Original Report

Original research

Amit Lahoti MD^a,

, Joseph L. Nates MD, MBA^a, Chris D. Wakefield BS^a, Kristen J. Price MD^a and Abdulla K. Salahudeen MD^a

Over the past several years, important advances have occurred in the treatment and supportive care of critically ill patients with cancer.^[1] However, acute kidney injury (AKI) remains a familiar complication and is a negative prognostic factor for overall survival.^{[2] and [3]} The development of AKI can limit further cancer treatment, increase toxicity of chemotherapy and reduce its delivery, and exclude patients from clinical trials. Further, patients with AKI have been shown to have longer hospitalizations and increased hospital costs.^{[4] and [5]} Recognized causes of AKI include acute tubular necrosis from medications or sepsis, volume depletion, tumor lysis syndrome, abdominal compartment syndrome, and obstruction from tumor or lymphadenopathy. Elevations in serum creatinine of as little as 0.3 mg/dL, which were previously considered insignificant, have been associated with a higher mortality rate in hospitalized patients.^[4] However, few of the numerous definitions of AKI used in the cancer literature incorporate these subtle declines in kidney function.

An increase in serum creatinine has traditionally been used as a reflection of AKI. However, it is well known that elevation in serum creatinine is a relatively late marker of kidney injury.^[6] In addition, patients with cancer often have decreased creatinine production secondary to cachexia, which may limit the sensitivity of creatinine as a marker of kidney injury. Other variables including total body volume, ethnicity, medications, and protein intake may also vary the serum creatinine level independent of renal function. Recent studies have demonstrated that a significant number of patients with cancer and normal serum creatinine have underlying chronic kidney disease (CKD) when renal function is estimated by the Cockcroft-Gault equation.^{[7] and [8]} Therefore, using an arbitrarily defined level of serum creatinine as an indicator of AKI (i.e. >1.5 or 2.0 mg/dL) may not be suitable.

What may be a more accurate measure of kidney injury is a classification system based on the percent increase in serum creatinine relative to baseline. One such model is the Risk, Injury, Failure, Loss, and End-Stage Kidney (RIFLE) classification, which defines three levels of severity of AKI (risk, injury, and failure).^[9] Previously, over 35 different definitions of AKI were used in the literature, which has made cross-comparisons between studies difficult.^[10] The RIFLE classification provides a uniform definition of AKI and has been validated in numerous studies.^{[11], [12], [13], [14], [15], [16], [17] and [18]} The aim of this analysis was to estimate the incidence, outcomes, and costs associated with AKI as defined by the RIFLE classification in critically ill patients with cancer.

Materials and Methods

The study included all patients ≥18 years of age who were admitted to the intensive care unit (ICU) at the University of Texas M.D. Anderson Cancer Center from December 2005 through December 2006. Patients with a baseline serum creatinine >1.5 mg/dL were excluded from the analysis. The protocol was approved by the institutional review board. Demographic and clinical data were obtained from the Department of Critical Care database, the Department of Pharmacy database, and the global institutional database (Enterprise Information Warehouse). The data were incorporated into a single spreadsheet using Excel 12.2 for Mac (Microsoft, Redmond, WA).

RIFLE categories for AKI were defined by the percent increase in serum creatinine from the time of ICU admission to the maximum creatinine at any point during the ICU stay: risk (≥50% rise in serum creatinine), injury (≥100% rise in serum creatinine), and failure (≥200% rise in serum creatinine). Consistent with the Acute Kidney Injury Network modifications of the original criteria, patients who required dialysis were classified into the RIFLE failure category, irrespective of the percent rise in serum creatinine.^[19] The modality for continuous renal replacement therapy used at our institution is continuous slow low-efficiency dialysis (c-SLED), which has been described previously.^[20] For patients who did not have an initial creatinine available within 24 hours after ICU admission, the most recent prior creatinine within the previous 48 hours was used.

Statistics

Descriptive data are presented as medians with interquartile ranges for continuous variables and absolute numbers with percentages for categorical variables. Survival of patients with AKI as defined by the RIFLE criteria was estimated by the Kaplan-Meier method. Patients were censored at death or last known follow-up, as determined by the clinical record. Statistical significance was determined by the log-rank test.

The primary end point for logistic regression was death at 60 days after ICU admission. Two separate models were developed, examining AKI as a categorical variable (RIFLE categories) and as a continuous variable (percent increase in creatinine from baseline). The variable “age” was significantly associated in a linear fashion with log odds of death but was dichotomized to provide a more meaningful odds ratio for the reader. Correlated data were assessed by correlation coefficients, and no variables were significantly correlated >0.6. Model reduction was achieved by variable elimination using the likelihood ratio test between nested models. Predictive ability and goodness-of-fit statistics were calculated, and the model was internally validated. No significant interactions were identified in either logistic regression model.

Lastly, a multivariate log linear regression model was developed to assess the relationship of AKI and dialysis with hospital cost. Cost was defined as hospital charges from the provider perspective. Log transformation of “cost” was used to account for skewness and heteroskedasticity. Coefficients in this model were multiplied by a factor of 100 to estimate a percent change in the dependent variable (cost) associated with a unit change in the independent variable.^[21]

A two-tailed P < 0.05 was considered statistically significant. No patients were excluded from the analysis because of missing data. Statistical analysis was performed with Stata 10 for Mac (StataCorp, College Station, TX).

Results

The data set included 2,398 patients. Patient characteristics are listed in Table 1. The median age was 59 years. The cohort was predominantly Caucasian (75%) and relatively balanced with respect to gender. The majority of patients on a medical service were admitted to the hospital from the emergency room (76%), compared to only 10% of patients on a surgical service. Sepsis was diagnosed in 23% of patients on a medical service vs. only 4% of patients on a surgical service. This is consistent with the large number of patients at our institution who were admitted to the ICU for routine monitoring after elective surgeries. A significant number of patients had underlying hypertension and diabetes (54% and 18%, respectively). One-third of patients had advanced malignancy by Surveillance, Epidemiology, and End Results (SEER) stage on initial presentation to our institution.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.

Correspondence to: Amit Lahoti, MD, MD Anderson Cancer Center, PO Box 301402, FCT 13.6068, Houston, TX, 77230-1402; telephone: (713) 563-6224; fax: (713) 745-3791

Original research

Amit Lahoti MD^a,

, Joseph L. Nates MD, MBA^a, Chris D. Wakefield BS^a, Kristen J. Price MD^a and Abdulla K. Salahudeen MD^a

Over the past several years, important advances have occurred in the treatment and supportive care of critically ill patients with cancer.^[1] However, acute kidney injury (AKI) remains a familiar complication and is a negative prognostic factor for overall survival.^{[2] and [3]} The development of AKI can limit further cancer treatment, increase toxicity of chemotherapy and reduce its delivery, and exclude patients from clinical trials. Further, patients with AKI have been shown to have longer hospitalizations and increased hospital costs.^{[4] and [5]} Recognized causes of AKI include acute tubular necrosis from medications or sepsis, volume depletion, tumor lysis syndrome, abdominal compartment syndrome, and obstruction from tumor or lymphadenopathy. Elevations in serum creatinine of as little as 0.3 mg/dL, which were previously considered insignificant, have been associated with a higher mortality rate in hospitalized patients.^[4] However, few of the numerous definitions of AKI used in the cancer literature incorporate these subtle declines in kidney function.

An increase in serum creatinine has traditionally been used as a reflection of AKI. However, it is well known that elevation in serum creatinine is a relatively late marker of kidney injury.^[6] In addition, patients with cancer often have decreased creatinine production secondary to cachexia, which may limit the sensitivity of creatinine as a marker of kidney injury. Other variables including total body volume, ethnicity, medications, and protein intake may also vary the serum creatinine level independent of renal function. Recent studies have demonstrated that a significant number of patients with cancer and normal serum creatinine have underlying chronic kidney disease (CKD) when renal function is estimated by the Cockcroft-Gault equation.^{[7] and [8]} Therefore, using an arbitrarily defined level of serum creatinine as an indicator of AKI (i.e. >1.5 or 2.0 mg/dL) may not be suitable.

What may be a more accurate measure of kidney injury is a classification system based on the percent increase in serum creatinine relative to baseline. One such model is the Risk, Injury, Failure, Loss, and End-Stage Kidney (RIFLE) classification, which defines three levels of severity of AKI (risk, injury, and failure).^[9] Previously, over 35 different definitions of AKI were used in the literature, which has made cross-comparisons between studies difficult.^[10] The RIFLE classification provides a uniform definition of AKI and has been validated in numerous studies.^{[11], [12], [13], [14], [15], [16], [17] and [18]} The aim of this analysis was to estimate the incidence, outcomes, and costs associated with AKI as defined by the RIFLE classification in critically ill patients with cancer.

Materials and Methods

The study included all patients ≥18 years of age who were admitted to the intensive care unit (ICU) at the University of Texas M.D. Anderson Cancer Center from December 2005 through December 2006. Patients with a baseline serum creatinine >1.5 mg/dL were excluded from the analysis. The protocol was approved by the institutional review board. Demographic and clinical data were obtained from the Department of Critical Care database, the Department of Pharmacy database, and the global institutional database (Enterprise Information Warehouse). The data were incorporated into a single spreadsheet using Excel 12.2 for Mac (Microsoft, Redmond, WA).

RIFLE categories for AKI were defined by the percent increase in serum creatinine from the time of ICU admission to the maximum creatinine at any point during the ICU stay: risk (≥50% rise in serum creatinine), injury (≥100% rise in serum creatinine), and failure (≥200% rise in serum creatinine). Consistent with the Acute Kidney Injury Network modifications of the original criteria, patients who required dialysis were classified into the RIFLE failure category, irrespective of the percent rise in serum creatinine.^[19] The modality for continuous renal replacement therapy used at our institution is continuous slow low-efficiency dialysis (c-SLED), which has been described previously.^[20] For patients who did not have an initial creatinine available within 24 hours after ICU admission, the most recent prior creatinine within the previous 48 hours was used.

Statistics

Descriptive data are presented as medians with interquartile ranges for continuous variables and absolute numbers with percentages for categorical variables. Survival of patients with AKI as defined by the RIFLE criteria was estimated by the Kaplan-Meier method. Patients were censored at death or last known follow-up, as determined by the clinical record. Statistical significance was determined by the log-rank test.

The primary end point for logistic regression was death at 60 days after ICU admission. Two separate models were developed, examining AKI as a categorical variable (RIFLE categories) and as a continuous variable (percent increase in creatinine from baseline). The variable “age” was significantly associated in a linear fashion with log odds of death but was dichotomized to provide a more meaningful odds ratio for the reader. Correlated data were assessed by correlation coefficients, and no variables were significantly correlated >0.6. Model reduction was achieved by variable elimination using the likelihood ratio test between nested models. Predictive ability and goodness-of-fit statistics were calculated, and the model was internally validated. No significant interactions were identified in either logistic regression model.

Lastly, a multivariate log linear regression model was developed to assess the relationship of AKI and dialysis with hospital cost. Cost was defined as hospital charges from the provider perspective. Log transformation of “cost” was used to account for skewness and heteroskedasticity. Coefficients in this model were multiplied by a factor of 100 to estimate a percent change in the dependent variable (cost) associated with a unit change in the independent variable.^[21]

A two-tailed P < 0.05 was considered statistically significant. No patients were excluded from the analysis because of missing data. Statistical analysis was performed with Stata 10 for Mac (StataCorp, College Station, TX).

Results

The data set included 2,398 patients. Patient characteristics are listed in Table 1. The median age was 59 years. The cohort was predominantly Caucasian (75%) and relatively balanced with respect to gender. The majority of patients on a medical service were admitted to the hospital from the emergency room (76%), compared to only 10% of patients on a surgical service. Sepsis was diagnosed in 23% of patients on a medical service vs. only 4% of patients on a surgical service. This is consistent with the large number of patients at our institution who were admitted to the ICU for routine monitoring after elective surgeries. A significant number of patients had underlying hypertension and diabetes (54% and 18%, respectively). One-third of patients had advanced malignancy by Surveillance, Epidemiology, and End Results (SEER) stage on initial presentation to our institution.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.

Correspondence to: Amit Lahoti, MD, MD Anderson Cancer Center, PO Box 301402, FCT 13.6068, Houston, TX, 77230-1402; telephone: (713) 563-6224; fax: (713) 745-3791

Original research

Amit Lahoti MD^a,

, Joseph L. Nates MD, MBA^a, Chris D. Wakefield BS^a, Kristen J. Price MD^a and Abdulla K. Salahudeen MD^a

Over the past several years, important advances have occurred in the treatment and supportive care of critically ill patients with cancer.^[1] However, acute kidney injury (AKI) remains a familiar complication and is a negative prognostic factor for overall survival.^{[2] and [3]} The development of AKI can limit further cancer treatment, increase toxicity of chemotherapy and reduce its delivery, and exclude patients from clinical trials. Further, patients with AKI have been shown to have longer hospitalizations and increased hospital costs.^{[4] and [5]} Recognized causes of AKI include acute tubular necrosis from medications or sepsis, volume depletion, tumor lysis syndrome, abdominal compartment syndrome, and obstruction from tumor or lymphadenopathy. Elevations in serum creatinine of as little as 0.3 mg/dL, which were previously considered insignificant, have been associated with a higher mortality rate in hospitalized patients.^[4] However, few of the numerous definitions of AKI used in the cancer literature incorporate these subtle declines in kidney function.

An increase in serum creatinine has traditionally been used as a reflection of AKI. However, it is well known that elevation in serum creatinine is a relatively late marker of kidney injury.^[6] In addition, patients with cancer often have decreased creatinine production secondary to cachexia, which may limit the sensitivity of creatinine as a marker of kidney injury. Other variables including total body volume, ethnicity, medications, and protein intake may also vary the serum creatinine level independent of renal function. Recent studies have demonstrated that a significant number of patients with cancer and normal serum creatinine have underlying chronic kidney disease (CKD) when renal function is estimated by the Cockcroft-Gault equation.^{[7] and [8]} Therefore, using an arbitrarily defined level of serum creatinine as an indicator of AKI (i.e. >1.5 or 2.0 mg/dL) may not be suitable.

What may be a more accurate measure of kidney injury is a classification system based on the percent increase in serum creatinine relative to baseline. One such model is the Risk, Injury, Failure, Loss, and End-Stage Kidney (RIFLE) classification, which defines three levels of severity of AKI (risk, injury, and failure).^[9] Previously, over 35 different definitions of AKI were used in the literature, which has made cross-comparisons between studies difficult.^[10] The RIFLE classification provides a uniform definition of AKI and has been validated in numerous studies.^{[11], [12], [13], [14], [15], [16], [17] and [18]} The aim of this analysis was to estimate the incidence, outcomes, and costs associated with AKI as defined by the RIFLE classification in critically ill patients with cancer.

Materials and Methods

The study included all patients ≥18 years of age who were admitted to the intensive care unit (ICU) at the University of Texas M.D. Anderson Cancer Center from December 2005 through December 2006. Patients with a baseline serum creatinine >1.5 mg/dL were excluded from the analysis. The protocol was approved by the institutional review board. Demographic and clinical data were obtained from the Department of Critical Care database, the Department of Pharmacy database, and the global institutional database (Enterprise Information Warehouse). The data were incorporated into a single spreadsheet using Excel 12.2 for Mac (Microsoft, Redmond, WA).

RIFLE categories for AKI were defined by the percent increase in serum creatinine from the time of ICU admission to the maximum creatinine at any point during the ICU stay: risk (≥50% rise in serum creatinine), injury (≥100% rise in serum creatinine), and failure (≥200% rise in serum creatinine). Consistent with the Acute Kidney Injury Network modifications of the original criteria, patients who required dialysis were classified into the RIFLE failure category, irrespective of the percent rise in serum creatinine.^[19] The modality for continuous renal replacement therapy used at our institution is continuous slow low-efficiency dialysis (c-SLED), which has been described previously.^[20] For patients who did not have an initial creatinine available within 24 hours after ICU admission, the most recent prior creatinine within the previous 48 hours was used.

Statistics

Descriptive data are presented as medians with interquartile ranges for continuous variables and absolute numbers with percentages for categorical variables. Survival of patients with AKI as defined by the RIFLE criteria was estimated by the Kaplan-Meier method. Patients were censored at death or last known follow-up, as determined by the clinical record. Statistical significance was determined by the log-rank test.

The primary end point for logistic regression was death at 60 days after ICU admission. Two separate models were developed, examining AKI as a categorical variable (RIFLE categories) and as a continuous variable (percent increase in creatinine from baseline). The variable “age” was significantly associated in a linear fashion with log odds of death but was dichotomized to provide a more meaningful odds ratio for the reader. Correlated data were assessed by correlation coefficients, and no variables were significantly correlated >0.6. Model reduction was achieved by variable elimination using the likelihood ratio test between nested models. Predictive ability and goodness-of-fit statistics were calculated, and the model was internally validated. No significant interactions were identified in either logistic regression model.

Lastly, a multivariate log linear regression model was developed to assess the relationship of AKI and dialysis with hospital cost. Cost was defined as hospital charges from the provider perspective. Log transformation of “cost” was used to account for skewness and heteroskedasticity. Coefficients in this model were multiplied by a factor of 100 to estimate a percent change in the dependent variable (cost) associated with a unit change in the independent variable.^[21]

A two-tailed P < 0.05 was considered statistically significant. No patients were excluded from the analysis because of missing data. Statistical analysis was performed with Stata 10 for Mac (StataCorp, College Station, TX).

Results

The data set included 2,398 patients. Patient characteristics are listed in Table 1. The median age was 59 years. The cohort was predominantly Caucasian (75%) and relatively balanced with respect to gender. The majority of patients on a medical service were admitted to the hospital from the emergency room (76%), compared to only 10% of patients on a surgical service. Sepsis was diagnosed in 23% of patients on a medical service vs. only 4% of patients on a surgical service. This is consistent with the large number of patients at our institution who were admitted to the ICU for routine monitoring after elective surgeries. A significant number of patients had underlying hypertension and diabetes (54% and 18%, respectively). One-third of patients had advanced malignancy by Surveillance, Epidemiology, and End Results (SEER) stage on initial presentation to our institution.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.

Correspondence to: Amit Lahoti, MD, MD Anderson Cancer Center, PO Box 301402, FCT 13.6068, Houston, TX, 77230-1402; telephone: (713) 563-6224; fax: (713) 745-3791

Lowell B. Anthony, MD,1 Nashat Y. Gabrail, MD,2 Hassan Ghazal, MD,3, Donald V. Woytowitz, MD,4 Marshall S. Flam, MD,5 Anibal Drelichman, MD,6, David M. Loesch, MD,7, Demi A. Niforos, MS,8, and Antoinette Mangione, MD, PharmD9; for the Iron Sucrose Study Group*

Lowell B. Anthony, MD,1 Nashat Y. Gabrail, MD,2 Hassan Ghazal, MD,3, Donald V. Woytowitz, MD,4 Marshall S. Flam, MD,5 Anibal Drelichman, MD,6, David M. Loesch, MD,7, Demi A. Niforos, MS,8, and Antoinette Mangione, MD, PharmD9; for the Iron Sucrose Study Group*

Lowell B. Anthony, MD,1 Nashat Y. Gabrail, MD,2 Hassan Ghazal, MD,3, Donald V. Woytowitz, MD,4 Marshall S. Flam, MD,5 Anibal Drelichman, MD,6, David M. Loesch, MD,7, Demi A. Niforos, MS,8, and Antoinette Mangione, MD, PharmD9; for the Iron Sucrose Study Group*

Oral anticancer and supportive care agents administered to cancer patients are costly and are associated with large copayment requirements or are often not fully reimbursed by private health insurers or Medicare.¹ To facilitate access to oral medications, pharmaceutical manufacturers have developed patient assistance programs (PAPs) that provide selected oral medications at no or reduced cost to financially eligible patients. Eligibility criteria, application processes, and program administration for PAPs differ by manufacturer and by product, which can ultimately present logistical barriers.^2–4 A systematic review of PAPs found improvements in disease indicator outcomes for patients with common chronic diseases who access these programs.⁵ However, knowledge about the use of PAPs among cancer patients is limited.⁶

The University of Texas MD Anderson Cancer Center (MDACC), the largest tertiary care cancer center in the country, has developed a systematic approach to administering a large number of PAPs. In 1996, the MDACC established an institutional program staffed by hospital pharmacy personnel, who navigate cancer patients through PAPs in inpatient and outpatient settings. This program removes the operational and administrative barriers often experienced by patients in smaller clinical settings.

Cancer patients eligible for PAPs at MDACC include those who are uninsured, those who are underinsured, those whose pharmacy benefit limits have been reached, and those whose private health or government insurance has denied coverage of certain oral medications. For example, the Texas Medicaid program limits its low-income beneficiaries to three prescriptions per month, which may lead some of them, particularly those with cancer, to require additional medication assistance through PAPs. As of April 2008, this institutional program established formal relationships with 29 pharmaceutical companies that provide 104 therapeutic or supportive care agents through PAPs to eligible cancer patients in the MDACC outpatient pharmacy.

Methods

Data source

Approval for this study was obtained from the MDACC Institutional Review Board. We conducted a retrospective, secondary analysis of noninvestigational prescription medications from the outpatient pharmacy at MDACC. Data from July 1, 2006, to December 31, 2007, were extracted from computerized pharmacy, medical, and cancer registry databases at MDACC. Prescriptions had to include both patient medical record and social security numbers to validate the patient’s identity as well as the date of pickup to validate that the medication had been dispensed during the study period. When the date of pickup was missing but billing was documented, the date the medication was dispensed was used as the pickup date. All data were de-identified prior to analysis.

PAPs

Prescriptions for oral medications were available to financially eligible individuals via two types of PAPs at MDACC: individual enrollment (60 distinct medications) and bulk drug replacement (44 distinct medications). Individual enrollment required that an eligible patient apply directly to a pharmaceutical company’s PAP for the medication (s) needed. Once approved, the requested medication was mailed directly to the patient or dispensed in the MDACC pharmacy. Given the purpose of this study, we were only interested in those PAP prescription medications dispensed at the outpatient pharmacy.

Bulk replacement PAPs provide available prescription medications in bulk quantities on a monthly (in some cases quarterly) basis to MDACC’s pharmacy to replace medications dispensed to patients who were classified as “indigent” by MDACC-established criteria. Financially indigent patients included those who were Texas residents, uninsured or insured by Medicaid, and not responsible for charges billed to MDACC. All eligible patients could apply for the 60 medications available through individual PAP enrollment, but only indigent patients qualified for the 44 medications available through bulk drug replacement to MDACC.

Patient classifications

Prescription data were extracted from a pharmacy administrative dispensing database; a systematic process was developed to identify case patients (based on financial eligibility) and control patients (similar to case patients with respect to treatments received but were nonusers of PAP programs). Only patients who were potentially eligible for PAPs were included in the study. The case selection was based on MDACC’s determination of a patient’s ability to pay, referred to as credit rating, at the time of a patient’s registration at the institution. Regardless of health insurance status, patients who had a low credit rating (responsible for 0%– 50% of their charges) were classified as being potentially eligible for PAPs. Patients with low credit ratings also included those who were indigent. The control selection identified a set of insured patients, including those with high credit ratings (responsible for 100% of their charges), who had been referred for special financial assistance to obtain specific medications through PAPs.

To be included in the study, patients identified based on a low credit rating had to receive at least 1 of the 104 medications through a PAP to be classified as a PAP user; these patients could receive other medications through traditional payment. PAP nonusers had to receive at least 1 of the 104 medications associated with PAPs through traditional payment or other third-party source, not through a PAP. Patients who had been referred for special assistance had to receive one or more of the PAP medications initially requested from a PAP.

For a drug to be verified as a PAP prescription medication, the pharmacy record could not have documentation of third-party payer or patient payment for that medication. The only exception made for payer and patient payment was for prescription medications provided by one particular pharmaceutical company, which required a $10 copayment for its PAP medications. Once PAP and non-PAP prescriptions were verified, they were aggregated by a unique patient identifier to yield prescriptionuse data for individual patients who were categorized as PAP users versus PAP nonusers.

Patient characteristics

Data on patient gender, race/ethnicity, age, insurance status, and primary cancer site were extracted. Race/ ethnicity was categorized as white, black, Hispanic, or Asian/other. Age was calculated as of July 1, 2006, from the patient’s birth date. Insurance status was based on the patient’s insurance status at the time of registration at MDACC and categorized as follows: no insurance (include self-payers and patients referred from the county public hospital), Medicare, Medicaid, or any of a variety of private/commercial insurances. Private insurances were combined into one category. Information on each patient’s primary cancer site was categorized as blood, breast, genitourinary, head and neck, or other (primarily brain, central nervous system, and an unknown primary site).

The gender and insurance variables had some missing data. When there were conflicting data for a particular patient’s gender, we coded gender as missing. When the insurance type was missing, data on the patient’s insurance status at the time of registration at MDACC were retrieved from MDACC’s financial department.

Prescription medication fills

Data on the prescription medication name (generic or brand) and institutional billing charges per fill were extracted from pharmacy records. Prescriptions were aggregated by generic and brand names, regardless of strength, dosage form, or method of administration, to identify the 20 most frequently dispensed medications overall and for the treatment of cancer. We then used Rxlist.com (www.rxlist.com), an online medication reference program, to identify each medication’s clinical indication(s). For example, the brand name medication Zofran would be aggregated with its generic, ondansetron, and would be considered as one medication indicated for nausea and vomiting.

We extracted patient billing charge per medication fill in dollars by the date of pickup in the outpatient pharmacy. Patient billing charge included patient copayments and did not include any payments from the patient’s payer or health plan. If the billing charge was missing for a medication fill, we applied a comparable charge from a prescription medication of the same name, dosage, quantity, date of pickup, and patient insurance status. When quantity, date, or patient insurance status differed, the lowest available charge was used. All charges were adjusted to the year 2008 using the US Bureau of Labor’s Annual Producer Price Index for pharmaceutical preparation and manufacturing.⁷

Data analysis

For patient-level analyses, a PAP user was a patient who received at least one medication through a PAP during the study period. We used descriptive statistics to compare patient characteristics of PAP users versus PAP nonusers. Next, we conducted separate unadjusted binary logit regression analyses (interpreted with odds ratios [ORs] and 95% confidence intervals [CIs]) to estimate the differences in the probability of being a PAP user for each of the patient characteristics. All patient characteristics that were statistically significant at P < 0.20 for the unadjusted analyses8 were included in the final multivariable model. The a priori level of significance was set at P < 0.05 for the multivariable model.

For other analyses conducted at the prescription level, a PAP medication was a medication verified as being provided through a PAP. We used descriptive statistics to compare the 20 most frequently dispensed prescription medications (overall and for anticancer agents specifically) by PAP status and clinical indication. Analyses were conducted in Microsoft Excel and STATA Version 11.⁹

Results

Study patients and prescription medications

During the 18-month observation period, a monthly mean of 1,550 patients received a monthly total of 19,000 noninvestigational medications in the outpatient pharmacy. Of these patients, 7.5% (n = 1,929) met study eligibility criteria for PAPs and received 1 of the 104 medications provided through PAPs. Thus, there were 979 PAP users and 950 PAP nonusers in the final study population. In total, the study population received 23.3% (n = 77,592) of all outpatient medications administered during this period, of which anticancer agents represented 4% (n = 3,105; Table 1).

Comparison of patient characteristics

In comparison to PAP nonusers, PAP users were, on average, younger (48 vs 52 years), indigent (73% vs 19%), white (50% vs 43%), and covered by Medicaid or were uninsured (75% versus 20%). PAP users also had more prescriptions fills (median = 30 vs 20) during the study period at the institution. Univariate analyses showed that all patient characteristics, except gender, significantly predicted PAP use. Given the strong correlation of indigent and insurance status to PAP use, we conducted post hoc analyses to assess the potential for multicollinearity between the two patient characteristics. The variance inflation factor (VIF = 4.57) did not indicate multicollinearity concerns.

In the adjusted model, patients who were indigent (OR = 16.95; 95% CI: 6.845, 41.960), uninsured (OR = 4.60; 95% CI: 2.118, 9.970), and under the age of 65 years (OR = 2.31; 95% CI: 1.517, 3.509) were 2- to 17- fold more likely than others to be PAP users. Black patients were 31% (P = 0.020) less likely to access PAPs than were white patients.

Overall prescription medication fills The top-20 prescription medication fills from the MDACC outpatient pharmacy differed by PAP user group and PAP status. For PAP users, 88% of the most common medications obtained from PAPs were supportive care agents, including treatments of bacterial infections (n =887 fills; 49/month), antiemetics (n = 492 fills; 27/month), and gastroesophageal reflux disease (n = 492 fills; 27/month). Conversely, treatments for neutropenia and anticoagulation represented nearly half ($1.8 million) of the total charges avoided through PAPs to PAP users ($3.9 million). The most common medications not obtained from PAPs were for treatment of pain (PAP users = 292 fills/month, nonusers = 218 fills/month), versus only 13 fills/ month for pain medications from PAPs. Medications indicated to treat pain and nausea/vomiting accounted for the largest proportion of charges for medications not filled by PAPs for both PAP users and nonusers.

Anticancer agent prescription fills

For both PAP users and nonusers, the top-20 anticancer oral agent fills represented 93% (n = 2,892 of 3,105) of all anticancer oral fills (Table 6), with 16% (n = 454) of these oral fills being provided through PAPs. Among PAP users, anticancer agents from PAPs accounted for 40% of their total charges and 35% of the total number of agents. Temozolomide (Temodar; mean charge/fill = $3,346) represented the highest amount of total charges ($220,857) from PAPs, whereas imatinib (Gleevec; mean charge/fill = $5,372) and dasatinib (Sprycel; mean charge/fill = $5,221) accounted for the highest average charges per fill. Anastrozole (Arimidex; n = 178 fills; 10/month), capecitabine (Xeloda; n = 91 fills; 5/month), and temozolomide (n = 66 fills; 4/month) accounted for 70% of agents from PAPs. PAP users who were given bicalutamide received 100% of those agents from PAPs. Five of the seven oral anticancer agents with no fills from PAPs had initial US Food and Drug Administration approval years before 2000.

Discussion

At MDACC, PAPs are designed to help cancer patients overcome financial barriers to accessing oral supportive and anticancer agents. Over an 18-month observation period, less than 5% of the cancer patients at MDACC who received prescription medications from the outpatient pharmacy were enrolled in a PAP— and these PAPs provided 13% of their medication fills, representing an annualized $3.6 million in pharmaceutical expenditures. In interpreting our findings, several factors should be considered.

Oral anticancer agents accounted for 4% of all prescription medication fills during the study period. Comparatively, an analysis of the 2007 National Ambulatory Medical Care Survey showed that less than 1% of cancer patients were prescribed at least one oral anticancer agent.¹⁰ This finding indicates that both nationally and at MDACC, chemotherapy continues to be largely provided parenterally, as there is more of a financial benefit from intravenous therapies that are often reimbursed by insurers as well as PAPs.

In the outpatient pharmacy at MDACC, PAPs provided nearly onethird of oral anticancer fills for PAP users—totaling a mean of $500,000 per month in expenditures. However, three agents, anastrozole (for breast cancer), capecitabine (for breast and GI cancers, primarily), and temozolomide (for brain tumors) accounted for 75% of all of the anticancer agents provided by PAPs. We also found that pharmaceutical companies provided expensive newer, targeted, anticancer agents (primarily dasatinib and imatinib, the two agents with the greatest pharmaceutical per-person expenditures by the PAP program) through PAPs.

Although PAPs filled a strong and focused need for a small number of oral chemotherapy agents for some individuals with breast, GI, and brain cancers, they did not provide much benefit for a wide range of supportive care agents, particularly those that are schedule C and are used to treatcancer pain. Pain is the most prevalent symptom reported by cancer patients, 11 but there were few schedule C pain medications among the most common medications provided through PAPs. These substances are generally not provided by PAPs because of legal and substance abuse concerns.12 However, these medications were commonly prescribed to PAP users and PAP nonusers alike, outside of the PAP program. It would be important to evaluate the comparative success in treating pain among cancer patients at MDACC who receive a limited array of pain medications from PAPs (usually agents that are not substance-controlled by the Drug Enforcement Administration) versus treatment of pain experienced by patients whose medications are not reimbursed by PAPs.

We found that being younger than 65 years old, being indigent, and having no health insurance were the strongest predictors of using a PAP. This finding was expected, given that US adults younger than age 65 are ineligible for outpatient prescription medication coverage through Medicare Part D. However, contrary to expectations, about 45% of PAP users had either private or governmentsupplied health insurance. Because it is not uncommon for cancer patients to endure economic hardship (including bankruptcy) when trying to finance their care,13 healthcare professionals could recommend PAPs and other relevant assistance programs to all of their cancer patients.

With the expansion of health insurance through the Patient Protection and Affordable Health Care Act of 2010, it is hoped that the need for cancer patients to enroll in PAPs will be diminished; yet, given the reality of the high cost of anticancer agents, reimbursement policies for these agents, and tiered formularies among insurers leading to high outof- pocket costs for patients, the need for PAPs is likely to remain. PAPs can be a viable option for some patients, but healthcare professionals should be aware that there are a number of concerns about these programs, including their complex and burdensome application process and often limited variety of available drugs.14

This study is not without its limitations. First, we may have underestimated our sample of PAP patients due to the fact that MDACC did not electronically or systematically track the use of PAPs within its pharmacy database at the time of the study. The institution is in the process of developing such a system.

Second, the data used in this study cannot be assumed to reflect a “closed pharmacy” setting because some patients, particularly those who have health insurance with prescription medication coverage, may have received some of their medications from outside pharmacies.

Third, because insurance status is not necessarily a static characteristic, insurance status in this study was classified based on that at the time of registration with MDACC’s financial department, and no account was taken of changes that might have occurred.

Last, our results are not necessarily generalizable to all cancer populations, time periods, or settings. Cancer patients treated in academic centers such as MDACC may differ from those who are treated in community settings. In particular, fewer than 10% of patients at MDACC qualified for indigent financial assistance in 2007,15 which is likely to have impacted the number of patients who were potentially eligible for PAPs. It is also likely that had our study been conducted prior to the implementation of Medicare Part D, our sample of PAP patients would have been older. Nevertheless, our results may be generalizable to cancer patients receiving care in other academic cancer centers.

Conclusion This study builds upon a previous description of implementing PAPs in a comprehensive cancer center16 as well as contributes to our limited knowledge of the use of PAPs among cancer patients.6 Future studies should prospectively examine cancer patients’ experiences and satisfaction with PAPs from the process of applying to the point of receiving requested therapies and evaluate the effect of PAPs on cancer outcomes in various care settings. Multidisciplinary teams, including pharmacists and clinicians, should establish and recommend valid and relevant clinical endpoints for researchers to use in effectiveness studies of PAPs and cancer patients, particularly as they relate to oral anticancer agent use. Given that these oral agents represent more than 25% of cancer therapies in development,17 future studies of PAPs are ideal for evaluating concerns of accessibility, affordability, and compliance related to these agents.

MDACC is a unique resource for observers of PAPs, as it is the largest cancer center in the United States. However, few cancer patients at MDACC were eligible for and accessed PAPs in the outpatient pharmacy. Although smaller cancer centers may not be able to devote the same degree of financial and personnel resources to their patients as does MDACC, these centers could seek to build relationships with specific pharmaceutical companies that provide PAPs for the oral anticancer and supportive care therapies most commonly prescribed and administered at their centers. Scarce resources could also be utilized in other ways, such as by developing public-private risk pools for establishment of indigent care funds.

Acknowledgments: The authors thank Chun Feng, Jason Lau, and Oliver Max for their special assistance; Dr. Phoenix Do for her study design recommendations; and Karyn Popham for her editorial support. They especially thank Rebecca Arbuckle, RPh, for her support of this project. At the time of the study, Dr. Felder was supported by a Predoctoral Fellowship from The University of Texas School of Public Health Cancer Education and Career Development Program, funded by National Cancer Institute/NIH Grant R25-CA-57712-17.

References

1. Hede K. Increase in oral cancer drugs raises thorny issues for oncology practices. J Natl Cancer Inst 2009;101:1534–1536.
2. Chisholm MA, DiPiro JT. Pharmaceutical manufacturer assistance programs. Arch Intern Med 2002;162:780–784.
3. Duke KS, Raube K, Lipton HL. Patientassistance programs: assessment of and use by safety-net clinics. Am J Health Syst Pharm 2005;62:726–731.
4. Pisu M, Richman J, Allison JJ, Williams OD, Kiefe CI. Pharmaceuticals companies’ medication assistance programs: potentially useful but too burdensome to use? South Med J 2009;102:139–144.
5. Felder TM, Palmer NR, Lal LS, Mullen PD. What is the evidence for pharmaceutical patient assistance programs? a systematic review. J Health Care Poor Underserved 2011;22:24–49.
6. Meropol NJ, Schrag D, Smith TJ, et al. American Society of Clinical Oncology guidance statement: the cost of cancer care. J Clin Oncol 2009;27:3868–3874.
7. United States Department of Labor, Bureau of Labor Statistics: Producer Price Index Industry Data—Pharmaceutical Preparation & Manufacturing; 2010 [updated April 26, 2010]. http://www.bls.gov/ppi/data.htm. Accessed June 22, 2011.
8. Hosmer DW, Lemeshow S. Applied Logistic
Logistic
Regression. New York: Wiley; 2000. 9. StataCorp LP. STATA statistical software. 2009; Release 11.
10. Arora S. Use of oral chemotherapeutic medications in non-traditional ambulatory settings; 2009. http://digarchive.library.vcu. edu/dspace/bitstream/10156/2711/1/Thesis_ MPH_sameer.pdf. Accessed June 22, 2011.
11. Cherny NI. The management of cancer pain. CA Cancer J Clin 2000;50:70–116.
12. Williams K. Accessing patient assistance programs to meet clients’ medication needs. J Am Acad Nurse Pract 2000;12:233– 235.
13. National Survey of Households Affected by Cancer: Kaiser Family Foundation; 2006 [updated November 2006]. http://kff.org/kaiserpolls/ upload/7591.pdf. Accessed June 22, 2011.
14. Choudhry NK, Lee JL, Agnew-Blais J, Corcoran C, Shrank WH. Drug company- sponsored patient assistance programs: a viable safety net? Health Aff (Millwood) 2009;28:827–834.
15. Ackerman T. M.D. Anderson submits its records on charitable care: cancer center hopes to quell Iowa senator’s investigation. Houston Chronicle. October 9, 2008. http:// www.chron.com/disp/story.mpl/metropolitan/ 6050254.html. Accessed June 22, 2011.
16. Johnson PE. Patient assistance programs and patient advocacy foundations: alternatives for obtaining prescription medications when insurance fails. Am J Health Syst Pharm 2006;63(21 suppl 7):S13–S17.
17. Weingart SA, Brown E, Bach PB, et al. National Comprehensive Cancer Network task force report: oral chemotherapy. JNCCN 2008;6(suppl 3):S1–S25.

Oral anticancer and supportive care agents administered to cancer patients are costly and are associated with large copayment requirements or are often not fully reimbursed by private health insurers or Medicare.¹ To facilitate access to oral medications, pharmaceutical manufacturers have developed patient assistance programs (PAPs) that provide selected oral medications at no or reduced cost to financially eligible patients. Eligibility criteria, application processes, and program administration for PAPs differ by manufacturer and by product, which can ultimately present logistical barriers.^2–4 A systematic review of PAPs found improvements in disease indicator outcomes for patients with common chronic diseases who access these programs.⁵ However, knowledge about the use of PAPs among cancer patients is limited.⁶

The University of Texas MD Anderson Cancer Center (MDACC), the largest tertiary care cancer center in the country, has developed a systematic approach to administering a large number of PAPs. In 1996, the MDACC established an institutional program staffed by hospital pharmacy personnel, who navigate cancer patients through PAPs in inpatient and outpatient settings. This program removes the operational and administrative barriers often experienced by patients in smaller clinical settings.

Cancer patients eligible for PAPs at MDACC include those who are uninsured, those who are underinsured, those whose pharmacy benefit limits have been reached, and those whose private health or government insurance has denied coverage of certain oral medications. For example, the Texas Medicaid program limits its low-income beneficiaries to three prescriptions per month, which may lead some of them, particularly those with cancer, to require additional medication assistance through PAPs. As of April 2008, this institutional program established formal relationships with 29 pharmaceutical companies that provide 104 therapeutic or supportive care agents through PAPs to eligible cancer patients in the MDACC outpatient pharmacy.

Methods

Data source

Approval for this study was obtained from the MDACC Institutional Review Board. We conducted a retrospective, secondary analysis of noninvestigational prescription medications from the outpatient pharmacy at MDACC. Data from July 1, 2006, to December 31, 2007, were extracted from computerized pharmacy, medical, and cancer registry databases at MDACC. Prescriptions had to include both patient medical record and social security numbers to validate the patient’s identity as well as the date of pickup to validate that the medication had been dispensed during the study period. When the date of pickup was missing but billing was documented, the date the medication was dispensed was used as the pickup date. All data were de-identified prior to analysis.

PAPs

Prescriptions for oral medications were available to financially eligible individuals via two types of PAPs at MDACC: individual enrollment (60 distinct medications) and bulk drug replacement (44 distinct medications). Individual enrollment required that an eligible patient apply directly to a pharmaceutical company’s PAP for the medication (s) needed. Once approved, the requested medication was mailed directly to the patient or dispensed in the MDACC pharmacy. Given the purpose of this study, we were only interested in those PAP prescription medications dispensed at the outpatient pharmacy.

Bulk replacement PAPs provide available prescription medications in bulk quantities on a monthly (in some cases quarterly) basis to MDACC’s pharmacy to replace medications dispensed to patients who were classified as “indigent” by MDACC-established criteria. Financially indigent patients included those who were Texas residents, uninsured or insured by Medicaid, and not responsible for charges billed to MDACC. All eligible patients could apply for the 60 medications available through individual PAP enrollment, but only indigent patients qualified for the 44 medications available through bulk drug replacement to MDACC.

Patient classifications

Prescription data were extracted from a pharmacy administrative dispensing database; a systematic process was developed to identify case patients (based on financial eligibility) and control patients (similar to case patients with respect to treatments received but were nonusers of PAP programs). Only patients who were potentially eligible for PAPs were included in the study. The case selection was based on MDACC’s determination of a patient’s ability to pay, referred to as credit rating, at the time of a patient’s registration at the institution. Regardless of health insurance status, patients who had a low credit rating (responsible for 0%– 50% of their charges) were classified as being potentially eligible for PAPs. Patients with low credit ratings also included those who were indigent. The control selection identified a set of insured patients, including those with high credit ratings (responsible for 100% of their charges), who had been referred for special financial assistance to obtain specific medications through PAPs.

To be included in the study, patients identified based on a low credit rating had to receive at least 1 of the 104 medications through a PAP to be classified as a PAP user; these patients could receive other medications through traditional payment. PAP nonusers had to receive at least 1 of the 104 medications associated with PAPs through traditional payment or other third-party source, not through a PAP. Patients who had been referred for special assistance had to receive one or more of the PAP medications initially requested from a PAP.

For a drug to be verified as a PAP prescription medication, the pharmacy record could not have documentation of third-party payer or patient payment for that medication. The only exception made for payer and patient payment was for prescription medications provided by one particular pharmaceutical company, which required a $10 copayment for its PAP medications. Once PAP and non-PAP prescriptions were verified, they were aggregated by a unique patient identifier to yield prescriptionuse data for individual patients who were categorized as PAP users versus PAP nonusers.

Patient characteristics

Data on patient gender, race/ethnicity, age, insurance status, and primary cancer site were extracted. Race/ ethnicity was categorized as white, black, Hispanic, or Asian/other. Age was calculated as of July 1, 2006, from the patient’s birth date. Insurance status was based on the patient’s insurance status at the time of registration at MDACC and categorized as follows: no insurance (include self-payers and patients referred from the county public hospital), Medicare, Medicaid, or any of a variety of private/commercial insurances. Private insurances were combined into one category. Information on each patient’s primary cancer site was categorized as blood, breast, genitourinary, head and neck, or other (primarily brain, central nervous system, and an unknown primary site).

The gender and insurance variables had some missing data. When there were conflicting data for a particular patient’s gender, we coded gender as missing. When the insurance type was missing, data on the patient’s insurance status at the time of registration at MDACC were retrieved from MDACC’s financial department.

Prescription medication fills

Data on the prescription medication name (generic or brand) and institutional billing charges per fill were extracted from pharmacy records. Prescriptions were aggregated by generic and brand names, regardless of strength, dosage form, or method of administration, to identify the 20 most frequently dispensed medications overall and for the treatment of cancer. We then used Rxlist.com (www.rxlist.com), an online medication reference program, to identify each medication’s clinical indication(s). For example, the brand name medication Zofran would be aggregated with its generic, ondansetron, and would be considered as one medication indicated for nausea and vomiting.

We extracted patient billing charge per medication fill in dollars by the date of pickup in the outpatient pharmacy. Patient billing charge included patient copayments and did not include any payments from the patient’s payer or health plan. If the billing charge was missing for a medication fill, we applied a comparable charge from a prescription medication of the same name, dosage, quantity, date of pickup, and patient insurance status. When quantity, date, or patient insurance status differed, the lowest available charge was used. All charges were adjusted to the year 2008 using the US Bureau of Labor’s Annual Producer Price Index for pharmaceutical preparation and manufacturing.⁷

Data analysis

For patient-level analyses, a PAP user was a patient who received at least one medication through a PAP during the study period. We used descriptive statistics to compare patient characteristics of PAP users versus PAP nonusers. Next, we conducted separate unadjusted binary logit regression analyses (interpreted with odds ratios [ORs] and 95% confidence intervals [CIs]) to estimate the differences in the probability of being a PAP user for each of the patient characteristics. All patient characteristics that were statistically significant at P < 0.20 for the unadjusted analyses8 were included in the final multivariable model. The a priori level of significance was set at P < 0.05 for the multivariable model.

For other analyses conducted at the prescription level, a PAP medication was a medication verified as being provided through a PAP. We used descriptive statistics to compare the 20 most frequently dispensed prescription medications (overall and for anticancer agents specifically) by PAP status and clinical indication. Analyses were conducted in Microsoft Excel and STATA Version 11.⁹

Results

Study patients and prescription medications

During the 18-month observation period, a monthly mean of 1,550 patients received a monthly total of 19,000 noninvestigational medications in the outpatient pharmacy. Of these patients, 7.5% (n = 1,929) met study eligibility criteria for PAPs and received 1 of the 104 medications provided through PAPs. Thus, there were 979 PAP users and 950 PAP nonusers in the final study population. In total, the study population received 23.3% (n = 77,592) of all outpatient medications administered during this period, of which anticancer agents represented 4% (n = 3,105; Table 1).

Comparison of patient characteristics

In comparison to PAP nonusers, PAP users were, on average, younger (48 vs 52 years), indigent (73% vs 19%), white (50% vs 43%), and covered by Medicaid or were uninsured (75% versus 20%). PAP users also had more prescriptions fills (median = 30 vs 20) during the study period at the institution. Univariate analyses showed that all patient characteristics, except gender, significantly predicted PAP use. Given the strong correlation of indigent and insurance status to PAP use, we conducted post hoc analyses to assess the potential for multicollinearity between the two patient characteristics. The variance inflation factor (VIF = 4.57) did not indicate multicollinearity concerns.

In the adjusted model, patients who were indigent (OR = 16.95; 95% CI: 6.845, 41.960), uninsured (OR = 4.60; 95% CI: 2.118, 9.970), and under the age of 65 years (OR = 2.31; 95% CI: 1.517, 3.509) were 2- to 17- fold more likely than others to be PAP users. Black patients were 31% (P = 0.020) less likely to access PAPs than were white patients.

Overall prescription medication fills The top-20 prescription medication fills from the MDACC outpatient pharmacy differed by PAP user group and PAP status. For PAP users, 88% of the most common medications obtained from PAPs were supportive care agents, including treatments of bacterial infections (n =887 fills; 49/month), antiemetics (n = 492 fills; 27/month), and gastroesophageal reflux disease (n = 492 fills; 27/month). Conversely, treatments for neutropenia and anticoagulation represented nearly half ($1.8 million) of the total charges avoided through PAPs to PAP users ($3.9 million). The most common medications not obtained from PAPs were for treatment of pain (PAP users = 292 fills/month, nonusers = 218 fills/month), versus only 13 fills/ month for pain medications from PAPs. Medications indicated to treat pain and nausea/vomiting accounted for the largest proportion of charges for medications not filled by PAPs for both PAP users and nonusers.

Anticancer agent prescription fills

For both PAP users and nonusers, the top-20 anticancer oral agent fills represented 93% (n = 2,892 of 3,105) of all anticancer oral fills (Table 6), with 16% (n = 454) of these oral fills being provided through PAPs. Among PAP users, anticancer agents from PAPs accounted for 40% of their total charges and 35% of the total number of agents. Temozolomide (Temodar; mean charge/fill = $3,346) represented the highest amount of total charges ($220,857) from PAPs, whereas imatinib (Gleevec; mean charge/fill = $5,372) and dasatinib (Sprycel; mean charge/fill = $5,221) accounted for the highest average charges per fill. Anastrozole (Arimidex; n = 178 fills; 10/month), capecitabine (Xeloda; n = 91 fills; 5/month), and temozolomide (n = 66 fills; 4/month) accounted for 70% of agents from PAPs. PAP users who were given bicalutamide received 100% of those agents from PAPs. Five of the seven oral anticancer agents with no fills from PAPs had initial US Food and Drug Administration approval years before 2000.

Discussion

At MDACC, PAPs are designed to help cancer patients overcome financial barriers to accessing oral supportive and anticancer agents. Over an 18-month observation period, less than 5% of the cancer patients at MDACC who received prescription medications from the outpatient pharmacy were enrolled in a PAP— and these PAPs provided 13% of their medication fills, representing an annualized $3.6 million in pharmaceutical expenditures. In interpreting our findings, several factors should be considered.

Oral anticancer agents accounted for 4% of all prescription medication fills during the study period. Comparatively, an analysis of the 2007 National Ambulatory Medical Care Survey showed that less than 1% of cancer patients were prescribed at least one oral anticancer agent.¹⁰ This finding indicates that both nationally and at MDACC, chemotherapy continues to be largely provided parenterally, as there is more of a financial benefit from intravenous therapies that are often reimbursed by insurers as well as PAPs.

In the outpatient pharmacy at MDACC, PAPs provided nearly onethird of oral anticancer fills for PAP users—totaling a mean of $500,000 per month in expenditures. However, three agents, anastrozole (for breast cancer), capecitabine (for breast and GI cancers, primarily), and temozolomide (for brain tumors) accounted for 75% of all of the anticancer agents provided by PAPs. We also found that pharmaceutical companies provided expensive newer, targeted, anticancer agents (primarily dasatinib and imatinib, the two agents with the greatest pharmaceutical per-person expenditures by the PAP program) through PAPs.

Although PAPs filled a strong and focused need for a small number of oral chemotherapy agents for some individuals with breast, GI, and brain cancers, they did not provide much benefit for a wide range of supportive care agents, particularly those that are schedule C and are used to treatcancer pain. Pain is the most prevalent symptom reported by cancer patients, 11 but there were few schedule C pain medications among the most common medications provided through PAPs. These substances are generally not provided by PAPs because of legal and substance abuse concerns.12 However, these medications were commonly prescribed to PAP users and PAP nonusers alike, outside of the PAP program. It would be important to evaluate the comparative success in treating pain among cancer patients at MDACC who receive a limited array of pain medications from PAPs (usually agents that are not substance-controlled by the Drug Enforcement Administration) versus treatment of pain experienced by patients whose medications are not reimbursed by PAPs.

We found that being younger than 65 years old, being indigent, and having no health insurance were the strongest predictors of using a PAP. This finding was expected, given that US adults younger than age 65 are ineligible for outpatient prescription medication coverage through Medicare Part D. However, contrary to expectations, about 45% of PAP users had either private or governmentsupplied health insurance. Because it is not uncommon for cancer patients to endure economic hardship (including bankruptcy) when trying to finance their care,13 healthcare professionals could recommend PAPs and other relevant assistance programs to all of their cancer patients.

With the expansion of health insurance through the Patient Protection and Affordable Health Care Act of 2010, it is hoped that the need for cancer patients to enroll in PAPs will be diminished; yet, given the reality of the high cost of anticancer agents, reimbursement policies for these agents, and tiered formularies among insurers leading to high outof- pocket costs for patients, the need for PAPs is likely to remain. PAPs can be a viable option for some patients, but healthcare professionals should be aware that there are a number of concerns about these programs, including their complex and burdensome application process and often limited variety of available drugs.14

This study is not without its limitations. First, we may have underestimated our sample of PAP patients due to the fact that MDACC did not electronically or systematically track the use of PAPs within its pharmacy database at the time of the study. The institution is in the process of developing such a system.

Second, the data used in this study cannot be assumed to reflect a “closed pharmacy” setting because some patients, particularly those who have health insurance with prescription medication coverage, may have received some of their medications from outside pharmacies.

Third, because insurance status is not necessarily a static characteristic, insurance status in this study was classified based on that at the time of registration with MDACC’s financial department, and no account was taken of changes that might have occurred.

Last, our results are not necessarily generalizable to all cancer populations, time periods, or settings. Cancer patients treated in academic centers such as MDACC may differ from those who are treated in community settings. In particular, fewer than 10% of patients at MDACC qualified for indigent financial assistance in 2007,15 which is likely to have impacted the number of patients who were potentially eligible for PAPs. It is also likely that had our study been conducted prior to the implementation of Medicare Part D, our sample of PAP patients would have been older. Nevertheless, our results may be generalizable to cancer patients receiving care in other academic cancer centers.

Conclusion This study builds upon a previous description of implementing PAPs in a comprehensive cancer center16 as well as contributes to our limited knowledge of the use of PAPs among cancer patients.6 Future studies should prospectively examine cancer patients’ experiences and satisfaction with PAPs from the process of applying to the point of receiving requested therapies and evaluate the effect of PAPs on cancer outcomes in various care settings. Multidisciplinary teams, including pharmacists and clinicians, should establish and recommend valid and relevant clinical endpoints for researchers to use in effectiveness studies of PAPs and cancer patients, particularly as they relate to oral anticancer agent use. Given that these oral agents represent more than 25% of cancer therapies in development,17 future studies of PAPs are ideal for evaluating concerns of accessibility, affordability, and compliance related to these agents.

MDACC is a unique resource for observers of PAPs, as it is the largest cancer center in the United States. However, few cancer patients at MDACC were eligible for and accessed PAPs in the outpatient pharmacy. Although smaller cancer centers may not be able to devote the same degree of financial and personnel resources to their patients as does MDACC, these centers could seek to build relationships with specific pharmaceutical companies that provide PAPs for the oral anticancer and supportive care therapies most commonly prescribed and administered at their centers. Scarce resources could also be utilized in other ways, such as by developing public-private risk pools for establishment of indigent care funds.

Acknowledgments: The authors thank Chun Feng, Jason Lau, and Oliver Max for their special assistance; Dr. Phoenix Do for her study design recommendations; and Karyn Popham for her editorial support. They especially thank Rebecca Arbuckle, RPh, for her support of this project. At the time of the study, Dr. Felder was supported by a Predoctoral Fellowship from The University of Texas School of Public Health Cancer Education and Career Development Program, funded by National Cancer Institute/NIH Grant R25-CA-57712-17.

References

1. Hede K. Increase in oral cancer drugs raises thorny issues for oncology practices. J Natl Cancer Inst 2009;101:1534–1536.
2. Chisholm MA, DiPiro JT. Pharmaceutical manufacturer assistance programs. Arch Intern Med 2002;162:780–784.
3. Duke KS, Raube K, Lipton HL. Patientassistance programs: assessment of and use by safety-net clinics. Am J Health Syst Pharm 2005;62:726–731.
4. Pisu M, Richman J, Allison JJ, Williams OD, Kiefe CI. Pharmaceuticals companies’ medication assistance programs: potentially useful but too burdensome to use? South Med J 2009;102:139–144.
5. Felder TM, Palmer NR, Lal LS, Mullen PD. What is the evidence for pharmaceutical patient assistance programs? a systematic review. J Health Care Poor Underserved 2011;22:24–49.
6. Meropol NJ, Schrag D, Smith TJ, et al. American Society of Clinical Oncology guidance statement: the cost of cancer care. J Clin Oncol 2009;27:3868–3874.
7. United States Department of Labor, Bureau of Labor Statistics: Producer Price Index Industry Data—Pharmaceutical Preparation & Manufacturing; 2010 [updated April 26, 2010]. http://www.bls.gov/ppi/data.htm. Accessed June 22, 2011.
8. Hosmer DW, Lemeshow S. Applied Logistic
Logistic
Regression. New York: Wiley; 2000. 9. StataCorp LP. STATA statistical software. 2009; Release 11.
10. Arora S. Use of oral chemotherapeutic medications in non-traditional ambulatory settings; 2009. http://digarchive.library.vcu. edu/dspace/bitstream/10156/2711/1/Thesis_ MPH_sameer.pdf. Accessed June 22, 2011.
11. Cherny NI. The management of cancer pain. CA Cancer J Clin 2000;50:70–116.
12. Williams K. Accessing patient assistance programs to meet clients’ medication needs. J Am Acad Nurse Pract 2000;12:233– 235.
13. National Survey of Households Affected by Cancer: Kaiser Family Foundation; 2006 [updated November 2006]. http://kff.org/kaiserpolls/ upload/7591.pdf. Accessed June 22, 2011.
14. Choudhry NK, Lee JL, Agnew-Blais J, Corcoran C, Shrank WH. Drug company- sponsored patient assistance programs: a viable safety net? Health Aff (Millwood) 2009;28:827–834.
15. Ackerman T. M.D. Anderson submits its records on charitable care: cancer center hopes to quell Iowa senator’s investigation. Houston Chronicle. October 9, 2008. http:// www.chron.com/disp/story.mpl/metropolitan/ 6050254.html. Accessed June 22, 2011.
16. Johnson PE. Patient assistance programs and patient advocacy foundations: alternatives for obtaining prescription medications when insurance fails. Am J Health Syst Pharm 2006;63(21 suppl 7):S13–S17.
17. Weingart SA, Brown E, Bach PB, et al. National Comprehensive Cancer Network task force report: oral chemotherapy. JNCCN 2008;6(suppl 3):S1–S25.

Oral anticancer and supportive care agents administered to cancer patients are costly and are associated with large copayment requirements or are often not fully reimbursed by private health insurers or Medicare.¹ To facilitate access to oral medications, pharmaceutical manufacturers have developed patient assistance programs (PAPs) that provide selected oral medications at no or reduced cost to financially eligible patients. Eligibility criteria, application processes, and program administration for PAPs differ by manufacturer and by product, which can ultimately present logistical barriers.^2–4 A systematic review of PAPs found improvements in disease indicator outcomes for patients with common chronic diseases who access these programs.⁵ However, knowledge about the use of PAPs among cancer patients is limited.⁶

The University of Texas MD Anderson Cancer Center (MDACC), the largest tertiary care cancer center in the country, has developed a systematic approach to administering a large number of PAPs. In 1996, the MDACC established an institutional program staffed by hospital pharmacy personnel, who navigate cancer patients through PAPs in inpatient and outpatient settings. This program removes the operational and administrative barriers often experienced by patients in smaller clinical settings.

Cancer patients eligible for PAPs at MDACC include those who are uninsured, those who are underinsured, those whose pharmacy benefit limits have been reached, and those whose private health or government insurance has denied coverage of certain oral medications. For example, the Texas Medicaid program limits its low-income beneficiaries to three prescriptions per month, which may lead some of them, particularly those with cancer, to require additional medication assistance through PAPs. As of April 2008, this institutional program established formal relationships with 29 pharmaceutical companies that provide 104 therapeutic or supportive care agents through PAPs to eligible cancer patients in the MDACC outpatient pharmacy.

Methods

Data source

Approval for this study was obtained from the MDACC Institutional Review Board. We conducted a retrospective, secondary analysis of noninvestigational prescription medications from the outpatient pharmacy at MDACC. Data from July 1, 2006, to December 31, 2007, were extracted from computerized pharmacy, medical, and cancer registry databases at MDACC. Prescriptions had to include both patient medical record and social security numbers to validate the patient’s identity as well as the date of pickup to validate that the medication had been dispensed during the study period. When the date of pickup was missing but billing was documented, the date the medication was dispensed was used as the pickup date. All data were de-identified prior to analysis.

PAPs

Prescriptions for oral medications were available to financially eligible individuals via two types of PAPs at MDACC: individual enrollment (60 distinct medications) and bulk drug replacement (44 distinct medications). Individual enrollment required that an eligible patient apply directly to a pharmaceutical company’s PAP for the medication (s) needed. Once approved, the requested medication was mailed directly to the patient or dispensed in the MDACC pharmacy. Given the purpose of this study, we were only interested in those PAP prescription medications dispensed at the outpatient pharmacy.

Bulk replacement PAPs provide available prescription medications in bulk quantities on a monthly (in some cases quarterly) basis to MDACC’s pharmacy to replace medications dispensed to patients who were classified as “indigent” by MDACC-established criteria. Financially indigent patients included those who were Texas residents, uninsured or insured by Medicaid, and not responsible for charges billed to MDACC. All eligible patients could apply for the 60 medications available through individual PAP enrollment, but only indigent patients qualified for the 44 medications available through bulk drug replacement to MDACC.

Patient classifications

Prescription data were extracted from a pharmacy administrative dispensing database; a systematic process was developed to identify case patients (based on financial eligibility) and control patients (similar to case patients with respect to treatments received but were nonusers of PAP programs). Only patients who were potentially eligible for PAPs were included in the study. The case selection was based on MDACC’s determination of a patient’s ability to pay, referred to as credit rating, at the time of a patient’s registration at the institution. Regardless of health insurance status, patients who had a low credit rating (responsible for 0%– 50% of their charges) were classified as being potentially eligible for PAPs. Patients with low credit ratings also included those who were indigent. The control selection identified a set of insured patients, including those with high credit ratings (responsible for 100% of their charges), who had been referred for special financial assistance to obtain specific medications through PAPs.

To be included in the study, patients identified based on a low credit rating had to receive at least 1 of the 104 medications through a PAP to be classified as a PAP user; these patients could receive other medications through traditional payment. PAP nonusers had to receive at least 1 of the 104 medications associated with PAPs through traditional payment or other third-party source, not through a PAP. Patients who had been referred for special assistance had to receive one or more of the PAP medications initially requested from a PAP.

For a drug to be verified as a PAP prescription medication, the pharmacy record could not have documentation of third-party payer or patient payment for that medication. The only exception made for payer and patient payment was for prescription medications provided by one particular pharmaceutical company, which required a $10 copayment for its PAP medications. Once PAP and non-PAP prescriptions were verified, they were aggregated by a unique patient identifier to yield prescriptionuse data for individual patients who were categorized as PAP users versus PAP nonusers.

Patient characteristics

Data on patient gender, race/ethnicity, age, insurance status, and primary cancer site were extracted. Race/ ethnicity was categorized as white, black, Hispanic, or Asian/other. Age was calculated as of July 1, 2006, from the patient’s birth date. Insurance status was based on the patient’s insurance status at the time of registration at MDACC and categorized as follows: no insurance (include self-payers and patients referred from the county public hospital), Medicare, Medicaid, or any of a variety of private/commercial insurances. Private insurances were combined into one category. Information on each patient’s primary cancer site was categorized as blood, breast, genitourinary, head and neck, or other (primarily brain, central nervous system, and an unknown primary site).

The gender and insurance variables had some missing data. When there were conflicting data for a particular patient’s gender, we coded gender as missing. When the insurance type was missing, data on the patient’s insurance status at the time of registration at MDACC were retrieved from MDACC’s financial department.

Prescription medication fills

Data on the prescription medication name (generic or brand) and institutional billing charges per fill were extracted from pharmacy records. Prescriptions were aggregated by generic and brand names, regardless of strength, dosage form, or method of administration, to identify the 20 most frequently dispensed medications overall and for the treatment of cancer. We then used Rxlist.com (www.rxlist.com), an online medication reference program, to identify each medication’s clinical indication(s). For example, the brand name medication Zofran would be aggregated with its generic, ondansetron, and would be considered as one medication indicated for nausea and vomiting.

We extracted patient billing charge per medication fill in dollars by the date of pickup in the outpatient pharmacy. Patient billing charge included patient copayments and did not include any payments from the patient’s payer or health plan. If the billing charge was missing for a medication fill, we applied a comparable charge from a prescription medication of the same name, dosage, quantity, date of pickup, and patient insurance status. When quantity, date, or patient insurance status differed, the lowest available charge was used. All charges were adjusted to the year 2008 using the US Bureau of Labor’s Annual Producer Price Index for pharmaceutical preparation and manufacturing.⁷

Data analysis

For patient-level analyses, a PAP user was a patient who received at least one medication through a PAP during the study period. We used descriptive statistics to compare patient characteristics of PAP users versus PAP nonusers. Next, we conducted separate unadjusted binary logit regression analyses (interpreted with odds ratios [ORs] and 95% confidence intervals [CIs]) to estimate the differences in the probability of being a PAP user for each of the patient characteristics. All patient characteristics that were statistically significant at P < 0.20 for the unadjusted analyses8 were included in the final multivariable model. The a priori level of significance was set at P < 0.05 for the multivariable model.

For other analyses conducted at the prescription level, a PAP medication was a medication verified as being provided through a PAP. We used descriptive statistics to compare the 20 most frequently dispensed prescription medications (overall and for anticancer agents specifically) by PAP status and clinical indication. Analyses were conducted in Microsoft Excel and STATA Version 11.⁹

Results

Study patients and prescription medications

During the 18-month observation period, a monthly mean of 1,550 patients received a monthly total of 19,000 noninvestigational medications in the outpatient pharmacy. Of these patients, 7.5% (n = 1,929) met study eligibility criteria for PAPs and received 1 of the 104 medications provided through PAPs. Thus, there were 979 PAP users and 950 PAP nonusers in the final study population. In total, the study population received 23.3% (n = 77,592) of all outpatient medications administered during this period, of which anticancer agents represented 4% (n = 3,105; Table 1).

Comparison of patient characteristics

In comparison to PAP nonusers, PAP users were, on average, younger (48 vs 52 years), indigent (73% vs 19%), white (50% vs 43%), and covered by Medicaid or were uninsured (75% versus 20%). PAP users also had more prescriptions fills (median = 30 vs 20) during the study period at the institution. Univariate analyses showed that all patient characteristics, except gender, significantly predicted PAP use. Given the strong correlation of indigent and insurance status to PAP use, we conducted post hoc analyses to assess the potential for multicollinearity between the two patient characteristics. The variance inflation factor (VIF = 4.57) did not indicate multicollinearity concerns.

In the adjusted model, patients who were indigent (OR = 16.95; 95% CI: 6.845, 41.960), uninsured (OR = 4.60; 95% CI: 2.118, 9.970), and under the age of 65 years (OR = 2.31; 95% CI: 1.517, 3.509) were 2- to 17- fold more likely than others to be PAP users. Black patients were 31% (P = 0.020) less likely to access PAPs than were white patients.

Overall prescription medication fills The top-20 prescription medication fills from the MDACC outpatient pharmacy differed by PAP user group and PAP status. For PAP users, 88% of the most common medications obtained from PAPs were supportive care agents, including treatments of bacterial infections (n =887 fills; 49/month), antiemetics (n = 492 fills; 27/month), and gastroesophageal reflux disease (n = 492 fills; 27/month). Conversely, treatments for neutropenia and anticoagulation represented nearly half ($1.8 million) of the total charges avoided through PAPs to PAP users ($3.9 million). The most common medications not obtained from PAPs were for treatment of pain (PAP users = 292 fills/month, nonusers = 218 fills/month), versus only 13 fills/ month for pain medications from PAPs. Medications indicated to treat pain and nausea/vomiting accounted for the largest proportion of charges for medications not filled by PAPs for both PAP users and nonusers.

Anticancer agent prescription fills

For both PAP users and nonusers, the top-20 anticancer oral agent fills represented 93% (n = 2,892 of 3,105) of all anticancer oral fills (Table 6), with 16% (n = 454) of these oral fills being provided through PAPs. Among PAP users, anticancer agents from PAPs accounted for 40% of their total charges and 35% of the total number of agents. Temozolomide (Temodar; mean charge/fill = $3,346) represented the highest amount of total charges ($220,857) from PAPs, whereas imatinib (Gleevec; mean charge/fill = $5,372) and dasatinib (Sprycel; mean charge/fill = $5,221) accounted for the highest average charges per fill. Anastrozole (Arimidex; n = 178 fills; 10/month), capecitabine (Xeloda; n = 91 fills; 5/month), and temozolomide (n = 66 fills; 4/month) accounted for 70% of agents from PAPs. PAP users who were given bicalutamide received 100% of those agents from PAPs. Five of the seven oral anticancer agents with no fills from PAPs had initial US Food and Drug Administration approval years before 2000.

Discussion

At MDACC, PAPs are designed to help cancer patients overcome financial barriers to accessing oral supportive and anticancer agents. Over an 18-month observation period, less than 5% of the cancer patients at MDACC who received prescription medications from the outpatient pharmacy were enrolled in a PAP— and these PAPs provided 13% of their medication fills, representing an annualized $3.6 million in pharmaceutical expenditures. In interpreting our findings, several factors should be considered.

Oral anticancer agents accounted for 4% of all prescription medication fills during the study period. Comparatively, an analysis of the 2007 National Ambulatory Medical Care Survey showed that less than 1% of cancer patients were prescribed at least one oral anticancer agent.¹⁰ This finding indicates that both nationally and at MDACC, chemotherapy continues to be largely provided parenterally, as there is more of a financial benefit from intravenous therapies that are often reimbursed by insurers as well as PAPs.

In the outpatient pharmacy at MDACC, PAPs provided nearly onethird of oral anticancer fills for PAP users—totaling a mean of $500,000 per month in expenditures. However, three agents, anastrozole (for breast cancer), capecitabine (for breast and GI cancers, primarily), and temozolomide (for brain tumors) accounted for 75% of all of the anticancer agents provided by PAPs. We also found that pharmaceutical companies provided expensive newer, targeted, anticancer agents (primarily dasatinib and imatinib, the two agents with the greatest pharmaceutical per-person expenditures by the PAP program) through PAPs.

Although PAPs filled a strong and focused need for a small number of oral chemotherapy agents for some individuals with breast, GI, and brain cancers, they did not provide much benefit for a wide range of supportive care agents, particularly those that are schedule C and are used to treatcancer pain. Pain is the most prevalent symptom reported by cancer patients, 11 but there were few schedule C pain medications among the most common medications provided through PAPs. These substances are generally not provided by PAPs because of legal and substance abuse concerns.12 However, these medications were commonly prescribed to PAP users and PAP nonusers alike, outside of the PAP program. It would be important to evaluate the comparative success in treating pain among cancer patients at MDACC who receive a limited array of pain medications from PAPs (usually agents that are not substance-controlled by the Drug Enforcement Administration) versus treatment of pain experienced by patients whose medications are not reimbursed by PAPs.

We found that being younger than 65 years old, being indigent, and having no health insurance were the strongest predictors of using a PAP. This finding was expected, given that US adults younger than age 65 are ineligible for outpatient prescription medication coverage through Medicare Part D. However, contrary to expectations, about 45% of PAP users had either private or governmentsupplied health insurance. Because it is not uncommon for cancer patients to endure economic hardship (including bankruptcy) when trying to finance their care,13 healthcare professionals could recommend PAPs and other relevant assistance programs to all of their cancer patients.

With the expansion of health insurance through the Patient Protection and Affordable Health Care Act of 2010, it is hoped that the need for cancer patients to enroll in PAPs will be diminished; yet, given the reality of the high cost of anticancer agents, reimbursement policies for these agents, and tiered formularies among insurers leading to high outof- pocket costs for patients, the need for PAPs is likely to remain. PAPs can be a viable option for some patients, but healthcare professionals should be aware that there are a number of concerns about these programs, including their complex and burdensome application process and often limited variety of available drugs.14

This study is not without its limitations. First, we may have underestimated our sample of PAP patients due to the fact that MDACC did not electronically or systematically track the use of PAPs within its pharmacy database at the time of the study. The institution is in the process of developing such a system.

Second, the data used in this study cannot be assumed to reflect a “closed pharmacy” setting because some patients, particularly those who have health insurance with prescription medication coverage, may have received some of their medications from outside pharmacies.

Third, because insurance status is not necessarily a static characteristic, insurance status in this study was classified based on that at the time of registration with MDACC’s financial department, and no account was taken of changes that might have occurred.

Last, our results are not necessarily generalizable to all cancer populations, time periods, or settings. Cancer patients treated in academic centers such as MDACC may differ from those who are treated in community settings. In particular, fewer than 10% of patients at MDACC qualified for indigent financial assistance in 2007,15 which is likely to have impacted the number of patients who were potentially eligible for PAPs. It is also likely that had our study been conducted prior to the implementation of Medicare Part D, our sample of PAP patients would have been older. Nevertheless, our results may be generalizable to cancer patients receiving care in other academic cancer centers.

Conclusion This study builds upon a previous description of implementing PAPs in a comprehensive cancer center16 as well as contributes to our limited knowledge of the use of PAPs among cancer patients.6 Future studies should prospectively examine cancer patients’ experiences and satisfaction with PAPs from the process of applying to the point of receiving requested therapies and evaluate the effect of PAPs on cancer outcomes in various care settings. Multidisciplinary teams, including pharmacists and clinicians, should establish and recommend valid and relevant clinical endpoints for researchers to use in effectiveness studies of PAPs and cancer patients, particularly as they relate to oral anticancer agent use. Given that these oral agents represent more than 25% of cancer therapies in development,17 future studies of PAPs are ideal for evaluating concerns of accessibility, affordability, and compliance related to these agents.

MDACC is a unique resource for observers of PAPs, as it is the largest cancer center in the United States. However, few cancer patients at MDACC were eligible for and accessed PAPs in the outpatient pharmacy. Although smaller cancer centers may not be able to devote the same degree of financial and personnel resources to their patients as does MDACC, these centers could seek to build relationships with specific pharmaceutical companies that provide PAPs for the oral anticancer and supportive care therapies most commonly prescribed and administered at their centers. Scarce resources could also be utilized in other ways, such as by developing public-private risk pools for establishment of indigent care funds.

Acknowledgments: The authors thank Chun Feng, Jason Lau, and Oliver Max for their special assistance; Dr. Phoenix Do for her study design recommendations; and Karyn Popham for her editorial support. They especially thank Rebecca Arbuckle, RPh, for her support of this project. At the time of the study, Dr. Felder was supported by a Predoctoral Fellowship from The University of Texas School of Public Health Cancer Education and Career Development Program, funded by National Cancer Institute/NIH Grant R25-CA-57712-17.

References

1. Hede K. Increase in oral cancer drugs raises thorny issues for oncology practices. J Natl Cancer Inst 2009;101:1534–1536.
2. Chisholm MA, DiPiro JT. Pharmaceutical manufacturer assistance programs. Arch Intern Med 2002;162:780–784.
3. Duke KS, Raube K, Lipton HL. Patientassistance programs: assessment of and use by safety-net clinics. Am J Health Syst Pharm 2005;62:726–731.
4. Pisu M, Richman J, Allison JJ, Williams OD, Kiefe CI. Pharmaceuticals companies’ medication assistance programs: potentially useful but too burdensome to use? South Med J 2009;102:139–144.
5. Felder TM, Palmer NR, Lal LS, Mullen PD. What is the evidence for pharmaceutical patient assistance programs? a systematic review. J Health Care Poor Underserved 2011;22:24–49.
6. Meropol NJ, Schrag D, Smith TJ, et al. American Society of Clinical Oncology guidance statement: the cost of cancer care. J Clin Oncol 2009;27:3868–3874.
7. United States Department of Labor, Bureau of Labor Statistics: Producer Price Index Industry Data—Pharmaceutical Preparation & Manufacturing; 2010 [updated April 26, 2010]. http://www.bls.gov/ppi/data.htm. Accessed June 22, 2011.
8. Hosmer DW, Lemeshow S. Applied Logistic
Logistic
Regression. New York: Wiley; 2000. 9. StataCorp LP. STATA statistical software. 2009; Release 11.
10. Arora S. Use of oral chemotherapeutic medications in non-traditional ambulatory settings; 2009. http://digarchive.library.vcu. edu/dspace/bitstream/10156/2711/1/Thesis_ MPH_sameer.pdf. Accessed June 22, 2011.
11. Cherny NI. The management of cancer pain. CA Cancer J Clin 2000;50:70–116.
12. Williams K. Accessing patient assistance programs to meet clients’ medication needs. J Am Acad Nurse Pract 2000;12:233– 235.
13. National Survey of Households Affected by Cancer: Kaiser Family Foundation; 2006 [updated November 2006]. http://kff.org/kaiserpolls/ upload/7591.pdf. Accessed June 22, 2011.
14. Choudhry NK, Lee JL, Agnew-Blais J, Corcoran C, Shrank WH. Drug company- sponsored patient assistance programs: a viable safety net? Health Aff (Millwood) 2009;28:827–834.
15. Ackerman T. M.D. Anderson submits its records on charitable care: cancer center hopes to quell Iowa senator’s investigation. Houston Chronicle. October 9, 2008. http:// www.chron.com/disp/story.mpl/metropolitan/ 6050254.html. Accessed June 22, 2011.
16. Johnson PE. Patient assistance programs and patient advocacy foundations: alternatives for obtaining prescription medications when insurance fails. Am J Health Syst Pharm 2006;63(21 suppl 7):S13–S17.
17. Weingart SA, Brown E, Bach PB, et al. National Comprehensive Cancer Network task force report: oral chemotherapy. JNCCN 2008;6(suppl 3):S1–S25.

The standard of care for locally advanced breast cancer (LABC) is neoadjuvant chemotherapy,¹ with LABC including clinical stages IIA, IIB, and IIIA. The goals of preoperative chemotherapy are to downstage so as to render breast conservation feasible, to eradicate disease in the axillary nodes, and to allow in vivo testing of tumor drug sensitivity, all with the ultimate aim of improving prognosis. Clinical trials have demonstrated that the pathologic in-breast response generally correlates with pathologic response in the lymph nodes. Furthermore, nodal status at the time of surgery correlates with overall survival (OS) and disease-free survival (DFS).²,³ A combined analysis of two large prospective neoadjuvant chemotherapy trials demonstrated significantly higher 5-year OS and DFS in patients achieving in-breast pathologic complete response (pCR), compared with those who did not (OS, 89% vs 64%; DFS, 87% vs 58%, respectively).⁴

At the start of this trial, the most effective neoad- juvant regimen remained in question. Even now, National Comprehensive Cancer Center guidelines suggest that any recommended adjuvant regimen can be used in the neoadjuvant setting.¹ Numerous phase II and III trials have evaluated single-agent^5–8 and combination^{9– 32} chemotherapies, most of which are anthracycline- based, with pCR rates reported between 7% and 36%. In the NSABP-B27 study, patients treated preoperatively with four cycles of doxorubicin and cyclophosphamide (AC) followed by four cycles of docetaxel (Taxotere) had a 26% pCR rate versus a 13% pCR rate in those receiving preoperative AC and postoperative docetaxel. Despite the doubling of pCR with neoadjuvant docetaxel, there was no difference in DFS or OS.9 However, as reported by Kuerer et al, patients achieving a pCR after completion of neoadjuvant chemotherapy appeared to have superior survival.⁴

Many previous trials (including the study reported here) did not exclude patients with human epidermal growth factor receptor 2 (HER2)-positive disease. It is now well established that such patients should be treated with neoadjuvant regimens incorporating HER2-targeted therapy. In fact, an early neoadjuvant study of paclitaxel followed by fluorouracil, epirubicin, and cyclophosphamide with or without 24 weeks of concurrent trastuzumab (Herceptin) in patients with HER2-positive tumors was closed early because patients receiving trastuzumab had a pCR rate of 65%, compared with 26% in those who did not receive it.³³ Expanded clinical trials of this approach are in progress.

The selection of capecitabine (Xeloda) and docetaxel in the present trial was based on the hypothesis that the upregulation of thymidine phosphorylase by docetaxel should increase the activity of capecitabine. ^34–36 Single-agent docetaxel in the neoadjuvant setting has yielded pCR rates of 7%–20%.^6–8 Treatment with docetaxel and capecitabine together has been reported to produce pCR rates of 10%–21%.^37–39 The addition of carboplatin was based on studies by Hurley et al at the University of Miami^{39– 41} suggesting that platinum salts appeared quite active in the neoadjuvant setting, with the combination of docetaxel and cisplatin producing a pCR rate of 20%, with no residual disease in the breast or axilla.40 Other regimens incorporating cisplatin or carboplatin have pCR rates ranging from 16% to 24%.^27,42–44

Patients and methods

Study design

In this phase II multicenter study, patients were assigned to receive docetaxel (30 mg/m2 IV) and carboplatin (AUC 2 IV) on days 1, 8, and 15 of each 28-day cycle plus capecitabine (625 mg/m2 PO) twice daily on days 5–18. The capecitabine dose was based on observations that this dose was effective and relatively nontoxic in metastatic breast cancer (C.L. Vogel, empirical observations). Patients were to receive four cycles prior to surgical resection.

Given that this neoadjuvant regimen was under study, all of the patients were scheduled to receive a proven standard postoperative adjuvant chemotherapy regimen, starting 4–6 weeks postoperatively, with doxorubicin (60 mg/m2 IV) and cyclophosphamide (600 mg/m2 IV) every 21 days for 4 cycles. This sequential design was prompted by studies such as the NSABP B-27 and Aberdeen trials.^9,32

Radiation therapy after lumpectomy or mastectomy was given according to individual institution guidelines. Patients with hormone receptor–positive tumors received appropriate antihormonal therapy. Tumor measurements were assessed at baseline and on day 1 of each cycle by physical examination with calipers. No breast or other imaging was required during the period of neoadjuvant chemotherapy or immediately preoperatively. Patients were considered evaluable if they proceeded to surgery after all intended cycles of neoadjuvant chemotherapy or if they developed disease progression during neoadjuvant therapy.

Patients

Eligible patients were men and women regardless of menopausal status ≥ 18 years of age with coreneedle biopsy proven locally advanced or inflammatory breast cancer. Breast cancer characteristics such as estrogen receptor (ER), progesterone receptor (PR), or HER2 status were collected but not used for inclusion/exclusion. Eligible tumors were T2 requiring mastectomy; T3N0–2; T4; and any TN2–3 that by calipers was > 2 cm or with fixed or matted axillary or imaging-detected internal mammary nodes. Patients with prior ductal carcinoma in situ (DCIS) were included, as were those with ≤ T2N0M0 breast cancer > 5 years prior.

Other requirements were an Eastern Cooperative Oncology Group (ECOG) performance status of 0–1; life expectancy > 6 months; negative metastatic workup (bone scan and CT chest/abdomen/pelvis); adequate bone marrow, liver, and kidney function; and peripheral neuropathy ≤ grade 1. All patients of child-bearing potential were required to consent to dual methods of contraception during treatment and for 3 months afterward. A negative pregnancy test was required for these women before treatment, and any suspicion of pregnancy had to be reported to the treating physician.

Study endpoints

The primary endpoint of the study was the in-breast pCR after four cycles of platinum-based neoadjuvant chemotherapy. Pathologic complete response was defined as complete disappearance of invasive and in situ disease or invasive disease alone. During the course of this trial, it became generally acceptable to include patients with only residual DCIS as equivalent to pCR.⁴⁵

The secondary endpoints were pCR in the lymph nodes; clinical response rate; tolerability; breast conservation; time to disease progression (local, regional, and distant); and OS. Also recorded was minimal residual disease (MRD), which we arbitrarily defined as ≤ 1 cm invasive carcinoma at resection. The overall treatment plan included postoperative AC to provide a standard-of-care regimen to maximize curative potential.

Statistical analysis

Data were analyzed on an intentto- treat basis. Although pCR rates with doxorubicin plus either cyclophosphamide or docetaxel have been < 15%, the studies by Smith et al26 and Hurley et al39 with in-breast pCR rates of at least 20% served as comparators (albeit imprecise).

Applying the min/max statistical design, the procedure tests the null hypothesis H0: P ≤ 0.15 against the alternative hypothesis H1: P ≥ 0.30. The overall level of significance and power for this design are 5% and 80%, respectively. The sample size needed for the first stage was 23 evaluable patients. If three or fewer pCR responses were observed, then the study would be terminated and the treatment regimen would not be investigated further. Otherwise, an additional 25 evaluable patients would be accrued for a total of 48 study patients. If 11 or fewer responses were observed, then the study would be terminated. Otherwise, this treatment regimen would be recommended to proceed to phase III for further investigation.

Tolerability assessment

At each visit, toxicities were assessed and graded according to the National Cancer Institute Common Toxicity Criteria, version 2.46 Two dose reductions were allowed for all drugs.

Ethical considerations

The investigational nature of this study was fully disclosed to each patient. In accordance with institutional and federal guidelines, the patients were guided through and subsequently signed the informed consent approved by the appropriate site Institutional Review Board.

Literature review

The terms “neoadjuvant” and “breast” were used in a literature search on PubMed, with filters “English” and “clinical trials.” Abstracts for each of the 398 results were reviewed We used phase II or III trials with at least 30 patients, at least four cycles of chemotherapy, and clearly defined pCR for comparison to this study.

Results Patients

Between June 2003 and December 2006, 50 women with a median age of 49 years (range, 28–75 years) were enrolled. One patient was ineligible due to preceding lumpectomy. The 49 eligible patients were treated with ≥ 1 cycle of neoadjuvant chemotherapy between June 27, 2003, and April 12, 2007.

The baseline characteristics of the 49 eligible patients are summarized in Table 3. Thirty-one patients (63%) were premenopausal. Twenty patients (41%) were positive for either ER or PR and were negative for HER2. Eight patients (16%) had HER2- positive tumors, and 23 (46%) had triple-negative tumors. At baseline, 22 patients (45%) had clinical lymphadenopathy, and 1 patient (2%) had inflammatory breast cancer.

The 41 patients (83%) who completed all four cycles of therapy were evaluable for response; 8 (16%) were inevaluable due to noncompliance (1), grade 3 or 4 toxicity (5), or withdrawal of consent (2). The following efficacy assessments apply to the 41 evaluable patients, whereas the toxicity assessments include the 49 patients who received at least one full cycle of chemotherapy.

Clinical response

At study onset, of the 49 eligible patients, 38 (78%) had a palpable inbreast tumor (median size, 5.5 cm); 22 (45%) had enlarged nodes, and 34 (69%) had confirmed nodal involvement (by biopsy or imaging). A clinical complete response (cCR) rate in the breast was seen in 23 of 41 (56%) evaluable patients. Of 22 patients with baseline lymphadenopathy (by imaging or physical examination), 13 had axillary assessment by physical examination throughout treatment, with 12 (92%) exhibiting a cCR in the axilla.

Pathologic response

After four cycles of chemotherapy, an in-breast pCR (the primary endpoint) was demonstrated in 6 of 41 patients (15%). One of these six patients had residual DCIS and is listed separately. All of these patients had nodal pCR, whereas overall, 20 patients (49%) had negative nodes at resection.

The pathology reports of two patients were read as having invasive tumor within lymphatics and lymphovascular invasion (one each) with no measurable disease, with tumor thus sized as Tx. Neither of these patients had involved lymph nodes. Fourteen patients (34%) had MRD in the breast, and 8 of these 14 patients (57%) had residual nodal disease. Nine patients (22%) had T1c tumors (> 1–2 cm), with five of these nine patients (55%) having nodal disease. Seven patients (17%) had T2 tumors (> 2–5 cm) tumors, with five of these seven patients (71%) having nodal disease. These findings are summarized in Table 4. The correlation between in-breast cCR and pCR was 26%.

Biologic features of responders

Of interest, five of the six patients with a pCR had triple-negative tumors. This translates to a 22% pCR rate (5 of 23) in the triple-negative subset, and a pCR rate of 6% (1 of 18) in patients with ER-positive and/ or PR-positive tumors. The remaining patient with a pCR had ER-, PR-, and HER2-positive disease.

One patient had inflammatory breast cancer at diagnosis, and another developed this during the course of chemotherapy; the latter patient was removed from the study for progressive disease. Interestingly, the patient who presented with inflammatory breast cancer was one of the six patients with a pCR. Both of these inflammatory disease patients had triple-negative tumors.

Conversion to breast conservation

Breast conservation was offered to patients if it was deemed appropriate by the treating surgeon. Preoperative imaging was not mandated and thus was not routinely performed. Mastectomy was ultimately performed in 4 of the 6 patients (67%) with pCR and in 22 of the 35 patients (63%) with less than a pCR. Thus, the choice for breast conservation did not correlate well with response to chemotherapy.

Time to disease progression

At a median follow-up of 48 months (range, 7–63), 36 of 41 patients (88%) remained free of disease (range, 19–63 months). Two patients had progressive disease while they were on study treatment and had T3 tumors on resection. Another three patients were found to have progressive disease at 10, 41, and 50 months from study day 1.

Of the nine patients with T1c disease, only one patient (who had positive nodes at resection) had a recurrence (at 41 months). Overall, the patients who had a recurrence had MRD (one patient), T1c (one patient), T2 (one patient), and T3 (the same two patients whose disease progressed while they were on treatment and continued to progress after surgery).

Disease-free and overall survival

Three patients were lost to followup, with point of last contact at 19, 34, and 59 months. Of the 41 evaluable patients, 5 patients developed progressive disease, with 2 of these patients progressing during the study treatment. Disease-free survival at 12, 24, and 36 months was 89%, 89%, and 78%, respectively. Overall survival at these same time points was 95%, 90%, and 76%. None of the patients with a pCR is known to have recurrent disease. Of the six patients achieving pCR, two were lost to follow-up after 34 and 59 months, and four continued diseasefree at 38, 39, 55, and 62 months.

Adverse events

Five patients were removed from the study secondary to toxicities. Grade 3 and 4 toxicity events are summarized in Table 5. Grade 3 toxicities were anemia (4), diarrhea (2), epigastric pain (1), fatigue (2), hand-foot syndrome (1), infection (1), leukopenia (9), pain (5), and peripheral sensory neuropathy (1). Grade 4 toxicities were depression (1) and leukopenia (4). Toxicities (all grades) occurring in ≥ 10% of the 49 treated patients were anemia (76%), leukopenia (70%), fatigue (67%), nausea (59%), alopecia (49%), thrombocytopenia (47%), diarrhea (47%), constipation (37%), pain (35%), vomiting (31%), epigastric pain (27%), nail changes (22%), epiphora (22%), hand-foot syndrome (20%), infection (18%), edema (16%), rash (16%), anorexia (16%), and depression (10%). In the intent-to-treat population, there were nine dose reductions among nine patients, and 19 dose delays among 15 patients.

Discussion

The combination of agents tested thus far in the neoadjuvant setting consistently produce pCR rates far less than 50% in unselected populations. This study was begun prior to the widespread use of personalized medicine. Most prior published trials had utilized anthracycline-based chemotherapy, with response rates generally ranging between 7% and 36%.6,9–26,28–31,41,42

The idea of thymidine phosphorylase upregulation by the combination of capecitabine and docetaxel upon which this study was largely based^34–36 has since been disputed.⁴⁷ The primary endpoint of this trial of a novel platinum- based regimen was a pCR rate of 15%. It is significant that 83% of the pCRs were in triple-negative tumors. A secondary endpoint of MRD was calculated, as this was in the original design of the study, but ultimately was not relevant to the primary endpoint.

Ultimately, pCR is the more relevant point of discussion for the modern era. The 15% pCR rate seen in this phase II study was within range of those achieved in numerous other phase II/III neoadjuvant chemotherapy trials with ≥ 25 patients, ≥ 3 cycles of chemotherapy, and pCR defined as absence of carcinoma in the breast and axilla. To date, no patient in our study with a pCR has been noted to have recurrent disease. However, a recently published French study found a 22% recurrence rate at 11 years in patients with triple-negative breast cancer achieving pCR, highlighting the importance of longer-term follow- up.⁴⁸.

The inclusion of patients with HER2-positive disease in neoadjuvant studies without HER2-targeted therapy was standard at the time that this study was conducted, but is no longer appropriate. If we were to exclude the eight HER2-positive patients from analysis, then there would be only 34 patients evaluable for response, with a pCR rate of 18%. Buzdar et al33 demonstrated a 65% pCR rate in women with HER2-positive disease treated with neoadjuvant chemotherapy plus trastuzumab. The improvement in pCR with the addition of trastuzumab is supported by other confirmatory trials. Authors of a single-arm trial of dose-dense epirubicin and cyclophosphamide followed by dosedense docetaxel and trastuzumab in a HER2-positive population reported a pCR rate of 57%.⁴⁹ The randomized NOAH study50 achieved a pCR rate of 23% in 115 patients treated with trastuzumab-based chemotherapy.

It is interesting to note that five of six patients (83%) achieving a pCR in our study had triple-negative tumors. Investigators at the University of Miami presented a retrospective review of locally advanced triple-negative breast cancer treated with docetaxel and a platinum salt, with 61% of patients also receiving AC. The authors reported a pCR rate of 34% overall and 40% for patients receiving AC.⁵¹ A pCR rate of 60% was noted in the triplenegative subset of patients in another study evaluating docetaxel, doxorubicin, and cyclophosphamide with or without vinorelbine/capecitabine (GeparTrio Study).⁵² Further, a pCR rate of 72% was achieved with singleagent cisplatin in a group of 25 women with BRCA1 mutations, suggesting, if confirmed by others, that this largely triple- negative population may be exquisitely sensitive to platinum salts.⁴³ In contrast, in a previous study of cisplatin in BRCA mutation carriers, Garber et al44 reported a pCR rate of 22%, suggesting that further trials are needed specifically in BRCA carriers and in triple-negative tumors to see whether these specific patient subsets preferentially derive benefit from platinum salts in the neoadjuvant setting.

The results of the current study are consistent with others indicating a low likelihood of pCR in patients with ERpositive tumors. In fact, none of our ER-positive patients had a pCR. Neoadjuvant endocrine therapy in postmenopausal women with ER- and/ or PR-positive disease is a reasonable treatment option for selected patients, but endpoints other than pCR have often been used.^53,54 It is therefore difficult to directly compare these two strategies. Currently, investigators are comparing the three aromatase inhibitors head to head in the neoadjuvant setting for postmenopausal women with hormone receptor–positive tumors.⁵⁵

The historic pCR ceiling appears to be rising, albeit slowly. Where targets such as HER2 overexpression and triple- negative biology are recognized, progress is being made. Patient eligibility criteria for neoadjuvant breast cancer studies at the time of this trial were quite broad, and it is now recognized that specific subsets of breast cancer respond differently to different classes of agents. Furthermore, our knowledge about breast cancer prognostic markers continues to expand. Had this study been designed in 2011, other data points such as Ki67 would have been collected. A recently published study on neoadjuvant triplenegative breast cancer found that only patients with baseline Ki67% expression > 10% achieved pCR.⁵⁶

Given the long-term implications of not achieving pCR, optimal treatment of patients in the adjuvant setting is critical. Although neoadjuvantly treated patients with ER-positive or HER2-positive disease go on to receive adjuvant agents (antihormonal therapy for ER-positive disease and trastuzumab for HER2-positive disease), patients with triple-negative disease lack long-term therapies of proven efficacy. Perhaps, as we edge closer to defining the optimal neoadjuvant agents for each subset of patients, this will be less of a concern. Many earlyphase neoadjuvant studies have been conducted, with promising reports, yet the results of larger, randomized trials continue to frustrate both investigators and clinicians. These deficits in care can only be answered by carefully planned randomized clinical trials.

Acknowledgment: Funding for this study was provided by sanofi-aventis, U.S.

1. Carlson RW, Allred DC, Anderson BO, et al. NCCN Clinical Practice Guidelines in Oncology: Breast Cancer, V.1.2009. http://www. nccn.org. Accessed May 12, 2011.
2. Gadjos C, Tartter PI, Estabrook A, et al. Relationship of clinical and pathologic response to neoadjuvant chemotherapy and outcome of locally advanced breast cancer. J Surg Oncol 2002;80:4–11.
3. Machiavelli MR, Romero AO, Perez JE, et al. Prognostic significance of pathologic response of primary tumor and metastatic axillary lymph nodes after neoadjuvant chemotherapy for locally advanced breast carcinoma. Cancer J Sci Am 1998;4:125–131.
4. Kuerer HM, Newman LA, Smith TL, et al. Clinical course of breast cancer patients with complete pathologic primary tumor and axillary lymph node response to doxorubicinbased neoadjuvant chemotherapy. J Clin Oncol 1999;17:441–444.
5. Schneeweiss A, Bastert G, Huober J, et al. Neoadjuvant therapy with gemcitabine in breast cancer. Oncology 2004;18:27–31.
6. Gradishar WJ, Wedam SB, Jahanzeb M, et al. Neoadjuvant docetaxel followed by adjuvant doxorubicin and cyclophosphamide in patients with stage III breast cancer. Ann Oncol 2005;8:1297–1304.
7. Amat S, Bougnoux P, Penault-Llorca F, et al. Neoadjuvant docetaxel for operable breast cancer induces a high pathologic response and breast conservation rate. Br J Cancer 2003;88:1339–1345.
8. Estevez LG, Cuevas JM, Anton A, et al. Weekly docetaxel as neoadjuvant chemotherapy for stage II and III breast cancer: efficacy and correlation with biologic markers in a phase II, multicenter study. Clin Cancer Res 2003;9:686–692.
9. Bear HD, Anderson S, Smith RE, et al. Sequential preoperative or postoperative docetaxel added to preoperative doxorubicin plus cyclophosphamide for operable breast cancer: National Surgical Adjuvant Breast and Bowel Protocol B-27. J Clin Oncol 2006;24:2019–2027.
10. Hamm JT, Wilson JW, Rastogi P, et al. Gemcitabine/epirubicin/paclitaxel as neoadjuvant chemotherapy in locally advanced breast cancer: a phase II trial of the NSABP Foundation Research Group. Clin Breast Cancer 2008;8:257–263
11. Steger GG, Galid A, Gnant M, et al. Pathologic complete response with six compared with three cycles of neoadjuvant epirubicin plus docetaxel and granulocyte colony- stimulating factor in operable breast cancer: results of ABCSG-14. J Clin Oncol 2007;20:2012–2018.
12. Sarid D, Ron IG, Sperber F et al. Neoadjuvant treatment with paclitaxel and epirubicin in invasive breast cancer: a phase II study. Clin Drug Investig 2006;26:691–701.
13. Thatai LC, Vishnubhotla P, Biernat L, et al. A phase II study of docetaxel, doxorubicin, and infusional 5-fluorouracil in the treatment of patients with locally advanced breast cancer. Am J Clin Oncol 2006;29:484–489.
14. Aguiar-Bujanda D, Bohn-Sarmiento U, Cabrera-Suarez MA, et al. Epirubicin, cyclophosphamide and weekly paclitaxel as neoadjuvant chemotherapy for stage II and III breast cancer. J Cancer Res Clin Oncol 2006;132:332–338.
15. Cooper BW, Radivovevitch T, Overmoyer BA, et al. Phase II study of dose-dense sequential doxorubicin and docetaxel for patients with advanced operable and inoperable breast cancer. Breast Cancer Res Treat 2006;97:311–318.
16. Ramaswamy B, Povoski SP, Rhoades C, et al. Phase II trial of neoadjuvant chemotherapy with docetaxel followed by epirubicin in stage II/III breast cancer. Breast Cancer Res Treat 2005;93:67–74.
17. Reitsamer R, Peintinger F, Prokop E, et al. Pathological complete response rates comparing 3 versus 6 cycles of epidoxorubicin and docetaxel in the neoadjuvant setting of patients with stage II and III breast cancer. Anticancer Drugs 2005;16:867–870.
18. O’Regan RM, Von Roenn JH, Carlson RW, et al. Final results of a phase II trial of preoperative TAC (docetaxel/doxorubicin/ cyclophosphamide) in stage III breast cancer. Clin Breast Cancer 2005;6:163–168.
19. Chua S, Smith IE, A’Hern RP, et al. Neoadjuvant vinorelbine/epirubicin (VE) versus standard Adriamycin/cyclophosphamide (AC) in operable breast cancer: analysis of response and tolerability in a randomized phase III trial (TOPIC 2). Ann Oncol 2005;16:1435–1441.
20. Chen SC, Chang HK, Lin YC, et al. Increased feasibility of weekly epirubicin and paclitaxel as neoadjuvant chemotherapy for locally advanced breast carcinoma. Onkologie 2005;28:339–344.
21. Evans TR, Yellowlees A, Foster E, et al. Phase III randomized trial of doxorubicin and docetaxel versus doxorubicin and cyclophosphamide as primary medical therapy in women with breast cancer: an Anglo-Celtic Cooperative Oncology Group study. J Clin Oncol 2005;23:2988–2995.
22. Abrial C, Van Praagh I, Delva R, et al. Pathologic and clinical response of a primary chemotherapy regimen combining vinorelbine, epirubicin, and paclitaxel as neoadjuvant treatment in patients with operable breast cancer. Oncologist 2005;10:242–249.
23. Dieras V, Fumoleau P, Romieu G, et al. Randomized parallel study of doxorubicin plus paclitaxel and doxorubicin plus cyclophosphamide as neoadjuvant treatment of patients with breast cancer. J Clin Oncol 2004;22:4958– 4965.
24. Mouret-Reynier MA, Abrial CJ, Ferriere JP, et al. Neoadjuvant FEC100 for operable breast cancer: eight year experience of Centre Jean Perrin. Clin Breast Cancer 2004;5:303–307.
25. Espinosa E, Morales S, Borrega P, et al. Docetaxel and high-dose epirubicin as neoadjuvant chemotherapy in locally advanced breast cancer. Cancer Chemother Pharmacol 2004;54:546–552.
26. Smith IE, A’Hern RP, Coombes GA, et al. A novel continuous infusional 5-fluorouracil- based chemotherapy regimen compared with conventional chemotherapy in the neo-adjuvant treatment of early breast cancer: 5 year results of the TOPIC trial. Ann Oncol 2004;15:751–758.
27. Ezzat AA, Ibrahim EM, Ajarim DS, et al. Phase II study of neoadjuvant paclitaxel and cisplatin for operable and locally advanced breast cancer: analysis of 126 patients. Br J Cancer 2004;90:968–974.
28. von Minckwitz G, Raab G, Caputo A, et al. Doxorubicin with cyclophosphamide followed by docetaxel every 21 days compared with doxorubicin and docetaxel every 14 days as preoperative treatment in operable breast cancer: the GEPARDUO study of the German Breast Group. J Clin Oncol 2005;23:2676–2685.
29. Gogas H, Papadimitriou C, Kalofonos HP, et al. Neoadjuvant chemotherapy with a combination of pegylated liposomal doxorubicin (Caelyx) and paclitaxel in locally advanced breast cancer: a phase II study by the Hellenic Cooperative Oncology Group. Ann Oncol 2002;13:1737–1742. 30. Braud AC, Levy E, Feuilhade F, et al. Combination of vinorelbine, epirubicin, and cyclophosphamide as neoadjuvant chemotherapy for locally advanced breast cancer: a phase II study. Am J Clin Oncol 2002;25:303–307. 31. de Matteis A, Nuzzo F, D’Aiuto G, et al. Docetaxel plus epidoxorubicin as neoadjuvant treatment in patients with large operable or locally advanced carcinoma of the breast: a single-center, phase II study. Cancer 2002;94:895–901. 32. Heys SD, Hutcheon AW, Sarkar TK, et al. Neoadjuvant docetaxel in breast cancer: 3-year survival results from the Aberdeen trial. Clin Breast Cancer 2002(suppl 3):S69–S74. 33. Buzdar AU, Ibrahim NK, Francis D, et al. Significantly higher pathologic complete remission rate after neoadjuvant therapy with trastuzumab, paclitaxel, and epirubicin chemotherapy: results of a randomized trial in human epidermal growth factor receptor 2-positive operable breast cancer. J Clin Oncol 2005;23:3676–3685. 34. Sawada N, Ishikawa T, Fukase Y, et al. Induction of thymidine phosphorylase activity and enhancement of capecitabine efficacy by Taxol/Taxotere in human cancer xenografts. Clin Cancer Res 1998;4:1013–1019.
35. Endo M, Shinbori N, Fukase Y, et al. Induction of thymidine phosphorylase expression and enhancement of efficacy of capecitabine or 5´deoxy-5-fluorouridine by cyclophosphamide in mammary tumor models. Int J Cancer 1999;83:127–134.
36. Yamamoto S, Kurebayashi J, Kurosumi M, et al. Combined effects of docetaxel and fluoropyrimidines on tumor growth and expression of interleukin-6 and thymidine phosphorylase in breast cancer xenografts. Cancer Chemother Pharmacol 2001;48:283–288.
37. Lebowitz PF, Eng-Wong J, Swain SM, et al. A phase II trial of neoadjuvant docetaxel and capecitabine for locally advanced breast cancer. Clin Cancer Res 2004;10:6764–6769.
38. Lee KS, Ro J, Nam BH, et al. A randomized phase-III trial of docetaxel/capecitabine versus doxorubicin/cyclophosphamide as primary chemotherapy for patients with stage II/III breast cancer. Breast Cancer Res Treat 2008;109:481–489.
39. Hurley J, Reis I, Silva O, et al. Weekly docetaxel/carboplatin as primary systemic therapy for Her2-negative locally advanced breast cancer. Clin Breast Cancer 2005;6:447–454.
40. Lee YJ, Doliny P, Gomez-Fernandez C, et al. Docetaxel and cisplatin as primary chemotherapy for treatment of locally advanced breast cancers. Clin Breast Cancer 2004;5:371–376.
41. Morrell LE, Lee YJ, Hurley J, et al. A phase II trial of neoadjuvant methotrexate, vinblastine, doxorubicin, and cisplatin in the treatment of patients with locally advanced breast carcinoma. Cancer 1998;82:503–511.
42. Villman K, Ohd JF, Lidbrink E, et al. A phase II study of epirubicin, cisplatin and capecitabine as neoadjuvant chemotherapy in locally advanced or inflammatory breast cancer. Eur J Cancer 2007;43:1153–1160.
43. Gronwald J, Byrski T, Huzarski T, et al. Neoadjuvant therapy with cisplatin in BRCA1-positive breast cancer patients. J Clin Oncol 2009;27(15S):502.
44. Garber J, Richardson A, Harris L, et al. Neoadjuvant cisplatin in “triple-negative” breast cancer. Presented at the 29th San Antonio Breast Cancer Symposium; December 14– 17, 2006; San Antonio, TX. Poster 3074.
45. Mazouni C, Peintinger F, Wan-Kau S, et al. Residual ductal carcinoma in situ in patients with complete eradication of invasive breast cancer after neoadjuvant chemotherapy does not adversely affect patient outcome. J Clin Oncol 2007;19:2650–2655.
46. Cancer Therapy Evaluation Program. Common Toxicity Criteria version 2.0. National Cancer Institute, 1999. http://ctep.cancer. gov. Accessed May 12, 2011.
47. Layman RM, Thomas DG, Griffith KA, et al. Neoadjuvant docetaxel and capecitabine and the use of thymidine phosphorylase as a predictive biomarker in breast cancer. Clin Cancer Res 2007;13:4092–4097.
48. Le Tourneau C, Dettwiler S, Beuzeboc P, et al. Pathologic response to short intensified taxane-free neoadjuvant chemotherapy in patients with highly proliferative operable breast cancer. Am J Clin Oncol 2011. doi: 10.1097/ COC.0b013e318209d34c (Epub ahead of print).
49. Blakely L, Somer B, Keaton M, et al. Neoadjuvant dose-dense sequential biweekly epirubicin and cyclophosphamide followed by docetaxel and trastuzumab for Her2+ operable breast cancer. J Clin Oncol 2009;27(15S):595.
50. Gianni L, Semiglazov V, Manikhas GM, et al. Neoadjuvant trastuzumab in locally advanced breast cancer (NOAH): antitumour and safety analysis. J Clin Oncol 2007;25(18S):532.
51. Leone JP, Guardiola V, Venkatraman A, et al. Neoadjuvant platinum-based chemotherapy (CT) for triple-negative locally advanced breast cancer (LABC): retrospective analysis of 125 patients. J Clin Oncol 2009;27(15S):625.
52. Huober J, von Minckwitz G, Denkert C, et al. Effect of neoadjuvant anthracyclinetaxane- based chemotherapy in different biological breast cancer phenotypes: overall results from the GeparTrio study. Breast Cancer Res Treat 2010;124:133–140.
53. Smith IE, Dowsett M, Ebbs SR, et al. Neoadjuvant treatment of postmenopausal breast cancer with anastrozole, tamoxifen, or both in combination: the Immediate Preoperative Anastrozole, Tamoxifen, or Combined with Tamoxifen (IMPACT) multicenter double-blind randomized trial. J Clin Oncol 2005;23:5108–5116.
54. Mamounas EP. Facilitating breastconserving surgery and preventing recurrence: aromatase inhibitors in the neoadjuvant and adjuvant settings. Ann Surg Oncol 2008;15:691–703.
55. American College of Surgeons, National Cancer Institute, and Cancer and Leukemia Group B. Exemestane, letrozole, or anastrozole in treating postmenopausal women who are undergoing surgery for stage II or stage III breast cancer. ClinicalTrials.gov NCT00265759. http://clinicaltrials.gov. Accessed May 12, 2011.
56. Keam B, Im SA, Lee KH, et al. Ki67 can be used for further classification of triple negative breast cancer into two subtypes with different response and prognosis. Breast Cancer Res 2011 March 2;13(2):R22 (Epub ahead of print).

ABOUT THE AUTHORS

Aruna Mani, MD; Sandra X. Franco, MD; Grace Wang, MD: Neil Abramson, MD; Lee S. Schwartzberg, MD: James Jakub, MD; Elizabeth Tan-Chiu, MD: Alisha Stein, RNC, BSN, OCN; Alejandra T. Perez, MD; and Charles L Vogel, MD.

Affiliations: Dr. Mani is a breast medical oncologist at Memorial Cancer Institute, Pembroke Pines, FL. Dr. Franco is now Chief of Oncology at the Oncology Center, Clinica del Country, Bogota, Colombia. Dr. Wang is an oncologist at Advanced Medical Specialties, Miami, FL. Dr. Abramson is Clinical Professor of Medicine and Emeritus Director of Education and Research at Baptist Cancer Institute, University of Florida, Jacksonville, FL. Dr. Schwartzberg is Medical Director of The West Clinic, Memphis, TN. Dr. Jakub is now Assistant Professor of Surgery, Division of Gastroenterology and General Surgery, Mayo Clinic, Rochester, MN. Dr. Tan-Chiu is Medical Director of Florida Cancer Care, Davie, FL. Dr. Schwartz is Principal Investigator at Mount Sinai Medical Center, Miami Beach, FL. Ms. Frankel is Director of Oncology Clinical Research and Development at Memorial Cancer Institute, Hollywood, FL. Dr. Krill-Jackson is an oncologist at Mount Sinai Comprehensive Cancer Center, Miami, FL. Ms. Stein is now Oncology Clinical Coordinator at Genentech Inc., Fort Lauderdale, FL. Dr. Perez is Director of the Breast Cancer Center at Memorial Cancer Institute, Hollywood, FL. Dr. Vogel is Professor of Clinical Medicine and Director of the Women’s Center, Sylvester Comprehensive Cancer Center, Deerfield Beach, FL.

Conflicts of interest: Dr. Vogel has served as an advisor and is a member of the speakers’ bureaus of sanofi-aventis U.S. and Roche, as well as many other companies whose products were not part of the current study plan. The other authors have no pertinent conflicts of interest to disclose.

The standard of care for locally advanced breast cancer (LABC) is neoadjuvant chemotherapy,¹ with LABC including clinical stages IIA, IIB, and IIIA. The goals of preoperative chemotherapy are to downstage so as to render breast conservation feasible, to eradicate disease in the axillary nodes, and to allow in vivo testing of tumor drug sensitivity, all with the ultimate aim of improving prognosis. Clinical trials have demonstrated that the pathologic in-breast response generally correlates with pathologic response in the lymph nodes. Furthermore, nodal status at the time of surgery correlates with overall survival (OS) and disease-free survival (DFS).²,³ A combined analysis of two large prospective neoadjuvant chemotherapy trials demonstrated significantly higher 5-year OS and DFS in patients achieving in-breast pathologic complete response (pCR), compared with those who did not (OS, 89% vs 64%; DFS, 87% vs 58%, respectively).⁴

At the start of this trial, the most effective neoad- juvant regimen remained in question. Even now, National Comprehensive Cancer Center guidelines suggest that any recommended adjuvant regimen can be used in the neoadjuvant setting.¹ Numerous phase II and III trials have evaluated single-agent^5–8 and combination^{9– 32} chemotherapies, most of which are anthracycline- based, with pCR rates reported between 7% and 36%. In the NSABP-B27 study, patients treated preoperatively with four cycles of doxorubicin and cyclophosphamide (AC) followed by four cycles of docetaxel (Taxotere) had a 26% pCR rate versus a 13% pCR rate in those receiving preoperative AC and postoperative docetaxel. Despite the doubling of pCR with neoadjuvant docetaxel, there was no difference in DFS or OS.9 However, as reported by Kuerer et al, patients achieving a pCR after completion of neoadjuvant chemotherapy appeared to have superior survival.⁴

Many previous trials (including the study reported here) did not exclude patients with human epidermal growth factor receptor 2 (HER2)-positive disease. It is now well established that such patients should be treated with neoadjuvant regimens incorporating HER2-targeted therapy. In fact, an early neoadjuvant study of paclitaxel followed by fluorouracil, epirubicin, and cyclophosphamide with or without 24 weeks of concurrent trastuzumab (Herceptin) in patients with HER2-positive tumors was closed early because patients receiving trastuzumab had a pCR rate of 65%, compared with 26% in those who did not receive it.³³ Expanded clinical trials of this approach are in progress.

The selection of capecitabine (Xeloda) and docetaxel in the present trial was based on the hypothesis that the upregulation of thymidine phosphorylase by docetaxel should increase the activity of capecitabine. ^34–36 Single-agent docetaxel in the neoadjuvant setting has yielded pCR rates of 7%–20%.^6–8 Treatment with docetaxel and capecitabine together has been reported to produce pCR rates of 10%–21%.^37–39 The addition of carboplatin was based on studies by Hurley et al at the University of Miami^{39– 41} suggesting that platinum salts appeared quite active in the neoadjuvant setting, with the combination of docetaxel and cisplatin producing a pCR rate of 20%, with no residual disease in the breast or axilla.40 Other regimens incorporating cisplatin or carboplatin have pCR rates ranging from 16% to 24%.^27,42–44

Patients and methods

Study design

In this phase II multicenter study, patients were assigned to receive docetaxel (30 mg/m2 IV) and carboplatin (AUC 2 IV) on days 1, 8, and 15 of each 28-day cycle plus capecitabine (625 mg/m2 PO) twice daily on days 5–18. The capecitabine dose was based on observations that this dose was effective and relatively nontoxic in metastatic breast cancer (C.L. Vogel, empirical observations). Patients were to receive four cycles prior to surgical resection.

Given that this neoadjuvant regimen was under study, all of the patients were scheduled to receive a proven standard postoperative adjuvant chemotherapy regimen, starting 4–6 weeks postoperatively, with doxorubicin (60 mg/m2 IV) and cyclophosphamide (600 mg/m2 IV) every 21 days for 4 cycles. This sequential design was prompted by studies such as the NSABP B-27 and Aberdeen trials.^9,32

Radiation therapy after lumpectomy or mastectomy was given according to individual institution guidelines. Patients with hormone receptor–positive tumors received appropriate antihormonal therapy. Tumor measurements were assessed at baseline and on day 1 of each cycle by physical examination with calipers. No breast or other imaging was required during the period of neoadjuvant chemotherapy or immediately preoperatively. Patients were considered evaluable if they proceeded to surgery after all intended cycles of neoadjuvant chemotherapy or if they developed disease progression during neoadjuvant therapy.

Patients

Eligible patients were men and women regardless of menopausal status ≥ 18 years of age with coreneedle biopsy proven locally advanced or inflammatory breast cancer. Breast cancer characteristics such as estrogen receptor (ER), progesterone receptor (PR), or HER2 status were collected but not used for inclusion/exclusion. Eligible tumors were T2 requiring mastectomy; T3N0–2; T4; and any TN2–3 that by calipers was > 2 cm or with fixed or matted axillary or imaging-detected internal mammary nodes. Patients with prior ductal carcinoma in situ (DCIS) were included, as were those with ≤ T2N0M0 breast cancer > 5 years prior.

Other requirements were an Eastern Cooperative Oncology Group (ECOG) performance status of 0–1; life expectancy > 6 months; negative metastatic workup (bone scan and CT chest/abdomen/pelvis); adequate bone marrow, liver, and kidney function; and peripheral neuropathy ≤ grade 1. All patients of child-bearing potential were required to consent to dual methods of contraception during treatment and for 3 months afterward. A negative pregnancy test was required for these women before treatment, and any suspicion of pregnancy had to be reported to the treating physician.

Study endpoints

The primary endpoint of the study was the in-breast pCR after four cycles of platinum-based neoadjuvant chemotherapy. Pathologic complete response was defined as complete disappearance of invasive and in situ disease or invasive disease alone. During the course of this trial, it became generally acceptable to include patients with only residual DCIS as equivalent to pCR.⁴⁵

The secondary endpoints were pCR in the lymph nodes; clinical response rate; tolerability; breast conservation; time to disease progression (local, regional, and distant); and OS. Also recorded was minimal residual disease (MRD), which we arbitrarily defined as ≤ 1 cm invasive carcinoma at resection. The overall treatment plan included postoperative AC to provide a standard-of-care regimen to maximize curative potential.

Statistical analysis

Data were analyzed on an intentto- treat basis. Although pCR rates with doxorubicin plus either cyclophosphamide or docetaxel have been < 15%, the studies by Smith et al26 and Hurley et al39 with in-breast pCR rates of at least 20% served as comparators (albeit imprecise).

Applying the min/max statistical design, the procedure tests the null hypothesis H0: P ≤ 0.15 against the alternative hypothesis H1: P ≥ 0.30. The overall level of significance and power for this design are 5% and 80%, respectively. The sample size needed for the first stage was 23 evaluable patients. If three or fewer pCR responses were observed, then the study would be terminated and the treatment regimen would not be investigated further. Otherwise, an additional 25 evaluable patients would be accrued for a total of 48 study patients. If 11 or fewer responses were observed, then the study would be terminated. Otherwise, this treatment regimen would be recommended to proceed to phase III for further investigation.

Tolerability assessment

At each visit, toxicities were assessed and graded according to the National Cancer Institute Common Toxicity Criteria, version 2.46 Two dose reductions were allowed for all drugs.

Ethical considerations

The investigational nature of this study was fully disclosed to each patient. In accordance with institutional and federal guidelines, the patients were guided through and subsequently signed the informed consent approved by the appropriate site Institutional Review Board.

Literature review

The terms “neoadjuvant” and “breast” were used in a literature search on PubMed, with filters “English” and “clinical trials.” Abstracts for each of the 398 results were reviewed We used phase II or III trials with at least 30 patients, at least four cycles of chemotherapy, and clearly defined pCR for comparison to this study.

Results Patients

Between June 2003 and December 2006, 50 women with a median age of 49 years (range, 28–75 years) were enrolled. One patient was ineligible due to preceding lumpectomy. The 49 eligible patients were treated with ≥ 1 cycle of neoadjuvant chemotherapy between June 27, 2003, and April 12, 2007.

The baseline characteristics of the 49 eligible patients are summarized in Table 3. Thirty-one patients (63%) were premenopausal. Twenty patients (41%) were positive for either ER or PR and were negative for HER2. Eight patients (16%) had HER2- positive tumors, and 23 (46%) had triple-negative tumors. At baseline, 22 patients (45%) had clinical lymphadenopathy, and 1 patient (2%) had inflammatory breast cancer.

The 41 patients (83%) who completed all four cycles of therapy were evaluable for response; 8 (16%) were inevaluable due to noncompliance (1), grade 3 or 4 toxicity (5), or withdrawal of consent (2). The following efficacy assessments apply to the 41 evaluable patients, whereas the toxicity assessments include the 49 patients who received at least one full cycle of chemotherapy.

Clinical response

At study onset, of the 49 eligible patients, 38 (78%) had a palpable inbreast tumor (median size, 5.5 cm); 22 (45%) had enlarged nodes, and 34 (69%) had confirmed nodal involvement (by biopsy or imaging). A clinical complete response (cCR) rate in the breast was seen in 23 of 41 (56%) evaluable patients. Of 22 patients with baseline lymphadenopathy (by imaging or physical examination), 13 had axillary assessment by physical examination throughout treatment, with 12 (92%) exhibiting a cCR in the axilla.

Pathologic response

After four cycles of chemotherapy, an in-breast pCR (the primary endpoint) was demonstrated in 6 of 41 patients (15%). One of these six patients had residual DCIS and is listed separately. All of these patients had nodal pCR, whereas overall, 20 patients (49%) had negative nodes at resection.

The pathology reports of two patients were read as having invasive tumor within lymphatics and lymphovascular invasion (one each) with no measurable disease, with tumor thus sized as Tx. Neither of these patients had involved lymph nodes. Fourteen patients (34%) had MRD in the breast, and 8 of these 14 patients (57%) had residual nodal disease. Nine patients (22%) had T1c tumors (> 1–2 cm), with five of these nine patients (55%) having nodal disease. Seven patients (17%) had T2 tumors (> 2–5 cm) tumors, with five of these seven patients (71%) having nodal disease. These findings are summarized in Table 4. The correlation between in-breast cCR and pCR was 26%.

Biologic features of responders

Of interest, five of the six patients with a pCR had triple-negative tumors. This translates to a 22% pCR rate (5 of 23) in the triple-negative subset, and a pCR rate of 6% (1 of 18) in patients with ER-positive and/ or PR-positive tumors. The remaining patient with a pCR had ER-, PR-, and HER2-positive disease.

One patient had inflammatory breast cancer at diagnosis, and another developed this during the course of chemotherapy; the latter patient was removed from the study for progressive disease. Interestingly, the patient who presented with inflammatory breast cancer was one of the six patients with a pCR. Both of these inflammatory disease patients had triple-negative tumors.

Conversion to breast conservation

Breast conservation was offered to patients if it was deemed appropriate by the treating surgeon. Preoperative imaging was not mandated and thus was not routinely performed. Mastectomy was ultimately performed in 4 of the 6 patients (67%) with pCR and in 22 of the 35 patients (63%) with less than a pCR. Thus, the choice for breast conservation did not correlate well with response to chemotherapy.

Time to disease progression

At a median follow-up of 48 months (range, 7–63), 36 of 41 patients (88%) remained free of disease (range, 19–63 months). Two patients had progressive disease while they were on study treatment and had T3 tumors on resection. Another three patients were found to have progressive disease at 10, 41, and 50 months from study day 1.

Of the nine patients with T1c disease, only one patient (who had positive nodes at resection) had a recurrence (at 41 months). Overall, the patients who had a recurrence had MRD (one patient), T1c (one patient), T2 (one patient), and T3 (the same two patients whose disease progressed while they were on treatment and continued to progress after surgery).

Disease-free and overall survival

Three patients were lost to followup, with point of last contact at 19, 34, and 59 months. Of the 41 evaluable patients, 5 patients developed progressive disease, with 2 of these patients progressing during the study treatment. Disease-free survival at 12, 24, and 36 months was 89%, 89%, and 78%, respectively. Overall survival at these same time points was 95%, 90%, and 76%. None of the patients with a pCR is known to have recurrent disease. Of the six patients achieving pCR, two were lost to follow-up after 34 and 59 months, and four continued diseasefree at 38, 39, 55, and 62 months.

Adverse events

Five patients were removed from the study secondary to toxicities. Grade 3 and 4 toxicity events are summarized in Table 5. Grade 3 toxicities were anemia (4), diarrhea (2), epigastric pain (1), fatigue (2), hand-foot syndrome (1), infection (1), leukopenia (9), pain (5), and peripheral sensory neuropathy (1). Grade 4 toxicities were depression (1) and leukopenia (4). Toxicities (all grades) occurring in ≥ 10% of the 49 treated patients were anemia (76%), leukopenia (70%), fatigue (67%), nausea (59%), alopecia (49%), thrombocytopenia (47%), diarrhea (47%), constipation (37%), pain (35%), vomiting (31%), epigastric pain (27%), nail changes (22%), epiphora (22%), hand-foot syndrome (20%), infection (18%), edema (16%), rash (16%), anorexia (16%), and depression (10%). In the intent-to-treat population, there were nine dose reductions among nine patients, and 19 dose delays among 15 patients.

Discussion

The combination of agents tested thus far in the neoadjuvant setting consistently produce pCR rates far less than 50% in unselected populations. This study was begun prior to the widespread use of personalized medicine. Most prior published trials had utilized anthracycline-based chemotherapy, with response rates generally ranging between 7% and 36%.6,9–26,28–31,41,42

The idea of thymidine phosphorylase upregulation by the combination of capecitabine and docetaxel upon which this study was largely based^34–36 has since been disputed.⁴⁷ The primary endpoint of this trial of a novel platinum- based regimen was a pCR rate of 15%. It is significant that 83% of the pCRs were in triple-negative tumors. A secondary endpoint of MRD was calculated, as this was in the original design of the study, but ultimately was not relevant to the primary endpoint.

Ultimately, pCR is the more relevant point of discussion for the modern era. The 15% pCR rate seen in this phase II study was within range of those achieved in numerous other phase II/III neoadjuvant chemotherapy trials with ≥ 25 patients, ≥ 3 cycles of chemotherapy, and pCR defined as absence of carcinoma in the breast and axilla. To date, no patient in our study with a pCR has been noted to have recurrent disease. However, a recently published French study found a 22% recurrence rate at 11 years in patients with triple-negative breast cancer achieving pCR, highlighting the importance of longer-term follow- up.⁴⁸.

The inclusion of patients with HER2-positive disease in neoadjuvant studies without HER2-targeted therapy was standard at the time that this study was conducted, but is no longer appropriate. If we were to exclude the eight HER2-positive patients from analysis, then there would be only 34 patients evaluable for response, with a pCR rate of 18%. Buzdar et al33 demonstrated a 65% pCR rate in women with HER2-positive disease treated with neoadjuvant chemotherapy plus trastuzumab. The improvement in pCR with the addition of trastuzumab is supported by other confirmatory trials. Authors of a single-arm trial of dose-dense epirubicin and cyclophosphamide followed by dosedense docetaxel and trastuzumab in a HER2-positive population reported a pCR rate of 57%.⁴⁹ The randomized NOAH study50 achieved a pCR rate of 23% in 115 patients treated with trastuzumab-based chemotherapy.

It is interesting to note that five of six patients (83%) achieving a pCR in our study had triple-negative tumors. Investigators at the University of Miami presented a retrospective review of locally advanced triple-negative breast cancer treated with docetaxel and a platinum salt, with 61% of patients also receiving AC. The authors reported a pCR rate of 34% overall and 40% for patients receiving AC.⁵¹ A pCR rate of 60% was noted in the triplenegative subset of patients in another study evaluating docetaxel, doxorubicin, and cyclophosphamide with or without vinorelbine/capecitabine (GeparTrio Study).⁵² Further, a pCR rate of 72% was achieved with singleagent cisplatin in a group of 25 women with BRCA1 mutations, suggesting, if confirmed by others, that this largely triple- negative population may be exquisitely sensitive to platinum salts.⁴³ In contrast, in a previous study of cisplatin in BRCA mutation carriers, Garber et al44 reported a pCR rate of 22%, suggesting that further trials are needed specifically in BRCA carriers and in triple-negative tumors to see whether these specific patient subsets preferentially derive benefit from platinum salts in the neoadjuvant setting.

The results of the current study are consistent with others indicating a low likelihood of pCR in patients with ERpositive tumors. In fact, none of our ER-positive patients had a pCR. Neoadjuvant endocrine therapy in postmenopausal women with ER- and/ or PR-positive disease is a reasonable treatment option for selected patients, but endpoints other than pCR have often been used.^53,54 It is therefore difficult to directly compare these two strategies. Currently, investigators are comparing the three aromatase inhibitors head to head in the neoadjuvant setting for postmenopausal women with hormone receptor–positive tumors.⁵⁵

The historic pCR ceiling appears to be rising, albeit slowly. Where targets such as HER2 overexpression and triple- negative biology are recognized, progress is being made. Patient eligibility criteria for neoadjuvant breast cancer studies at the time of this trial were quite broad, and it is now recognized that specific subsets of breast cancer respond differently to different classes of agents. Furthermore, our knowledge about breast cancer prognostic markers continues to expand. Had this study been designed in 2011, other data points such as Ki67 would have been collected. A recently published study on neoadjuvant triplenegative breast cancer found that only patients with baseline Ki67% expression > 10% achieved pCR.⁵⁶

Given the long-term implications of not achieving pCR, optimal treatment of patients in the adjuvant setting is critical. Although neoadjuvantly treated patients with ER-positive or HER2-positive disease go on to receive adjuvant agents (antihormonal therapy for ER-positive disease and trastuzumab for HER2-positive disease), patients with triple-negative disease lack long-term therapies of proven efficacy. Perhaps, as we edge closer to defining the optimal neoadjuvant agents for each subset of patients, this will be less of a concern. Many earlyphase neoadjuvant studies have been conducted, with promising reports, yet the results of larger, randomized trials continue to frustrate both investigators and clinicians. These deficits in care can only be answered by carefully planned randomized clinical trials.

Acknowledgment: Funding for this study was provided by sanofi-aventis, U.S.

1. Carlson RW, Allred DC, Anderson BO, et al. NCCN Clinical Practice Guidelines in Oncology: Breast Cancer, V.1.2009. http://www. nccn.org. Accessed May 12, 2011.
2. Gadjos C, Tartter PI, Estabrook A, et al. Relationship of clinical and pathologic response to neoadjuvant chemotherapy and outcome of locally advanced breast cancer. J Surg Oncol 2002;80:4–11.
3. Machiavelli MR, Romero AO, Perez JE, et al. Prognostic significance of pathologic response of primary tumor and metastatic axillary lymph nodes after neoadjuvant chemotherapy for locally advanced breast carcinoma. Cancer J Sci Am 1998;4:125–131.
4. Kuerer HM, Newman LA, Smith TL, et al. Clinical course of breast cancer patients with complete pathologic primary tumor and axillary lymph node response to doxorubicinbased neoadjuvant chemotherapy. J Clin Oncol 1999;17:441–444.
5. Schneeweiss A, Bastert G, Huober J, et al. Neoadjuvant therapy with gemcitabine in breast cancer. Oncology 2004;18:27–31.
6. Gradishar WJ, Wedam SB, Jahanzeb M, et al. Neoadjuvant docetaxel followed by adjuvant doxorubicin and cyclophosphamide in patients with stage III breast cancer. Ann Oncol 2005;8:1297–1304.
7. Amat S, Bougnoux P, Penault-Llorca F, et al. Neoadjuvant docetaxel for operable breast cancer induces a high pathologic response and breast conservation rate. Br J Cancer 2003;88:1339–1345.
8. Estevez LG, Cuevas JM, Anton A, et al. Weekly docetaxel as neoadjuvant chemotherapy for stage II and III breast cancer: efficacy and correlation with biologic markers in a phase II, multicenter study. Clin Cancer Res 2003;9:686–692.
9. Bear HD, Anderson S, Smith RE, et al. Sequential preoperative or postoperative docetaxel added to preoperative doxorubicin plus cyclophosphamide for operable breast cancer: National Surgical Adjuvant Breast and Bowel Protocol B-27. J Clin Oncol 2006;24:2019–2027.
10. Hamm JT, Wilson JW, Rastogi P, et al. Gemcitabine/epirubicin/paclitaxel as neoadjuvant chemotherapy in locally advanced breast cancer: a phase II trial of the NSABP Foundation Research Group. Clin Breast Cancer 2008;8:257–263
11. Steger GG, Galid A, Gnant M, et al. Pathologic complete response with six compared with three cycles of neoadjuvant epirubicin plus docetaxel and granulocyte colony- stimulating factor in operable breast cancer: results of ABCSG-14. J Clin Oncol 2007;20:2012–2018.
12. Sarid D, Ron IG, Sperber F et al. Neoadjuvant treatment with paclitaxel and epirubicin in invasive breast cancer: a phase II study. Clin Drug Investig 2006;26:691–701.
13. Thatai LC, Vishnubhotla P, Biernat L, et al. A phase II study of docetaxel, doxorubicin, and infusional 5-fluorouracil in the treatment of patients with locally advanced breast cancer. Am J Clin Oncol 2006;29:484–489.
14. Aguiar-Bujanda D, Bohn-Sarmiento U, Cabrera-Suarez MA, et al. Epirubicin, cyclophosphamide and weekly paclitaxel as neoadjuvant chemotherapy for stage II and III breast cancer. J Cancer Res Clin Oncol 2006;132:332–338.
15. Cooper BW, Radivovevitch T, Overmoyer BA, et al. Phase II study of dose-dense sequential doxorubicin and docetaxel for patients with advanced operable and inoperable breast cancer. Breast Cancer Res Treat 2006;97:311–318.
16. Ramaswamy B, Povoski SP, Rhoades C, et al. Phase II trial of neoadjuvant chemotherapy with docetaxel followed by epirubicin in stage II/III breast cancer. Breast Cancer Res Treat 2005;93:67–74.
17. Reitsamer R, Peintinger F, Prokop E, et al. Pathological complete response rates comparing 3 versus 6 cycles of epidoxorubicin and docetaxel in the neoadjuvant setting of patients with stage II and III breast cancer. Anticancer Drugs 2005;16:867–870.
18. O’Regan RM, Von Roenn JH, Carlson RW, et al. Final results of a phase II trial of preoperative TAC (docetaxel/doxorubicin/ cyclophosphamide) in stage III breast cancer. Clin Breast Cancer 2005;6:163–168.
19. Chua S, Smith IE, A’Hern RP, et al. Neoadjuvant vinorelbine/epirubicin (VE) versus standard Adriamycin/cyclophosphamide (AC) in operable breast cancer: analysis of response and tolerability in a randomized phase III trial (TOPIC 2). Ann Oncol 2005;16:1435–1441.
20. Chen SC, Chang HK, Lin YC, et al. Increased feasibility of weekly epirubicin and paclitaxel as neoadjuvant chemotherapy for locally advanced breast carcinoma. Onkologie 2005;28:339–344.
21. Evans TR, Yellowlees A, Foster E, et al. Phase III randomized trial of doxorubicin and docetaxel versus doxorubicin and cyclophosphamide as primary medical therapy in women with breast cancer: an Anglo-Celtic Cooperative Oncology Group study. J Clin Oncol 2005;23:2988–2995.
22. Abrial C, Van Praagh I, Delva R, et al. Pathologic and clinical response of a primary chemotherapy regimen combining vinorelbine, epirubicin, and paclitaxel as neoadjuvant treatment in patients with operable breast cancer. Oncologist 2005;10:242–249.
23. Dieras V, Fumoleau P, Romieu G, et al. Randomized parallel study of doxorubicin plus paclitaxel and doxorubicin plus cyclophosphamide as neoadjuvant treatment of patients with breast cancer. J Clin Oncol 2004;22:4958– 4965.
24. Mouret-Reynier MA, Abrial CJ, Ferriere JP, et al. Neoadjuvant FEC100 for operable breast cancer: eight year experience of Centre Jean Perrin. Clin Breast Cancer 2004;5:303–307.
25. Espinosa E, Morales S, Borrega P, et al. Docetaxel and high-dose epirubicin as neoadjuvant chemotherapy in locally advanced breast cancer. Cancer Chemother Pharmacol 2004;54:546–552.
26. Smith IE, A’Hern RP, Coombes GA, et al. A novel continuous infusional 5-fluorouracil- based chemotherapy regimen compared with conventional chemotherapy in the neo-adjuvant treatment of early breast cancer: 5 year results of the TOPIC trial. Ann Oncol 2004;15:751–758.
27. Ezzat AA, Ibrahim EM, Ajarim DS, et al. Phase II study of neoadjuvant paclitaxel and cisplatin for operable and locally advanced breast cancer: analysis of 126 patients. Br J Cancer 2004;90:968–974.
28. von Minckwitz G, Raab G, Caputo A, et al. Doxorubicin with cyclophosphamide followed by docetaxel every 21 days compared with doxorubicin and docetaxel every 14 days as preoperative treatment in operable breast cancer: the GEPARDUO study of the German Breast Group. J Clin Oncol 2005;23:2676–2685.
29. Gogas H, Papadimitriou C, Kalofonos HP, et al. Neoadjuvant chemotherapy with a combination of pegylated liposomal doxorubicin (Caelyx) and paclitaxel in locally advanced breast cancer: a phase II study by the Hellenic Cooperative Oncology Group. Ann Oncol 2002;13:1737–1742. 30. Braud AC, Levy E, Feuilhade F, et al. Combination of vinorelbine, epirubicin, and cyclophosphamide as neoadjuvant chemotherapy for locally advanced breast cancer: a phase II study. Am J Clin Oncol 2002;25:303–307. 31. de Matteis A, Nuzzo F, D’Aiuto G, et al. Docetaxel plus epidoxorubicin as neoadjuvant treatment in patients with large operable or locally advanced carcinoma of the breast: a single-center, phase II study. Cancer 2002;94:895–901. 32. Heys SD, Hutcheon AW, Sarkar TK, et al. Neoadjuvant docetaxel in breast cancer: 3-year survival results from the Aberdeen trial. Clin Breast Cancer 2002(suppl 3):S69–S74. 33. Buzdar AU, Ibrahim NK, Francis D, et al. Significantly higher pathologic complete remission rate after neoadjuvant therapy with trastuzumab, paclitaxel, and epirubicin chemotherapy: results of a randomized trial in human epidermal growth factor receptor 2-positive operable breast cancer. J Clin Oncol 2005;23:3676–3685. 34. Sawada N, Ishikawa T, Fukase Y, et al. Induction of thymidine phosphorylase activity and enhancement of capecitabine efficacy by Taxol/Taxotere in human cancer xenografts. Clin Cancer Res 1998;4:1013–1019.
35. Endo M, Shinbori N, Fukase Y, et al. Induction of thymidine phosphorylase expression and enhancement of efficacy of capecitabine or 5´deoxy-5-fluorouridine by cyclophosphamide in mammary tumor models. Int J Cancer 1999;83:127–134.
36. Yamamoto S, Kurebayashi J, Kurosumi M, et al. Combined effects of docetaxel and fluoropyrimidines on tumor growth and expression of interleukin-6 and thymidine phosphorylase in breast cancer xenografts. Cancer Chemother Pharmacol 2001;48:283–288.
37. Lebowitz PF, Eng-Wong J, Swain SM, et al. A phase II trial of neoadjuvant docetaxel and capecitabine for locally advanced breast cancer. Clin Cancer Res 2004;10:6764–6769.
38. Lee KS, Ro J, Nam BH, et al. A randomized phase-III trial of docetaxel/capecitabine versus doxorubicin/cyclophosphamide as primary chemotherapy for patients with stage II/III breast cancer. Breast Cancer Res Treat 2008;109:481–489.
39. Hurley J, Reis I, Silva O, et al. Weekly docetaxel/carboplatin as primary systemic therapy for Her2-negative locally advanced breast cancer. Clin Breast Cancer 2005;6:447–454.
40. Lee YJ, Doliny P, Gomez-Fernandez C, et al. Docetaxel and cisplatin as primary chemotherapy for treatment of locally advanced breast cancers. Clin Breast Cancer 2004;5:371–376.
41. Morrell LE, Lee YJ, Hurley J, et al. A phase II trial of neoadjuvant methotrexate, vinblastine, doxorubicin, and cisplatin in the treatment of patients with locally advanced breast carcinoma. Cancer 1998;82:503–511.
42. Villman K, Ohd JF, Lidbrink E, et al. A phase II study of epirubicin, cisplatin and capecitabine as neoadjuvant chemotherapy in locally advanced or inflammatory breast cancer. Eur J Cancer 2007;43:1153–1160.
43. Gronwald J, Byrski T, Huzarski T, et al. Neoadjuvant therapy with cisplatin in BRCA1-positive breast cancer patients. J Clin Oncol 2009;27(15S):502.
44. Garber J, Richardson A, Harris L, et al. Neoadjuvant cisplatin in “triple-negative” breast cancer. Presented at the 29th San Antonio Breast Cancer Symposium; December 14– 17, 2006; San Antonio, TX. Poster 3074.
45. Mazouni C, Peintinger F, Wan-Kau S, et al. Residual ductal carcinoma in situ in patients with complete eradication of invasive breast cancer after neoadjuvant chemotherapy does not adversely affect patient outcome. J Clin Oncol 2007;19:2650–2655.
46. Cancer Therapy Evaluation Program. Common Toxicity Criteria version 2.0. National Cancer Institute, 1999. http://ctep.cancer. gov. Accessed May 12, 2011.
47. Layman RM, Thomas DG, Griffith KA, et al. Neoadjuvant docetaxel and capecitabine and the use of thymidine phosphorylase as a predictive biomarker in breast cancer. Clin Cancer Res 2007;13:4092–4097.
48. Le Tourneau C, Dettwiler S, Beuzeboc P, et al. Pathologic response to short intensified taxane-free neoadjuvant chemotherapy in patients with highly proliferative operable breast cancer. Am J Clin Oncol 2011. doi: 10.1097/ COC.0b013e318209d34c (Epub ahead of print).
49. Blakely L, Somer B, Keaton M, et al. Neoadjuvant dose-dense sequential biweekly epirubicin and cyclophosphamide followed by docetaxel and trastuzumab for Her2+ operable breast cancer. J Clin Oncol 2009;27(15S):595.
50. Gianni L, Semiglazov V, Manikhas GM, et al. Neoadjuvant trastuzumab in locally advanced breast cancer (NOAH): antitumour and safety analysis. J Clin Oncol 2007;25(18S):532.
51. Leone JP, Guardiola V, Venkatraman A, et al. Neoadjuvant platinum-based chemotherapy (CT) for triple-negative locally advanced breast cancer (LABC): retrospective analysis of 125 patients. J Clin Oncol 2009;27(15S):625.
52. Huober J, von Minckwitz G, Denkert C, et al. Effect of neoadjuvant anthracyclinetaxane- based chemotherapy in different biological breast cancer phenotypes: overall results from the GeparTrio study. Breast Cancer Res Treat 2010;124:133–140.
53. Smith IE, Dowsett M, Ebbs SR, et al. Neoadjuvant treatment of postmenopausal breast cancer with anastrozole, tamoxifen, or both in combination: the Immediate Preoperative Anastrozole, Tamoxifen, or Combined with Tamoxifen (IMPACT) multicenter double-blind randomized trial. J Clin Oncol 2005;23:5108–5116.
54. Mamounas EP. Facilitating breastconserving surgery and preventing recurrence: aromatase inhibitors in the neoadjuvant and adjuvant settings. Ann Surg Oncol 2008;15:691–703.
55. American College of Surgeons, National Cancer Institute, and Cancer and Leukemia Group B. Exemestane, letrozole, or anastrozole in treating postmenopausal women who are undergoing surgery for stage II or stage III breast cancer. ClinicalTrials.gov NCT00265759. http://clinicaltrials.gov. Accessed May 12, 2011.
56. Keam B, Im SA, Lee KH, et al. Ki67 can be used for further classification of triple negative breast cancer into two subtypes with different response and prognosis. Breast Cancer Res 2011 March 2;13(2):R22 (Epub ahead of print).

ABOUT THE AUTHORS

Aruna Mani, MD; Sandra X. Franco, MD; Grace Wang, MD: Neil Abramson, MD; Lee S. Schwartzberg, MD: James Jakub, MD; Elizabeth Tan-Chiu, MD: Alisha Stein, RNC, BSN, OCN; Alejandra T. Perez, MD; and Charles L Vogel, MD.

Affiliations: Dr. Mani is a breast medical oncologist at Memorial Cancer Institute, Pembroke Pines, FL. Dr. Franco is now Chief of Oncology at the Oncology Center, Clinica del Country, Bogota, Colombia. Dr. Wang is an oncologist at Advanced Medical Specialties, Miami, FL. Dr. Abramson is Clinical Professor of Medicine and Emeritus Director of Education and Research at Baptist Cancer Institute, University of Florida, Jacksonville, FL. Dr. Schwartzberg is Medical Director of The West Clinic, Memphis, TN. Dr. Jakub is now Assistant Professor of Surgery, Division of Gastroenterology and General Surgery, Mayo Clinic, Rochester, MN. Dr. Tan-Chiu is Medical Director of Florida Cancer Care, Davie, FL. Dr. Schwartz is Principal Investigator at Mount Sinai Medical Center, Miami Beach, FL. Ms. Frankel is Director of Oncology Clinical Research and Development at Memorial Cancer Institute, Hollywood, FL. Dr. Krill-Jackson is an oncologist at Mount Sinai Comprehensive Cancer Center, Miami, FL. Ms. Stein is now Oncology Clinical Coordinator at Genentech Inc., Fort Lauderdale, FL. Dr. Perez is Director of the Breast Cancer Center at Memorial Cancer Institute, Hollywood, FL. Dr. Vogel is Professor of Clinical Medicine and Director of the Women’s Center, Sylvester Comprehensive Cancer Center, Deerfield Beach, FL.

Conflicts of interest: Dr. Vogel has served as an advisor and is a member of the speakers’ bureaus of sanofi-aventis U.S. and Roche, as well as many other companies whose products were not part of the current study plan. The other authors have no pertinent conflicts of interest to disclose.

The standard of care for locally advanced breast cancer (LABC) is neoadjuvant chemotherapy,¹ with LABC including clinical stages IIA, IIB, and IIIA. The goals of preoperative chemotherapy are to downstage so as to render breast conservation feasible, to eradicate disease in the axillary nodes, and to allow in vivo testing of tumor drug sensitivity, all with the ultimate aim of improving prognosis. Clinical trials have demonstrated that the pathologic in-breast response generally correlates with pathologic response in the lymph nodes. Furthermore, nodal status at the time of surgery correlates with overall survival (OS) and disease-free survival (DFS).²,³ A combined analysis of two large prospective neoadjuvant chemotherapy trials demonstrated significantly higher 5-year OS and DFS in patients achieving in-breast pathologic complete response (pCR), compared with those who did not (OS, 89% vs 64%; DFS, 87% vs 58%, respectively).⁴

At the start of this trial, the most effective neoad- juvant regimen remained in question. Even now, National Comprehensive Cancer Center guidelines suggest that any recommended adjuvant regimen can be used in the neoadjuvant setting.¹ Numerous phase II and III trials have evaluated single-agent^5–8 and combination^{9– 32} chemotherapies, most of which are anthracycline- based, with pCR rates reported between 7% and 36%. In the NSABP-B27 study, patients treated preoperatively with four cycles of doxorubicin and cyclophosphamide (AC) followed by four cycles of docetaxel (Taxotere) had a 26% pCR rate versus a 13% pCR rate in those receiving preoperative AC and postoperative docetaxel. Despite the doubling of pCR with neoadjuvant docetaxel, there was no difference in DFS or OS.9 However, as reported by Kuerer et al, patients achieving a pCR after completion of neoadjuvant chemotherapy appeared to have superior survival.⁴

Many previous trials (including the study reported here) did not exclude patients with human epidermal growth factor receptor 2 (HER2)-positive disease. It is now well established that such patients should be treated with neoadjuvant regimens incorporating HER2-targeted therapy. In fact, an early neoadjuvant study of paclitaxel followed by fluorouracil, epirubicin, and cyclophosphamide with or without 24 weeks of concurrent trastuzumab (Herceptin) in patients with HER2-positive tumors was closed early because patients receiving trastuzumab had a pCR rate of 65%, compared with 26% in those who did not receive it.³³ Expanded clinical trials of this approach are in progress.

The selection of capecitabine (Xeloda) and docetaxel in the present trial was based on the hypothesis that the upregulation of thymidine phosphorylase by docetaxel should increase the activity of capecitabine. ^34–36 Single-agent docetaxel in the neoadjuvant setting has yielded pCR rates of 7%–20%.^6–8 Treatment with docetaxel and capecitabine together has been reported to produce pCR rates of 10%–21%.^37–39 The addition of carboplatin was based on studies by Hurley et al at the University of Miami^{39– 41} suggesting that platinum salts appeared quite active in the neoadjuvant setting, with the combination of docetaxel and cisplatin producing a pCR rate of 20%, with no residual disease in the breast or axilla.40 Other regimens incorporating cisplatin or carboplatin have pCR rates ranging from 16% to 24%.^27,42–44

Patients and methods

Study design

In this phase II multicenter study, patients were assigned to receive docetaxel (30 mg/m2 IV) and carboplatin (AUC 2 IV) on days 1, 8, and 15 of each 28-day cycle plus capecitabine (625 mg/m2 PO) twice daily on days 5–18. The capecitabine dose was based on observations that this dose was effective and relatively nontoxic in metastatic breast cancer (C.L. Vogel, empirical observations). Patients were to receive four cycles prior to surgical resection.

Given that this neoadjuvant regimen was under study, all of the patients were scheduled to receive a proven standard postoperative adjuvant chemotherapy regimen, starting 4–6 weeks postoperatively, with doxorubicin (60 mg/m2 IV) and cyclophosphamide (600 mg/m2 IV) every 21 days for 4 cycles. This sequential design was prompted by studies such as the NSABP B-27 and Aberdeen trials.^9,32

Radiation therapy after lumpectomy or mastectomy was given according to individual institution guidelines. Patients with hormone receptor–positive tumors received appropriate antihormonal therapy. Tumor measurements were assessed at baseline and on day 1 of each cycle by physical examination with calipers. No breast or other imaging was required during the period of neoadjuvant chemotherapy or immediately preoperatively. Patients were considered evaluable if they proceeded to surgery after all intended cycles of neoadjuvant chemotherapy or if they developed disease progression during neoadjuvant therapy.

Patients

Eligible patients were men and women regardless of menopausal status ≥ 18 years of age with coreneedle biopsy proven locally advanced or inflammatory breast cancer. Breast cancer characteristics such as estrogen receptor (ER), progesterone receptor (PR), or HER2 status were collected but not used for inclusion/exclusion. Eligible tumors were T2 requiring mastectomy; T3N0–2; T4; and any TN2–3 that by calipers was > 2 cm or with fixed or matted axillary or imaging-detected internal mammary nodes. Patients with prior ductal carcinoma in situ (DCIS) were included, as were those with ≤ T2N0M0 breast cancer > 5 years prior.

Other requirements were an Eastern Cooperative Oncology Group (ECOG) performance status of 0–1; life expectancy > 6 months; negative metastatic workup (bone scan and CT chest/abdomen/pelvis); adequate bone marrow, liver, and kidney function; and peripheral neuropathy ≤ grade 1. All patients of child-bearing potential were required to consent to dual methods of contraception during treatment and for 3 months afterward. A negative pregnancy test was required for these women before treatment, and any suspicion of pregnancy had to be reported to the treating physician.

Study endpoints

The primary endpoint of the study was the in-breast pCR after four cycles of platinum-based neoadjuvant chemotherapy. Pathologic complete response was defined as complete disappearance of invasive and in situ disease or invasive disease alone. During the course of this trial, it became generally acceptable to include patients with only residual DCIS as equivalent to pCR.⁴⁵

The secondary endpoints were pCR in the lymph nodes; clinical response rate; tolerability; breast conservation; time to disease progression (local, regional, and distant); and OS. Also recorded was minimal residual disease (MRD), which we arbitrarily defined as ≤ 1 cm invasive carcinoma at resection. The overall treatment plan included postoperative AC to provide a standard-of-care regimen to maximize curative potential.

Statistical analysis

Data were analyzed on an intentto- treat basis. Although pCR rates with doxorubicin plus either cyclophosphamide or docetaxel have been < 15%, the studies by Smith et al26 and Hurley et al39 with in-breast pCR rates of at least 20% served as comparators (albeit imprecise).

Applying the min/max statistical design, the procedure tests the null hypothesis H0: P ≤ 0.15 against the alternative hypothesis H1: P ≥ 0.30. The overall level of significance and power for this design are 5% and 80%, respectively. The sample size needed for the first stage was 23 evaluable patients. If three or fewer pCR responses were observed, then the study would be terminated and the treatment regimen would not be investigated further. Otherwise, an additional 25 evaluable patients would be accrued for a total of 48 study patients. If 11 or fewer responses were observed, then the study would be terminated. Otherwise, this treatment regimen would be recommended to proceed to phase III for further investigation.

Tolerability assessment

At each visit, toxicities were assessed and graded according to the National Cancer Institute Common Toxicity Criteria, version 2.46 Two dose reductions were allowed for all drugs.

Ethical considerations

The investigational nature of this study was fully disclosed to each patient. In accordance with institutional and federal guidelines, the patients were guided through and subsequently signed the informed consent approved by the appropriate site Institutional Review Board.

Literature review

The terms “neoadjuvant” and “breast” were used in a literature search on PubMed, with filters “English” and “clinical trials.” Abstracts for each of the 398 results were reviewed We used phase II or III trials with at least 30 patients, at least four cycles of chemotherapy, and clearly defined pCR for comparison to this study.

Results Patients

Between June 2003 and December 2006, 50 women with a median age of 49 years (range, 28–75 years) were enrolled. One patient was ineligible due to preceding lumpectomy. The 49 eligible patients were treated with ≥ 1 cycle of neoadjuvant chemotherapy between June 27, 2003, and April 12, 2007.

The baseline characteristics of the 49 eligible patients are summarized in Table 3. Thirty-one patients (63%) were premenopausal. Twenty patients (41%) were positive for either ER or PR and were negative for HER2. Eight patients (16%) had HER2- positive tumors, and 23 (46%) had triple-negative tumors. At baseline, 22 patients (45%) had clinical lymphadenopathy, and 1 patient (2%) had inflammatory breast cancer.

The 41 patients (83%) who completed all four cycles of therapy were evaluable for response; 8 (16%) were inevaluable due to noncompliance (1), grade 3 or 4 toxicity (5), or withdrawal of consent (2). The following efficacy assessments apply to the 41 evaluable patients, whereas the toxicity assessments include the 49 patients who received at least one full cycle of chemotherapy.

Clinical response

At study onset, of the 49 eligible patients, 38 (78%) had a palpable inbreast tumor (median size, 5.5 cm); 22 (45%) had enlarged nodes, and 34 (69%) had confirmed nodal involvement (by biopsy or imaging). A clinical complete response (cCR) rate in the breast was seen in 23 of 41 (56%) evaluable patients. Of 22 patients with baseline lymphadenopathy (by imaging or physical examination), 13 had axillary assessment by physical examination throughout treatment, with 12 (92%) exhibiting a cCR in the axilla.

Pathologic response

After four cycles of chemotherapy, an in-breast pCR (the primary endpoint) was demonstrated in 6 of 41 patients (15%). One of these six patients had residual DCIS and is listed separately. All of these patients had nodal pCR, whereas overall, 20 patients (49%) had negative nodes at resection.

The pathology reports of two patients were read as having invasive tumor within lymphatics and lymphovascular invasion (one each) with no measurable disease, with tumor thus sized as Tx. Neither of these patients had involved lymph nodes. Fourteen patients (34%) had MRD in the breast, and 8 of these 14 patients (57%) had residual nodal disease. Nine patients (22%) had T1c tumors (> 1–2 cm), with five of these nine patients (55%) having nodal disease. Seven patients (17%) had T2 tumors (> 2–5 cm) tumors, with five of these seven patients (71%) having nodal disease. These findings are summarized in Table 4. The correlation between in-breast cCR and pCR was 26%.

Biologic features of responders

Of interest, five of the six patients with a pCR had triple-negative tumors. This translates to a 22% pCR rate (5 of 23) in the triple-negative subset, and a pCR rate of 6% (1 of 18) in patients with ER-positive and/ or PR-positive tumors. The remaining patient with a pCR had ER-, PR-, and HER2-positive disease.

One patient had inflammatory breast cancer at diagnosis, and another developed this during the course of chemotherapy; the latter patient was removed from the study for progressive disease. Interestingly, the patient who presented with inflammatory breast cancer was one of the six patients with a pCR. Both of these inflammatory disease patients had triple-negative tumors.

Conversion to breast conservation

Breast conservation was offered to patients if it was deemed appropriate by the treating surgeon. Preoperative imaging was not mandated and thus was not routinely performed. Mastectomy was ultimately performed in 4 of the 6 patients (67%) with pCR and in 22 of the 35 patients (63%) with less than a pCR. Thus, the choice for breast conservation did not correlate well with response to chemotherapy.

Time to disease progression

At a median follow-up of 48 months (range, 7–63), 36 of 41 patients (88%) remained free of disease (range, 19–63 months). Two patients had progressive disease while they were on study treatment and had T3 tumors on resection. Another three patients were found to have progressive disease at 10, 41, and 50 months from study day 1.

Of the nine patients with T1c disease, only one patient (who had positive nodes at resection) had a recurrence (at 41 months). Overall, the patients who had a recurrence had MRD (one patient), T1c (one patient), T2 (one patient), and T3 (the same two patients whose disease progressed while they were on treatment and continued to progress after surgery).

Disease-free and overall survival

Three patients were lost to followup, with point of last contact at 19, 34, and 59 months. Of the 41 evaluable patients, 5 patients developed progressive disease, with 2 of these patients progressing during the study treatment. Disease-free survival at 12, 24, and 36 months was 89%, 89%, and 78%, respectively. Overall survival at these same time points was 95%, 90%, and 76%. None of the patients with a pCR is known to have recurrent disease. Of the six patients achieving pCR, two were lost to follow-up after 34 and 59 months, and four continued diseasefree at 38, 39, 55, and 62 months.

Adverse events

Five patients were removed from the study secondary to toxicities. Grade 3 and 4 toxicity events are summarized in Table 5. Grade 3 toxicities were anemia (4), diarrhea (2), epigastric pain (1), fatigue (2), hand-foot syndrome (1), infection (1), leukopenia (9), pain (5), and peripheral sensory neuropathy (1). Grade 4 toxicities were depression (1) and leukopenia (4). Toxicities (all grades) occurring in ≥ 10% of the 49 treated patients were anemia (76%), leukopenia (70%), fatigue (67%), nausea (59%), alopecia (49%), thrombocytopenia (47%), diarrhea (47%), constipation (37%), pain (35%), vomiting (31%), epigastric pain (27%), nail changes (22%), epiphora (22%), hand-foot syndrome (20%), infection (18%), edema (16%), rash (16%), anorexia (16%), and depression (10%). In the intent-to-treat population, there were nine dose reductions among nine patients, and 19 dose delays among 15 patients.

Discussion

The combination of agents tested thus far in the neoadjuvant setting consistently produce pCR rates far less than 50% in unselected populations. This study was begun prior to the widespread use of personalized medicine. Most prior published trials had utilized anthracycline-based chemotherapy, with response rates generally ranging between 7% and 36%.6,9–26,28–31,41,42

The idea of thymidine phosphorylase upregulation by the combination of capecitabine and docetaxel upon which this study was largely based^34–36 has since been disputed.⁴⁷ The primary endpoint of this trial of a novel platinum- based regimen was a pCR rate of 15%. It is significant that 83% of the pCRs were in triple-negative tumors. A secondary endpoint of MRD was calculated, as this was in the original design of the study, but ultimately was not relevant to the primary endpoint.

Ultimately, pCR is the more relevant point of discussion for the modern era. The 15% pCR rate seen in this phase II study was within range of those achieved in numerous other phase II/III neoadjuvant chemotherapy trials with ≥ 25 patients, ≥ 3 cycles of chemotherapy, and pCR defined as absence of carcinoma in the breast and axilla. To date, no patient in our study with a pCR has been noted to have recurrent disease. However, a recently published French study found a 22% recurrence rate at 11 years in patients with triple-negative breast cancer achieving pCR, highlighting the importance of longer-term follow- up.⁴⁸.

The inclusion of patients with HER2-positive disease in neoadjuvant studies without HER2-targeted therapy was standard at the time that this study was conducted, but is no longer appropriate. If we were to exclude the eight HER2-positive patients from analysis, then there would be only 34 patients evaluable for response, with a pCR rate of 18%. Buzdar et al33 demonstrated a 65% pCR rate in women with HER2-positive disease treated with neoadjuvant chemotherapy plus trastuzumab. The improvement in pCR with the addition of trastuzumab is supported by other confirmatory trials. Authors of a single-arm trial of dose-dense epirubicin and cyclophosphamide followed by dosedense docetaxel and trastuzumab in a HER2-positive population reported a pCR rate of 57%.⁴⁹ The randomized NOAH study50 achieved a pCR rate of 23% in 115 patients treated with trastuzumab-based chemotherapy.

It is interesting to note that five of six patients (83%) achieving a pCR in our study had triple-negative tumors. Investigators at the University of Miami presented a retrospective review of locally advanced triple-negative breast cancer treated with docetaxel and a platinum salt, with 61% of patients also receiving AC. The authors reported a pCR rate of 34% overall and 40% for patients receiving AC.⁵¹ A pCR rate of 60% was noted in the triplenegative subset of patients in another study evaluating docetaxel, doxorubicin, and cyclophosphamide with or without vinorelbine/capecitabine (GeparTrio Study).⁵² Further, a pCR rate of 72% was achieved with singleagent cisplatin in a group of 25 women with BRCA1 mutations, suggesting, if confirmed by others, that this largely triple- negative population may be exquisitely sensitive to platinum salts.⁴³ In contrast, in a previous study of cisplatin in BRCA mutation carriers, Garber et al44 reported a pCR rate of 22%, suggesting that further trials are needed specifically in BRCA carriers and in triple-negative tumors to see whether these specific patient subsets preferentially derive benefit from platinum salts in the neoadjuvant setting.

The results of the current study are consistent with others indicating a low likelihood of pCR in patients with ERpositive tumors. In fact, none of our ER-positive patients had a pCR. Neoadjuvant endocrine therapy in postmenopausal women with ER- and/ or PR-positive disease is a reasonable treatment option for selected patients, but endpoints other than pCR have often been used.^53,54 It is therefore difficult to directly compare these two strategies. Currently, investigators are comparing the three aromatase inhibitors head to head in the neoadjuvant setting for postmenopausal women with hormone receptor–positive tumors.⁵⁵

The historic pCR ceiling appears to be rising, albeit slowly. Where targets such as HER2 overexpression and triple- negative biology are recognized, progress is being made. Patient eligibility criteria for neoadjuvant breast cancer studies at the time of this trial were quite broad, and it is now recognized that specific subsets of breast cancer respond differently to different classes of agents. Furthermore, our knowledge about breast cancer prognostic markers continues to expand. Had this study been designed in 2011, other data points such as Ki67 would have been collected. A recently published study on neoadjuvant triplenegative breast cancer found that only patients with baseline Ki67% expression > 10% achieved pCR.⁵⁶

Given the long-term implications of not achieving pCR, optimal treatment of patients in the adjuvant setting is critical. Although neoadjuvantly treated patients with ER-positive or HER2-positive disease go on to receive adjuvant agents (antihormonal therapy for ER-positive disease and trastuzumab for HER2-positive disease), patients with triple-negative disease lack long-term therapies of proven efficacy. Perhaps, as we edge closer to defining the optimal neoadjuvant agents for each subset of patients, this will be less of a concern. Many earlyphase neoadjuvant studies have been conducted, with promising reports, yet the results of larger, randomized trials continue to frustrate both investigators and clinicians. These deficits in care can only be answered by carefully planned randomized clinical trials.

Acknowledgment: Funding for this study was provided by sanofi-aventis, U.S.

1. Carlson RW, Allred DC, Anderson BO, et al. NCCN Clinical Practice Guidelines in Oncology: Breast Cancer, V.1.2009. http://www. nccn.org. Accessed May 12, 2011.
2. Gadjos C, Tartter PI, Estabrook A, et al. Relationship of clinical and pathologic response to neoadjuvant chemotherapy and outcome of locally advanced breast cancer. J Surg Oncol 2002;80:4–11.
3. Machiavelli MR, Romero AO, Perez JE, et al. Prognostic significance of pathologic response of primary tumor and metastatic axillary lymph nodes after neoadjuvant chemotherapy for locally advanced breast carcinoma. Cancer J Sci Am 1998;4:125–131.
4. Kuerer HM, Newman LA, Smith TL, et al. Clinical course of breast cancer patients with complete pathologic primary tumor and axillary lymph node response to doxorubicinbased neoadjuvant chemotherapy. J Clin Oncol 1999;17:441–444.
5. Schneeweiss A, Bastert G, Huober J, et al. Neoadjuvant therapy with gemcitabine in breast cancer. Oncology 2004;18:27–31.
6. Gradishar WJ, Wedam SB, Jahanzeb M, et al. Neoadjuvant docetaxel followed by adjuvant doxorubicin and cyclophosphamide in patients with stage III breast cancer. Ann Oncol 2005;8:1297–1304.
7. Amat S, Bougnoux P, Penault-Llorca F, et al. Neoadjuvant docetaxel for operable breast cancer induces a high pathologic response and breast conservation rate. Br J Cancer 2003;88:1339–1345.
8. Estevez LG, Cuevas JM, Anton A, et al. Weekly docetaxel as neoadjuvant chemotherapy for stage II and III breast cancer: efficacy and correlation with biologic markers in a phase II, multicenter study. Clin Cancer Res 2003;9:686–692.
9. Bear HD, Anderson S, Smith RE, et al. Sequential preoperative or postoperative docetaxel added to preoperative doxorubicin plus cyclophosphamide for operable breast cancer: National Surgical Adjuvant Breast and Bowel Protocol B-27. J Clin Oncol 2006;24:2019–2027.
10. Hamm JT, Wilson JW, Rastogi P, et al. Gemcitabine/epirubicin/paclitaxel as neoadjuvant chemotherapy in locally advanced breast cancer: a phase II trial of the NSABP Foundation Research Group. Clin Breast Cancer 2008;8:257–263
11. Steger GG, Galid A, Gnant M, et al. Pathologic complete response with six compared with three cycles of neoadjuvant epirubicin plus docetaxel and granulocyte colony- stimulating factor in operable breast cancer: results of ABCSG-14. J Clin Oncol 2007;20:2012–2018.
12. Sarid D, Ron IG, Sperber F et al. Neoadjuvant treatment with paclitaxel and epirubicin in invasive breast cancer: a phase II study. Clin Drug Investig 2006;26:691–701.
13. Thatai LC, Vishnubhotla P, Biernat L, et al. A phase II study of docetaxel, doxorubicin, and infusional 5-fluorouracil in the treatment of patients with locally advanced breast cancer. Am J Clin Oncol 2006;29:484–489.
14. Aguiar-Bujanda D, Bohn-Sarmiento U, Cabrera-Suarez MA, et al. Epirubicin, cyclophosphamide and weekly paclitaxel as neoadjuvant chemotherapy for stage II and III breast cancer. J Cancer Res Clin Oncol 2006;132:332–338.
15. Cooper BW, Radivovevitch T, Overmoyer BA, et al. Phase II study of dose-dense sequential doxorubicin and docetaxel for patients with advanced operable and inoperable breast cancer. Breast Cancer Res Treat 2006;97:311–318.
16. Ramaswamy B, Povoski SP, Rhoades C, et al. Phase II trial of neoadjuvant chemotherapy with docetaxel followed by epirubicin in stage II/III breast cancer. Breast Cancer Res Treat 2005;93:67–74.
17. Reitsamer R, Peintinger F, Prokop E, et al. Pathological complete response rates comparing 3 versus 6 cycles of epidoxorubicin and docetaxel in the neoadjuvant setting of patients with stage II and III breast cancer. Anticancer Drugs 2005;16:867–870.
18. O’Regan RM, Von Roenn JH, Carlson RW, et al. Final results of a phase II trial of preoperative TAC (docetaxel/doxorubicin/ cyclophosphamide) in stage III breast cancer. Clin Breast Cancer 2005;6:163–168.
19. Chua S, Smith IE, A’Hern RP, et al. Neoadjuvant vinorelbine/epirubicin (VE) versus standard Adriamycin/cyclophosphamide (AC) in operable breast cancer: analysis of response and tolerability in a randomized phase III trial (TOPIC 2). Ann Oncol 2005;16:1435–1441.
20. Chen SC, Chang HK, Lin YC, et al. Increased feasibility of weekly epirubicin and paclitaxel as neoadjuvant chemotherapy for locally advanced breast carcinoma. Onkologie 2005;28:339–344.
21. Evans TR, Yellowlees A, Foster E, et al. Phase III randomized trial of doxorubicin and docetaxel versus doxorubicin and cyclophosphamide as primary medical therapy in women with breast cancer: an Anglo-Celtic Cooperative Oncology Group study. J Clin Oncol 2005;23:2988–2995.
22. Abrial C, Van Praagh I, Delva R, et al. Pathologic and clinical response of a primary chemotherapy regimen combining vinorelbine, epirubicin, and paclitaxel as neoadjuvant treatment in patients with operable breast cancer. Oncologist 2005;10:242–249.
23. Dieras V, Fumoleau P, Romieu G, et al. Randomized parallel study of doxorubicin plus paclitaxel and doxorubicin plus cyclophosphamide as neoadjuvant treatment of patients with breast cancer. J Clin Oncol 2004;22:4958– 4965.
24. Mouret-Reynier MA, Abrial CJ, Ferriere JP, et al. Neoadjuvant FEC100 for operable breast cancer: eight year experience of Centre Jean Perrin. Clin Breast Cancer 2004;5:303–307.
25. Espinosa E, Morales S, Borrega P, et al. Docetaxel and high-dose epirubicin as neoadjuvant chemotherapy in locally advanced breast cancer. Cancer Chemother Pharmacol 2004;54:546–552.
26. Smith IE, A’Hern RP, Coombes GA, et al. A novel continuous infusional 5-fluorouracil- based chemotherapy regimen compared with conventional chemotherapy in the neo-adjuvant treatment of early breast cancer: 5 year results of the TOPIC trial. Ann Oncol 2004;15:751–758.
27. Ezzat AA, Ibrahim EM, Ajarim DS, et al. Phase II study of neoadjuvant paclitaxel and cisplatin for operable and locally advanced breast cancer: analysis of 126 patients. Br J Cancer 2004;90:968–974.
28. von Minckwitz G, Raab G, Caputo A, et al. Doxorubicin with cyclophosphamide followed by docetaxel every 21 days compared with doxorubicin and docetaxel every 14 days as preoperative treatment in operable breast cancer: the GEPARDUO study of the German Breast Group. J Clin Oncol 2005;23:2676–2685.
29. Gogas H, Papadimitriou C, Kalofonos HP, et al. Neoadjuvant chemotherapy with a combination of pegylated liposomal doxorubicin (Caelyx) and paclitaxel in locally advanced breast cancer: a phase II study by the Hellenic Cooperative Oncology Group. Ann Oncol 2002;13:1737–1742. 30. Braud AC, Levy E, Feuilhade F, et al. Combination of vinorelbine, epirubicin, and cyclophosphamide as neoadjuvant chemotherapy for locally advanced breast cancer: a phase II study. Am J Clin Oncol 2002;25:303–307. 31. de Matteis A, Nuzzo F, D’Aiuto G, et al. Docetaxel plus epidoxorubicin as neoadjuvant treatment in patients with large operable or locally advanced carcinoma of the breast: a single-center, phase II study. Cancer 2002;94:895–901. 32. Heys SD, Hutcheon AW, Sarkar TK, et al. Neoadjuvant docetaxel in breast cancer: 3-year survival results from the Aberdeen trial. Clin Breast Cancer 2002(suppl 3):S69–S74. 33. Buzdar AU, Ibrahim NK, Francis D, et al. Significantly higher pathologic complete remission rate after neoadjuvant therapy with trastuzumab, paclitaxel, and epirubicin chemotherapy: results of a randomized trial in human epidermal growth factor receptor 2-positive operable breast cancer. J Clin Oncol 2005;23:3676–3685. 34. Sawada N, Ishikawa T, Fukase Y, et al. Induction of thymidine phosphorylase activity and enhancement of capecitabine efficacy by Taxol/Taxotere in human cancer xenografts. Clin Cancer Res 1998;4:1013–1019.
35. Endo M, Shinbori N, Fukase Y, et al. Induction of thymidine phosphorylase expression and enhancement of efficacy of capecitabine or 5´deoxy-5-fluorouridine by cyclophosphamide in mammary tumor models. Int J Cancer 1999;83:127–134.
36. Yamamoto S, Kurebayashi J, Kurosumi M, et al. Combined effects of docetaxel and fluoropyrimidines on tumor growth and expression of interleukin-6 and thymidine phosphorylase in breast cancer xenografts. Cancer Chemother Pharmacol 2001;48:283–288.
37. Lebowitz PF, Eng-Wong J, Swain SM, et al. A phase II trial of neoadjuvant docetaxel and capecitabine for locally advanced breast cancer. Clin Cancer Res 2004;10:6764–6769.
38. Lee KS, Ro J, Nam BH, et al. A randomized phase-III trial of docetaxel/capecitabine versus doxorubicin/cyclophosphamide as primary chemotherapy for patients with stage II/III breast cancer. Breast Cancer Res Treat 2008;109:481–489.
39. Hurley J, Reis I, Silva O, et al. Weekly docetaxel/carboplatin as primary systemic therapy for Her2-negative locally advanced breast cancer. Clin Breast Cancer 2005;6:447–454.
40. Lee YJ, Doliny P, Gomez-Fernandez C, et al. Docetaxel and cisplatin as primary chemotherapy for treatment of locally advanced breast cancers. Clin Breast Cancer 2004;5:371–376.
41. Morrell LE, Lee YJ, Hurley J, et al. A phase II trial of neoadjuvant methotrexate, vinblastine, doxorubicin, and cisplatin in the treatment of patients with locally advanced breast carcinoma. Cancer 1998;82:503–511.
42. Villman K, Ohd JF, Lidbrink E, et al. A phase II study of epirubicin, cisplatin and capecitabine as neoadjuvant chemotherapy in locally advanced or inflammatory breast cancer. Eur J Cancer 2007;43:1153–1160.
43. Gronwald J, Byrski T, Huzarski T, et al. Neoadjuvant therapy with cisplatin in BRCA1-positive breast cancer patients. J Clin Oncol 2009;27(15S):502.
44. Garber J, Richardson A, Harris L, et al. Neoadjuvant cisplatin in “triple-negative” breast cancer. Presented at the 29th San Antonio Breast Cancer Symposium; December 14– 17, 2006; San Antonio, TX. Poster 3074.
45. Mazouni C, Peintinger F, Wan-Kau S, et al. Residual ductal carcinoma in situ in patients with complete eradication of invasive breast cancer after neoadjuvant chemotherapy does not adversely affect patient outcome. J Clin Oncol 2007;19:2650–2655.
46. Cancer Therapy Evaluation Program. Common Toxicity Criteria version 2.0. National Cancer Institute, 1999. http://ctep.cancer. gov. Accessed May 12, 2011.
47. Layman RM, Thomas DG, Griffith KA, et al. Neoadjuvant docetaxel and capecitabine and the use of thymidine phosphorylase as a predictive biomarker in breast cancer. Clin Cancer Res 2007;13:4092–4097.
48. Le Tourneau C, Dettwiler S, Beuzeboc P, et al. Pathologic response to short intensified taxane-free neoadjuvant chemotherapy in patients with highly proliferative operable breast cancer. Am J Clin Oncol 2011. doi: 10.1097/ COC.0b013e318209d34c (Epub ahead of print).
49. Blakely L, Somer B, Keaton M, et al. Neoadjuvant dose-dense sequential biweekly epirubicin and cyclophosphamide followed by docetaxel and trastuzumab for Her2+ operable breast cancer. J Clin Oncol 2009;27(15S):595.
50. Gianni L, Semiglazov V, Manikhas GM, et al. Neoadjuvant trastuzumab in locally advanced breast cancer (NOAH): antitumour and safety analysis. J Clin Oncol 2007;25(18S):532.
51. Leone JP, Guardiola V, Venkatraman A, et al. Neoadjuvant platinum-based chemotherapy (CT) for triple-negative locally advanced breast cancer (LABC): retrospective analysis of 125 patients. J Clin Oncol 2009;27(15S):625.
52. Huober J, von Minckwitz G, Denkert C, et al. Effect of neoadjuvant anthracyclinetaxane- based chemotherapy in different biological breast cancer phenotypes: overall results from the GeparTrio study. Breast Cancer Res Treat 2010;124:133–140.
53. Smith IE, Dowsett M, Ebbs SR, et al. Neoadjuvant treatment of postmenopausal breast cancer with anastrozole, tamoxifen, or both in combination: the Immediate Preoperative Anastrozole, Tamoxifen, or Combined with Tamoxifen (IMPACT) multicenter double-blind randomized trial. J Clin Oncol 2005;23:5108–5116.
54. Mamounas EP. Facilitating breastconserving surgery and preventing recurrence: aromatase inhibitors in the neoadjuvant and adjuvant settings. Ann Surg Oncol 2008;15:691–703.
55. American College of Surgeons, National Cancer Institute, and Cancer and Leukemia Group B. Exemestane, letrozole, or anastrozole in treating postmenopausal women who are undergoing surgery for stage II or stage III breast cancer. ClinicalTrials.gov NCT00265759. http://clinicaltrials.gov. Accessed May 12, 2011.
56. Keam B, Im SA, Lee KH, et al. Ki67 can be used for further classification of triple negative breast cancer into two subtypes with different response and prognosis. Breast Cancer Res 2011 March 2;13(2):R22 (Epub ahead of print).

ABOUT THE AUTHORS

Aruna Mani, MD; Sandra X. Franco, MD; Grace Wang, MD: Neil Abramson, MD; Lee S. Schwartzberg, MD: James Jakub, MD; Elizabeth Tan-Chiu, MD: Alisha Stein, RNC, BSN, OCN; Alejandra T. Perez, MD; and Charles L Vogel, MD.

Affiliations: Dr. Mani is a breast medical oncologist at Memorial Cancer Institute, Pembroke Pines, FL. Dr. Franco is now Chief of Oncology at the Oncology Center, Clinica del Country, Bogota, Colombia. Dr. Wang is an oncologist at Advanced Medical Specialties, Miami, FL. Dr. Abramson is Clinical Professor of Medicine and Emeritus Director of Education and Research at Baptist Cancer Institute, University of Florida, Jacksonville, FL. Dr. Schwartzberg is Medical Director of The West Clinic, Memphis, TN. Dr. Jakub is now Assistant Professor of Surgery, Division of Gastroenterology and General Surgery, Mayo Clinic, Rochester, MN. Dr. Tan-Chiu is Medical Director of Florida Cancer Care, Davie, FL. Dr. Schwartz is Principal Investigator at Mount Sinai Medical Center, Miami Beach, FL. Ms. Frankel is Director of Oncology Clinical Research and Development at Memorial Cancer Institute, Hollywood, FL. Dr. Krill-Jackson is an oncologist at Mount Sinai Comprehensive Cancer Center, Miami, FL. Ms. Stein is now Oncology Clinical Coordinator at Genentech Inc., Fort Lauderdale, FL. Dr. Perez is Director of the Breast Cancer Center at Memorial Cancer Institute, Hollywood, FL. Dr. Vogel is Professor of Clinical Medicine and Director of the Women’s Center, Sylvester Comprehensive Cancer Center, Deerfield Beach, FL.

Conflicts of interest: Dr. Vogel has served as an advisor and is a member of the speakers’ bureaus of sanofi-aventis U.S. and Roche, as well as many other companies whose products were not part of the current study plan. The other authors have no pertinent conflicts of interest to disclose.

Original research

Thomas J. Smith MD

^{, a,}

, Lindsay A. Dow MD^a, Enid A. Virago MDiv^a, James Khatcheressian MD^a, Robin Matsuyama PhD^a and Laurel J. Lyckholm MD^a

References¹

1 S.E. Harrington and T.J. Smith, The role of chemotherapy at the end of life: “when is enough, enough?”, JAMA 299 (22) (2008), pp. 2667–2678. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (42)

2 C.G. Koedoot, F.J. Oort, R.J. de Haan, P.J. Bakker, A. de Graeff and J.C. de Haes, The content and amount of information given by medical oncologists when telling patients with advanced cancer what their treatment options are: Palliative chemotherapy and watchful-waiting, Eur J Cancer 40 (2) (2004), pp. 225–235. Article |

PDF (265 K)

| View Record in Scopus | Cited By in Scopus (34)

3 A.M. Sullivan, M.D. Lakoma, R.K. Matsuyama, L. Rosenblatt, R.M. Arnold and S.D. Block, Diagnosing and discussing imminent death in the hospital: a secondary analysis of physician interviews, J Palliat Med 10 (4) (2007), pp. 882–893. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (12)

4 C.K. Daugherty and F.J. Hlubocky, What are terminally ill cancer patients told about their expected deaths?: A study of cancer physicians' self-reports of prognosis disclosure, J Clin Oncol 26 (36) (2008), pp. 5988–5993. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (10)

5 A.A. Wright, B. Zhang, A. Ray, J.W. Mack, E. Trice, T. Balboni, S.L. Mitchell, V.A. Jackson, S.D. Block, P.K. Maciejewski and H.G. Prigerson, Associations between end-of-life discussions, patient mental health, medical care near death, and caregiver bereavement adjustment, JAMA 300 (14) (2008), pp. 1665–1673. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (131)

6 B. Zhang, A.A. Wright, H.A. Huskamp, M.E. Nilsson, M.L. Maciejewski, C.C. Earle, S.D. Block, P.K. Maciejewski and H.G. Prigerson, Health care costs in the last week of life: associations with end-of-life conversations, Arch Intern Med 169 (5) (2009), pp. 480–488. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (47)

7 E.B. Lamont and N.A. Christakis, Prognostic disclosure to patients with cancer near the end of life, Ann Intern Med 134 (12) (2001), pp. 1096–1105. View Record in Scopus | Cited By in Scopus (166)

8 N. Christakis, Death Foretold: Prophecy and Prognosis in Medical Care, University of Chicago Press, Chicago (1999).

9 K.N. Swisher, D.L. Ulmer, W. Carpenter, R.B. Mitchell, T.H. Wan, M. Dowdy and T.J. Smith, Patient indemnification as a way to involve patients in economic decision-making about their care: barriers in the current system, Proc ASCO (1996), p. 939a.

10 L.A. Siminoff, N.H. Gordon, P. Silverman, T. Budd and P.M. Ravdin, A decision aid to assist in adjuvant therapy choices for breast cancer, Psychooncology 15 (11) (2006), pp. 1001–1013. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (26)

11 P.B. Peele, L.A. Siminoff, Y. Xu and P.M. Ravdin, Decreased use of adjuvant breast cancer therapy in a randomized controlled trial of a decision aid with individualized risk information, Med Decis Making 25 (3) (2005), pp. 301–307. View Record in Scopus | Cited By in Scopus (42)

12 T.J. Smith, Tell it like it is, J Clin Oncol 18; (2000), pp. 3441–3445. View Record in Scopus | Cited By in Scopus (17)

13 K. Herth, Abbreviated instrument to measure hope: development and psychometric evaluation, J Adv Nurs 17 (10) (1992), pp. 1251–1259. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (129)

14 I.J. Higginson and N. Donaldson, Relationship between three palliative care outcome scales, Health Qual Life Outcomes 2 (2004), p. 68.

15 M.A. Murray, T. Miller, V. Fiset, A. O'Connor and M.J. Jacobsen, Decision support: helping patients and families to find a balance at the end of life, Int J Palliat Nurs 10 (6) (2004), pp. 270–277. View Record in Scopus | Cited By in Scopus (13)

16 S.H. Woolf, E.C. Chan, R. Harris, S.L. Sheridan, C.H. Braddock 3rd, R.M. Kaplan, A. Krist, A.M. O'Connor and S. Tunis, Promoting informed choice: transforming health care to dispense knowledge for decision making, Ann Intern Med 143 (4) (2005), pp. 293–300. View Record in Scopus | Cited By in Scopus (102)

17 A.M. O'Connor, D. Stacey, V. Entwistle, H. Llewellyn-Thomas, D. Rovner, M. Holmes-Rovner, V. Tait, J. Tetroe, V. Fiset, M. Barry and J. Jones, Decision aids for people facing health treatment or screening decisions, Cochrane Database Syst Rev (2) (2003) CD001431.

18 Smith TJ, Dow LA, Virago E, Khatcheressian J, Lyckholm L, Matsuyama R. Giving honest information to patients with advanced cancer maintains hope. Oncologist (Williston Park) 24(6):521–525.

19 J.A. Tulsky, M.A. Chesney and B. Lo, How do medical residents discuss resuscitation with patients?, J Gen Intern Med 10 (8) (1995), pp. 436–442. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (132)

20 T.J. Smith, C.E. Desch, M.H. Hackney and J.E. Shaw, How long does it take to get a “do not resuscitate” order?, J Palliat Care 13 (1) (1997), pp. 5–8. View Record in Scopus | Cited By in Scopus (16)

21 T.J. Whelan and C.L. Loprinzi, Physician/patient decision aids for adjuvant therapy, J Clin Oncol 23 (2005), pp. 1627–1630. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (16)

22 T.M. Whelan, M.A. O'Brien and M. Villasis-Keever et al., Impact of Cancer-Related Decision Aids. Evidence Report/Technology Assessment Number 46. (Prepared by McMaster University under contract no. 290-97-0017.), Agency for Healthcare Research and Quality, Rockville, MD (2002) AHRQ Publication 02-E004.

23 B.R. Cassileth, M.S. Soloway and N.J. Vogelzang et al., Patients' choice of treatment in stage D prostate cancer, Urology 33 (suppl 5) (1989), pp. S57–S62.

24 D.J. Chadwick, D.A. Gillatt and J.C. Gingell, Medical or surgical orchidectomy: The patients' choice, BMJ 302 (1991), p. 572. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (18)

25 L.M. Elit, M.N. Levine and A. Gafni et al., Patients' preferences for therapy in advanced epithelial ovarian cancer: Development, testing, and application of a bedside decision instrument, Gynecol Oncol 62 (1996), pp. 329–335. Abstract |

PDF (131 K)

| View Record in Scopus | Cited By in Scopus (66)

26 V. Fiset, A.M. O'Connor and W. Evans et al., Development and evaluation of a decision aid for patients with stage IV non-small cell lung cancer, Health Expect 3 (2000), pp. 125–136. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (26)

27 M.D. Brundage, D. Feldman-Steward and R. Cosby et al., Cancer patients' attitudes toward treatment options for advanced non-small cell lung cancer: Implications for patient education and decision support, Patient Educ Couns 45 (2001), pp. 149–157. Article |

PDF (260 K)

| View Record in Scopus | Cited By in Scopus (33)

28 M.D. Brundage, D. Feldman-Stewart and R. Cosby et al., Phase I study of a decision aid for patients with locally advanced non-small-cell lung cancer, J Clin Oncol 19 (2001), pp. 1326–1335. View Record in Scopus | Cited By in Scopus (27)

29 K.S. Chiew, H. Shepherd, J. Vardy, M.H. Tattersall, P.N. Butow and N.B. Leighl, Development and evaluation of a decision aid for patients considering first-line chemotherapy for metastatic breast cancer, Health Expect 11 (1) (2008), pp. 35–45. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (4)

30 W.J. Mackillop, W.E. Stewart, A.D. Ginsburg and S.S. Stewart, Cancer patients' perceptions of their disease and its treatment, Br J Cancer 58 (1988), pp. 355–358. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (148)

31 E. Chow, L. Andersson, R. Wong, M. Vachon, G. Hruby and E. Franssen et al., Patients with advanced cancer: a survey of the understanding of their illness and expectations from palliative radiotherapy for symptomatic metastases, Clin Oncol (R Coll Radiol) 13 (2001), pp. 204–208. Abstract |

PDF (84 K)

| Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (29)

32 A.K. Ganti, S.J. Lee, J.M. Vose, M.P. Devetten, R.G. Bociek, J.O. Armitage, P.J. Bierman, L.J. Maness, E.C. Reed and F.R. Loberiza Jr, Outcomes after hematopoietic stem-cell transplantation for hematologic malignancies in patients with or without advance care planning, J Clin Oncol 25 (35) (2007), pp. 5643–5648. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (9)

33 S.J. Lee, D. Fairclough, J.H. Antin and J.C. Weeks, Discrepancies between patient and physician estimates for the success of stem cell transplantation, JAMA 285 (8) (2001), pp. 1034–1038. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (34)

34 S.J. Lee, F.R. Loberiza, J.D. Rizzo, R.J. Soiffer, J.H. Antin and J.C. Weeks, Optimistic expectations and survival after hematopoietic stem cell transplantation, Biol Blood Marrow Transplant 9 (2003), pp. 389–396. Article |

PDF (118 K)

| View Record in Scopus | Cited By in Scopus (14)

35 J.C. Weeks, E.F. Cook, S.J. O'Day, L.M. Peterson, N. Wenger and D. Reding et al., Relationship between cancer patients' predictions of prognosis and their treatment preferences, JAMA 279 (21) (1998), pp. 1709–1714. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (350)

36 C.C. Earle, B.A. Neville, M.B. Landrum, J.Z. Ayanian, S.D. Block and J.C. Weeks, Trends in the aggressiveness of cancer care near the end of life, J Clin Oncol 22 (2) (2004), pp. 315–321. View Record in Scopus | Cited By in Scopus (185)

37 R. Matsuyama, S.N. Reddy and T.J. Smith, Why do patients choose chemotherapy near the end of life?, J Clin Oncol 24 (2006), pp. 1–7.

38 A. Dufresne, X. Pivot, C. Tournigand, T. Facchini, T. Altweegg, L. Chaigneau and A. De Gramont, Impact of chemotherapy beyond the first line in patients with metastatic breast cancer, Breast Cancer Res Treat 107 (2) (2008), pp. 275–279. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (14)

39 P. Kirk, I. Kirk and L.J. Kristjanson, What do patients receiving palliative care for cancer and their families want to be told?: A Canadian and Australian qualitative study, BMJ 328 (7452) (2004), p. 1343. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (110)

40 L.L. Emanuel, F.D. Ferris, C.F. von Gunten and J. Von Roenn, EPEC-O: Education in Palliative and End of Life Care for Oncology, EPEC Project, Chicago (2005).

41 M.J. Field and C.K. Cassel, Approaching Death: Improving Care at the End of Life, National Academy Press, Washington DC (1997), pp. 59–64.

42 H.A. Huskamp, N.L. Keating, J.L. Malin, A.M. Zaslavsky, J.C. Weeks, C.C. Earle, J.M. Teno, B.A. Virnig, K.L. Kahn, Y. He and J.Z. Ayanian, Discussions with physicians about hospice among patients with metastatic lung cancer, Arch Intern Med 169 (10) (2009), pp. 954–962. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (4)

43 P. Glare, K. Virik, M. Jones, M. Hudson, S. Eychmuller and J. Simes et al., A systematic review of physicians' survival predictions in terminally ill cancer patients, BMJ 327 (2003), pp. 195–200.

44 T. Smith and K. Swisher, Telling the truth about terminal cancer, JAMA 279 (21) (1998), pp. 1746–1748. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (37)

45 T. Beauchamp and J. Childress, Principles of Biomedical Ethics (5th ed), Oxford University Press, New York (2001).

46 E. Panagopoulou, G. Mintziori, A. Montgomery, D. Kapoukranidou and A. Benos, Concealment of information in clinical practice: is lying less stressful than telling the truth?, J Clin Oncol 26 (7) (2008), pp. 1175–1177. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (9)

47 L. Fallowfield and V. Jenkins, Communicating sad, bad, and difficult news in medicine, Lancet 363 (9405) (2004), pp. 312–319. Article |

PDF (110 K)

| View Record in Scopus | Cited By in Scopus (195)

48 L.A. Dow, R.K. Matsuyama, V. Ramakrishnan, L. Kuhn, E.B. Lamont, L. Lyckholm and T.J. Smith, Paradoxes in advance care planning: the complex relationship of oncology patients, their physicians, and advance medical directives, J Clin Oncol 28 (2) (2010), pp. 299–304. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (2)

Correspondence to: Thomas J. Smith, MD, Virginia Commonwealth University, Division of Hematology/Oncology and Palliative Care, MCV Box 980230, Richmond, VA 23298–0230; telephone: (804) 828–9723; fax: (804) 828–8079

Original research

Thomas J. Smith MD

^{, a,}

, Lindsay A. Dow MD^a, Enid A. Virago MDiv^a, James Khatcheressian MD^a, Robin Matsuyama PhD^a and Laurel J. Lyckholm MD^a

References¹

1 S.E. Harrington and T.J. Smith, The role of chemotherapy at the end of life: “when is enough, enough?”, JAMA 299 (22) (2008), pp. 2667–2678. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (42)

2 C.G. Koedoot, F.J. Oort, R.J. de Haan, P.J. Bakker, A. de Graeff and J.C. de Haes, The content and amount of information given by medical oncologists when telling patients with advanced cancer what their treatment options are: Palliative chemotherapy and watchful-waiting, Eur J Cancer 40 (2) (2004), pp. 225–235. Article |

PDF (265 K)

| View Record in Scopus | Cited By in Scopus (34)

3 A.M. Sullivan, M.D. Lakoma, R.K. Matsuyama, L. Rosenblatt, R.M. Arnold and S.D. Block, Diagnosing and discussing imminent death in the hospital: a secondary analysis of physician interviews, J Palliat Med 10 (4) (2007), pp. 882–893. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (12)

4 C.K. Daugherty and F.J. Hlubocky, What are terminally ill cancer patients told about their expected deaths?: A study of cancer physicians' self-reports of prognosis disclosure, J Clin Oncol 26 (36) (2008), pp. 5988–5993. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (10)

5 A.A. Wright, B. Zhang, A. Ray, J.W. Mack, E. Trice, T. Balboni, S.L. Mitchell, V.A. Jackson, S.D. Block, P.K. Maciejewski and H.G. Prigerson, Associations between end-of-life discussions, patient mental health, medical care near death, and caregiver bereavement adjustment, JAMA 300 (14) (2008), pp. 1665–1673. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (131)

6 B. Zhang, A.A. Wright, H.A. Huskamp, M.E. Nilsson, M.L. Maciejewski, C.C. Earle, S.D. Block, P.K. Maciejewski and H.G. Prigerson, Health care costs in the last week of life: associations with end-of-life conversations, Arch Intern Med 169 (5) (2009), pp. 480–488. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (47)

7 E.B. Lamont and N.A. Christakis, Prognostic disclosure to patients with cancer near the end of life, Ann Intern Med 134 (12) (2001), pp. 1096–1105. View Record in Scopus | Cited By in Scopus (166)

8 N. Christakis, Death Foretold: Prophecy and Prognosis in Medical Care, University of Chicago Press, Chicago (1999).

9 K.N. Swisher, D.L. Ulmer, W. Carpenter, R.B. Mitchell, T.H. Wan, M. Dowdy and T.J. Smith, Patient indemnification as a way to involve patients in economic decision-making about their care: barriers in the current system, Proc ASCO (1996), p. 939a.

10 L.A. Siminoff, N.H. Gordon, P. Silverman, T. Budd and P.M. Ravdin, A decision aid to assist in adjuvant therapy choices for breast cancer, Psychooncology 15 (11) (2006), pp. 1001–1013. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (26)

11 P.B. Peele, L.A. Siminoff, Y. Xu and P.M. Ravdin, Decreased use of adjuvant breast cancer therapy in a randomized controlled trial of a decision aid with individualized risk information, Med Decis Making 25 (3) (2005), pp. 301–307. View Record in Scopus | Cited By in Scopus (42)

12 T.J. Smith, Tell it like it is, J Clin Oncol 18; (2000), pp. 3441–3445. View Record in Scopus | Cited By in Scopus (17)

13 K. Herth, Abbreviated instrument to measure hope: development and psychometric evaluation, J Adv Nurs 17 (10) (1992), pp. 1251–1259. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (129)

14 I.J. Higginson and N. Donaldson, Relationship between three palliative care outcome scales, Health Qual Life Outcomes 2 (2004), p. 68.

15 M.A. Murray, T. Miller, V. Fiset, A. O'Connor and M.J. Jacobsen, Decision support: helping patients and families to find a balance at the end of life, Int J Palliat Nurs 10 (6) (2004), pp. 270–277. View Record in Scopus | Cited By in Scopus (13)

16 S.H. Woolf, E.C. Chan, R. Harris, S.L. Sheridan, C.H. Braddock 3rd, R.M. Kaplan, A. Krist, A.M. O'Connor and S. Tunis, Promoting informed choice: transforming health care to dispense knowledge for decision making, Ann Intern Med 143 (4) (2005), pp. 293–300. View Record in Scopus | Cited By in Scopus (102)

17 A.M. O'Connor, D. Stacey, V. Entwistle, H. Llewellyn-Thomas, D. Rovner, M. Holmes-Rovner, V. Tait, J. Tetroe, V. Fiset, M. Barry and J. Jones, Decision aids for people facing health treatment or screening decisions, Cochrane Database Syst Rev (2) (2003) CD001431.

18 Smith TJ, Dow LA, Virago E, Khatcheressian J, Lyckholm L, Matsuyama R. Giving honest information to patients with advanced cancer maintains hope. Oncologist (Williston Park) 24(6):521–525.

19 J.A. Tulsky, M.A. Chesney and B. Lo, How do medical residents discuss resuscitation with patients?, J Gen Intern Med 10 (8) (1995), pp. 436–442. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (132)

20 T.J. Smith, C.E. Desch, M.H. Hackney and J.E. Shaw, How long does it take to get a “do not resuscitate” order?, J Palliat Care 13 (1) (1997), pp. 5–8. View Record in Scopus | Cited By in Scopus (16)

21 T.J. Whelan and C.L. Loprinzi, Physician/patient decision aids for adjuvant therapy, J Clin Oncol 23 (2005), pp. 1627–1630. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (16)

22 T.M. Whelan, M.A. O'Brien and M. Villasis-Keever et al., Impact of Cancer-Related Decision Aids. Evidence Report/Technology Assessment Number 46. (Prepared by McMaster University under contract no. 290-97-0017.), Agency for Healthcare Research and Quality, Rockville, MD (2002) AHRQ Publication 02-E004.

23 B.R. Cassileth, M.S. Soloway and N.J. Vogelzang et al., Patients' choice of treatment in stage D prostate cancer, Urology 33 (suppl 5) (1989), pp. S57–S62.

24 D.J. Chadwick, D.A. Gillatt and J.C. Gingell, Medical or surgical orchidectomy: The patients' choice, BMJ 302 (1991), p. 572. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (18)

25 L.M. Elit, M.N. Levine and A. Gafni et al., Patients' preferences for therapy in advanced epithelial ovarian cancer: Development, testing, and application of a bedside decision instrument, Gynecol Oncol 62 (1996), pp. 329–335. Abstract |

PDF (131 K)

| View Record in Scopus | Cited By in Scopus (66)

26 V. Fiset, A.M. O'Connor and W. Evans et al., Development and evaluation of a decision aid for patients with stage IV non-small cell lung cancer, Health Expect 3 (2000), pp. 125–136. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (26)

27 M.D. Brundage, D. Feldman-Steward and R. Cosby et al., Cancer patients' attitudes toward treatment options for advanced non-small cell lung cancer: Implications for patient education and decision support, Patient Educ Couns 45 (2001), pp. 149–157. Article |

PDF (260 K)

| View Record in Scopus | Cited By in Scopus (33)

28 M.D. Brundage, D. Feldman-Stewart and R. Cosby et al., Phase I study of a decision aid for patients with locally advanced non-small-cell lung cancer, J Clin Oncol 19 (2001), pp. 1326–1335. View Record in Scopus | Cited By in Scopus (27)

29 K.S. Chiew, H. Shepherd, J. Vardy, M.H. Tattersall, P.N. Butow and N.B. Leighl, Development and evaluation of a decision aid for patients considering first-line chemotherapy for metastatic breast cancer, Health Expect 11 (1) (2008), pp. 35–45. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (4)

30 W.J. Mackillop, W.E. Stewart, A.D. Ginsburg and S.S. Stewart, Cancer patients' perceptions of their disease and its treatment, Br J Cancer 58 (1988), pp. 355–358. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (148)

31 E. Chow, L. Andersson, R. Wong, M. Vachon, G. Hruby and E. Franssen et al., Patients with advanced cancer: a survey of the understanding of their illness and expectations from palliative radiotherapy for symptomatic metastases, Clin Oncol (R Coll Radiol) 13 (2001), pp. 204–208. Abstract |

PDF (84 K)

| Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (29)

32 A.K. Ganti, S.J. Lee, J.M. Vose, M.P. Devetten, R.G. Bociek, J.O. Armitage, P.J. Bierman, L.J. Maness, E.C. Reed and F.R. Loberiza Jr, Outcomes after hematopoietic stem-cell transplantation for hematologic malignancies in patients with or without advance care planning, J Clin Oncol 25 (35) (2007), pp. 5643–5648. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (9)

33 S.J. Lee, D. Fairclough, J.H. Antin and J.C. Weeks, Discrepancies between patient and physician estimates for the success of stem cell transplantation, JAMA 285 (8) (2001), pp. 1034–1038. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (34)

34 S.J. Lee, F.R. Loberiza, J.D. Rizzo, R.J. Soiffer, J.H. Antin and J.C. Weeks, Optimistic expectations and survival after hematopoietic stem cell transplantation, Biol Blood Marrow Transplant 9 (2003), pp. 389–396. Article |

PDF (118 K)

| View Record in Scopus | Cited By in Scopus (14)

35 J.C. Weeks, E.F. Cook, S.J. O'Day, L.M. Peterson, N. Wenger and D. Reding et al., Relationship between cancer patients' predictions of prognosis and their treatment preferences, JAMA 279 (21) (1998), pp. 1709–1714. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (350)

36 C.C. Earle, B.A. Neville, M.B. Landrum, J.Z. Ayanian, S.D. Block and J.C. Weeks, Trends in the aggressiveness of cancer care near the end of life, J Clin Oncol 22 (2) (2004), pp. 315–321. View Record in Scopus | Cited By in Scopus (185)

37 R. Matsuyama, S.N. Reddy and T.J. Smith, Why do patients choose chemotherapy near the end of life?, J Clin Oncol 24 (2006), pp. 1–7.

38 A. Dufresne, X. Pivot, C. Tournigand, T. Facchini, T. Altweegg, L. Chaigneau and A. De Gramont, Impact of chemotherapy beyond the first line in patients with metastatic breast cancer, Breast Cancer Res Treat 107 (2) (2008), pp. 275–279. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (14)

39 P. Kirk, I. Kirk and L.J. Kristjanson, What do patients receiving palliative care for cancer and their families want to be told?: A Canadian and Australian qualitative study, BMJ 328 (7452) (2004), p. 1343. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (110)

40 L.L. Emanuel, F.D. Ferris, C.F. von Gunten and J. Von Roenn, EPEC-O: Education in Palliative and End of Life Care for Oncology, EPEC Project, Chicago (2005).

41 M.J. Field and C.K. Cassel, Approaching Death: Improving Care at the End of Life, National Academy Press, Washington DC (1997), pp. 59–64.

42 H.A. Huskamp, N.L. Keating, J.L. Malin, A.M. Zaslavsky, J.C. Weeks, C.C. Earle, J.M. Teno, B.A. Virnig, K.L. Kahn, Y. He and J.Z. Ayanian, Discussions with physicians about hospice among patients with metastatic lung cancer, Arch Intern Med 169 (10) (2009), pp. 954–962. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (4)

43 P. Glare, K. Virik, M. Jones, M. Hudson, S. Eychmuller and J. Simes et al., A systematic review of physicians' survival predictions in terminally ill cancer patients, BMJ 327 (2003), pp. 195–200.

44 T. Smith and K. Swisher, Telling the truth about terminal cancer, JAMA 279 (21) (1998), pp. 1746–1748. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (37)

45 T. Beauchamp and J. Childress, Principles of Biomedical Ethics (5th ed), Oxford University Press, New York (2001).

46 E. Panagopoulou, G. Mintziori, A. Montgomery, D. Kapoukranidou and A. Benos, Concealment of information in clinical practice: is lying less stressful than telling the truth?, J Clin Oncol 26 (7) (2008), pp. 1175–1177. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (9)

47 L. Fallowfield and V. Jenkins, Communicating sad, bad, and difficult news in medicine, Lancet 363 (9405) (2004), pp. 312–319. Article |

PDF (110 K)

| View Record in Scopus | Cited By in Scopus (195)

48 L.A. Dow, R.K. Matsuyama, V. Ramakrishnan, L. Kuhn, E.B. Lamont, L. Lyckholm and T.J. Smith, Paradoxes in advance care planning: the complex relationship of oncology patients, their physicians, and advance medical directives, J Clin Oncol 28 (2) (2010), pp. 299–304. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (2)