Jennifer A Ligibel1, Constance T Cirrincione2, Minetta Liu2, Marc Citron2, James N Ingle2, William Gradishar2, Silvana Martino2, William Sikov2, Richard Michaelson2, Elaine Mardis2, Charles M Perou2, Matthew Ellis2, Eric Winer2, Clifford A Hudis2, Donald Berry2, William T Barry2. 1. Dana-Farber Cancer Institute, Boston, MA (JAL, EW); Alliance Statistics and Data Center, Durham, NC (CTC); Mayo Clinic, Rochester, MN (ML, JNI); Hofstra North Shore - LIJ School of Medicine, ProHEALTH Care Associates, Lake Success, NY (MC); Northwestern University Feinberg School of Medicine, Chicago, IL (WG); The Angeles Clinic and Research Institute, Santa Monica, CA (SM); Rhode Island Hospital, Providence, RI (WS); St. Barnabas Medical Center, Livingston, NJ (RM); The Genome Institute, Washington University in St. Louis, St. Louis, MO (EM); Department of Genetics, Lineberger Cancer Center, University of North Carolina, Chapel Hill, NC (CMP); Breast Cancer Program, Siteman Cancer Center and Washington University School of Medicine, St. Louis, MO (ME); Memorial Sloan Kettering Cancer Center, New York, NY (CAH); Alliance Statistics and Data Center, MD Anderson Cancer Center, Houston, TX (DB); Alliance Statistics and Data Center, Dana-Farber Cancer Institute, Boston, MA (WTB). jligibel@partners.org. 2. Dana-Farber Cancer Institute, Boston, MA (JAL, EW); Alliance Statistics and Data Center, Durham, NC (CTC); Mayo Clinic, Rochester, MN (ML, JNI); Hofstra North Shore - LIJ School of Medicine, ProHEALTH Care Associates, Lake Success, NY (MC); Northwestern University Feinberg School of Medicine, Chicago, IL (WG); The Angeles Clinic and Research Institute, Santa Monica, CA (SM); Rhode Island Hospital, Providence, RI (WS); St. Barnabas Medical Center, Livingston, NJ (RM); The Genome Institute, Washington University in St. Louis, St. Louis, MO (EM); Department of Genetics, Lineberger Cancer Center, University of North Carolina, Chapel Hill, NC (CMP); Breast Cancer Program, Siteman Cancer Center and Washington University School of Medicine, St. Louis, MO (ME); Memorial Sloan Kettering Cancer Center, New York, NY (CAH); Alliance Statistics and Data Center, MD Anderson Cancer Center, Houston, TX (DB); Alliance Statistics and Data Center, Dana-Farber Cancer Institute, Boston, MA (WTB).
Abstract
BACKGROUND: Obesity at diagnosis is associated with poor prognosis in women with breast cancer, but few reports have been adjusted for treatment factors. METHODS: CALGB 9741 was a randomized trial of dose density and sequence of chemotherapy for node-positive breast cancer. All patients received doxorubicin, cyclophosphamide, and paclitaxel, dosed by actual body weight. Height and weight at diagnosis were abstracted from patient records, and the PAM50 assay was performed from archived specimens using the NanoString platform. Relationships between body mass index (BMI), PAM50, and recurrence-free and overall survival (RFS and OS) were evaluated using proportional hazards regression, adjusting for number of involved nodes, estrogen receptor (ER) status, tumor size, menopausal status, drug sequence, and dose density. All statistical tests were two-sided. RESULTS: Baseline height and weight were available for 1909 of 2005 enrolled patients; 1272 additionally had subtype determination by PAM50. Median baseline BMI was 27.4kg/m(2). After 11 years of median follow-up, there were 619 RFS events and 543 deaths. Baseline BMI was a statistically significant predictor of RFS (adjusted hazard ratio [HR] for each five-unit increase in BMI = 1.08, 95% confidence interval [CI] = 1.02 to 1.14, P = .01) and OS (adjusted HR = 1.08, 95% CI = 1.01 to 1.14, P = .02) BMI and molecular phenotypes were independent prognostic factors for RFS, with no statistically significant interactions detected. CONCLUSIONS: BMI at diagnosis was a statistically significant prognostic factor in a group of patients receiving optimally dosed chemotherapy. Additional research is needed to determine the impact of weight loss on breast cancer outcomes and to evaluate whether this impact is maintained across tumor subtypes.
RCT Entities:
BACKGROUND: Obesity at diagnosis is associated with poor prognosis in women with breast cancer, but few reports have been adjusted for treatment factors. METHODS: CALGB 9741 was a randomized trial of dose density and sequence of chemotherapy for node-positive breast cancer. All patients received doxorubicin, cyclophosphamide, and paclitaxel, dosed by actual body weight. Height and weight at diagnosis were abstracted from patient records, and the PAM50 assay was performed from archived specimens using the NanoString platform. Relationships between body mass index (BMI), PAM50, and recurrence-free and overall survival (RFS and OS) were evaluated using proportional hazards regression, adjusting for number of involved nodes, estrogen receptor (ER) status, tumor size, menopausal status, drug sequence, and dose density. All statistical tests were two-sided. RESULTS: Baseline height and weight were available for 1909 of 2005 enrolled patients; 1272 additionally had subtype determination by PAM50. Median baseline BMI was 27.4kg/m(2). After 11 years of median follow-up, there were 619 RFS events and 543 deaths. Baseline BMI was a statistically significant predictor of RFS (adjusted hazard ratio [HR] for each five-unit increase in BMI = 1.08, 95% confidence interval [CI] = 1.02 to 1.14, P = .01) and OS (adjusted HR = 1.08, 95% CI = 1.01 to 1.14, P = .02) BMI and molecular phenotypes were independent prognostic factors for RFS, with no statistically significant interactions detected. CONCLUSIONS: BMI at diagnosis was a statistically significant prognostic factor in a group of patients receiving optimally dosed chemotherapy. Additional research is needed to determine the impact of weight loss on breast cancer outcomes and to evaluate whether this impact is maintained across tumor subtypes.
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