Development and Validation of a Tool Integrating the 21-Gene Recurrence Score and Clinical-Pathological Features to Individualize Prognosis and Prediction of Chemotherapy Benefit in Early Breast Cancer

PURPOSEThe 21-gene recurrence score (RS) is prognostic for distant recurrence (DR) and predictive for chemotherapy benefit in early breast cancer, whereas clinical-pathological factors are only prognostic. Integration of genomic and clinical features offers the potential to guide adjuvant chemotherapy use with greater precision.METHODSWe developed a new tool (RSClin) that integrates RS with tumor grade, tumor size, and age using a patient-specific meta-Analysis including 10,004 women with hormone receptor-positive, human epidermal growth factor receptor 2-negative, and node-negative breast cancer who received endocrine therapy alone in the B-14 (n = 577) and TAILORx (n = 4,854) trials or plus chemotherapy in TAILORx (n = 4,573). Cox models for RSClin were compared with RS alone and clinical-pathological features alone using likelihood ratio tests. RSClin estimates of DR used a baseline risk with TAILORx event rates to reflect current medical practice. A patient-specific estimator of absolute chemotherapy benefit was computed using individualized relative chemotherapy effect from the randomized TAILORx and B-20 trials. External validation of risk estimation was performed by comparing RSClin estimated risk and observed risk in 1,098 women in the Clalit registry.RESULTSRSClin provides more prognostic information (likelihood ratio χ) for DR than RS or clinical-pathological factors alone (both P <.001, likelihood ratio test). In external validation, the RSClin risk estimate was prognostic for DR risk in the Clalit registry (P <.001) and the estimated risk closely approximated the observed 10-year risk (Lin concordance 0.962). The absolute chemotherapy benefit estimate ranges from 0% to 15% as the RS ranges from 11 to 50 using RSClin in a 55-year-old woman with a 1.5-cm intermediate-grade tumor.CONCLUSIONThe RSClin tool integrates clinical-pathological and genomic risk to guide adjuvant chemotherapy in node-negative breast cancer and provides more individualized information than clinical-pathological or genomic data alone.