Differential pathologic complete response rates after neoadjuvant chemotherapy among molecular subtypes of triple-negative breast cancer.

Breast Cancer - Triple-Negative/Cytotoxics/Local Therapy
Session Type and Session Title: 
Oral Abstract Session, Breast Cancer - Triple-Negative/Cytotoxics/Local Therapy
Abstract Number: 



J Clin Oncol 31, 2013 (suppl; abstr 1005)


Hiroko Masuda, Keith A. Baggerly, Ying Wang, Ya Zhang, Ana M. Gonzalez-Angulo, Funda Meric-Bernstam, Vicente Valero, Brian D. Lehmann, Jennifer A. Pietenpol, Gabriel N. Hortobagyi, William Fraser Symmans, Naoto T. Ueno; Morgan Welch Inflammatory Breast Cancer Program and Clinic, The University of Texas MD Anderson Cancer Center, Houston, TX; The University of Texas MD Anderson Cancer Center, Houston, TX; Vanderbilt-Ingram Cancer Center; School of Medicine Department of Biochemistry, Vanderbilt University, Nashville, TN; Vanderbilt-Ingram Cancer Center, Nashville, TN; Morgan Welch Inflammatory Breast Cancer Program and Clinic, University of Texas M. D. Anderson Cancer Center, Houston, TX

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Abstract Disclosures


Background: By gene profiling, Lehmann et al. (J Clin Invest 121:2750-2767, 2011) reported that triple-negative breast cancer (TNBC) can be classified into 6 clusters—basal-like 1 (BL1), basal-like 2 (BL2), immunomodulatory (IM), mesenchymal (M), mesenchymal stem-like (MSL), and luminal androgen receptor (LAR)—plus an unstable (UNS) cluster. While it is clear that patients with TNBC differently respond to chemotherapy, the clinical relevance of these molecular TNBC subtypes is unknown. Methods: We qualitatively reproduced the Lehmann et al. experiments using Affymetrix CEL files from the public datasets. We identified 130 TNBC gene expression microarrays obtained from 03/00 to 03/10. All patients had received neoadjuvant chemotherapy containing sequential taxane and anthracycline-based regimens and had evaluable pathological tumor response data. Median follow-up was 68.1 months. (5.1-147.5). We classified TNBC samples using Lehmann’s gene signatures, then performed Fisher's exact test to correlate TNBC subtype and pCR status. To assess the independent utility of TNBC subtype for predicting pCR status, we fit a logistic regression model to our data and used age, clinical stage, treatment regimens, and nuclear grade as potential explanatory factors. We also performed comparison of the subtypes with the PAM50 intrinsic subtypes and RCB index. Results: The BL1 subtype had the highest pCR rate (52%); BL2 and LAR the lowest (0% and 10%, respectively). TNBC subtype and pCR status were significantly associated (p = 0.044). TNBC subtype was an independent predictor of pCR status (p = 0.022) by a likelihood ratio test.The Lehmann’s subtype classification better predicted pCR status than did the PAM50 intrinsic subtypes (basal-like vs. non basal-like). Conclusions: Dividing TNBC into 7 subtypes predicts high vs. low pCR rate. The 7-subtype classification may lead to innovative personalized medicine strategies for patients with TNBC. There is a need for prospective validation of the hypothesis that pCR rates associated with the seven TNBC subtypes will predict long-term patient outcome.