Predicting response to systemic treatments: learning from the past to plan for the future
Meredith M Regan1, Richard D Gelber
1IBCSG Statistical Center, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 44 Binney St., Boston, MA 02115, USA. mregan@jimmy.harvard.edu
Abstract:
Therapeutic effects of adjuvant therapies for breast cancer have been assessed "across the board" and implemented using the principle that if a treatment is effective "on average" then it is effective "for all patients." Exploration and improved understanding of the biological basis for predicting response to available adjuvant therapies is essential to enhance patient care. As illustration, we consider the effects of chemotherapy and tamoxifen in two International Breast Cancer Study Group (IBCSG) trials for postmenopausal women. The level of estrogen receptor (ER) expression in the primary tumor is a powerful predictor of response to adjuvant therapy. Absence of ER identifies a chemosensitive cohort for which concurrent tamoxifen significantly blunts the large chemotherapy effect. High levels of ER expression are associated with good results using tamoxifen alone; adding chemotherapy provides little additional benefit. By contrast, adding chemotherapy to tamoxifen provides additional benefit for patients with node-positive disease and tumors expressing intermediate levels of ER. Identification of chemosensitive targets, e.g., absence of PgR, in tumors with intermediate ER expression is required to further tailor, adding chemotherapy within this otherwise endocrine-responsive cohort. Age is not a therapeutic target. Thus, the biological bases for treatment responsiveness must be defined. All findings from clinical trials and meta-analyses should be presented primarily according to steroid hormone receptor status and future studies should be designed as tailored treatment investigations.
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