As molecular subtyping of breast cancer influences clinical management, evaluation of screening and adjuvant treatment interventions at the population level needs to account for molecular subtyping. Performing such analyses are challenging because molecular-subtype-specific long-term outcomes are not readily accessible as these markers were not historically recorded in tumor registries. We present a modeling approach to estimate historical survival outcomes by estrogen-receptor (ER) and human epidermal growth factor 2 (HER2) status. Our approach leverages a simulation model of breast cancer outcomes and integrates data from two sources: the Surveillance Epidemiology and End Results (SEER) databases and the Breast Cancer Surveillance Consortium (BCSC). We not only produce ER,HER2-specific estimates of breast cancer survival in the absence of screening and adjuvant treatment, but we also estimate mean tumor volume doubling time (TVDT) and mean mammographic detection threshold by ER/HER2-status. In general, we found that tumors with ER-negative and HER2-positive status are associated with more aggressive growth, have lower TVDTs, are harder to detect by mammography and have worse survival outcomes in the absence of screening and adjuvant treatment. Our estimates have been used as inputs into model-based analyses that evaluate the effects of screening and adjuvant treatment interventions on population outcomes by ER and HER2 status developed by the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Working Group. In addition, our estimates enable a re-assessment of historical trends in breast cancer incidence and mortality in terms of contemporary molecular tumor characteristics. Our approach can be generalized beyond breast cancer and to more complex molecular profiles.
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