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  • Author: Daniel G. Stover x
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Sarah Asad, Carlos H. Barcenas, Richard J. Bleicher, Adam L. Cohen, Sara H. Javid, Ellis G. Levine, Nancy U. Lin, Beverly Moy, Joyce Niland, Antonio C. Wolff, Michael J. Hassett and Daniel G. Stover

Background: Triple-negative breast cancer (TNBC) accounts for disproportionately poor outcomes in breast cancer, driven by a subset of rapid-relapse TNBC (rrTNBC) with marked chemoresistance, rapid metastatic spread, and poor survival. Our objective was to evaluate clinicopathologic and sociodemographic features associated with rrTNBC. Methods: We included patients diagnosed with stage I–III TNBC in 1996 through 2012 who received chemotherapy at 1 of 10 academic cancer centers. rrTNBC was defined as a distant metastatic recurrence event or death ≤24 months after diagnosis. Features associated with rrTNBC were included in a multivariable logistic model upon which backward elimination was performed with a P<.10 criterion, with a final multivariable model applied to training (70%) and independent validation (30%) cohorts. Results: Among all patients with breast cancer treated at these centers, 3,016 fit the inclusion criteria. Training cohort (n=2,112) bivariable analyses identified disease stage, insurance type, age, body mass index, race, and income as being associated with rrTNBC (P<.10). In the final multivariable model, rrTNBC was significantly associated with higher disease stage (adjusted odds ratio for stage III vs I, 16.0; 95% CI, 9.8–26.2; P<.0001), Medicaid/indigent insurance, lower income (by 2000 US Census tract), and younger age at diagnosis. Model performance was consistent between the training and validation cohorts. In sensitivity analyses, insurance type, low income, and young age were associated with rrTNBC among patients with stage I/II but not stage III disease. When comparing rrTNBC versus late relapse (>24 months), we found that insurance type and young age remained significant. Conclusions: Timing of relapse in TNBC is associated with stage of disease and distinct sociodemographic features, including insurance type, income, and age at diagnosis.