Breast Cancer Index (BCI) prognostic and predictive classification in older versus younger patients with early-stage HR+ breast cancer (BC) and correlation with clinical risk factors.

Abstract

546 Background: Older patients with HR+ BC may present distinct challenges for treatment decision-making. BCI is a validated gene expression assay for pts with early stage HR+ BC that provides 2 results: BCI Predictive, based on HoxB13/IL17BR (H/I ratio), reports a prediction of likelihood of benefit from extended endocrine therapy (EET); BCI Prognostic, based on the combination of H/I and proliferation-based genes, reports individualized risk of late distant recurrence (DR). Here, the predictive and prognostic value of BCI results across the aging spectrum ( < 64y, 65-74y, ≥75y) and correlation with clinical risk factors were examined. Methods: The BCI Clinical Database for Correlative Studies is an IRB-approved de-identified database that contains > 50 clinicopathologic and molecular variables from cases submitted for BCI in clinical practice (N = 14,463). Clinicopathologic variables were abstracted from pathology reports, and were available for a subset of cases. Chi-squared tests were used to compare BCI results between age groups and clinical subsets. Results: Analyses included LN- pts (N = 3,395): median age 59.1y; 5.5% ≥75y (N = 188), 24.0% 65-74y (N = 814), 70.5% ≤64y (N = 2,393). BCI Prognostic had a wide distribution of individual risk assessments in all age groups. The proportion of pts classified as high risk was similar between age groups (48.4%, 48.4% and 49.8% in ≥75y, 65-74y and ≤64y; p = 0.76). The proportion of pts with high risk results increased with increasing tumor size and grade in all age groups (P < 0.05 for all). Notably, in pts ≥75y, BCI identified 31.9% of T1a/b and 21.1% of Grade 1 tumors as high risk of late DR. BCI Predictive (H/I) also identified similar proportions as High H/I across age groups (42.0%, 41.8%, and 40.6% in ≥75y, 65-74y, and ≤64y; p = 0.81). Similar proportions of pts ≥75y were identified as high H/I across size and grade subsets (P = 0.702, P = 0.193, respectively). Conclusions: BCI identifies pts with high risk disease and who may benefit from EET across the aging spectrum. Individualized decisions for EET also must include life expectancy, competing comorbidities and potential toxicities from therapy.