Abstract PS1-11-13: Ai-driven chemotherapy decision support via genomic subgroup identification in fertility-conscious and low-risk er+/her2− invasive ductal breast carcinoma

Abstract Background: Invasive ductal carcinoma is the most common subtype of breast cancer, with endocrine therapy the standard treatment for ER+ patients. However, certain clinical and genomic risk factors support adding chemotherapy. Among young women under 50, treatment decisions must balance survival benefit with fertility preservation and overtreatment avoidance. Similarly, patients with low Nottingham Prognostic Index (NPI) scores are generally spared chemotherapy, yet a subset may still benefit from combination treatment. In this study, we apply a two-stage machine learning framework to identify (1) a clinically meaningful subgroup of young ER+/HER2− patients likely to benefit from combined chemo-endocrine therapy despite fertility concerns, and (2) genomic subgroups associated with chemotherapy benefit among patients with low-risk NPI profiles. Methods: We analyzed a longitudinal cohort of 423 IDC patients who received adjuvant treatment post-surgery, with 5-year overall survival (OS) and recurrence-free survival (RFS) as outcomes. A two-stage Virtual Twins (VT) ML framework was applied to identify subgroups benefiting from combination therapy (hormonal therapy + chemotherapy ± radiation) versus hormonal therapy alone, focusing on patients 5.0) and harbored amplifications at 11q13/14 (CCND1, PAK1, RSF1, EMSY), 8p12, 8q, and 20q—indicative of proliferative and endocrine-resistant biology. These tumors also exhibited TRG/TRA deletions and high CD8+ infiltration. In contrast, younger patients with genomically quiet tumors-defined by isolated 8p12 amplification, 1q gain, 16q loss, or modest 8q/20q gains-and Luminal A or Claudin-low subtypes showed minimal benefit from combination therapy. Secondly, in patients with low NPI (