Abstract PD7-09: Feasibility of a machine learning-based peripheral blood immunoprofiling platform to stratify patients with early-stage triple-negative breast cancer (eTNBC) by neoadjuvant therapy response

Abstract Background: Tissue-based biomarkers for predicting breast cancer outcomes are limited by sampling bias and tumor heterogeneity. Blood-based immunoprofiling offers a promising alternative. A machine learning-based platform using multiparameter flow cytometry (MFC) analysis of peripheral blood mononuclear cells (PBMCs) previously identified 5 immunotypes — G1-Naïve (Naïve T cell enriched), G2-Primed (Memory CD4+ T cell enriched), G3-Progressive (DC/NK/Memory CD8+ T cell enriched), G4-Chronic (Effector/Exhausted CD8+ T cell enriched), G5-Suppressive (Myeloid/MDSC enriched), with potential associations to treatment outcomes (Dyikanov et al. Cancer Cell 2024). We aimed to assess whether applying this platform to serial PBMC samples from 2 eTNBC cohorts treated with neoadjuvant therapy (NAT) (1: chemotherapy [ChT]; 2: ChT+immunotherapy [IO]) could stratify patients (pts) into response groups. Methods: We identified from the prospective DFCI Multicenter TNBC registry pts with eTNBC, available pathological response information, and PBMCs at both baseline (treatment-naive) and on-NAT (weeks 4-7). PBMCs were stained with 10 custom antibody panels (mean 14 antibodies/panel) and analyzed by MFC. A regression model calculated immune signature scores (ISS; 0: least aligned, 10: most aligned), assigning samples to one of 5 immunotypes based on maximum ISS. Associations with pathologic complete response (pCR, yes/no) were assessed by Fisher’s exact test, Mann-Whitney U test (p