AI-driven H&E plasma cell to predict pathological complete response in IO-treated triple-negative breast cancer (Yale; NCT02489448).

Abstract

1122 Background: Pathological complete response (pCR) remains difficult to predict in triple-negative breast cancer (TNBC) patients treated with immune checkpoint inhibitors due to limited validated biomarkers. While tumor-infiltrating lymphocytes (TILs) are prognostic in TNBC, they are commonly treated as a composite population. Plasma cells are central to humoral immunity and can influence antitumor response through antibody production, antigen presentation, and T-cell modulation. However, the independent and cooperative prognostic role of plasma cells, either alone or in spatial interaction with other immune cells has not been systematically evaluated, particularly in TNBC. Here, we evaluated the value of plasma cells to predict pathological complete response using AI-based analysis of routine H&E slides. Methods: We analyzed 239 stage I–III TNBC patients across four cohorts. Models were trained on KEYNOTE-522 regimen treated patients (D1, N=130) and validated in both the Yale TNBC immunotherapy trial cohort (D2, N=65; NCT02489448) treated with durvalumab (anti-PD-L1 antibody) and University Hospital cohort (D3, N=25) treated with pembrolizumab. Plasma cells and lymphocytes were automatically detected on whole-slide H&E images using deep learning–based nuclear segmentation and tumor region classification. Quantified metrics included immune cell density, tumor-infiltrating plasma cell (TIP) ratios, and spatial features capturing plasma cell clustering and interaction with lymphocytes. Density-based classifiers were trained on D1 and evaluated for pCR prediction. Results: Plasma cell density alone predicted pCR with higher accuracy than lymphocyte density across both validation cohorts. In D2), plasma cell density achieved an AUC of 73.2 compared with 62.9 for lymphocyte density. Similarly, in the external institutional cohort (D3), plasma cell density outperformed lymphocyte density (AUC 69.2 vs 60.8, respectively; Table 1). These findings demonstrate the superior predictive value of plasma cell density over conventional lymphocyte-based measures for pCR in immunotherapy-treated TNBC. Conclusions: Plasma cells demonstrate independent and cooperative predictive value in immunotherapy-treated TNBC. AI-derived plasma cell density and spatial interaction features improve pCR prediction and survival stratification beyond conventional lymphocyte-based metrics. Comparison of plasma cell performance compared to lymphocytes on holdout set D2 (Yale TNBC clinical trial NCT02489448) and D3. Holdout 1 Holdout 2 Yale NCT02489448 (N=64), D2 UH Hospital (N=25), D3 M plasma 73.2 69.2 M lymph 62.9 60.8