Abstract 6179: Machine learning (ML)-spatial quantification of the tumor microenvironment (TME) identifies differences associated with response to bintrafusp alfa (BA) vs pembrolizumab (PEM) treatment in the Phase 3 INTR@PID Lung 037 study

Abstract Background: ML digital pathology models can accurately quantify predictive biomarkers like PD-L1. 1 We combined tissue conserving ML assessment of the TME in hematoxylin and eosin (H macrophages, fibroblasts, and granulocytes in stroma; and the abundance of cancer cells relative to all other cells in tumor. Bulk RNAseq was performed for 186 samples yielding 177 WSI-RNAseq pairs. Spearman correlation between HIFs and gene expression or signature was computed. Top gene associations were analyzed via gene-set enrichment analysis. HIFs were used for immunophenotyping (immune excluded, inflamed, and desert) via prespecified cutoffs on tumor-infiltrating lymphocyte abundance, and for biomarker discovery via Cox modeling of PFS and OS. Results: Gene expression and signatures were associated with HIFs quantifying immune spatial abundance in TME (e. g. , cTILs vs. immune checkpoint signature2). Improved PFS and OS were associated with an immune-inflamed phenotype for PEM (PFS, HR=0. 36, p=0. 007, 95% CI 0. 17-0. 75; OS, HR=0. 37, p=0. 065, 95% CI 0. 13-1. 06). In interaction with BA, stromal macrophages were associated with improved survival (PFS, HR=0. 72, p=0. 062, 95% CI 0. 53-1. 02; OS, HR=0. 62, p=0. 058, 95% CI 0. 38-1. 02) and an increased likelihood of treatment response (odds ratio=1. 61, p=0. 015). PFS and OS were nearly identical between arms in immune-excluded cases (PEM vs. BA PFS, HR=0. 93, p=0. 73, 95% CI 0. 63-1. 39; OS, HR=1. 09, p=0. 77, 95% CI 0. 62-1. 92. Conclusion: Together HIFs and immunophenotyping enabled response prediction to BA and PEM. PEM response was greater for immune-inflamed tumors, while BA response was associated with macrophage infiltration of stroma. ML-based spatial analysis of the TME shows promise for immunotherapy biomarker discovery and validation. 1V Baxi, et al. Modern Pathology. 2022; 35 (1): 23-32. 2S Mariathasan, et al. Nature. 2018; 554 (7693): 544-548. Citation Format: John Abel, Andreas Machl, Aslihan Gerhold-Ay, Limin Yu, Darpan Sanghavi, Ben Trotter, Neel Patel, Ylaine Gerardin, Ramprakash Srinivasan, Sergine Brutus, Thomas Mrowiec. Machine learning (ML) -spatial quantification of the tumor microenvironment (TME) identifies differences associated with response to bintrafusp alfa (BA) vs pembrolizumab (PEM) treatment in the Phase 3 INTR@PID Lung 037 study abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 6179.