Abstract 4951: Integrative multi-omic machine learning model predicts neoadjuvant immunotherapy response using molecular data and deep learning-derived features from digital pathology

Abstract Background: Over the past decade, there has been an exponential accumulation of digital patient data through molecular profiling and digitization of pathology slides. However, integrating this to advance diagnostics and cancer patient care remains a significant challenge. By integrating information from diverse modalities to capture aspects of the tumor microenvironment (TME) and immune landscape, we aim to improve patient stratification for immunotherapy. Methods: We collected data from baseline biopsies from patients with palpable melanoma stage III (n=195) who received neoadjuvant immunotherapy followed by surgical resection of largest lymph node metastasis. The different modalities included clinical information, whole-genome sequencing (WGS), RNAseq, laboratory tests of blood, haematoxylin and eosin (H Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 4951.