Abstract LB016: Deep learning identifies pathobiological features within H&E images associated with genomic alterations and progression on anti-PD(L)1 in HUDSON, an AstraZeneca-sponsored Phase II clinical trial

Introduction Machine learning (ML) models offer the potential to provide rich, quantitative characterizations of the tumor and tumor micro-environment (TME). Here we deployed a machine learning-based approach to the analysis of H&E images from HUDSON (NCT03334617), an AstraZeneca Phase II Platform clinical trial, to identify and quantify cellular composition and tissue architecture features in the TME that are associated with genomic alterations and time to progression on anti-PD(L)1 therapies. Methods PathAI previously trained ML models on non-small cell lung carcinoma (NSCLC) samples from commercial and clinical datasets to identify cell types and tissue regions within the TME. With no additional training, the models were deployed on 169 digitized whole slide images (WSIs) of H&E-stained biopsies from an international, multi-site AstraZeneca-sponsored Phase II clinical trial of novel anti-cancer agents in subjects with metastatic NSCLC. Biopsies were across multiple body sites, and taken both pre- and post-checkpoint progression. ML models generated human interpretable features (HIFs) that characterize the cell composition and tissue architecture from each biopsied sample. HIFs from baseline samples that met minimum image quality thresholds (n=89) were clustered to reduce redundancy and were tested for association with weeks to progression on anti-PD(L)1 therapy using Cox regression analysis. Results The PathAI ML models were successfully deployed on WSIs from the HUDSON clinical trial. Following correction for biopsy timing and location, a total of 59 HIFs were found to be significantly associated (p