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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 Conclusions PathAI models were able to identify TME-associated features from WSIs from a Phase II clinical trial which were associated with therapy failure and genomic alterations. These results suggest the power of deploying pre-trained ML-based systems in a clinical trial setting to identify pathobiological features associated with tumor characteristics and time to progression from only H&E images. Citation Format: Laura Dillon, Marylens Hernandez, Ben Glass, Guillaume Chhor, Sara Hoffman, Varsha Chinnaobireddy, Sai Chowdary Gullapally, Kris Sachsenmeier, Andy Beck, Jason Hipp. 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 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB016.

Rendering cancer diagnoses from tissues is a highly complex process that requires many years of expert training. It involves challenging tasks for pathologists, such as identifying rare events in very large images (e.g., micrometastases in lymph nodes) or classifying subtle differences between normal vs tumor or among similar-looking tumors that have very different treatment plans. These tasks are typically more challenging for human cognition, and, consequently, over- and underdiagnoses are not uncommon, resulting in nonoptimal treatment selection. Deep learning, a type of machine intelligence, has been successfully applied to solve similar identification and classification problems for Gmail, Google Maps, Google Photos, Google Translate, etc., and may have the potential to solve these similar challenges in the practice of pathology and help improve patient's diagnoses. This talk will begin by providing a brief background on deep learning and how it has been utilized at Google for health care and non-health care applications. The latter half of the talk will discuss the potential of deep learning to improve the accuracy and efficiency of the pathology workflow while highlighting recent advances towards that goal. Citation Format: Jason D. Hipp, Martin Stumpe. Advancing cancer diagnostics with artificial intelligence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr PL02-01.