Synthetic lethality-based prediction of cancer treatment response from histopathology images

Danh-Tai Hoang, Gal Dinstag, Leandro C. Hermida, Doreen S. Ben-Zvi, Efrat Elis, Katherine Caley, Sanju Sinha, Neelam Sinha, Christopher H. Dampier, Tuvik Beker, Kenneth Aldape, Ranit Aharonov, Eric A. Stone, Eytan Ruppin

June, 2022

Advances in artificial intelligence have paved the way for predicting cancer patients’ survival and response to treatment from hematoxylin and eosin (H&E)-stained tumor slides. Extant approaches do so either directly from the H&E images or via prediction of actionable mutations and gene fusions. Here we present the first genetic interactions (GI)-based approach for predicting patient response to treatment, founded on two conceptual steps: (1) First, we build DeepPT, a deep-learning framework that predicts tumor gene expression from H&E slides, and subsequently, (2) we apply ENLIGHT – a previously published GI based approach – to predict patient treatment response from the inferred tumor expression. DeepPT was trained on images and corresponding transcriptomics data of TCGA breast, kidney, lung, and brain tumor samples. Testing DeepPT transcriptomics prediction ability, we find that it generalizes well to predicting the expression of two breast and brain cancer unseen independent datasets. Studying samples from a recently published large multi-omics breast cancer clinical trial, we applied ENLIGHT to the expression predicted by DeepPT from the tumor slides. We find that it successfully predicts true responders with a clinically meaningful hazard ratio of about six. These results put forward a general framework for predicting patient response to a broad array of targeted and checkpoint therapies from the histological images. If corroborated further, the new approach could augment the feasibility of precision oncology in developing countries and in other situations where comprehensive molecular profiling is not available.  


Read the full publication in BioRxiv