Identifying commonalities between cell lines and tumors at the single cell level using Sobolev Alignment of deep generative models

Abstract

Preclinical models are essential to cancer research, however, key biological differences with patient tumors result in reduced translatability to the clinic and high attrition rates in drug development. Variability among and between patients, preclinical models, and individual cells obscures commonalities which could otherwise be exploited therapeutically. To discover the shared biological processes between cell line models and clinical tumors we developed Sobolev Alignment, a computational framework which uses deep generative models to capture non-linear processes in single-cell RNA sequencing data and kernel methods to align and interpret these processes. We show that our approach faithfully captures shared processes on a set of three synthetic datasets. Exploiting two large panels of untreated non-small cell lung cancer cell lines and patients, we identify the similarities between cell lines and tumors and show the conservation of key mitotic and immune-related pathways. Employing our approach on a large in-vitro perturbation screen, we show that processes captured by our method faithfully recapitulate the known modes of action of clinically approved drugs and allow investigation into the mode of action of an uncharacterized drug.