Massively parallel phenotyping of coding variants in cancer with Perturb-seq

Abstract

Genome sequencing studies have identified millions of somatic variants in cancer, but it remains challenging to predict the phenotypic impact of most. Experimental approaches to distinguish impactful variants often use phenotypic assays that report on predefined gene-specific functional effects in bulk cell populations. Here, we develop an approach to functionally assess variant impact in single cells by pooled Perturb-seq. We measured the impact of 200 TP53 and KRAS variants on RNA profiles in over 300,000 single lung cancer cells, and used the profiles to categorize variants into phenotypic subsets to distinguish gain-of-function, loss-of-function and dominant negative variants, which we validated by comparison with orthogonal assays. We discovered that KRAS variants did not merely fit into discrete functional categories, but spanned a continuum of gain-of-function phenotypes, and that their functional impact could not have been predicted solely by their frequency in patient cohorts. Our work provides a scalable, gene-agnostic method for coding variant impact phenotyping, with potential applications in multiple disease settings.