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

Oana Ursu1,2, James T Neal1, Emily Shea1,3, Pratiksha I Thakore1,2, Livnat Jerby-Arnon1,4,5, Lan Nguyen1, Danielle Dionne1, Celeste Diaz1,6, Julia Bauman1,5, Mariam Mounir Mosaad1, Christian Fagre1, April Lo7, Maria McSharry7, Andrew O Giacomelli1,8,9, Seav Huong Ly1,8,10, Orit Rozenblatt-Rosen1,2, William C Hahn1,8,11, Andrew J Aguirre1,8,11, Alice H Berger7, Aviv Regev12,13,14,15, Jesse S Boehm16

  1. Broad Institute of Harvard and MIT, Cambridge, MA, USA.
  2. Genentech, South San Francisco, CA, USA.
  3. Department of Cancer Biology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
  4. Chan Zuckerberg Biohub, San Francisco, CA, USA.
  5. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  6. Department of Cancer Biology, Stanford University School of Medicine, Stanford, CA, USA.
  7. Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
  8. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
  9. Princess Margaret Cancer Centre, Toronto, ON, Canada.
  10. Duke-NUS Medical School, Singapore, Singapore.
  11. Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
  12. Broad Institute of Harvard and MIT, Cambridge, MA, USA. aviv.regev.sc@gmail.com.
  13. Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. aviv.regev.sc@gmail.com.
  14. Howard Hughes Medical Institute, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA. aviv.regev.sc@gmail.com.
  15. Genentech, South San Francisco, CA, USA. aviv.regev.sc@gmail.com.
  16. Broad Institute of Harvard and MIT, Cambridge, MA, USA. boehm@broadinstitute.org.

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.

Presented By Oana Ursu | ORCID iD

Cancer Biology, GenomicsNicholas Liao2022-06 (Jun)