2. Academic Validation
  3. The GENEVA platform models tumor mosaicism to reveal variations of responses to KRAS inhibitors and identify improved drug combinations

The GENEVA platform models tumor mosaicism to reveal variations of responses to KRAS inhibitors and identify improved drug combinations

  • Nat Cancer. 2026 Mar;7(3):522-537. doi: 10.1038/s43018-026-01130-5.
Johnny X Yu # 1 2 3 4 5,  Jung Min Suh # 1 2 3 4 5 6,  Katerina D Popova 5 7 8,  Kristle Garcia 1 2 3 4,  Tanvi Joshi 1 2 3 4,  Bruce Culbertson 1 2 3 4,  Jessica B Spinelli 9,  Vishvak Subramanyam 1 2 3 4,  Kevin Lou 5 8 10,  Trey Charbonneau 6,  Kevan M Shokat 5 8 10,  Jonathan Weissman 7,  Hani Goodarzi 11 12 13 14 15
Affiliations

Affiliations

  • 1 Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA.
  • 2 Department of Urology, University of California, San Francisco, San Francisco, CA, USA.
  • 3 Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
  • 4 Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
  • 5 Biomedical Sciences Graduate Program, University of California, San Francsicso, San Francisco, CA, USA.
  • 6 Arc Institute, Palo Alto, CA, USA.
  • 7 Department of Biology, Whitehead Institute, MIT, Cambridge, MA, USA.
  • 8 Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.
  • 9 Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA.
  • 10 Howard Hughes Medical Institute, Bethesda, MD, USA.
  • 11 Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA. [email protected].
  • 12 Department of Urology, University of California, San Francisco, San Francisco, CA, USA. [email protected].
  • 13 Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA. [email protected].
  • 14 Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA. [email protected].
  • 15 Arc Institute, Palo Alto, CA, USA. [email protected].
  • # Contributed equally.
PMID: 41735640 DOI: 10.1038/s43018-026-01130-5
Abstract

The clinical success of Cancer drug candidates depends on efficacy across many different individuals. Because xenografts are challenging to scale, we currently rely on a limited set of in vivo preclinical models. Here, to address this limitation, we introduce GENEVA, a scalable single-cell-resolution platform for measuring responses to drug perturbations. GENEVA models Cancer genetic diversity by combining multiple patient-derived cell lines and Cancer cell lines into pooled three-dimensional cultures and xenograft models, allowing us to study drug responses across a wide range of genetic backgrounds within a single experiment. We apply GENEVA to investigate KRAS-G12C inhibitors and demonstrate that mitochondrial activation is a key driver of cell death following KRAS inhibition, while epithelial-to-mesenchymal transition is a prominent resistance mechanism. These findings highlight the utility of GENEVA to identify therapeutic targets and optimize combination therapies with the potential to bridge the gap between preclinical Cancer models and patient outcomes.

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