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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月24日

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  • Johnny X. Yu,
  • Jung Min Suh,
  • Katerina D. Popova,
  • Kristle Garcia,
  • Tanvi Joshi,
  • Bruce Culbertson,
  • Jessica B. Spinelli,
  • Vishvak Subramanyam,
  • Kevin Lou,
  • Trey Charbonneau,
  • Kevan M. Shokat,
  • Jonathan Weissman,
  • Hani Goodarzi

摘要

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.