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<p>Oncogenic mutations shape colorectal cancer (CRC) biology, yet their impact on transcriptional phenotypes remains incompletely understood, and their individual prognostic value is limited. Here, we perform a pooled single-cell transcriptomic screen of over 100,000 CRC cells with a comprehensive barcoded library of oncogenic variants across genetically diverse CRC lines. Using a variational autoencoder-based interpretable factor model, we identify ten conserved oncogene-driven transcriptional modules (TMOs) representing core cancer phenotypes such as cellular plasticity, inflammatory response, replicative stress, and epithelial-to-mesenchymal transition. Engagement of these modules can be context-dependent, reflecting interactions between oncogene-induced driver pathways and background genetics. TMO activity in patient tumors stratifies CRC cohorts into high- and low-risk groups, improving relapse-free survival prediction beyond existing classification systems. Our study systematically links oncogenic signaling to transcriptional states and clinical outcomes, establishing a functional framework for module-based patient stratification in precision oncology.</p>

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Pooled single-cell screen in colorectal cancer defines transcriptional modules linked to oncogenes

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月19日

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  • Viola Hollek,
  • Francisca Böhning,
  • Catalina Florez Vargas,
  • Anja Sieber,
  • Markus Morkel,
  • Nils Blüthgen

摘要

Oncogenic mutations shape colorectal cancer (CRC) biology, yet their impact on transcriptional phenotypes remains incompletely understood, and their individual prognostic value is limited. Here, we perform a pooled single-cell transcriptomic screen of over 100,000 CRC cells with a comprehensive barcoded library of oncogenic variants across genetically diverse CRC lines. Using a variational autoencoder-based interpretable factor model, we identify ten conserved oncogene-driven transcriptional modules (TMOs) representing core cancer phenotypes such as cellular plasticity, inflammatory response, replicative stress, and epithelial-to-mesenchymal transition. Engagement of these modules can be context-dependent, reflecting interactions between oncogene-induced driver pathways and background genetics. TMO activity in patient tumors stratifies CRC cohorts into high- and low-risk groups, improving relapse-free survival prediction beyond existing classification systems. Our study systematically links oncogenic signaling to transcriptional states and clinical outcomes, establishing a functional framework for module-based patient stratification in precision oncology.