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Pathway-Centric Integration of CRISPR Fitness with Molecular Features Draws Cancer State Maps

Hagiwara, T.; Ito, K. K.; Kuroki, K.; Niikura, R.; Hirota, M.; Kawai, K.; Uga, H.; Iwakiri, J.; Terai, G.; Asai, K.; Goyama, S.; Shikata, S.; Kanai, A.; Suzuki, Y.; Shimada, K.; Fukuyama, M.; Hata, S.; Yamamoto, S.; Chinen, T.; Kitagawa, D.

2026-05-14 cancer biology
10.64898/2026.05.12.724547 bioRxiv
Show abstract

Cancer cells display heterogeneous pathway activity that shapes therapeutic vulnerability, but mapping it remains challenging. Transcriptomic scores do not directly measure functional activity, and CRISPR knockout data alone lack molecular interpretability. We introduce StateMap, a pathway-centric framework integrating gene expression and genome-wide CRISPR knockout fitness data from the Cancer Dependency Map. For a given pathway, StateMap selects features by co-dependency and mutual information, then projects cell lines into a low-dimensional space reflecting pathway activity and molecular state. Applied to the Hippo pathway, it resolved five functional states refining the YAP-on/YAP-off dichotomy. Notably, the Hippo-strong state showed selective dependence on integrin V{beta}5; ITGAV depletion triggered Hippo-dependent cell aggregation and G1 arrest via enhanced cell-cell adhesion. Machine learning transfer to TCGA identified a matching subtype with poor prognosis, nominated NNMT as a biomarker, and predicted sensitivity to the V inhibitor Cilengitide. StateMap enables pathway-specific state mapping and discovery of state-selective therapeutic vulnerabilities.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.