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Spatial domain analysis predicts risk of colorectal cancer recurrence and infers associated tumor microenvironment networks

Author

Listed:
  • Shikhar Uttam

    (University of Pittsburgh)

  • Andrew M. Stern

    (University of Pittsburgh
    University of Pittsburgh)

  • Christopher J. Sevinsky

    (GE Global Research Center)

  • Samantha Furman

    (University of Pittsburgh)

  • Filippo Pullara

    (University of Pittsburgh)

  • Daniel Spagnolo

    (University of Pittsburgh)

  • Luong Nguyen

    (University of Pittsburgh)

  • Albert Gough

    (University of Pittsburgh
    University of Pittsburgh)

  • Fiona Ginty

    (GE Global Research Center)

  • D. Lansing Taylor

    (University of Pittsburgh
    University of Pittsburgh)

  • S. Chakra Chennubhotla

    (University of Pittsburgh)

Abstract

An unmet clinical need in solid tumor cancers is the ability to harness the intrinsic spatial information in primary tumors that can be exploited to optimize prognostics, diagnostics and therapeutic strategies for precision medicine. Here, we develop a transformational spatial analytics computational and systems biology platform (SpAn) that predicts clinical outcomes and captures emergent spatial biology that can potentially inform therapeutic strategies. We apply SpAn to primary tumor tissue samples from a cohort of 432 chemo-naïve colorectal cancer (CRC) patients iteratively labeled with a highly multiplexed (hyperplexed) panel of 55 fluorescently tagged antibodies. We show that SpAn predicts the 5-year risk of CRC recurrence with a mean AUROC of 88.5% (SE of 0.1%), significantly better than current state-of-the-art methods. Additionally, SpAn infers the emergent network biology of tumor microenvironment spatial domains revealing a spatially-mediated role of CRC consensus molecular subtype features with the potential to inform precision medicine.

Suggested Citation

  • Shikhar Uttam & Andrew M. Stern & Christopher J. Sevinsky & Samantha Furman & Filippo Pullara & Daniel Spagnolo & Luong Nguyen & Albert Gough & Fiona Ginty & D. Lansing Taylor & S. Chakra Chennubhotla, 2020. "Spatial domain analysis predicts risk of colorectal cancer recurrence and infers associated tumor microenvironment networks," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17083-x
    DOI: 10.1038/s41467-020-17083-x
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    References listed on IDEAS

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    1. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    2. Simon, Noah & Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2011. "Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i05).
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