Spatial domain analysis predicts risk of colorectal cancer recurrence and infers associated tumor microenvironment networks.
IF: 17.694
Cited by: 15


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.


Gene Expression

MeSH terms

Colorectal Neoplasms
Fluorescent Antibody Technique
Gene Expression Regulation, Neoplastic
Neoplasm Recurrence, Local
Oligonucleotide Array Sequence Analysis
Precision Medicine
Systems Biology
Tumor Microenvironment


Uttam, Shikhar
Stern, Andrew M
Sevinsky, Christopher J
Furman, Samantha
Pullara, Filippo
Spagnolo, Daniel
Nguyen, Luong
Gough, Albert
Ginty, Fiona
Lansing Taylor, D
Chakra Chennubhotla, S

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