RT Journal Article
SR Electronic
T1 A spatial analytics computational and systems biology platform predicts risk of colorectal cancer recurrence and identifies emergent spatial domain networks associated with recurrence
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 635730
DO 10.1101/635730
A1 Shikhar Uttam
A1 Andrew M. Stern
A1 Samantha Furman
A1 Filippo Pullara
A1 Daniel Spagnolo
A1 Luong Nguyen
A1 Albert Gough
A1 Christopher J. Sevinsky
A1 Fiona Ginty
A1 D. Lansing Taylor
A1 S. Chakra Chennubhotla
YR 2019
UL http://biorxiv.org/content/early/2019/05/13/635730.abstract
AB 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. We have developed a transformational spatial analytics (SpAn) computational and systems biology platform that predicts clinical outcomes and captures emergent spatial biology that can potentially inform therapeutic strategies. Here 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 fifty-five fluorescently tagged antibodies. SpAn predicted the 5-year risk of CRC recurrence with a mean area under the ROC curve of 88.5% (SE of 0.1%), significantly better than current state-of-the-art methods. SpAn also inferred the emergent network biology of the tumor spatial domains revealing a synergistic role of known features from CRC consensus molecular subtypes that will enhance precision medicine.