Predicting Cancer Phenotypes

This project aims to significantly advance our ability to study the complex mechanistic underpinnings of cancer using integrated multi-omic network models. Towards this goal, we are building metabolic networks relevant to cancer biology to serve as a mechanistic basis on which to ground statistical analyses of experimental multi-omic data. This collaborative systems-based approach for integrating and analyzing multi-omic information from regulatory network topology, signaling and metabolic pathways will lead to the identification of efficacious prognostic and predictive signatures for cancer phenotypes from high-dimensional omics data.

Funding supporting this work:

Hardwiring mechanism into predicting caner phenotypes by computational learning (PI Luigi Marchonni, Johns Hopkins University)

Recent Relevant Publications:

Digitizing omics profiles by divergence from a baseline. Dinalankara W, Ke Q, Xu Y, Ji L, Pagane N, Lien A, Matam T, Fertig EJ, Price ND, Younes L, Marchionni L, Geman D. Proc Natl Acad Sci U S A. 2018 May 1;115(18):4545-4552. doi: 10.1073/pnas.1721628115. Epub 2018 Apr 16. PMID: 29666255

The building blocks of successful translation of proteomics to the clinic. Kearney P, Boniface JJ, Price ND, Hood L. Curr Opin Biotechnol. 2018 Jun;51:123-129. doi: 10.1016/j.copbio.2017.12.011. Epub 2018 Feb 7. Review. PMID: 29427919

Current Project Leads:

Priyanka Baloni	John Earls	Nathan Price