Mult-iomic integrated curvature study on pan-cancer genomic data

Zhu, J.; Tran, A. P.; Deasy, J. O.; Tannenbaum, A. R.

doi:10.1101/2022.03.24.485712

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bioRxiv · 10.1101/2022.03.24.485712

Mult-iomic integrated curvature study on pan-cancer genomic data

Zhu, J.·Tran, A. P.·Deasy, J. O.·Tannenbaum, A. R.

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Abstract

In this work, we introduce a new mathematical framework based on network curvature to extract significant cancer subtypes from multi-omics data. This extends our previous work that was based on analyzing a fixed single-omics data class (e,g, CNA, gene expression, etc.). Notably, we are able to show that this new methodology provided us with significant survival differences on Kaplan-Meier curves across almost every cancer that we considered. Moreover, the variances in Ollivier-Ricci curvature was explored to investigate its usefulness in network topology analysis as this curvature may be capturing subtle functional changes between various cancer subtypes.

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Zhu, J., Tran, A. P., Deasy, J. O., Tannenbaum, A. R.. 2022-03-27. Mult-iomic integrated curvature study on pan-cancer genomic data. https://doi.org/10.1101/2022.03.24.485712

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