The blessings of dimensionality: unsupervised network-based learning on omic data using Ollivier-Ricci curvature.
ORCO: A tool for measuring the robustness of a network and its application on RNAseq data from 700 multiple myeloma patients.
Here, we used a novel measure of network robustness called Ollivier-Ricci curvature (ORC) to characterize cancers. This innovative geometric network analysis integrates complex gene-product interactions to uncover patterns in cancer biology that were previously undetectable.
ORC designed for -omic data was released as a stand-alone Python package, “ORCO,” available for download here. Details about the ORCO implementation can be found on Bioinformatics (PDF).
A.K. Simhal, C. Weistuch, K.A. Murgas, D. Grange, J. Zhu, J.H. Oh, R. Elkin, J.O. Deasy. “ORCO: Ollivier-Ricci Curvature-Omics: an unsupervised method for analyzing robustness in biological systems.” 2025. Bioinformatics.
Using Ollivier-Ricci curvature (a measure of network robustness), we identified 118 genes previously unassociated with MM that identify a high risk subtype. This paper described a novel eight gene signature associated with high-risk multiple myeloma: BUB1, MCM6, NOSTRIN, PAM, RNF115, SNCAIP, SPRR2A, & WEE1.
A.K. Simhal, K.H. Maclachlan, R. Elkin, J. Zhu, L. Norton, J.O. Deasy, J.H. Oh, S.Z. Usmani, A. Tannenbaum. Gene interaction network analysis in multiple myeloma detects complex immune dysregulation associated with shorter survival. 2023. Blood Cancer Journal.
WEE1 is a strong prognostic marker of high-risk MM
Of all 8 genes, WEE1 was the most prognostic. When researching WEE1 expression further, WEE1 was found to be incredibly prognostic for MM outcomes independent of known MM biomarkers. Additionally, the results show WEE1 expression differentiates outcomes associated with known markers, is upregulated independently of its interacting neighbors, and is associated with dysregulated P53 pathways. This suggests that WEE1 expression levels may have clinical utility in prognosticating outcomes in newly diagnosed MM and may support the application of WEE1 inhibitors to MM preclinical models.
A.K. Simhal, R.S. Firestone, J.H. Oh, V. Avutu, L. Norton, M. Hultcrantz, S.Z. Usmani, K.H. Maclachlan, J.O. Deasy. “High WEE1 expression is independently linked to poor survival in multiple myeloma.” 2025. Blood Cancer Journal.
If you work on similar projects — or would like to use this tool on your data, I’d love to hear from you! Please email me at simhala [at] mskcc [dot] org.