Causal integration of multi‐omics data with prior knowledge to generate mechanistic hypotheses. Issue 1 (27th January 2021)

• Record Type: Journal Article
• Authors: Dugourd, Aurelien; Kuppe, Christoph; Sciacovelli, Marco; Gjerga, Enio; Gabor, Attila; Emdal, Kristina B.; Vieira, Vitor; Bekker‐Jensen, Dorte B.; Kranz, Jennifer; Bindels, Eric.M.J.; Costa, Ana S.H.; Sousa, Abel; Beltrao, Pedro; Rocha, Miguel; Olsen, Jesper V.; Frezza, Christian; Kramann, Rafael; Saez‐Rodriguez, Julio
• Is Part Of: Molecular systems biology. Volume 17:Issue 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-01-27
• Subjects: causal reasoning -- kidney cancer -- metabolism -- multi‐omics -- signaling; Molecular biology -- Periodicals; Systems biology -- Periodicals; 572.8
• DOI: 10.15252/msb.20209730
• Languages: English
• ISSNs: 1744-4292

Abstract

Abstract: Multi‐omics datasets can provide molecular insights beyond the sum of individual omics. Various tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi‐Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network‐level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi‐omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from eleven clear cell renal cell carcinoma (ccRCC) patients. COSMOS was able to capture relevant crosstalks within and between multiple omics layers, such as known ccRCC drug targets. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi‐omics studies. SYNOPSIS: A new approach integrates multi‐omics datasets with a prior knowledge network spanning signaling, metabolism and allosteric regulations. Application to a kidney cancer patient cohort captures relevant cross‐talks among deregulated processes. A causal multi‐omics network is built by integrating multiple ressources spanning signaling, metabolism and allosteric regulations. Transcriptomics, phosphoproteomics and metabolomics data are integrated in a set of coherent mechanistic hypotheses using CARNIVAL, a tool contextualizing causal networks. This set of coherent mechanistic hypotheses can be mined to identify disease mechanisms and therapeutic targets. A network built for a cohort of kidney cancer patients shows coherence with other studies and known therapeutic targets. Abstract : A new approach integrates multi‐omics datasets with a prior knowledge network spanning signaling, metabolism and allosteric regulations. Application to a kidney cancer patient cohort captures relevant cross‐talks among deregulated processes.