Tumour-specific Causal Inference Discovers Distinct Disease Mechanisms Underlying Cancer Subtypes

Abstract Cancer is a disease mainly caused by somatic genome alterations (SGAs) that perturb cellular signalling systems. Furthermore, the combination of pathway aberrations in a tumour defines its disease mechanism, and distinct disease mechanisms underlie the inter-tumour heterogeneity in terms of...

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Autores principales:	Yifan Xue, Gregory Cooper, Chunhui Cai, Songjian Lu, Baoli Hu, Xiaojun Ma, Xinghua Lu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/edd3506aa54e4a72847c9a111af4d6ee
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spelling	oai:doaj.org-article:edd3506aa54e4a72847c9a111af4d6ee2021-12-02T15:09:28ZTumour-specific Causal Inference Discovers Distinct Disease Mechanisms Underlying Cancer Subtypes10.1038/s41598-019-48318-72045-2322https://doaj.org/article/edd3506aa54e4a72847c9a111af4d6ee2019-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-48318-7https://doaj.org/toc/2045-2322Abstract Cancer is a disease mainly caused by somatic genome alterations (SGAs) that perturb cellular signalling systems. Furthermore, the combination of pathway aberrations in a tumour defines its disease mechanism, and distinct disease mechanisms underlie the inter-tumour heterogeneity in terms of disease progression and responses to therapies. Discovering common disease mechanisms shared by tumours would provide guidance for precision oncology but remains a challenge. Here, we present a novel computational framework for revealing distinct combinations of aberrant signalling pathways in tumours. Specifically, we applied the tumour-specific causal inference algorithm (TCI) to identify causal relationships between SGAs and differentially expressed genes (DEGs) within tumours from the Cancer Genome Atlas (TCGA) study. Based on these causal inferences, we adopted a network-based method to identify modules of DEGs, such that the member DEGs within a module tend to be co-regulated by a common pathway. Using the expression status of genes in a module as a surrogate measure of the activation status of the corresponding pathways, we divided breast cancers (BRCAs) into five subgroups and glioblastoma multiformes (GBMs) into six subgroups with distinct combinations of pathway aberrations. The patient groups exhibited significantly different survival patterns, indicating that our approach can identify clinically relevant disease subtypes.Yifan XueGregory CooperChunhui CaiSongjian LuBaoli HuXiaojun MaXinghua LuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-13 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yifan Xue Gregory Cooper Chunhui Cai Songjian Lu Baoli Hu Xiaojun Ma Xinghua Lu Tumour-specific Causal Inference Discovers Distinct Disease Mechanisms Underlying Cancer Subtypes
description	Abstract Cancer is a disease mainly caused by somatic genome alterations (SGAs) that perturb cellular signalling systems. Furthermore, the combination of pathway aberrations in a tumour defines its disease mechanism, and distinct disease mechanisms underlie the inter-tumour heterogeneity in terms of disease progression and responses to therapies. Discovering common disease mechanisms shared by tumours would provide guidance for precision oncology but remains a challenge. Here, we present a novel computational framework for revealing distinct combinations of aberrant signalling pathways in tumours. Specifically, we applied the tumour-specific causal inference algorithm (TCI) to identify causal relationships between SGAs and differentially expressed genes (DEGs) within tumours from the Cancer Genome Atlas (TCGA) study. Based on these causal inferences, we adopted a network-based method to identify modules of DEGs, such that the member DEGs within a module tend to be co-regulated by a common pathway. Using the expression status of genes in a module as a surrogate measure of the activation status of the corresponding pathways, we divided breast cancers (BRCAs) into five subgroups and glioblastoma multiformes (GBMs) into six subgroups with distinct combinations of pathway aberrations. The patient groups exhibited significantly different survival patterns, indicating that our approach can identify clinically relevant disease subtypes.
format	article
author	Yifan Xue Gregory Cooper Chunhui Cai Songjian Lu Baoli Hu Xiaojun Ma Xinghua Lu
author_facet	Yifan Xue Gregory Cooper Chunhui Cai Songjian Lu Baoli Hu Xiaojun Ma Xinghua Lu
author_sort	Yifan Xue
title	Tumour-specific Causal Inference Discovers Distinct Disease Mechanisms Underlying Cancer Subtypes
title_short	Tumour-specific Causal Inference Discovers Distinct Disease Mechanisms Underlying Cancer Subtypes
title_full	Tumour-specific Causal Inference Discovers Distinct Disease Mechanisms Underlying Cancer Subtypes
title_fullStr	Tumour-specific Causal Inference Discovers Distinct Disease Mechanisms Underlying Cancer Subtypes
title_full_unstemmed	Tumour-specific Causal Inference Discovers Distinct Disease Mechanisms Underlying Cancer Subtypes
title_sort	tumour-specific causal inference discovers distinct disease mechanisms underlying cancer subtypes
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/edd3506aa54e4a72847c9a111af4d6ee
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Tumour-specific Causal Inference Discovers Distinct Disease Mechanisms Underlying Cancer Subtypes

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