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ORN: Inferring patient-specific dysregulation status of pathway modules in cancer with OR-gate Network

Pathway level understanding of cancer plays a key role in precision oncology. However, the current amount of high-throughput data cannot support the elucidation of full pathway topology. In this study, instead of directly learning the pathway network, we adapted the probabilistic OR gate to model th...

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Detalles Bibliográficos
Autores principales: Liang, Lifan, Zhu, Kunju, Tao, Junyan, Lu, Songjian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8049496/
https://www.ncbi.nlm.nih.gov/pubmed/33819263
http://dx.doi.org/10.1371/journal.pcbi.1008792
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author Liang, Lifan
Zhu, Kunju
Tao, Junyan
Lu, Songjian
author_facet Liang, Lifan
Zhu, Kunju
Tao, Junyan
Lu, Songjian
author_sort Liang, Lifan
collection PubMed
description Pathway level understanding of cancer plays a key role in precision oncology. However, the current amount of high-throughput data cannot support the elucidation of full pathway topology. In this study, instead of directly learning the pathway network, we adapted the probabilistic OR gate to model the modular structure of pathways and regulon. The resulting model, OR-gate Network (ORN), can simultaneously infer pathway modules of somatic alterations, patient-specific pathway dysregulation status, and downstream regulon. In a trained ORN, the differentially expressed genes (DEGs) in each tumour can be explained by somatic mutations perturbing a pathway module. Furthermore, the ORN handles one of the most important properties of pathway perturbation in tumours, the mutual exclusivity. We have applied the ORN to lower-grade glioma (LGG) samples and liver hepatocellular carcinoma (LIHC) samples in TCGA and breast cancer samples from METABRIC. Both datasets have shown abnormal pathway activities related to immune response and cell cycles. In LGG samples, ORN identified pathway modules closely related to glioma development and revealed two pathways closely related to patient survival. We had similar results with LIHC samples. Additional results from the METABRIC datasets showed that ORN could characterize critical mechanisms of cancer and connect them to less studied somatic mutations (e.g., BAP1, MIR604, MICAL3, and telomere activities), which may generate novel hypothesis for targeted therapy.
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spelling pubmed-80494962021-04-28 ORN: Inferring patient-specific dysregulation status of pathway modules in cancer with OR-gate Network Liang, Lifan Zhu, Kunju Tao, Junyan Lu, Songjian PLoS Comput Biol Research Article Pathway level understanding of cancer plays a key role in precision oncology. However, the current amount of high-throughput data cannot support the elucidation of full pathway topology. In this study, instead of directly learning the pathway network, we adapted the probabilistic OR gate to model the modular structure of pathways and regulon. The resulting model, OR-gate Network (ORN), can simultaneously infer pathway modules of somatic alterations, patient-specific pathway dysregulation status, and downstream regulon. In a trained ORN, the differentially expressed genes (DEGs) in each tumour can be explained by somatic mutations perturbing a pathway module. Furthermore, the ORN handles one of the most important properties of pathway perturbation in tumours, the mutual exclusivity. We have applied the ORN to lower-grade glioma (LGG) samples and liver hepatocellular carcinoma (LIHC) samples in TCGA and breast cancer samples from METABRIC. Both datasets have shown abnormal pathway activities related to immune response and cell cycles. In LGG samples, ORN identified pathway modules closely related to glioma development and revealed two pathways closely related to patient survival. We had similar results with LIHC samples. Additional results from the METABRIC datasets showed that ORN could characterize critical mechanisms of cancer and connect them to less studied somatic mutations (e.g., BAP1, MIR604, MICAL3, and telomere activities), which may generate novel hypothesis for targeted therapy. Public Library of Science 2021-04-05 /pmc/articles/PMC8049496/ /pubmed/33819263 http://dx.doi.org/10.1371/journal.pcbi.1008792 Text en © 2021 Liang et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Liang, Lifan
Zhu, Kunju
Tao, Junyan
Lu, Songjian
ORN: Inferring patient-specific dysregulation status of pathway modules in cancer with OR-gate Network
title ORN: Inferring patient-specific dysregulation status of pathway modules in cancer with OR-gate Network
title_full ORN: Inferring patient-specific dysregulation status of pathway modules in cancer with OR-gate Network
title_fullStr ORN: Inferring patient-specific dysregulation status of pathway modules in cancer with OR-gate Network
title_full_unstemmed ORN: Inferring patient-specific dysregulation status of pathway modules in cancer with OR-gate Network
title_short ORN: Inferring patient-specific dysregulation status of pathway modules in cancer with OR-gate Network
title_sort orn: inferring patient-specific dysregulation status of pathway modules in cancer with or-gate network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8049496/
https://www.ncbi.nlm.nih.gov/pubmed/33819263
http://dx.doi.org/10.1371/journal.pcbi.1008792
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