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De novo individualized disease modules reveal the synthetic penetrance of genes and inform personalized treatment regimens
Current understandings of individual disease etiology and therapeutics are limited despite great need. To fill the gap, we propose a novel computational pipeline that collects potent disease gene cooperative pathways to envision individualized disease etiology and therapies. Our algorithm constructs...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8744682/ https://www.ncbi.nlm.nih.gov/pubmed/34876496 http://dx.doi.org/10.1101/gr.275889.121 |
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author | Weiskittel, Taylor M. Ung, Choong Y. Correia, Cristina Zhang, Cheng Li, Hu |
author_facet | Weiskittel, Taylor M. Ung, Choong Y. Correia, Cristina Zhang, Cheng Li, Hu |
author_sort | Weiskittel, Taylor M. |
collection | PubMed |
description | Current understandings of individual disease etiology and therapeutics are limited despite great need. To fill the gap, we propose a novel computational pipeline that collects potent disease gene cooperative pathways to envision individualized disease etiology and therapies. Our algorithm constructs individualized disease modules de novo, which enables us to elucidate the importance of mutated genes in specific patients and to understand the synthetic penetrance of these genes across patients. We reveal that importance of the notorious cancer drivers TP53 and PIK3CA fluctuate widely across breast cancers and peak in tumors with distinct numbers of mutations and that rarely mutated genes such as XPO1 and PLEKHA1 have high disease module importance in specific individuals. Furthermore, individualized module disruption enables us to devise customized singular and combinatorial target therapies that were highly varied across patients, showing the need for precision therapeutics pipelines. As the first analysis of de novo individualized disease modules, we illustrate the power of individualized disease modules for precision medicine by providing deep novel insights on the activity of diseased genes in individuals. |
format | Online Article Text |
id | pubmed-8744682 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Cold Spring Harbor Laboratory Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-87446822022-01-20 De novo individualized disease modules reveal the synthetic penetrance of genes and inform personalized treatment regimens Weiskittel, Taylor M. Ung, Choong Y. Correia, Cristina Zhang, Cheng Li, Hu Genome Res Method Current understandings of individual disease etiology and therapeutics are limited despite great need. To fill the gap, we propose a novel computational pipeline that collects potent disease gene cooperative pathways to envision individualized disease etiology and therapies. Our algorithm constructs individualized disease modules de novo, which enables us to elucidate the importance of mutated genes in specific patients and to understand the synthetic penetrance of these genes across patients. We reveal that importance of the notorious cancer drivers TP53 and PIK3CA fluctuate widely across breast cancers and peak in tumors with distinct numbers of mutations and that rarely mutated genes such as XPO1 and PLEKHA1 have high disease module importance in specific individuals. Furthermore, individualized module disruption enables us to devise customized singular and combinatorial target therapies that were highly varied across patients, showing the need for precision therapeutics pipelines. As the first analysis of de novo individualized disease modules, we illustrate the power of individualized disease modules for precision medicine by providing deep novel insights on the activity of diseased genes in individuals. Cold Spring Harbor Laboratory Press 2022-01 /pmc/articles/PMC8744682/ /pubmed/34876496 http://dx.doi.org/10.1101/gr.275889.121 Text en © 2022 Weiskittel et al.; Published by Cold Spring Harbor Laboratory Press https://creativecommons.org/licenses/by-nc/4.0/This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Method Weiskittel, Taylor M. Ung, Choong Y. Correia, Cristina Zhang, Cheng Li, Hu De novo individualized disease modules reveal the synthetic penetrance of genes and inform personalized treatment regimens |
title | De novo individualized disease modules reveal the synthetic penetrance of genes and inform personalized treatment regimens |
title_full | De novo individualized disease modules reveal the synthetic penetrance of genes and inform personalized treatment regimens |
title_fullStr | De novo individualized disease modules reveal the synthetic penetrance of genes and inform personalized treatment regimens |
title_full_unstemmed | De novo individualized disease modules reveal the synthetic penetrance of genes and inform personalized treatment regimens |
title_short | De novo individualized disease modules reveal the synthetic penetrance of genes and inform personalized treatment regimens |
title_sort | de novo individualized disease modules reveal the synthetic penetrance of genes and inform personalized treatment regimens |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8744682/ https://www.ncbi.nlm.nih.gov/pubmed/34876496 http://dx.doi.org/10.1101/gr.275889.121 |
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