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Integrative omics analyses broaden treatment targets in human cancer

BACKGROUND: Although large-scale, next-generation sequencing (NGS) studies of cancers hold promise for enabling precision oncology, challenges remain in integrating NGS with clinically validated biomarkers. METHODS: To overcome such challenges, we utilized the Database of Evidence for Precision Onco...

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Autores principales: Sengupta, Sohini, Sun, Sam Q., Huang, Kuan-lin, Oh, Clara, Bailey, Matthew H., Varghese, Rajees, Wyczalkowski, Matthew A., Ning, Jie, Tripathi, Piyush, McMichael, Joshua F., Johnson, Kimberly J., Kandoth, Cyriac, Welch, John, Ma, Cynthia, Wendl, Michael C., Payne, Samuel H., Fenyö, David, Townsend, Reid R., Dipersio, John F., Chen, Feng, Ding, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6064051/
https://www.ncbi.nlm.nih.gov/pubmed/30053901
http://dx.doi.org/10.1186/s13073-018-0564-z
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author Sengupta, Sohini
Sun, Sam Q.
Huang, Kuan-lin
Oh, Clara
Bailey, Matthew H.
Varghese, Rajees
Wyczalkowski, Matthew A.
Ning, Jie
Tripathi, Piyush
McMichael, Joshua F.
Johnson, Kimberly J.
Kandoth, Cyriac
Welch, John
Ma, Cynthia
Wendl, Michael C.
Payne, Samuel H.
Fenyö, David
Townsend, Reid R.
Dipersio, John F.
Chen, Feng
Ding, Li
author_facet Sengupta, Sohini
Sun, Sam Q.
Huang, Kuan-lin
Oh, Clara
Bailey, Matthew H.
Varghese, Rajees
Wyczalkowski, Matthew A.
Ning, Jie
Tripathi, Piyush
McMichael, Joshua F.
Johnson, Kimberly J.
Kandoth, Cyriac
Welch, John
Ma, Cynthia
Wendl, Michael C.
Payne, Samuel H.
Fenyö, David
Townsend, Reid R.
Dipersio, John F.
Chen, Feng
Ding, Li
author_sort Sengupta, Sohini
collection PubMed
description BACKGROUND: Although large-scale, next-generation sequencing (NGS) studies of cancers hold promise for enabling precision oncology, challenges remain in integrating NGS with clinically validated biomarkers. METHODS: To overcome such challenges, we utilized the Database of Evidence for Precision Oncology (DEPO) to link druggability to genomic, transcriptomic, and proteomic biomarkers. Using a pan-cancer cohort of 6570 tumors, we identified tumors with potentially druggable biomarkers consisting of drug-associated mutations, mRNA expression outliers, and protein/phosphoprotein expression outliers identified by DEPO. RESULTS: Within the pan-cancer cohort of 6570 tumors, we found that 3% are druggable based on FDA-approved drug-mutation interactions in specific cancer types. However, mRNA/phosphoprotein/protein expression outliers and drug repurposing across cancer types suggest potential druggability in up to 16% of tumors. The percentage of potential drug-associated tumors can increase to 48% if we consider preclinical evidence. Further, our analyses showed co-occurring potentially druggable multi-omics alterations in 32% of tumors, indicating a role for individualized combinational therapy, with evidence supporting mTOR/PI3K/ESR1 co-inhibition and BRAF/AKT co-inhibition in 1.6 and 0.8% of tumors, respectively. We experimentally validated a subset of putative druggable mutations in BRAF identified by a protein structure-based computational tool. Finally, analysis of a large-scale drug screening dataset lent further evidence supporting repurposing of drugs across cancer types and the use of expression outliers for inferring druggability. CONCLUSIONS: Our results suggest that an integrated analysis platform can nominate multi-omics alterations as biomarkers of druggability and aid ongoing efforts to bring precision oncology to patients. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13073-018-0564-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-60640512018-07-31 Integrative omics analyses broaden treatment targets in human cancer Sengupta, Sohini Sun, Sam Q. Huang, Kuan-lin Oh, Clara Bailey, Matthew H. Varghese, Rajees Wyczalkowski, Matthew A. Ning, Jie Tripathi, Piyush McMichael, Joshua F. Johnson, Kimberly J. Kandoth, Cyriac Welch, John Ma, Cynthia Wendl, Michael C. Payne, Samuel H. Fenyö, David Townsend, Reid R. Dipersio, John F. Chen, Feng Ding, Li Genome Med Research BACKGROUND: Although large-scale, next-generation sequencing (NGS) studies of cancers hold promise for enabling precision oncology, challenges remain in integrating NGS with clinically validated biomarkers. METHODS: To overcome such challenges, we utilized the Database of Evidence for Precision Oncology (DEPO) to link druggability to genomic, transcriptomic, and proteomic biomarkers. Using a pan-cancer cohort of 6570 tumors, we identified tumors with potentially druggable biomarkers consisting of drug-associated mutations, mRNA expression outliers, and protein/phosphoprotein expression outliers identified by DEPO. RESULTS: Within the pan-cancer cohort of 6570 tumors, we found that 3% are druggable based on FDA-approved drug-mutation interactions in specific cancer types. However, mRNA/phosphoprotein/protein expression outliers and drug repurposing across cancer types suggest potential druggability in up to 16% of tumors. The percentage of potential drug-associated tumors can increase to 48% if we consider preclinical evidence. Further, our analyses showed co-occurring potentially druggable multi-omics alterations in 32% of tumors, indicating a role for individualized combinational therapy, with evidence supporting mTOR/PI3K/ESR1 co-inhibition and BRAF/AKT co-inhibition in 1.6 and 0.8% of tumors, respectively. We experimentally validated a subset of putative druggable mutations in BRAF identified by a protein structure-based computational tool. Finally, analysis of a large-scale drug screening dataset lent further evidence supporting repurposing of drugs across cancer types and the use of expression outliers for inferring druggability. CONCLUSIONS: Our results suggest that an integrated analysis platform can nominate multi-omics alterations as biomarkers of druggability and aid ongoing efforts to bring precision oncology to patients. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13073-018-0564-z) contains supplementary material, which is available to authorized users. BioMed Central 2018-07-27 /pmc/articles/PMC6064051/ /pubmed/30053901 http://dx.doi.org/10.1186/s13073-018-0564-z Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Sengupta, Sohini
Sun, Sam Q.
Huang, Kuan-lin
Oh, Clara
Bailey, Matthew H.
Varghese, Rajees
Wyczalkowski, Matthew A.
Ning, Jie
Tripathi, Piyush
McMichael, Joshua F.
Johnson, Kimberly J.
Kandoth, Cyriac
Welch, John
Ma, Cynthia
Wendl, Michael C.
Payne, Samuel H.
Fenyö, David
Townsend, Reid R.
Dipersio, John F.
Chen, Feng
Ding, Li
Integrative omics analyses broaden treatment targets in human cancer
title Integrative omics analyses broaden treatment targets in human cancer
title_full Integrative omics analyses broaden treatment targets in human cancer
title_fullStr Integrative omics analyses broaden treatment targets in human cancer
title_full_unstemmed Integrative omics analyses broaden treatment targets in human cancer
title_short Integrative omics analyses broaden treatment targets in human cancer
title_sort integrative omics analyses broaden treatment targets in human cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6064051/
https://www.ncbi.nlm.nih.gov/pubmed/30053901
http://dx.doi.org/10.1186/s13073-018-0564-z
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