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PATH-SURVEYOR: pathway level survival enquiry for immuno-oncology and drug repurposing
Pathway-level survival analysis offers the opportunity to examine molecular pathways and immune signatures that influence patient outcomes. However, available survival analysis algorithms are limited in pathway-level function and lack a streamlined analytical process. Here we present a comprehensive...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10303868/ https://www.ncbi.nlm.nih.gov/pubmed/37380943 http://dx.doi.org/10.1186/s12859-023-05393-y |
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author | Obermayer, Alyssa N. Chang, Darwin Nobles, Gabrielle Teng, Mingxiang Tan, Aik-Choon Wang, Xuefeng Chen, Y. Ann Eschrich, Steven Rodriguez, Paulo C. Grass, G. Daniel Meshinchi, Soheil Tarhini, Ahmad Chen, Dung-tsa Shaw, Timothy I. |
author_facet | Obermayer, Alyssa N. Chang, Darwin Nobles, Gabrielle Teng, Mingxiang Tan, Aik-Choon Wang, Xuefeng Chen, Y. Ann Eschrich, Steven Rodriguez, Paulo C. Grass, G. Daniel Meshinchi, Soheil Tarhini, Ahmad Chen, Dung-tsa Shaw, Timothy I. |
author_sort | Obermayer, Alyssa N. |
collection | PubMed |
description | Pathway-level survival analysis offers the opportunity to examine molecular pathways and immune signatures that influence patient outcomes. However, available survival analysis algorithms are limited in pathway-level function and lack a streamlined analytical process. Here we present a comprehensive pathway-level survival analysis suite, PATH-SURVEYOR, which includes a Shiny user interface with extensive features for systematic exploration of pathways and covariates in a Cox proportional-hazard model. Moreover, our framework offers an integrative strategy for performing Hazard Ratio ranked Gene Set Enrichment Analysis and pathway clustering. As an example, we applied our tool in a combined cohort of melanoma patients treated with checkpoint inhibition (ICI) and identified several immune populations and biomarkers predictive of ICI efficacy. We also analyzed gene expression data of pediatric acute myeloid leukemia (AML) and performed an inverse association of drug targets with the patient’s clinical endpoint. Our analysis derived several drug targets in high-risk KMT2A-fusion-positive patients, which were then validated in AML cell lines in the Genomics of Drug Sensitivity database. Altogether, the tool offers a comprehensive suite for pathway-level survival analysis and a user interface for exploring drug targets, molecular features, and immune populations at different resolutions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05393-y. |
format | Online Article Text |
id | pubmed-10303868 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-103038682023-06-29 PATH-SURVEYOR: pathway level survival enquiry for immuno-oncology and drug repurposing Obermayer, Alyssa N. Chang, Darwin Nobles, Gabrielle Teng, Mingxiang Tan, Aik-Choon Wang, Xuefeng Chen, Y. Ann Eschrich, Steven Rodriguez, Paulo C. Grass, G. Daniel Meshinchi, Soheil Tarhini, Ahmad Chen, Dung-tsa Shaw, Timothy I. BMC Bioinformatics Software Pathway-level survival analysis offers the opportunity to examine molecular pathways and immune signatures that influence patient outcomes. However, available survival analysis algorithms are limited in pathway-level function and lack a streamlined analytical process. Here we present a comprehensive pathway-level survival analysis suite, PATH-SURVEYOR, which includes a Shiny user interface with extensive features for systematic exploration of pathways and covariates in a Cox proportional-hazard model. Moreover, our framework offers an integrative strategy for performing Hazard Ratio ranked Gene Set Enrichment Analysis and pathway clustering. As an example, we applied our tool in a combined cohort of melanoma patients treated with checkpoint inhibition (ICI) and identified several immune populations and biomarkers predictive of ICI efficacy. We also analyzed gene expression data of pediatric acute myeloid leukemia (AML) and performed an inverse association of drug targets with the patient’s clinical endpoint. Our analysis derived several drug targets in high-risk KMT2A-fusion-positive patients, which were then validated in AML cell lines in the Genomics of Drug Sensitivity database. Altogether, the tool offers a comprehensive suite for pathway-level survival analysis and a user interface for exploring drug targets, molecular features, and immune populations at different resolutions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05393-y. BioMed Central 2023-06-28 /pmc/articles/PMC10303868/ /pubmed/37380943 http://dx.doi.org/10.1186/s12859-023-05393-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Obermayer, Alyssa N. Chang, Darwin Nobles, Gabrielle Teng, Mingxiang Tan, Aik-Choon Wang, Xuefeng Chen, Y. Ann Eschrich, Steven Rodriguez, Paulo C. Grass, G. Daniel Meshinchi, Soheil Tarhini, Ahmad Chen, Dung-tsa Shaw, Timothy I. PATH-SURVEYOR: pathway level survival enquiry for immuno-oncology and drug repurposing |
title | PATH-SURVEYOR: pathway level survival enquiry for immuno-oncology and drug repurposing |
title_full | PATH-SURVEYOR: pathway level survival enquiry for immuno-oncology and drug repurposing |
title_fullStr | PATH-SURVEYOR: pathway level survival enquiry for immuno-oncology and drug repurposing |
title_full_unstemmed | PATH-SURVEYOR: pathway level survival enquiry for immuno-oncology and drug repurposing |
title_short | PATH-SURVEYOR: pathway level survival enquiry for immuno-oncology and drug repurposing |
title_sort | path-surveyor: pathway level survival enquiry for immuno-oncology and drug repurposing |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10303868/ https://www.ncbi.nlm.nih.gov/pubmed/37380943 http://dx.doi.org/10.1186/s12859-023-05393-y |
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