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DRPPM-PATH-SURVEIOR: Plug-and-Play Survival Analysis of Pathway-level Signatures and Immune Components

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...

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Autores principales: Obermayer, Alyssa, Chang, Darwin, Nobles, Gabrielle, Teng, Mingxiang, Tan, Aik-Choon, Wang, Xuefeng, Eschrich, Steven, Rodriguez, Paulo, Grass, G Daniel, Meshinchi, Soheil, Tarhini, Ahmad, Chen, Dung-tsa, Shaw, Timothy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Journal Experts 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055629/
https://www.ncbi.nlm.nih.gov/pubmed/36993526
http://dx.doi.org/10.21203/rs.3.rs-2688545/v1
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author Obermayer, Alyssa
Chang, Darwin
Nobles, Gabrielle
Teng, Mingxiang
Tan, Aik-Choon
Wang, Xuefeng
Eschrich, Steven
Rodriguez, Paulo
Grass, G Daniel
Meshinchi, Soheil
Tarhini, Ahmad
Chen, Dung-tsa
Shaw, Timothy
author_facet Obermayer, Alyssa
Chang, Darwin
Nobles, Gabrielle
Teng, Mingxiang
Tan, Aik-Choon
Wang, Xuefeng
Eschrich, Steven
Rodriguez, Paulo
Grass, G Daniel
Meshinchi, Soheil
Tarhini, Ahmad
Chen, Dung-tsa
Shaw, Timothy
author_sort Obermayer, Alyssa
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, DRPPM-PATH-SURVEIOR, 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 (GSEA) 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.
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spelling pubmed-100556292023-03-30 DRPPM-PATH-SURVEIOR: Plug-and-Play Survival Analysis of Pathway-level Signatures and Immune Components Obermayer, Alyssa Chang, Darwin Nobles, Gabrielle Teng, Mingxiang Tan, Aik-Choon Wang, Xuefeng Eschrich, Steven Rodriguez, Paulo Grass, G Daniel Meshinchi, Soheil Tarhini, Ahmad Chen, Dung-tsa Shaw, Timothy Res Sq Article 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, DRPPM-PATH-SURVEIOR, 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 (GSEA) 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. American Journal Experts 2023-03-14 /pmc/articles/PMC10055629/ /pubmed/36993526 http://dx.doi.org/10.21203/rs.3.rs-2688545/v1 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Obermayer, Alyssa
Chang, Darwin
Nobles, Gabrielle
Teng, Mingxiang
Tan, Aik-Choon
Wang, Xuefeng
Eschrich, Steven
Rodriguez, Paulo
Grass, G Daniel
Meshinchi, Soheil
Tarhini, Ahmad
Chen, Dung-tsa
Shaw, Timothy
DRPPM-PATH-SURVEIOR: Plug-and-Play Survival Analysis of Pathway-level Signatures and Immune Components
title DRPPM-PATH-SURVEIOR: Plug-and-Play Survival Analysis of Pathway-level Signatures and Immune Components
title_full DRPPM-PATH-SURVEIOR: Plug-and-Play Survival Analysis of Pathway-level Signatures and Immune Components
title_fullStr DRPPM-PATH-SURVEIOR: Plug-and-Play Survival Analysis of Pathway-level Signatures and Immune Components
title_full_unstemmed DRPPM-PATH-SURVEIOR: Plug-and-Play Survival Analysis of Pathway-level Signatures and Immune Components
title_short DRPPM-PATH-SURVEIOR: Plug-and-Play Survival Analysis of Pathway-level Signatures and Immune Components
title_sort drppm-path-surveior: plug-and-play survival analysis of pathway-level signatures and immune components
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055629/
https://www.ncbi.nlm.nih.gov/pubmed/36993526
http://dx.doi.org/10.21203/rs.3.rs-2688545/v1
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