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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.
Afiliação
  • Obermayer A; H. Lee Moffitt Cancer Center and Research Institute.
  • Chang D; H. Lee Moffitt Cancer Center and Research Institute.
  • Nobles G; H. Lee Moffitt Cancer Center and Research Institute.
  • Teng M; H. Lee Moffitt Cancer Center and Research Institute.
  • Tan AC; Huntsman Cancer Institute, University of Utah, Salt Lake City, UT.
  • Wang X; H. Lee Moffitt Cancer Center and Research Institute.
  • Eschrich S; H. Lee Moffitt Cancer Center and Research Institute.
  • Rodriguez P; H. Lee Moffitt Cancer Center and Research Institute.
  • Grass GD; H. Lee Moffitt Cancer Center and Research Institute.
  • Meshinchi S; Fred Hutchinson Cancer Research Center, Seattle, WA.
  • Tarhini A; H. Lee Moffitt Cancer Center and Research Institute.
  • Chen DT; H. Lee Moffitt Cancer Center and Research Institute.
  • Shaw T; H. Lee Moffitt Cancer Center and Research Institute.
Res Sq ; 2023 Mar 14.
Article em En | MEDLINE | ID: mdl-36993526
ABSTRACT
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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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article