PATH-SURVEYOR: pathway level survival enquiry for immuno-oncology and drug repurposing

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.

Abstract

AbstractPathway-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.

Funder

NIH

American Cancer Society

Team Science Award from the Moffitt Cancer Center Quantitative Science Program

Moffitt Cancer Center Department of Biostatistics and Bioinformatics Pilot Award

Florida Department of Health Live Like Bella Pediatric Research Initiative

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology

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