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Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

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Bayesian semi-nonnegative matrix tri-factorization to identify pathways associated with cancer phenotypes.

Sunho Park¹, Nabhonil Kar, Jae-Ho Cheong

¹Quantitative Health Sciences, Cleveland Clinic, 9500 Euclid Ave., Cleveland, OH 44195, USA.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|December 5, 2019

Summary

This study introduces a new Bayesian method for identifying cancer pathways from gene expression data, improving upon previous limitations with real-valued inputs and uncertainty. The method successfully identifies biologically relevant pathways for cancer subtypes and predicts patient survival outcomes.

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

Area of Science:

Bioinformatics
Computational Biology
Cancer Genomics

Background:

Identifying cancer-associated pathways aids in discovering prognostic and predictive biomarkers for patient stratification and treatment guidance.
Non-negative matrix tri-factorization (NMTF) has been used for pathway identification but struggles with real-valued (positive/negative) molecular data and uncertainty.
Existing methods lack the ability to handle negative input values, common in gene expression data, and typically provide single point estimates.

Purpose of the Study:

To develop a Bayesian semi-nonnegative matrix trifactorization (BSNMTF) method for identifying cancer pathways from real-valued gene expression data.
To address limitations of NMTF, including handling negative values and incorporating uncertainty.
To identify biologically and clinically relevant pathways associated with cancer phenotypes and predict patient outcomes.

Main Methods:

Proposed a Bayesian semi-nonnegative matrix trifactorization (BSNMTF) method allowing a real-valued centroid matrix to represent gene up/down-regulation.
Incorporated structured spike-and-slab priors with pathway and gene-gene interaction (GGI) network information for stochastic gene involvement.
Developed update rules for posterior distributions using variational inference.

Main Results:

Demonstrated advantages over NMTF using synthetic datasets.
Identified biologically and clinically relevant pathways associated with molecular subtypes in The Cancer Genome Atlas (TCGA) gastric cancer data.
Identified pathways linked to immunotherapy response in metastatic gastric cancer clinical trial data.

Conclusions:

The BSNMTF method effectively identifies cancer-associated pathways from real-valued gene expression data, overcoming NMTF limitations.
Identified pathways serve as reliable prognostic biomarkers for stratifying patients based on survival outcomes.
The method provides a robust framework for cancer pathway analysis and biomarker discovery.