Individualized multi-omic pathway deviation scores using multiple factor analysis. (6th August 2020)

• Record Type: Journal Article
• Authors: Rau, Andrea; Manansala, Regina; Flister, Michael J; Rui, Hallgeir; Jaffrézic, Florence; Laloë, Denis; Auer, Paul L
• Is Part Of: Biostatistics. Volume 23:Number 2(2022)
• Page Start: 362
• Page End: 379
• Publication Date: 2020-08-06
• Subjects: Cancer genomics -- Multi-omic data -- Multiple factor analysis -- Pathways; Medical statistics -- Periodicals; Biometry -- Periodicals; Health risk assessment -- Periodicals; Medicine -- Research -- Statistical methods -- Periodicals; 610.727
• DOI: 10.1093/biostatistics/kxaa029
• Languages: English
• ISSNs: 1465-4644

Abstract

Summary: Malignant progression of normal tissue is typically driven by complex networks of somatic changes, including genetic mutations, copy number aberrations, epigenetic changes, and transcriptional reprogramming. To delineate aberrant multi-omic tumor features that correlate with clinical outcomes, we present a novel pathway-centric tool based on the multiple factor analysis framework called padma . Using a multi-omic consensus representation, padma quantifies and characterizes individualized pathway-specific multi-omic deviations and their underlying drivers, with respect to the sampled population. We demonstrate the utility of padma to correlate patient outcomes with complex genetic, epigenetic, and transcriptomic perturbations in clinically actionable pathways in breast and lung cancer.