Discovering Multi-omic Biomarkers for Prostate Cancer Severity Using Machine Learning

TL;DR

This study employs machine learning and multi-omics data to identify biomarkers that can improve the accuracy of prostate cancer severity grading, potentially aiding in better treatment decisions.

Contribution

It introduces a machine-driven approach to discover biomarkers for prostate cancer Gleason scores using multi-omics data, combining statistical and deep learning methods.

Findings

Identified genes like COL1A1 and SFRP4 as important biomarkers.

Highlighted cell cycle pathways such as G2M checkpoint and E2F targets.

Demonstrated potential for more accurate prostate cancer grading.

Abstract

Prostate cancer is the second most common form of cancer, though most patients have a positive prognosis with many experiencing long-term survival with current treatment options. Yet, each treatment carries varying levels of intensity and side effects, therefore determining the severity of prostate cancer is an important criteria in selecting the most appropriate treatment. The Gleason score is the most common grading system used to judge the severity of prostate cancer, but much of the grading process can be affected by human error or subjectivity. Finding biomarkers for prostate cancer Gleason scores in a quantitative, machine-driven approach could enable pathologists to validate their assessment of a patient cancer sample by examining such biomarkers. In our study, we identified biomarkers from multi-omics data using machine learning, statistical tools, and deep learning to train models against the Gleason score and capture the most important features that could potentially serve as biomarkers for the Gleason score. Through this process, multiple genes, such as COL1A1 and SFRP4, and cell cycle pathways, such as G2M checkpoint, E2F targets, and the PLK1 pathways, were found to be important predictive features for particular Gleason scores. The combination of these analytical methods shows potential for more accurate grading of prostate cancer, and greater understanding of biological processes behind prostate cancer severity that could provide additional therapeutic targets.