Identification of Somatic Gene Signatures in Circulating Cell-Free DNA Associated with Disease Progression in Metastatic Prostate Cancer by a Novel Machine Learning Platform.

ABSTRACT:

Purpose

Progression from metastatic castration-sensitive prostate cancer (mCSPC) to a castration-resistant (mCRPC) state heralds the lethal phenotype of prostate cancer. Identifying genomic alterations associated with mCRPC may help find new targets for drug development. In the majority of patients, obtaining a tumor biopsy is challenging because of the predominance of bone-only metastasis. In this study, we hypothesize that machine learning (ML) algorithms can identify clinically relevant patterns of genomic alterations (GAs) that distinguish mCRPC from mCSPC, as assessed by next-generation sequencing (NGS) of circulating cell-free DNA (cfDNA).

Experimental design

Retrospective clinical data from men with metastatic prostate cancer were collected. Men with NGS of cfDNA perf

SUBMITTER: Lin E 

PROVIDER: S-EPMC8417886 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature