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 performed at a Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory at time of diagnosis of mCSPC or mCRPC were included. A combination of supervised and unsupervised ML algorithms was used to obtain biologically interpretable, potentially actionable insights into genomic signatures that distinguish mCRPC from mCSPC.
Results: GAs that distinguish patients with mCRPC (n = 187) from patients with mCSPC (n = 154) (positive predictive value = 94%, specificity = 91%) were identified using supervised ML algorithms. These GAs, primarily amplifications, corresponded to androgen receptor, Mitogen-activated protein kinase (MAPK) signaling, Phosphoinositide 3-kinase (PI3K) signaling, G1/S cell cycle, and receptor tyrosine kinases. We also identified recurrent patterns of gene- and pathway-level alterations associated with mCRPC by using Bayesian networks, an unsupervised machine learning algorithm.
Conclusion: These results provide clinical evidence that progression from mCSPC to mCRPC is associated with stereotyped concomitant gain-of-function aberrations in these pathways. Furthermore, detection of these aberrations in cfDNA may overcome the challenges associated with obtaining tumor bone biopsies and allow contemporary investigation of combinatorial therapies that target these aberrations.
Implications for practice: The progression from castration-sensitive to castration-resistant prostate cancer is characterized by worse prognosis and there is a pressing need for targeted drugs to prevent or delay this transition. This study used machine learning algorithms to examine the cell-free DNA of patients to identify alterations to specific pathways and genes associated with progression. Detection of these alterations in cell-free DNA may overcome the challenges associated with obtaining tumor bone biopsies and allow contemporary investigation of combinatorial therapies that target these aberrations.
Keywords: Castration-resistant; Castration-sensitive; Cell-free DNA; Genomics; Machine learning; Metastatic prostate cancer; Next-generation sequencing.
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