Clinical and biological relevance of the transcriptomic-based prostate cancer metastasis subtypes MetA-C.

Thysell E, Köhn L, Semenas J, Järemo H, Freyhult E, Lundholm M, Thellenberg Karlsson C, Damber J, Widmark A, Crnalic S, Josefsson A, Welén K, Nilsson RJA, Bergh A, Wikström P

Mol Oncol - (-) - [2021-12-10; online 2021-12-10]

To improve treatment of metastatic prostate cancer, the biology of metastases needs to be understood. We recently described three subtypes of prostate cancer bone metastases (MetA-C), based on differential gene expression. The aim of this study was to verify the clinical relevance of these subtypes and to explore their biology and relations to genetic drivers. Freshly-frozen metastasis samples were obtained as hormone-naive (n = 17), short-term castrated (n = 21), or castration-resistant (n = 65) from a total of 67 patients. Previously published sequencing data from 573 metastasis samples were also analyzed. Through transcriptome profiling and sample classification based on a set of predefined MetA-C-differentiating genes, we found that most metastases were heterogeneous for the MetA-C subtypes. Overall, MetA was the most common subtype, while MetB was significantly enriched in castration-resistant samples and in liver metastases, and consistently associated with poor prognosis. By gene set enrichment analysis, the phenotype of MetA was described by high androgen response, protein secretion and adipogenesis, MetB by high cell cycle activity and DNA repair, and MetC by epithelial-to-mesenchymal transition and inflammation. The MetB subtype demonstrated single nucleotide variants of RB transcriptional corepressor 1 (RB1) and loss of 21 genes at chromosome 13, including RB1, but provided independent prognostic value to those genetic aberrations. In conclusion, a distinct set of gene transcripts can be used to classify prostate cancer metastases into the subtypes MetA-C. The MetA-C subtypes show diverse biology, organ tropism, and prognosis. The MetA-C classification may be used independently, or in combination with genetic markers, primarily to identify MetB patients in need of complementary therapy to conventional androgen receptor-targeting treatments.

Bioinformatics Support and Infrastructure [Collaborative]

Bioinformatics Support, Infrastructure and Training [Collaborative]

PubMed 34889043

DOI 10.1002/1878-0261.13158

Crossref 10.1002/1878-0261.13158


Publications 7.1.2