Molecular Profile Changes in Patients with Castrate-Resistant Prostate Cancer Pre- and Post-Abiraterone/Prednisone Treatment

NetActivity enhances transcriptional signals by combining gene expression into robust gene set activity scores through interpretable autoencoders

Grouping gene expression into gene set activity scores (GSAS) provides better biological insights than studying individual genes. However, existing gene set projection methods cannot return representative, robust, and interpretable GSAS. We developed NetActivity, a machine learning framework that generates GSAS based on a sparsely-connected autoencoder, where each neuron in the inner layer represents a gene set. We proposed a three-tier training that yielded representative, robust, and interpretable GSAS. NetActivity model was trained with 1518 GO biological processes terms and KEGG pathways and all GTEx samples. NetActivity generates GSAS robust to the initialization parameters and representative of the original transcriptome, and assigned higher importance to more biologically relevant genes. Moreover, NetActivity returns GSAS with a more consistent definition and higher interpretability than GSVA and hipathia, state-of-the-art gene set projection methods. Finally, NetActivity enables combining bulk RNA-seq and microarray datasets in a meta-analysis of prostate cancer progression, highlighting gene sets related to cell division, key for disease progression. When applied to metastatic prostate cancer, gene sets associated with cancer progression were also altered due to drug resistance, while a classical enrichment analysis identified gene sets irrelevant to the phenotype. NetActivity is publicly available in Bioconductor and GitHub.

Regulation of Molecular Biomarkers Associated with the Progression of Prostate Cancer

Androgen receptor signaling regulates the normal and pathological growth of the prostate. In particular, the growth and survival of prostate cancer cells is initially dependent on androgen receptor signaling. Exposure to androgen deprivation therapy leads to the development of castration-resistant prostate cancer. There is a multitude of molecular and cellular changes that occur in prostate tumor cells, including the expression of neuroendocrine features and various biomarkers, which promotes the switch of cancer cells to androgen-independent growth. These biomarkers include transcription factors (TP53, REST, BRN2, INSM1, c-Myc), signaling molecules (PTEN, Aurora kinases, retinoblastoma tumor suppressor, calcium-binding proteins), and receptors (glucocorticoid, androgen receptor-variant 7), among others. It is believed that genetic modifications, therapeutic treatments, and changes in the tumor microenvironment are contributing factors to the progression of prostate cancers with significant heterogeneity in their phenotypic characteristics. However, it is not well understood how these phenotypic characteristics and molecular modifications arise under specific treatment conditions. In this work, we summarize some of the most important molecular changes associated with the progression of prostate cancers and we describe some of the factors involved in these cellular processes.

Single Nucleotide Polymorphisms Associated with Prostate Cancer Progression: A Systematic Review

BACKGROUND

New biomarkers of progression in patients with prostate cancer (PCa) are needed to improve their classification and clinical management. This systematic review investigated the relationship between single nucleotide polymorphisms (SNPs) and PCa progression.

METHODS

A keyword search was performed in Pubmed, EMBASE, Scopus, Web of Science, and Cochrane for publications between 2007 and 2022. We included articles with adjusted and significant associations, a median follow-up greater than or equal to 24 months, patients taken to radical prostatectomy (RP) as a first therapeutic option, and results presented based on biochemical recurrence (BCR).

RESULTS

In the 27 articles selected, 73 SNPs were identified in 39 genes, organized in seven functional groups. Of these, 50 and 23 SNPs were significantly associated with a higher and lower risk of PCa progression, respectively. Likewise, four haplotypes were found to have a significant association with PCa progression.

CONCLUSION

This article highlights the importance of SNPs as potential markers of PCa progression and their possible functional relationship with some genes relevant to its development and progression. However, most variants were identified only in cohorts from two countries; no additional studies reproduce these findings.