The heterogeneity and clonal evolution analysis of the advanced prostate cancer with castration resistance

Abstract

Background Nowadays, the incidence rate of advanced prostate cancer at the first time of diagnosis grows higher in China yearly. At present, androgen deprivation therapy is the standard first-line treatment of advanced prostate cancer. However, after several years of ADT, most patients will inevitably progress to castration-resistant prostate cancer (CRPC). Previous studies mainly focus on Caucasian and very few on East Asian patients. Methods In this study, the pre-and post-androgen deprivation therapy tumor samples were collected from five Chinese patients with advanced prostate cancer. The whole-exome sequencing, tumor heterogeneity, and clonal evolution pattern were analyzed. Results The results showed that the gene mutation pattern and heterogeneity changed significantly after androgen deprivation therapy. Tumor Mutational Burden (TMB) and Copy Number Alteration (CNA) were substantially reduced in the post-treatment group, but the Mutant-allele tumor heterogeneity (MATH), Socio-Demographic Index (SDI), Intratumor heterogeneity (ITH), and weighted Genome Instability Index (wGII) had no significant difference. According to the clone types and characteristics, the presence of main clones in five pre-and post-treatment samples, the clonal evolution pattern can be further divided into two sub-groups (the Homogeneous origin clonal model or the Heterogeneous origin clonal model). The Progression-free survival of the patients with the “Homogeneous origin clonal model” was shorter than the “Heterogeneous origin clonal model”. The longer PFS might relate to MUC7 and MUC5B mutations repaired. ZNF91 mutation might contribute to resistance to androgen deprivation therapy. Conclusion Our findings revealed potential genetic regulators to predict the castration resistance and provide insights into the castration resistance processes in advanced prostate cancer.

Efficacy and safety of Chinese patent medicine in the adjuvant treatment of prostate cancer: A Bayesian network meta-analysis

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