Introduction: Prostate cancer (PCa) progression from benign androgen (AR) dependent stage to AR independent castration resistant and then a neuroendocrine stage is often accompanied by resistance to therapy. This resistance calls for ways that can help in understanding the etiology of progression in order to improve outcome. Similarly, Immune checkpoint inhibition (ICI) has revolutionized treatment of many tumor types, including prostate cancer. Such therapies focus on restoring antibody diversity to eliminate tumor cells that evaded immune detection, but is effective only in a subset of patients. This limited efficacy be in part from tumor heterogeneity and the unique ability of PCa to evolve. Therefore, this study focuses on evaluating the immunomodulatory patterns that are correlated to stages of PCa progression.
Methods: The analysis contains 3 parts: PCa subclassification, mutation analysis, and Ig expression. First, we obtained all RNAseq from the PRAD project contained in the TCGA (n=500). A general PCA was performed, and then patients were stratified into subclasses by using gene signatures obtained from the Disease/Gene Database (disgene.net). The results were then classified using basic clustering to refine these genes, and finalize the subclasses. Second, mutations were downloaded for the same PRAD TCGA dataset. Mutation types as well as number of mutations were classified per patient in order to come up with a mutational profile per patient. These profiles were then used to scan for mutations across severity by Gleason score (GS). Thirdly, Ig profiles were evaluated using the software packages TRUST and MiXCR. Total CDR3 sequences as well as AA length was analyzed and compared with mutation rates as well as switching events. Finally, all stratifications were compared to known tumor severity by GS.
Results: We observed patterns of Immunoglobulin (Ig) expression that are co-relatable to a) PCa subclasses such as Prostate Adenocarcinoma, Recurrent PCa, and Invasive PCa, b) mutation landscape (with focus on mutation types SNPs, SVs, and fusions), and c) Ig expression. Also, there are immunomodulatory patterns in IGHG1 and IGHG2 that are correlated with tumor severity and stages of PCa progression. Also, diversity variations exist in Shannon entropy with lower diversity in low grade tumor severity suggesting a significant change in the immune landscape.
Conclusions: The results suggest there are immune modulatory patterns are correlated with stages of PCa, and their correlation with components such as mutations, GS, subclasses of PCa can help in better understanding PCa progression and design therapeutic strategy more effectively.
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