Understanding dynamic interactions in the prostate tumor microenvironment

Therapy resistance in prostate cancer: mechanism, signaling and reversal strategies

Prostate cancer (PC) depicts a major health challenge all over the globe due to its complexities in the treatment and diverse clinical trajectories. Even in the advances in the modern treatment strategies, the spectrum of resistance to the therapies continues to be a significant challenge. This review comprehensively examines the underlying mechanisms of the therapy resistance occurred in PC, focusing on both the tumor microenvironment and the signaling pathways implicated in the resistance. Tumor microenvironment comprises of stromal and epithelial cells, which influences tumor growth, response to therapy and progression. Mechanisms such as microenvironmental epithelial-mesenchymal transition (EMT), anoikis suppression and stimulation of angiogenesis results in therapy resistance. Moreover, dysregulation of signaling pathways including androgen receptor (AR), mammalian target of rapamycin/phosphoinositide 3 kinase/AKT (mTOR/PI3K/AKT), DNA damage repair and Janus kinase/signal transducers and activators of transcription (JAK/STAT) pathways drive therapy resistance by promoting tumor survival and proliferation. Understanding these molecular pathways is important for developing targeted therapeutic interventions which overcomes resistance. In conclusion, a complete grasp of mechanisms and pathways underlying medication resistance in PC is important for the development of individualized treatment plans and enhancements of clinical outcomes. By studying and understanding the complex mechanisms of signaling pathways and microenvironmental factors contributing to therapy resistance, this study focuses and aims to guide the development of innovative therapeutic approaches to effectively overcome the PC progression and improve the survival rate of patients.

Identifying prognostic signatures in the microenvironment of prostate cancer

Background

An increasing number of studies has indicated that the tumor microenvironment (TME), an important component of tumor tissue, has clinicopathological significance in predicting disease outcome and therapeutic efficacy. However, little evidence in prostate cancer (PCa) is available.

Methods

The cohort of TCGA-PRAD (n=477) was used in this study. Based on the proportion of 22 types of immune cells calculated by CIBERSORT, the TME was classified by K-means clustering and differentially expressed genes (DEGs) were determined. The TMEscore was calculated based on cluster signature genes, which were obtained from DEGs by the random forest method, and the samples were classified into two subtypes. Analyses of somatic mutation and copy number variation (CNVs) were further conducted to identify the genetic characteristics of the two subtypes. Correlation analysis was performed to explore the correlation between TMEscore and the tumor response to immune checkpoint inhibitors (ICIs) as well as the prognosis of PCa.

Results

Based on the distribution of infiltrating immune cells in the TME, we constructed the TMEscore model and classified PCa samples into high and low TMEscore groups. Survival analysis indicated that the high TMEscore group had significantly better survival outcome than the low TMEscore group. Correlation analysis showed a significantly positive correlation between TMEscore and the known prognostic factors of PCa.

Conclusions

Our study indicates that the TMEscore could be a potential prognostic biomarker in PCa. A comprehensive description of the characteristics of TME may help predict the response to therapies and provide new treatment strategies for PCa patients.