The potential of tumour microenvironment markers to stratify the risk of recurrence in prostate cancer patients

Prostate cancer microenvironment: multidimensional regulation of immune cells, vascular system, stromal cells, and microbiota

BACKGROUND

Prostate cancer (PCa) is one of the most prevalent malignancies in males worldwide. Increasing research attention has focused on the PCa microenvironment, which plays a crucial role in tumor progression and therapy resistance. This review aims to provide a comprehensive overview of the key components of the PCa microenvironment, including immune cells, vascular systems, stromal cells, and microbiota, and explore their implications for diagnosis and treatment.

METHODS

Keywords such as "prostate cancer", "tumor microenvironment", "immune cells", "vascular system", "stromal cells", and "microbiota" were used for literature retrieval through online databases including PubMed and Web of Science. Studies related to the PCa microenvironment were selected, with a particular focus on those discussing the roles of immune cells, vascular systems, stromal cells, and microbiota in the development, progression, and treatment of PCa. The selection criteria prioritized peer-reviewed articles published in the last five years, aiming to summarize and analyze the latest research advancements and clinical relevance regarding the PCa microenvironment.

RESULTS

The PCa microenvironment is highly complex and dynamic, with immune cells contributing to immunosuppressive conditions, stromal cells promoting tumor growth, and microbiota potentially affecting androgen metabolism. Vascular systems support angiogenesis, which fosters tumor expansion. Understanding these components offers insight into the mechanisms driving PCa progression and opens avenues for novel therapeutic strategies targeting the tumor microenvironment.

CONCLUSIONS

A deeper understanding of the PCa microenvironment is crucial for advancing diagnostic techniques and developing precision therapies. This review highlights the potential of targeting the microenvironment to improve patient outcomes, emphasizing its significance in the broader context of PCa research and treatment innovation.

The estrogen signaling pathway reprograms prostate cancer cell metabolism and supports proliferation and disease progression

Just like the androgen receptor (AR), the estrogen receptor α (ERα) is expressed in the prostate and is thought to influence prostate cancer (PCa) biology. Yet the incomplete understanding of ERα functions in PCa hinders our ability to fully comprehend its clinical relevance and restricts the repurposing of estrogen-targeted therapies for the treatment of this disease. Using 2 human PCa tissue microarray cohorts, we first demonstrate that nuclear ERα expression was heterogeneous among patients, being detected in only half of the tumors. Positive nuclear ERα levels were correlated with disease recurrence, progression to metastatic PCa, and patient survival. Using in vitro and in vivo models of the normal prostate and PCa, bulk and single-cell RNA-Seq analyses revealed that estrogens partially mimicked the androgen transcriptional response and activated specific biological pathways linked to proliferation and metabolism. Bioenergetic flux assays and metabolomics confirmed the regulation of cancer metabolism by estrogens, supporting proliferation. Using cancer cell lines and patient-derived organoids, selective estrogen receptor modulators, a pure anti-estrogen, and genetic approaches impaired cancer cell proliferation and growth in an ERα-dependent manner. Overall, our study revealed that, when expressed, ERα functionally reprogrammed PCa metabolism, was associated with disease progression, and could be targeted for therapeutic purposes.

The tumor microenvironment in the postsurgical liver: Mechanisms and potential targets of postoperative recurrence in human hepatocellular carcinoma

Surgery remains to be the mainstay of treatment for hepatocellular carcinoma (HCC). Nonetheless, its therapeutic efficacy is significantly impaired by postoperative recurrence, which occurs in more than half of cases as a result of intrahepatic metastasis or de novo tumorigenesis. For decades, most therapeutic strategies on inhibiting postoperative HCC recurrence have been focused on the residual tumor cells but satisfying therapeutic outcomes are barely observed in the clinic. In recent years, a better understanding of tumor biology allows us to shift our focus from tumor cells toward the postoperative tumor microenvironment (TME), which is gradually identified to play a pivotal role in tumor recurrence. In this review, we describe various surgical stress and surgical perturbation on postoperative TME. Besides, we discuss how such alternations in TME give rise to postoperative recurrence of HCC. Based on its clinical significance, we additionally highlight the potential of the postoperative TME as a target for postoperative adjuvant therapeutics.

Novel Multiparametric Magnetic Resonance Imaging-Based Deep Learning and Clinical Parameter Integration for the Prediction of Long-Term Biochemical Recurrence-Free Survival in Prostate Cancer after Radical Prostatectomy

Radical prostatectomy (RP) is the main treatment of prostate cancer (PCa). Biochemical recurrence (BCR) following RP remains the first sign of aggressive disease; hence, better assessment of potential long-term post-RP BCR-free survival is crucial. Our study aimed to evaluate a combined clinical-deep learning (DL) model using multiparametric magnetic resonance imaging (mpMRI) for predicting long-term post-RP BCR-free survival in PCa. A total of 437 patients with PCa who underwent mpMRI followed by RP between 2008 and 2009 were enrolled; radiomics features were extracted from T2-weighted imaging, apparent diffusion coefficient maps, and contrast-enhanced sequences by manually delineating the index tumors. Deep features from the same set of imaging were extracted using a deep neural network based on pretrained EfficentNet-B0. Here, we present a clinical model (six clinical variables), radiomics model, DL model (DLM-Deep feature), combined clinical-radiomics model (CRM-Multi), and combined clinical-DL model (CDLM-Deep feature) that were built using Cox models regularized with the least absolute shrinkage and selection operator. We compared their prognostic performances using stratified fivefold cross-validation. In a median follow-up of 61 months, 110/437 patients experienced BCR. CDLM-Deep feature achieved the best performance (hazard ratio [HR] = 7.72), followed by DLM-Deep feature (HR = 4.37) or RM-Multi (HR = 2.67). CRM-Multi performed moderately. Our results confirm the superior performance of our mpMRI-derived DL algorithm over conventional radiomics.

Prostate Cancer Tumor Stroma: Responsibility in Tumor Biology, Diagnosis and Treatment

Simple Summary

The crosstalk between prostate stroma and its epithelium is essential to tissue homeostasis. Likewise, reciprocal signaling between tumor cells and the stromal compartment is required in tumor progression to facilitate or stimulate key processes such as cell proliferation and invasion. The aim of the present work was to review the current state of knowledge on the significance of tumor stroma in the genesis, progression and therapeutic response of prostate carcinoma. Additionally, we addressed the future therapeutic opportunities.

Abstract

Prostate cancer (PCa) is a common cancer among males globally, and its occurrence is growing worldwide. Clinical decisions about the combination of therapies are becoming highly relevant. However, this is a heterogeneous disease, ranging widely in prognosis. Therefore, new approaches are needed based on tumor biology, from which further prognostic assessments can be established and complementary strategies can be identified. The knowledge of both the morphological structure and functional biology of the PCa stroma compartment can provide new diagnostic, prognostic or therapeutic possibilities. In the present review, we analyzed the aspects related to the tumor stromal component (both acellular and cellular) in PCa, their influence on tumor behavior and the therapeutic response and their consideration as a new therapeutic target.

Tumour microenvironment and focal therapy for prostate cancer

PURPOSE OF REVIEW

Although most studies focus on the tumour component of prostate cancer (PCa), increasing attention is being paid to the prostatic tumour microenvironment (TME) and its role in diagnosis, prognosis, and therapy development. Herein, we review the prognostic capability of tumour and nontumour derived biomarkers, the immunomodulatory effects of focal therapy (FT) on TME, and its potential as part of a multidisciplinary approach to PCa treatment.

RECENT FINDINGS

Tumour cells have always been the natural candidates to explore new biomarkers, but recent evidence highlights the prognostic contribution of TME cell markers. TME plays a critical role in PCa progression and tumours may escape from the immune system by establishing a microenvironment that suppresses effective antitumour immunity. It has been demonstrated that FT has an immunomodulatory effect and may elicit an immune response that can either favour or inhibit tumorigenesis. TME shows to be an additional target to enhance oncological control.

SUMMARY

A better understanding of TME has the potential to reliably elucidate PCa heterogeneity and assign a prognostic profile in accordance with prostate tumour foci. The joint contribution of biomarkers derived from both tumour and TME compartments may improve patient selection for FT by accurately stratifying disease aggressivity according to the characteristics of tumour foci. Preclinical studies have suggested that FT may act as a TME modulator, highlighting its promising role in multimodal therapeutic management.

Tumor microenvironment mechanisms and bone metastatic disease progression of prostate cancer

During disease progression from primary towards metastatic prostate cancer (PCa), and in particular bone metastases, the tumor microenvironment (TME) evolves in parallel with the cancer clones, altering extracellular matrix composition (ECM), vasculature architecture, and recruiting specialized tumor-supporting cells that favor tumor spread and colonization at distant sites. We introduce the clinical profile of advanced metastatic PCa in terms of common genetic alterations. Findings from recently developed models of PCa metastatic spread are discussed, focusing mainly on the role of the TME (mainly matrix and fibroblast cell types), at distinct stages: premetastatic niche orchestrated by the primary tumor towards the metastatic site and bone metastasis. We report evidence of premetastatic niche formation, such as the mechanisms of distant site conditioning by extracellular vesicles, chemokines and other tumor-derived mechanisms, including altered cancer cell-ECM interactions. Furthermore, evidence supporting the similarities of stroma alterations among the primary PCa and bone metastasis, and contribution of TME to androgen deprivation therapy resistance are also discussed. We summarize the available bone metastasis transgenic mouse models of PCa from a perspective of pro-metastatic TME alterations during disease progression and give an update on the current diagnostic and therapeutic radiological strategies for bone metastasis clinical management.

Molecular Characterization of Prostate Cancers in the Precision Medicine Era

Prostate cancer (PCa) therapy has been recently revolutionized by the approval of new therapeutic agents in the metastatic setting. However, the optimal therapeutic strategy in such patients should be individualized in the light of prognostic and predictive molecular factors, which have been recently studied: androgen receptor (AR) alterations, PTEN-PI3K-AKT pathway deregulation, homologous recombination deficiency (HRD), mismatch repair deficiency (MMRd), and tumor microenvironment (TME) modifications. In this review, we highlighted the clinical impact of prognostic and predictive molecular factors in PCa patients' outcomes, identifying biologically distinct subtypes. We further analyzed the relevant methods to detect these factors, both on tissue, i.e., immunohistochemistry (IHC) and molecular tests, and blood, i.e., analysis of circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA). Moreover, we discussed the main pros and cons of such techniques, depicting their present and future roles in PCa management, throughout the precision medicine era.

The Role of Mathematical Models in Immuno-Oncology: Challenges and Future Perspectives

Immuno-oncology (IO) focuses on the ability of the immune system to detect and eliminate cancer cells. Since the approval of the first immune checkpoint inhibitor, immunotherapies have become a major player in oncology treatment and, in 2021, represented the highest number of approved drugs in the field. In spite of this, there is still a fraction of patients that do not respond to these therapies and develop resistance mechanisms. In this sense, mathematical models offer an opportunity to identify predictive biomarkers, optimal dosing schedules and rational combinations to maximize clinical response. This work aims to outline the main therapeutic targets in IO and to provide a description of the different mathematical approaches (top-down, middle-out, and bottom-up) integrating the cancer immunity cycle with immunotherapeutic agents in clinical scenarios. Among the different strategies, middle-out models, which combine both theoretical and evidence-based description of tumor growth and immunological cell-type dynamics, represent an optimal framework to evaluate new IO strategies.