A Systematic Review of Prostate Cancer Heterogeneity: Understanding the Clonal Ancestry of Multifocal Disease

Stemness regulation in prostate cancer: prostate cancer stem cells and targeted therapy

BACKGROUND

Increasing evidence indicates that cancer stem cells (CSCs) and cancer stem-like cells form a special subpopulation of cells that are ubiquitous in tumors. These cells exhibit similar characteristics to those of normal stem cells in tissues; moreover, they are capable of self-renewal and differentiation, as well as high tumorigenicity and drug resistance. In prostate cancer (PCa), it is difficult to kill these cells using androgen signaling inhibitors and chemotherapy drugs. Consequently, the residual prostate cancer stem cells (PCSCs) mediate tumor recurrence and progression.

OBJECTIVE

This review aims to provide a comprehensive and up-to-date overview of PCSCs, with a particular emphasis on potential therapeutic strategies targeting these cells.

METHODS

After searching in PubMed and Embase databases using 'prostate cancer' and 'cancer stem cells' as keywords, studies related were compiled and examined.

RESULTS

In this review, we detail the origin and characteristics of PCSCs, introduce the regulatory pathways closely related to CSC survival and stemness maintenance, and discuss the link between epithelial-mesenchymal transition, tumor microenvironment and tumor stemness. Furthermore, we introduce the currently available therapeutic strategies targeting CSCs, including signaling pathway inhibitors, anti-apoptotic protein inhibitors, microRNAs, nanomedicine, and immunotherapy. Lastly, we summarize the limitations of current CSC research and mention future research directions.

CONCLUSION

A deeper understanding of the regulatory network and molecular markers of PCSCs could facilitate the development of novel therapeutic strategies targeting these cells. Previous preclinical studies have demonstrated the potential of this treatment approach. In the future, this may offer alternative treatment options for PCa patients.

Refining partial gland ablation for localised prostate cancer: the FALCON project

OBJECTIVES

To provide a contemporary statement on focal therapy (FT) for localised prostate cancer (PCa) from an international and diverse group of physicians treating localised PCa, with the aim of overcoming the limitations of previous consensus statements, which were restricted to early adopters, and to offer direction regarding the various aspects of FT application that are currently not well defined.

MATERIALS AND METHODS

The FocAL therapy CONsensus (FALCON) project began with a 154-item online survey, developed following a steering committee discussion and literature search. Invitations to participate were extended to a large, diverse group of professionals experienced in PCa management. From 2022 to 2023, a Delphi consensus study consisting of three online rounds was conducted using the Modified Delphi method. A 1-9 Likert scale was used for the survey, which was followed by an in-person expert meeting. The threshold for achieving consensus was set at 70% agreement/disagreement. Six main aspects of FT were covered: (i) patient selection; (ii) energy source selection; (iii) treatment approach; (iv) treatment evaluation and follow-up; (v) treatment cost and accessibility; and (vi) future perspectives.

RESULTS

Of 246 initial participants, 148 (60%) completed all three rounds. Based on participant feedback, 27 new statements were added in the second round, and 33 questions related to personal expertise, for which consensus was not necessary, were excluded. After the third and final round, consensus had not been reached for 69 items. These items were discussed at the in-person meeting, resulting in a consensus of 57 additional items. Consensus was finally not reached on 12 items. Given the volume of data, the voting outcomes are summarised in this article, with a detailed breakdown presented in the form of figures and tables.

CONCLUSIONS

The FALCON project delivered a significant consensus on the approach to FT for localised PCa. Additionally, it highlighted gaps in our knowledge that may provide guidance for future research.

Computational Identification and Validation of Metabolic Cell Death-Related Prognostic Biomarkers for Personalized Treatment Strategies in Prostate Cancer

Prostate cancer (PCa) is a prevalent malignancy characterized by metabolic dysregulation and varied prognosis. Identifying prognostic biomarkers related to metabolic cell death could enhance risk stratification and treatment strategies. The purpose of this study was to identify prognostic genes associated with metabolic cell death in PCa and formulate a risk model for improved patient stratification. We identified genes that exhibit differential expression in The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) cohort (n = 394), with validation using GSE70769 (n = 92) and RT-qPCR on tissue samples from 5 patients. Candidate genes were intersected with metabolic cell death-related genes to identify prognostic markers. Independent prognostic factors were determined utilizing univariate and multivariate Cox regression analyses (p < 0.05, HR ≠ 1). A nomogram was designed, and the validation of gene expression was carried out using RT-qPCR on tissue samples from five PCa patients. A total of 78 candidate genes were identified, with ASNS and ZNF419 emerging as independent prognostic factors. The gene-based risk model successfully stratified patients into high- and low-risk groups, demonstrating correlations with overall survival and clinicopathological features, while also revealing significant differences in immune cell infiltration patterns through immune microenvironment analysis. Additionally, somatic mutation analysis indicated TP53, TTN, and SPOP as frequently mutated genes. This study identifies ASNS and ZNF419 as novel prognostic biomarkers in PCa, contributing to improved risk stratification and personalized treatment strategies. Further investigation into their functional roles may provide insights into therapeutic targets for PCa management.

Triplet therapy for metastatic castration-sensitive prostate cancer: Rationale and clinical evidence

Prostate cancer (PC) growth is hormone-dependent and it frequently develops distant metastases as disease progresses. Patients with metastatic castration-sensitive prostate cancer (mCSPC) initially respond to androgen deprivation therapy (ADT) but eventually become refractory and develop metastatic castration-resistant prostate cancer (mCRPC). Castration-resistance is associated with high lethality and metastases confer poor prognosis, therefore unmet needs in treatment for mCSPC remain high. So far, improvements in survival in mCSPC have been achieved by doublet combination therapy such as docetaxel or an androgen-receptor signaling inhibitor (ARSI) in addition to ADT. Further, recent phase 3 trials have shown that triplet therapy-a combination of ARSI, docetaxel, and ADT improves prognosis compared with docetaxel plus ADT in mCSPC. PC tumors manifest intra- and inter-tumoral heterogeneity at both the genetic and phenotypic level. As heterogeneity increases during sequential treatment and disease progression, it is reasonable to initiate combination therapy using drugs with different mechanisms of action early in the course of disease, such as mCSPC. Previous research about tumor heterogeneity and drug resistant mechanism support this rationale, as well as preclinical studies and real-world data provide the scientific evidence of benefit by combining ARSI and docetaxel. Here, we review the rationale and clinical evidence for triplet therapy in patients with mCSPC.

Clonal phylogenies inferred from bulk, single cell, and spatial transcriptomic analysis of epithelial cancers

Epithelial cancers are typically heterogeneous with primary prostate cancer being a typical example of histological and genomic variation. Prior studies of primary prostate cancer tumour genetics revealed extensive inter and intra-patient genomic tumour heterogeneity. Recent advances in machine learning have enabled the inference of ground-truth genomic single-nucleotide and copy number variant status from transcript data. While these inferred SNV and CNV states can be used to resolve clonal phylogenies, however, it is still unknown how faithfully transcript-based tumour phylogenies reconstruct ground truth DNA-based tumour phylogenies. We sought to study the accuracy of inferred-transcript to recapitulate DNA-based tumour phylogenies. We first performed in-silico comparisons of inferred and directly resolved SNV and CNV status, from single cancer cells, from three different cell lines. We found that inferred SNV phylogenies accurately recapitulate DNA phylogenies (entanglement = 0.097). We observed similar results in iCNV and CNV based phylogenies (entanglement = 0.11). Analysis of published prostate cancer DNA phylogenies and inferred CNV, SNV and transcript based phylogenies demonstrated phylogenetic concordance. Finally, a comparison of pseudo-bulked spatial transcriptomic data to adjacent sections with WGS data also demonstrated recapitulation of ground truth (entanglement = 0.35). These results suggest that transcript-based inferred phylogenies recapitulate conventional genomic phylogenies. Further work will need to be done to increase accuracy, genomic, and spatial resolution.

Clinical Implications of Basic Research: Exploring the Transformative Potential of Spatial 'Omics in Uro-oncology

New spatial molecular technologies are poised to transform our understanding and treatment of urological cancers. By mapping the spatial molecular architecture of tumours, these platforms uncover the complex heterogeneity within and around individual malignancies, offering novel insights into disease development, progression, diagnosis, and treatment. They enable tracking of clonal phylogenetics in situ and immune-cell interactions in the tumour microenvironment. A whole transcriptome/genome/proteome-level spatial analysis is hypothesis generating, particularly in the areas of risk stratification and precision medicine. Current challenges include reagent costs, harmonisation of protocols, and computational demands. Nonetheless, the evolving landscape of the technology and evolving machine learning applications have the potential to overcome these barriers, pushing towards a future of personalised cancer therapy, leveraging detailed spatial cellular and molecular data. PATIENT SUMMARY: Tumours are complex and contain many different components. Although we have been able to observe some of these differences visually under the microscope, until recently, we have not been able to observe the genetic changes that underpin cancer development. Scientists are now able to explore molecular/genetic differences using approaches such as "spatial transcriptomics" and "spatial proteomics", which allow them to see genetic and cellular variation across a region of normal and cancerous tissue without destroying the tissue architecture. Currently, these technologies are limited by high associated costs, and a need for powerful and complex computational analysis workflows. Future advancements and results through these new technologies may assist patients and their doctors as they make decisions about treating their cancer.

Assessing the Molecular Heterogeneity of Prostate Cancer Biopsy Sampling: Insights from the MAST Trial

BACKGROUND AND OBJECTIVE

Prostate cancer (PC) heterogeneity can result in sampling discrepancies during biopsy, leading to inaccurate molecular classifications that affect treatment decisions. We evaluated transcriptomic profile variability between multiparametric magnetic resonance imaging (mpMRI)-targeted biopsy (TBx) and systematic biopsy (SBx) methods using the Decipher GRID platform.

METHODS

The study included 205 men from the MAST trial. We analyzed 408 biopsy samples, of which 149 were TBx and 259 were SBx samples. Three prognostic signatures-the Decipher genomic classifier (DGC), cell cycle progression (CCP), and Genomic Prostate Score-were assessed in relation to grade group (GG) and MRI phenotype. Multivariable linear regression was conducted to adjust for the confounding effects of GG and tumor purity.

KEY FINDINGS AND LIMITATIONS

Unpaired analysis revealed that TBx samples had higher derived GPS and CCP scores than SBx samples (p < 0.05), but the difference was no longer significant after multiple-test adjustment. There was no significant difference in scores between SBx and TBx samples in the subgroup with GG 1 disease. For TBx cores, higher genomic scores were associated with higher Prostate Imaging-Reporting and Data System (PI-RADS) scores in the overall cohort, but not in the GG 1 subgroup. Multivariable analysis revealed significant associations between DGC and CCP scores and PI-RADS scores (p < 0.01). Higher DGC score concordance between TBx and SBx lesions was observed in the low-risk subgroup. A limitation of the study is the small sample size, so further validation is required.

CONCLUSIONS AND CLINICAL IMPLICATIONS

TBx samples yield higher genomic scores than SBx samples, with grade influencing the association between PI-RADS score and genomic risk. For the GG 1 subgroup, there was no correlation between PI-RADS and genomic scores. These findings need further validation to assess the impact of TBx on genomic risk assessment in active surveillance.

PATIENT SUMMARY

We examined the effectiveness of two different biopsy methods in assessing the risk of prostate cancer (PC) progression. We found that while biopsy samples guided by MRI (magnetic resonance imaging) scans often showed higher genetic risk scores than biopsy samples without MRI guidance, the difference was not significant for men with lower-grade PC. Our findings suggest that MRI targeting for biopsy might not always provide additional information about cancer aggressiveness for patients with low-risk PC.

Intrapatient Intermetastatic Heterogeneity Determined by Triple-Tracer PET Imaging in mCRPC Patients and Correlation to Survival: The 3TMPO Cohort Study

Intrapatient intermetastatic heterogeneity (IIH) has been demonstrated in metastatic castration-resistant prostate cancer (mCRPC) patients and is of the utmost importance for radiopharmaceutical therapy (RPT) eligibility. This study was designed to determine the prevalence of IIH and RPT eligibility in mCRPC patients through a triple-tracer PET imaging strategy. Methods: This was a multisite prospective observational study in which mCRPC patients underwent both 18F-FDG and 68Ga-prostate-specific membrane antigen (PSMA)-617 PET/CT scans. A third scan with 68Ga-DOTATATE, a potential biomarker of neuroendocrine differentiation, was performed if an 18F-FDG-positive/68Ga-PSMA-negative lesion was found. Per-tracer lesion positivity was defined as having an uptake at least 50% above that of the liver. IIH prevalence was defined as the percentage of participants having at least 2 lesions with discordant features on multitracer PET. Results: IIH was observed in 81 patients (82.7%), and at least 1 18F-FDG-positive/68Ga-PSMA-negative lesion was found in 45 patients (45.9%). Of the 37 participants who also underwent 68Ga-DOTATATE PET/CT, 6 (16.2%) had at least 1 68Ga-DOTATATE-positive lesion. In total, 12 different combinations of lesion imaging phenotypes were observed. On the basis of our prespecified criteria, 52 (53.1%) participants were determined to be eligible for PSMA RPT, but none for DOTATATE RPT. Patients with IIH had a significantly shorter median overall survival than patients without IIH (9.5 mo vs. not reached; log-rank P = 0.03; hazard ratio, 2.7; 95% CI, 1.1-6.8). Conclusion: Most mCRPC patients showed IIH, which was associated with shorter overall survival. On the basis of a triple-tracer PET approach, multiple phenotypic combinations were found. Correlation of these imaging phenotypes with genomics and treatment response will be relevant for precision medicine.

Immunohistochemical markers as predictors of prognosis in multifocal prostate cancer

The impact of tumor focality on prostate cancer (PCa) prognosis has been addressed in several studies with conflicting results. Tumor foci from multifocal (MF) PCa can show highly heterogeneous molecular features. Our aim was to analyze the protein expression of PTEN, SPOP, SLC45A3, ETV1, ERG and the "triple hit" (ERG overexpression, PTEN plus SLC45A3 loss) in unifocal (UF) and MF PCa, to evaluate their value as prognostic markers according to focality, and the role of tumor heterogeneity in MF disease. PTEN, SPOP, SLC45A3, ETV1 and ERG immunohistochemical expression was evaluated in 185 PCa from 9 TMAs, 51 UF and 134 MF. In a subset of 69 MF cases, the dominant and secondary foci (DF and SF) were compared. Heterogeneity was considered when both tumor foci presented different expression patterns. Relationship with clinicopathological features was also analyzed. MF PCa was diagnosed in significantly younger patients when compared to UF ones (p = 0.007). ETV1 overexpression was associated with UF disease (p = 0.028). A shorter time to PSA recurrence was related to SLC45A3 wt expression in UF PCa (p = 0.052), and to SPOP expression loss (p = 0.043) or "triple hit" phenotype in MF PCa (p = 0.041). In MF cases, PTEN loss, SLC45A3 loss and "triple hit" phenotype were associated with the DF and had significant heterogeneity. In conclusion, our results indicate that UF and MF PCa have relevant and consistent molecular differences. The analysis of an immunohistochemical panel, composed by PTEN, SPOP, SLC45A3, ETV1 and ERG, could be useful to predict outcome in MF cases.

Evaluating the Initial Experience and Clinical Impact of Prostate-Specific Membrane Antigen (PSMA) Positron Emission Tomography/Computed Tomography (PET/CT) Scans in Prostate Cancer Management: A Retrospective Study in Iraq

Background Prostate cancer is a significant health concern globally, especially in the Middle East, including Iraq. This study explores the adoption and impact of prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) scans in Erbil, Iraq, from 2020 to 2023, marking a pivotal advancement in prostate cancer diagnostics in a region where the disease's prevalence is rising.  Materials and methods Through a retrospective analysis at Medya Diagnostic Center in Erbil, Iraq, involving 172 patients, we assessed the feasibility, applicability, and clinical utility of PSMA PET/CT in the local population.  Results The study highlights the modality's enhanced sensitivity and specificity in detecting prostate cancer and its metastases, with bone being the most frequent metastasis site. Despite positive outcomes, challenges such as integration into clinical practice, adherence to guidelines, and financial implications were identified. The majority of referrals came from medical oncologists, primarily for staging and response evaluation, indicating PSMA PET/CT's critical role in managing prostate cancer. The findings suggest a need for national guidelines, interdisciplinary collaboration, and educational initiatives to optimize the use of PSMA PET/CT in Iraq's healthcare setting.  Conclusions This study contributes valuable insights into the early experiences with PSMA PET/CT, paving the way for improved prostate cancer diagnostics and management in similar contexts.

Identifying proteomic risk factors for overall, aggressive, and early onset prostate cancer using Mendelian Randomisation and tumour spatial transcriptomics

BACKGROUND

Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention.

METHODS

We investigated the association of 2002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomisation (MR) and colocalisation. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalisation were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumour tissue to assess their role in tumour aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets.

FINDINGS

We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which the majority replicated where data were available. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirmed an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also found an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that comparatively had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk also mapped to existing therapeutic interventions.

INTERPRETATION

Our findings emphasise the importance of proteomics for improving our understanding of prostate cancer aetiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumours. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer.

FUNDING

This work was supported by Cancer Research UK (grant no. C8221/A29017).

Characterization of prostatic cancer lesion and gleason grade using a continuous-time random-walk diffusion model at high b-values

Background

Distinguishing between prostatic cancer (PCa) and chronic prostatitis (CP) is sometimes challenging, and Gleason grading is strongly associated with prognosis in PCa. The continuous-time random-walk diffusion (CTRW) model has shown potential in distinguishing between PCa and CP as well as predicting Gleason grading.

Purpose

This study aimed to quantify the CTRW parameters (α, β & Dm) and apparent diffusion coefficient (ADC) of PCa and CP tissues; and then assess the diagnostic value of CTRW and ADC parameters in differentiating CP from PCa and low-grade PCa from high-grade PCa lesions.

Study type

Retrospective (retrospective analysis using prospective designed data).

Population

Thirty-one PCa patients undergoing prostatectomy (mean age 74 years, range 64-91 years), and thirty CP patients undergoing prostate needle biopsies (mean age 68 years, range 46-79 years).

Field strength/Sequence

MRI scans on a 3.0T scanner (uMR790, United Imaging Healthcare, Shanghai, China). DWI were acquired with 12 b-values (0, 50, 100, 150, 200, 500, 800, 1200, 1500, 2000, 2500, 3000 s/mm2).

Assessment

CTRW parameters and ADC were quantified in PCa and CP lesions.

Statistical tests

The Mann-Whitney U test was used to evaluate the differences in CTRW parameters and ADC between PCa and CP, high-grade PCa, and low-grade PCa. Spearman's correlation of the pathologic grading group (GG) with CTRW parameters and ADC was evaluated. The usefulness of CTRW parameters, ADC, and their combinations (Dm, α and β; Dm, α, β, and ADC) to differentiate PCa from CP and high-grade PCa from low-grade PCa was determined by logistic regression and receiver operating characteristic curve (ROC) analysis. Delong test was used to compare the differences among AUCs.

Results

Significant differences were found for the CTRW parameters (α, Dm) between CP and PCa (all P<0.001), high-grade PCa, and low-grade PCa (α:P=0.024, Dm:P=0.021). GG is correlated with certain CTRW parameters and ADC(α:P<0.001,r=-0.795; Dm:P<0.001,r=-0.762;ADC:P<0.001,r=-0.790). Moreover, CTRW parameters (α, β, Dm) combined with ADC showed the best diagnostic efficacy for distinguishing between PCa and CP as well as predicting Gleason grading. The differences among AUCs of ADC, CTRW parameters and their combinations were not statistically significant (P=0.051-0.526).

Conclusion

CTRW parameters α and Dm, as well as their combination were beneficial to distinguish between CA and PCa, low-grade PCa and high-grade PCa lesions, and CTRW parameters and ADC had comparable diagnostic performance.

Aligning germline and somatic mutations in prostate cancer. Are genetics changing practice?

OBJECTIVE

To review the current status of germline and somatic (tumour) genetic testing for prostate cancer (PCa), and its relevance for clinical practice.

METHODS

A narrative synthesis of various molecular profiles related to their clinical context was carried out. Current guidelines for genetic testing and its feasibility in clinical practice were analysed. We report the main identified genetic sequencing results or functional genomic scores for PCa published in the literature or obtained from the French PROGENE study.

RESULTS

The molecular alterations observed in PCa are mostly linked to disruption of the androgen receptor (AR) pathway or DNA repair deficiency. The main known germline mutations affect the BReast CAncer gene 2 (BRCA2) and homeobox B13 (HOXB13) genes, whereas AR and tumour protein p53 (TP53) are the genes with most frequent somatic alterations in tumours from men with metastatic PCa. Molecular tests are now available for detecting some of these germline or somatic alterations and sometimes recommended by guidelines, but their utilisation must combine rationality and feasibility. They can guide specific therapies, notably for the management of metastatic disease. Indeed, following androgen deprivation, targeted therapies for PCa currently include poly-(ADP-ribose)-polymerase (PARP) inhibitors, immune checkpoint inhibitors, and prostate-specific membrane antigen (PSMA)-guided radiotherapy. The genetic tests currently approved for targeted therapies remain limited to the detection of BRCA1 and BRCA2 mutation and DNA mismatch repair deficiency, while large panels are recommended for germline analyses, not only for inherited cancer predisposing syndrome, but also for metastatic PCa.

CONCLUSIONS

Further consensus aligning germline with somatic molecular analysis in metastatic PCa is required, including genomics scars, emergent immunohistochemistry, or functional pre-screen imaging. With rapid advances in knowledge and technology in the field, continuous updating of guidelines to help the clinical management of these individuals, and well-conducted studies to evaluate the benefits of genetic testing are needed.

Cancer origin tracing and timing in two high-risk prostate cancers using multisample whole genome analysis: prospects for personalized medicine

BACKGROUND

Prostate cancer (PrCa) genomic heterogeneity causes resistance to therapies such as androgen deprivation. Such heterogeneity can be deciphered in the context of evolutionary principles, but current clinical trials do not include evolution as an essential feature. Whether or not analysis of genomic data in an evolutionary context in primary prostate cancer can provide unique added value in the research and clinical domains remains an open question.

METHODS

We used novel processing techniques to obtain whole genome data together with 3D anatomic and histomorphologic analysis in two men (GP5 and GP12) with high-risk PrCa undergoing radical prostatectomy. A total of 22 whole genome-sequenced sites (16 primary cancer foci and 6 lymph node metastatic) were analyzed using evolutionary reconstruction tools and spatio-evolutionary models. Probability models were used to trace spatial and chronological origins of the primary tumor and metastases, chart their genetic drivers, and distinguish metastatic and non-metastatic subclones.

RESULTS

In patient GP5, CDK12 inactivation was among the first mutations, leading to a PrCa tandem duplicator phenotype and initiating the cancer around age 50, followed by rapid cancer evolution after age 57, and metastasis around age 59, 5 years prior to prostatectomy. In patient GP12, accelerated cancer progression was detected after age 54, and metastasis occurred around age 56, 3 years prior to prostatectomy. Multiple metastasis-originating events were identified in each patient and tracked anatomically. Metastasis from prostate to lymph nodes occurred strictly ipsilaterally in all 12 detected events. In this pilot, metastatic subclone content analysis appears to substantially enhance the identification of key drivers. Evolutionary analysis' potential impact on therapy selection appears positive in these pilot cases.

CONCLUSIONS

PrCa evolutionary analysis allows tracking of anatomic site of origin, timing of cancer origin and spread, and distinction of metastatic-capable from non-metastatic subclones. This enables better identification of actionable targets for therapy. If extended to larger cohorts, it appears likely that similar analyses could add substantial biological insight and clinically relevant value.

Spatial transcriptomic analysis of virtual prostate biopsy reveals confounding effect of tissue heterogeneity on genomic signatures

Genetic signatures have added a molecular dimension to prognostics and therapeutic decision-making. However, tumour heterogeneity in prostate cancer and current sampling methods could confound accurate assessment. Based on previously published spatial transcriptomic data from multifocal prostate cancer, we created virtual biopsy models that mimic conventional biopsy placement and core size. We then analysed the gene expression of different prognostic signatures (OncotypeDx®, Decipher®, Prostadiag®) using a step-wise approach with increasing resolution from pseudo-bulk analysis of the whole biopsy, to differentiation by tissue subtype (benign, stroma, tumour), followed by distinct tumour grade and finally clonal resolution. The gene expression profile of virtual tumour biopsies revealed clear differences between grade groups and tumour clones, compared to a benign control, which were not reflected in bulk analyses. This suggests that bulk analyses of whole biopsies or tumour-only areas, as used in clinical practice, may provide an inaccurate assessment of gene profiles. The type of tissue, the grade of the tumour and the clonal composition all influence the gene expression in a biopsy. Clinical decision making based on biopsy genomics should be made with caution while we await more precise targeting and cost-effective spatial analyses.

Identifying proteomic risk factors for overall, aggressive and early onset prostate cancer using Mendelian randomization and tumor spatial transcriptomics

Background

Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention.

Methods

We investigated the association of 2,002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomization (MR) and colocalization. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalization were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumor tissue to assess their role in tumor aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets.

Results

We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which a majority were novel and replicated. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirm an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also find an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk mapped to existing therapeutic interventions.

Conclusion

Our findings emphasize the importance of proteomics for improving our understanding of prostate cancer etiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumors. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer.

The Transcriptomic Profiles of ESR1 and MMP3 Stratify the Risk of Biochemical Recurrence in Primary Prostate Cancer beyond Clinical Features

The molecular determinants of the heterogenic course of prostate cancer (PC) remain elusive. We aimed to determine the drivers predisposing to unfavorable PC outcomes anticipated by BCR events among patients of similar preoperative characteristics. The TCGA transcriptomic and clinical data of 497 PC individuals were used, stratified according to the risk of BCR by EAU-EANM-ESTRO-ESUR-SIOG. The relevance of the functional markers regarding BCR-free survival was examined by the cutp algorithm. Through UpSetR, subgroups of PC patients bearing an unfavorable signature were identified, followed by the hierarchical clustering of the major markers of the epithelial-to-mesenchymal transition (EMT). BCR-free survival was estimated with the Cox proportional hazards regression model. ESR1 significantly differentiated BCR-free survival, whereas AR did not. An elevation in KLK3 correlated with better prognosis, although PGR, KLK3, CDH1, and MMP3 predicted BCR better than the preoperative PSA level. Patients sharing an unfavorable profile of ESR1 and MMP3 together with lymph node status, Gleason score, T, and EAU risk groups were at a higher risk of BCR originating from mesenchymal features of PC cells. To conclude, we revealed an ESR1-driven unfavorable profile of EMT underpinning a worse PC trajectory. ESR1 may have a major role in PC progression; therefore, it could become a major focus for further investigations.

Understanding and targeting prostate cancer cell heterogeneity and plasticity

Prostate cancer (PCa) is a prevalent malignancy that occurs primarily in old males. Prostate tumors in different patients manifest significant inter-patient heterogeneity with respect to histo-morphological presentations and molecular architecture. An individual patient tumor also harbors genetically distinct clones in which PCa cells display intra-tumor heterogeneity in molecular features and phenotypic marker expression. This inherent PCa cell heterogeneity, e.g., in the expression of androgen receptor (AR), constitutes a barrier to the long-term therapeutic efficacy of AR-targeting therapies. Furthermore, tumor progression as well as therapeutic treatments induce PCa cell plasticity such that AR-positive PCa cells may turn into AR-negative cells and prostate tumors may switch lineage identity from adenocarcinomas to neuroendocrine-like tumors. This induced PCa cell plasticity similarly confers resistance to AR-targeting and other therapies. In this review, I first discuss PCa from the perspective of an abnormal organ development and deregulated cellular differentiation, and discuss the luminal progenitor cells as the likely cells of origin for PCa. I then focus on intrinsic PCa cell heterogeneity in treatment-naïve tumors with the presence of prostate cancer stem cells (PCSCs). I further elaborate on PCa cell plasticity induced by genetic alterations and therapeutic interventions, and present potential strategies to therapeutically tackle PCa cell heterogeneity and plasticity. My discussions will make it clear that, to achieve enduring clinical efficacy, both intrinsic PCa cell heterogeneity and induced PCa cell plasticity need to be targeted with novel combinatorial approaches.

Molecular Mechanisms Related with Oligometastatic Prostate Cancer-Is It Just a Matter of Numbers?

During the last decade, the body of knowledge regarding the oligometastatic state has increased exponentially. Several molecular frameworks have been established, aiding our understanding of metastatic spread caused by genetically unstable cells that adapt to a tissue environment which is distant from the primary tumor. In the current narrative review, we provide an overview of the current treatment landscape of oligometastatic cancer, focusing on the current biomarkers used in the identification of true oligometastatic disease and highlighting the impact of molecular imaging on stage shift in different scenarios. Finally, we address current and future directions regarding the use of genetic and epigenetic targeting treatments in oligometastatic prostate cancer.

Prostate Cancer Radiogenomics-From Imaging to Molecular Characterization

Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radiological assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-designed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.