Molecular Pathology of Prostate Cancer

Framework for the Pathology Workup of Metastatic Castration-Resistant Prostate Cancer Biopsies

Lineage plasticity and histologic transformation from prostate adenocarcinoma to neuroendocrine (NE) prostate cancer (NEPC) occur in up to 15% to 20% of patients with castration-resistant prostate cancer (CRPC) as a mechanism of treatment resistance and are associated with aggressive disease and poor prognosis. NEPC tumors typically display small cell carcinoma morphology with loss of androgen receptor (AR) expression and gain of NE lineage markers. However, there is a spectrum of phenotypes that are observed during the lineage plasticity process, and the clinical significance of mixed histologies or those that co-express AR and NE markers or lack all markers is not well defined. Translational research studies investigating NEPC have used variable definitions, making clinical trial design challenging. In this manuscript, we discuss the diagnostic workup of metastatic biopsies to help guide the reproducible classification of phenotypic CRPC subtypes. We recommend classifying CRPC tumors based on histomorphology (adenocarcinoma, small cell carcinoma, poorly differentiated carcinoma, other morphologic variant, or mixed morphology) and IHC markers with a priority for AR, NK3 homeobox 1, insulinoma-associated protein 1, synaptophysin, and cell proliferation based on Ki-67 positivity, with additional markers to be considered based on the clinical context. Ultimately, a unified workup of metastatic CRPC biopsies can improve clinical trial design and eventually practice.

Reporting tumor genomic test results to SEER registries via linkages

BACKGROUND

Precision medicine has become a mainstay of cancer care in recent years. The National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) Program has been an authoritative source of cancer statistics and data since 1973. However, tumor genomic information has not been adequately captured in the cancer surveillance data, which impedes population-based research on molecular subtypes. To address this, the SEER Program has developed and implemented a centralized process to link SEER registries' tumor cases with genomic test results that are provided by molecular laboratories to the registries.

METHODS

Data linkages were carried out following operating procedures for centralized linkages established by the SEER Program. The linkages used Match*Pro, a probabilistic linkage software, and were facilitated by the registries' trusted third party (an honest broker). The SEER registries provide to NCI limited datasets that undergo preliminary evaluation prior to their release to the research community.

RESULTS

Recently conducted genomic linkages included OncotypeDX Breast Recurrence Score, OncotypeDX Breast Ductal Carcinoma in Situ, OncotypeDX Genomic Prostate Score, Decipher Prostate Genomic Classifier, DecisionDX Uveal Melanoma, DecisionDX Preferentially Expressed Antigen in Melanoma, DecisionDX Melanoma, and germline tests results in Georgia and California SEER registries.

CONCLUSIONS

The linkages of cancer cases from SEER registries with genomic test results obtained from molecular laboratories offer an effective approach for data collection in cancer surveillance. By providing de-identified data to the research community, the NCI's SEER Program enables scientists to investigate numerous research inquiries.

Assessment of TROP2, CEACAM5 and DLL3 in metastatic prostate cancer: Expression landscape and molecular correlates

Therapeutic approaches targeting proteins on the surface of cancer cells have emerged as an important strategy for precision oncology. To capitalize on the potential impact of drugs targeting surface proteins, detailed knowledge about the expression patterns of the target proteins in tumor tissues is required. In castration-resistant prostate cancer (CRPC), agents targeting prostate-specific membrane antigen (PSMA) have demonstrated clinical activity. However, PSMA expression is lost in a significant number of CRPC tumors. The identification of additional cell surface targets is necessary to develop new therapeutic approaches. Here, we performed a comprehensive analysis of the expression heterogeneity and co-expression patterns of trophoblast cell-surface antigen 2 (TROP2), delta-like ligand 3 (DLL3), and carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) in CRPC samples from a rapid autopsy cohort. We show that DLL3 and CEACAM5 exhibit the highest expression in neuroendocrine prostate cancer (NEPC), while TROP2 is expressed across different CRPC molecular subtypes, except for NEPC. We further demonstrated that AR alterations were associated with higher expression of PSMA and TROP2. Conversely, PSMA and TROP2 expression was lower in RB1-altered tumors. In addition to genomic alterations, we show a tight correlation between epigenetic states, particularly histone H3 lysine 27 methylation (H3K27me3) at the transcriptional start site and gene body of TACSTD2 (encoding TROP2), DLL3, and CEACAM5, and their respective protein expression in CRPC patient-derived xenografts. Collectively, these findings provide insights into patterns and determinants of expression of TROP2, DLL3, and CEACAM5 with implications for the clinical development of cell surface targeting agents in CRPC.

Advanced Molecular Solutions for Cancer Therapy-The Good, the Bad, and the Ugly of the Biomarker Paradigm

Identifying the most effective actionable molecules whose "smart" manipulation might selectively kill/slow down/stop the proliferation of cancer cells, with few side effects on the normal cells of the tissue, was for decades the single major objective of countless investigators [...].

Renal Function Parameters in Distinctive Molecular Subtypes of Prostate Cancer

Prostate cancer is a prevalent malignancy in male patients, having diverse clinical outcomes. The follow-up of patients diagnosed with prostate cancer involves the evaluation of renal function, because its impairment reduces patient survival rates and adds complexity to their treatment and clinical care. This study aimed to investigate the relationship between renal function parameters and distinctive molecular subtypes of prostate adenocarcinomas, defined by the immunoexpression of the SPINK1, ERG, HOXB13, and TFF3 markers. The study group comprised 72 patients with prostate cancer and associated chronic kidney disease (CKD) who underwent radical prostatectomy. Histopathological, molecular, and renal parameters were analyzed. Patients were categorized based on ERG/SPINK1 and HOXB13/TFF3 status, and correlations with renal function and prognostic grade groups were assessed. The ERG+/SPINK1+ subgroup exhibited significantly higher postoperative CKD stages and serum creatinine levels compared to the ERG+/SPINK1- subgroup. This suggests an intricate relationship between SPINK1 overexpression and renal function dynamics. The HOXB13-/TFF3+ subgroup displayed higher preoperative serum creatinine levels and CKD stages than the HOXB13-/TFF3- subgroup, aligning with TFF3's potential role in renal function. Furthermore, the study revealed associations between CKD stages and prognostic grade groups in different molecular subtypes, pointing out an intricate interplay between renal function and tumor behavior. Although the molecular classification of prostate acinar ADK is not yet implemented, this research underscores the variability of renal function parameters in different molecular subtypes, offering potential insights into patient prognosis.

Novel device for dividing core needle biopsy specimens to provide paired mirror image-like tissues for genetic and pathological tests

In a world that seeks precision medicine, genetic testing is gaining importance in clinical decision making. We previously reported the utility of a novel tool for longitudinally dividing core needle biopsy (CNB) tissues into two filamentous tissues that can provide paired mirror image-like tissues (mirror-tissues) that spatially match each other. In this study, we investigated its application in gene panel testing in patients who underwent prostate CNB. Four hundred and forty-three biopsy cores were obtained from 40 patients. Of them, 361 biopsy cores (81.5%) were judged by a physician to be appropriate for dividing into two pieces using the new device, of which a histopathological diagnosis was successfully reached in 358 biopsy cores (99.2%). Among them, the quality and quantity of nucleic acid in 16 appropriately divided cores were assessed and found to be sufficient for gene panel testing, and histopathological diagnosis was successfully obtained from the remaining divided cores. The novel device for longitudinally-dividing CNB tissue provided mirror image-like paired-tissues for gene panel and pathology testing. The device might be a promising tool for obtaining genetic and molecular biological information, in addition to histopathological diagnosis, helping to advance personalized medicine.

Prognostic Values of Gene Copy Number Alterations in Prostate Cancer

Whilst risk prediction for individual prostate cancer (PCa) cases is of a high priority, the current risk stratification indices for PCa management have severe limitations. This study aimed to identify gene copy number alterations (CNAs) with prognostic values and to determine if any combination of gene CNAs could have risk stratification potentials. Clinical and genomic data of 500 PCa cases from the Cancer Genome Atlas stable were retrieved from the Genomic Data Commons and cBioPortal databases. The CNA statuses of a total of 52 genetic markers, including 21 novel markers and 31 previously identified potential prognostic markers, were tested for prognostic significance. The CNA statuses of a total of 51/52 genetic markers were significantly associated with advanced disease at an odds ratio threshold of ≥1.5 or ≤0.667. Moreover, a Kaplan-Meier test identified 27/52 marker CNAs which correlated with disease progression. A Cox Regression analysis showed that the amplification of MIR602 and deletions of MIR602, ZNF267, MROH1, PARP8, and HCN1 correlated with a progression-free survival independent of the disease stage and Gleason prognostic group grade. Furthermore, a binary logistic regression analysis identified twenty-two panels of markers with risk stratification potentials. The best model of 7/52 genetic CNAs, which included the SPOP alteration, SPP1 alteration, CCND1 amplification, PTEN deletion, CDKN1B deletion, PARP8 deletion, and NKX3.1 deletion, stratified the PCa cases into a localised and advanced disease with an accuracy of 70.0%, sensitivity of 85.4%, specificity of 44.9%, positive predictive value of 71.67%, and negative predictive value of 65.35%. This study validated prognostic gene level CNAs identified in previous studies, as well as identified new genetic markers with CNAs that could potentially impact risk stratification in PCa.

Value of machine learning-based transrectal multimodal ultrasound combined with PSA-related indicators in the diagnosis of clinically significant prostate cancer

Objective

To investigate the effect of transrectal multimodal ultrasound combined with serum prostate-specific antigen (PSA)-related indicators and machine learning for the diagnosis of clinically significant prostate cancer.

Methods

Based on Gleason score of postoperative pathological results, the subjects were divided into clinically significant prostate cancer groups(GS>6)and non-clinically significant prostate cancer groups(GS ≤ 6). The independent risk factors were obtained by univariate logistic analysis. Artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and K-nearest neighbor (KNN) machine learning models were combined with clinically significant prostate cancer risk factors to establish the machine learning model, calculate the model evaluation indicators, construct the receiver operating characteristic curve (ROC), and calculate the area under the curve (AUC).

Results

Independent risk factor items (P< 0.05) were entered into the machine learning model. A comparison of the evaluation indicators of the model and the area under the ROC curve showed the ANN model to be best at predicting clinically significant prostate cancer, with a sensitivity of 80%, specificity of 88.6%, F1 score of 0.897, and the AUC was 0.855.

Conclusion

Establishing a machine learning model by rectal multimodal ultrasound and combining it with PSA-related indicators has definite application value in predicting clinically significant prostate cancer.

Clonal relationships of adjacent Gleason pattern 3 and Gleason pattern 5 lesions in Gleason Scores 3+5=8 and 5+3=8

Genomic studies have demonstrated a high level of intra-tumoral heterogeneity in prostate cancer. There is strong evidence suggesting that individual tumor foci can arise as genetically distinct, clonally independent lesions. However, recent studies have also demonstrated that adjacent Gleason pattern (GP) 3 and GP4 lesions can originate from the same clone but follow divergent genetic and morphologic evolution. The clonal relationship of adjacent GP3 and GP5 lesions has thus far not been investigated. Here we analyzed a cohort of 14 cases-11 biopsy and 3 radical prostatectomy specimens-with a Gleason score of 3 + 5 = 8 or 5 + 3 = 8 present in the same biopsy or in a single dominant tumor nodule at radical prostatectomy. Clonal and subclonal relationships between GP3 and GP5 lesions were assessed using genetically validated immunohistochemical assays for ERG, PTEN, and P53. 9/14 (64%) cases showed ERG reactivity in both GP3 and GP5 lesions. Only 1/14 (7%) cases showed a discordant pattern with ERG staining present only in GP3. PTEN expression was lost in 2/14 (14%) cases with perfect concordance between GP5 and GP3. P53 nuclear reactivity was present in 1/14 (7%) case in both GP5 and GP3. This study provides first evidence that the majority of adjacent GP3 and GP5 lesions share driver alterations and are clonally related. In addition, we observed a lower-than-expected rate of PTEN loss in GP5 in the context of Gleason score 3 + 5 = 8 or 5 + 3 = 8 tumors.

The Immunotherapy and Immunosuppressive Signaling in Therapy-Resistant Prostate Cancer

Prostate cancer is one of the most common malignant tumors in men. Initially, it is androgen-dependent, but it eventually develops into castration-resistant prostate cancer (CRPC), which is incurable with current androgen receptor signaling target therapy and chemotherapy. Immunotherapy, specifically with immune checkpoint inhibitors, has brought hope for the treatment of this type of prostate cancer. Approaches such as vaccines, adoptive chimeric antigen receptor-T (CAR-T) cells, and immune checkpoint inhibitors have been employed to activate innate and adaptive immune responses to treat prostate cancer, but with limited success. Only Sipuleucel-T and the immune checkpoint inhibitor pembrolizumab are approved by the US FDA for the treatment of limited prostate cancer patients. Prostate cancer has a complex tumor microenvironment (TME) in which various immunosuppressive molecules and mechanisms coexist and interact. Additionally, prostate cancer is considered a “cold” tumor with low levels of tumor mutational burden, low amounts of antigen-presenting and cytotoxic T-cell activation, and high levels of immunosuppressive molecules including cytokines/chemokines. Thus, understanding the mechanisms of immunosuppressive signaling activation and immune evasion will help develop more effective treatments for prostate cancer. The purpose of this review is to summarize emerging advances in prostate cancer immunotherapy, with a particular focus on the molecular mechanisms that lead to immune evasion in prostate cancer. At the same time, we also highlight some potential therapeutic targets to provide a theoretical basis for the treatment of prostate cancer.

In situ expression of ERG protein in the context of tumor heterogeneity identifies prostate cancer patients with inferior prognosis

Prognostic biomarkers for prostate cancer are needed to improve prediction of disease course and guide treatment decisions. However, biomarker development is complicated by the common multifocality and heterogeneity of the disease. We aimed to determine the prognostic value of candidate biomarkers transcriptional regulator ERG and related ETS family genes, while considering tumor heterogeneity. In a multisampled, prospective, and treatment‐naïve radical prostatectomy cohort from one tertiary center (2010–2012, median follow‐up 8.1 years), we analyzed ERG protein (480 patients; 2047 tissue cores), and RNA of several ETS genes in a subcohort (165 patients; 778 fresh‐frozen tissue samples). Intra‐ and interfocal heterogeneity was identified in 29% and 33% (ERG protein) and 39% and 27% (ETS RNA) of patients, respectively. ERG protein and ETS RNA was identified exclusively in a nonindex tumor in 31% and 32% of patients, respectively. ERG protein demonstrated independent prognostic value in predicting biochemical (P = 0.04) and clinical recurrence (P = 0.004) and appeared to have greatest prognostic value for patients with Grade Groups 4–5. In conclusion, when heterogeneity is considered, ERG protein is a robust prognostic biomarker for prostate cancer.

Personalized 3-Gene Panel for Prostate Cancer Target Therapy

Many years and billions spent for research did not yet produce an effective answer to prostate cancer (PCa). Not only each human, but even each cancer nodule in the same tumor, has unique transcriptome topology. The differences go beyond the expression level to the expression control and networking of individual genes. The unrepeatable heterogeneous transcriptomic organization among men makes the quest for universal biomarkers and “fit-for-all” treatments unrealistic. We present a bioinformatics procedure to identify each patient’s unique triplet of PCa Gene Master Regulators (GMRs) and predict consequences of their experimental manipulation. The procedure is based on the Genomic Fabric Paradigm (GFP), which characterizes each individual gene by the independent expression level, expression variability and expression coordination with each other gene. GFP can identify the GMRs whose controlled alteration would selectively kill the cancer cells with little consequence on the normal tissue. The method was applied to microarray data on surgically removed prostates from two men with metastatic PCas (each with three distinct cancer nodules), and DU145 and LNCaP PCa cell lines. The applications verified that each PCa case is unique and predicted the consequences of the GMRs’ manipulation. The predictions are theoretical and need further experimental validation.