Multigene panels in prostate cancer risk assessment: a systematic review

Genetic Analysis Reveals the Prognostic Significance of the DNA Mismatch Repair Gene MSH2 in Advanced Prostate Cancer

Simple Summary

Androgen deprivation therapy is the most effective and widely used treatment for advanced prostate cancer, but its efficacy is highly variable among patients. Therefore, the identification of potent prognostic biomarkers is needed to determine patients at risk. We demonstrated that MSH2 rs1400633 was notably associated with patient survival during androgen deprivation therapy even after adjustment for clinical predictors and false discovery rate correction. Furthermore, our meta-analyses demonstrated that the MSH2 gene is highly expressed in prostate cancer and correlates positively with poor prognosis for this disease.

Abstract

DNA damage repair is frequently dysregulated in advanced prostate cancer and has been linked to cancer susceptibility and survival outcomes. The aim of this study is to assess the influence of genetic variants in DNA damage repair pathways on the prognosis of prostate cancer. Specifically, 167 single nucleotide polymorphisms (SNPs) in 18 DNA damage repair pathway genes were assessed for association with cancer-specific survival (CSS), overall survival (OS), and progression-free survival (PFS) in a cohort of 630 patients with advanced prostate cancer receiving androgen deprivation therapy. Univariate analysis identified four SNPs associated with CSS, four with OS, and two with PFS. However, only MSH2 rs1400633 C > G showed a significant association upon multivariate analysis and multiple testing adjustments (hazard ratio = 0.75, 95% confidence interval = 0.63–0.90, p = 0.002). Furthermore, rs1400633 risk allele C increased MSH2 expression in the prostate and other tissues, which correlated with more aggressive prostate cancer characteristics. A meta-analysis of 31 gene expression datasets revealed significantly higher MSH2 expression in prostate cancer than in normal tissues (p < 0.001), and this high expression was associated with a poor prognosis of prostate cancer (p = 0.002). In summary, we identified MSH2 rs1400633 as an independent prognostic biomarker for prostate cancer survival, and the association of MSH2 with cancer progression lends relevance to our findings.

Identification of a survival-related signature for sarcoma patients through integrated transcriptomic and proteomic profiling analyses

Sarcoma (SARC) represents a group of highly histological and molecular heterogeneous rare malignant tumors with poor prognosis. There are few proposed classifiers for predicting patient's outcome. The Cancer Proteome Atlas (TPCA) and The Cancer Genome Atlas (TCGA) databases provide multi-omics datasets that enable a comprehensive investigation for this disease. The proteomic expression profile of SARC patients along with the clinical information was downloaded. 55 proteins were found to be associated with overall survival (OS) of patients using univariate Cox regression analysis. We developed a prognostic risk signature that comprises seven proteins (AMPKALPHA, CHK1, S6, ARID1A, RBM15, ACETYLATUBULINLYS40, and MSH6) with robust predictive performance using multivariate Cox stepwise regression analysis. Additionally, the signature could be an independent prognostic predictor after adjusting for clinicopathological parameters. Patients in high-risk group also have worse progression free intervals (PFI) than that of patients in low-risk group, but not for disease free intervals (DFI). The signature was validated using transcriptomic profile of SARC patients from TCGA. Potential mechanisms between high- and low-risk groups were identified using differentially expressed genes (DEGs) analysis. These DEGs were primarily enriched in RAS and MPAK signaling pathways. The signature protein molecules are candidate biomarkers for SARC, and the analysis of computational biology in tumor infiltrating lymphocytes and immune checkpoint molecules revealed distinctly immune landscapes of high- and low-risk patients. Together, we constructed a prognostic signature for predicting outcomes for SARC integrating proteomic and transcriptomic profiles, this might have value in guiding clinical practice.

Improved detection of prostate cancer using a magneto-nanosensor assay for serum circulating autoantibodies

Purpose

To develop a magneto-nanosensor (MNS) based multiplex assay to measure protein and autoantibody biomarkers from human serum for prostate cancer (CaP) diagnosis.

Materials and methods

A 4-panel MNS autoantibody assay and a MNS protein assay were developed and optimized in our labs. Using these assays, serum concentration of six biomarkers including prostate-specific antigen (PSA) protein, free/total PSA ratio, as well as four autoantibodies against Parkinson disease 7 (PARK7), TAR DNA-binding protein 43 (TARDBP), Talin 1 (TLN1), and Caldesmon 1 (CALD1) and were analyzed. Human serum samples from 99 patients (50 with non-cancer and 49 with clinically localized CaP) were evaluated.

Results

The MNS assay showed excellent performance characteristics and no cross-reactivity. All autoantibody assays showed a statistically significant difference between CaP and non-cancer samples except for PARK7. The most significant difference was the combination of the four autoantibodies as a panel in addition to the free/total PSA ratio. This combination had the highest area under the curve (AUC)– 0.916 in ROC analysis.

Conclusions

Our results suggest that this autoantibody panel along with PSA and free PSA have potential to segregate patients without cancer from those with prostate cancer with higher sensitivity and specificity than PSA alone.

Germline Genetics of Prostate Cancer: Time to Incorporate Genetics into Early Detection Tools

BACKGROUND

Prostate cancer (PCa) remains the most common solid malignancy in men, and its prevalence makes understanding its heritability of paramount importance. To date, the most common factors used to estimate a man's risk of developing PCa are age, race, and family history. Despite recent advances in its utility in multiple malignancies (e.g., breast and colon cancer), genetic testing is still relatively underutilized in PCa.

CONTENT

Multiple highly penetrant genes (HPGs) and single-nucleotide polymorphisms (SNPs) have been show to increase a patient's risk of developing PCa. Mutations in the former, like DNA damage repair genes, can confer a 2- to 3-fold increased risk of developing PCa and can increase the risk of aggressive disease. Similarly, PCa-risk SNPs can be used to create risk scores (e.g., genetic or polygenic risk scores) that can be used to further stratify an individual's disease susceptibility. Specifically, these genetic risk scores can provide more specific estimates of a man's lifetime risk ranging up to >6-fold higher risk of PCa.

SUMMARY

It is becoming increasingly evident that in addition to the standard family history and race information, it is necessary to obtain genetic testing (including an assessment of HPG mutation status and genetic risk score) to provide a full risk assessment. The additional information derived thereby will improve current practices in PCa screening by risk-stratifying patients before initial prostate-specific antigen testing, determining a patient's frequency of visits, and even help identify potentially at-risk family members.

Clinical Proof-of-concept of a Novel Platform Utilizing Biopsy-derived Live Single Cells, Phenotypic Biomarkers, and Machine Learning Toward a Precision Risk Stratification Test for Prostate Cancer Grade Groups 1 and 2 (Gleason 3 + 3 and 3 + 4)

OBJECTIVE

To examine the ability of a novel live primary-cell phenotypic (LPCP) test to predict postsurgical adverse pathology (P-SAP) features and risk stratify patients based on SAP features in a blinded study utilizing radical prostatectomy (RP) surgical specimens.

METHODS

Two hundred fifty-one men undergoing RP were enrolled in a prospective, multicenter (10), and proof-of-concept study in the United States. Fresh prostate samples were taken from known areas of cancer in the operating room immediately after RP. Samples were shipped and tested at a central laboratory. Utilizing the LPCP test, a suite of phenotypic biomarkers was analyzed and quantified using objective machine vision software. Biomarkers were objectively ranked via machine learning-derived statistical algorithms (MLDSA) to predict postsurgical adverse pathological features. Sensitivity and specificity were determined by comparing blinded predictions and unblinded RP surgical pathology reports, training MLDSAs on 70% of biopsy cells and testing MLDSAs on the remaining 30% of biopsy cells across the tested patient population.

RESULTS

The LPCP test predicted adverse pathologies post-RP with area under the curve (AUC) via receiver operating characteristics analysis of greater than 0.80 and distinguished between Prostate Cancer Grade Groups 1, 2, and 3/Gleason Scores 3 + 3, 3 + 4, and 4 + 3. Further, LPCP derived-biomarker scores predicted Gleason pattern, stage, and adverse pathology with high precision-AUCs>0.80.

CONCLUSION

Using MLDSA-derived phenotypic biomarker scores, the LPCP test successfully risk stratified Prostate Cancer Grade Groups 1, 2, and 3 (Gleason 3 + 3 and 7) into distinct subgroups predicted to have surgical adverse pathologies or not with high performance (>0.85 AUC).

Live-cell phenotypic-biomarker microfluidic assay for the risk stratification of cancer patients via machine learning

The risk stratification of prostate cancer and breast cancer tumours from patients relies on histopathology, selective genomic testing, or on other methods employing fixed formalin tissue samples. However, static biomarker measurements from bulk fixed-tissue samples provide limited accuracy and actionability. Here, we report the development of a live-primary-cell phenotypic-biomarker assay with single-cell resolution, and its validation with prostate cancer and breast cancer tissue samples for the prediction of post-surgical adverse pathology. The assay includes a collagen-I/fibronectin extracellular-matrix formulation, dynamic live-cell biomarkers, a microfluidic device, machine-vision analysis and machine-learning algorithms, and generates predictive scores of adverse pathology at the time of surgery. Predictive scores for the risk stratification of 59 prostate cancer patients and 47 breast cancer patients, with values for area under the curve in receiver-operating-characteristic curves surpassing 80%, support the validation of the assay and its potential clinical applicability for the risk stratification of cancer patients.

MC1R variants as melanoma risk factors independent of at-risk phenotypic characteristics: a pooled analysis from the M-SKIP project

PURPOSE

Melanoma represents an important public health problem, due to its high case-fatality rate. Identification of individuals at high risk would be of major interest to improve early diagnosis and ultimately survival. The aim of this study was to evaluate whether MC1R variants predicted melanoma risk independently of at-risk phenotypic characteristics.

MATERIALS AND METHODS

Data were collected within an international collaboration - the M-SKIP project. The present pooled analysis included data on 3,830 single, primary, sporadic, cutaneous melanoma cases and 2,619 controls from seven previously published case-control studies. All the studies had information on MC1R gene variants by sequencing analysis and on hair color, skin phototype, and freckles, ie, the phenotypic characteristics used to define the red hair phenotype.

RESULTS

The presence of any MC1R variant was associated with melanoma risk independently of phenotypic characteristics (OR 1.60; 95% CI 1.36-1.88). Inclusion of MC1R variants in a risk prediction model increased melanoma predictive accuracy (area under the receiver-operating characteristic curve) by 0.7% over a base clinical model (P=0.002), and 24% of participants were better assessed (net reclassification index 95% CI 20%-30%). Subgroup analysis suggested a possibly stronger role of MC1R in melanoma prediction for participants without the red hair phenotype (net reclassification index: 28%) compared to paler skinned participants (15%).

CONCLUSION

The authors suggest that measuring the MC1R genotype might result in a benefit for melanoma prediction. The results could be a valid starting point to guide the development of scientific protocols assessing melanoma risk prediction tools incorporating the MC1R genotype.

Male Oncology Research and Education program for men at high risk for prostate cancer

Three groups of men are at high risk of developing prostate cancer: men with a strong family history of prostate cancer, men of West African or Caribbean ancestry, and men with a germline pathogenic variant in a prostate cancer-associated gene. Despite the fact that those men constitute a significant portion of the male population in North America, few recommendations for prostate cancer screening specific to them have been developed. For men at general population risk for prostate cancer, screening based on prostate-specific antigen (psa) has remained controversial despite the abundance of literature on the topic. As a result, recommendations made by major screening authorities are inconsistent (ranging from no psa screening to baseline psa screening at age 45), allowing physicians to pick and choose how to screen their patients. The Male Oncology Research and Education (more) program is an observational research program that serves as an academic platform for multiple research foci. For its participants, serum and dna are biobanked, medical information is collected, and contact for relevant research-related opportunities is maintained. This research program is paired with a specialized clinic called the more clinic, where men at high risk are regularly screened for prostate cancer in a standard approach that includes physical examination and serum psa measurement. In this article, we describe the goals, participant accrual to date, and projects specific to this unique program.

Exome-based genome-wide association study and risk assessment using genetic risk score to prostate cancer in the Korean population

PURPOSE

To investigate exome-wide genetic variants associated with prostate cancer (PCa) in Koreans and evaluate the discriminative ability by the genetic risk score (GRS).

PATIENTS AND METHODS

We prospectively recruited 1,001 PCa cases from a tertiary hospital and conducted a case-control study including 2,641 healthy men (Stage I). Participants were analyzed using HumanExome BeadChip. For the external validation, additionally enrolled 514 PCa cases and 548 controls (independent cohort) were analyzed for the identified single nucleotide polymorphisms (SNPs) of Stage I (Stage II). The GRS was calculated as a non-weighted sum of the risk allele counts and investigated for accuracy of prediction of PCa.

RESULTS

the mean age was 66.3 years, and the median level of prostate specific antigen (PSA) was 9.19 ng/ml in PCa cases. In Stage I, 4 loci containing 5 variants (rs1512268 on 8p21.2; rs1016343 and rs7837688 on 8q24.21; rs7501939 on 17q12, and rs2735839 on 19q13.33) were confirmed to reach exome-wide significance (p<8.3x10-7). In Stage II, the mean GRS was 4.23 ± 1.44 for the controls and 4.78 ± 1.43 for the cases. As a reference to GRS 4, GRS 6, 7 and 8 showed a statistically significant risk of PCa (OR=1.85, 2.11 and 3.34, respectively).

CONCLUSIONS

The five variants were validated to associate with PCa in firstly performed exome-wide study in Koreans. The addition of individualized calculated GRS effectively enhanced the accuracy of prediction. These results need to be validated in future studies.