Systematic evaluation of underlying defects in DNA repair as an approach to case-only assessment of familial prostate cancer

Candidate variants in DNA replication and repair genes in early-onset renal cell carcinoma patients referred for germline testing

BACKGROUND

Early-onset renal cell carcinoma (eoRCC) is typically associated with pathogenic germline variants (PGVs) in RCC familial syndrome genes. However, most eoRCC patients lack PGVs in familial RCC genes and their genetic risk remains undefined.

METHODS

Here, we analyzed biospecimens from 22 eoRCC patients that were seen at our institution for genetic counseling and tested negative for PGVs in RCC familial syndrome genes.

RESULTS

Analysis of whole-exome sequencing (WES) data found enrichment of candidate pathogenic germline variants in DNA repair and replication genes, including multiple DNA polymerases. Induction of DNA damage in peripheral blood monocytes (PBMCs) significantly elevated numbers of [Formula: see text]H2AX foci, a marker of double-stranded breaks, in PBMCs from eoRCC patients versus PBMCs from matched cancer-free controls. Knockdown of candidate variant genes in Caki RCC cells increased [Formula: see text]H2AX foci. Immortalized patient-derived B cell lines bearing the candidate variants in DNA polymerase genes (POLD1, POLH, POLE, POLK) had DNA replication defects compared to control cells. Renal tumors carrying these DNA polymerase variants were microsatellite stable but had a high mutational burden. Direct biochemical analysis of the variant Pol δ and Pol η polymerases revealed defective enzymatic activities.

CONCLUSIONS

Together, these results suggest that constitutional defects in DNA repair underlie a subset of eoRCC cases. Screening patient lymphocytes to identify these defects may provide insight into mechanisms of carcinogenesis in a subset of genetically undefined eoRCCs. Evaluation of DNA repair defects may also provide insight into the cancer initiation mechanisms for subsets of eoRCCs and lay the foundation for targeting DNA repair vulnerabilities in eoRCC.

Therapeutic implications of germline vulnerabilities in DNA repair for precision oncology

DNA repair vulnerabilities are present in a significant proportion of cancers. Specifically, germline alterations in DNA repair not only increase cancer risk but are associated with treatment response and clinical outcomes. The therapeutic landscape of cancer has rapidly evolved with the FDA approval of therapies that specifically target DNA repair vulnerabilities. The clinical success of synthetic lethality between BRCA deficiency and poly(ADP-ribose) polymerase (PARP) inhibition has been truly revolutionary. Defective mismatch repair has been validated as a predictor of response to immune checkpoint blockade associated with durable responses and long-term benefit in many cancer patients. Advances in next generation sequencing technologies and their decreasing cost have supported increased genetic profiling of tumors coupled with germline testing of cancer risk genes in patients. The clinical adoption of panel testing for germline assessment in high-risk individuals has generated a plethora of genetic data, particularly on DNA repair genes. Here, we highlight the therapeutic relevance of germline aberrations in DNA repair to identify patients eligible for precision treatments such as PARP inhibitors (PARPis), immune checkpoint blockade, chemotherapy, radiation therapy and combined treatment. We also discuss emerging mechanisms that regulate DNA repair.

Biomarkers of therapeutic response with immune checkpoint inhibitors

Immune checkpoint inhibitors (ICPIs) have revolutionized the treatment paradigm of a wide range of malignancies with durable responses seen in even advanced, refractory cancers. Unfortunately, only a small proportion of patients with cancer derive meaningful benefit to ICPI therapy, and its use is also limited by significant immune and financial toxicities. Thus, there is a critical need for the development of biomarkers to reliably predict response to ICPI therapy. Only a few biomarkers are validated and approved for use with currently Food and Drug administration (FDA)-approved ICPIs. The development and broad application of biomarkers is limited by the lack of complete understanding of the complex interactions of tumor-host environment, the effect of immunotherapies on these already complex interactions, a lack of standardization and interpretation of biomarker assays across tumor types. Despite these challenges, the field of identifying predictive biomarkers is evolving at an unprecedented pace leaving the clinician responsible for identifying the patients that may derive optimal benefit from ICPIs. In this review, we provide clinicians with a current and practical update on the key, clinically relevant biomarkers of response to ICPIs. We categorize the current and emerging biomarkers of response to ICPIs in four major categories that govern anticancer response—the inflamed tumor, tumor antigens, immune suppression, and overall host environment.

The effect of neighborhood social environment on prostate cancer development in black and white men at high risk for prostate cancer

INTRODUCTION

Neighborhood socioeconomic (nSES) factors have been implicated in prostate cancer (PCa) disparities. In line with the Precision Medicine Initiative that suggests clinical and socioenvironmental factors can impact PCa outcomes, we determined whether nSES variables are associated with time to PCa diagnosis and could inform PCa clinical risk assessment.

MATERIALS AND METHODS

The study sample included 358 high risk men (PCa family history and/or Black race), aged 35-69 years, enrolled in an early detection program. Patient variables were linked to 78 nSES variables (employment, income, etc.) from previous literature via geocoding. Patient-level models, including baseline age, prostate specific antigen (PSA), digital rectal exam, as well as combined models (patient plus nSES variables) by race/PCa family history subgroups were built after variable reduction methods using Cox regression and LASSO machine-learning. Model fit of patient and combined models (AIC) were compared; p-values<0.05 were significant. Model-based high/low nSES exposure scores were calculated and the 5-year predicted probability of PCa was plotted against PSA by high/low neighborhood score to preliminarily assess clinical relevance.

RESULTS

In combined models, nSES variables were significantly associated with time to PCa diagnosis. Workers mode of transportation and low income were significant in White men with a PCa family history. Homeownership (%owner-occupied houses with >3 bedrooms) and unemployment were significant in Black men with and without a PCa family history, respectively. The 5-year predicted probability of PCa was higher in men with a high neighborhood score (weighted combination of significant nSES variables) compared to a low score (e.g., Baseline PSA level of 4ng/mL for men with PCa family history: White-26.7% vs 7.7%; Black-56.2% vs 29.7%).

DISCUSSION

Utilizing neighborhood data during patient risk assessment may be useful for high risk men affected by disparities. However, future studies with larger samples and validation/replication steps are needed.

A Systematic Literature Review of Whole Exome and Genome Sequencing Population Studies of Genetic Susceptibility to Cancer

The application of next-generation sequencing (NGS) technologies in cancer research has accelerated the discovery of somatic mutations; however, progress in the identification of germline variation associated with cancer risk is less clear. We conducted a systematic literature review of cancer genetic susceptibility studies that used NGS technologies at an exome/genome-wide scale to obtain a fuller understanding of the research landscape to date and to inform future studies. The variability across studies on methodologies and reporting was considerable. Most studies sequenced few high-risk (mainly European) families, used a candidate analysis approach, and identified potential cancer-related germline variants or genes in a small fraction of the sequenced cancer cases. This review highlights the importance of establishing consensus on standards for the application and reporting of variants filtering strategies. It also describes the progress in the identification of cancer-related germline variation to date. These findings point to the untapped potential in conducting studies with appropriately sized and racially diverse families and populations, combining results across studies and expanding beyond a candidate analysis approach to advance the discovery of genetic variation that accounts for the unexplained cancer heritability.

A Hierarchical Machine Learning Model to Discover Gleason Grade-Specific Biomarkers in Prostate Cancer

(1) Background:One of the most common cancers that affect North American men and men worldwide is prostate cancer. The Gleason score is a pathological grading system to examine the potential aggressiveness of the disease in the prostate tissue. Advancements in computing and next-generation sequencing technology now allow us to study the genomic profiles of patients in association with their different Gleason scores more accurately and effectively. (2) Methods: In this study, we used a novel machine learning method to analyse gene expression of prostate tumours with different Gleason scores, and identify potential genetic biomarkers for each Gleason group. We obtained a publicly-available RNA-Seq dataset of a cohort of 104 prostate cancer patients from the National Center for Biotechnology Information's (NCBI) Gene Expression Omnibus (GEO) repository, and categorised patients based on their Gleason scores to create a hierarchy of disease progression. A hierarchical model with standard classifiers in different Gleason groups, also known as nodes, was developed to identify and predict nodes based on their mRNA or gene expression. In each node, patient samples were analysed via class imbalance and hybrid feature selection techniques to build the prediction model. The outcome from analysis of each node was a set of genes that could differentiate each Gleason group from the remaining groups. To validate the proposed method, the set of identified genes were used to classify a second dataset of 499 prostate cancer patients collected from cBioportal. (3) Results: The overall accuracy of applying this novel method to the first dataset was 93.3%; the method was further validated to have 87% accuracy using the second dataset. This method also identified genes that were not previously reported as potential biomarkers for specific Gleason groups. In particular, PIAS3 was identified as a potential biomarker for Gleason score 4 + 3 = 7, and UBE2V2 for Gleason score 6. (4) Insight: Previous reports show that the genes predicted by this newly proposed method strongly correlate with prostate cancer development and progression. Furthermore, pathway analysis shows that both PIAS3 and UBE2V2 share similar protein interaction pathways, the JAK/STAT signaling process.

Existing and Emerging Biomarkers for Immune Checkpoint Immunotherapy in Solid Tumors

In the last few years, immunotherapy has transformed the way we treat solid tumors, including melanoma, lung, head neck, breast, renal, and bladder cancers. Durable responses and long-term survival benefit has been experienced by many cancer patients, with favorable toxicity profiles of immunotherapeutic agents relative to chemotherapy. Cures have become possible in some patients with metastatic disease. Additional approvals of immunotherapy drugs and in combination with other agents are anticipated in the near future. Multiple additional immunotherapy drugs are in earlier stages of clinical development, and their testing in additional tumor types is under way. Despite considerable early success and relatively fewer side effects, the majority of cancer patients do not respond to checkpoint inhibitors. Additionally, while the drugs are generally well tolerated, there is still the potential for significant, unpredictable and even fatal toxicity with these agents. Improved biomarkers may help to better select patients who are more likely to respond to these drugs. Two key biologically important predictive tissue biomarkers, specifically, PD-L1 and mismatch repair deficiency, have been FDA-approved in conjunction with the checkpoint inhibitor, pembrolizumab. Tumor mutation burden, another promising biomarker, is emerging in several tumor types, and may also soon receive approval. Finally, several other tissue and liquid biomarkers are emerging that could help guide single-agent immunotherapy and in combination with other agents. Of these, one promising investigational biomarker is alteration or deficiency in DNA damage response (DDR) pathways, with altered DDR observed in a broad spectrum of tumors. Here, we provide a critical overview of current, emerging, and investigational biomarkers in the context of response to immunotherapy in solid tumors.

A systematic review of the prevalence of DNA damage response gene mutations in prostate cancer

Several ongoing international prostate cancer (PC) clinical trials are exploring therapies that target the DNA damage response (DDR) pathway. This systematic review summarizes the prevalence of DDR mutation carriers in the unselected (general) PC and familial PC populations. A total of 11 electronic databases, 10 conference proceedings, and grey literature sources were searched from their inception to December 2017. Studies reporting the prevalence of somatic and/or germline DDR mutations were summarized. Metastatic PC (mPC), castration‑resistant PC (CRPC) and metastatic CRPC (mCRPC) subgroups were included. A total of 11,648 records were retrieved, and 80 studies (103 records) across all PC populations were included; 59 records were of unselected PC and 13 records of familial PC. Most data were available for DDR panels (n=12 studies), ataxia telangiectasia mutated (ATM; n=13), breast cancer susceptibility gene (BRCA)1 (n=14) and BRCA2 (n=20). ATM, BRCA2 and partner and localizer of BRCA2 (PALB2) had the highest mutation rates (≥4%). Median prevalence rates for DDR germline mutations were 18.6% in PC (range, 17.2‑19%; three studies, n=1,712), 11.6% in mPC (range, 11.4‑11.8%; two studies, n=1,261) and 8.3% in mCRPC (range, 7.5‑9.1%; two studies, n=738). Median prevalence rates for DDR somatic mutations were 10.7% in PC (range, 4.9‑22%; three studies, n=680), 13.2% in mPC (range, 10‑16.4%; two studies, n=105) and not reported (NR) in mCRPC. The prevalence of DDR germline and/or somatic mutations was 27% in PC (one study, n=221), 22.67% in mCRPC (one study, n=150) and NR in mPC. In familial PC, median mutation prevalence was 12.1% (range, 7.3‑16.9%) for germline DDR (two studies, n=315) and 3.7% (range, 1.3‑7.9%) for BRCA2 (six studies, n=945). In total, 88% of studies were at a high risk of bias. The prevalence of DDR gene mutations in PC varied widely within somatic subgroups depending on study size, genetic screening techniques, DDR mutation definition and PC diagnosis; somatic and/or germline DDR mutation prevalence was in the range of 23‑27% in PC. These findings support DDR mutation testing for all patients with PC (including those with mCRPC). With the advent of the latest clinical practice PC guidelines highlighting the importance of DDR mutation screening, and ongoing mCRPC clinical trials evaluating DDR mutation‑targeted drugs, future larger epidemiological studies are warranted to further quantify the international burden of DDR mutations in PC.

Molecular mechanisms of the preventable causes of cancer in the United States

Annually, there are 1.6 million new cases of cancer and nearly 600,000 cancer deaths in the United States alone. The public health burden associated with these numbers has motivated enormous research efforts into understanding the root causes of cancer. These efforts have led to the recognition that between 40% and 45% of cancers are associated with preventable risk factors and, importantly, have identified specific molecular mechanisms by which these exposures modify human physiology to induce or promote cancer. The increasingly refined knowledge of these mechanisms, which we summarize here, emphasizes the need for greater efforts toward primary cancer prevention through mitigation of modifiable risk factors. It also suggests exploitable avenues for improved secondary prevention (which includes the development of therapeutics designed for cancer interception and enhanced techniques for noninvasive screening and early detection) based on detailed knowledge of early neoplastic pathobiology. Such efforts would complement the current emphasis on the development of therapeutic approaches to treat established cancers and are likely to result in far greater gains in reducing morbidity and mortality.

Functional analysis of rare variants in mismatch repair proteins augments results from computation-based predictive methods

The cancer-predisposing Lynch Syndrome (LS) arises from germline mutations in DNA mismatch repair (MMR) genes, predominantly MLH1, MSH2, MSH6, and PMS2. A major challenge for clinical diagnosis of LS is the frequent identification of variants of uncertain significance (VUS) in these genes, as it is often difficult to determine variant pathogenicity, particularly for missense variants. Generic programs such as SIFT and PolyPhen-2, and MMR gene-specific programs such as PON-MMR and MAPP-MMR, are often used to predict deleterious or neutral effects of VUS in MMR genes. We evaluated the performance of multiple predictive programs in the context of functional biologic data for 15 VUS in MLH1, MSH2, and PMS2. Using cell line models, we characterized VUS predicted to range from neutral to pathogenic on mRNA and protein expression, basal cellular viability, viability following treatment with a panel of DNA-damaging agents, and functionality in DNA damage response (DDR) signaling, benchmarking to wild-type MMR proteins. Our results suggest that the MMR gene-specific classifiers do not always align with the experimental phenotypes related to DDR. Our study highlights the importance of complementary experimental and computational assessment to develop future predictors for the assessment of VUS.