Cancer Predisposition Sequencing Reporter (CPSR): a flexible variant report engine for high-throughput germline screening in cancer

AbstractThe value of high-throughput germline genetic testing is increasingly recognized in clinical cancer care. Disease-associated germline variants in cancer patients are important for risk management and surveillance, surgical decisions, and can also have major implications for treatment strategies since many are in DNA repair genes. With the increasing availability of high-throughput DNA sequencing in cancer clinics and research, there is thus a need to provide clinically oriented sequencing reports for germline variants and their potential therapeutic relevance on a per-patient basis. To meet this need we have developed the Cancer Predisposition Sequencing Reporter (CPSR), an open-source computational workflow that generates a structured report of germline variants identified in known cancer predisposition genes, highlighting markers of therapeutic, prognostic, and diagnostic relevance. A fully automated variant classification procedure based on more than 30 refined ACMG criteria represents an integral part of the workflow. Importantly, the set of cancer predisposition genes profiled in the report can be flexibly chosen from more than 40 virtual gene panels established by scientific experts, enabling customization of the report for different screening purposes and clinical contexts. The report can be configured to also list actionable secondary variant findings as recommended by ACMG, as well as the status of low-risk variants from genome-wide association studies in cancer. CPSR demonstrates superior sensitivity and comparable specificity for the detection of pathogenic variants when compared to existing algorithms. Technically, the tool is implemented in Python/R, and is freely available through Docker technology. Source code, documentation, example reports, and installation instructions are accessible via the project GitHub page: https://github.com/sigven/cpsr.

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Risk of Breast Cancer Among Carriers of Pathogenic Variants in Breast Cancer Predisposition Genes Varies by Polygenic Risk Score

Journal of Clinical Oncology ◽

10.1200/jco.20.01992 ◽

2021 ◽

pp. JCO.20.01992

Author(s):

Chi Gao ◽

Eric C. Polley ◽

Steven N. Hart ◽

Hongyan Huang ◽

Chunling Hu ◽

...

Keyword(s):

Breast Cancer ◽

General Population ◽

Association Studies ◽

Polygenic Risk Score ◽

Genome Wide Association Studies ◽

Polygenic Risk ◽

First Degree Relatives ◽

Pathogenic Variants ◽

Predisposition Genes ◽

Brca1 Brca2

PURPOSE This study assessed the joint association of pathogenic variants (PVs) in breast cancer (BC) predisposition genes and polygenic risk scores (PRS) with BC in the general population. METHODS A total of 26,798 non-Hispanic white BC cases and 26,127 controls from predominately population-based studies in the Cancer Risk Estimates Related to Susceptibility consortium were evaluated for PVs in BRCA1, BRCA2, ATM, CHEK2, PALB2, BARD1, BRIP1, CDH1, and NF1. PRS based on 105 common variants were created using effect estimates from BC genome-wide association studies; the performance of an overall BC PRS and estrogen receptor–specific PRS were evaluated. The odds of BC based on the PVs and PRS were estimated using penalized logistic regression. The results were combined with age-specific incidence rates to estimate 5-year and lifetime absolute risks of BC across percentiles of PRS by PV status and first-degree family history of BC. RESULTS The estimated lifetime risks of BC among general-population noncarriers, based on 10th and 90th percentiles of PRS, were 9.1%-23.9% and 6.7%-18.2% for women with or without first-degree relatives with BC, respectively. Taking PRS into account, more than 95% of BRCA1, BRCA2, and PALB2 carriers had > 20% lifetime risks of BC, whereas, respectively, 52.5% and 69.7% of ATM and CHEK2 carriers without first-degree relatives with BC, and 78.8% and 89.9% of those with a first-degree relative with BC had > 20% risk. CONCLUSION PRS facilitates personalization of BC risk among carriers of PVs in predisposition genes. Incorporating PRS into BC risk estimation may help identify > 30% of CHEK2 and nearly half of ATM carriers below the 20% lifetime risk threshold, suggesting the addition of PRS may prevent overscreening and enable more personalized risk management approaches.

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Risks and Function of Breast Cancer Susceptibility Alleles

Cancers ◽

10.3390/cancers13163953 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3953

Author(s):

Saeideh Torabi Dalivandan ◽

Jasmine Plummer ◽

Simon A. Gayther

Keyword(s):

Breast Cancer ◽

Risk Factors ◽

Disease Risk ◽

Cancer Susceptibility ◽

Association Studies ◽

Treatment Strategies ◽

Polygenic Risk Score ◽

Genome Wide Association Studies ◽

Degree Relative ◽

Risk Variants

Family history remains one of the strongest risk factors for breast cancer. It is well established that women with a first-degree relative affected by breast cancer are twice as likely to develop the disease themselves. Twins studies indicate that this is most likely due to shared genetics rather than shared epidemiological/lifestyle risk factors. Linkage and targeted sequencing studies have shown that rare high- and moderate-penetrance germline variants in genes involved in the DNA damage response (DDR) including BRCA1, BRCA2, PALB2, ATM, and TP53 are responsible for a proportion of breast cancer cases. However, breast cancer is a heterogeneous disease, and there is now strong evidence that different risk alleles can predispose to different subtypes of breast cancer. Here, we review the associations between the different genes and subtype-specificity of breast cancer based on the most comprehensive genetic studies published. Genome-wide association studies (GWAS) have also been used to identify an additional hereditary component of breast cancer, and have identified hundreds of common, low-penetrance susceptibility alleles. The combination of these low penetrance risk variants, summed as a polygenic risk score (PRS), can identify individuals across the spectrum of disease risk. However, there remains a substantial bottleneck between the discovery of GWAS-risk variants and their contribution to tumorigenesis mainly because the majority of these variants map to the non-protein coding genome. A range of functional genomic approaches are needed to identify the causal risk variants and target susceptibility genes and establish their underlying role in disease biology. We discuss how the application of these multidisciplinary approaches to understand genetic risk for breast cancer can be used to identify individuals in the population that may benefit from clinical interventions including screening for early detection and prevention, and treatment strategies to reduce breast cancer-related mortalities.

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Germline cancer predisposition variants and pediatric glioma: a population-based study in California

Neuro-Oncology ◽

10.1093/neuonc/noaa014 ◽

2020 ◽

Vol 22 (6) ◽

pp. 864-874 ◽

Cited By ~ 4

Author(s):

Ivo S Muskens ◽

Adam J de Smith ◽

Chenan Zhang ◽

Helen M Hansen ◽

Libby Morimoto ◽

...

Keyword(s):

Cell Cycle Control ◽

Population Level ◽

Cancer Predisposition ◽

Sequencing Data ◽

Pediatric Glioma ◽

Germline Variants ◽

Pathogenic Variants ◽

Predisposition Genes ◽

Pediatric Astrocytoma ◽

Pediatric Glioblastoma

Abstract Background Pediatric astrocytoma constitutes a majority of malignant pediatric brain tumors. Previous studies that investigated pediatric cancer predisposition have primarily been conducted in tertiary referral centers and focused on cancer predisposition genes. In this study, we investigated the contribution of rare germline variants to risk of malignant pediatric astrocytoma on a population level. Methods DNA samples were extracted from neonatal dried bloodspots from 280 pediatric astrocytoma patients (predominantly high grade) born and diagnosed in California and were subjected to whole-exome sequencing. Sequencing data were analyzed using agnostic exome-wide gene-burden testing and variant identification for putatively pathogenic variants in 175 a priori candidate cancer-predisposition genes. Results We identified 33 putatively pathogenic germline variants among 31 patients (11.1%) which were located in 24 genes largely involved in DNA repair and cell cycle control. Patients with pediatric glioblastoma were most likely to harbor putatively pathogenic germline variants (14.3%, N = 9/63). Five variants were located in tumor protein 53 (TP53), of which 4 were identified among patients with glioblastoma (6.3%, N = 4/63). The next most frequently mutated gene was neurofibromatosis 1 (NF1), in which putatively pathogenic variants were identified in 4 patients with astrocytoma not otherwise specified. Gene-burden testing also revealed that putatively pathogenic variants in TP53 were significantly associated with pediatric glioblastoma on an exome-wide level (odds ratio, 32.8, P = 8.04 × 10−7). Conclusion A considerable fraction of pediatric glioma patients, especially those of higher grade, harbor a putatively pathogenic variant in a cancer predisposition gene. Some of these variants may be clinically actionable or may warrant genetic counseling.

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Genetic Predisposition to Childhood Cancer in the Genomic Era

Annual Review of Genomics and Human Genetics ◽

10.1146/annurev-genom-083118-015415 ◽

2019 ◽

Vol 20 (1) ◽

pp. 241-263 ◽

Cited By ~ 5

Author(s):

Sharon E. Plon ◽

Philip J. Lupo

Keyword(s):

Childhood Cancer ◽

Pediatric Cancer ◽

Association Studies ◽

Cancer Predisposition ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Pathogenic Variants ◽

Pediatric Cancer Patients ◽

Genes Encoding ◽

Genome Analyses

Developments over the past five years have significantly advanced our ability to use genome-scale analyses—including high-density genotyping, transcriptome sequencing, exome sequencing, and genome sequencing—to identify the genetic basis of childhood cancer. This article reviews several key results from an expanding number of genomic studies of pediatric cancer: ( a) Histopathologic subtypes of cancers can be associated with a high incidence of germline predisposition, ( b) neurodevelopmental disorders or highly penetrant cancer predisposition syndromes can result from specific patterns of variation in genes encoding the SMARC family of chromatin remodelers, ( c) genome-wide association studies with relatively small pediatric cancer cohorts have successfully identified single-nucleotide polymorphisms with large effect sizes and provided insight into population differences in cancer risk, and ( d) multiple exome or genome analyses of unselected childhood cancer cohorts have yielded a 7–10% incidence of pathogenic variants in cancer predisposition genes. This work supports the increasing use of genomic sequencing in the care of pediatric cancer patients and at-risk family members.

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The Genetic Basis of Hypertriglyceridemia

Current Atherosclerosis Reports ◽

10.1007/s11883-021-00939-y ◽

2021 ◽

Vol 23 (8) ◽

Author(s):

Germán D. Carrasquilla ◽

Malene Revsbech Christiansen ◽

Tuomas O. Kilpeläinen

Keyword(s):

Genetic Basis ◽

Association Studies ◽

Cumulative Effect ◽

Genome Wide Association Studies ◽

Genetic Loci ◽

Risk Variants ◽

Genome Wide ◽

Severe Hypertriglyceridemia ◽

Increased Risk ◽

Polygenic Scores

Abstract Purpose of Review Hypertriglyceridemia is a common dyslipidemia associated with an increased risk of cardiovascular disease and pancreatitis. Severe hypertriglyceridemia may sometimes be a monogenic condition. However, in the vast majority of patients, hypertriglyceridemia is due to the cumulative effect of multiple genetic risk variants along with lifestyle factors, medications, and disease conditions that elevate triglyceride levels. In this review, we will summarize recent progress in the understanding of the genetic basis of hypertriglyceridemia. Recent Findings More than 300 genetic loci have been identified for association with triglyceride levels in large genome-wide association studies. Studies combining the loci into polygenic scores have demonstrated that some hypertriglyceridemia phenotypes previously attributed to monogenic inheritance have a polygenic basis. The new genetic discoveries have opened avenues for the development of more effective triglyceride-lowering treatments and raised interest towards genetic screening and tailored treatments against hypertriglyceridemia. Summary The discovery of multiple genetic loci associated with elevated triglyceride levels has led to improved understanding of the genetic basis of hypertriglyceridemia and opened new translational opportunities.

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Functional genomics of autoimmune diseases

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2019-216794 ◽

2021 ◽

pp. annrheumdis-2019-216794

Author(s):

Akari Suzuki ◽

Matteo Maurizio Guerrini ◽

Kazuhiko Yamamoto

Keyword(s):

Autoimmune Diseases ◽

Association Studies ◽

Linkage Disequilibrium Block ◽

Causal Variant ◽

Genome Wide Association Studies ◽

Coding Region ◽

Risk Variants ◽

Non Coding Rna ◽

Genome Wide ◽

Long Non Coding Rna

For more than a decade, genome-wide association studies have been applied to autoimmune diseases and have expanded our understanding on the pathogeneses. Genetic risk factors associated with diseases and traits are essentially causative. However, elucidation of the biological mechanism of disease from genetic factors is challenging. In fact, it is difficult to identify the causal variant among multiple variants located on the same haplotype or linkage disequilibrium block and thus the responsible biological genes remain elusive. Recently, multiple studies have revealed that the majority of risk variants locate in the non-coding region of the genome and they are the most likely to regulate gene expression such as quantitative trait loci. Enhancer, promoter and long non-coding RNA appear to be the main target mechanisms of the risk variants. In this review, we discuss functional genetics to challenge these puzzles.

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Genetic association studies of alterations in protein function expose recessive effects on cancer predisposition

Scientific Reports ◽

10.1038/s41598-021-94252-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Nadav Brandes ◽

Nathan Linial ◽

Michal Linial

Keyword(s):

Cancer Risk ◽

Protein Function ◽

Association Studies ◽

Genetic Effects ◽

Cancer Predisposition ◽

Genome Wide Association Studies ◽

Genetic Associations ◽

Functional Interpretation ◽

Gene Damage ◽

Genomic Regions

AbstractThe characterization of germline genetic variation affecting cancer risk, known as cancer predisposition, is fundamental to preventive and personalized medicine. Studies of genetic cancer predisposition typically identify significant genomic regions based on family-based cohorts or genome-wide association studies (GWAS). However, the results of such studies rarely provide biological insight or functional interpretation. In this study, we conducted a comprehensive analysis of cancer predisposition in the UK Biobank cohort using a new gene-based method for detecting protein-coding genes that are functionally interpretable. Specifically, we conducted proteome-wide association studies (PWAS) to identify genetic associations mediated by alterations to protein function. With PWAS, we identified 110 significant gene-cancer associations in 70 unique genomic regions across nine cancer types and pan-cancer. In 48 of the 110 PWAS associations (44%), estimated gene damage is associated with reduced rather than elevated cancer risk, suggesting a protective effect. Together with standard GWAS, we implicated 145 unique genomic loci with cancer risk. While most of these genomic regions are supported by external evidence, our results also highlight many novel loci. Based on the capacity of PWAS to detect non-additive genetic effects, we found that 46% of the PWAS-significant cancer regions exhibited exclusive recessive inheritance. These results highlight the importance of recessive genetic effects, without relying on familial studies. Finally, we show that many of the detected genes exert substantial cancer risk in the studied cohort determined by a quantitative functional description, suggesting their relevance for diagnosis and genetic consulting.

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Exome-Wide Pan-Cancer Analysis of Germline Variants in 8,719 Individuals Finds Little Evidence of Rare Variant Associations

Human Heredity ◽

10.1159/000519355 ◽

2021 ◽

pp. 1-10

Author(s):

Zoe Guan ◽

Ronglai Shen ◽

Colin B. Begg

Keyword(s):

Rare Variant ◽

Rare Variants ◽

Association Studies ◽

The Cancer Genome Atlas ◽

Considerable Proportion ◽

Genome Wide Association Studies ◽

Sequencing Data ◽

Risk Variants ◽

Cancer Types ◽

Pan Cancer

Background: Many cancer types show considerable heritability, and extensive research has been done to identify germline susceptibility variants. Linkage studies have discovered many rare high-risk variants, and genome-wide association studies (GWAS) have discovered many common low-risk variants. However, it is believed that a considerable proportion of the heritability of cancer remains unexplained by known susceptibility variants. The “rare variant hypothesis” proposes that much of the missing heritability lies in rare variants that cannot reliably be detected by linkage analysis or GWAS. Until recently, high sequencing costs have precluded extensive surveys of rare variants, but technological advances have now made it possible to analyze rare variants on a much greater scale. Objectives: In this study, we investigated associations between rare variants and 14 cancer types. Methods: We ran association tests using whole-exome sequencing data from The Cancer Genome Atlas (TCGA) and validated the findings using data from the Pan-Cancer Analysis of Whole Genomes Consortium (PCAWG). Results: We identified four significant associations in TCGA, only one of which was replicated in PCAWG (BRCA1 and ovarian cancer). Conclusions: Our results provide little evidence in favor of the rare variant hypothesis. Much larger sample sizes may be needed to detect undiscovered rare cancer variants.

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Primer on Hereditary Cancer Predisposition Genes Included Within Somatic Next-Generation Sequencing Panels

JCO Precision Oncology ◽

10.1200/po.18.00258 ◽

2019 ◽

pp. 1-11

Author(s):

Zade Akras ◽

Brandon Bungo ◽

Brandie H. Leach ◽

Jessica Marquard ◽

Manmeet Ahluwalia ◽

...

Keyword(s):

Hereditary Cancer ◽

Cancer Susceptibility ◽

Cancer Predisposition ◽

Comprehensive Overview ◽

Germline Variants ◽

Cancer Susceptibility Genes ◽

Germline Genes ◽

Number Of Genes ◽

Predisposition Genes ◽

Hereditary Cancer Predisposition

PURPOSE It has been estimated that 5% to 10% of cancers are due to hereditary causes. Recent data sets indicate that the incidence of hereditary cancer may be as high as 17.5% in patients with cancer, and a notable subset is missed if screening is solely by family history and current syndrome-based testing guidelines. Identification of germline variants has implications for both patients and their families. There is currently no comprehensive overview of cancer susceptibility genes or inclusion of these genes in commercially available somatic testing. We aimed to summarize genes linked to hereditary cancer and the somatic and germline panels that include such genes. METHODS Germline predisposition genes were chosen if commercially available for testing. Penetrance was defined as low, moderate, or high according to whether the gene conferred a 0% to 20%, 20% to 50%, or 50% to 100% lifetime risk of developing the cancer or, when percentages were not available, was estimated on the basis of existing literature descriptions. RESULTS We identified a total of 89 genes linked to hereditary cancer predisposition, and we summarized these genes alphabetically and by organ system. We considered four germline and six somatic commercially available panel tests and quantified the coverage of germline genes across them. Comparison between the number of genes that had germline importance and the number of genes included in somatic testing showed that many but not all germline genes are tested by frequently used somatic panels. CONCLUSION The inclusion of cancer-predisposing genes in somatic variant testing panels makes incidental germline findings likely. Although somatic testing can be used to screen for germline variants, this strategy is inadequate for comprehensive screening. Access to genetic counseling is essential for interpretation of germline implications of somatic testing and implementation of appropriate screening and follow-up.

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Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes

10.1101/011569 ◽

2014 ◽

Cited By ~ 1

Author(s):

Daniel S Himmelstein ◽

Sergio E Baranzini

Keyword(s):

Data Integration ◽

Protein Interactions ◽

Heterogeneous Network ◽

Association Studies ◽

Genome Wide Association Studies ◽

Genetic Associations ◽

Protein Coding ◽

Multiple Node ◽

Pathogenic Variants ◽

Disease Associated Genes

The first decade of Genome Wide Association Studies (GWAS) has uncovered a wealth of disease-associated variants. Two important derivations will be the translation of this information into a multiscale understanding of pathogenic variants, and leveraging existing data to increase the power of existing and future studies through prioritization. We explore edge prediction on heterogeneous networks—graphs with multiple node and edge types—for accomplishing both tasks. First we constructed a network with 18 node types—genes, diseases, tissues, pathophysiologies, and 14 MSigDB (molecular signatures database)collections—and 19 edge types from high-throughput publicly-available resources. From this network composed of 40,343 nodes and 1,608,168 edges, we extracted features that describe the topology between specific genes and diseases. Next, we trained a model from GWAS associations and predicted the probability of association between each protein-coding gene and each of 29 well-studied complex diseases. The model, which achieved 132-fold enrichment in precision at 10% recall, outperformed any individual domain, highlighting the benefit of integrative approaches. We identified pleiotropy, transcriptional signatures of perturbations, pathways, and protein interactions as fundamental mechanisms explaining pathogenesis. Our method successfully predicted the results (with AUROC = 0.79) from a withheld multiple sclerosis (MS) GWAS despite starting with only 13 previously associated genes. Finally, we combined our network predictions with statistical evidence of association to propose four novel MS genes, three of which (JAK2, REL, RUNX3) validated on the masked GWAS. Furthermore, our predictions provide biological support highlighting REL as the causal gene within its gene-rich locus. Users can browse all predictions online (http://het.io). Heterogeneous network edge prediction effectively prioritized genetic associations and provides a powerful new approach for data integration across multiple domains.

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