Raymond Moore

Associations Between Cancer Predisposition Testing Panel Genes and Breast Cancer

Importance Germline pathogenic variants in BRCA1 and BRCA2 predispose to an increased lifetime risk of breast cancer. However, the relevance of germline variants in other genes from multigene hereditary cancer testing panels is not well defined. Objective To determine the risks of breast cancer associated with germline variants in cancer predisposition genes. Design, Setting, and Participants A study population of 65 057 patients with breast cancer receiving germline genetic testing of cancer predisposition genes with hereditary cancer multigene panels. Associations between pathogenic variants in non-BRCA1 and non-BRCA2 predisposition genes and breast cancer risk were estimated in a case-control analysis of patients with breast cancer and Exome Aggregation Consortium reference controls. The women underwent testing between March 15, 2012, and June 30, 2016. Main Outcomes and Measures Breast cancer risk conferred by pathogenic variants in non-BRCA1 and non-BRCA2 predisposition genes. Results The mean (SD) age at diagnosis for the 65 057 women included in the analysis was 48.5 (11.1) years. The frequency of pathogenic variants in 21 panel genes identified in 41 611 consecutively tested white women with breast cancer was estimated at 10.2%. After exclusion of BRCA1, BRCA2, and syndromic breast cancer genes (CDH1, PTEN, and TP53), observed pathogenic variants in 5 of 16 genes were associated with high or moderately increased risks of breast cancer: ATM (OR, 2.78; 95% CI, 2.22-3.62), BARD1 (OR, 2.16; 95% CI, 1.31-3.63), CHEK2 (OR, 1.48; 95% CI, 1.31-1.67), PALB2 (OR, 7.46; 95% CI, 5.12-11.19), and RAD51D (OR, 3.07; 95% CI, 1.21-7.88). Conversely, variants in the BRIP1 and RAD51C ovarian cancer risk genes; the MRE11A, RAD50, and NBN MRN complex genes; the MLH1 and PMS2 mismatch repair genes; and NF1 were not associated with increased risks of breast cancer. Conclusions and Relevance This study establishes several panel genes as high- and moderate-risk breast cancer genes and provides estimates of breast cancer risk associated with pathogenic variants in these genes among individuals qualifying for clinical genetic testing.

Evaluation of ACMG-Guideline-Based Variant Classification of Cancer Susceptibility and Non-Cancer-Associated Genes in Families Affected by Breast Cancer.

Sequencing tests assaying panels of genes or whole exomes are widely available for cancer risk evaluation. However, methods for classification of variants resulting from this testing are not well studied. We evaluated the ability of a variant-classification methodology based on American College of Medical Genetics and Genomics (ACMG) guidelines to define the rate of mutations and variants of uncertain significance (VUS) in 180 medically relevant genes, including all ACMG-designated reportable cancer and non-cancer-associated genes, in individuals who met guidelines for hereditary cancer risk evaluation. We performed whole-exome sequencing in 404 individuals in 253 families and classified 1,640 variants. Potentially clinically actionable (likely pathogenic [LP] or pathogenic [P]) versus nonactionable (VUS, likely benign, or benign) calls were 95% concordant with locus-specific databases and Clinvar. LP or P mutations were identified in 12 of 25 breast cancer susceptibility genes in 26 families without identified BRCA1/2 mutations (11%). Evaluation of 84 additional genes associated with autosomal-dominant cancer susceptibility identified LP or P mutations in only two additional families (0.8%). However, individuals from 10 of 253 families (3.9%) had incidental LP or P mutations in 32 non-cancer-associated genes, and 9% of individuals were monoallelic carriers of a rare LP or P mutation in 39 genes associated with autosomal-recessive cancer susceptibility. Furthermore, 95% of individuals had at least one VUS. In summary, these data support the clinical utility of ACMG variant-classification guidelines. Additionally, evaluation of extended panels of cancer-associated genes in breast/ovarian cancer families leads to only an incremental clinical benefit but substantially increases the complexity of the results.

Prevalence of Pathogenic Mutations in Cancer Predisposition Genes among Pancreatic Cancer Patients

The prevalence of germline pathogenic mutations in a comprehensive panel of cancer predisposition genes is not well-defined for patients with pancreatic ductal adenocarcinoma (PDAC). To estimate the frequency of mutations in a panel of 22 cancer predisposition genes, 96 patients unselected for a family history of cancer who were recruited to the Mayo Clinic Pancreatic Cancer patient registry over a 12-month period were screened by next-generation sequencing. Fourteen pathogenic mutations in 13 patients (13.5%) were identified in eight genes: four in ATM, two in BRCA2, CHEK2, and MSH6, and one in BARD1, BRCA1, FANCM, and NBN. These included nine mutations (9.4%) in established pancreatic cancer genes. Three mutations were found in patients with a first-degree relative with PDAC, and 10 mutations were found in patients with first- or second-degree relatives with breast, pancreas, colorectal, ovarian, or endometrial cancers. These results suggest that a substantial proportion of patients with PDAC carry germline mutations in predisposition genes associated with other cancers and that a better understanding of pancreatic cancer risk will depend on evaluation of families with broad constellations of tumors. These findings highlight the need for recommendations governing germline gene-panel testing of patients with pancreatic cancer. Cancer Epidemiol Biomarkers Prev; 25(1); 207–11. ©2015 AACR.

SoftSearch: integration of multiple sequence features to identify breakpoints of structural variations.

Background Structural variation (SV) represents a significant, yet poorly understood contribution to an individual’s genetic makeup. Advanced next-generation sequencing technologies are widely used to discover such variations, but there is no single detection tool that is considered a community standard. In an attempt to fulfil this need, we developed an algorithm, SoftSearch, for discovering structural variant breakpoints in Illumina paired-end next-generation sequencing data. SoftSearch combines multiple strategies for detecting SV including split-read, discordant read-pair, and unmated pairs. Co-localized split-reads and discordant read pairs are used to refine the breakpoints. Results We developed and validated SoftSearch using real and synthetic datasets. SoftSearch’s key features are 1) not requiring secondary (or exhaustive primary) alignment, 2) portability into established sequencing workflows, and 3) is applicable to any DNA-sequencing experiment (e.g. whole genome, exome, custom capture, etc.). SoftSearch identifies breakpoints from a small number of soft-clipped bases from split reads and a few discordant read-pairs which on their own would not be sufficient to make an SV call. Conclusions We show that SoftSearch can identify more true SVs by combining multiple sequence features. SoftSearch was able to call clinically relevant SVs in the BRCA2 gene not reported by other tools while offering significantly improved overall performance.

The Biological Reference Repository (BioR): a rapid and flexible system for genomics annotation

Motivation: The Biological Reference Repository (BioR) is a toolkit for annotating variants. BioR stores public and user-specific annotation sources in indexed JSON-encoded flat files (catalogs). The BioR toolkit provides the functionality to combine and retrieve annotation from these catalogs via the command-line interface. Several catalogs from commonly used annotation sources and instructions for creating user-specific catalogs are provided. Commands from the toolkit can be combined with other UNIX commands for advanced annotation processing. We also provide instructions for the development of custom annotation pipelines. Availability and implementation: The package is implemented in Java and makes use of external tools written in Java and Perl. The toolkit can be executed on Mac OS X 10.5 and above or any Linux distribution. The BioR application, quickstart, and user guide documents and many biological examples are available at http://bioinformaticstools.mayo.edu. Contact: Kocher.JeanPierre@mayo.edu Supplementary information: Supplementary data are available at Bioinformatics online.

HiChIP: a high-throughput pipeline for integrative analysis of ChIP-Seq data

BackgroundChromatin immunoprecipitation (ChIP) followed by next-generation sequencing (ChIP-Seq) has been widely used to identify genomic loci of transcription factor (TF) binding and histone modifications. ChIP-Seq data analysis involves multiple steps from read mapping and peak calling to data integration and interpretation. It remains challenging and time-consuming to process large amounts of ChIP-Seq data derived from different antibodies or experimental designs using the same approach. To address this challenge, there is a need for a comprehensive analysis pipeline with flexible settings to accelerate the utilization of this powerful technology in epigenetics research.ResultsWe have developed a highly integrative pipeline, termed HiChIP for systematic analysis of ChIP-Seq data. HiChIP incorporates several open source software packages selected based on internal assessments and published comparisons. It also includes a set of tools developed in-house. This workflow enables the analysis of both paired-end and single-end ChIP-Seq reads, with or without replicates for the characterization and annotation of both punctate and diffuse binding sites. The main functionality of HiChIP includes: (a) read quality checking; (b) read mapping and filtering; (c) peak calling and peak consistency analysis; and (d) result visualization. In addition, this pipeline contains modules for generating binding profiles over selected genomic features, de novo motif finding from transcription factor (TF) binding sites and functional annotation of peak associated genes.ConclusionsHiChIP is a comprehensive analysis pipeline that can be configured to analyze ChIP-Seq data derived from varying antibodies and experiment designs. Using public ChIP-Seq data we demonstrate that HiChIP is a fast and reliable pipeline for processing large amounts of ChIP-Seq data.

The contribution of pathogenic variants in breast cancer susceptibility genes to familial breast cancer risk

Understanding the gene-specific risks for development of breast cancer will lead to improved clinical care for those carrying germline mutations in cancer predisposition genes. We sought to detail the spectrum of mutations and refine risk estimates for known and proposed breast cancer susceptibility genes. Targeted massively-parallel sequencing was performed to identify mutations and copy number variants in 26 known or proposed breast cancer susceptibility genes in 2134 BRCA1/2-negative women with familial breast cancer (proband with breast cancer and a family history of breast or ovarian cancer) from a largely European–Caucasian multi-institutional cohort. Case–control analysis was performed comparing the frequency of internally classified mutations identified in familial breast cancer women to Exome Aggregation Consortium controls. Mutations were identified in 8.2% of familial breast cancer women, including mutations in high-risk (odds ratio > 5) (1.4%) and moderate-risk genes (2 < odds ratio < 5) (2.9%). The remaining familial breast cancer women had mutations in proposed breast cancer genes (1.7%), Lynch syndrome genes (0.5%), and six cases had two mutations (0.3%). Case–control analysis demonstrated associations with familial breast cancer for ATM, PALB2, and TP53 mutations (odds ratio > 3.0, p < 10−4), BARD1 mutations (odds ratio = 3.2, p = 0.012), and CHEK2 truncating mutations (odds ratio = 1.6, p = 0.041). Our results demonstrate that approximately 4.7% of BRCA1/2 negative familial breast cancer women have mutations in genes statistically associated with breast cancer. We classified PALB2 and TP53 as high-risk, ATM and BARD1 as moderate risk, and CHEK2 truncating mutations as low risk breast cancer predisposition genes. This study demonstrates that large case–control studies are needed to fully evaluate the breast cancer risks associated with mutations in moderate-risk and proposed susceptibility genes.Familial breast cancer: Pinning down susceptibility genes beyond BRCAWomen with the heritable form of breast cancer often harbor mutations in cancer-linked genes other than the usual suspects, BRCA1 and BRCA2. Slavin, Maxwell, Lilyquist, Joseph, and colleagues from major national and international cancer centers studied 2134 women with familial breast cancer who tested negative for BRCA1/2 gene mutations. The researchers sequenced 26 known or proposed breast cancer susceptibility genes and found mutations in approximately 1 in every 12 of the study subjects. They then further broke down the susceptibility genes into those that confer high-, moderate- or low-risk—although not all the proposed breast cancer genes reached statistical significance and, as such, their clinical importance remains unclear. The results support adding some of the high- and moderate-risk genes to multi-panel diagnostic tests that aim to determine the likelihood of a women developing heritable breast cancer.

Association between inherited germline mutations in cancer predisposition genes and risk of pancreatic cancer

Importance Individuals genetically predisposed to pancreatic cancer may benefit from early detection. Genes that predispose to pancreatic cancer and the risks of pancreatic cancer associated with mutations in these genes are not well defined. Objective To determine whether inherited germline mutations in cancer predisposition genes are associated with increased risks of pancreatic cancer. Design, Setting, and Participants Case-control analysis to identify pancreatic cancer predisposition genes; longitudinal analysis of patients with pancreatic cancer for prognosis. The study included 3030 adults diagnosed as having pancreatic cancer and enrolled in a Mayo Clinic registry between October 12, 2000, and March 31, 2016, with last follow-up on June 22, 2017. Reference controls were 123 136 individuals with exome sequence data in the public Genome Aggregation Database and 53 105 in the Exome Aggregation Consortium database. Exposures Individuals were classified based on carrying a deleterious mutation in cancer predisposition genes and having a personal or family history of cancer. Main Outcomes and Measures Germline mutations in coding regions of 21 cancer predisposition genes were identified by sequencing of products from a custom multiplex polymerase chain reaction–based panel; associations of genes with pancreatic cancer were assessed by comparing frequency of mutations in genes of pancreatic cancer patients with those of reference controls. Results Comparing 3030 case patients with pancreatic cancer (43.2% female; 95.6% non-Hispanic white; mean age at diagnosis, 65.3 [SD, 10.7] years) with reference controls, significant associations were observed between pancreatic cancer and mutations in CDKN2A (0.3% of cases and 0.02% of controls; odds ratio [OR], 12.33; 95% CI, 5.43-25.61); TP53 (0.2% of cases and 0.02% of controls; OR, 6.70; 95% CI, 2.52-14.95); MLH1 (0.13% of cases and 0.02% of controls; OR, 6.66; 95% CI, 1.94-17.53); BRCA2 (1.9% of cases and 0.3% of controls; OR, 6.20; 95% CI, 4.62-8.17); ATM (2.3% of cases and 0.37% of controls; OR, 5.71; 95% CI, 4.38-7.33); and BRCA1 (0.6% of cases and 0.2% of controls; OR, 2.58; 95% CI, 1.54-4.05). Conclusions and Relevance In this case-control study, mutations in 6 genes associated with pancreatic cancer were found in 5.5% of all pancreatic cancer patients, including 7.9% of patients with a family history of pancreatic cancer and 5.2% of patients without a family history of pancreatic cancer. Further research is needed for replication in other populations.

Label-Free Proteomic Analysis of Protein Changes in the Striatum during Chronic Ethanol Use and Early Withdrawal.

The molecular mechanisms underlying the neuronal signaling changes in alcohol addiction and withdrawal are complex and multifaceted. The cortico-striatal circuit is highly implicated in these processes, and the striatum plays a significant role not only in the early stages of addiction, but in the developed-addictive state as well, including withdrawal symptoms. Transcriptional analysis is a useful method for determining changes in gene expression, however, the results do not always accurately correlate with protein levels. In this study, we employ label-free proteomic analysis to determine changes in protein expression within the striatum during chronic ethanol use and early withdrawal. The striatum, composed primarily of medium spiny GABAergic neurons, glutamatergic and dopaminergic nerve terminals and astrocytes, is relatively homogeneous for proteomic analysis. We were able to analyze more than 5000 proteins from both the dorsal (caudate and putamen) and ventral (nucleus accumbens) striatum and identified significant changes following chronic intermittent ethanol exposure and acute (8 h) withdrawal compared to ethanol naïve and ethanol exposure groups respectively. Our results showed significant changes in proteins involved in glutamate and opioid peptide signaling, and also uncovered novel pathways including mitochondrial function and lipid/cholesterol metabolism, as revealed by changes in electron transport chain proteins and RXR activation pathways. These results will be useful in the development of novel treatments for alcohol withdrawal and thereby aid in recovery from alcohol use disorder.

PANDA: pathway and annotation explorer for visualizing and interpreting gene-centric data

Objective. Bringing together genomics, transcriptomics, proteomics, and other -omics technologies is an important step towards developing highly personalized medicine. However, instrumentation has advances far beyond expectations and now we are able to generate data faster than it can be interpreted. Materials and Methods. We have developed PANDA (Pathway AND Annotation) Explorer, a visualization tool that integrates gene-level annotation in the context of biological pathways to help interpret complex data from disparate sources. PANDA is a web-based application that displays data in the context of well-studied pathways like KEGG, BioCarta, and PharmGKB. PANDA represents data/annotations as icons in the graph while maintaining the other data elements (i.e., other columns for the table of annotations). Custom pathways from underrepresented diseases can be imported when existing data sources are inadequate. PANDA also allows sharing annotations among collaborators. Results. In our first use case, we show how easy it is to view supplemental data from a manuscript in the context of a user’s own data. Another use-case is provided describing how PANDA was leveraged to design a treatment strategy from the somatic variants found in the tumor of a patient with metastatic sarcomatoid renal cell carcinoma. Conclusion. PANDA facilitates the interpretation of gene-centric annotations by visually integrating this information with context of biological pathways. The application can be downloaded or used directly from our website: http://bioinformaticstools.mayo.edu/research/panda-viewer/.