Hart, Steven N

Abstract Known gene mutations account for approximately 50% of the hereditary risk for breast cancer. Moderate and low penetrance variants, discovered by genomic approaches, account for an as-yet-unknown proportion of the remaining heritability. A truncating mutation c.325C>T:p.Arg109* (R109X) in the ATP-dependent helicase ERCC3 was observed recurrently among exomes sequenced in BRCA wild-type, breast cancer–affected individuals of Ashkenazi Jewish ancestry. Modeling of the mutation in ERCC3-deficient or CRISPR/Cas9-edited cell lines showed a consistent pattern of reduced expression of the protein and concomitant hypomorphic functionality when challenged with UVC exposure or treatment with the DNA alkylating agent IlludinS. Overexpressing the mutant protein in ERCC3-deficient cells only partially rescued their DNA repair–deficient phenotype. Comparison of frequency of this recurrent mutation in over 6,500 chromosomes of breast cancer cases and 6,800 Ashkenazi controls showed significant association with breast cancer risk (ORBC = 1.53, ORER+ = 1.73), particularly for the estrogen receptor–positive subset (P < 0.007). Significance: A functionally significant recurrent ERCC3 mutation increased the risk for breast cancer in a genetic isolate. Mutated cell lines showed lower survival after in vitro exposure to DNA-damaging agents. Thus, similar to tumors arising in the background of homologous repair defects, mutations in nucleotide excision repair genes such as ERCC3 could constitute potential therapeutic targets in a subset of hereditary breast cancers. Cancer Discov; 6(11); 1267–75. ©2016 AACR. This article is highlighted in the In This Issue feature, p. 1197

Background BRCA1 interacting protein C-terminal helicase 1 (BRIP1) is one of the Fanconi Anaemia Complementation (FANC) group family of DNA repair proteins. Biallelic mutations in BRIP1 are responsible for FANC group J, and previous studies have also suggested that rare protein truncating variants in BRIP1 are associated with an increased risk of breast cancer. These studies have led to inclusion of BRIP1 on targeted sequencing panels for breast cancer risk prediction. Methods We evaluated a truncating variant, p.Arg798Ter (rs137852986), and 10 missense variants of BRIP1, in 48 144 cases and 43 607 controls of European origin, drawn from 41 studies participating in the Breast Cancer Association Consortium (BCAC). Additionally, we sequenced the coding regions of BRIP1 in 13 213 cases and 5242 controls from the UK, 1313 cases and 1123 controls from three population-based studies as part of the Breast Cancer Family Registry, and 1853 familial cases and 2001 controls from Australia. Results The rare truncating allele of rs137852986 was observed in 23 cases and 18 controls in Europeans in BCAC (OR 1.09, 95% CI 0.58 to 2.03, p=0.79). Truncating variants were found in the sequencing studies in 34 cases (0.21%) and 19 controls (0.23%) (combined OR 0.90, 95% CI 0.48 to 1.70, p=0.75). Conclusions These results suggest that truncating variants in BRIP1, and in particular p.Arg798Ter, are not associated with a substantial increase in breast cancer risk. Such observations have important implications for the reporting of results from breast cancer screening panels.

Abstract Many somatic mutations have been detected in pancreatic ductal adenocarcinoma (PDAC), leading to the identification of some key drivers of disease progression, but the involvement of large genomic rearrangements has often been overlooked. In this study, we performed mate pair sequencing (MPseq) on genomic DNA from 24 PDAC tumors, including 15 laser-captured microdissected PDAC and 9 patient-derived xenografts, to identify genome-wide rearrangements. Large genomic rearrangements with intragenic breakpoints altering key regulatory genes involved in PDAC progression were detected in all tumors. SMAD4, ZNF521, and FHIT were among the most frequently hit genes. Conversely, commonly reported genes with copy number gains, including MYC and GATA6, were frequently observed in the absence of direct intragenic breakpoints, suggesting a requirement for sustaining oncogenic function during PDAC progression. Integration of data from MPseq, exome sequencing, and transcriptome analysis of primary PDAC cases identified limited overlap in genes affected by both rearrangements and point mutations. However, significant overlap was observed in major PDAC-associated signaling pathways, with all PDAC exhibiting reduced SMAD4 expression, reduced SMAD-dependent TGFβ signaling, and increased WNT and Hedgehog signaling. The frequent loss of SMAD4 and FHIT due to genomic rearrangements strongly implicates these genes as key drivers of PDAC, thus highlighting the strengths of an integrated genomic and transcriptomic approach for identifying mechanisms underlying disease initiation and progression. Cancer Res; 76(3); 749–61. ©2015 AACR.

Abstract 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.

We used a target-centric strategy to identify transporter proteins upregulated in pancreatic ductal adenocarcinoma (PDAC) as potential targets for a functional imaging probe to complement existing anatomical imaging approaches. We performed transcriptomic profiling (microarray and RNASeq) on histologically confirmed primary PDAC tumors and normal pancreas tissue from 33 patients, including five patients whose tumors were not visible on computed tomography. Target expression was confirmed with immunohistochemistry on tissue microarrays from 94 PDAC patients. The best imaging target identified was SLC6A14 (a neutral and basic amino acid transporter). SLC6A14 was overexpressed at the transcriptional level in all patients and expressed at the protein level in 95% of PDAC tumors. Very little is known about the role of SLC6A14 in PDAC and our results demonstrate that this target merits further investigation as a candidate transporter for functional imaging of PDAC.

Purpose Recent advances in DNA sequencing have led to the development of breast cancer susceptibility gene panels for germline genetic testing of patients. We assessed the frequency of mutations in 17 predisposition genes, including BRCA1 and BRCA2, in a large cohort of patients with triple-negative breast cancer (TNBC) unselected for family history of breast or ovarian cancer to determine the utility of germline genetic testing for those with TNBC. Patients and Methods Patients with TNBC (N = 1,824) unselected for family history of breast or ovarian cancer were recruited through 12 studies, and germline DNA was sequenced to identify mutations. Results Deleterious mutations were identified in 14.6% of all patients. Of these, 11.2% had mutations in the BRCA1 (8.5%) and BRCA2 (2.7%) genes. Deleterious mutations in 15 other predisposition genes were detected in 3.7% of patients, with the majority observed in genes involved in homologous recombination, including PALB2 (1.2%) and BARD1, RAD51D, RAD51C, and BRIP1 (0.3% to 0.5%). Patients with TNBC with mutations were diagnosed at an earlier age (P < .001) and had higher-grade tumors (P = .01) than those without mutations. Conclusion Deleterious mutations in predisposition genes are present at high frequency in patients with TNBC unselected for family history of cancer. Mutation prevalence estimates suggest that patients with TNBC, regardless of age at diagnosis or family history of cancer, should be considered for germline genetic testing of BRCA1 and BRCA2. Although mutations in other predisposition genes are observed among patients with TNBC, better cancer risk estimates are needed before these mutations are used for clinical risk assessment in relatives.

Abstract BACKGROUND Next generation sequencing (NGS)-based assays continue to redefine the field of genetic testing. Owing to the complexity of the data, bioinformatics has become a necessary component in any laboratory implementing a clinical NGS test. CONTENT The computational components of an NGS-based work flow can be conceptualized as primary, secondary, and tertiary analytics. Each of these components addresses a necessary step in the transformation of raw data into clinically actionable knowledge. Understanding the basic concepts of these analysis steps is important in assessing and addressing the informatics needs of a molecular diagnostics laboratory. Equally critical is a familiarity with the regulatory requirements addressing the bioinformatics analyses. These and other topics are covered in this review article. SUMMARY Bioinformatics has become an important component in clinical laboratories generating, analyzing, maintaining, and interpreting data from molecular genetics testing. Given the rapid adoption of NGS-based clinical testing, service providers must develop informatics work flows that adhere to the rigor of clinical laboratory standards, yet are flexible to changes as the chemistry and software for analyzing sequencing data mature.

Significance The major portion of hereditary breast cancer still remains unexplained, and many susceptibility loci are yet to be found. Exome sequencing of 24 high-risk familial BRCA1/2 -negative breast cancer patients and further genotyping of a large sample set of breast/ovarian cancer cases and controls was used to discover previously unidentified susceptibility alleles and genes. A significant association of a FANCM nonsense mutation with breast cancer, especially triple-negative breast cancer, identifies FANCM as a breast cancer susceptibility gene. Identification of such risk alleles is expected to improve cancer risk assessment for breast cancer patients and families, and may lead to improvements in the prevention, early diagnosis, and treatment of cancer.

Abstract Motivation: Exome sequencing (exome-seq) data, which are typically used for calling exonic mutations, have also been utilized in detecting DNA copy number variations (CNVs). Despite the existence of several CNV detection tools, there is still a great need for a sensitive and an accurate CNV-calling algorithm with built-in QC steps, and does not require a paired reference for each sample. Results: We developed a novel method named PatternCNV, which (i) accounts for the read coverage variations between exons while leveraging the consistencies of this variability across different samples; (ii) reduces alignment BAM files to WIG format and therefore greatly accelerates computation; (iii) incorporates multiple QC measures designed to identify outlier samples and batch effects; and (iv) provides a variety of visualization options including chromosome, gene and exon-level views of CNVs, along with a tabular summarization of the exon-level CNVs. Compared with other CNV-calling algorithms using data from a lymphoma exome-seq study, PatternCNV has higher sensitivity and specificity. Availability and implementation: The software for PatternCNV is implemented using Perl and R, and can be used in Mac or Linux environments. Software and user manual are available at http://bioinformaticstools.mayo.edu/research/patterncnv/ , and R package at https://github.com/topsoil/patternCNV/ . Contact: Asmann.Yan@mayo.edu Supplementary information: Supplementary data are available at Bioinformatics online.