Strong functional data for pathogenicity or neutrality classify BRCA2 DNA-binding-domain variants of uncertain significance.

Determination of the clinical relevance of rare germline variants of uncertain significance (VUSs) in the BRCA2 cancer predisposition gene remains a challenge as a result of limited availability of data for use in classification models. However, laboratory-based functional data derived from validated functional assays of known sensitivity and specificity may influence the interpretation of VUSs. We evaluated 252 missense VUSs from the BRCA2 DNA-binding domain by using a homology-directed DNA repair (HDR) assay and identified 90 as non-functional and 162 as functional. The functional assay results were integrated with other available data sources into an ACMG/AMP rules-based classification framework used by a hereditary cancer testing laboratory. Of the 186 missense variants observed by the testing laboratory, 154 were classified as VUSs without functional data. However, after applying protein functional data, 86% (132/154) of the VUSs were reclassified as either likely pathogenic/pathogenic (39/132) or likely benign/benign (93/132), which impacted testing results for 1,900 individuals. These results indicate that validated functional assay data can have a substantial impact on VUS classification and associated clinical management for many individuals with inherited alterations in BRCA2.

A Population-Based Study of Genes Previously Implicated in Breast Cancer.

BACKGROUND: Population-based estimates of the risk of breast cancer associated with germline pathogenic variants in cancer-predisposition genes are critically needed for risk assessment and management in women with inherited pathogenic variants. METHODS: In a population-based case-control study, we performed sequencing using a custom multigene amplicon-based panel to identify germline pathogenic variants in 28 cancer-predisposition genes among 32,247 women with breast cancer (case patients) and 32,544 unaffected women (controls) from population-based studies in the Cancer Risk Estimates Related to Susceptibility (CARRIERS) consortium. Associations between pathogenic variants in each gene and the risk of breast cancer were assessed. RESULTS: Pathogenic variants in 12 established breast cancer-predisposition genes were detected in 5.03% of case patients and in 1.63% of controls. Pathogenic variants in BRCA1 and BRCA2 were associated with a high risk of breast cancer, with odds ratios of 7.62 (95% confidence interval [CI], 5.33 to 11.27) and 5.23 (95% CI, 4.09 to 6.77), respectively. Pathogenic variants in PALB2 were associated with a moderate risk (odds ratio, 3.83; 95% CI, 2.68 to 5.63). Pathogenic variants in BARD1, RAD51C, and RAD51D were associated with increased risks of estrogen receptor-negative breast cancer and triple-negative breast cancer, whereas pathogenic variants in ATM, CDH1, and CHEK2 were associated with an increased risk of estrogen receptor-positive breast cancer. Pathogenic variants in 16 candidate breast cancer-predisposition genes, including the c.657_661del5 founder pathogenic variant in NBN, were not associated with an increased risk of breast cancer. CONCLUSIONS: This study provides estimates of the prevalence and risk of breast cancer associated with pathogenic variants in known breast cancer-predisposition genes in the U.S. population. These estimates can inform cancer testing and screening and improve clinical management strategies for women in the general population with inherited pathogenic variants in these genes. (Funded by the National Institutes of Health and the Breast Cancer Research Foundation.).

Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer.

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Whole-exome sequencing of non-BRCA1/BRCA2 mutation carrier cases at high-risk for hereditary breast/ovarian cancer.

The current study aimed to identify new breast and/or ovarian cancer predisposition genes. For that, whole-exome sequencing (WES) was performed in the germline DNA of 52 non-BRCA1/BRCA2/TP53 mutation carrier women at high-risk for hereditary breast and ovarian cancer (HBOC). All variants were classified using information from population and disease specific databases, in silico prediction tools and the American College of Medical Genetics and Genomics (ACMG) criteria. Loss of heterozygosity (LOH) of tumor samples and segregation analyses were performed whenever possible. The variants identified were investigated in a second, independent cohort of 17 BC cases. Pathogenic/Likely Pathogenic variants were identified in known cancer genes such as CHEK2, MUTYH, PMS2, and RAD51C. Rare and potentially pathogenic variants were identified in DNA repair genes (FAN1, POLQ, and RAD54L) and other cancer-related genes such as DROSHA and SLC34A2. Interestingly, the variant c.149T>G in the FAN1 gene was identified in two unrelated families, and exhibited LOH in the tumor tissue of one of them. In conclusion, this is the largest Brazilian WES study involving families at high-risk for HBOC which has brought novel insights into the role of potentially new genetic risk factors for hereditary breast and ovarian cancer.

Mutation prevalence tables for hereditary cancer derived from multigene panel testing.

Multigene panel testing for cancer predisposition mutations is becoming routine in clinical care. However, the gene content of panels offered by testing laboratories vary significantly, and data on mutation detection rates by gene and by the panel is limited, causing confusion among clinicians on which test to order. Using results from 147,994 multigene panel tests conducted at Ambry Genetics, we built an interactive prevalence tool to explore how differences in ethnicity, age of onset, and personal and family history of different cancers affect the prevalence of pathogenic mutations in 31 cancer predisposition genes, across various clinically available hereditary cancer gene panels. Over 13,000 mutation carriers were identified in this high-risk population. Most were non-Hispanic white (74%, n = 109,537), but also Black (n = 10,875), Ashkenazi Jewish (n = 10,464), Hispanic (n = 10,028), and Asian (n = 7,090). The most prevalent cancer types were breast (50%), ovarian (6.6%), and colorectal (4.7%), which is expected based on genetic testing guidelines and clinician referral for testing. The Hereditary Cancer Multi-Gene Panel Prevalence Tool presented here can be used to provide insight into the prevalence of mutations on a per-gene and per-multigene panel basis, while conditioning on multiple custom phenotypic variables to include race and cancer type.

Classification of variants of uncertain significance in BRCA1 and BRCA2 using personal and family history of cancer from individuals in a large hereditary cancer multigene panel testing cohort.

PURPOSE: Genetic testing of individuals often results in identification of genomic variants of unknown significance (VUS). Multiple lines of evidence are used to help determine the clinical significance of these variants. METHODS: We analyzed ~138,000 individuals tested by multigene panel testing (MGPT). We used logistic regression to predict carrier status based on personal and family history of cancer. This was applied to 4644 tested individuals carrying 2383 BRCA1/2 variants to calculate likelihood ratios informing pathogenicity for each. Heterogeneity tests were performed for specific classes of variants defined by in silico predictions. RESULTS: Twenty-two variants labeled as VUS had odds of >10:1 in favor of pathogenicity. The heterogeneity analysis found that among variants in functional domains that were predicted to be benign by in silico tools, a significantly higher proportion of variants were estimated to be pathogenic than previously indicated; that missense variants outside of functional domains should be considered benign; and that variants predicted to create de novo donor sites were also largely benign. CONCLUSION: The evidence presented here supports the use of personal and family history from MGPT in the classification of VUS and will be integrated into ongoing efforts to provide large-scale multifactorial classification.

A clinical guide to hereditary cancer panel testing: evaluation of gene-specific cancer associations and sensitivity of genetic testing criteria in a cohort of 165,000 high-risk patients.

PURPOSE: Despite the rapid uptake of multigene panel testing (MGPT) for hereditary cancer predisposition, there is limited guidance surrounding indications for testing and genes to include. METHODS: To inform the clinical approach to hereditary cancer MGPT, we comprehensively evaluated 32 cancer predisposition genes by assessing phenotype-specific pathogenic variant (PV) frequencies, cancer risk associations, and performance of genetic testing criteria in a cohort of 165,000 patients referred for MGPT. RESULTS: We identified extensive genetic heterogeneity surrounding predisposition to cancer types commonly referred for germline testing (breast, ovarian, colorectal, uterine/endometrial, pancreatic, and melanoma). PV frequencies were highest among patients with ovarian cancer (13.8%) and lowest among patients with melanoma (8.1%). Fewer than half of PVs identified in patients meeting testing criteria for only BRCA1/2 or only Lynch syndrome occurred in the respective genes (33.1% and 46.2%). In addition, 5.8% of patients with PVs in BRCA1/2 and 26.9% of patients with PVs in Lynch syndrome genes did not meet respective testing criteria. CONCLUSION: Opportunities to improve upon identification of patients at risk for hereditary cancer predisposition include revising BRCA1/2 and Lynch syndrome testing criteria to include additional clinically actionable genes with overlapping phenotypes and relaxing testing criteria for associated cancers.

Recommendations for performance optimizations when using GATK3.8 and GATK4.

BACKGROUND: Use of the Genome Analysis Toolkit (GATK) continues to be the standard practice in genomic variant calling in both research and the clinic. Recently the toolkit has been rapidly evolving. Significant computational performance improvements have been introduced in GATK3.8 through collaboration with Intel in 2017. The first release of GATK4 in early 2018 revealed rewrites in the code base, as the stepping stone toward a Spark implementation. As the software continues to be a moving target for optimal deployment in highly productive environments, we present a detailed analysis of these improvements, to help the community stay abreast with changes in performance. RESULTS: We re-evaluated multiple options, such as threading, parallel garbage collection, I/O options and data-level parallelization. Additionally, we considered the trade-offs of using GATK3.8 and GATK4. We found optimized parameter values that reduce the time of executing the best practices variant calling procedure by 29.3% for GATK3.8 and 16.9% for GATK4. Further speedups can be accomplished by splitting data for parallel analysis, resulting in run time of only a few hours on whole human genome sequenced to the depth of 20X, for both versions of GATK. Nonetheless, GATK4 is already much more cost-effective than GATK3.8. Thanks to significant rewrites of the algorithms, the same analysis can be run largely in a single-threaded fashion, allowing users to process multiple samples on the same CPU. CONCLUSIONS: In time-sensitive situations, when a patient has a critical or rapidly developing condition, it is useful to minimize the time to process a single sample. In such cases we recommend using GATK3.8 by splitting the sample into chunks and computing across multiple nodes. The resultant walltime will be nnn.4 hours at the cost of $41.60 on 4 c5.18xlarge instances of Amazon Cloud. For cost-effectiveness of routine analyses or for large population studies, it is useful to maximize the number of samples processed per unit time. Thus we recommend GATK4, running multiple samples on one node. The total walltime will be ∼34.1 hours on 40 samples, with 1.18 samples processed per hour at the cost of $2.60 per sample on c5.18xlarge instance of Amazon Cloud.

Correction to: Recommendations for performance optimizations when using GATK3.8 and GATK4.

Following publication of the original article [1], the author explained that Table 2 is displayed incorrectly. The correct Table 2 is given below. The original article has been corrected.

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.

Comprehensive annotation of BRCA1 and BRCA2 missense variants by functionally validated sequence-based computational prediction models.

PURPOSE: To improve methods for predicting the impact of missense variants of uncertain significance (VUS) in BRCA1 and BRCA2 on protein function. METHODS: Functional data for 248 BRCA1 and 207 BRCA2 variants from assays with established high sensitivity and specificity for damaging variants were used to recalibrate 40 in silico algorithms predicting the impact of variants on protein activity. Additional random forest (RF) and naïve voting method (NVM) metapredictors for both BRCA1 and BRCA2 were developed to increase predictive accuracy. RESULTS: Optimized thresholds for in silico prediction models significantly improved the accuracy of predicted functional effects for BRCA1 and BRCA2 variants. In addition, new BRCA1-RF and BRCA2-RF metapredictors showed area under the curve (AUC) values of 0.92 (95% confidence interval [CI]: 0.88-0.96) and 0.90 (95% CI: 0.84-0.95), respectively. Similarly, the BRCA1-NVM and BRCA2-NVM models had AUCs of 0.93 and 0.90. The RF and NVM models were used to predict the pathogenicity of all possible missense variants in BRCA1 and BRCA2. CONCLUSION: The recalibrated algorithms and new metapredictors significantly improved upon current models for predicting the impact of variants in cancer risk-associated domains of BRCA1 and BRCA2. Prediction of the functional impact of all possible variants in BRCA1 and BRCA2 provides important information about the clinical relevance of variants in these genes.

Cancer susceptibility gene mutations in type I and II endometrial cancer.

OBJECTIVES: To determine the incidence of germline cancer predisposition gene mutations in patients with endometrial cancer (EC) subtypes. METHODS: Germline DNA was extracted from whole blood collected from consenting patients undergoing primary surgery for EC between 5/2005 and 11/2016. DNA samples were evaluated by product sequencing from a targeted multiplex PCR panel including 21 known/suspected cancer predisposition genes. Variants were classified as pathogenic/likely pathogenic based on allele frequency (<0.003), effects on protein function, and ClinVar assertions. RESULTS: Germline panel testing was performed on 1170 cases of EC; 849 (72.6%) were type I, and 321 (27.4%) were type II EC, including 135 (11.5%) uterine serous cancers (USC). BRCA1 mutations were enriched in Type II EC compared to Type I EC (0.93% vs. 0.12%, p = 0.07). Lynch Syndrome (LS) mutations were identified in 1.4% of type I and 1.6% of type II EC (p = 0.79), including 1.5% for USC. In total, predisposition gene mutations were present in 4.2% of type I and 5.3% of type II EC, as well as 6.7% of patients with USC). CONCLUSIONS: BRCA1/2 and Lynch mutations were rare in this cohort of unselected patients with type I and II EC, including USC. However, the presence of predisposition gene mutations in 4.2% of EC type I, 5.3% of EC type II, and 6.7% of USC suggests that somatic mutation testing should be considered for all EC patients.

VCF-Miner: GUI-based application for mining variants and annotations stored in VCF files.

Next-generation sequencing platforms are widely used to discover variants associated with disease. The processing of sequencing data involves read alignment, variant calling, variant annotation and variant filtering. The standard file format to hold variant calls is the variant call format (VCF) file. According to the format specifications, any arbitrary annotation can be added to the VCF file for downstream processing. However, most downstream analysis programs disregard annotations already present in the VCF and re-annotate variants using the annotation provided by that particular program. This precludes investigators who have collected information on variants from literature or other sources from including these annotations in the filtering and mining of variants. We have developed VCF-Miner, a graphical user interface-based stand-alone tool, to mine variants and annotation stored in the VCF. Powered by a MongoDB database engine, VCF-Miner enables the stepwise trimming of non-relevant variants. The grouping feature implemented in VCF-Miner can be used to identify somatic variants by contrasting variants in tumor and in normal samples or to identify recessive/dominant variants in family studies. It is not limited to human data, but can also be extended to include non-diploid organisms. It also supports copy number or any other variant type supported by the VCF specification. VCF-Miner can be used on a personal computer or large institutional servers and is freely available for download from http://bioinformaticstools.mayo.edu/research/vcf-miner/.

Collaborative science in the next-generation sequencing era: a viewpoint on how to combine exome sequencing data across sites to identify novel disease susceptibility genes.

The purpose of this article is to inform readers about technical challenges that we encountered when assembling exome sequencing data from the 'Simplifying Complex Exomes' (SIMPLEXO) consortium-whose mandate is the discovery of novel genes predisposing to breast and ovarian cancers. Our motivation is to share these obstacles-and our solutions to them-as a means of communicating important technical details that should be discussed early in projects involving massively parallel sequencing.

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.

Frequency of mutations in a large series of clinically ascertained ovarian cancer cases tested on multi-gene panels compared to reference controls.

OBJECTIVES: Given the lack of adequate screening modalities, knowledge of ovarian cancer risks for carriers of pathogenic alterations in predisposition genes is important for decisions about risk-reduction by salpingo-oophorectomy. We sought to determine which genes assayed on multi-gene panels are associated with ovarian cancer, the magnitude of the associations, and for which clinically meaningful associations could be ruled out. METHODS: 7768 adult ovarian cancer cases of European ancestry referred to a single clinical testing laboratory underwent multi-gene panel testing for detection of pathogenic alterations in known or suspected ovarian cancer susceptibility genes. A targeted capture approach was employed to assay each of 19 genes for the presence of pathogenic or likely pathogenic alterations. Mutation frequencies in ovarian cancer cases were compared to mutation frequencies in individuals from the Exome Aggregation Consortium (ExAC). Analyses stratified by family and personal history of other cancers and age at diagnosis were also performed. RESULTS: Significant associations (p<0.001) were identified between alterations in 11 genes and ovarian cancer, with eight of these displaying ≥5-fold increased risk (BRCA1, BRCA2, BRIP1, MSH2, MSH6, RAD51C, RAD51D). Relative risks of ovarian cancer greater than two-fold were also observed for ATM, but could reliably be ruled out for RAD50 and CHEK2. CONCLUSIONS: These results will inform clinical management of women found to carry pathogenic alterations in genes tested on multi-gene panels. The knowledge that some genes are not associated with OC can reduce concerns of women found to carry pathogenic alterations in those genes.

TP53 mutations, tetraploidy and homologous recombination repair defects in early stage high-grade serous ovarian cancer.

To determine early somatic changes in high-grade serous ovarian cancer (HGSOC), we performed whole genome sequencing on a rare collection of 16 low stage HGSOCs. The majority showed extensive structural alterations (one had an ultramutated profile), exhibited high levels of p53 immunoreactivity, and harboured a TP53 mutation, deletion or inactivation. BRCA1 and BRCA2 mutations were observed in two tumors, with nine showing evidence of a homologous recombination (HR) defect. Combined Analysis with The Cancer Genome Atlas (TCGA) indicated that low and late stage HGSOCs have similar mutation and copy number profiles. We also found evidence that deleterious TP53 mutations are the earliest events, followed by deletions or loss of heterozygosity (LOH) of chromosomes carrying TP53, BRCA1 or BRCA2. Inactivation of HR appears to be an early event, as 62.5% of tumours showed a LOH pattern suggestive of HR defects. Three tumours with the highest ploidy had little genome-wide LOH, yet one of these had a homozygous somatic frame-shift BRCA2 mutation, suggesting that some carcinomas begin as tetraploid then descend into diploidy accompanied by genome-wide LOH. Lastly, we found evidence that structural variants (SV) cluster in HGSOC, but are absent in one ultramutated tumor, providing insights into the pathogenesis of low stage HGSOC.

Exome sequencing identifies FANCM as a susceptibility gene for triple-negative breast cancer.

Inherited predisposition to breast cancer is known to be caused by loss-of-function mutations in BRCA1, BRCA2, PALB2, CHEK2, and other genes involved in DNA repair. However, most families severely affected by breast cancer do not harbor mutations in any of these genes. In Finland, founder mutations have been observed in each of these genes, suggesting that the Finnish population may be an excellent resource for the identification of other such genes. To this end, we carried out exome sequencing of constitutional genomic DNA from 24 breast cancer patients from 11 Finnish breast cancer families. From all rare damaging variants, 22 variants in 21 DNA repair genes were genotyped in 3,166 breast cancer patients, 569 ovarian cancer patients, and 2,090 controls, all from the Helsinki or Tampere regions of Finland. In Fanconi anemia complementation gene M (FANCM), nonsense mutation c.5101C>T (p.Q1701X) was significantly more frequent among breast cancer patients than among controls [odds ratio (OR) = 1.86, 95% CI = 1.26-2.75; P = 0.0018], with particular enrichment among patients with triple-negative breast cancer (TNBC; OR = 3.56, 95% CI = 1.81-6.98, P = 0.0002). In the Helsinki and Tampere regions, respectively, carrier frequencies of FANCM p.Q1701X were 2.9% and 4.0% of breast cancer patients, 5.6% and 6.6% of TNBC patients, 2.2% of ovarian cancer patients (from Helsinki), and 1.4% and 2.5% of controls. These findings identify FANCM as a breast cancer susceptibility gene, mutations in which confer a particularly strong predisposition for TNBC.

Patient survival and tumor characteristics associated with CHEK2:p.I157T - findings from the Breast Cancer Association Consortium.

BACKGROUND: P.I157T is a CHEK2 missense mutation associated with a modest increase in breast cancer risk. Previously, another CHEK2 mutation, the protein truncating c.1100delC has been associated with poor prognosis of breast cancer patients. Here, we have investigated patient survival and characteristics of breast tumors of germ line p.I157T carriers. METHODS: We included in the analyses 26,801 European female breast cancer patients from 15 studies participating in the Breast Cancer Association Consortium. We analyzed the association between p.I157T and the clinico-pathological breast cancer characteristics by comparing the p.I157T carrier tumors to non-carrier and c.1100delC carrier tumors. Similarly, we investigated the p.I157T associated risk of early death, breast cancer-associated death, distant metastasis, locoregional relapse and second breast cancer using Cox proportional hazards models. Additionally, we explored the p.I157T-associated genomic gene expression profile using data from breast tumors of 183 Finnish female breast cancer patients (ten p.I157T carriers) (GEO: GSE24450). Differential gene expression analysis was performed using a moderated t test. Functional enrichment was investigated using the DAVID functional annotation tool and gene set enrichment analysis (GSEA). The tumors were classified into molecular subtypes according to the St Gallen 2013 criteria and the PAM50 gene expression signature. RESULTS: P.I157T was not associated with increased risk of early death, breast cancer-associated death or distant metastasis relapse, and there was a significant difference in prognosis associated with the two CHEK2 mutations, p.I157T and c.1100delC. Furthermore, p.I157T was associated with lobular histological type and clinico-pathological markers of good prognosis, such as ER and PR expression, low TP53 expression and low grade. Gene expression analysis suggested luminal A to be the most common subtype for p.I157T carriers and CDH1 (cadherin 1) target genes to be significantly enriched among genes, whose expression differed between p.I157T and non-carrier tumors. CONCLUSIONS: Our analyses suggest that there are fundamental differences in breast tumors of CHEK2:p.I157T and c.1100delC carriers. The poor prognosis associated with c.1100delC cannot be generalized to other CHEK2 mutations.