Rare, protein-truncating variants in ATM, CHEK2 and PALB2, but not XRCC2, are associated with increased breast cancer risks

Background Breast cancer (BC) is the most common malignancy in women and has a major heritable component. The risks associated with most rare susceptibility variants are not well estimated. To better characterise the contribution of variants in ATM, CHEK2, PALB2 and XRCC2, we sequenced their coding regions in 13 087 BC cases and 5488 controls from East Anglia, UK. Methods Gene coding regions were enriched via PCR, sequenced, variant called and filtered for quality. ORs for BC risk were estimated separately for carriers of truncating variants and of rare missense variants, which were further subdivided by functional domain and pathogenicity as predicted by four in silico algorithms. Results Truncating variants in PALB2 (OR=4.69, 95% CI 2.27 to 9.68), ATM (OR=3.26; 95% CI 1.82 to 6.46) and CHEK2 (OR=3.11; 95% CI 2.15 to 4.69), but not XRCC2 (OR=0.94; 95% CI 0.26 to 4.19) were associated with increased BC risk. Truncating variants in ATM and CHEK2 were more strongly associated with risk of oestrogen receptor (ER)-positive than ER-negative disease, while those in PALB2 were associated with similar risks for both subtypes. There was also some evidence that missense variants in ATM, CHEK2 and PALB2 may contribute to BC risk, but larger studies are necessary to quantify the magnitude of this effect. Conclusions Truncating variants in PALB2 are associated with a higher risk of BC than those in ATM or CHEK2. A substantial risk of BC due to truncating XRCC2 variants can be excluded.

[1]  S. Bojesen,et al.  Increased Risk for Other Cancers in Addition to Breast Cancer for CHEK2*1100delC Heterozygotes Estimated From the Copenhagen General Population Study. , 2016, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[2]  Michael Jones,et al.  Age- and Tumor Subtype-Specific Breast Cancer Risk Estimates for CHEK2*1100delC Carriers. , 2016, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[3]  E. John,et al.  Multigene testing of moderate-risk genes: be mindful of the missense , 2016, Journal of Medical Genetics.

[4]  James Y. Zou Analysis of protein-coding genetic variation in 60,706 humans , 2015, Nature.

[5]  Nazneen Rahman,et al.  Gene-panel sequencing and the prediction of breast-cancer risk. , 2015, The New England journal of medicine.

[6]  C. Mathers,et al.  Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012 , 2015, International journal of cancer.

[7]  Nazneen Rahman,et al.  Breast-cancer risk in families with mutations in PALB2. , 2014, The New England journal of medicine.

[8]  J. Shendure,et al.  A general framework for estimating the relative pathogenicity of human genetic variants , 2014, Nature Genetics.

[9]  D. Easton,et al.  BOADICEA breast cancer risk prediction model: updates to cancer incidences, tumour pathology and web interface , 2013, British Journal of Cancer.

[10]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[11]  Mauricio O. Carneiro,et al.  From FastQ Data to High‐Confidence Variant Calls: The Genome Analysis Toolkit Best Practices Pipeline , 2013, Current protocols in bioinformatics.

[12]  Jaana M. Hartikainen,et al.  Large-scale genotyping identifies 41 new loci associated with breast cancer risk , 2013, Nature Genetics.

[13]  Daniel J. Park,et al.  Rare mutations in XRCC2 increase the risk of breast cancer. , 2012, American journal of human genetics.

[14]  Heng Li,et al.  Exploring single-sample SNP and INDEL calling with whole-genome de novo assembly , 2012, Bioinform..

[15]  P. Oefner,et al.  Rare variants in the ATM gene and risk of breast cancer , 2011, Breast Cancer Research.

[16]  M. DePristo,et al.  A framework for variation discovery and genotyping using next-generation DNA sequencing data , 2011, Nature Genetics.

[17]  Daniel J. Park,et al.  A PALB2 mutation associated with high risk of breast cancer , 2010, Breast Cancer Research.

[18]  M. DePristo,et al.  The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. , 2010, Genome research.

[19]  Daniel Rios,et al.  Bioinformatics Applications Note Databases and Ontologies Deriving the Consequences of Genomic Variants with the Ensembl Api and Snp Effect Predictor , 2022 .

[20]  P. Bork,et al.  A method and server for predicting damaging missense mutations , 2010, Nature Methods.

[21]  Alun Thomas,et al.  Rare, evolutionarily unlikely missense substitutions in ATM confer increased risk of breast cancer. , 2009, American journal of human genetics.

[22]  R. Warren,et al.  Genetic Models for the Familial Aggregation of Mammographic Breast Density , 2009, Cancer Epidemiology Biomarkers & Prevention.

[23]  C. Béroud,et al.  Human Splicing Finder: an online bioinformatics tool to predict splicing signals , 2009, Nucleic acids research.

[24]  J. Hopper,et al.  Penetrance Analysis of the PALB2 c.1592delT Founder Mutation , 2008, Clinical Cancer Research.

[25]  M. Southey,et al.  PenetranceAnalysis of the PALB 2 c . 1592 delT Founder Mutation , 2008 .

[26]  Katri Pylkäs,et al.  A recurrent mutation in PALB2 in Finnish cancer families , 2007, Nature.

[27]  A. Whittemore,et al.  Population‐based estimates of breast cancer risks associated with ATM gene variants c.7271T>G and c.1066–6T>G (IVS10–6T>G) from the Breast Cancer Family Registry , 2006, Human mutation.

[28]  A. Zharkikh,et al.  Comprehensive statistical study of 452 BRCA1 missense substitutions with classification of eight recurrent substitutions as neutral , 2005, Journal of Medical Genetics.

[29]  Päivi Heikkilä,et al.  CHEK2 variant I157T may be associated with increased breast cancer risk , 2004, International journal of cancer.

[30]  Christopher B. Burge,et al.  Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals , 2003, RECOMB '03.

[31]  W. Pearson,et al.  Current Protocols in Bioinformatics , 2002 .

[32]  S. Henikoff,et al.  Predicting deleterious amino acid substitutions. , 2001, Genome research.

[33]  S. Salzberg,et al.  GeneSplicer: a new computational method for splice site prediction. , 2001, Nucleic acids research.

[34]  J. Kaprio,et al.  Environmental and heritable factors in the causation of cancer--analyses of cohorts of twins from Sweden, Denmark, and Finland. , 2000, The New England journal of medicine.

[35]  B. Ponder,et al.  A Systematic Review Of Genetic Polymorphisms and Breast Cancer Risk 1 , 2000 .

[36]  N. Day,et al.  EPIC-Norfolk: study design and characteristics of the cohort. European Prospective Investigation of Cancer. , 1999, British journal of cancer.

[37]  Michael Ruogu Zhang,et al.  Statistical features of human exons and their flanking regions. , 1998, Human molecular genetics.

[38]  D. Easton,et al.  ATM mutations and phenotypes in ataxia-telangiectasia families in the British Isles: expression of mutant ATM and the risk of leukemia, lymphoma, and breast cancer. , 1998, American journal of human genetics.

[39]  David Haussler,et al.  Improved splice site detection in Genie , 1997, RECOMB '97.

[40]  J. Benítez,et al.  Rare Mutations in XRCC 2 Increase the Risk of Breast Cancer , 2022 .