Publications by authors named "Amanda B Spurdle"

298 Publications

Altered regulation of BRCA1 exon 11 splicing is associated with breast cancer risk in carriers of BRCA1 pathogenic variants.

Hum Mutat 2021 Nov 31;42(11):1488-1502. Epub 2021 Aug 31.

Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK.

Germline pathogenic variants in BRCA1 confer a high risk of developing breast and ovarian cancer. The BRCA1 exon 11 (formally exon 10) is one of the largest exons and codes for the nuclear localization signals of the corresponding gene product. This exon can be partially or entirely skipped during pre-mRNA splicing, leading to three major in-frame isoforms that are detectable in most cell types and tissue, and in normal and cancer settings. However, it is unclear whether the splicing imbalance of this exon is associated with cancer risk. Here we identify a common genetic variant in intron 10, rs5820483 (NC_000017.11:g.43095106_43095108dup), which is associated with exon 11 isoform expression and alternative splicing, and with the risk of breast cancer, but not ovarian cancer, in BRCA1 pathogenic variant carriers. The identification of this genetic effect was confirmed by analogous observations in mouse cells and tissue in which a loxP sequence was inserted in the syntenic intronic region. The prediction that the rs5820483 minor allele variant would create a binding site for the splicing silencer hnRNP A1 was confirmed by pull-down assays. Our data suggest that perturbation of BRCA1 exon 11 splicing modifies the breast cancer risk conferred by pathogenic variants of this gene.
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http://dx.doi.org/10.1002/humu.24276DOI Listing
November 2021

Large-scale cross-cancer fine-mapping of the 5p15.33 region reveals multiple independent signals.

HGG Adv 2021 Jul 12;2(3). Epub 2021 Jun 12.

Department of Laboratory Medicine and Pathology, Mayo Clinic Comprehensive Cancer Center, Mayo Clinic, Rochester, MN, USA.

Genome-wide association studies (GWASs) have identified thousands of cancer risk loci revealing many risk regions shared across multiple cancers. Characterizing the cross-cancer shared genetic basis can increase our understanding of global mechanisms of cancer development. In this study, we collected GWAS summary statistics based on up to 375,468 cancer cases and 530,521 controls for fourteen types of cancer, including breast (overall, estrogen receptor [ER]-positive, and ER-negative), colorectal, endometrial, esophageal, glioma, head/neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancer, to characterize the shared genetic basis of cancer risk. We identified thirteen pairs of cancers with statistically significant local genetic correlations across eight distinct genomic regions. Specifically, the 5p15.33 region, harboring the and genes, showed statistically significant local genetic correlations for multiple cancer pairs. We conducted a cross-cancer fine-mapping of the 5p15.33 region based on eight cancers that showed genome-wide significant associations in this region (ER-negative breast, colorectal, glioma, lung, melanoma, ovarian, pancreatic, and prostate cancer). We used an iterative analysis pipeline implementing a subset-based meta-analysis approach based on cancer-specific conditional analyses and identified ten independent cross-cancer associations within this region. For each signal, we conducted cross-cancer fine-mapping to prioritize the most plausible causal variants. Our findings provide a more in-depth understanding of the shared inherited basis across human cancers and expand our knowledge of the 5p15.33 region in carcinogenesis.
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http://dx.doi.org/10.1016/j.xhgg.2021.100041DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8336922PMC
July 2021

An updated quantitative model to classify missense variants in the TP53 gene: A novel multifactorial strategy.

Hum Mutat 2021 Oct 4;42(10):1351-1361. Epub 2021 Aug 4.

Parkville Familial Cancer Centre, Peter MacCallum Cancer Centre and Royal Melbourne Hospital, Melbourne, Australia.

Multigene panel testing has led to an increase in the number of variants of uncertain significance identified in the TP53 gene, associated with Li-Fraumeni syndrome. We previously developed a quantitative model for predicting the pathogenicity of P53 missense variants based on the combination of calibrated bioinformatic information and somatic to germline ratio. Here, we extended this quantitative model for the classification of P53 predicted missense variants by adding new pieces of evidence (personal and family history parameters, loss-of-function results, population allele frequency, healthy individual status by age 60, and breast tumor pathology). We also annotated which missense variants might have an effect on splicing based on bioinformatic predictions. This updated model plus annotation led to the classification of 805 variants into a clinically relevant class, which correlated well with existing ClinVar classifications, and resolved a large number of conflicting and uncertain classifications. We propose this model as a reliable approach to TP53 germline variant classification and emphasize its use in contributing to optimize TP53-specific ACMG/AMP guidelines.
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http://dx.doi.org/10.1002/humu.24264DOI Listing
October 2021

Genetic analyses of gynecological disease identify genetic relationships between uterine fibroids and endometrial cancer, and a novel endometrial cancer genetic risk region at the WNT4 1p36.12 locus.

Hum Genet 2021 Sep 15;140(9):1353-1365. Epub 2021 Jul 15.

Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.

Endometriosis, polycystic ovary syndrome (PCOS) and uterine fibroids have been proposed as endometrial cancer risk factors; however, disentangling their relationships with endometrial cancer is complicated due to shared risk factors and comorbidities. Using genome-wide association study (GWAS) data, we explored the relationships between these non-cancerous gynecological diseases and endometrial cancer risk by assessing genetic correlation, causal relationships and shared risk loci. We found significant genetic correlation between endometrial cancer and PCOS, and uterine fibroids. Adjustment for genetically predicted body mass index (a risk factor for PCOS, uterine fibroids and endometrial cancer) substantially attenuated the genetic correlation between endometrial cancer and PCOS but did not affect the correlation with uterine fibroids. Mendelian randomization analyses suggested a causal relationship between only uterine fibroids and endometrial cancer. Gene-based analyses revealed risk regions shared between endometrial cancer and endometriosis, and uterine fibroids. Multi-trait GWAS analysis of endometrial cancer and the genetically correlated gynecological diseases identified a novel genome-wide significant endometrial cancer risk locus at 1p36.12, which replicated in an independent endometrial cancer dataset. Interrogation of functional genomic data at 1p36.12 revealed biologically relevant genes, including WNT4 which is necessary for the development of the female reproductive system. In summary, our study provides genetic evidence for a causal relationship between uterine fibroids and endometrial cancer. It further provides evidence that the comorbidity of endometrial cancer, PCOS and uterine fibroids may partly be due to shared genetic architecture. Notably, this shared architecture has revealed a novel genome-wide risk locus for endometrial cancer.
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http://dx.doi.org/10.1007/s00439-021-02312-0DOI Listing
September 2021

Case-case analysis addressing ascertainment bias for multigene panel testing implicates BRCA1 and PALB2 in endometrial cancer.

Hum Mutat 2021 Oct 21;42(10):1265-1278. Epub 2021 Jul 21.

Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Australia.

Hereditary endometrial cancer (EC) is most commonly attributed to pathogenic variants in mismatch repair genes. Evidence supports the existence of additional genetic risk factors in the context of multiple cancer diagnoses and/or family history of EC. EC patients (n = 5292) referred for diagnostic multigene cancer panel testing were annotated for presence of a pathogenic gene variant; personal history of prior, concurrent, or subsequent cancer of another type; reported family history of Lynch syndrome or EC. The Pearson χ test was used to assess differences in gene variant prevalence between case sub-groups defined by personal and/or family history of cancer/s, using cases with no family history of Lynch/EC as reference. Another cancer diagnosis was reported for 55% of EC cases. EC cases with a prior and reported family history of Lynch cancer were enriched for variants in MLH1 (p = 3.5 × 10 ), MSH2 (p = 3.1 × 10 ), and PMS2 (p = .02). Consistent with expectations for a breast cancer gene also predisposing to EC, the variant frequency was increased in EC patients with prior BC and family history of EC for BRCA1 (p = 1.7 × 10 ) and PALB2 (p = .0002). Strategic case-case analyses to address cohort ascertainment bias have provided a rationale to direct future studies of candidate hereditary EC genes.
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http://dx.doi.org/10.1002/humu.24256DOI Listing
October 2021

Associations between Genetically Predicted Circulating Protein Concentrations and Endometrial Cancer Risk.

Cancers (Basel) 2021 Apr 26;13(9). Epub 2021 Apr 26.

Population Sciences in the Pacific Program, Cancer Epidemiology Division, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI 96813, USA.

Endometrial cancer (EC) is the leading female reproductive tract malignancy in developed countries. Currently, genome-wide association studies (GWAS) have identified 17 risk loci for EC. To identify novel EC-associated proteins, we used previously reported protein quantitative trait loci for 1434 plasma proteins as instruments to evaluate associations between genetically predicted circulating protein concentrations and EC risk. We studied 12,906 cases and 108,979 controls of European descent included in the Endometrial Cancer Association Consortium, the Epidemiology of Endometrial Cancer Consortium, and the UK Biobank. We observed associations between genetically predicted concentrations of nine proteins and EC risk at a false discovery rate of <0.05 (-values range from 1.14 × 10 to 3.04 × 10). Except for vascular cell adhesion protein 1, all other identified proteins were independent from known EC risk variants identified in EC GWAS. The respective odds ratios (95% confidence intervals) per one standard deviation increase in genetically predicted circulating protein concentrations were 1.21 (1.13, 1.30) for DNA repair protein RAD51 homolog 4, 1.27 (1.14, 1.42) for desmoglein-2, 1.14 (1.07, 1.22) for MHC class I polypeptide-related sequence B, 1.05 (1.02, 1.08) for histo-blood group ABO system transferase, 0.77 (0.68, 0.89) for intestinal-type alkaline phosphatase, 0.82 (0.74, 0.91) for carbohydrate sulfotransferase 15, 1.07 (1.03, 1.11) for D-glucuronyl C5-epimerase, and 1.07 (1.03, 1.10) for CD209 antigen. In conclusion, we identified nine potential EC-associated proteins. If validated by additional studies, our findings may contribute to understanding the pathogenesis of endometrial tumor development and identifying women at high risk of EC along with other EC risk factors and biomarkers.
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http://dx.doi.org/10.3390/cancers13092088DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8123478PMC
April 2021

Tumor Signature Analysis Implicates Hereditary Cancer Genes in Endometrial Cancer Development.

Cancers (Basel) 2021 04 7;13(8). Epub 2021 Apr 7.

Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane 4006, Australia.

Risk of endometrial cancer (EC) is increased ~2-fold for women with a family history of cancer, partly due to inherited pathogenic variants in mismatch repair (MMR) genes. We explored the role of additional genes as explanation for familial EC presentation by investigating germline and EC tumor sequence data from The Cancer Genome Atlas ( = 539; 308 European ancestry), and germline data from 33 suspected familial European ancestry EC patients demonstrating immunohistochemistry-detected tumor MMR proficiency. Germline variants in MMR and 26 other known/candidate EC risk genes were annotated for pathogenicity in the two EC datasets, and also for European ancestry individuals from gnomAD as a population reference set ( = 59,095). Ancestry-matched case-control comparisons of germline variant frequency and/or sequence data from suspected familial EC cases highlighted , , , and as candidates for large-scale risk association studies. Tumor mutational signature analysis identified a microsatellite-high signature for all cases with a germline pathogenic MMR gene variant. Signature analysis also indicated that germline loss-of-function variants in homologous recombination (, , ) or base excision (, ) repair genes can contribute to EC development in some individuals with germline variants in these genes. These findings have implications for expanded therapeutic options for EC cases.
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http://dx.doi.org/10.3390/cancers13081762DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8067736PMC
April 2021

Considerations for using population frequency data in germline variant interpretation: Cancer syndrome genes as a model.

Hum Mutat 2021 May 1;42(5):530-536. Epub 2021 Mar 1.

Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia.

Aggregate population genomics data from large cohorts are vital for assessing germline variant pathogenicity. However, there are no specifications on how sequencing quality metrics should be considered, and whether exome-derived and genome-derived allele frequencies should be considered in isolation. Germline genome sequence data were simulated for nine read-depths to identify a minimum acceptable read-depth for detecting variants. gnomAD exome-derived and genome-derived datasets were assessed for read-depth, for six key cancer genes selected for variant curation by ClinGen expert panels. Non-Finnish European allele frequency (AF) or filter AF of coding variants in these genes, assigned into frequency bins using modified ACMG-AMP criteria, was compared between exome-derived and genome-derived datasets. A 30X read-depth achieved acceptable precision and recall for detection of substitutions, but poor recall for small insertions/deletions. Exome-derived and genome-derived datasets exhibited low read-depth for different gene exons. Individual variants were mostly assigned to non-divergent AF bins (>95%) or filter AF bins (>97%). Two major bin divergences were resolved by applying the minimal acceptable read-depth threshold. These findings show the importance of assessing read-depth separately for population datasets sourced from different short-read sequencing technologies before assigning a frequency-based ACMG-AMP classification code for variant interpretation.
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http://dx.doi.org/10.1002/humu.24183DOI Listing
May 2021

Breast Cancer Risk Genes - Association Analysis in More than 113,000 Women.

N Engl J Med 2021 02 20;384(5):428-439. Epub 2021 Jan 20.

The authors' affiliations are as follows: the Centre for Cancer Genetic Epidemiology, Departments of Public Health and Primary Care (L.D., S. Carvalho, J.A., K.A.P., Q.W., M.K.B., J.D., B.D., N. Mavaddat, K. Michailidou, A.C.A., P.D.P.P., D.F.E.) and Oncology (C.L., P.A.H., C. Baynes, D.M.C., L.F., V.R., M. Shah, P.D.P.P., A.M.D., D.F.E.), University of Cambridge, Cambridge, the Centre for Genomic and Experimental Medicine, MRC Institute of Genetics and Molecular Medicine (A. Campbell, D.J.P.), and the Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology (D.J.P.), University of Edinburgh, the Cancer Research UK Edinburgh Centre (D.A.C., J.F.), and the Usher Institute of Population Health Sciences and Informatics, University of Edinburgh Medical School (A. Campbell, J.F.), Edinburgh, the Divisions of Informatics, Imaging, and Data Sciences (E.F.H.), Cancer Sciences (A. Howell), Population Health, Health Services Research, and Primary Care (A. Lophatananon, K. Muir), and Evolution and Genomic Sciences, School of Biological Sciences (W.G.N., E.M.V., D.G.E.), University of Manchester, the NIHR Manchester Biomedical Research Unit (E.F.H.) and the Nightingale Breast Screening Centre, Wythenshawe Hospital (E.F.H., H.I.), Academic Health Science Centre and North West Genomics Laboratory Hub, and the Manchester Centre for Genomic Medicine, St. Mary's Hospital, Manchester University NHS Foundation Trust (W.G.N., E.M.V., D.G.E.), Manchester, the School of Cancer and Pharmaceutical Sciences, Comprehensive Cancer Centre, Guy's Campus, King's College London, London (E.J.S.), the Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham (I.T.), and the Wellcome Trust Centre for Human Genetics and Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford (I.T.) - all in the United Kingdom; the Human Genotyping-CEGEN Unit, Human Cancer Genetic Program (A.G.-N., M.R.A., N.Á., B.H., R.N.-T.), and the Human Genetics Group (V.F., A.O., J.B.), Spanish National Cancer Research Center, Centro de Investigación en Red de Enfermedades Raras (A.O., J.B.), Servicio de Oncología Médica, Hospital Universitario La Paz (M.P.Z.), and Molecular Oncology Laboratory, Hospital Clinico San Carlos, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (M. de la Hoya), Madrid, the Genomic Medicine Group, Galician Foundation of Genomic Medicine, Instituto de Investigación Sanitaria de Santiago de Compostela, Complejo Hospitalario Universitario de Santiago (A. Carracedo, M.G.-D.), and Centro de Investigación en Red de Enfermedades Raras y Centro Nacional de Genotipado, Universidad de Santiago de Compostela (A. Carracedo), Santiago de Compostela, the Oncology and Genetics Unit, Instituto de Investigacion Sanitaria Galicia Sur, Xerencia de Xestion Integrada de Vigo-Servizo Galeo de Saúde, Vigo (J.E.C.), and Servicio de Cirugía General y Especialidades, Hospital Monte Naranco, Oviedo (J.I.A.P.) - all in Spain; the Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Lund (C. Wahlström, J.V., M.L., T. Törngren, Å.B., A.K.), the Department of Oncology, Örebro University Hospital, Örebro (C. Blomqvist), and the Departments of Medical Epidemiology and Biostatistics (K.C., M.E., M.G., P. Hall, W.H., K.H.), Oncology, Södersjukhuset (P. Hall, S. Margolin), Molecular Medicine and Surgery (A. Lindblom), and Clinical Science and Education, Södersjukhuset (S. Margolin, C. Wendt), Karolinska Institutet, and the Department of Clinical Genetics, Karolinska University Hospital (A. Lindblom), Stockholm - all in Sweden; the Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD (M.T.P., C.F., G.C.-T., A.B.S.), the Cancer Epidemiology Division, Cancer Council Victoria (G.G.G., R.J.M., R.L.M.), the Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health (G.G.G., R.J.M., R.L.M.), and the Department of Clinical Pathology (M.C.S.), University of Melbourne, Anatomical Pathology, Alfred Hospital (C.M.), and the Cancer Epidemiology Division, Cancer Council Victoria (M.C.S.), Melbourne, VIC, and Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC (G.G.G., M.C.S., R.L.M.) - all in Australia; the Division of Molecular Pathology (R.K., S. Cornelissen, M.K.S.), Family Cancer Clinic (F.B.L.H., L.E.K.), Department of Epidemiology (M.A.R.), and Division of Psychosocial Research and Epidemiology (M.K.S.), the Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, Division Laboratories, Pharmacy and Biomedical Genetics, Department of Genetics, University Medical Center, Utrecht (M.G.E.M.A.), the Department of Clinical Genetics, Erasmus University Medical Center (J.M.C., A.M.W.O.), and the Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute (B.A.M.H.-G., A. Hollestelle, M.J.H.), Rotterdam, the Department of Clinical Genetics, Maastricht University Medical Center, Maastricht (E.B.G.G.), the Departments of Human Genetics (I.M.M.L., M.P.G.V., P.D.), Clinical Genetics (C.J.A.), and Pathology (P.D.), Leiden University Medical Center, Leiden, the Department of Human Genetics, Radboud University Medical Center, Nijmegen (A.R.M.), and the Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen (J.C.O.) - all in the Netherlands; the Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute (B.D.), and the Division of Cancer Epidemiology and Genetics, National Cancer Institute (T.A., S.J.C., X.R.Y., M.G.-C.), National Institutes of Health, Bethesda, MD; the Department of Pathology, Brigham and Women's Hospital, Harvard Medical School (B.D.), and the Department of Nutrition, Harvard T.H. Chan School of Public Health (R.M.V.D.), Boston; the Departments of Clinical Genetics (K.A.), Oncology (C. Blomqvist), and Obstetrics and Gynecology (H.N., M. Suvanto), Helsinki University Hospital, University of Helsinki, Helsinki, and the Unit of Clinical Oncology, Kuopio University Hospital (P. Auvinen), the Institute of Clinical Medicine, Oncology (P. Auvinen), the Translational Cancer Research Area (J.M.H., V.-M.K., A. Mannermaa), and the Institute of Clinical Medicine, Pathology, and Forensic Medicine (J.M.H., V.-M.K., A. Mannermaa), University of Eastern Finland, and the Biobank of Eastern Finland, Kuopio University Hospital (V.-M.K., A. Mannermaa), Kuopio - both in Finland; the N.N. Alexandrov Research Institute of Oncology and Medical Radiology, Minsk, Belarus (N.N.A., N.V.B.); the Department of Gynecology and Obstetrics and Institute of Clinical Molecular Biology, University Hospital of Schleswig-Holstein, Campus Kiel, Christian-Albrechts University Kiel, Kiel (N.A.), the Institute of Medical Biometry and Epidemiology (H. Becher) and Cancer Epidemiology Group (T.M., J.C.-C.), University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, the Department of Gynecology and Obstetrics (M.W.B., P.A.F., L.H.) and Institute of Human Genetics (A.B.E.), University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-European Metropolitan Region of Nuremberg, Erlangen, the Division of Cancer Epidemiology (S.B., A. Jung, P.M.K., J.C.-C.), Molecular Epidemiology Group, C080 (B. Burwinkel, H.S.), Division of Pediatric Neurooncology (A.F.), and Molecular Genetics of Breast Cancer (U.H., M.M., M.U.R., D.T.), German Cancer Research Center, Molecular Biology of Breast Cancer, University Women's Clinic Heidelberg, University of Heidelberg (B. Burwinkel, A.S., H.S.), Hopp Children's Cancer Center (A.F.), Faculty of Medicine, University of Heidelberg (P.M.K.), and National Center for Tumor Diseases, University Hospital and German Cancer Research Center (A.S., C.S.), Heidelberg, the Department of Radiation Oncology (N.V.B., M. Bremer, H.C.) and the Gynecology Research Unit (N.V.B., T.D., P. Hillemanns, T.-W.P.-S., P.S.), Hannover Medical School, Hannover, the Institute of Human Genetics, University of Münster, Münster (N.B.-M.), Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart (H. Brauch, W.-Y.L.), iFIT-Cluster of Excellence, University of Tübingen, and the German Cancer Consortium, German Cancer Research Center, Partner Site Tübingen (H. Brauch), and the University of Tübingen (W.-Y.L.), Tübingen, Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum, Bochum (T.B.), Institute for Medical Informatics, Statistics, and Epidemiology, University of Leipzig, Leipzig (C.E.), Center for Hereditary Breast and Ovarian Cancer (E.H., R.K.S.) and Center for Integrated Oncology (E.H., R.K.S.), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, the Department of Internal Medicine, Evangelische Kliniken Bonn, Johanniter Krankenhaus, Bonn (Y.-D.K.), the Department of Gynecology and Obstetrics, University of Munich, Campus Großhadern, Munich (A. Meindl), and the Institute of Pathology, Städtisches Klinikum Karlsruhe, Karlsruhe (T.R.) - all in Germany; the Gynecological Cancer Registry, Centre Georges-François Leclerc, Dijon (P. Arveux), and the Center for Research in Epidemiology and Population Health, Team Exposome and Heredity, INSERM, University Paris-Saclay, Villejuif (E.C.-D., P.G., T. Truong) - both in France; the Institute of Biochemistry and Genetics, Ufa Federal Research Center of the Russian Academy of Sciences (M. Bermisheva, E.K.), the Department of Genetics and Fundamental Medicine, Bashkir State University (E.K., D.P., Y.V.), and the Ufa Research Institute of Occupational Health and Human Ecology (Y.V.), Ufa, Russia; the Department of Genetics and Pathology (K.B., A. Jakubowska, J. Lubiński, K.P.) and the Independent Laboratory of Molecular Biology and Genetic Diagnostics (A. Jakubowska), Pomeranian Medical University, Szczecin, Poland; the Copenhagen General Population Study, the Department of Clinical Biochemistry (S.E.B., B.G.N.), and the Department of Breast Surgery (H.F.), Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, and the Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen (S.E.B., B.G.N.) - both in Denmark; the Division of Cancer Prevention and Genetics, European Institute of Oncology Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) (B. Bonanni), the Unit of Medical Genetics, Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano (S. Manoukian), the Genome Diagnostics Program, FIRC Institute of Molecular Oncology (P.P.), and the Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori (P.R.), Milan; the Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet (A.-L.B.-D., G.I.G.A., V.N.K.), and the Institute of Clinical Medicine, Faculty of Medicine, University of Oslo (A.-L.B.-D., V.N.K.), Oslo; Medical Faculty, Universidad de La Sabana (I.B.), and the Clinical Epidemiology and Biostatistics Department (F.G.) and Institute of Human Genetics (D.T.), Pontificia Universidad Javeriana, Bogota, Colombia; the Department of Internal Medicine and Huntsman Cancer Institute, University of Utah (N.J.C., M.J.M., J.A.W.), and the Intermountain Healthcare Biorepository and Department of Pathology, Intermountain Healthcare (M.H.C.), Salt Lake City; the David Geffen School of Medicine, Department of Medicine Division of Hematology and Oncology, University of California, Los Angeles (P.A.F.), and Moores Cancer Center (M.G.-D., M.E.M.) and the Department of Family Medicine and Public Health (M.E.M.), University of California San Diego, La Jolla; the Departments of Medical Oncology (V.G., D.M.) and Pathology (M.T.), University Hospital of Heraklion, Heraklion, and the Department of Oncology, University Hospital of Larissa, Larissa (E.S.) - both in Greece; the Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital (G.G., I.L.A.), the Departments of Laboratory Medicine and Pathobiology (A.M.M.) and Molecular Genetics (I.L.A.), University of Toronto, and the Laboratory Medicine Program, University Health Network (A.M.M.), Toronto, and the Genomics Center, Centre Hospitalier Universitaire de Québec-Université Laval Research Center, Québec City, QC (J.S.) - both in Canada; the Department of Electron Microscopy and Molecular Pathology (A. Hadjisavvas, K.K., M.A.L.), the Cyprus School of Molecular Medicine (A. Hadjisavvas, K.K., M.A.L., K. Michailidou), and the Biostatistics Unit (K. Michailidou), Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus; the Saw Swee Hock School of Public Health (M. Hartman, R.M.V.D.) and the Department of Medicine, Yong Loo Lin School of Medicine (R.M.V.D.), National University of Singapore, the Department of Surgery, National University Health System (M. Hartman, J. Li), and the Human Genetics Division, Genome Institute of Singapore (J. Li), Singapore; the Department of Mathematical Sciences, Faculty of Science and Engineering, University of Nottingham Malaysia (W.K.H.), and the Breast Cancer Research Programme, Cancer Research Malaysia (W.K.H., P.S.N., S.-Y.Y., S.H.T.), Selangor, and the Breast Cancer Research Unit, Cancer Research Institute (N.A.M.T.), and the Department of Surgery, Faculty of Medicine (N.A.M.T., P.S.N., S.H.T.), University Malaya, Kuala Lumpur - both in Malaysia; Surgery, School of Medicine, National University of Ireland, Galway (M.J.K., N. Miller); the Department of Surgery, Daerim Saint Mary's Hospital (S.-W.K.), the Department of Surgery, Ulsan University College of Medicine and Asan Medical Center (J.W.L.), the Department of Surgery, Soonchunhyang University College of Medicine and Soonchunhyang University Hospital (M.H.L.), Integrated Major in Innovative Medical Science, Seoul National University College of Medicine (S.K.P.), and the Cancer Research Institute, Seoul National University (S.K.P.), Seoul, South Korea; the Department of Basic Sciences, Shaukat Khanum Memorial Cancer Hospital and Research Center, Lahore, Pakistan (M.U.R.); and the National Cancer Institute, Ministry of Public Health, Nonthaburi, Thailand (S.T.).

Background: Genetic testing for breast cancer susceptibility is widely used, but for many genes, evidence of an association with breast cancer is weak, underlying risk estimates are imprecise, and reliable subtype-specific risk estimates are lacking.

Methods: We used a panel of 34 putative susceptibility genes to perform sequencing on samples from 60,466 women with breast cancer and 53,461 controls. In separate analyses for protein-truncating variants and rare missense variants in these genes, we estimated odds ratios for breast cancer overall and tumor subtypes. We evaluated missense-variant associations according to domain and classification of pathogenicity.

Results: Protein-truncating variants in 5 genes (, , , , and ) were associated with a risk of breast cancer overall with a P value of less than 0.0001. Protein-truncating variants in 4 other genes (, , , and ) were associated with a risk of breast cancer overall with a P value of less than 0.05 and a Bayesian false-discovery probability of less than 0.05. For protein-truncating variants in 19 of the remaining 25 genes, the upper limit of the 95% confidence interval of the odds ratio for breast cancer overall was less than 2.0. For protein-truncating variants in and , odds ratios were higher for estrogen receptor (ER)-positive disease than for ER-negative disease; for protein-truncating variants in , , , , , and , odds ratios were higher for ER-negative disease than for ER-positive disease. Rare missense variants (in aggregate) in , , and were associated with a risk of breast cancer overall with a P value of less than 0.001. For , , and , missense variants (in aggregate) that would be classified as pathogenic according to standard criteria were associated with a risk of breast cancer overall, with the risk being similar to that of protein-truncating variants.

Conclusions: The results of this study define the genes that are most clinically useful for inclusion on panels for the prediction of breast cancer risk, as well as provide estimates of the risks associated with protein-truncating variants, to guide genetic counseling. (Funded by European Union Horizon 2020 programs and others.).
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http://dx.doi.org/10.1056/NEJMoa1913948DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7611105PMC
February 2021

Germline and Tumor Sequencing as a Diagnostic Tool To Resolve Suspected Lynch Syndrome.

J Mol Diagn 2021 03 29;23(3):358-371. Epub 2020 Dec 29.

Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia; Centre for Cancer Research, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia; Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Victoria, Australia. Electronic address:

Patients in whom mismatch repair (MMR)-deficient cancer develops in the absence of pathogenic variants of germline MMR genes or somatic hypermethylation of the MLH1 gene promoter are classified as having suspected Lynch syndrome (SLS). Germline whole-genome sequencing (WGS) and targeted and genome-wide tumor sequencing were applied to identify the underlying cause of tumor MMR deficiency in SLS. Germline WGS was performed on samples from 14 cancer-affected patients with SLS, including two sets of first-degree relatives. MMR genes were assessed for germline pathogenic variants, including complex structural rearrangements and noncoding variants. Tumor tissue was assessed for somatic MMR gene mutations using targeted, whole-exome sequencing or WGS. Germline WGS identified pathogenic MMR variants in 3 of the 14 cases (21.4%), including a 9.5-megabase inversion disrupting MSH2 in a mother and daughter. Excluding these 3 MMR carriers, tumor sequencing identified at least two somatic MMR gene mutations in 8 of 11 tumors tested (72.7%). In a second mother-daughter pair, a somatic cause of tumor MMR deficiency was supported by the presence of double somatic MSH2 mutations in their respective tumors. More than 70% of SLS cases had double somatic MMR mutations in the absence of germline pathogenic variants in the MMR or other DNA repair-related genes on WGS, and, therefore, were confidently assigned a noninherited cause of tumor MMR deficiency.
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http://dx.doi.org/10.1016/j.jmoldx.2020.12.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7927277PMC
March 2021

Specifications of the ACMG/AMP variant interpretation guidelines for germline TP53 variants.

Hum Mutat 2021 Mar 25;42(3):223-236. Epub 2020 Dec 25.

Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA.

Germline pathogenic variants in TP53 are associated with Li-Fraumeni syndrome, a cancer predisposition disorder inherited in an autosomal dominant pattern associated with a high risk of malignancy, including early-onset breast cancers, sarcomas, adrenocortical carcinomas, and brain tumors. Intense cancer surveillance for individuals with TP53 germline pathogenic variants is associated with reduced cancer-related mortality. Accurate and consistent classification of germline variants across clinical and research laboratories is important to ensure appropriate cancer surveillance recommendations. Here, we describe the work performed by the Clinical Genome Resource TP53 Variant Curation Expert Panel (ClinGen TP53 VCEP) focused on specifying the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) guidelines for germline variant classification to the TP53 gene. Specifications were developed for 20 ACMG/AMP criteria, while nine were deemed not applicable. The original strength level for the 10 criteria was also adjusted due to current evidence. Use of TP53-specific guidelines and sharing of clinical data among experts and clinical laboratories led to a decrease in variants of uncertain significance from 28% to 12% compared with the original guidelines. The ClinGen TP53 VCEP recommends the use of these TP53-specific ACMG/AMP guidelines as the standard strategy for TP53 germline variant classification.
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http://dx.doi.org/10.1002/humu.24152DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8374922PMC
March 2021

Implementing gene curation for hereditary cancer susceptibility in Australia: achieving consensus on genes with clinical utility.

J Med Genet 2020 Nov 9. Epub 2020 Nov 9.

Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

Background: The strength of evidence supporting the validity of gene-disease relationships is variable. Hereditary cancer has the additional complexity of low or moderate penetrance for some confirmed disease-associated alleles.

Methods: To promote national consistency in interpretation of hereditary cancer/tumour gene test results, we requested opinions of representatives from Australian Family Cancer Clinics regarding the clinical utility of 157 genes initially collated for a national research project. Viewpoints were sought by initial survey, face-to-face workshop and follow-up survey. Subsequent review was undertaken by the eviQ Cancer Genetics Reference Committee, a national resource providing evidence-based and consensus-driven cancer treatment protocols.

Results: Genes were categorised by clinical actionability as: relevant for testing on presentation of common cancer/tumour types (n=45); relevant for testing in the context of specific rare phenotypes (n=74); insufficient clinical utility (n=34) or contentious clinical utility (n=3). Opinions for several genes altered during the study time frame, due to new information.

Conclusion: Through an iterative process, consensus was achieved on genes with clinical utility for hereditary cancer/tumour conditions in the Australian setting. This study highlighted need for regular review of gene-disease lists, a role assumed in Australia for hereditary cancer/tumour predisposition genes by the eviQ Cancer Genetics Reference Committee.
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http://dx.doi.org/10.1136/jmedgenet-2020-107140DOI Listing
November 2020

Cross-Cancer Genome-Wide Association Study of Endometrial Cancer and Epithelial Ovarian Cancer Identifies Genetic Risk Regions Associated with Risk of Both Cancers.

Cancer Epidemiol Biomarkers Prev 2021 01 3;30(1):217-228. Epub 2020 Nov 3.

Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota.

Background: Accumulating evidence suggests a relationship between endometrial cancer and ovarian cancer. Independent genome-wide association studies (GWAS) for endometrial cancer and ovarian cancer have identified 16 and 27 risk regions, respectively, four of which overlap between the two cancers. We aimed to identify joint endometrial and ovarian cancer risk loci by performing a meta-analysis of GWAS summary statistics from these two cancers.

Methods: Using LDScore regression, we explored the genetic correlation between endometrial cancer and ovarian cancer. To identify loci associated with the risk of both cancers, we implemented a pipeline of statistical genetic analyses (i.e., inverse-variance meta-analysis, colocalization, and M-values) and performed analyses stratified by subtype. Candidate target genes were then prioritized using functional genomic data.

Results: Genetic correlation analysis revealed significant genetic correlation between the two cancers ( = 0.43, = 2.66 × 10). We found seven loci associated with risk for both cancers ( < 2.4 × 10). In addition, four novel subgenome-wide regions at 7p22.2, 7q22.1, 9p12, and 11q13.3 were identified ( < 5 × 10). Promoter-associated HiChIP chromatin loops from immortalized endometrium and ovarian cell lines and expression quantitative trait loci data highlighted candidate target genes for further investigation.

Conclusions: Using cross-cancer GWAS meta-analysis, we have identified several joint endometrial and ovarian cancer risk loci and candidate target genes for future functional analysis.

Impact: Our research highlights the shared genetic relationship between endometrial cancer and ovarian cancer. Further studies in larger sample sets are required to confirm our findings.
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http://dx.doi.org/10.1158/1055-9965.EPI-20-0739DOI Listing
January 2021

Under-ascertainment of breast cancer susceptibility gene carriers in a cohort of New Zealand female breast cancer patients.

Breast Cancer Res Treat 2021 Feb 28;185(3):583-590. Epub 2020 Oct 28.

Mackenzie Cancer Research Group, Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand.

Background: Diagnostic screening for pathogenic variants in breast cancer susceptibility genes, including BRCA1, BRCA2, PALB2, PTEN and TP53, may be offered to New Zealanders from suspected high-risk breast (and ovarian) cancer families. However, it is unknown how many high-risk pathogenic variant carriers in New Zealand are not offered genetic screening using existing triage tools and guidelines for breast (and ovarian) cancer patients.

Methods: Panel-gene sequencing of the coding and non-coding regions of the BRCA1 and BRCA2 genes, and the coding regions and splice sites of CDH1, PALB2, PTEN and TP53, was undertaken for an unselected cohort of 367 female breast cancer patients. A total of 1685 variants were evaluated using the ENIGMA and the ACMG/AMP variant classification guidelines.

Results: Our study identified that 13 (3.5%) breast cancer patients carried a pathogenic or likely pathogenic variant in BRCA1, BRCA2, PALB2, or PTEN. A significantly higher number of pathogenic variant carriers had grade 3 tumours (10/13) when compared to non-carriers; however, no other clinicopathological characteristics were found to be significantly different between (likely) pathogenic variant carriers and non-carriers, nor between variant of unknown significance carriers and non-carriers. Notably, 46% of the identified (likely) pathogenic variant carriers had not been referred for a genetic assessment and consideration of genetic testing.

Conclusion: Our study shows a potential under-ascertainment of women carrying a (likely) pathogenic variant in a high-risk breast cancer susceptibility gene. These results suggest that further research into testing pathways for New Zealand breast cancer patients may be required to reduce the impact of hereditary cancer syndromes for these individuals and their families.
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http://dx.doi.org/10.1007/s10549-020-05986-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7921023PMC
February 2021

Pregnancy outcomes and risk of endometrial cancer: A pooled analysis of individual participant data in the Epidemiology of Endometrial Cancer Consortium.

Int J Cancer 2021 05 17;148(9):2068-2078. Epub 2020 Nov 17.

Department of Clinical and Biological Sciences, University of Turin, Orbassano, Italy.

A full-term pregnancy is associated with reduced endometrial cancer risk; however, whether the effect of additional pregnancies is independent of age at last pregnancy is unknown. The associations between other pregnancy-related factors and endometrial cancer risk are less clear. We pooled individual participant data from 11 cohort and 19 case-control studies participating in the Epidemiology of Endometrial Cancer Consortium (E2C2) including 16 986 women with endometrial cancer and 39 538 control women. We used one- and two-stage meta-analytic approaches to estimate pooled odds ratios (ORs) for the association between exposures and endometrial cancer risk. Ever having a full-term pregnancy was associated with a 41% reduction in risk of endometrial cancer compared to never having a full-term pregnancy (OR = 0.59, 95% confidence interval [CI] 0.56-0.63). The risk reduction appeared the greatest for the first full-term pregnancy (OR = 0.78, 95% CI 0.72-0.84), with a further ~15% reduction per pregnancy up to eight pregnancies (OR = 0.20, 95% CI 0.14-0.28) that was independent of age at last full-term pregnancy. Incomplete pregnancy was also associated with decreased endometrial cancer risk (7%-9% reduction per pregnancy). Twin births appeared to have the same effect as singleton pregnancies. Our pooled analysis shows that, while the magnitude of the risk reduction is greater for a full-term pregnancy than an incomplete pregnancy, each additional pregnancy is associated with further reduction in endometrial cancer risk, independent of age at last full-term pregnancy. These results suggest that the very high progesterone level in the last trimester of pregnancy is not the sole explanation for the protective effect of pregnancy.
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http://dx.doi.org/10.1002/ijc.33360DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7969437PMC
May 2021

Genetic characterization of pancreatic cancer patients and prediction of carrier status of germline pathogenic variants in cancer-predisposing genes.

EBioMedicine 2020 Oct 24;60:103033. Epub 2020 Sep 24.

Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. Electronic address:

Background: National Comprehensive Cancer Network (NCCN) recently recommended germline genetic testing for all pancreatic cancer patients. However, the genes targeted by genetic testing and the feasibility of selecting patients likely to carry pathogenic variants have not been sufficiently verified. The purpose of this study was to genetically characterize Japanese patients and examine whether the current guideline is applicable in this population.

Methods: Using targeted sequencing, we analyzed the coding regions of 27 cancer-predisposing genes in 1,005 pancreatic cancer patients and 23,705 controls in Japan. We compared the pathogenic variant frequency between cases and controls and documented the demographic and clinical characteristics of carrier patients. We then examined if it was possible to use machine learning to predict carrier status based on those characteristics.

Findings: We identified 205 pathogenic variants across the 27 genes. Pathogenic variants in BRCA2, ATM, and BRCA1 were significantly associated with pancreatic cancer. Characteristics associated with carrier status were inconsistent with previous investigations. Machine learning classifiers had a low performance in determining the carrier status of pancreatic cancer patients, while the same classifiers, when applied to breast cancer data as a positive control, had a higher performance that was comparable to that of the NCCN guideline.

Interpretation: Our findings support the clinical significance of multigene panel testing for pancreatic cancer and indicate that at least 3.4% of Japanese patients may respond to poly (ADP ribose) polymerase inhibitor treatments. The difficulty in predicting carrier status suggests that offering germline genetic testing for all pancreatic cancer patients is reasonable.

Funding: AMED under Grant Number JP19kk0305010 and Australian National Health and Medical Research funding (ID177524).
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http://dx.doi.org/10.1016/j.ebiom.2020.103033DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7519363PMC
October 2020

Genotype-phenotype correlations among TP53 carriers: Literature review and analysis of probands undergoing multi-gene panel testing and single-gene testing.

Cancer Genet 2020 10 11;248-249:11-17. Epub 2020 Sep 11.

QIMR Berghofer Medical Research Institute, Genetics and Computational Division, 300 Herston Rd, Herston, QLD 4006, Australia. Electronic address:

Pathogenic germline variants in the TP53 gene predispose to a wide range of cancers, known collectively as Li-Fraumeni syndrome (LFS). There has been much research aimed to identify genotype-phenotype correlations, that is, differences between variant location and/or effect and cancer spectrum. These correlations, should they exist, have potential to impact clinical management of carriers. Review of previously published studies showed a variety of study designs and inconsistency in reported findings. Here, we used pooled data from 427 TP53 carriers who had undergone multigene panel testing and 154 TP53 carriers identified by single-gene testing to investigate correlations between TP53 genotype (truncating variants, hotspot variants, other missense variants with dominant-negative effect, missense variants without dominant-negative effect) and a number of LFS-selected malignancies. Our results suggest that carriers of truncating and hotspot variants might be more likely to present with LFS cancers and have shorter time to first cancer diagnosis compared to carriers of other variant types. However, the differences observed were minor, and we conclude that there is currently insufficient evidence to consider location and/or molecular effect of pathogenic variants to assist with clinical management of TP53 carriers. Larger studies are necessary to confirm the correlations suggested by our analysis.
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http://dx.doi.org/10.1016/j.cancergen.2020.09.002DOI Listing
October 2020

Association of germline variation with the survival of women with pathogenic variants and breast cancer.

NPJ Breast Cancer 2020 10;6:44. Epub 2020 Sep 10.

Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Fred A. Litwin Center for Cancer Genetics, Toronto, ON Canada.

Germline genetic variation has been suggested to influence the survival of breast cancer patients independently of tumor pathology. We have studied survival associations of genetic variants in two etiologically unique groups of breast cancer patients, the carriers of germline pathogenic variants in or genes. We found that rs57025206 was significantly associated with the overall survival, predicting higher mortality of carrier patients with estrogen receptor-negative breast cancer, with a hazard ratio 4.37 (95% confidence interval 3.03-6.30,  = 3.1 × 10). Multivariable analysis adjusted for tumor characteristics suggested that rs57025206 was an independent survival marker. In addition, our exploratory analyses suggest that the associations between genetic variants and breast cancer patient survival may depend on tumor biological subgroup and clinical patient characteristics.
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http://dx.doi.org/10.1038/s41523-020-00185-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7483417PMC
September 2020

Mendelian randomization analyses suggest a role for cholesterol in the development of endometrial cancer.

Int J Cancer 2021 01 7;148(2):307-319. Epub 2020 Aug 7.

Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, Georgia, USA.

Blood lipids have been associated with the development of a range of cancers, including breast, lung and colorectal cancer. For endometrial cancer, observational studies have reported inconsistent associations between blood lipids and cancer risk. To reduce biases from unmeasured confounding, we performed a bidirectional, two-sample Mendelian randomization analysis to investigate the relationship between levels of three blood lipids (low-density lipoprotein [LDL] and high-density lipoprotein [HDL] cholesterol, and triglycerides) and endometrial cancer risk. Genetic variants associated with each of these blood lipid levels (P < 5 × 10 ) were identified as instrumental variables, and assessed using genome-wide association study data from the Endometrial Cancer Association Consortium (12 906 cases and 108 979 controls) and the Global Lipids Genetic Consortium (n = 188 578). Mendelian randomization analyses found genetically raised LDL cholesterol levels to be associated with lower risks of endometrial cancer of all histologies combined, and of endometrioid and non-endometrioid subtypes. Conversely, higher genetically predicted HDL cholesterol levels were associated with increased risk of non-endometrioid endometrial cancer. After accounting for the potential confounding role of obesity (as measured by genetic variants associated with body mass index), the association between genetically predicted increased LDL cholesterol levels and lower endometrial cancer risk remained significant, especially for non-endometrioid endometrial cancer. There was no evidence to support a role for triglycerides in endometrial cancer development. Our study supports a role for LDL and HDL cholesterol in the development of non-endometrioid endometrial cancer. Further studies are required to understand the mechanisms underlying these findings.
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http://dx.doi.org/10.1002/ijc.33206DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7757859PMC
January 2021

Contribution of mRNA Splicing to Mismatch Repair Gene Sequence Variant Interpretation.

Front Genet 2020 27;11:798. Epub 2020 Jul 27.

Genetics and Computational Biology Department, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.

Functional assays that assess mRNA splicing can be used in interpretation of the clinical significance of sequence variants, including the Lynch syndrome-associated mismatch repair (MMR) genes. The purpose of this study was to investigate the contribution of splicing assay data to the classification of MMR gene sequence variants. We assayed mRNA splicing for 24 sequence variants in , , and , including 12 missense variants that were also assessed using a cell-free MMR activity (CIMRA) assay. Multifactorial likelihood analysis was conducted for each variant, combining CIMRA outputs and clinical data where available. We collated these results with existing public data to provide a dataset of splicing assay results for a total of 671 MMR gene sequence variants (328 missense/in-frame indel), and published and unpublished repair activity measurements for 154 of these variants. There were 241 variants for which a splicing aberration was detected: 92 complete impact, 33 incomplete impact, and 116 where it was not possible to determine complete versus incomplete splicing impact. Splicing results mostly aided in the interpretation of intronic (72%) and silent (92%) variants and were the least useful for missense substitutions/in-frame indels (10%). MMR protein functional activity assays were more useful in the analysis of these exonic variants but by design they were not able to detect clinically important splicing aberrations identified by parallel mRNA assays. The development of high throughput assays that can quantitatively assess impact on mRNA transcript expression and protein function in parallel will streamline classification of MMR gene sequence variants.
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http://dx.doi.org/10.3389/fgene.2020.00798DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7398121PMC
July 2020

ROR1 is upregulated in endometrial cancer and represents a novel therapeutic target.

Sci Rep 2020 08 17;10(1):13906. Epub 2020 Aug 17.

Gynaecological Cancer Research Group, Lowy Cancer Research Centre, School of Women's and Children's Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, 2052, Australia.

ROR1 and ROR2 are receptor tyrosine kinases with altered expression in a range of cancers. Silencing ROR1 or ROR2 in different tumour types has been shown to inhibit proliferation and decrease metastatic potential. The aim of this study was to investigate the role of ROR1 and ROR2 in endometrial cancer via immunohistochemistry (IHC) in a large endometrial cancer patient cohort (n = 499) and through in vitro analysis in endometrial cancer cell lines. Correlation was assessed between ROR1/2 expression and clinicopathological parameters. Kaplan Meier curves were produced for 5-year progression free survival (PFS) and overall survival (OS) with low/moderate versus high ROR1/2 intensity. Cox multivariate regression was applied to analyse the effect of selected covariates on the PFS and OS. The effect of ROR1 and/or ROR2 modulation on cell proliferation, adhesion, migration and invasion was analysed in two endometrial cancer cell lines (KLE and MFE-296). We observed a significant decrease in OS and PFS in patients with high ROR1 expression. ROR1 silencing and ROR2 overexpression significantly inhibited proliferation of KLE endometrial cancer cells and decreased migration. This study supports the oncogenic role of ROR1 in endometrial cancer, and warrants investigation of future application of ROR1-targeting therapies in endometrial cancer patients.
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http://dx.doi.org/10.1038/s41598-020-70924-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7431863PMC
August 2020

Considerations in assessing germline variant pathogenicity using cosegregation analysis.

Genet Med 2020 12 10;22(12):2052-2059. Epub 2020 Aug 10.

University of Utah, Salt Lake City, UT, USA.

Purpose: The American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) have developed guidelines for classifying germline variants as pathogenic or benign to interpret genetic testing results. Cosegregation analysis is an important component of the guidelines. There are two main approaches for cosegregation analysis: meiosis counting and Bayes factor-based quantitative methods. Of these, the ACMG/AMP guidelines employ only meiosis counting. The accuracy of either approach has not been sufficiently addressed in previous works.

Methods: We analyzed hypothetical, simulated, and real-life data to evaluate the accuracy of each approach for cancer-associated genes.

Results: We demonstrate that meiosis counting can provide incorrect classifications when the underlying genetic basis of the disease departs from simple Mendelian situations. Some Bayes factor approaches are currently implemented with inappropriate penetrance. We propose an improved penetrance model and describe several critical considerations, including the accuracy of cosegregation for moderate-risk genes and the impact of pleiotropy, population, and birth year. We highlight a webserver, COOL (Co-segregation Online, http://BJFengLab.org/ ), that implements an accurate Bayes factor cosegregation analysis.

Conclusion: An appropriate penetrance model improves the accuracy of Bayes factor cosegregation analysis for high-penetrant variants, and is a better choice than meiosis counting whenever feasible.
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http://dx.doi.org/10.1038/s41436-020-0920-4DOI Listing
December 2020

Polygenic risk scores and breast and epithelial ovarian cancer risks for carriers of BRCA1 and BRCA2 pathogenic variants.

Genet Med 2020 10 15;22(10):1653-1666. Epub 2020 Jul 15.

Royal Devon & Exeter Hospital, Department of Clinical Genetics, Exeter, UK.

Purpose: We assessed the associations between population-based polygenic risk scores (PRS) for breast (BC) or epithelial ovarian cancer (EOC) with cancer risks for BRCA1 and BRCA2 pathogenic variant carriers.

Methods: Retrospective cohort data on 18,935 BRCA1 and 12,339 BRCA2 female pathogenic variant carriers of European ancestry were available. Three versions of a 313 single-nucleotide polymorphism (SNP) BC PRS were evaluated based on whether they predict overall, estrogen receptor (ER)-negative, or ER-positive BC, and two PRS for overall or high-grade serous EOC. Associations were validated in a prospective cohort.

Results: The ER-negative PRS showed the strongest association with BC risk for BRCA1 carriers (hazard ratio [HR] per standard deviation = 1.29 [95% CI 1.25-1.33], P = 3×10). For BRCA2, the strongest association was with overall BC PRS (HR = 1.31 [95% CI 1.27-1.36], P = 7×10). HR estimates decreased significantly with age and there was evidence for differences in associations by predicted variant effects on protein expression. The HR estimates were smaller than general population estimates. The high-grade serous PRS yielded the strongest associations with EOC risk for BRCA1 (HR = 1.32 [95% CI 1.25-1.40], P = 3×10) and BRCA2 (HR = 1.44 [95% CI 1.30-1.60], P = 4×10) carriers. The associations in the prospective cohort were similar.

Conclusion: Population-based PRS are strongly associated with BC and EOC risks for BRCA1/2 carriers and predict substantial absolute risk differences for women at PRS distribution extremes.
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http://dx.doi.org/10.1038/s41436-020-0862-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7521995PMC
October 2020

The MLH1 polymorphism rs1800734 and risk of endometrial cancer with microsatellite instability.

Clin Epigenetics 2020 07 8;12(1):102. Epub 2020 Jul 8.

Cancer Gene Regulation Group, Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK.

Both colorectal (CRC, 15%) and endometrial cancers (EC, 30%) exhibit microsatellite instability (MSI) due to MLH1 hypermethylation and silencing. The MLH1 promoter polymorphism, rs1800734 is associated with MSI CRC risk, increased methylation and reduced MLH1 expression. In EC samples, we investigated rs1800734 risk using MSI and MSS cases and controls. We found no evidence that rs1800734 or other MLH1 SNPs were associated with the risk of MSI EC. We found the rs1800734 risk allele had no effect on MLH1 methylation or expression in ECs. We propose that MLH1 hypermethylation occurs by different mechanisms in CRC and EC.
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http://dx.doi.org/10.1186/s13148-020-00889-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7346630PMC
July 2020

Variant effect on splicing regulatory elements, branchpoint usage, and pseudoexonization: Strategies to enhance bioinformatic prediction using hereditary cancer genes as exemplars.

Hum Mutat 2020 Oct 17;41(10):1705-1721. Epub 2020 Jul 17.

Genetics and Computational Biology Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.

It is possible to estimate the prior probability of pathogenicity for germline disease gene variants based on bioinformatic prediction of variant effect/s. However, routinely used approaches have likely led to the underestimation and underreporting of variants located outside donor and acceptor splice site motifs that affect messenger RNA (mRNA) processing. This review presents information about hereditary cancer gene germline variants, outside native splice sites, with experimentally validated splicing effects. We list 95 exonic variants that impact splicing regulatory elements (SREs) in BRCA1, BRCA2, MLH1, MSH2, MSH6, and PMS2. We utilized a pre-existing large-scale BRCA1 functional data set to map functional SREs, and assess the relative performance of different tools to predict effects of 283 variants on such elements. We also describe rare examples of intronic variants that impact branchpoint (BP) sites and create pseudoexons. We discuss the challenges in predicting variant effect on BP site usage and pseudoexonization, and suggest strategies to improve the bioinformatic prioritization of such variants for experimental validation. Importantly, our review and analysis highlights the value of considering impact of variants outside donor and acceptor motifs on mRNA splicing and disease causation.
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http://dx.doi.org/10.1002/humu.24074DOI Listing
October 2020

Assessment of polygenic architecture and risk prediction based on common variants across fourteen cancers.

Nat Commun 2020 07 3;11(1):3353. Epub 2020 Jul 3.

Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK.

Genome-wide association studies (GWAS) have led to the identification of hundreds of susceptibility loci across cancers, but the impact of further studies remains uncertain. Here we analyse summary-level data from GWAS of European ancestry across fourteen cancer sites to estimate the number of common susceptibility variants (polygenicity) and underlying effect-size distribution. All cancers show a high degree of polygenicity, involving at a minimum of thousands of loci. We project that sample sizes required to explain 80% of GWAS heritability vary from 60,000 cases for testicular to over 1,000,000 cases for lung cancer. The maximum relative risk achievable for subjects at the 99th risk percentile of underlying polygenic risk scores (PRS), compared to average risk, ranges from 12 for testicular to 2.5 for ovarian cancer. We show that PRS have potential for risk stratification for cancers of breast, colon and prostate, but less so for others because of modest heritability and lower incidence.
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http://dx.doi.org/10.1038/s41467-020-16483-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7335068PMC
July 2020

A Genetic Risk Score to Personalize Prostate Cancer Screening, Applied to Population Data.

Cancer Epidemiol Biomarkers Prev 2020 09 24;29(9):1731-1738. Epub 2020 Jun 24.

Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota.

Background: A polygenic hazard score (PHS), the weighted sum of 54 SNP genotypes, was previously validated for association with clinically significant prostate cancer and for improved prostate cancer screening accuracy. Here, we assess the potential impact of PHS-informed screening.

Methods: United Kingdom population incidence data (Cancer Research United Kingdom) and data from the Cluster Randomized Trial of PSA Testing for Prostate Cancer were combined to estimate age-specific clinically significant prostate cancer incidence (Gleason score ≥7, stage T3-T4, PSA ≥10, or nodal/distant metastases). Using HRs estimated from the ProtecT prostate cancer trial, age-specific incidence rates were calculated for various PHS risk percentiles. Risk-equivalent age, when someone with a given PHS percentile has prostate cancer risk equivalent to an average 50-year-old man (50-year-standard risk), was derived from PHS and incidence data. Positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was calculated using PHS-adjusted age groups.

Results: The expected age at diagnosis of clinically significant prostate cancer differs by 19 years between the 1st and 99th PHS percentiles: men with PHS in the 1st and 99th percentiles reach the 50-year-standard risk level at ages 60 and 41, respectively. PPV of PSA was higher for men with higher PHS-adjusted age.

Conclusions: PHS provides individualized estimates of risk-equivalent age for clinically significant prostate cancer. Screening initiation could be adjusted by a man's PHS.

Impact: Personalized genetic risk assessments could inform prostate cancer screening decisions.
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http://dx.doi.org/10.1158/1055-9965.EPI-19-1527DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7483627PMC
September 2020

Generating high-quality data abstractions from scanned clinical records: text-mining-assisted extraction of endometrial carcinoma pathology features as proof of principle.

BMJ Open 2020 06 11;10(6):e037740. Epub 2020 Jun 11.

Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.

Objective: Medical research studies often rely on the manual collection of data from scanned typewritten clinical records, which can be laborious, time consuming and error prone because of the need to review individual clinical records. We aimed to use text mining to assist with the extraction of clinical features from complex text-based scanned pathology records for medical research studies.

Design: Text mining performance was measured by extracting and annotating three distinct pathological features from scanned photocopies of endometrial carcinoma clinical pathology reports, and comparing results to manually abstracted terms. Inclusion and exclusion keyword trigger terms to capture leiomyomas, endometriosis and adenomyosis were provided based on expert knowledge. Terms were expanded with character variations based on common optical character recognition (OCR) error patterns as well as negation phrases found in sample reports. The approach was evaluated on an unseen test set of 1293 scanned pathology reports originating from laboratories across Australia.

Setting: Scanned typewritten pathology reports for women aged 18-79 years with newly diagnosed endometrial cancer (2005-2007) in Australia.

Results: High concordance with final abstracted codes was observed for identifying the presence of three pathology features (94%-98% F-measure). The approach was more consistent and reliable than manual abstractions, identifying 3%-14% additional feature instances.

Conclusion: Keyword trigger-based automation with OCR error correction and negation handling proved not only to be rapid and convenient, but also providing consistent and reliable data abstractions from scanned clinical records. In conjunction with manual review, it can assist in the generation of high-quality data abstractions for medical research studies.
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http://dx.doi.org/10.1136/bmjopen-2020-037740DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7295399PMC
June 2020

Suggested application of HER2+ breast tumor phenotype for germline TP53 variant classification within ACMG/AMP guidelines.

Hum Mutat 2020 09 12;41(9):1555-1562. Epub 2020 Jun 12.

Genetics and Computational Division, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia.

Early onset breast cancer is the most common malignancy in women with Li-Fraumeni syndrome, caused by germline TP53 pathogenic variants. It has repeatedly been suggested that breast tumors from TP53 carriers are more likely to be HER2+ than those of noncarriers, but this information has not been incorporated into variant interpretation models for TP53. Breast tumor pathology is already being used quantitatively for assessing pathogenicity of germline variants in other genes, and it has been suggested that this type of evidence can be incorporated into current American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) guidelines for germline variant classification. Here, by reviewing published data and using internal datasets separated by different age groups, we investigated if breast tumor HER2+ status has utility as a predictor of TP53 germline variant pathogenicity, considering age at diagnosis. Overall, our results showed that the identification of HER2+ breast tumors diagnosed before the age of 40 can be conservatively incorporated into the current TP53-specific ACMG/AMP PP4 criterion, following a point system detailed in this manuscript. Further larger studies will be needed to reassess the value of HER2+ breast tumors diagnosed at a later age.
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http://dx.doi.org/10.1002/humu.24060DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7484289PMC
September 2020
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