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18 maart 2025: Bron: Science Direct Available online 11 March 2025

Uit groot DNA - Whole Exome Sequencing onderzoek van de UK Biobank onder data gegevens van ruim 400.000 mensen blijkt dat bepaalde afwijkende genencombinaties duiden op erfelijkheid van bepaalde vormen van kanker. Uit de recente studie kwam naar voren dat eierstokkanker geassocieerd werd voor 9 genen (46 procent), blaaskanker voor 8 genen (72 procent) en ook alvleesklierkanker voor 8 genen, wat volgens de onderzoekers 100 procent is voor deze vorm van kanker. Wat betekent wie combinaties heeft van deze 8 genen voor 100 procent zeker kan zijn dat de alvleesklierkanker bij die persoon 100 procent erfelijk is. Er moet bij worden gezegd dat op dit moment al 20.000 verschillende genenafwijkingen bekend zijn en daarvan zijn er 15.000 genen dus  getest bij dit onderzoek. Daarnaast zijn in eerste instantie 11 vormen van kanker gerelateerd aan dit onderzoek.   

Zie deze grafiek voor andere vormen van erfelijkheid van kanker:

 Table 2. Summary of results for PTV burden tests for 11 cancers

CancerWald testFirth regression
Number of genes at a p value thresholdGenes with p < 2.5 × 106COSMIC TSGs with p < 1 × 104Genes with p < 2.5 × 106
p < 0.001p < 1 × 104p < 2.5 × 106
Breast 30 9 6 BRCA2BRCA1CHEK2PALB2ATMMAP3K1 BRCA2BRCA1CHEK2PALB2ATMMAP3K1LZTR1BARD1 BRCA2BRCA1PALB2CHEK2ATMMAP3K1
Prostate 35 8 3 BRCA2CHEK2ATM BRCA2CHEK2ATM BRCA2CHEK2ATM
Bowel 42 9 5 MSH6MSH2MLH1APCGAPDH MSH6MSH2MLH1APCFLCNSMAD4 MSH6MSH2MLH1APC
Lung 46 8 0 N/A ARHGAP35BIRC3 N/A
Pancreatic 100 38 8 ATMMEN1RCN2YPEL3SMC2SEC14L3GNG10ZNF461 ATMMEN1 ATM
Endometrial 80 26 5 MSH6MLH1ACRV1STK32CPSMC6 MSH6MLH1MSH2 MSH6
Ovarian 115 35 9 BRCA2BRCA1IVDJAMLKCNAB2ZFP14TMEM163TMEM167ANHEJ1 BRCA2BRCA1NFRSF14 BRCA2BRCA1
Esophagus 117 32 2 KNL1IRF2BP2 KNL1ATMFUS N/A
Kidney 89 27 6 PKD1FGL2TTC9EX0C7NCK2TMEM174 N/A N/A
Bladder 72 27 8 DLX2ZNF506CDCP2TMEM222KDM1AARHGEF6HRNLRP10 N/A N/A
Malignant melanoma 45 12 4 DCXMED9MRPL44CDKN2A CDKN2A MED9
Genes not previously mentioned: LZTR1 (MIM: 600574), GAPDH (MIM: 138400), FLCN [MIM: 607273), SMAD4 (MIM: 600993), ARHGAP35 (MIM: 605277), BIRC3 (MIM: 601721), MEN1 (MIM: 613733), RCN2 (MIM: 602584), YPEL3 (MIM: 609724), SMC2 (MIM: 605576), SEC14L3 (MIM: 612824), GNG10 (MIM: 604389), ZNF461 (MIM: 608640), ACRV1 (MIM: 102525), STK32C (MIM: N/A), PSMC6 (MIM: 602708), IVD (MIM: 607036), JAML (MIM: 609770), KCNAB2 (MIM: 601142), ZFP15 (MIM: 620163), TMEM163 (MIM: 618978), TMEM167A (MIM: 620000), NHEJ1 (MIM: 611290), NFRSF14 (MIM: N/A), KNL1 (MIM: 609173), IRF2BP2 (MIM: 615332), FUS (MIM: 137070), PKD1 (MIM: 601313), FGL2 (MIM: 605351), TTC9 (MIM: 610488), EX0C7 (MIM: 608163), NCK2 (MIM: 604930), TMEM174 (MIM: 614909), DLX2 (MIM: 126255), ZNF506 (MIM: N/A), CDCP2 (MIM: 612320), TMEM222 (MIM: 619469), KDM1A (MIM: 609132), ARHGEF6 (MIM: 300267), HR (MIM: 602302), NLRP10 (MIM: 609662), DCX (MIM: 300121), MRPL44 (MIM: 611849), and CDKN2A (MIM: 600160). The table includes the number of genes reaching different significance thresholds as well as the list of genes that reach exome-wide significance and COSMIC TSGs with Wald test p < 1 × 10−4 listed in ascending p value order. For breast cancer, the results are from the meta-analysis of the UK Biobank and BCAC datasets as reported by Wilcox et al.,5 apart from for Firth regression, which was just run in the UK Biobank. The final column shows the genes that remained exome wide significant using Firth regression. N/A, not applicable.

Resultaten PTV belasting:

De PTV belasting voor de 11 agenoemde vormen van kanker zijn samengevat in Table 2. Associaties voor vormen van kanker die niet eerder werden gerapporteerd en een statistische significantie van p < 0.001 bereikten zie referenties 5 en 27 en Tables S1–S7. De corresponderende Manhattan en quantile-quantile (QQ) plots zijn te lezen  in Figures S1–S14. Andere resultaten zijn te lezen in Table S8.


Het volledige studierapport is gepubliceerd in Science Direct en is gratis in te zien of te downloaden. Klik daarvoor op de titel van het abstract:

The contribution of coding variants to the heritability of multiple cancer types using UK Biobank whole-exome sequencing data

https://doi.org/10.1016/j.ajhg.2025.02.013Get rights and content
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Summary

Genome-wide association studies have been highly successful at identifying common variants associated with cancer; however, they do not explain all the inherited risks of cancer. Family-based studies, targeted sequencing, and, more recently, exome-wide association studies have identified rare coding variants in some genes associated with cancer risk, but the overall contribution of these variants to the heritability of cancer is less clear. Here, we describe a method to estimate the genome-wide contribution of rare coding variants to heritability that fits models to the burden effect sizes using an empirical Bayesian approach. We apply this method to the burden of protein-truncating variants in over 15,000 genes for 11 cancers in the UK Biobank using whole-exome sequencing data on over 400,000 individuals. We extend the method to consider the overlap of genes contributing to pairs of cancers. We found ovarian cancer to have the greatest proportion of heritability attributable to protein-truncating variants in genes (46%). The joint cancer models highlight significant clustering of cancer types, including a near-complete overlap in susceptibility genes for breast, ovarian, prostate, and pancreatic cancer. Our results provide insights into the contribution of rare coding variants to the heritability of cancer and identify additional genes with strong evidence of susceptibility to multiple cancer types.

Data and code availability

Requests for access to UK Biobank data should be made to the UK Biobank access management team (access@ukbiobank.ac.uk). QC filtering of VCF files was performed using vcftools v.0.1.15, bcftools v.1.9, picard v.2.22.2, and plink v.1.90b, as outlined in the material and methods. Variants were annotated using Ensembl VEP v.101 with assembly GRCh38. The code for each software is available at the website of each package. Data manipulation and analysis were performed using R-4.3.3 with the packages clusterProfiler (4.2.2), data.table (1.14.2), dplyr (1.0.9), dbplyr (2.5.0), gtools (3.9.5), HGNChelper (0.8.9), SKAT (2.2.5), tibble (3.2.1), and tidyr (1.3.1). Plots were created using the additional packages ggplot2 (3.5.1) and ggrepel (0.9.5). The code for each of the R packages can be found in their associated vignettes. Burden test results are available for each cancer from the GWAS Catalog (https://www.ebi.ac.uk/gwas/https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/). The accession numbers for the burden test results reported in this paper are GWAS Catalog: GCST90503274 (pancreatic cancer), GCST90503275 (endometrial cancer), GCST90503276 (ovarian cancer), GCST90503277 (oesophagus cancer), GSCST90503278 (kidney cancer), GCST90503279 (bladder cancer), and GCST90503280 (malignant melanoma).

Acknowledgments

QC of the UK Biobank sequencing data was funded by the Medical Research Council (unit programs: MC_UU_12015/2 and MC_UU_00006/2). The research was conducted using the UK Biobank Resource under application no. 28126. N.W. was supported by the International Alliance for Cancer Early Detection, an alliance between Cancer Research UK (C14478/A29329), the Canary Center at Stanford University, the University of Cambridge, the OHSU Knight Cancer InstituteUniversity College London, and the University of Manchester. J.D. was supported by core funding from the NIHR Cambridge Biomedical Research Centre (NIHR203312). X.Y. and J.P.T. were supported by Cancer Research UK (PPRPGM-Nov20\100002 and PRCPJT-May21\100006).

Author contributions

D.F.E. supervised this work and directed the overall analysis. N.W. performed the statistical analysis. N.W., E.J.G., J.P.T., and J.D.P. developed the bioinformatics and computational pipelines. X.Y. and J.D. acquired data, and X.Y. extracted cancer phenotypes. N.W. and D.F.E. drafted the manuscript. All authors reviewed and approved the paper.

Declaration of interests

N.W. has been an employee and shareholder of Illumina since October 1, 2024. J.R.B.P. and E.J.G. are employees of Insmed Innovation UK and hold stock/stock options in Insmed, Inc. J.R.B.P. also receives research funding from GSK and engages in paid consultancy for WW International, Inc.

Supplemental information

What’s this?
Document S1. Figures S1–S15, Tables S8–S15, S17–S26, S28, and S29, and supplemental methodsData S1. Tables S1–S7, S16, S27, and S30Document S2. Article plus supplemental information

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References

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