Abstract
Human cancers arise from environmental, heritable and somatic factors, but how these mechanisms interact in tumorigenesis is poorly understood. Studying 17,152 prospectively sequenced patients with cancer, we identified pathogenic germline variants in cancer predisposition genes, and assessed their zygosity and co-occurring somatic alterations in the concomitant tumors. Two major routes to tumorigenesis were apparent. In carriers of pathogenic germline variants in high-penetrance genes (5.1% overall), lineage-dependent patterns of biallelic inactivation led to tumors exhibiting mechanism-specific somatic phenotypes and fewer additional somatic oncogenic drivers. Nevertheless, 27% of cancers in these patients, and most tumors in patients with pathogenic germline variants in lower-penetrance genes, lacked particular hallmarks of tumorigenesis associated with the germline allele. The dependence of tumors on pathogenic germline variants is variable and often dictated by both penetrance and lineage, a finding with implications for clinical management.
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Data availability
Study results including cohort-wide prevalence and zygosity of germline and somatic mutations are available at https://www.signaldb.org and may be subject to a registration process and certain terms of use specified at https://www.signaldb.org/terms, including that the results may be used only for noncommercial research purposes without a license agreement with MSK Cancer Center. Germline variants and tumor-specific zygosity estimates are available from the National Center for Biotechnology Information dbGaP archive at accession no. phs001858.v1.p1. In addition, the following publicly available data were used: annotations indicating statistically significant somatic mutations were derived from Hotspots (http://www.cancerhotspots.org); biological effects, prognostic information and treatment implications of specific cancer gene alterations were obtained from OncoKB as of June 2018 (http://www.oncokb.org); variant-level annotations aggregated from data resources for germline alterations were obtained from myvariant.info as of August 2017 (https://myvariant.info); population frequencies for observed germline alterations were derived from gnomAD r2.0.2 (https://gnomad.broadinstitute.org); and annotations regarding the deleterious nature of known germline variants and associated phenotypes were downloaded from ClinVar as of September 2017 (https://www.ncbi.nlm.nih.gov/clinvar).
Code availability
Source code is available at https://github.com/taylor-lab/somatic-germline.
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Acknowledgements
We thank our patients and their families for participating in this study. We thank the members of the Molecular Diagnostics Service in the Department of Pathology, the Marie-Josée and Henry R. Kravis Center for Molecular Oncology, the Robert and Kate Niehaus Center for Inherited Cancer Genomics, and the Berger and Taylor labs for discussions and support. This work was supported by National Institutes of Health awards (nos. P30 CA008748 and R01 CA227534 to M.F.B.), the Breast Cancer Research Foundation (to M.E.R. and K.O.), the Fund for Innovation in Cancer Informatics (to J.G.) and Cycle for Survival.
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Contributions
P.S., C.B., M.F.B. and B.S.T. designed the study. P.S., C.B., P.J., S.S.C., A.L.R., A.V.P., C.M.B., M.F.B. and B.S.T. designed and performed data analysis. C.F., A.S., G.J., M.P., J.H., N.S., R.C., J. Galle, A.Z., M.L., D.M.H., D.B.S. and M.F.B. assisted with prospective genomic and clinical data collection and sample annotation. P.S., C.B., Y.K., S.M., J.V., K.A.C., M.I.C., M.F.W., D.M., O.C.B., L.Z., K.O., M.E.R. and Z.K.S. assisted with germline variant pathogenicity and penetrance annotation. S.O.S., I.D., X.L., J. Gao and N.S. assisted in the development of the SignalDB portal. J.S. assisted with pathology. P.S., C.B., M.F.B. and B.S.T. wrote the manuscript with input from all authors. M.F.B. and B.S.T. contributed equally as senior authors.
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Competing interests
K.A.C. reports advisory or consulting activities with AstraZeneca, MSD Ireland and GSK Ireland, and receives honoraria from Pfizer. M.I.C. has had an advisory role with Pfizer. D.M.H. received personal fees from Chugai Pharma, Boehringer Ingelheim, AstraZeneca, Pfizer, Bayer, Debiopharm Group and Genentech, and grants from AstraZeneca, Puma Biotechnology, Loxo Oncology (now owned by Eli Lilly) and Bayer. L.Z. received honoraria from Future Technology Research LLC, Roche Diagnostics Asia Pacific, BGI and Illumina, and has family members with leadership positions and ownership interests in Decipher Medicine. M.E.R. reports honoraria from Research to Practice, Intellisphere and Physician’s Education Resource; consulting and advisory activities for AstraZeneca, Daiichi-Sankyo, Epic Science, Merck and Pfizer (all uncompensated), and Change Healthcare; institutional research funding from AbbVie, AstraZeneca, Merck and Pfizer; and editorial services for AstraZeneca and Pfizer. D.B.S. has consulted with and received honoraria from Pfizer, Loxo/Lilly Oncology, Illumina, Vividion Therapeutics, Scorpion Therapeutics, Fore Biotherapeutics and BioBridge Pharma. Z.K.S. has an immediate family member who serves as a consultant in Ophthalmology for Alcon, Adverum, Gyroscope Therapeutics Ltd, Neurogene and RegenexBio, outside the submitted work. M.F.B. reports receiving research funding from Illumina and Grail and advisory board activities for Roche. B.S.T. reports advisory board activities for Boehringer Ingelheim and Loxo Oncology at Lilly, and research support from Genentech. The remaining authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Variant discovery and pathogenicity classification.
a) Schematic of the workflow for the germline variant discovery pipeline. b) Contribution of most important features used in classification of pathogenicity. c) Evidence for pathogenicity in classifier-based pathogenicity calls. Proportion of variant calls predicted as pathogenic (first four columns) or benign (fifth column) that exhibited orthogonal evidence of pathogenicity from ClinVar (April, 2020), by medical geneticist review, or as truncating mutations in tumor suppressor genes (excluding last exon or within 50 amino acids at the C-terminus end of the protein).
Extended Data Fig. 2 Ancestry inference.
a) The inference of ancestry from polymorphic SNPs with sufficient coverage in the MSK-IMPACT assay (see Supplementary Note). b) Breakdown of pathogenic variants by ancestry subpopulation. EUR, European; ASJ, Ashkenazi Jewish; AFR, African/African American; ASN, East/South Asian; OTH, Other.
Extended Data Fig. 3 Mechanisms of biallelic inactivation.
Different classes of somatic alterations leading to biallelic loss in carriers of pathogenic variants are shown by gene. LOH, Copy number loss of heterozygosity.
Extended Data Fig. 4 Biallelic inactivation via epigenetic means in TCGA germline carriers.
a) The number of carriers of one of 310 pathogenic germline mutations in 17 high penetrance genes for which biallelic inactivation was apparent by promoter methylation (dark blue; n = 10 total, 3.2% of such patients). Data suggests that heterozygous carriers do not acquire biallelic inactivation via epigenetic silencing of the remaining allele in appreciable numbers as an alternative mechanism to LOH. b) Germline mutations and promoter methylation combined for BRCA1 in ovarian cancers and MLH1 in colorectal cancers indicate that they arise mutually exclusively in affected cancers. All data were acquired from the PanCancerAtlas of The Cancer Genome Atlas project (see Supplementary Note).
Extended Data Fig. 5 Tumor-specific zygosity inference by various classes of variants and specimen types.
a) The rate of biallelic inactivation in the tumors for pathogenic variants as well as multiple classes of non-pathogenic variants including all variants of unknown significance (VUS, as in Fig. 1b), all common variants (MAF > 5%), and all variants annotated as benign in ClinVar. b) The rates of biallelic inactivation in primary and metastasis samples compared with those in benign variants in the corresponding specimen types (primary or metastasis). c) By penetrance level and specimen type (primary or metastasis), the rate of somatic biallelic inactivation of pathogenic variants in cancer types that are either associated or not with increased prevalence in carriers. In gray are benign germline variants in the same genes and cancer types. Points represent biallelic rates for high (n = 714), moderate (n = 354) and low/uncertain (n = 1,353) pathogenic variants. Error bars are 95% binomial CIs. d) As in Main Text Fig. 2c, the rate of somatic biallelic inactivation of germline pathogenic variants in high penetrance genes by association with increased prevalence. Not shown here are those genes with no association to any cancer type or those genes with fewer than five pathogenic variants. Shown are the fraction with somatic biallelic inactivation among carriers of pathogenic variants (red) and benign variants (gray) within the same gene. Error bars are 95% binomial CIs.
Extended Data Fig. 6 Somatic mutations in APC I1307K carriers.
APC I1307K, classified as low penetrance (see Supplementary Note), is a T > A polymorphism that creates a hypermutable tract of eight adenines that increases the propensity for polymerase slippage leading to an additional insertion of adenine, which generates a frameshift. In our cohort, this somatic APC I1307fs* frameshift mutation that results from the aforementioned polymerase slippage on the allele carrying the germline I1307K variant occurred only in colorectal cancers. Seven of these eight colorectal cancers harbored a second somatic mutation leading to biallelic inactivation, which reaffirmed the ‘three-hit’ model for this variant.
Extended Data Fig. 7 Age of onset in germline carriers by association with cancer type.
Age of cancer diagnosis is shown for carriers of high penetrance pathogenic germline variants stratified by association with cancer type and zygosity. Data shown for 10,076 germline WT patients, 330 carriers in associated lineages (276 with biallelic loss) and 176 carriers in non-associated lineages (67 with biallelic loss). Linear regression adjusting for cancer type, specimen type (primary vs. metastasis), genomic instability and sex. For the boxplots, the center red line is the median, the lower and the upper hinges represent the first and third quartiles for the ages of onset. The upper and lower whiskers extend up to 1.5 * IQR (interquartile range) above and below the upper and lower hinges, respectively.
Extended Data Fig. 8 Mutational signatures associated with germline alleles.
Spectrum of mutational signatures in germline carriers of pathogenic alleles in the indicated genes. A signature is considered present if 30% or greater of all somatic mutations are attributed to it. Only signatures that are detected in at least 5% of the carriers are shown. In parentheses is the number of carriers of pathogenic alleles that had 10 or more somatic mutations for robust mutational signature inference.
Extended Data Fig. 9 Cancer type-specific differences in somatic alterations in carriers.
A) The gene-specific pattern of somatic alteration differences in GISTs among carriers of high penetrance alleles that are biallelic in the corresponding tumors (left) versus the rest. B) As in panel (A) but for breast cancers harboring canonical PIK3CA or CCND1 alterations (black and gray are carriers of germline alleles that are somatic biallelic or not in the corresponding cancers, respectively). Related to main text Fig. 3.
Extended Data Fig. 10 Somatic phenotypes of MSI-positive tumors in germline MMR carriers.
a) The proportion of germline MMR carriers among patients who presented with Lynch-associated cancers by gene altered, grouped by zygosity and MSI phenotype. b) Tumor mutational burden (TMB) for MSI tumors in carriers of pathogenic germline variants in MLH1, MSH2, MSH6, and PMS2 indicating no significant difference between the mutational burden of MSI tumors in carriers of different MMR gene mutations. For the boxplots, the center line is the median, the lower and the upper hinges represent the first and third quartiles. The upper and lower whiskers extend up to 1.5 * IQR (interquartile range) above and below the upper and lower hinges, respectively. c) MSIsensor score as a function of the proportion of indels among somatic mutations is shown for MSS tumors (black) compared to those germline pathogenic MSH6 (light blue), PMS2 (dark blue), MLH1 (light green), and MSH2 (dark green) carriers indicate gene-specific differences in their somatic mutational phenotype. Specifically, MSH6-mutant patients had a lower intensity of the MSI phenotype as measured by MSIsensor (P = 2.1 × 10-3, Mann Whitney U test) and a lower proportion of somatic indels in affected tumors (P = 3.7 × 10-7, Mann Whitney U test). d) Three distinct classes of somatic mutations (insertions, deletions, and substitutions) in the affected tumors of germline carriers of the indicated MMR genes (same as in panel c) indicates different germline MMR dysfunctions drive mutation class-specific differences in the somatic MSI phenotype. For the boxplots, the center line is the median, the lower and the upper hinges represent the first and third quartiles. The upper and lower whiskers extend up to 1.5 * IQR (interquartile range) above and below the upper and lower hinges, respectively. e) Immunogenic burden, determined as the ratio of total number of mutation derived epitopes that are strong binders to the total number of non-synonymous mutations, is shown for MSI tumors harboring pathogenic germline variants in MMR genes along with tumors that are germline wild-type (Non-carrier, including non-MSI tumors) with TMB > 20. Tumors from carriers of germline mutations in MLH1/MSH2 had significantly higher immunogenic burden than those tumors that are carriers of germline mutations in MSH6/PMS2 (P = 1.2 × 10-5, after adjusting for tumor type and sample type). Compared with Non-carriers, carriers of germline mutations in MLH1 and MSH2 had significantly higher immunogenic burden (P = 1.3 × 10-5, P = 8 × 10-4, respectively, Wilcoxon test) while carriers with germline mutations MSH6 and PMS2 did not differ from Non-carriers. For the boxplots, the center line is the median, the lower and the upper hinges represent the first and third quartiles. The upper and lower whiskers extend up to 1.5 * IQR (interquartile range) above and below the upper and lower hinges, respectively.
Supplementary information
Supplementary Information
Supplementary Tables 1–6, Note and Figs. 1–10.
Supplementary Tables
Supplementary Table 1 Cancer types in cohort. The number of samples of each cancer type among those studied here along with OncoTree node mapping. Supplementary Table 2 Gene and variant level stratification by penetrance. The penetrance level of genes and alleles utilized here. Supplementary Table 3 Germline pathogenicity by cancer type and penetrance. The percentage of patients with the indicated cancer types that were carriers of germline pathogenic alleles are shown, stratified by penetrance level. Supplementary Table 4 Germline pathogenicity by gene and penetrance. The percentage of patients who were carriers of pathogenic alleles in the indicated genes, stratified by penetrance level. Supplementary Table 5 Gene and cancer type associations. The associations among genes and cancer types for those pathogenic alleles shown by ancestry-corrected association testing in the study cohort or those curated by literature review. Excluded here were those genes and cancer types for which no prior evidence was found and insufficient power was available for testing.
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Srinivasan, P., Bandlamudi, C., Jonsson, P. et al. The context-specific role of germline pathogenicity in tumorigenesis. Nat Genet 53, 1577–1585 (2021). https://doi.org/10.1038/s41588-021-00949-1
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DOI: https://doi.org/10.1038/s41588-021-00949-1
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