Germline determinants of aberrant signaling pathways in cancer.
Journal
NPJ precision oncology
ISSN: 2397-768X
Titre abrégé: NPJ Precis Oncol
Pays: England
ID NLM: 101708166
Informations de publication
Date de publication:
01 Mar 2024
01 Mar 2024
Historique:
received:
25
05
2023
accepted:
16
02
2024
medline:
2
3
2024
pubmed:
2
3
2024
entrez:
1
3
2024
Statut:
epublish
Résumé
Cancer is a complex disease influenced by a heterogeneous landscape of both germline genetic variants and somatic aberrations. While there is growing evidence suggesting an interplay between germline and somatic variants, and a substantial number of somatic aberrations in specific pathways are now recognized as hallmarks in many well-known forms of cancer, the interaction landscape between germline variants and the aberration of those pathways in cancer remains largely unexplored. Utilizing over 8500 human samples across 33 cancer types characterized by TCGA and considering binary traits defined using a large collection of somatic aberration profiles across ten well-known oncogenic signaling pathways, we conducted a series of GWAS and identified genome-wide and suggestive associations involving 276 SNPs. Among these, 94 SNPs revealed cis-eQTL links with cancer-related genes or with genes functionally correlated with the corresponding traits' oncogenic pathways. GWAS summary statistics for all tested traits were then used to construct a set of polygenic scores employing a customized computational strategy. Polygenic scores for 24 traits demonstrated significant performance and were validated using data from PCAWG and CCLE datasets. These scores showed prognostic value for clinical variables and exhibited significant effectiveness in classifying patients into specific cancer subtypes or stratifying patients with cancer-specific aggressive phenotypes. Overall, we demonstrate that germline genetics can describe patients' genetic liability to develop specific cancer molecular and clinical profiles.
Identifiants
pubmed: 38429380
doi: 10.1038/s41698-024-00546-5
pii: 10.1038/s41698-024-00546-5
doi:
Types de publication
Journal Article
Langues
eng
Pagination
57Subventions
Organisme : Fondazione Italiana per la Ricerca sul Cancro (Italian Foundation for Cancer Research)
ID : MFAG 20621
Informations de copyright
© 2024. The Author(s).
Références
Sud, A., Kinnersley, B. & Houlston, R. S. Genome-wide association studies of cancer: current insights and future perspectives. Nat. Rev. Cancer 17, 692–704 (2017).
pubmed: 29026206
doi: 10.1038/nrc.2017.82
Hosking, F. J., Dobbins, S. E. & Houlston, R. S. Genome-wide association studies for detecting cancer susceptibility. Br. Med. Bull. 97, 27–46 (2011).
pubmed: 21247937
doi: 10.1093/bmb/ldq038
Galvan, A., Ioannidis, J. P. A. & Dragani, T. A. Beyond genome-wide association studies: genetic heterogeneity and individual predisposition to cancer. Trends Genet. 26, 132–141 (2010).
pubmed: 20106545
pmcid: 2826571
doi: 10.1016/j.tig.2009.12.008
Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).
pubmed: 19812666
pmcid: 2831613
doi: 10.1038/nature08494
Chang, C. Q. et al. A systematic review of cancer GWAS and candidate gene meta-analyses reveals limited overlap but similar effect sizes. Eur. J. Hum. Genet. 22, 402–408 (2014).
pubmed: 23881057
doi: 10.1038/ejhg.2013.161
Varghese, J. S. & Easton, D. F. Genome-wide association studies in common cancers—what have we learnt? Curr. Opin. Genet. Dev. 20, 201–209 (2010).
pubmed: 20418093
doi: 10.1016/j.gde.2010.03.012
Torkamani, A., Wineinger, N. E. & Topol, E. J. The personal and clinical utility of polygenic risk scores. Nat. Rev. Genet. 19, 581–590 (2018).
pubmed: 29789686
doi: 10.1038/s41576-018-0018-x
Stratton, M. R., Campbell, P. J. & Futreal, P. A. The cancer genome. Nature 458, 719–724 (2009).
pubmed: 19360079
pmcid: 2821689
doi: 10.1038/nature07943
Vogelstein, B. et al. Cancer genome landscapes. Science 339, 1546–1558 (2013).
pubmed: 23539594
pmcid: 3749880
doi: 10.1126/science.1235122
Carter, H. et al. Interaction landscape of inherited polymorphisms with somatic events in cancer. Cancer Discov. 7, 410–423 (2017).
pubmed: 28188128
pmcid: 5460679
doi: 10.1158/2159-8290.CD-16-1045
Castro, M. A. A. et al. Regulators of genetic risk of breast cancer identified by integrative network analysis. Nat. Genet. 48, 12–21 (2016).
pubmed: 26618344
doi: 10.1038/ng.3458
Romanel, A. et al. Inherited determinants of early recurrent somatic mutations in prostate cancer. Nat. Commun. 8, 48 (2017).
pubmed: 28663546
pmcid: 5491529
doi: 10.1038/s41467-017-00046-0
Liu, Y., Gusev, A. & Kraft, P. Germline cancer gene expression quantitative trait loci are associated with local and global tumor mutations. Cancer Res. 83, 1191–1202 (2023).
pubmed: 36745477
pmcid: 10106413
doi: 10.1158/0008-5472.CAN-22-2624
Guo, J. et al. Inherited polygenic effects on common hematological traits influence clonal selection on JAK2V617F and the development of myeloproliferative neoplasms. Nat. Genet. https://doi.org/10.1038/s41588-023-01638-x (2024).
Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).
pubmed: 21376230
doi: 10.1016/j.cell.2011.02.013
The Cancer Genome Atlas Research Network, Weinstein, J. N. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).
doi: 10.1038/ng.2764
ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).
doi: 10.1038/s41586-020-1969-6
Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).
pubmed: 22460905
pmcid: 3320027
doi: 10.1038/nature11003
Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).
pubmed: 31068700
pmcid: 6697103
doi: 10.1038/s41586-019-1186-3
Sanchez-Vega, F. et al. Oncogenic signaling pathways in The Cancer Genome Atlas. Cell 173, 321–337.e10 (2018).
pubmed: 29625050
pmcid: 6070353
doi: 10.1016/j.cell.2018.03.035
Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019).
pubmed: 30445434
doi: 10.1093/nar/gky1120
Mittlböck, M. & Heinzl, H. A simulation study comparing properties of heterogeneity measures in meta-analyses. Stat. Med. 25, 4321–4333 (2006).
pubmed: 16991104
doi: 10.1002/sim.2692
Dalfovo, V., Valentini, S. & Romanel, A. Exploring functionally annotated transcriptional consensus regulatory elements with CONREL. Database 2020, baaa071 (2020).
pubmed: 33186463
pmcid: 7805434
doi: 10.1093/database/baaa071
Valentini, S. et al. Polympact: exploring functional relations among common human genetic variants. Nucleic Acids Res. https://doi.org/10.1093/nar/gkac024 (2022).
Lonsdale, J. et al. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).
doi: 10.1038/ng.2653
Murphree, A. L. & Benedict, W. F. Retinoblastoma: clues to human oncogenesis. Science 223, 1028–1033 (1984).
pubmed: 6320372
doi: 10.1126/science.6320372
Liu, J. et al. An integrated TCGA Pan-Cancer clinical data resource to drive high-quality survival outcome analytics. Cell 173, 400–416.e11 (2018).
pubmed: 29625055
pmcid: 6066282
doi: 10.1016/j.cell.2018.02.052
Demichelis, F. et al. Identification of functionally active, low frequency copy number variants at 15q21.3 and 12q21.31 associated with prostate cancer risk. Proc. Natl Acad. Sci. USA 109, 6686–6691 (2012).
pubmed: 22496589
pmcid: 3340033
doi: 10.1073/pnas.1117405109
Schaefer, G. et al. Distinct ERG rearrangement prevalence in prostate cancer: higher frequency in young age and in low PSA prostate cancer. Prostate Cancer Prostatic Dis. 16, 132–138 (2013).
pubmed: 23381693
pmcid: 3655380
doi: 10.1038/pcan.2013.4
Gordetsky, J. & Epstein, J. Grading of prostatic adenocarcinoma: current state and prognostic implications. Diagn. Pathol. 11, 25 (2016).
pubmed: 26956509
pmcid: 4784293
doi: 10.1186/s13000-016-0478-2
Sayaman, R. W. et al. Germline genetic contribution to the immune landscape of cancer. Immunity 54, 367–386.e8 (2021).
pubmed: 33567262
pmcid: 8414660
doi: 10.1016/j.immuni.2021.01.011
Musa, J. & Grünewald, T. G. P. Interaction between somatic mutations and germline variants contributes to clinical heterogeneity in cancer. Mol. Cell. Oncol. 7, 1682924 (2020).
pubmed: 31993496
doi: 10.1080/23723556.2019.1682924
Mamidi, T. K. K., Wu, J. & Hicks, C. Integrating germline and somatic variation information using genomic data for the discovery of biomarkers in prostate cancer. BMC Cancer 19, 229 (2019).
pubmed: 30871495
pmcid: 6417124
doi: 10.1186/s12885-019-5440-8
Musa, J. et al. Cooperation of cancer drivers with regulatory germline variants shapes clinical outcomes. Nat. Commun. 10, 4128 (2019).
pubmed: 31511524
pmcid: 6739408
doi: 10.1038/s41467-019-12071-2
Liu, H.-M. et al. Recessive/dominant model: alternative choice in case-control-based genome-wide association studies. PLoS ONE 16, e0254947 (2021).
pubmed: 34288964
pmcid: 8294554
doi: 10.1371/journal.pone.0254947
Guindo-Martínez, M. et al. The impact of non-additive genetic associations on age-related complex diseases. Nat. Commun. 12, 2436 (2021).
pubmed: 33893285
pmcid: 8065056
doi: 10.1038/s41467-021-21952-4
Liu, N., Zhao, H., Patki, A., Limdi, N. A. & Allison, D. B. Controlling population structure in human genetic association studies with samples of unrelated individuals. Stat. Interface 4, 317–326 (2011).
pubmed: 22308192
pmcid: 3269890
doi: 10.4310/SII.2011.v4.n3.a6
Astle, W. & Balding, D. J. Population structure and cryptic relatedness in genetic association studies. Statist. Sci. 24, 451–471 (2009).
doi: 10.1214/09-STS307
Yang, J., Zaitlen, N. A., Goddard, M. E., Visscher, P. M. & Price, A. L. Advantages and pitfalls in the application of mixed-model association methods. Nat. Genet. 46, 100–106 (2014).
pubmed: 24473328
pmcid: 3989144
doi: 10.1038/ng.2876
Sul, J. H. & Eskin, E. Mixed models can correct for population structure for genomic regions under selection. Nat. Rev. Genet. 14, 300–300 (2013).
pubmed: 23438871
doi: 10.1038/nrg2813-c1
Tucker, G., Price, A. L. & Berger, B. Improving the power of GWAS and avoiding confounding from population stratification with PC-Select. Genetics 197, 1045–1049 (2014).
pubmed: 24788602
pmcid: 4096359
doi: 10.1534/genetics.114.164285
TCGA Analysis Network, Oak, N. et al. Ancestry-specific predisposing germline variants in cancer. Genome Med. 12, 51 (2020).
doi: 10.1186/s13073-020-00744-3
Martinez, V. D. et al. Disruption of KEAP1/CUL3/RBX1 E3-ubiquitin ligase complex components by multiple genetic mechanisms: association with poor prognosis in head and neck cancer. Head. Neck 37, 727–734 (2015).
pubmed: 24596130
doi: 10.1002/hed.23663
Martinez, V. D. et al. Unique pattern of component gene disruption in the NRF2 inhibitor KEAP1/CUL3/RBX1 E3-ubiquitin ligase complex in serous ovarian cancer. Biomed. Res. Int. 2014, 159459 (2014).
pubmed: 25114896
pmcid: 4121105
doi: 10.1155/2014/159459
Porta-Pardo, E., Sayaman, R., Ziv, E. & Valencia, A. The landscape of interactions between cancer polygenic risk scores and somatic alterations in cancer cells. Preprint at bioRxiv https://doi.org/10.1101/2020.09.28.316851 (2020).
Schizophrenia Working Group of the Psychiatric Genomics Consortium et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
pmcid: 4495769
doi: 10.1038/ng.3211
Cowen, L., Ideker, T., Raphael, B. J. & Sharan, R. Network propagation: a universal amplifier of genetic associations. Nat. Rev. Genet. 18, 551–562 (2017).
pubmed: 28607512
doi: 10.1038/nrg.2017.38
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaSci 4, 7 (2015).
doi: 10.1186/s13742-015-0047-8
Carrot-Zhang, J. et al. Comprehensive analysis of genetic ancestry and its molecular correlates in cancer. Cancer Cell 37, 639–654.e6 (2020).
pubmed: 32396860
pmcid: 7328015
doi: 10.1016/j.ccell.2020.04.012
Pluzhnikov, A. et al. Spoiling the whole bunch: quality control aimed at preserving the integrity of high-throughput genotyping. Am. J. Hum. Genet. 87, 123–128 (2010).
pubmed: 20598280
pmcid: 2896766
doi: 10.1016/j.ajhg.2010.06.005
Romanel, A., Zhang, T., Elemento, O. & Demichelis, F. EthSEQ: ethnicity annotation from whole exome sequencing data. Bioinformatics 33, 2402–2404 (2017).
pubmed: 28369222
pmcid: 5818140
doi: 10.1093/bioinformatics/btx165
Dalfovo, D. & Romanel, A. Analysis of genetic ancestry from NGS data using EthSEQ. Curr. Protoc. 3, e663 (2023).
pubmed: 36779822
doi: 10.1002/cpz1.663
Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).
pubmed: 16862161
doi: 10.1038/ng1847
Cerami, E. et al. The cBio Cancer Genomics Portal: an open platform for exploring multidimensional cancer genomics data: Fig. 1. Cancer Discov. 2, 401–404 (2012).
pubmed: 22588877
doi: 10.1158/2159-8290.CD-12-0095
Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).
pubmed: 23550210
pmcid: 4160307
doi: 10.1126/scisignal.2004088
Oughtred, R. et al. The BioGRID interaction database: 2019 update. Nucleic Acids Res. 47, D529–D541 (2019).
pubmed: 30476227
doi: 10.1093/nar/gky1079
Peri, S. et al. Human protein reference database as a discovery resource for proteomics. Nucleic Acids Res. 32, D497–D501 (2004).
pubmed: 14681466
pmcid: 308804
doi: 10.1093/nar/gkh070
Orchard, S. et al. The MIntAct project-IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. 42, D358–D363 (2014).
pubmed: 24234451
doi: 10.1093/nar/gkt1115
Huttlin, E. L. et al. Architecture of the human interactome defines protein communities and disease networks. Nature 545, 505–509 (2017).
pubmed: 28514442
pmcid: 5531611
doi: 10.1038/nature22366
Szklarczyk, D. et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).
pubmed: 30476243
doi: 10.1093/nar/gky1131
Wilks, C. et al. recount3: summaries and queries for large-scale RNA-seq expression and splicing. Genome Biol. 22, 323 (2021).
pubmed: 34844637
pmcid: 8628444
doi: 10.1186/s13059-021-02533-6
Ritchie, M.E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
pubmed: 25605792
pmcid: 4402510
doi: 10.1093/nar/gkv007
Choi, S. W., Mak, T. S.-H. & O’Reilly, P. F. Tutorial: a guide to performing polygenic risk score analyses. Nat. Protoc. 15, 2759–2772 (2020).
pubmed: 32709988
pmcid: 7612115
doi: 10.1038/s41596-020-0353-1
Zhang, Y. D. et al. Assessment of polygenic architecture and risk prediction based on common variants across fourteen cancers. Nat. Commun. 11, 3353 (2020).
pubmed: 32620889
pmcid: 7335068
doi: 10.1038/s41467-020-16483-3
Chatterjee, N., Shi, J. & García-Closas, M. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nat. Rev. Genet. 17, 392–406 (2016).
pubmed: 27140283
pmcid: 6021129
doi: 10.1038/nrg.2016.27
Robin, X. et al. pROC: an open-source package for R and S. to analyze and compare ROC curves. BMC Bioinformatics 12, 77 (2011).
pubmed: 21414208
pmcid: 3068975
doi: 10.1186/1471-2105-12-77
Therneau, M. T. & Patricia, M. G. Modeling Survival Data: Extending the Cox Model (Springer, New York, 2000).
Pounds, S. & Cheng, C. Robust estimation of the false discovery rate. Bioinformatics 22, 1979–1987 (2006).
pubmed: 16777905
doi: 10.1093/bioinformatics/btl328