Polygenic and Network-based studies in risk identification and demystification of cancer

References

• Metzker ML. Sequencing technologies — the next generation. Nat Rev Genet. 2010;11:31–46.
   PubMed Web of Science ®Google Scholar
• Landau YE, Lichter-Konecki U, Levy HL. Genomics in newborn screening. J Pediatr. 2014;164:14–19.
   PubMed Web of Science ®Google Scholar
• Boyd SD. Diagnostic applications of high-throughput DNA sequencing. Annu Rev Pathol Mech Dis. 2013;8:381–410.
   PubMed Web of Science ®Google Scholar
• Home - OMIM - NCBI [Internet]. [ cited 2021 Jun 28]. Available from: https://www.ncbi.nlm.nih.gov/omim.
   Google Scholar
• Karki R, Pandya D, Elston RC, et al. Defining “mutation” and “polymorphism” in the era of personal genomics. BMC Med Genomics. 2015;8:37.
   PubMed Web of Science ®Google Scholar
• Kosvyra A, Ntzioni E, Chouvarda I. Network analysis with biological data of cancer patients: a scoping review. J Biomed Inform. 2021;120:103873.
   PubMed Web of Science ®Google Scholar
• Kaushik S, Kaushik S, Sharma D. Functional Genomics. Encycl Bioinforma Comput Biol [Internet]. 2019;2:118–133. [cited 2021 Aug 8]. https://linkinghub.elsevier.com/retrieve/pii/B9780128096338202227.
   Google Scholar
• Barabási A-L, Oltvai ZN. Network biology: understanding the cell’s functional organization. Nat Rev Genet. 2004;5:101–113.
   PubMed Web of Science ®Google Scholar
• Venter JC, Adams MD, Myers EW, et al. The sequence of the human genome. Science. 2001;291:1304–1351.
   PubMed Web of Science ®Google Scholar
• Levy S, Sutton G, Ng PC, et al. The Diploid Genome Sequence of an Individual Human. In: Rubin EM, editor. PLoS Biol. 2007. Vol. 5, pp. e254.
   PubMed Web of Science ®Google Scholar
• Myles S, Davison D, Barrett J, et al. Worldwide population differentiation at disease-associated SNPs. BMC Med Genomics. 2008;1:22.
   PubMed Web of Science ®Google Scholar
• Piel FB, Patil AP, Howes RE, et al. Global distribution of the sickle cell gene and geographical confirmation of the malaria hypothesis. Nat Commun. 2010;1:104.
   PubMed Web of Science ®Google Scholar
• Hassell KL. Population estimates of sickle cell disease in the U.S. Am J Prev Med. 2010;38:S512–S521.
   PubMed Web of Science ®Google Scholar
• Lanclos K, Oner C, Dimovski A, et al. Sequence variations in the 5’ flanking and IVS-II regions of the G gamma- and A gamma-globin genes of beta S chromosomes with five different haplotypes. Blood. 1991;77:2488–2496.
   PubMed Web of Science ®Google Scholar
• Öner C, Dimovski A, Olivieri N, et al. Beta S Haplotypes in various world populations. Hum Genet. 1992;89(1):99–104.
   PubMed Web of Science ®Google Scholar
• Lapouniéroulie C, Dunda O, Ducrocq R, et al. A novel sickle cell mutation of yet another origin in Africa: the cameroon type. Hum Genet [Internet]. 1992;89(3). cited 2021 Jun 28. Available from: https://doi.org/10.1007/BF00220553
   Google Scholar
• McCarthy MI.Genomics, Type 2 Diabetes, and Obesity. In: Feero WG, Guttmacher AE, editors. N Engl J Med. 2010. Vol. 363, pp. 2339–2350.
   PubMed Web of Science ®Google Scholar
• Richards S, Aziz N, Bale S, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17:405–423.
   PubMed Web of Science ®Google Scholar
• Visscher PM, Wray NR, Zhang Q, et al. 10 years of GWAS discovery: biology, function, and translation. Am J Hum Genet. 2017;101:5–22.
   PubMed Web of Science ®Google Scholar
• Calabrese B. Linkage Disequilibrium. Encycl Bioinforma Comput Biol [Internet]. Elsevier. 2019;1 :p. 763–765. cited 2021 Jun 28. Elsevier: https://linkinghub.elsevier.com/retrieve/pii/B9780128096338202343.
   Google Scholar
• Welter D, MacArthur J, Morales J, et al. The NHGRI GWAS catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2014;42:D1001–D1006.
   PubMed Web of Science ®Google Scholar
• Uitterlinden A. An introduction to Genome-Wide association studies: GWAS for dummies. Semin Reprod Med. 2016;34:196–204.
   PubMed Web of Science ®Google Scholar
• Björkegren JLM, Kovacic JC, Dudley JT, et al. Genome-Wide significant loci: how important are they? J Am Coll Cardiol. 2015;65:830–845.
   PubMed Web of Science ®Google Scholar
• Westra H-J, Peters MJ, Esko T, et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat Genet. 2013;45:1238–1243.
   PubMed Web of Science ®Google Scholar
• GTEx Consortium. Genetic effects on gene expression across human tissues. Nature. 2017;550:204–213.
   PubMed Web of Science ®Google Scholar
• Hirschhorn JN. Genetic approaches to studying common diseases and complex traits. Pediatr Res. 2005;57:74R–77R.
   PubMed Web of Science ®Google Scholar
• Johnson G. Strategies in complex disease mapping. Curr Opin Genet Dev. 2000;10:330–334.
   PubMed Web of Science ®Google Scholar
• The CARDIoGRAMplusC4D Consortium, DIAGRAM Consortium, CARDIOGENICS Consortium, et al. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet. 2013;45:25–33.
   PubMed Web of Science ®Google Scholar
• The Electronic Medical Records and Genomics (eMERGE) Consortium, The MIGen Consortium, The PAGE Consortium, Wood AR, Esko T, Yang J, et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet. 2014;46:1173–1186.
   PubMed Web of Science ®Google Scholar
• Marenberg ME, Risch N, Berkman LF, et al. Genetic susceptibility to death from coronary heart disease in a study of twins. N Engl J Med. 1994;330:1041–1046.
   PubMed Web of Science ®Google Scholar
• Yang J, Benyamin B, McEvoy BP, et al. Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 2010;42:565–569.
   PubMed Web of Science ®Google Scholar
• Hughes V. Epigenetics: the sins of the father. Nature. 2014;507:22–24.
   PubMed Web of Science ®Google Scholar
• Hägg S, Skogsberg J, Lundström J, et al. Multi-Organ Expression Profiling Uncovers a Gene Module in Coronary Artery Disease Involving Transendothelial Migration of Leukocytes and LIM Domain Binding 2: The Stockholm Atherosclerosis Gene Expression (STAGE) Study. In: Kerr K, editor. PLoS Genet. 2009. Vol. 5, pp. e1000754.
   PubMed Web of Science ®Google Scholar
• Smith EN, Kruglyak L.Gene–Environment Interaction in Yeast Gene Expression. In: Mackay T,editor. PLoS Biol. 2008. Vol. 6, pp. e83.
   PubMed Web of Science ®Google Scholar
• Smirnov DA, Morley M, Shin E, et al. Genetic analysis of radiation-induced changes in human gene expression. Nature. 2009;459:587–591.
   PubMed Web of Science ®Google Scholar
• Lusk CM, Dyson G, Clark AG, et al. Validated context-dependent associations of coronary heart disease risk with genotype variation in the chromosome 9p21 region: the atherosclerosis risk in communities study. Hum Genet. 2014;133:1105–1116.
   PubMed Web of Science ®Google Scholar
• Smith CJ, Steinbrekera B, Dagle JM. Genetic Basis of Patent Ductus Arteriosus. Hematology, Immunology and Genetics. 3rd ed. Amsterdam, Netherlands: Elsevier; 2019. p. 137–148. [cited 2021 Jun 28]. https://linkinghub.elsevier.com/retrieve/pii/B9780323544009000126
   Google Scholar
• Kidd BA, Peters LA, Schadt EE, et al. Unifying immunology with informatics and multiscale biology. Nat Immunol. 2014;15:118–127.
   PubMed Web of Science ®Google Scholar
• Lusis AJ, Weiss JN. Cardiovascular Networks: systems-based approaches to cardiovascular disease. Circulation. 2010;121:157–170.
   PubMed Web of Science ®Google Scholar
• Barabási A-L, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011;12:56–68.
   PubMed Web of Science ®Google Scholar
• Vogelstein B, Lane D, Levine AJ. Surfing the p53 network. Nature. 2000;408:307–310.
   PubMed Web of Science ®Google Scholar
• Jeong H, Tombor B, Albert R, et al. The large-scale organization of metabolic networks. Nature. 2000;407:651–654.
   PubMed Web of Science ®Google Scholar
• Wagner A, Fell DA. The small world inside large metabolic networks. Proc R Soc Lond B Biol Sci. 2001;268:1803–1810.
   PubMed Web of Science ®Google Scholar
• Hartwell LH, Hopfield JJ, Leibler S, et al. From molecular to modular cell biology. Nature. 1999;402:C47–C52.
   PubMed Web of Science ®Google Scholar
• Wall ME, Hlavacek WS, Savageau MA. Design of gene circuits: lessons from bacteria. Nat Rev Genet. 2004;5:34–42.
   PubMed Web of Science ®Google Scholar
• Simon I, Barnett J, Hannett N, et al. Serial regulation of transcriptional regulators in the yeast cell cycle. Cell. 2001;106:697–708.
   PubMed Web of Science ®Google Scholar
• Tyson JJ, Csikasz-Nagy A, Novak B. The dynamics of cell cycle regulation. BioEssays. 2002;24:1095–1109.
   PubMed Web of Science ®Google Scholar
• Ravasz E, Somera AL, Mongru DA. Hierarchical organization of modularity in metabolic networks. Science. 2002;297:1551–1555.
   PubMed Web of Science ®Google Scholar
• Barkai N, Leibler S. Robustness in simple biochemical networks. Nature. 1997;387:913–917.
   PubMed Web of Science ®Google Scholar
• Alon U, Surette MG, Barkai N, et al. Robustness in bacterial chemotaxis. Nature. 1999;397:168–171.
   PubMed Web of Science ®Google Scholar
• Albert R, Jeong H, Barabási A-L. Error and attack tolerance of complex networks. Nature. 2000;406:378–382.
   PubMed Web of Science ®Google Scholar
• van Dam S, Võsa U, van der Graaf A, et al. Gene co-expression analysis for functional classification and gene–disease predictions. Brief Bioinform. 2017;19:bbw139.
   Web of Science ®Google Scholar
• Zhang B, Horvath S. A general framework for weighted Gene co-expression Network analysis. Stat Appl Genet Mol Biol [Internet]. 2005;4. [cited 2021 Jun 28]. Available from: https://doi.org/10.2202/1544-6115.1128/html
   Google Scholar
• Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559.
   PubMed Web of Science ®Google Scholar
• Xue Z, Huang K, Cai C, et al. Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing. Nature. 2013;500:593–597.
   PubMed Web of Science ®Google Scholar
• Li Y, Sahni N, Yi S. Comparative analysis of protein interactome networks prioritizes candidate genes with cancer signatures. Oncotarget. 2016;7:78841–78849.
   PubMedGoogle Scholar
• The International ACTANE Consortium. Results of a genome-wide linkage analysis in prostate cancer families ascertained through the ACTANE consortium. Prostate. 2003;57:270–279.
   PubMed Web of Science ®Google Scholar
• Kurian AW, Hare EE, Mills MA, et al. Clinical evaluation of a Multiple-Gene sequencing panel for hereditary cancer risk assessment. J Clin Oncol. 2014;32:2001–2009.
   PubMed Web of Science ®Google Scholar
• MacArthur J, Bowler E, Cerezo M, et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 2017;45:D896–D901.
   PubMed Web of Science ®Google Scholar
• Manolio TA, Collins FS, Cox NJ, et al. Finding the missing heritability of complex diseases. Nature. 2009;461:747–753.
   PubMed Web of Science ®Google Scholar
• The Profile Study, Australian Prostate Cancer BioResource (APCB), The IMPACT Study, Schumacher FR, Al Olama AA, Berndt SI, et al. Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nat Genet. 2018;50:928–936.
   PubMed Web of Science ®Google Scholar
• The CHEK2-Breast Cancer Consortium. Low-penetrance susceptibility to breast cancer due to CHEK2*1100delC in noncarriers of BRCA1 or BRCA2 mutations. Nat Genet. 2002;31:55–59.
   PubMed Web of Science ®Google Scholar
• Pritchard CC, Mateo J, Walsh MF, et al. Inherited DNA-repair Gene mutations in men with metastatic prostate cancer. N Engl J Med. 2016;375:443–453.
   PubMed Web of Science ®Google Scholar
• Farashi S, Kryza T, Clements J, et al. Post-GWAS in prostate cancer: from genetic association to biological contribution. Nat Rev Cancer. 2019;19:46–59.
   PubMed Web of Science ®Google Scholar
• Eeles R, Goh C, Castro E, et al. The genetic epidemiology of prostate cancer and its clinical implications. Nat Rev Urol. 2014;11:18–31.
   PubMed Web of Science ®Google Scholar
• Agarwal D, Nowak C, Zhang NR, et al. Functional germline variants as potential co-oncogenes. Npj Breast Cancer. 2017;3:46.
   PubMed Web of Science ®Google Scholar
• CAMCAP Study Group, The TCGA Consortium, Wedge DC, Gundem G, Mitchell T, et al. Sequencing of prostate cancers identifies new cancer genes, routes of progression and drug targets. Nat Genet. 2018;50:682–692.
   PubMed Web of Science ®Google Scholar
• Lin H-Y, Chen D-T, Huang P-Y, et al. SNP interaction pattern identifier (SIPI): an intensive search for SNP–SNP interaction patterns. Bioinformatics. 2016;33:btw762.
   Web of Science ®Google Scholar
• Vaidyanathan V, Naidu V, Karunasinghe N, et al. SNP-SNP interactions as risk factors for aggressive prostate cancer. F1000Res. 2017;6:621.
   PubMedGoogle Scholar
• the PRACTICAL Consortium, the CRUK GWAS, the BCAC Consortium, Zuber V, Bettella F, Witoelar A, et al. Bromodomain protein 4 discriminates tissue-specific super-enhancers containing disease-specific susceptibility loci in prostate and breast cancer. BMC Genomics. 2017;18:270.
   PubMed Web of Science ®Google Scholar
• Thompson DJ, O’Mara TA, Glubb DM, et al. CYP19A1 fine-mapping and Mendelian randomization: estradiol is causal for endometrial cancer. Endocr Relat Cancer. 2016;23:77–91.
   PubMed Web of Science ®Google Scholar
• Haenszel W, Kurihara M. Studies of Japanese Migrants. I. Mortality from cancer and other diseases among Japanese in the United States. J Natl Cancer Inst. 1968;40:43–68. [cited 2021 Jun 28]. Available from: https://academic.oup.com/jnci/article/40/1/43/932035/Studies-of-Japanese-Migrants-I-Mortality-From
   PubMed Web of Science ®Google Scholar
• Sampson JN, Wheeler WA, Yeager M, et al. Analysis of heritability and shared heritability based on genome-wide association studies for thirteen cancer types. J Natl Cancer Inst. 2015;107:djv279.
   PubMedGoogle Scholar
• Bossé Y, Amos CI. A decade of GWAS results in lung cancer. Cancer Epidemiol Biomarkers Prev. 2018;27:363–379.
   PubMed Web of Science ®Google Scholar
• Dai J, Lv J, Zhu M, et al. Identification of risk loci and a polygenic risk score for lung cancer: a large-scale prospective cohort study in Chinese populations. Lancet Respir Med. 2019;7:881–891.
   PubMed Web of Science ®Google Scholar
• Choi H, Na KJ. A risk stratification model for lung cancer based on Gene coexpression Network and deep learning. BioMed Res Int. 2018;2018:1–11.
   Web of Science ®Google Scholar
• Jia X, Miao Z, Li W, et al. Cancer-risk module identification and module-based disease risk evaluation: a case study on lung cancer. In: Xu Y, editor. PLoS ONE. 2014. Vol. 9, p. e92395.
   PubMed Web of Science ®Google Scholar
• NBCS Collaborators, ABCTB Investigators, ConFab/AOCS Investigators, Michailidou K, Lindström S, Dennis J, et al. Association analysis identifies 65 new breast cancer risk loci. Nature. 2017;551:92–94.
   PubMed Web of Science ®Google Scholar
• ABCTB Investigators, EMBRACE, GEMO Study Collaborators, Milne RL, Kuchenbaecker KB, Michailidou K, et al. Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer. Nat Genet. 2017;49:1767–1778.
   PubMed Web of Science ®Google Scholar
• Pashayan N, Duffy SW, Chowdhury S, et al. Polygenic susceptibility to prostate and breast cancer: implications for personalised screening. Br J Cancer. 2011;104:1656–1663.
   PubMed Web of Science ®Google Scholar
• Hall P, Easton D. Breast cancer screening: time to target women at risk. Br J Cancer. 2013;108:2202–2204.
   PubMed Web of Science ®Google Scholar
• Burton H, Chowdhury S, Dent T, et al. Public health implications from COGS and potential for risk stratification and screening. Nat Genet. 2013;45:349–351.
   PubMed Web of Science ®Google Scholar
• Torkamani A, Wineinger NE, Topol EJ. The personal and clinical utility of polygenic risk scores. Nat Rev Genet. 2018;19:581–590.
   PubMed Web of Science ®Google Scholar
• Mavaddat N, Michailidou K, Dennis J, et al. Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. Am J Hum Genet. 2019;104:21–34.
   PubMed Web of Science ®Google Scholar
• Khera AV, Chaffin M, Aragam KG, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50:1219–1224.
   PubMed Web of Science ®Google Scholar
• Rivandi M, Martens JWM, Hollestelle A. Elucidating the underlying functional mechanisms of breast cancer susceptibility through post-GWAS analyses. Front Genet. 2018;9:280.
   PubMed Web of Science ®Google Scholar
• Shi J, Park J-H, Duan J, et al. Winner’s curse correction and variable thresholding improve performance of polygenic risk modeling based on genome-wide association study summary-level data. In: Ripatti S, editor. PLOS Genet. 2016. Vol, 12. pp. e1006493.
   PubMed Web of Science ®Google Scholar
• Pereira M, Thompson JR, Weichenberger CX, et al. Inclusion of biological knowledge in a Bayesian shrinkage model for joint estimation of SNP effects: pereira et al. Genet Epidemiol. 2017;41:320–331.
   PubMed Web of Science ®Google Scholar
• Yang L, Li X, Luo Y, et al. Weighted gene co‑expression network analysis of the association between upregulated AMD1, EN1 and VGLL1 and the progression and poor prognosis of breast cancer. Exp Ther Med. 2021;22:1030.
   PubMed Web of Science ®Google Scholar
• Cao W, Jiang Y, Ji X, et al. Identification of novel prognostic genes of triple-negative breast cancer using meta-analysis and weighted gene co-expressed network analysis. Ann Transl Med. 2021;9:205.
   PubMed Web of Science ®Google Scholar
• Guo L, Mao L, Lu W, et al. Identification of breast cancer prognostic modules via differential module selection based on weighted gene Co-expression network analysis. Biosystems. 2021;199:104317.
   PubMed Web of Science ®Google Scholar
• Guo L, Jing Y. Construction and identification of a Novel 5-Gene signature for predicting the prognosis in breast cancer. Front Med. 2021;8:669931.
   Web of Science ®Google Scholar
• Maas P, Barrdahl M, Joshi AD, et al. Breast cancer risk from modifiable and nonmodifiable risk factors among white women in the United States. JAMA Oncol. 2016;2:1295.
   PubMed Web of Science ®Google Scholar
• Kuchenbaecker KB, McGuffog L, Barrowdale D, et al. Evaluation of polygenic risk scores for breast and ovarian cancer risk prediction in BRCA1 and BRCA2 mutation carriers. JNCI J Natl Cancer Inst [Internet]. 2017;109. cited 2021 Jun 28. Available from: https://doi.org/10.1093/jnci/djw302/3064534
   Google Scholar
• Sanati N, Iancu OD, Wu G, et al. Network-based predictors of progression in head and neck squamous cell carcinoma. Front Genet. 2018;9:183.
   PubMed Web of Science ®Google Scholar
• Tong M, Lloyd B, Pei P, et al. Human head and neck squamous cell carcinoma cells are both targets and effectors for the angiogenic cytokine, VEGF. Journal of Cellular Biochemistry. 2008;105(5):1202–1210.
   PubMed Web of Science ®Google Scholar
• Lucas JT, Salimath BP, Slomiany MG, et al. Regulation of invasive behavior by vascular endothelial growth factor is HEF1-dependent. Oncogene. 2010;29:4449–4459.
   PubMed Web of Science ®Google Scholar
• Gold B, Rabiner LR. Theory and application of digital signal processing. 1st ed. Hoboken, New Jersey, United States: Prentice Hall; 2009.
   Google Scholar
• Hofree M, Shen JP, Carter H, et al. Network-based stratification of tumor mutations. Nat Methods. 2013;10:1108–1115.
   PubMed Web of Science ®Google Scholar
• Liu Y, Sun Y, Broaddus R, et al. Integrated analysis of gene expression and tumor nuclear image profiles associated with chemotherapy response in serous ovarian carcinoma. In: Tan P, editor. PLoS ONE. 2012. Vol. 7, p. e36383.
   PubMedGoogle Scholar
• Keiser MJ, Setola V, Irwin JJ, et al. Predicting new molecular targets for known drugs. Nature. 2009;462:175–181.
   PubMed Web of Science ®Google Scholar
• Jahchan NS, Dudley JT, Mazur PK, et al. A drug repositioning approach identifies tricyclic antidepressants as inhibitors of small cell lung cancer and other neuroendocrine tumors. Cancer Discov. 2013;3:1364–1377.
   PubMed Web of Science ®Google Scholar
• Zhao D, Lu X, Wang G, et al. Synthetic essentiality of chromatin remodelling factor CHD1 in PTEN-deficient cancer. Nature. 2017;542:484–488.
   PubMed Web of Science ®Google Scholar
• Mair B, Moffat J, Boone C, et al. Genetic interaction networks in cancer cells. Curr Opin Genet Dev. 2019;54:64–72.
   PubMed Web of Science ®Google Scholar
• Wang Z, Wu D, Xia Y, et al. Identification of hub genes and compounds controlling ovarian cancer stem cell characteristics via stemness indices analysis. Ann Transl Med. 2021;9:379.
   PubMed Web of Science ®Google Scholar
• Steinhart Z, Pavlovic Z, Chandrashekhar M, et al. Genome-wide CRISPR screens reveal a Wnt–FZD5 signaling circuit as a druggable vulnerability of RNF43-mutant pancreatic tumors. Nat Med. 2017;23:60–68.
   PubMed Web of Science ®Google Scholar
• Erb MA, Scott TG, Li BE, et al. Transcription control by the ENL YEATS domain in acute leukaemia. Nature. 2017;543:270–274.
   PubMed Web of Science ®Google Scholar
• Ashworth A, Lord CJ. Synthetic lethal therapies for cancer: what’s next after PARP inhibitors? Nat Rev Clin Oncol. 2018;15:564–576.
   PubMed Web of Science ®Google Scholar