A double primary colorectal cancer (CRC) in a familial setting signals a high risk of CRC. In order to identify novel CRC susceptibility genes, we whole-exome sequenced germline DNA from nine persons with a double primary CRC and a family history of CRC. The detected variants were processed by bioinformatics filtering and prioritization, including STRING protein-protein interaction and pathway analysis. A total of 150 missense, 19 stop-gain, 22 frameshift and 13 canonical splice site variants fulfilled our filtering criteria. The STRING analysis identified 20 DNA repair/cell cycle proteins as the main cluster, related to genes CHEK2, EXO1, FAAP24, FANCI, MCPH1, POLL, PRC1, RECQL, RECQL5, RRM2, SHCBP1, SMC2, XRCC1, in addition to CDK18, ENDOV, ZW10 and the known mismatch repair genes. Another STRING network included extracellular matrix genes and TGFβ signaling genes. In the nine whole-exome sequenced patients, eight harbored at least two candidate DNA repair/cell cycle/TGFβ signaling gene variants. The number of families is too small to provide evidence for individual variants but, considering the known role of DNA repair/cell cycle genes in CRC, the clustering of multiple deleterious variants in the present families suggests that these, perhaps jointly, contributed to CRC development in these families.

Biomedical Center Faculty of Medicine Charles University Pilsen Pilsen Czech Republic

Department of Genetics and Pathology International Hereditary Cancer Center Pomeranian Medical University in Szczecin Szczecin Poland

Division of Cancer Epidemiology German Cancer Research Center Heidelberg Germany

Division of Pediatric Neurooncology German Cancer Research Center Heidelberg Germany

Hopp Children's Cancer Center Heidelberg Germany

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Valle L, de Voer RM, Goldberg Y, et al. Update on genetic predisposition to colorectal cancer and polyposis. Mol Aspects Med. 2019;69:10‐26. doi:10.1016/j.mam.2019.03.001 PubMed DOI

Chubb D, Broderick P, Frampton M, et al. Genetic diagnosis of high‐penetrance susceptibility for colorectal cancer (CRC) is achievable for a high proportion of familial CRC by exome sequencing. J Clin Oncol. 2015;33(5):426‐432. doi:10.1200/JCO.2014.56.5689 PubMed DOI

Dominguez‐Valentin M, Sampson JR, Seppälä TT, et al. Cancer risks by gene, age, and gender in 6350 carriers of pathogenic mismatch repair variants: findings from the Prospective Lynch Syndrome Database. Genet Med. 2020;22(1):15‐25. doi:10.1038/s41436-019-0596-9 PubMed DOI PMC

Försti A, Kumar A, Paramasivam N, et al. Pedigree based DNA sequencing pipeline for germline genomes of cancer families. Hered Cancer Clin Pract. 2016;14:16. doi:10.1186/s13053-016-0058-1 PubMed DOI PMC

Skopelitou D, Miao B, Srivastava A, et al. Whole exome sequencing identifies APCDD1 and HDAC5 genes as potentially cancer predisposing in familial colorectal cancer. Int J Mol Sci. 2021;22(4):1837. doi:10.3390/ijms22041837 PubMed DOI PMC

Zhu L, Miao B, Dymerska D, et al. Germline variants of CYBA and TRPM4 predispose to familial colorectal cancer. Cancer. 2022;14(3):670. doi:10.3390/cancers14030670 PubMed DOI PMC

Daca Alvarez M, Quintana I, Terradas M, Mur P, Balaguer F, Valle L. The inherited and familial component of early‐onset colorectal cancer. Cells. 2021;10(3):710. doi:10.3390/cells10030710 PubMed DOI PMC

Thomas M, Su YR, Rosenthal EA, et al. Combining Asian and European genome‐wide association studies of colorectal cancer improves risk prediction across racial and ethnic populations. Nat Commun. 2023;14(1):6147. doi:10.1038/s41467-023-41819-0 PubMed DOI PMC

Chen Z, Guo X, Tao R, et al. Fine‐mapping analysis including over 254,000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes. Nat Commun. 2024;15(1):3557. doi:10.1038/s41467-024-47399-x PubMed DOI PMC

Hemminki K, Sundquist K, Sundquist J, Försti A, Hemminki A, Li X. Familial risks and proportions describing population landscape of familial cancer. Cancer. 2021;13(17):4385. doi:10.3390/cancers13174385 PubMed DOI PMC

Yu H, Hemminki K. Genetic epidemiology of colorectal cancer and associated cancers. Mutagenesis. 2020;35(3):207‐219. doi:10.1093/mutage/gez022 PubMed DOI

Hemminki K, Li X, Dong C. Second primary cancers after sporadic and familial colorectal cancer. Cancer Epidemiol Biomarkers Prev. 2001;10(7):793‐798. PubMed

McLaren W, Gil L, Hunt SE, et al. The Ensembl variant effect predictor. Genome Biol. 2016;17(1):122. doi:10.1186/s13059-016-0974-4 PubMed DOI PMC

Rentzsch P, Schubach M, Shendure J, Kircher M. CADD‐splice‐improving genome‐wide variant effect prediction using deep learning‐derived splice scores. Genome Med. 2021;13(1):31. doi:10.1186/s13073-021-00835-9 PubMed DOI PMC

Kumar P, Henikoff S, Ng PC. Predicting the effects of coding non‐synonymous variants on protein function using the SIFT algorithm. Nat Protoc. 2009;4(7):1073‐1081. doi:10.1038/nprot.2009.86 PubMed DOI

Adzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen‐2. Curr Protoc Hum Genet. 2013;7:Unit7.20. doi:10.1002/0471142905.hg0720s76 PubMed DOI PMC

Chun S, Fay JC. Identification of deleterious mutations within three human genomes. Genome Res. 2009;19(9):1553‐1561. doi:10.1101/gr.092619.109 PubMed DOI PMC

Schwarz JM, Rodelsperger C, Schuelke M, Seelow D. MutationTaster evaluates disease‐causing potential of sequence alterations. Nat Methods. 2010;7(8):575‐576. doi:10.1038/nmeth0810-575 PubMed DOI

Reva B, Antipin Y, Sander C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 2011;39(17):e118. doi:10.1093/nar/gkr407 PubMed DOI PMC

Shihab HA, Gough J, Cooper DN, et al. Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Hum Mutat. 2013;34(1):57‐65. doi:10.1002/humu.22225 PubMed DOI PMC

Liu X, Wu C, Li C, Boerwinkle E. dbNSFP v3.0: a one‐stop database of functional predictions and annotations for human nonsynonymous and splice‐site SNVs. Hum Mutat. 2016;37(3):235‐241. doi:10.1002/humu.22932 PubMed DOI PMC

Choi Y, Sims GE, Murphy S, Miller JR, Chan AP. Predicting the functional effect of amino acid substitutions and indels. PLoS One. 2012;7(10):e46688. doi:10.1371/journal.pone.0046688 PubMed DOI PMC

Cooper GM, Stone EA, Asimenos G, Green ED, Batzoglou S, Sidow A. Distribution and intensity of constraint in mammalian genomic sequence. Genome Res. 2005;15(7):901‐913. doi:10.1101/gr.3577405 PubMed DOI PMC

Siepel A, Bejerano G, Pedersen JS, et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 2005;15(8):1034‐1050. doi:10.1101/gr.3715005 PubMed DOI PMC

Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 2010;20(1):110‐121. doi:10.1101/gr.097857.109 PubMed DOI PMC

Karczewski KJ, Francioli LC, Tiao G, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581(7809):434‐443. doi:10.1038/s41586-020-2308-7 PubMed DOI PMC

Cheng J, Novati G, Pan J, et al. Accurate proteome‐wide missense variant effect prediction with AlphaMissense. Science. 2023;381(6664):eadg7492. doi:10.1126/science.adg7492 PubMed DOI

Ioannidis NM, Rothstein JH, Pejaver V, et al. REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am J Hum Genet. 2016;99(4):877‐885. doi:10.1016/j.ajhg.2016.08.016 PubMed DOI PMC

Kaiser VB, Svinti V, Prendergast JG, et al. Homozygous loss‐of‐function variants in European cosmopolitan and isolate populations. Hum Mol Genet. 2015;24(19):5464‐5474. doi:10.1093/hmg/ddv272 PubMed DOI PMC

Pejaver V, Urresti J, Lugo‐Martinez J, et al. Inferring the molecular and phenotypic impact of amino acid variants with MutPred2. Nat Commun. 2020;11(1):5918. doi:10.1038/s41467-020-19669-x PubMed DOI PMC

Jaganathan K, Kyriazopoulou Panagiotopoulou S, McRae JF, et al. Predicting splicing from primary sequence with deep learning. Cell. 2019;176(3):535‐548.e24. doi:10.1016/j.cell.2018.12.015 PubMed DOI

Cheng J, Nguyen TYD, Cygan KJ, et al. MMSplice: modular modeling improves the predictions of genetic variant effects on splicing. Genome Biol. 2019;20(1):48. doi:10.1186/s13059-019-1653-z PubMed DOI PMC

Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein‐protein association networks with increased coverage, supporting functional discovery in genome‐wide experimental datasets. Nucleic Acids Res. 2019;47(D1):D607‐D613. doi:10.1093/nar/gky1131 PubMed DOI PMC

Valle L, Vilar E, Tavtigian SV, Stoffel EM. Genetic predisposition to colorectal cancer: syndromes, genes, classification of genetic variants and implications for precision medicine. J Pathol. 2019;247(5):574‐588. doi:10.1002/path.5229 PubMed DOI PMC

Cybulski C, Wokolorczyk D, Kladny J, et al. Germline CHEK2 mutations and colorectal cancer risk: different effects of a missense and truncating mutations? Eur J Hum Genet. 2007;15(2):237‐241. doi:10.1038/sj.ejhg.5201734 PubMed DOI

Suchy J, Cybulski C, Wokołorczyk D, et al. CHEK2 mutations and HNPCC‐related colorectal cancer. Int J Cancer. 2010;126(12):3005‐3009. doi:10.1002/ijc.25003 PubMed DOI

Landrum MJ, Lee JM, Benson M, et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 2018;46(D1):D1062‐D1067. doi:10.1093/nar/gkx1153 PubMed DOI PMC

Fokkema I, Kroon M, López Hernández JA, et al. The LOVD3 platform: efficient genome‐wide sharing of genetic variants. Eur J Hum Genet. 2021;29(12):1796‐1803. doi:10.1038/s41431-021-00959-x PubMed DOI PMC

Mollenhauer J, Herbertz S, Holmskov U, et al. DMBT1 encodes a protein involved in the immune defense and in epithelial differentiation and is highly unstable in cancer. Cancer Res. 2000;60(6):1704‐1710. PubMed

Rahman N. Realizing the promise of cancer predisposition genes. Nature. 2014;505(7483):302‐308. doi:10.1038/nature12981 PubMed DOI PMC

Sud A, Kinnersley B, Houlston RS. Genome‐wide association studies of cancer: current insights and future perspectives. Nat Rev Cancer. 2017;17(11):692‐704. doi:10.1038/nrc.2017.82 PubMed DOI

Fukui H, Sekikawa A, Tanaka H, et al. DMBT1 is a novel gene induced by IL‐22 in ulcerative colitis. Inflamm Bowel Dis. 2011;17(5):1177‐1188. doi:10.1002/ibd.21473 PubMed DOI

Pajares JA, Perea J. Multiple primary colorectal cancer: individual or familial predisposition? World J Gastrointest Oncol. 2015;7(12):434‐444. doi:10.4251/wjgo.v7.i12.434 PubMed DOI PMC

Díaz‐Gay M, Franch‐Expósito S, Arnau‐Collell C, et al. Integrated analysis of germline and tumor DNA identifies new candidate genes involved in familial colorectal cancer. Cancer. 2019;11(3):362. doi:10.3390/cancers11030362 PubMed DOI PMC

Hemminki K, Niazi Y, Vodickova L, Vodicka P, Försti A. Genetic and environmental associations of nonspecific chromosomal aberrations. Mutagenesis. 2024;geae006. doi:10.1093/mutage/geae006 PubMed DOI PMC

Itatani Y, Kawada K, Sakai Y. Transforming growth factor‐β signaling pathway in colorectal cancer and its tumor microenvironment. Int J Mol Sci. 2019;20(23):5822. doi:10.3390/ijms20235822 PubMed DOI PMC

Lu J, Kornmann M, Traub B. Role of epithelial to mesenchymal transition in colorectal cancer. Int J Mol Sci. 2023;24(19):14815. doi:10.3390/ijms241914815 PubMed DOI PMC

Salimian N, Peymani M, Ghaedi K, Hashemi M, Rahimi E. Collagen 1A1 (COL1A1) and Collagen11A1(COL11A1) as diagnostic biomarkers in breast, colorectal and gastric cancers. Gene. 2024;892:147867. doi:10.1016/j.gene.2023.147867 PubMed DOI

Cornish AJ, Gruber AJ, Kinnersley B, et al. The genomic landscape of 2,023 colorectal cancers. Nature. 2024;633:127‐136. doi:10.1038/s41586-024-07747-9 PubMed DOI PMC