Differentiating between cancer and normal tissue samples using multi-hit combinations of genetic mutations

Bioinformatics
  • 1.

    Tomasetti, C., Marchionni, L., Nowak, M. A., Parmigiani, G. & Vogelstein, B. Only three driver gene mutations are required for the development of lung and colorectal cancers. Proc Natl Acad Sci USA 112, 118–123 (2015).

  • 2.

    Zhang, X. & Simon, R. Estimating the number of rate limiting genomic changes for human breast cancer. Breast Cancer Res Treat 91, 121–124 (2005).

  • 3.

    Luebeck, E. G. & Moolgavkar, S. H. Multistage carcinogenesis and the incidence of colorectal cancer. Proc Natl Acad Sci USA 99, 15095–15100 (2002).

  • 4.

    Little, M. & Wright, E. A stochastic carcinogenesis model incorporating genomic instability fitted to colon cancer data. Mathematical biosciences 183, 111–134 (2003).

  • 5.

    Ashley, D. The two “hit” and multiple “hit” theories of carcinogenesis. Br J Cancer 23, 313 (1969).

  • 6.

    Armitage, P. & Doll, R. The age distribution of cancer and a multi-stage theory of carcinogenesis. Br J Cancer 8, 1 (1954).

  • 7.

    Nordling, C. A new theory on the cancer-inducing mechanism. Br J Cancer 7, 68 (1953).

  • 8.

    Anandakrishnan, R. Estimating the number of genetic mutations (hits) required for carcinogenesis based on the distribution of somatic mutations. PLOS Comp Bio In Review (2018).

  • 9.

    Tian, R., Basu, M. & Capriotti, E. Contrastrank: a new method for ranking putative cancer driver genes and classification of tumor samples. Bioinformatics 30, 572–578 (2014).

  • 10.

    Tamborero, D., Gonzalez-Perez, A. & Lopez-Bigas, N. Oncodriveclust: exploiting the positional clustering of somatic mutations to identify cancer genes. Bioinformatics 29, 2238–2242 (2013).

  • 11.

    Dees, N. D. et al. Music: identifying mutational significance in cancer genomes. Genome Res 22, 1589–1598 (2012).

  • 12.

    Kumar, R. D., Swamidass, S. J. & Bose, R. Unsupervised detection of cancer driver mutations with parsimony-guided learning. Nat Genet 48, 1288–1294 (2016).

  • 13.

    Kuchenbaecker, K. B. et al. Risks of breast, ovarian, and contralateral breast cancer for BRCA1 and BRCA2 mutation carriers. JAMA 317, 2402–2416 (2017).

  • 14.

    Mai, P. et al. Li-Fraumeni syndrome: report of a clinical research workshop and creation of a research consortium. Cancer Genet 205, 479–87 (2012).

  • 15.

    Pantziarka, P. Primed for cancer: Li Fraumeni Syndrome and the pre-cancerous niche. Ecancermedicalscience 9, 541 (2015).

  • 16.

    Guha, T. & Malkin, D. Inherited TP53 mutations and the Li-Fraumeni syndrome. Cold Spring Harb Perspect Med 7, a026187 (2017).

  • 17.

    Amadou, A., Waddington Achatz, M. & Hainaut, P. Revisiting tumor patterns and penetrance in germline TP53 mutation carriers: temporal phases of Li-Fraumeni syndrome. Curr Opin Oncol 30, 23–29 (2018).

  • 18.

    Grant, R. C. et al. Prevalence of germline mutations in cancer predisposition genes in patients with pancreatic cancer. Gastroenterology 148, 556–564 (2015).

  • 19.

    Kinzler, K. W. & Vogelstein, B. Lessons from hereditary colorectal cancer. Cell 87, 159–170 (1996).

  • 20.

    Stahl, M. et al. Epigenetics in Cancer: A hematological perspective. PLoS Genet 12, e1006193 (2016).

  • 21.

    Schneider G, R. R. S. D. & Schmidt-Supprian, M. Tissue-specific tumorigenesis: context matters. Nat Rev Cancer 17, 239–53 (2017).

  • 22.

    Almassalha, L. et al. The greater genomic landscape: The heterogeneous rvolution of cancer. Cancer Res 76, 5605–9 (2016).

  • 23.

    Vogelstein, B. et al. Cancer genome landscapes. Science 339, 1546–58 (2013).

  • 24.

    Chvatal, V. A greedy heuristic for the set-covering problem. Mathematics of Operations Research 4, 233–235 (1979).

  • 25.

    Feige, U. A threshold of ln n for approximating set cover. Journal of the ACM (JACM) 45, 634–652 (1998).

  • 26.

    Al-Lazikani, B., Banerji, U. & Workman, P. Combinatorial drug therapy for cancer in the post-genomic era. Nature biotechnology 30, 679 (2012).

  • 27.

    Ledford, H. Cocktails for cancer with a measure of immunotherapy. Nature 532, 162–164 (2016).

  • 28.

    Pleasance, E. et al. A comprehensive catalogue of somatic mutations from a human cancer genome. Nature 463, 191–196 (2010).

  • 29.

    Xi, J., Wang, M. & Li, A. Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network. BMC Bioinformatics 19, 1–14 (2018).

  • 30.

    Spencer, D. H. et al. Performance of common analysis methods for detecting low-frequency single nucleotide variants in targeted next-generation sequence data. J Mol Diag 16, 75–88 (2014).

  • 31.

    Sandmann, S. et al. Evaluating variant calling tools for non-matched next-generation sequencing data. Sci Rep 7, 43169 (2017).

  • 32.

    Pearson, K. Mathematical contributions to the theory of evolution. iii. regression, heredity, and panmixia. Philosophical Transactions of the Royal Society of London. Series A, containing papers of a mathematical or physical character 187, 253–318 (1896).

  • 33.

    Liu, X. & Ling, Z.-Q. Role of isocitrate dehydrogenase 1/2 (IDH 1/2) gene mutations in human tumors. Histology and Histopathology 30, 1155–1160 (2015).

  • 34.

    Merid, S. K., Goranskaya, D. & Alexeyenko, A. Distinguishing between driver and passenger mutations in individual cancer genomes by network enrichment analysis. BMC Bioinformatics 14, 308 (2014).

  • 35.

    Berger, A. et al. High-throughput phenotyping of lung cancer somatic mutations. Cancer Cell 30, 214–228 (2016).

  • 36.

    Leiserson, M. D., Reyna, M. A. & Raphael, B. J. A weighted exact test for mutually exclusive mutations in cancer. Bioinformatics 32, 736–745 (2016).

  • 37.

    Parrales, A. & Iwakuma, T. Targeting oncogenic mutant p53 for cancer therapy. Front Oncol 5, 288 (2015).

  • 38.

    Pan, B., Zheng, S., Liu, C. & Xu, Y. Suppression of IGHG1 gene expression by siRNA leads to growth inhibition and apoptosis induction in human prostate cancer cell. Mol Biol Rep 40, 27–33 (2013).

  • 39.

    Xu, Y. et al. IgG silencing induces apoptosis and suppresses proliferation, migration and invasion in LNCaP prostate cancer cells. Cell Mol Biol Lett 21, 27 (2016).

  • 40.

    Weinstein, J. et al. The cancer genome atlas pan-cancer analysis project. Nat Genet 48, 1288–1294 (2016).

  • 41.

    Copson, E. R. et al. Germline BRCA mutation and outcome in young-onset breast cancer (POSH): a prospective cohort study. Lancet Oncol 19, 169–180 (2018).

  • 42.

    Berchuck, A. et al. Frequency of germline and somatic BRCA1 mutations in ovarian cancer. Clin Cancer Res 4, 2433–2437 (1998).

  • 43.

    Zhang, H., Meltzer, P. & Davis, S. Rcircos: an R package for Circos 2D track plots. BMC Bioinformatics 14, 244 (2013).

  • 44.

    Cerami, E. et al. The cbio cancer genomics portal: An open platform for exploring multidimensional cancer genomics data. Cancer Discovery 2, 401–404 (2012).

  • 45.

    Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cbioportal. Sci. Signal. 6, pl1–pl1 (2013).

  • 46.

    Song, Y. & C.J., Z. Effect of hydralazine on demethylation status and expression of APCgene, proliferation and apoptosis of human cervical cancer cell lines. Chinese journal of pathology 36, 614–8 (2007).

  • 47.

    Wang, T. et al. Increased nucleotide polymorphic changes in the 5′-untranslated region of δ-catenin (CTNND2) gene in prostate cancer. Oncogene 28, 555–564 (2009).

  • 48.

    Dhillon, S. Ivosidenib: First global approval. Drugs 78, 1509–1516 (2018).

  • 49.

    Liu, X., Jakubowski, M. & Hunt, J. KRAS gene mutation in colorectal cancer is correlated with increased proliferation and spontaneous apoptosis. Am J Clin Pathol 135, 245–52 (2011).

  • 50.

    Matsuyama, T. et al. MUC12 mRNA expression is an independent marker of prognosis in stage II and stage III colorectal cancer. Int J Cancer 127, 2292–9 (2010).

  • 51.

    Vincent, A. et al. Epigenetic regulation (DNA methylation, histone modifications) of the 11p15 mucin genes (MUC2, MUC5AC, MUC5B, MUC6) in epithelial cancer cells. Oncogene 26, 6566–76 (2007).

  • 52.

    Yim, E. et al. Rak functions as a tumor suppressor by regulating PTEN protein stability and function. Cancer Cell 15, 304–14 (2009).

  • 53.

    Arima, Y. et al. Rb depletion results in deregulation of E-cadherin and induction of cellular phenotypic changes that are characteristic of the epithelial-to-mesenchymal transition. Cancer Res 68, 5104–12 (2008).

  • 54.

    Vannini, I. et al. Role of p53 codon 72 arginine allele in cell survival in vitro and in the clinical outcome of patients with advanced breast cancer. Tumour Biol 29, 145–51 (2008).

  • 55.

    Ma, J. et al. 15-lipoxygenase-1/15-hydroxyeicosatetraenoic acid promotes hepatocellular cancer cells growth through protein kinase b and heat shock protein 90 complex activation. Int J Biochem Cell Biol 45, 1031–41 (2013).

  • 56.

    Ben-Arie, A., Hagay, Z., Ben-Hur, H., Open, M. & Dgani, R. Elevated serum alkaline phosphatase may enable early diagnosis of ovarian cancer. Eur J Obstet Gynecol Reprod Biol 86, 69–71 (1999).

  • 57.

    Natrajan, R. et al. Amplification and overexpression of CACNA1E correlates with relapse in favorable histology Wilms’ tumors. Clin Cancer Res 12, 7284–93 (2006).

  • 58.

    Ritterhouse, L. L. et al. Ros1 rearrangement in thyroid cancer. Thyroid 26, 1 (2016).

  • 59.

    Tan, E., Richard, C., Zhang, H., Hoskin, D. & Blay, J. Adenosine downregulates DPPIV on HT-29 colon cancer cells by stimulating protein tyrosine phosphatase(s) and reducing ERK1/2 activity via a novel pathway. Am J Physiol Cell Physiol 291, 433–44 (2006).

  • 60.

    Paul, N. et al. α5β1 integrin recycling promotes Arp2/3-independent cancer cell invasion via the formin FHOD3. J Cell Biol 210, 1013–31 (2015).

  • 61.

    An, Q. et al. Heterogeneous breakpoints in patients with acute lymphoblastic leukemia and the dic(9; 20)(p11-13; q11) show recurrent involvement of genes at 20q11.21. Haematologica 94, 1164–9 (2009).

  • 62.

    Verheyden, S. et al. Role of the inhibitory KIR ligand HLA-Bw4 and HLA-C expression levels in the recognition of leukemic cells by natural killer cells. Cancer Immunol Immunother 58, 855–65 (2009).

  • 63.

    Mundhada, S., Luthra, R. & Cano, P. Association of HLA class i and class ii genes with bcr-abl transcripts in leukemia patients with t(9; 22) (q34; q11). BMC Cancer 4, 25 (2004).

  • 64.

    Fleming, J., Ginsburg, E., Oliver, S., Goldsmith, P. & Vonderhaar, B. Hornerin, an s100 family protein, is functional in breast cells and aberrantly expressed in breast cancer. BMC Cancer 12, 266 (2012).

  • 65.

    Coma, M. et al. Impaired voltage-gated K+ channel expression in brain during experimental cancer cachexia. FEBS Lett 536, 45–50 (2003).

  • 66.

    Qin, Y., Tang, X. & Liu, M. Tumor-suppressor gene NBPF1 inhibits invasion and PI3K/mTOR signaling in cervical cancer cells. Oncol Res 23, 13–20 (2016).

  • 67.

    Tsai, L. et al. The sodium-dependent glucose cotransporter SLC5A11 as an autoimmune modifier gene in SLE. Tissue Antigens 71, 114–126 (2007).

  • Articles You May Like

    Spintronics by ‘straintronics’
    Bugs Vs. Superbugs: Insects Offer Promise In Fight Against Antibiotic Resistance
    A signal processing method for alignment-free metagenomic binning: multi-resolution genomic binary patterns
    Improved RNA data visualization method gets to the bigger picture faster
    Verbalizing phylogenomic conflict: Representation of node congruence across competing reconstructions of the neoavian explosion

    Leave a Reply

    Your email address will not be published. Required fields are marked *