Several tumour types are sensitive to deactivation of just one or very few genes that are constantly active in the cancer cells, a phenomenon that is termed ‘oncogene addiction’. Drugs that target the products of those oncogenes can yield a temporary relief, and even complete remission. Unfortunately, many patients receiving oncogene-targeted therapies relapse on treatment. This often happens due to somatic mutations in the oncogene (‘resistance mutations’). ‘Compound mutations’, which in the context of cancer drug resistance are defined as two or more mutations of the drug target in the same clone may lead to enhanced resistance against the most selective inhibitors. Here, it is shown that the vast majority of the resistance mutations occurring in cancer patients treated with tyrosin kinase inhibitors aimed at three different proteins follow an evolutionary pathway. Using bioinformatic analysis tools, it is found that the drug-resistance mutations in the tyrosine kinase domains of Abl1, ALK and exons 20 and 21 of EGFR favour transformations to residues that can be identified in similar positions in evolutionary related proteins. The results demonstrate that evolutionary pressure shapes the mutational landscape in the case of drug-resistance somatic mutations. The constraints on the mutational landscape suggest that it may be possible to counter single drug-resistance point mutations. The observation of relatively many resistance mutations in Abl1, but not in the other genes, is explained by the fact that mutations in Abl1 tend to be biochemically conservative, whereas mutations in EGFR and ALK tend to be radical. Analysis of Abl1 compound mutations suggests that such mutations are more prevalent than hitherto reported and may be more difficult to counter. This supports the notion that such mutations may provide an escape route for targeted cancer drug resistance.
References
[1]
Henkes M, van der Kuip H, Aulitzky WE (2008) Therapeutic options for chronic myeloid leukemia: focus on imatinib (Glivec ？, Gleevec？). Ther Clin Risk Manag 4: 163–187.
[2]
Zhang J, Yang PL, Gray NS (2009) Targeting cancer with small molecule kinase inhibitors. Nat Rev Cancer 9: 28–39.
[3]
Pao W, Miller VA, Politi KA, Riely GJ, Somwar R, et al. (2005) Acquired resistance of lung adenocarcinomas to gefitinib or erlotinib is associated with a second mutation in the egfr kinase domain. PLoS Med 2.
[4]
Choi YL, Soda M, Yamashita Y, Ueno T, Takashima J, et al. (2010) EML4-ALK mutations in lung cancer that confer resistance to ALK inhibitors. N Engl J Med 363: 1734–1739.
[5]
J？nne PA, Gray N, Settleman J (2009) Factors underlying sensitivity of cancers to small-molecule kinase inhibitors. Nat Rev Drug Discov 8: 709–723.
[6]
O'Hare T, Shakespeare WC, Zhu X, Eide CA, Rivera VM, et al. (2009) AP24534, a pan-BCRABL inhibitor for chronic myeloid leukemia, potently inhibits the T315I mutant and overcomes mutation-based resistance. Cancer Cell 16: 401–412.
[7]
Eide CA, Adrian LT, Tyner JW, Mac Partlin M, Anderson DJ, et al. (2011) The ABL switch control inhibitor DCC-2036 is active against the chronic myeloid leukemia mutant BCR-ABLT315I and exhibits a narrow resistance profile. Cancer Res 71: 3189–3195.
[8]
Michor F, Hughes TP, Iwasa Y, Branford S, Shah NP, et al. (2005) Dynamics of chronic myeloid leukaemia. Nature 435: 1267–1270.
[9]
Iwasa Y, Nowak MA, Michor F (2006) Evolution of resistance during clonal expansion. Genetics 172: 2557–2566.
[10]
Komarova NL, Wodarz D (2009) Combination therapies against chronic myeloid leukemia: shortterm versus long-term strategies. Cancer Res 69: 4904–4910.
[11]
Leder K, Foo J, Skaggs B, Gorre M, Sawyers CL, et al. (2011) Fitness conferred by BCR-ABL kinase domain mutations determines the risk of pre-existing resistance in chronic myeloid leukemia. PLoS One 6: e27682.
[12]
Silva AS, Kam Y, Khin ZP, Minton SE, Gillies RJ, et al. (2012) Evolutionary approaches to prolong progression-free survival in breast cancer. Cancer Res 72: 6362–6370.
[13]
Ohta T (1973) Slightly deleterious mutant substitutions in evolution. Nature 246: 96–98.
[14]
Lynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA, et al. (2004) Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med 350: 2129–2139.
[15]
Paez JG, J？nne PA, Lee JC, Tracy S, Greulich H, et al. (2004) EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304: 1497–1500.
[16]
Pao W, Miller V, Zakowski M, Doherty J, Politi K, et al. (2004) EGF receptor gene mutations are common in lung cancers from “never smokers” and are associated with sensitivity of tumors to gefitinib and erlotinib. Proc Natl Acad Sci U S A 101: 13306–13311.
[17]
Ohashi K, Maruvka YE, Michor F, Pao W (2013) Epidermal growth factor receptor tyrosine kinase inhibitor-resistant disease. J Clin Oncol
[18]
Sharma SV, Bell DW, Settleman J, Haber DA (2007) Epidermal growth factor receptor mutations in lung cancer. Nat Rev Cancer 7: 169–181.
[19]
Soda M, Choi YL, Enomoto M, Takada S, Yamashita Y, et al. (2007) Identification of the transforming EML4-ALK fusion gene in non-small-cell lung cancer. Nature 448: 561–566.
[20]
Chen Y, Takita J, Choi YL, Kato M, Ohira M, et al. (2008) Oncogenic mutations of ALK kinase in neuroblastoma. Nature 455: 971–974.
[21]
George RE, Sanda T, Hanna M, Frohling S, Luther W, et al. (2008) Activating mutations in ALK provide a therapeutic target in neuroblastoma. Nature 455: 975–978.
[22]
Janoueix-Lerosey I, Lequin D, Brugieres L, Ribeiro A, de Pontual L, et al. (2008) Somatic and germline activating mutations of the ALK kinase receptor in neuroblastoma. Nature 455: 967–970.
[23]
Katayama R, Shaw AT, Khan TM, Mino-Kenudson M, Solomon BJ, et al. (2012) Mechanisms of acquired crizotinib resistance in ALK-rearranged lung cancers. Sci Transl Med 4: 120–120.
[24]
Soverini S, Hochhaus A, Nicolini FE, Gruber F, Lange T, et al. (2011) BCR-ABL kinase domain mutation analysis in chronic myeloid leukemia patients treated with tyrosine kinase inhibitors: recommendations from an expert panel on behalf of european leukemianet. Blood 118: 1208–1215.
[25]
O'Hare T, Eide CA, Deininger MW (2007) Bcr-Abl kinase domain mutations, drug resistance, and the road to a cure for chronic myeloid leukemia. Blood 110: 2242–2249.
[26]
Willis SG, Lange T, Demehri S, Otto S, Crossman L, et al. (2005) High-sensitivity detection of BCR-ABL kinase domain mutations in imatinib-naive patients: correlation with clonal cytogenetic evolution but not response to therapy. Blood 106: 2128–2137.
[27]
Yun CH, Mengwasser KE, Toms AV, Woo MS, Greulich H, et al. (2008) The T790M mutation in EGFR kinase causes drug resistance by increasing the affinity for ATP. Proc Natl Acad Sci U S A 105: 2070–2075.
[28]
Bean J, Riely GJ, Balak M, Marks JL, Ladanyi M, et al. (2008) Acquired resistance to epidermal growth factor receptor kinase inhibitors associated with a novel T854A mutation in a patient with EGFR-mutant lung adenocarcinoma. Clin Cancer Res 14: 7519–7525.
[29]
Marchler-Bauer A, Zheng C, Chitsaz F, Derbyshire MK, Geer LY, et al. (2013) CDD: conserved domains and protein three-dimensional structure. Nucleic Acids Res 41: 348–352.
[30]
Khorashad JS, Kelley TW, Szankasi P, Mason CC, Soverini S, et al. (2013) BCR-ABL1 compound mutations in tyrosine kinase inhibitor-resistant cml: frequency and clonal relationships. Blood 121: 489–498.
[31]
Forshew T, Murtaza M, Parkinson C, Gale D, Tsui DW, et al. (2012) Noninvasive identification and monitoring of cancer mutations by targeted deep sequencing of plasma dna. Sci Transl Med 4: 136–136.
Nei M, Suzuki Y, Nozawa M (2010) The neutral theory of molecular evolution in the genomic era. Annu Rev Genomics Hum Genet 11: 265–289.
[34]
Pupko T, Bell RE, Mayrose I, Glaser F, Ben-Tal N (2002) Rate4Site: an algorithmic tool for the identification of functional regions in proteins by surface mapping of evolutionary determinants within their homologues. Bioinformatics 18 (Suppl 1) 71–77.
[35]
Glaser F, Pupko T, Paz I, Bell RE, Bechor-Shental D, et al. (2003) Consurf: identification of functional regions in proteins by surface-mapping of phylogenetic information. Bioinformatics 19: 163–4.
[36]
Glaser F, Rosenberg Y, Kessel A, Pupko T, Ben-Tal N (2005) The ConSurf-HSSP database: the mapping of evolutionary conservation among homologs onto PDB structures. Proteins 58: 610–617.
[37]
Bashashati A, Haffari G, Ding J, Ha G, Lui K, et al. (2012) DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer. Genome Biol 13: R124.
[38]
Grantham R (1974) Amino acid difference formula to help explain protein evolution. Science 185: 862–864.
[39]
Ohta T, Tachida H (1990) Theoretical study of near neutrality. i. heterozygosity and rate of mutant substitution. Genetics 126: 219–229.
[40]
Berge EM, Aisner DL, Doebele RC (2013) Erlotinib response in an nsclc patient with a novel compound g719d+l861r mutation in egfr. J Thorac Oncol 8: 83–84.
[41]
Kobayashi S, Canepa HM, Bailey AS, Nakayama S, Yamaguchi N, et al. (2013) Compound egfr mutations and response to egfr tyrosine kinase inhibitors. J Thorac Oncol 8: 45–51.
[42]
De Grassi A, Segala C, Iannelli F, Volorio S, Bertario L, et al. (2010) Ultradeep sequencing of a human ultraconserved region reveals somatic and constitutional genomic instability. PLoS Biol 8: e1000275.
[43]
D'Antonio M, Pendino V, Sinha S, Ciccarelli FD (2012) Network of cancer genes (ncg 3.0): integration and analysis of genetic and network properties of cancer genes. Nucleic Acids Res 40: 978–983.
[44]
Diaz LA, Williams RT, Wu J, Kinde I, Hecht JR, et al. (2012) The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers. Nature 486: 537–540.
[45]
Martins FC, De S, Almendro V, G？nen M, Park SY, et al. (2012) Evolutionary pathways in BRCA1-associated breast tumors. Cancer Discov 2: 503–511.
[46]
Podlaha O, Riester M, De S, Michor F (2012) Evolution of the cancer genome. Trends Genet 28: 155–163.
[47]
Foo J, Leder K, Mumenthaler SM (2013) Cancer as a moving target: understanding the composition and rebound growth kinetics of recurrent tumors. Evol Appl 6: 54–69.
[48]
Kaminker JS, Zhang Y, Watanabe C, Zhang Z (2007) CanPredict: a computational tool for predicting cancer-associated missense mutations. Nucleic Acids Res 35: 595–598.
[49]
Torkamani A, Schork NJ (2008) Prediction of cancer driver mutations in protein kinases. Cancer Res 68: 1675–1682.
[50]
Wong WC, Kim D, Carter H, Diekhans M, Ryan MC, et al. (2011) CHASM and SNVBox: toolkit for detecting biologically important single nucleotide mutations in cancer. Bioinformatics 27: 2147–2148.
[51]
Gonzalez-Perez A, Deu-Pons J, Lopez-Bigas N (2012) Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation. Genome Med 4: 89–89.
[52]
Goymer P (2008) Natural selection: The evolution of cancer. Nature 454: 1046–1048.
[53]
Fillon M (2012) Cancer and natural selection. J Natl Cancer Inst 104: 1773–1774.
[54]
Gatenby R (2012) Perspective: Finding cancer's first principles. Nature 491.
[55]
Gillies RJ, Verduzco D, Gatenby RA (2012) Evolutionary dynamics of carcinogenesis and why targeted therapy does not work. Nat Rev Cancer 12: 487–493.
[56]
Aktipis CA, Nesse RM (2013) Evolutionary foundations for cancer biology. Evol Appl 6: 144–159.
[57]
McFarland CD, Korolev KS, Kryukov GV, Sunyaev SR, Mirny LA (2013) Impact of deleterious passenger mutations on cancer progression. Proc Natl Acad Sci U S A 110: 2910–2915.
[58]
Nunney L (2013) The real war on cancer: the evolutionary dynamics of cancer suppression. Evol Appl 6: 11–19.
[59]
Boratyn GM, Sch？ffer AA, Agarwala R, Altschul SF, Lipman DJ, et al. (2012) Domain enhanced lookup time accelerated BLAST. Biol Direct 7: 12–12.
[60]
Bairoch A, Apweiler R (2000) The swiss-prot protein sequence database and its supplement trembl in 2000. Nucleic Acids Res 28: 45–48.
[61]
Rice P, Longden I, Bleasby A (2000) EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet 16: 276–277.
[62]
Needleman SB, Wunsch CD (1970) A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol 48: 443–453.
[63]
Katoh K, Misawa K, Kuma K, Miyata T (2002) MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res 30: 3059–3066.
[64]
Penn O, Privman E, Landan G, Graur D, Pupko T (2010) An alignment confidence score capturing robustness to guide tree uncertainty. Mol Biol Evol 27: 1759–1767.
[65]
Biegert A, S？ding J (2009) Sequence context-specific profiles for homology searching. Proc Natl Acad Sci U S A 106: 3770–3775.
[66]
Angermüller C, Biegert A, S？ding J (2012) Discriminative modelling of context-specific amino acid substitution probabilities. Bioinformatics 28: 3240–3247.
[67]
Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14: 33–38.
[68]
Solca F, Dahl G, Zoephel A, Bader G, Sanderson M, et al. (2012) Target binding properties and cellular activity of afatinib (BIBW 2992), an irreversible ErbB family blocker. J Pharmacol Exp Ther 343: 342–350.
[69]
Lewis RT, Bode CM, Choquette DM, Potashman M, Romero K, et al. (2012) The discovery and optimization of a novel class of potent, selective, and orally bioavailable anaplastic lymphoma kinase (ALK) inhibitors with potential utility for the treatment of cancer. J Med Chem 55: 6523–6540.
[70]
Weisberg E, Manley PW, Breitenstein W, Brüggen J, Cowan-Jacob SW, et al. (2005) Characterization of AMN107, a selective inhibitor of native and mutant Bcr-Abl. Cancer Cell 7: 129–141.
