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The influence of subclonal resistance mutations on targeted cancer therapy

Key Points

  • All cancers probably contain an enormous number of coexisting subclonal mutations; in some cases, every possible mutation could exist in at least one cell in the tumour

  • Resistance to molecularly targeted therapies can arise from selective growth of pre-existing subclones within the bulk of the tumour that carry drug-resistance mutations and thus have a survival advantage

  • Drug-resistance mutations can be found in variable proportions of tumour cells before therapy; their early detection enables stratification of patients to more-effective treatments and avoidance of treatments that are destined to fail

  • Accurate identification of resistance mutations requires highly sensitive detection techniques and representative tumour sampling

  • Routine interrogation of the subclonal genetic structure of tumours will be critical to the success of personalized cancer medicine

Abstract

Clinical oncology is being revolutionized by the increasing use of molecularly targeted therapies. This paradigm holds great promise for improving cancer treatment; however, allocating specific therapies to the patients who are most likely to derive a durable benefit continues to represent a considerable challenge. Evidence continues to emerge that cancers are characterized by extensive intratumour genetic heterogeneity, and that patients being considered for treatment with a targeted agent might, therefore, already possess resistance to the drug in a minority of cells. Indeed, multiple examples of pre-existing subclonal resistance mutations to various molecularly targeted agents have been described, which we review herein. Early detection of pre-existing or emerging drug resistance could enable more personalized use of targeted cancer therapy, as patients could be stratified to receive the therapies that are most likely to be effective. We consider how monitoring of drug resistance could be incorporated into clinical practice to optimize the use of targeted therapies in individual patients.

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Figure 1: The evolution and detection of drug resistance in patients with cancer.
Figure 2: Core elements of the human EGFR–MAPK pathway.
Figure 3: The ability to detect mutations that are present at a low frequency depends on the assay error rate.

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References

  1. Schmitt, M. W., Prindle, M. J. & Loeb, L. A. Implications of genetic heterogeneity in cancer. Ann. N. Y. Acad. Sci. 1267, 110–116 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Cull, E. H. & Altman, J. K. Contemporary treatment of APL. Curr. Hematol. Malig. Rep. 9, 193–201 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Welch, J. S. et al. The origin and evolution of mutations in acute myeloid leukemia. Cell 150, 264–278 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Zhu, H.-H., Qin, Y.-Z. & Huang, X.-J. Resistance to arsenic therapy in acute promyelocytic leukemia. N. Engl. J. Med. 370, 1864–1866 (2014).

    Article  CAS  PubMed  Google Scholar 

  7. Loeb, L. A., Springgate, C. F. & Battula, N. Errors in DNA replication as a basis of malignant changes. Cancer Res. 34, 2311–2321 (1974).

    CAS  PubMed  Google Scholar 

  8. Greaves, M. & Maley, C. C. Clonal evolution in cancer. Nature 481, 306–313 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Nowell, P. C. The clonal evolution of tumor cell populations. Science 194, 23–28 (1976).

    Article  CAS  PubMed  Google Scholar 

  10. Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    Article  CAS  PubMed  Google Scholar 

  11. Berger, M. F. et al. Melanoma genome sequencing reveals frequent PREX2 mutations. Nature 485, 502–506 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Campbell, P. J. et al. Subclonal phylogenetic structures in cancer revealed by ultra-deep sequencing. Proc. Natl Acad. Sci. USA 105, 13081–13086 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Ding, L. et al. Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature 481, 506–509 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Naxerova, K. et al. Hypermutable DNA chronicles the evolution of human colon cancer. Proc. Natl Acad. Sci. USA 111, E1889–E1898 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Mullighan, C. G. et al. Genomic analysis of the clonal origins of relapsed acute lymphoblastic leukemia. Science 322, 1377–1380 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Landau, D. A. et al. Evolution and impact of subclonal mutations in chronic lymphocytic leukemia. Cell 152, 714–726 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Rossi, D. et al. Clinical impact of small TP53 mutated subclones in chronic lymphocytic leukemia. Blood 123, 2139–2147 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Wong, T. N. et al. Role of TP53 mutations in the origin and evolution of therapy-related acute myeloid leukaemia. Nature 518, 552–555 (2015).

    Article  CAS  PubMed  Google Scholar 

  20. Bhang, H.-E. et al. Studying clonal dynamics in response to cancer therapy using high-complexity barcoding. Nat. Med. 21, 440–448 (2015).

    Article  CAS  PubMed  Google Scholar 

  21. Mroz, E. A. et al. High intratumor genetic heterogeneity is related to worse outcome in patients with head and neck squamous cell carcinoma. Cancer 119, 3034–3042 (2013).

    Article  PubMed  Google Scholar 

  22. Mroz, E. A., Tward, A. M., Hammon, R. J., Ren, Y. & Rocco, J. W. Intra-tumor genetic heterogeneity and mortality in head and neck cancer: analysis of data from The Cancer Genome Atlas. PLoS Med. 12, e1001786 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Cooke, S. L. et al. Intra-tumour genetic heterogeneity and poor chemoradiotherapy response in cervical cancer. 104, 361–368 (2010).

  24. Park, S. Y., Gonen, M., Kim, H. J., Michor, F. & Polyak, K. Cellular and genetic diversity in the progression of in situ human breast carcinomas to an invasive phenotype. J. Clin. Invest. 120, 636–644 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Carter, S. L., Eklund, A. C., Kohane, I. S., Harris, L. N. & Szallasi, Z. A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers. Nat. Genet. 38, 1043–1048 (2006).

    Article  CAS  PubMed  Google Scholar 

  26. Merlo, L. M. et al. A comprehensive survey of clonal diversity measures in Barrett's esophagus as biomarkers of progression to esophageal adenocarcinoma. Cancer Prev. Res. (Phila.) 3, 1388–1397 (2010).

    Article  Google Scholar 

  27. Wang, Y. et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512, 155–160 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Foulkes, W. D., Reis-Filho, J. S. & Narod, S. A. Tumor size and survival in breast cancer—a reappraisal. Nat. Rev. Clin. Oncol. 7, 348–353 (2010).

    Article  CAS  PubMed  Google Scholar 

  29. Sinicrope, F. A. & Yang, Z. J. Prognostic and predictive impact of DNA mismatch repair in the management of colorectal cancer. Future Oncol. 7, 467–474 (2011).

    Article  CAS  PubMed  Google Scholar 

  30. Fox, E. J. & Loeb, L. A. Lethal mutagenesis: targeting the mutator phenotype in cancer. Semin. Cancer Biol. 20, 353–359 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Le, D. T. et al. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 372, 2509–2520 (2015).

    PubMed  PubMed Central  CAS  Google Scholar 

  33. Chereda, B. & Melo, J. V. Natural course and biology of CML. Ann. Hematol. 94 (Suppl. 2), S107–S121 (2015).

    Article  CAS  PubMed  Google Scholar 

  34. Cilloni, D. & Saglio, G. Molecular pathways: BCR–ABL. Clin. Cancer Res. 18, 930–937 (2012).

    Article  CAS  PubMed  Google Scholar 

  35. Druker, B. J. et al. Five-year follow-up of patients receiving imatinib for chronic myeloid leukemia. N. Engl. J. Med. 355, 2408–2417 (2006).

    Article  CAS  PubMed  Google Scholar 

  36. Moorman, A. V. et al. Karyotype is an independent prognostic factor in adult acute lymphoblastic leukemia (ALL): analysis of cytogenetic data from patients treated on the Medical Research Council (MRC) UKALLXII/Eastern Cooperative Oncology Group (ECOG) 2993 trial. Blood 109, 3189–3197 (2007).

    Article  CAS  PubMed  Google Scholar 

  37. Lee, K.-H. et al. Clinical effect of imatinib added to intensive combination chemotherapy for newly diagnosed Philadelphia chromosome-positive acute lymphoblastic leukemia. Leukemia 19, 1509–1516 (2005).

    Article  CAS  PubMed  Google Scholar 

  38. Soverini, S. et al. Implications of BCR–ABL1 kinase domain-mediated resistance in chronic myeloid leukemia. Leukemia Res. 38, 10–20 (2014).

    Article  CAS  Google Scholar 

  39. Roche-Lestienne, C. et al. Several types of mutations of the Abl gene can be found in chronic myeloid leukemia patients resistant to STI571, and they can pre-exist to the onset of treatment. Blood 100, 1014–1018 (2002).

    Article  CAS  PubMed  Google Scholar 

  40. Roche-Lestienne, C., Laï, J.-L., Darré, S., Facon, T. & Preudhomme, C. A mutation conferring resistance to imatinib at the time of diagnosis of chronic myelogenous leukemia. N. Engl. J. Med. 348, 2265–2266 (2003).

    Article  PubMed  Google Scholar 

  41. Pfeifer, H. et al. Kinase domain mutations of BCR–ABL frequently precede imatinib-based therapy and give rise to relapse in patients with de novo Philadelphia-positive acute lymphoblastic leukemia (Ph+ ALL). Blood 110, 727–734 (2007).

    Article  CAS  PubMed  Google Scholar 

  42. Shah, N. P. et al. Multiple BCR–ABL kinase domain mutations confer polyclonal resistance to the tyrosine kinase inhibitor imatinib (STI571) in chronic phase and blast crisis chronic myeloid leukemia. Cancer Cell 2, 117–125 (2002).

    Article  CAS  PubMed  Google Scholar 

  43. Hughes, T. et al. Impact of baseline BCR–ABL mutations on response to nilotinib in patients with chronic myeloid leukemia in chronic phase. J. Clin. Oncol. 27, 4204–4210 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Fox, E. J., Reid-Bayliss, K. S., Emond, M. J. & Loeb, L. A. Accuracy of next generation sequencing platforms. Next Gener. Seq. Appl. 1, 1000106 (2014).

    PubMed  PubMed Central  Google Scholar 

  45. Parker, W. T. et al. Sensitive detection of BCR–ABL1 mutations in patients with chronic myeloid leukemia after imatinib resistance is predictive of outcome during subsequent therapy. J. Clin. Oncol. 29, 4250–4259 (2011).

    Article  CAS  PubMed  Google Scholar 

  46. Parker, W. T., Ho, M., Scott, H. S., Hughes, T. P. & Branford, S. Poor response to second-line kinase inhibitors in chronic myeloid leukemia patients with multiple low-level mutations, irrespective of their resistance profile. Blood 119, 2234–2238 (2012).

    Article  CAS  PubMed  Google Scholar 

  47. Rubin, B. P. et al. KIT activation is a ubiquitous feature of gastrointestinal stromal tumors. Cancer Res. 61, 8118–8121 (2001).

    CAS  PubMed  Google Scholar 

  48. Lennartsson, J. & Ronnstrand, L. Stem cell factor receptor/c-Kit: from basic science to clinical implications. Physiol. Rev. 92, 1619–1649 (2012).

    Article  CAS  PubMed  Google Scholar 

  49. Corless, C. L. et al. PDGFRA mutations in gastrointestinal stromal tumors: frequency, spectrum and in vitro sensitivity to imatinib. J. Clin. Oncol. 23, 5357–5364 (2005).

    Article  CAS  PubMed  Google Scholar 

  50. Blanke, C. D. et al. Long-term results from a randomized phase II trial of standard- versus higher-dose imatinib mesylate for patients with unresectable or metastatic gastrointestinal stromal tumors expressing KIT. J. Clin. Oncol. 26, 620–625 (2008).

    Article  CAS  PubMed  Google Scholar 

  51. Heinrich, M. C. et al. Molecular correlates of imatinib resistance in gastrointestinal stromal tumors. J. Clin. Oncol. 24, 4764–4774 (2006).

    Article  CAS  PubMed  Google Scholar 

  52. Guo, T. et al. Sorafenib inhibits the imatinib-resistant KITT670I gatekeeper mutation in gastrointestinal stromal tumor. Clin. Cancer Res. 13, 4874–4881 (2007).

    Article  CAS  PubMed  Google Scholar 

  53. Heinrich, M. C. et al. Correlation of kinase genotype and clinical outcome in the North American Intergroup Phase III trial of imatinib mesylate for treatment of advanced gastrointestinal stromal tumor: CALGB 150105 Study by Cancer and Leukemia Group B and Southwest Oncology Group. J. Clin. Oncol. 26, 5360–5367 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Heinrich, M. C. et al. Kinase mutations and imatinib response in patients with metastatic gastrointestinal stromal tumor. J. Clin. Oncol. 21, 4342–4349 (2003).

    Article  CAS  PubMed  Google Scholar 

  55. Tomasetti, C., Demetri, G. D. & Parmigiani, G. Why tyrosine kinase inhibitor resistance is common in advanced gastrointestinal stromal tumors. F1000Res. 2, 152 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Patel, S. Exploring novel therapeutic targets in GIST: focus on the PI3K/Akt/mTOR pathway. Curr. Oncol. Rep. 15, 386–395 (2013).

    Article  CAS  PubMed  Google Scholar 

  57. Baker, N. M. & Der, C. J. Cancer: drug for an 'undruggable' protein. Nature 497, 577–578 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Shi, Y. et al. A prospective, molecular epidemiology study of EGFR mutations in Asian patients with advanced non-small-cell lung cancer of adenocarcinoma histology (PIONEER). J. Thorac. Oncol. 9, 154–162 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Zhou, C. et al. Erlotinib versus chemotherapy as first-line treatment for patients with advanced EGFR mutation-positive non-small-cell lung cancer (OPTIMAL, CTONG-0802): a multicentre, open-label, randomised, phase 3 study. Lancet Oncol. 12, 735–742 (2011).

    Article  CAS  PubMed  Google Scholar 

  60. Yang, J. C. et al. Clinical activity of afatinib in patients with advanced non-small-cell lung cancer harbouring uncommon EGFR mutations: a combined post-hoc analysis of LUX-Lung 2, LUX-Lung 3, and LUX-Lung 6. Lancet Oncol. 16, 830–838 (2015).

    Article  CAS  PubMed  Google Scholar 

  61. Fukuoka, M. et al. Biomarker analyses and final overall survival results from a phase III, randomized, open-label, first-line study of gefitinib versus carboplatin/paclitaxel in clinically selected patients with advanced non-small-cell lung cancer in Asia (IPASS). J. Clin. Oncol. 29, 2866–2874 (2011).

    Article  CAS  PubMed  Google Scholar 

  62. Moore, M. J. et al. Erlotinib plus gemcitabine compared with gemcitabine alone in patients with advanced pancreatic cancer: a phase III trial of the National Cancer Institute of Canada Clinical Trials Group. J. Clin. Oncol. 25, 1960–1966 (2007).

    Article  CAS  PubMed  Google Scholar 

  63. Wang, J. P. et al. Erlotinib is effective in pancreatic cancer with epidermal growth factor receptor mutations: a randomized, open-label, prospective trial. Oncotarget 6, 18162–18173 (2015).

    PubMed  PubMed Central  Google Scholar 

  64. Seo, J. S. et al. The transcriptional landscape and mutational profile of lung adenocarcinoma. Genome Res. 22, 2109–2119 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Zhou, Q. et al. Relative abundance of EGFR mutations predicts benefit from gefitinib treatment for advanced non-small-cell lung cancer. J. Clin. Oncol. 29, 3316–3321 (2011).

    Article  CAS  PubMed  Google Scholar 

  66. Lee, J.-K. et al. Epidermal growth factor receptor tyrosine kinase inhibitors vs conventional chemotherapy in non-small cell lung cancer harboring wild-type epidermal growth factor receptor. JAMA 311, 1430 (2014).

    Article  CAS  PubMed  Google Scholar 

  67. de Bruin, E. C. et al. Spatial and temporal diversity in genomic instability processes defines lung cancer evolution. Science 346, 251–256 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Zhang, J. et al. Intratumor heterogeneity in localized lung adenocarcinomas delineated by multiregion sequencing. Science 346, 256–259 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Balak, M. N. et al. Novel D761Y and common secondary T790M mutations in epidermal growth factor receptor-mutant lung adenocarcinomas with acquired resistance to kinase inhibitors. Clin. Cancer Res. 12, 6494–6501 (2006).

    Article  CAS  PubMed  Google Scholar 

  70. Chong, C. R. & Janne, P. A. The quest to overcome resistance to EGFR-targeted therapies in cancer. Nat. Med. 19, 1389–1400 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Su, K. Y. et al. Pretreatment epidermal growth factor receptor (EGFR) T790M mutation predicts shorter EGFR tyrosine kinase inhibitor response duration in patients with non-small-cell lung cancer. J. Clin. Oncol. 30, 433–440 (2012).

    Article  CAS  PubMed  Google Scholar 

  72. Lee, Y. et al. Clinical outcome according to the level of preexisting epidermal growth factor receptor T790M mutation in patients with lung cancer harboring sensitive epidermal growth factor receptor mutations. Cancer 120, 2090–2098 (2014).

    Article  CAS  PubMed  Google Scholar 

  73. Karapetis, C. S. et al. K-ras mutations and benefit from cetuximab in advanced colorectal cancer. N. Engl. J. Med. 359, 1757–1765 (2008).

    Article  CAS  PubMed  Google Scholar 

  74. Dolgin, E. FDA narrows drug label usage. Nature 460, 1069 (2009).

    Article  CAS  PubMed  Google Scholar 

  75. Riely, G. J., Marks, J. & Pao, W. KRAS mutations in non-small cell lung cancer. Proc. Am. Thorac. Soc. 6, 201–205 (2009).

    Article  CAS  PubMed  Google Scholar 

  76. Misale, S. et al. Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer. Nature 486, 532–536 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  77. Molinari, F. et al. Increased detection sensitivity for KRAS mutations enhances the prediction of anti-EGFR monoclonal antibody resistance in metastatic colorectal cancer. Clin. Cancer Res. 17, 4901–4914 (2011).

    Article  CAS  PubMed  Google Scholar 

  78. Dono, M. et al. Low percentage of KRAS mutations revealed by locked nucleic acid polymerase chain reaction: implications for treatment of metastatic colorectal cancer. Mol. Med. 18, 1519–1526 (2012).

    Article  PubMed Central  CAS  Google Scholar 

  79. Tougeron, D. et al. Effect of low-frequency KRAS mutations on the response to anti-EGFR therapy in metastatic colorectal cancer. Ann. Oncol. 24, 1267–1273 (2013).

    Article  CAS  PubMed  Google Scholar 

  80. Long, G. V. et al. Prognostic and clinicopathologic associations of oncogenic BRAF in metastatic melanoma. J. Clin. Oncol. 29, 1239–1246 (2011).

    Article  PubMed  Google Scholar 

  81. Su, F. et al. RAS mutations in cutaneous squamous-cell carcinomas in patients treated with BRAF inhibitors. N. Engl. J. Med. 366, 207–215 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  82. Robert, C. et al. Improved overall survival in melanoma with combined dabrafenib and trametinib. N. Engl. J. Med. 372, 30–39 (2015).

    Article  CAS  PubMed  Google Scholar 

  83. Flaherty, K. T. et al. Combined BRAF and MEK inhibition in melanoma with BRAF V600 mutations. N. Engl. J. Med. 367, 1694–1703 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  84. Long, G. V. et al. Dabrafenib and trametinib versus dabrafenib and placebo for Val600 BRAF-mutant melanoma: a multicentre, double-blind, phase 3 randomised controlled trial. Lancet 386, 444–451 (2015).

    Article  CAS  PubMed  Google Scholar 

  85. Long, G. V. et al. Increased MAPK reactivation in early resistance to dabrafenib/trametinib combination therapy of BRAF-mutant metastatic melanoma. Nat. Commun. 5, 5694 (2014).

    Article  CAS  PubMed  Google Scholar 

  86. Girotti, M. R. et al. Paradox-breaking RAF inhibitors that also target SRC are effective in drug-resistant BRAF mutant melanoma. Cancer Cell 27, 85–96 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Bottema, C. D. & Sommer, S. S. PCR amplification of specific alleles: rapid detection of known mutations and polymorphisms. Mutat. Res. 288, 93–102 (1993).

    Article  CAS  PubMed  Google Scholar 

  88. Tost, J. & Gut, I. G. Genotyping single nucleotide polymorphisms by MALDI mass spectrometry in clinical applications. Clin. Biochem. 38, 335–350 (2005).

    Article  CAS  PubMed  Google Scholar 

  89. Bielas, J. H. & Loeb, L. A. Quantification of random genomic mutations. Nat. Methods 2, 285–290 (2005).

    Article  CAS  PubMed  Google Scholar 

  90. Day, E., Dear, P. H. & McCaughan, F. Digital PCR strategies in the development and analysis of molecular biomarkers for personalized medicine. Methods 59, 101–107 (2013).

    Article  CAS  PubMed  Google Scholar 

  91. Schmitt, M. W. et al. Detection of ultra-rare mutations by next-generation sequencing. Proc. Natl Acad. Sci. USA 109, 14508–14513 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Kinde, I., Wu, J., Papadopoulos, N., Kinzler, K. W. & Vogelstein, B. Detection and quantification of rare mutations with massively parallel sequencing. Proc. Natl Acad. Sci. USA 108, 9530–9535 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  93. Lou, D. I. et al. High-throughput DNA sequencing errors are reduced by orders of magnitude using circle sequencing. Proc. Natl Acad. Sci. USA 110, 19872–19877 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Schmitt, M. W. et al. Sequencing small genomic targets with high efficiency and extreme accuracy. Nat. Methods 12, 423–425 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  95. Klco, J. M. et al. Functional heterogeneity of genetically defined subclones in acute myeloid leukemia. Cancer Cell 25, 379–392 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  96. Bettegowda, C. et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci. Transl. Med. 6, 224ra24 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  97. Zill, O. A. et al. Cell-free DNA next-generation sequencing in pancreatobiliary carcinomas. Cancer Discov. http://dx.doi.org/10.1158/2159–8290CD-15-0274 (2015).

  98. Lebofsky, R. et al. Circulating tumor DNA as a non-invasive substitute to metastasis biopsy for tumor genotyping and personalized medicine in a prospective trial across all tumor types. Mol. Oncol. 9, 783–790 (2015).

    Article  CAS  PubMed  Google Scholar 

  99. Piotrowska, Z. et al. Heterogeneity underlies the emergence of EGFRT790 wild-type clones following treatment of T790M-positive cancers with a third-generation EGFR inhibitor. Cancer Discov. 5, 713–722 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  100. Siravegna, G. et al. Clonal evolution and resistance to EGFR blockade in the blood of colorectal cancer patients. Nat. Med. 21, 795–801 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  101. Thress, K. S. et al. Acquired EGFR C797S mutation mediates resistance to AZD9291 in non-small cell lung cancer harboring EGFR T790M. Nat. Med. 21, 560–562 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  102. Diaz, L. A. et al. The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers. Nature 486, 537–540 (2012).

    PubMed  PubMed Central  CAS  Google Scholar 

  103. Bardelli, A. et al. Amplification of the MET receptor drives resistance to anti-EGFR therapies in colorectal cancer. Cancer Discov. 3, 658–673 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  104. Maheswaran, S. et al. Detection of mutations in EGFR in circulating lung-cancer cells. N. Engl. J. Med. 359, 366–377 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  105. Yu, M. et al. Cancer therapy. Ex vivo culture of circulating breast tumor cells for individualized testing of drug susceptibility. Science 345, 216–220 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  106. Chu, S. et al. Detection of BCR–ABL kinase mutations in CD34+ cells from chronic myelogenous leukemia patients in complete cytogenetic remission on imatinib mesylate treatment. Blood 105, 2093–2098 (2005).

    Article  CAS  PubMed  Google Scholar 

  107. Kim, T. M. et al. Regional biases in mutation screening due to intratumoural heterogeneity of prostate cancer. J. Pathol. 233, 425–435 (2014).

    Article  CAS  PubMed  Google Scholar 

  108. Kumar, A. et al. Deep sequencing of multiple regions of glial tumors reveals spatial heterogeneity for mutations in clinically relevant genes. Genome Biol. 15, 530 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  109. Montagut, C. et al. Identification of a mutation in the extracellular domain of the epidermal growth factor receptor conferring cetuximab resistance in colorectal cancer. Nat. Med. 18, 221–223 (2012).

    Article  CAS  PubMed  Google Scholar 

  110. Lee, M. S. & Kopetz, S. Current and future approaches to target the epidermal growth factor receptor and its downstream signaling in metastatic colorectal cancer. Clin. Colorectal Cancer http://dx.doi.org/10.1016/j.clcc.2015.05.006 (2015).

  111. Byrd, J. C. et al. Ibrutinib versus ofatumumab in previously treated chronic lymphoid leukemia. N. Engl. J. Med. 371, 213–223 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  112. Byrd, J. C. et al. Three-year follow-up of treatment-naive and previously treated patients with CLL and SLL receiving single-agent ibrutinib. Blood 125, 2497–2506 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  113. Advani, R. H. et al. Bruton tyrosine kinase inhibitor ibrutinib (PCI-32765) has significant activity in patients with relapsed/refractory B-cell malignancies. J. Clin. Oncol. 31, 88–94 (2013).

    Article  CAS  PubMed  Google Scholar 

  114. Woyach, J. A. et al. Resistance mechanisms for the Bruton's tyrosine kinase inhibitor ibrutinib. N. Engl. J. Med. 370, 2286–2294 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  115. Chiron, D. et al. Cell-cycle reprogramming for PI3K inhibition overrides a relapse-specific C481S BTK mutation revealed by longitudinal functional genomics in mantle cell lymphoma. Cancer Discov. 4, 1022–1035 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  116. Daver, N. et al. Secondary mutations as mediators of resistance to targeted therapy in leukemia. Blood 125, 3236–3245 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  117. Das Thakur, M. et al. Modelling vemurafenib resistance in melanoma reveals a strategy to forestall drug resistance. Nature 494, 251–255 (2013).

    Article  PubMed  CAS  Google Scholar 

  118. Pattabiraman, D. R. & Weinberg, R. A. Tackling the cancer stem cells—what challenges do they pose? Nat. Rev. Drug Discov. 13, 497–512 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  119. Jordan, C. T. Cancer stem cells: controversial or just misunderstood? Cell Stem Cell 4, 203–205 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  120. Willis, S. G. et al. 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 (2005).

    Article  CAS  PubMed  Google Scholar 

  121. Nucifora, G., Larson, R. A. & Rowley, J. D. Persistence of the 8;21 translocation in patients with acute myeloid leukemia type M2 in long-term remission. Blood 82, 712–715 (1993).

    CAS  PubMed  Google Scholar 

  122. Beckman, R. A. & Loeb, L. A. Efficiency of carcinogenesis with and without a mutator mutation. Proc. Natl Acad. Sci. USA 103, 14140–14145 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Bielas, J. H., Loeb, K. R., Rubin, B. P., True, L. D. & Loeb, L. A. Human cancers express a mutator phenotype. Proc. Natl Acad. Sci. USA 103, 18238–18242 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Kumar, A. et al. Exome sequencing identifies a spectrum of mutation frequencies in advanced and lethal prostate cancers. Proc. Natl Acad. Sci. USA 108, 17087–17092 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Shlien, A. et al. Combined hereditary and somatic mutations of replication error repair genes result in rapid onset of ultra-hypermutated cancers. Nat. Genet. 47, 257–262 (2015).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We gratefully acknowledge our many colleagues, collaborators, and patients for the stimulating discussions that lead to the conception of this manuscript. The work of the authors is funded by NIH grants: P50 CA097186 to M.W.S.; R01 CA160674 and R33 CA181771 to L.A.L.; and T32 HL007093 to J.J.S.

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M.W.S. and J.J.S. researched data for article. M.W.S., L.A.L., and J.J.S. all contributed to discussion of content and writing of the manuscript, and reviewed/edited the manuscript before submission.

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Correspondence to Michael W. Schmitt.

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Schmitt, M., Loeb, L. & Salk, J. The influence of subclonal resistance mutations on targeted cancer therapy. Nat Rev Clin Oncol 13, 335–347 (2016). https://doi.org/10.1038/nrclinonc.2015.175

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