1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Biochemistry
  4. Volume 88, 2019
  5. Article

Annual Review of Biochemistry

Volume 88, 2019

Cancer Treatment in the Genomic Era

Abstract

The complexity of human cancer underlies its devastating clinical consequences. Drugs designed to target the genetic alterations that drive cancer have improved the outcome for many patients, but not the majority of them. Here, we review the genomic landscape of cancer, how genomic data can provide much more than a sum of its parts, and the approaches developed to identify and validate genomic alterations with potential therapeutic value. We highlight notable successes and pitfalls in predicting the value of potential therapeutic targets and discuss the use of multi-omic data to better understand cancer dependencies and drug sensitivity. We discuss how integrated approaches to collecting, curating, and sharing these large data sets might improve the identification and prioritization of cancer vulnerabilities as well as patient stratification within clinical trials. Finally, we outline how future approaches might improve the efficiency and speed of translating genomic data into clinically effective therapies and how the use of unbiased genome-wide information can identify novel predictive biomarkers that can be either simple or complex.

Keyword(s): cancer genomics, cancer treatment, personalized cancer medicine, precision oncology, therapeutic actionability
Loading

Article metrics loading...

/content/journals/10.1146/annurev-biochem-062917-011840
2019-06-20
2024-04-18
Loading full text...

Full text loading...

/deliver/fulltext/biochem/88/1/annurev-biochem-062917-011840.html?itemId=/content/journals/10.1146/annurev-biochem-062917-011840&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Brufsky AM. 2017. Long-term management of patients with hormone receptor-positive metastatic breast cancer: concepts for sequential and combination endocrine-based therapies. Cancer Treat. Rev. 59:22–32
     [Google Scholar]
  2. 2. 
    Loibl S, Gianni L. 2017. HER2-positive breast cancer. Lancet 389:100872415–29
     [Google Scholar]
  3. 3. 
    Dietel M. 2016. Molecular pathology: a requirement for precision medicine in cancer. Oncol. Res. Treat. 39:12804–10
     [Google Scholar]
  4. 4. 
    Hutter C, Zenklusen JC. 2018. The Cancer Genome Atlas: creating lasting value beyond its data. Cell 173:2283–85
     [Google Scholar]
  5. 5. 
    Sanchez-Vega F, Mina M, Armenia J, Chatila WK, Luna A et al. 2018. Oncogenic signaling pathways in The Cancer Genome Atlas. Cell 173:2321–37.e10
     [Google Scholar]
  6. 6. 
    Hoadley KA, Yau C, Hinoue T, Wolf DM, Lazar AJ et al. 2018. Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell 173:2291–304.e6
     [Google Scholar]
  7. 7. 
    Ciriello G, Miller ML, Aksoy BA, Senbabaoglu Y, Schultz N, Sander C 2013. Emerging landscape of oncogenic signatures across human cancers. Nat. Genet. 45:101127–33
     [Google Scholar]
  8. 8. 
    Lefebvre C, Bachelot T, Filleron T, Pedrero M, Campone M et al. 2016. Mutational profile of metastatic breast cancers: a retrospective analysis. PLOS Med 13:12e1002201
     [Google Scholar]
  9. 9. 
    Hyman DM, Piha-Paul SA, Won H, Rodon J, Saura C et al. 2018. HER kinase inhibition in patients with HER2- and HER3-mutant cancers. Nature 554:7691189–94
     [Google Scholar]
  10. 10. 
    Hyman DM, Puzanov I, Subbiah V, Faris JE, Chau I et al. 2015. Vemurafenib in multiple nonmelanoma cancers with BRAF V600 mutations. N. Engl. J. Med. 373:8726–36
     [Google Scholar]
  11. 11. 
    McGranahan N, Swanton C. 2017. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell 168:4613–28
     [Google Scholar]
  12. 12. 
    Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D et al. 2012. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366:10883–92
     [Google Scholar]
  13. 13. 
    Turajlic S, Xu H, Litchfield K, Rowan A, Horswell S et al. 2018. Deterministic evolutionary trajectories influence primary tumor growth: TRACERx Renal. Cell 173:3595–610.e11
     [Google Scholar]
  14. 14. 
    Burrell RA, Swanton C. 2016. Re-evaluating clonal dominance in cancer evolution. Trends Cancer 2:5263–76
     [Google Scholar]
  15. 15. 
    Aparicio S, Caldas C. 2013. The implications of clonal genome evolution for cancer medicine. N. Engl. J. Med. 368:9842–51
     [Google Scholar]
  16. 16. 
    Offit K. 2016. The future of clinical cancer genomics. Semin. Oncol. 43:5615–22
     [Google Scholar]
  17. 17. 
    Hyman DM, Solit DB, Arcila ME, Cheng DT, Sabbatini P et al. 2015. Precision medicine at Memorial Sloan Kettering Cancer Center: clinical next-generation sequencing enabling next-generation targeted therapy trials. Drug Discov. Today 20:121422–28
     [Google Scholar]
  18. 18. 
    Hyman DM, Taylor BS, Baselga J 2017. Implementing genome-driven oncology. Cell 168:4584–99
     [Google Scholar]
  19. 19. 
    Giesen C, Wang HAO, Schapiro D, Zivanovic N, Jacobs A et al. 2014. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11:4417–22
     [Google Scholar]
  20. 20. 
    Cheng ML, Berger MF, Hyman DM, Solit DB 2018. Clinical tumour sequencing for precision oncology: time for a universal strategy. Nat. Rev. Cancer 18:527–28
     [Google Scholar]
  21. 21. 
    Cardoso F, van't Veer LJ, Bogaerts J, Slaets L, Viale G et al. 2016. 70-gene signature as an aid to treatment decisions in early-stage breast cancer. N. Engl. J. Med. 375:8717–29
     [Google Scholar]
  22. 22. 
    Sparano JA, Gray RJ, Makower DF, Pritchard KI, Albain KS et al. 2018. Adjuvant chemotherapy guided by a 21-gene expression assay in breast cancer. N. Engl. J. Med. 379:2111–21
     [Google Scholar]
  23. 23. 
    Lyle S, Powers W, Xu J, Huettner CS, Russell M, Protopopov A 2017. Comparison of a large NGS panel to hot-spot testing and small panels for the ability to accurately stratify advanced colorectal cancer patients to appropriate treatment pathways. J. Clin. Oncol. 34:510
     [Google Scholar]
  24. 24. 
    Perakis S, Speicher MR. 2017. Emerging concepts in liquid biopsies. BMC Med 15:175
     [Google Scholar]
  25. 25. 
    Wan JCM, Massie C, Garcia-Corbacho J, Mouliere F, Brenton JD et al. 2017. Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat. Rev. Cancer 17:4223–38
     [Google Scholar]
  26. 26. 
    Dawson S-J, Tsui DWY, Murtaza M, Biggs H, Rueda OM et al. 2013. Analysis of circulating tumor DNA to monitor metastatic breast cancer. N. Engl. J. Med. 368:131199–209
     [Google Scholar]
  27. 27. 
    Murtaza M, Dawson S-J, Tsui DWY, Gale D, Forshew T et al. 2013. Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA. Nature 497:7447108–12
     [Google Scholar]
  28. 28. 
    Cohen JD, Li L, Wang Y, Thoburn C, Afsari B et al. 2018. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 359:6378926–30
     [Google Scholar]
  29. 29. 
    Snyder MW, Kircher M, Hill AJ, Daza RM, Shendure J 2016. Cell-free DNA comprises an in vivo nucleosome footprint that informs its tissues-of-origin. Cell 164:1–257–68
     [Google Scholar]
  30. 30. 
    Thiele J-A, Bethel K, Králíčková M, Kuhn P 2017. Circulating tumor cells: fluid surrogates of solid tumors. Annu. Rev. Pathol. Mech. Dis. 12:419–47
     [Google Scholar]
  31. 31. 
    Everaert C, Luypaert M, Maag JLV, Cheng QX, Dinger ME et al. 2017. Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data. Sci. Rep. 7:11559
     [Google Scholar]
  32. 32. 
    Baslan T, Hicks J. 2017. Unravelling biology and shifting paradigms in cancer with single-cell sequencing. Nat. Rev. Cancer 17:9557–69
     [Google Scholar]
  33. 33. 
    Geiss GK, Bumgarner RE, Birditt B, Dahl T, Dowidar N et al. 2008. Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat. Biotechnol. 26:3317–25
     [Google Scholar]
  34. 34. 
    Veldman-Jones MH, Brant R, Rooney C, Geh C, Emery H et al. 2015. Evaluating robustness and sensitivity of the NanoString technologies nCounter platform to enable multiplexed gene expression analysis of clinical samples. Cancer Res 75:132587–93
     [Google Scholar]
  35. 35. 
    Nebbioso A, Tambaro FP, Dell'Aversana C, Altucci L 2018. Cancer epigenetics: moving forward. PLOS Genet 14:6e1007362
     [Google Scholar]
  36. 36. 
    Belczacka I, Latosinska A, Metzger J, Marx D, Vlahou A et al. 2018. Proteomics biomarkers for solid tumors: current status and future prospects. Mass Spectrom. Rev. 38:49–78
     [Google Scholar]
  37. 37. 
    da Cunha Santos G, Shepherd FA, Tsao MS 2011. EGFR mutations and lung cancer. Annu. Rev. Pathol. Mech. Dis. 6:49–69
     [Google Scholar]
  38. 38. 
    Amado RG, Wolf M, Peeters M, Van Cutsem E, Siena S et al. 2008. Wild-type KRAS is required for panitumumab efficacy in patients with metastatic colorectal cancer. J. Clin. Oncol. 26:101626–34
     [Google Scholar]
  39. 39. 
    Karapetis CS, Khambata-Ford S, Jonker DJ, O'Callaghan CJ, Tu D et al. 2008. K-ras mutations and benefit from cetuximab in advanced colorectal cancer. N. Engl. J. Med. 359:171757–65
     [Google Scholar]
  40. 40. 
    Hofree M, Carter H, Kreisberg JF, Bandyopadhyay S, Mischel PS et al. 2016. Challenges in identifying cancer genes by analysis of exome sequencing data. Nat. Commun. 7:12096
     [Google Scholar]
  41. 41. 
    Koboldt DC, Zhang Q, Larson DE, Shen D, McLellan MD et al. 2012. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res 22:3568–76
     [Google Scholar]
  42. 42. 
    Magi A, Tattini L, Cifola I, D'Aurizio R, Benelli M et al. 2013. EXCAVATOR: detecting copy number variants from whole-exome sequencing data. Genome Biol 14:10R120
     [Google Scholar]
  43. 43. 
    Forbes SA, Beare D, Boutselakis H, Bamford S, Bindal N et al. 2017. COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Res 45:D1D777–83
     [Google Scholar]
  44. 44. 
    Landrum MJ, Lee JM, Benson M, Brown GR, Chao C et al. 2018. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res 46:D1D1062–67
     [Google Scholar]
  45. 45. 
    Turajlic S, Xu H, Litchfield K, Rowan A, Chambers T et al. 2018. Tracking cancer evolution reveals constrained routes to metastases: TRACERx Renal. Cell 173:3581–94.e12
     [Google Scholar]
  46. 46. 
    Robichaux JP, Elamin YY, Tan Z, Carter BW, Zhang S et al. 2018. Mechanisms and clinical activity of an EGFR and HER2 exon 20-selective kinase inhibitor in non-small cell lung cancer. Nat. Med. 24:5638–46
     [Google Scholar]
  47. 47. 
    Chakravarty D, Gao J, Phillips S, Kundra R, Zhang H et al. 2017. OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. https://doi.org/10.1200/PO.17.00011
    [Crossref] [Google Scholar]
  48. 48. 
    Taylor AD, Micheel CM, Anderson IA, Levy MA, Lovly CM 2016. The path(way) less traveled: a pathway-oriented approach to providing information about precision cancer medicine on My Cancer Genome. Transl. Oncol. 9:2163–65
     [Google Scholar]
  49. 49. 
    Tamborero D, Rubio-Perez C, Deu-Pons J, Schroeder MP, Vivancos A et al. 2018. Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations. Genome Med 10:125
     [Google Scholar]
  50. 50. 
    Dumbrava EI, Meric-Bernstam F. 2018. Personalized cancer therapy—leveraging a knowledge base for clinical decision-making. Cold Spring Harb. Mol. Case Stud. 4:2a001578
     [Google Scholar]
  51. 51. 
    Bailey MH, Tokheim C, Porta-Pardo E, Sengupta S, Bertrand D et al. 2018. Comprehensive characterization of cancer driver genes and mutations. Cell 173:2371–85.e18
     [Google Scholar]
  52. 52. 
    Sanchez-Vega F, Mina M, Armenia J, Chatila WK, Luna A et al. 2018. Oncogenic signaling pathways in The Cancer Genome Atlas. Cell 173:2321–37.e10
     [Google Scholar]
  53. 53. 
    Niinuma T, Suzuki H, Sugai T 2018. Molecular characterization and pathogenesis of gastrointestinal stromal tumor. Transl. Gastroenterol. Hepatol. 3:2
     [Google Scholar]
  54. 54. 
    Staedtke V, Dzaye OD, Holdhoff M 2016. Actionable molecular biomarkers in primary brain tumors. Trends Cancer 2:7338–49
     [Google Scholar]
  55. 55. 
    Mak IW, Evaniew N, Ghert M 2014. Lost in translation: animal models and clinical trials in cancer treatment. Am. J. Transl. Res. 6:2114–18
     [Google Scholar]
  56. 56. 
    Nagano M, Kohsaka S, Ueno T, Kojima S, Saka K et al. 2018. High-throughput functional evaluation of variants of unknown significance in ERBB2. Clin. Cancer Res. 24: https://doi.org/10.1158/1078-0432.CCR-18-0991
    [Crossref] [Google Scholar]
  57. 57. 
    Brenan L, Andreev A, Cohen O, Pantel S, Kamburov A et al. 2016. Phenotypic characterization of a comprehensive set of MAPK1/ERK2 missense mutants. Cell Rep 17:41171–83
     [Google Scholar]
  58. 58. 
    Strickler JH, Wu C, Bekaii-Saab T 2017. Targeting BRAF in metastatic colorectal cancer: maximizing molecular approaches. Cancer Treat. Rev. 60:109–19
     [Google Scholar]
  59. 59. 
    Hasty P, Montagna C. 2014. Chromosomal rearrangements in cancer: detection and potential causal mechanisms. Mol. Cell. Oncol. 1:1e29904
     [Google Scholar]
  60. 60. 
    Mertens F, Johansson B, Fioretos T, Mitelman F 2015. The emerging complexity of gene fusions in cancer. Nat. Rev. Cancer 15:6371–81
     [Google Scholar]
  61. 61. 
    Tsujimoto Y, Cossman J, Jaffe E, Croce CM 1985. Involvement of the bcl-2 gene in human follicular lymphoma. Science 228:47061440–43
     [Google Scholar]
  62. 62. 
    Kridel R, Sehn LH, Gascoyne RD 2012. Pathogenesis of follicular lymphoma. J. Clin. Investig. 122:103424–31
     [Google Scholar]
  63. 63. 
    Ren R. 2005. Mechanisms of BCR-ABL in the pathogenesis of chronic myelogenous leukaemia. Nat. Rev. Cancer 5:3172–83
     [Google Scholar]
  64. 64. 
    O'Brien SG, Guilhot F, Larson RA, Gathmann I, Baccarani M et al. 2003. Imatinib compared with interferon and low-dose cytarabine for newly diagnosed chronic-phase chronic myeloid leukemia. N. Engl. J. Med. 348:11994–1004
     [Google Scholar]
  65. 65. 
    Viganò I, Di Giacomo N, Bozzani S, Antolini L, Piazza R, Gambacorti Passerini C 2014. First-line treatment of 102 chronic myeloid leukemia patients with imatinib: a long-term single institution analysis. Am. J. Hematol. 89:10E184–87
     [Google Scholar]
  66. 66. 
    Dupain C, Harttrampf AC, Urbinati G, Geoerger B, Massaad-Massade L 2017. Relevance of fusion genes in pediatric cancers: toward precision medicine. Mol. Ther. Nucleic Acids 6:315–26
     [Google Scholar]
  67. 67. 
    Peters S, Camidge DR, Shaw AT, Gadgeel S, Ahn JS et al. 2017. Alectinib versus crizotinib in untreated ALK-positive non-small-cell lung cancer. N. Engl. J. Med. 377:9829–38
     [Google Scholar]
  68. 68. 
    Ardini E, Menichincheri M, Banfi P, Bosotti R, De Ponti C et al. 2016. Entrectinib, a pan-TRK, ROS1, and ALK inhibitor with activity in multiple molecularly defined cancer indications. Mol. Cancer Ther. 15:4628–39
     [Google Scholar]
  69. 69. 
    Drilon A, Laetsch TW, Kummar S, DuBois SG, Lassen UN et al. 2018. Efficacy of larotrectinib in TRK fusion–positive cancers in adults and children. N. Engl. J. Med. 378:8731–39
     [Google Scholar]
  70. 70. 
    Stransky N, Cerami E, Schalm S, Kim JL, Lengauer C 2014. The landscape of kinase fusions in cancer. Nat. Commun. 5:14846
     [Google Scholar]
  71. 71. 
    Drilon A, Siena S, Ou S-HI, Patel M, Ahn MJ et al. 2017. Safety and antitumor activity of the multitargeted pan-TRK, ROS1, and ALK inhibitor entrectinib: combined results from two phase I trials (ALKA-372-001 and STARTRK-1). Cancer Discov 7:4400–9
     [Google Scholar]
  72. 72. 
    Ashworth A, Lord CJ. 2018. Synthetic lethal therapies for cancer: What's next after PARP inhibitors. ? Nat. Rev. Clin. Oncol. 15:564–76
     [Google Scholar]
  73. 73. 
    Venkitaraman AR. 2014. Cancer suppression by the chromosome custodians, BRCA1 and BRCA2. Science 343:61781470–75
     [Google Scholar]
  74. 74. 
    Bryant HE, Schultz N, Thomas HD, Parker KM, Flower D et al. 2005. Specific killing of BRCA2-deficient tumours with inhibitors of poly(ADP-ribose) polymerase. Nature 434:7035913–17
     [Google Scholar]
  75. 75. 
    Farmer H, McCabe N, Lord CJ, Tutt ANJ, Johnson DA et al. 2005. Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature 434:7035917–21
     [Google Scholar]
  76. 76. 
    Ström CE, Johansson F, Uhlén M, Szigyarto CA-K, Erixon K, Helleday T 2011. Poly (ADP-ribose) polymerase (PARP) is not involved in base excision repair but PARP inhibition traps a single-strand intermediate. Nucleic Acids Res 39:83166–75
     [Google Scholar]
  77. 77. 
    Chang HHY, Pannunzio NR, Adachi N, Lieber MR 2017. Non-homologous DNA end joining and alternative pathways to double-strand break repair. Nat. Rev. Mol. Cell Biol. 18:8495–506
     [Google Scholar]
  78. 78. 
    Mirza MR, Pignata S, Ledermann JA 2018. Latest clinical evidence and further development of PARP inhibitors in ovarian cancer. Ann. Oncol. 29:61366–76
     [Google Scholar]
  79. 79. 
    Lee J-M, Ledermann JA, Kohn EC 2014. PARP inhibitors for BRCA1/2 mutation-associated and BRCA-like malignancies. Ann. Oncol. 25:132–40
     [Google Scholar]
  80. 80. 
    Mateos-Gomez PA, Gong F, Nair N, Miller KM, Lazzerini-Denchi E, Sfeir A 2015. Mammalian polymerase θ promotes alternative NHEJ and suppresses recombination. Nature 518:7538254–57
     [Google Scholar]
  81. 81. 
    Ceccaldi R, Liu JC, Amunugama R, Hajdu I, Primack B et al. 2015. Homologous-recombination-deficient tumours are dependent on Polθ-mediated repair. Nature 518:7538258–62
     [Google Scholar]
  82. 82. 
    Feng Z, Scott SP, Bussen W, Sharma GG, Guo G et al. 2011. Rad52 inactivation is synthetically lethal with BRCA2 deficiency. PNAS 108:2686–91
     [Google Scholar]
  83. 83. 
    Kadoch C, Hargreaves DC, Hodges C, Elias L, Ho L et al. 2013. Proteomic and bioinformatic analysis of mammalian SWI/SNF complexes identifies extensive roles in human malignancy. Nat. Genet. 45:6592–601
     [Google Scholar]
  84. 84. 
    Biegel JA, Zhou JY, Rorke LB, Stenstrom C, Wainwright LM, Fogelgren B 1999. Germ-line and acquired mutations of INI1 in atypical teratoid and rhabdoid tumors. Cancer Res 59:174–79
     [Google Scholar]
  85. 85. 
    Kim KH, Kim W, Howard TP, Vazquez F, Tsherniak A et al. 2015. SWI/SNF-mutant cancers depend on catalytic and non-catalytic activity of EZH2. Nat. Med. 21:121491–96
     [Google Scholar]
  86. 86. 
    Cardaci S, Zheng L, MacKay G, van den Broek NJF, MacKenzie ED et al. 2015. Pyruvate carboxylation enables growth of SDH-deficient cells by supporting aspartate biosynthesis. Nat. Cell Biol. 17:101317–26
     [Google Scholar]
  87. 87. 
    Westcott PMK, Halliwill KD, To MD, Rashid M, Rust AG et al. 2015. The mutational landscapes of genetic and chemical models of Kras-driven lung cancer. Nature 517:7535489–92
     [Google Scholar]
  88. 88. 
    Doherty GJ, Kerr EM, Martins CP 2017. KRAS allelic imbalance: strengths and weaknesses in numbers. Trends Mol. Med. 23:5377–78
     [Google Scholar]
  89. 89. 
    Chapman PB, Hauschild A, Robert C, Haanen JB, Ascierto P et al. 2011. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N. Engl. J. Med. 364:262507–16
     [Google Scholar]
  90. 90. 
    Hauschild A, Grob J-J, Demidov LV, Jouary T, Gutzmer R et al. 2012. Dabrafenib in BRAF-mutated metastatic melanoma: a multicentre, open-label, phase 3 randomised controlled trial. Lancet 380:9839358–65
     [Google Scholar]
  91. 91. 
    Flaherty KT, Robert C, Hersey P, Nathan P, Garbe C et al. 2012. Improved survival with MEK inhibition in BRAF-mutated melanoma. N. Engl. J. Med. 367:2107–14
     [Google Scholar]
  92. 92. 
    Robert C, Karaszewska B, Schachter J, Rutkowski P, Mackiewicz A et al. 2015. Improved overall survival in melanoma with combined dabrafenib and trametinib. N. Engl. J. Med. 372:130–39
     [Google Scholar]
  93. 93. 
    Larkin J, Ascierto PA, Dréno B, Atkinson V, Liszkay G et al. 2014. Combined vemurafenib and cobimetinib in BRAF-mutated melanoma. N. Engl. J. Med. 371:201867–76
     [Google Scholar]
  94. 94. 
    McCormick F. 2018. Targeting KRAS directly. Annu. Rev. Cancer Biol. 2:81–90
     [Google Scholar]
  95. 95. 
    Kerr EM, Gaude E, Turrell FK, Frezza C, Martins CP 2016. Mutant Kras copy number defines metabolic reprogramming and therapeutic susceptibilities. Nature 531:7592110–13
     [Google Scholar]
  96. 96. 
    Janes MR, Zhang J, Li L-S, Hansen R, Peters U et al. 2018. Targeting KRAS mutant cancers with a covalent G12C-specific inhibitor. Cell 172:3578–89.e17
     [Google Scholar]
  97. 97. 
    Jänne PA, van den Heuvel MM, Barlesi F, Cobo M, Mazieres J et al. 2017. Selumetinib plus docetaxel compared with docetaxel alone and progression-free survival in patients with KRAS-mutant advanced non-small cell lung cancer. JAMA 317:181844–53
     [Google Scholar]
  98. 98. 
    Molina-Arcas M, Hancock DC, Sheridan C, Kumar MS, Downward J 2013. Coordinate direct input of both KRAS and IGF1 receptor to activation of PI3 kinase in KRAS-mutant lung cancer. Cancer Discov 3:5548–63
     [Google Scholar]
  99. 99. 
    Sun C, Hobor S, Bertotti A, Zecchin D, Huang S et al. 2014. Intrinsic resistance to MEK inhibition in KRAS mutant lung and colon cancer through transcriptional induction of ERBB3. . Cell Rep 7:186–93
     [Google Scholar]
  100. 100. 
    Kruspig B, Monteverde T, Neidler S, Hock A, Kerr E et al. 2018. The ERBB network facilitates KRAS-driven lung tumorigenesis. Sci. Transl. Med. 10:446eaao2565
     [Google Scholar]
  101. 101. 
    Zhang Y-L, Yuan J-Q, Wang K-F, Fu X-H, Han X-R et al. 2016. The prevalence of EGFR mutation in patients with non-small cell lung cancer: a systematic review and meta-analysis. Oncotarget 7:4878985–93
     [Google Scholar]
  102. 102. 
    Ahluwalia MS, Becker K, Levy BP 2018. Epidermal growth factor receptor tyrosine kinase inhibitors for central nervous system metastases from non‐small cell lung cancer. Oncologist 23:101199–209
     [Google Scholar]
  103. 103. 
    Ko B, Paucar D, Halmos B 2017. EGFR T790M: revealing the secrets of a gatekeeper. Lung Cancer 8:147–59
     [Google Scholar]
  104. 104. 
    Cross DAE, Ashton SE, Ghiorghiu S, Eberlein C, Nebhan CA et al. 2014. AZD9291, an irreversible EGFR TKI, overcomes T790M-mediated resistance to EGFR inhibitors in lung cancer. Cancer Discov 4:91046–61
     [Google Scholar]
  105. 105. 
    Soria J-C, Ohe Y, Vansteenkiste J, Reungwetwattana T, Chewaskulyong B et al. 2018. Osimertinib in untreated EGFR-mutated advanced non-small-cell lung cancer. N. Engl. J. Med. 378:2113–25
     [Google Scholar]
  106. 106. 
    O'Kane GM, Bradbury PA, Feld R, Leighl NB, Liu G et al. 2017. Uncommon EGFR mutations in advanced non-small cell lung cancer. Lung Cancer 109:137–44
     [Google Scholar]
  107. 107. 
    Albertson DG. 2006. Gene amplification in cancer. Trends Genet 22:8447–55
     [Google Scholar]
  108. 108. 
    Curtis C, Shah SP, Chin S-F, Turashvili G, Rueda OM et al. 2012. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486:7403346–52
     [Google Scholar]
  109. 109. 
    Pereira B, Chin S-F, Rueda OM, Vollan H-KM, Provenzano E et al. 2016. The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes. Nat. Commun. 7:11479
     [Google Scholar]
  110. 110. 
    Macintyre G, Goranova TE, De Silva D, Ennis D, Piskorz AM et al. 2018. Copy number signatures and mutational processes in ovarian carcinoma. Nat. Genet. 50:1262–70
     [Google Scholar]
  111. 111. 
    Parakh S, Gan HK, Parslow AC, Burvenich IJG, Burgess AW, Scott AM 2017. Evolution of anti-HER2 therapies for cancer treatment. Cancer Treat. Rev. 59:1–21
     [Google Scholar]
  112. 112. 
    Pearson A, Smyth E, Babina IS, Herrera-Abreu MT, Tarazona N et al. 2016. High-level clonal FGFR amplification and response to FGFR inhibition in a translational clinical trial. Cancer Discov 6:8838–51
     [Google Scholar]
  113. 113. 
    Beck A, Goetsch L, Dumontet C, Corvaïa N 2017. Strategies and challenges for the next generation of antibody-drug conjugates. Nat. Rev. Drug Discov. 16:5315–37
     [Google Scholar]
  114. 114. 
    Verma S, Miles D, Gianni L, Krop IE, Welslau M et al. 2012. Trastuzumab emtansine for HER2-positive advanced breast cancer. N. Engl. J. Med. 367:1783–91
     [Google Scholar]
  115. 115. 
    Pritchard CC, Mateo J, Walsh MF, De Sarkar N, Abida W et al. 2016. Inherited DNA-repair gene mutations in men with metastatic prostate cancer. N. Engl. J. Med. 375:5443–53
     [Google Scholar]
  116. 116. 
    Carlo MI, Mukherjee S, Mandelker D, Vijai J, Kemel Y et al. 2018. Prevalence of germline mutations in cancer susceptibility genes in patients with advanced renal cell carcinoma. JAMA Oncol 4:1228–35
     [Google Scholar]
  117. 117. 
    Swisher EM, Lin KK, Oza AM, Scott CL, Giordano H et al. 2017. Rucaparib in relapsed, platinum-sensitive high-grade ovarian carcinoma (ARIEL2 Part 1): an international, multicentre, open-label, phase 2 trial. Lancet Oncol 18:175–87
     [Google Scholar]
  118. 118. 
    Sansregret L, Vanhaesebroeck B, Swanton C 2018. Determinants and clinical implications of chromosomal instability in cancer. Nat. Rev. Clin. Oncol. 15:3139–50
     [Google Scholar]
  119. 119. 
    Lee J-K, Choi Y-L, Kwon M, Park PJ 2016. Mechanisms and consequences of cancer genome instability: lessons from genome sequencing studies. Annu. Rev. Pathol. Mech. Dis. 11:283–312
     [Google Scholar]
  120. 120. 
    Pikor L, Thu K, Vucic E, Lam W 2013. The detection and implication of genome instability in cancer. Cancer Metastasis Rev 32:3–4341–52
     [Google Scholar]
  121. 121. 
    Negrini S, Gorgoulis VG, Halazonetis TD 2010. Genomic instability—an evolving hallmark of cancer. Nat. Rev. Mol. Cell Biol. 11:3220–28
     [Google Scholar]
  122. 122. 
    Fragkos M, Naim V. 2017. Rescue from replication stress during mitosis. Cell Cycle 16:7613–33
     [Google Scholar]
  123. 123. 
    Técher H, Koundrioukoff S, Nicolas A, Debatisse M 2017. The impact of replication stress on replication dynamics and DNA damage in vertebrate cells. Nat. Rev. Genet. 18:9535–50
     [Google Scholar]
  124. 124. 
    Gordon DJ, Resio B, Pellman D 2012. Causes and consequences of aneuploidy in cancer. Nat. Rev. Genet. 13:3189–203
     [Google Scholar]
  125. 125. 
    Sheltzer JM, Ko JH, Replogle JM, Habibe Burgos NC, Chung ES et al. 2017. Single-chromosome gains commonly function as tumor suppressors. Cancer Cell 31:2240–55
     [Google Scholar]
  126. 126. 
    Santaguida S, Richardson A, Iyer DR, M'Saad O, Zasadil L et al. 2017. Chromosome mis-segregation generates cell-cycle-arrested cells with complex karyotypes that are eliminated by the immune system. Dev. Cell 41:6638–51.e5
     [Google Scholar]
  127. 127. 
    Boland CR, Goel A. 2010. Microsatellite instability in colorectal cancer. Gastroenterology 138:62073–87.e3
     [Google Scholar]
  128. 128. 
    Dieci MV, Mathieu MC, Guarneri V, Conte P, Delaloge S et al. 2015. Prognostic and predictive value of tumor-infiltrating lymphocytes in two phase III randomized adjuvant breast cancer trials. Ann. Oncol. 26:81698–704
     [Google Scholar]
  129. 129. 
    Maleki Vareki S, Garrigós C, Duran I 2017. Biomarkers of response to PD-1/PD-L1 inhibition. Crit. Rev. Oncol. Hematol. 116:116–24
     [Google Scholar]
  130. 130. 
    Pardoll DM. 2012. The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer 12:4252–64
     [Google Scholar]
  131. 131. 
    Sharma P, Allison JP. 2015. Immune checkpoint targeting in cancer therapy: toward combination strategies with curative potential. Cell 161:2205–14
     [Google Scholar]
  132. 132. 
    Goodman AM, Kato S, Bazhenova L, Patel SP, Frampton GM et al. 2017. Tumor mutational burden as an independent predictor of response to immunotherapy in diverse cancers. Mol. Cancer Ther. 16:112598–608
     [Google Scholar]
  133. 133. 
    Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H et al. 2015. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 372:262509–20
     [Google Scholar]
  134. 134. 
    Dawson S-J, Rueda OM, Aparicio S, Caldas C 2013. A new genome-driven integrated classification of breast cancer and its implications. EMBO J 32:5617–28
     [Google Scholar]
  135. 135. 
    Mukherjee A, Russell R, Chin S-F, Liu B, Rueda OM et al. 2018. Associations between genomic stratification of breast cancer and centrally reviewed tumour pathology in the METABRIC cohort. npj Breast Cancer 4:15
     [Google Scholar]
  136. 136. 
    Greaves M. 2015. Evolutionary determinants of cancer. Cancer Discov 5:8806–20
     [Google Scholar]
  137. 137. 
    Murtaza M, Dawson S-J, Pogrebniak K, Rueda OM, Provenzano E et al. 2015. Multifocal clonal evolution characterized using circulating tumour DNA in a case of metastatic breast cancer. Nat. Commun. 6:18760
     [Google Scholar]
  138. 138. 
    Deleted in proof
  139. 139. 
    McGranahan N, Favero F, de Bruin EC, Birkbak NJ, Szallasi Z, Swanton C 2015. Clonal status of actionable driver events and the timing of mutational processes in cancer evolution. Sci. Transl. Med. 7:283283ra54
     [Google Scholar]
  140. 140. 
    Shah SP, Roth A, Goya R, Oloumi A, Ha G et al. 2012. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 486:7403395–99
     [Google Scholar]
  141. 141. 
    Baca SC, Prandi D, Lawrence MS, Mosquera JM, Romanel A et al. 2013. Punctuated evolution of prostate cancer genomes. Cell 153:3666–77
     [Google Scholar]
  142. 142. 
    Collins DC, Sundar R, Lim JSJ, Yap TA 2017. Towards precision medicine in the clinic: from biomarker discovery to novel therapeutics. Trends Pharmacol. Sci. 38:125–40
     [Google Scholar]
  143. 143. 
    Le Tourneau C, Delord J-P, Gonçalves A, Gavoille C, Dubot C et al. 2015. Molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer (SHIVA): a multicentre, open-label, proof-of-concept, randomised, controlled phase 2 trial. Lancet Oncol 16:131324–34
     [Google Scholar]
  144. 144. 
    Massard C, Michiels S, Ferté C, Le Deley M-C, Lacroix L et al. 2017. High-throughput genomics and clinical outcome in hard-to-treat advanced cancers: results of the MOSCATO 01 Trial. Cancer Discov 7:586–95
     [Google Scholar]
  145. 145. 
    Tsimberidou AM, Hong DS, Wheler JW, Falchook GS, Naing A et al. 2018. Precision medicine: clinical outcomes including long-term survival according to the pathway targeted and treatment period—The IMPACT study. J. Clin. Oncol. 36: https://doi.org/10.1200/JCO.2018.36.18_suppl.LBA2553
    [Crossref] [Google Scholar]
  146. 146. 
    Do K, O'Sullivan Coyne G, Chen AP 2015. An overview of the NCI precision medicine trials—NCI MATCH and MPACT. Chin. Clin. Oncol. 4:331
     [Google Scholar]
  147. 147. 
    Eggermont AMM, Caldas C, Ringborg U, Medema R, Tabernero J, Wiestler O 2014. Cancer Core Europe: a consortium to address the cancer care–cancer research continuum challenge. Eur. J. Cancer 50:162745–46
     [Google Scholar]
  148. 148. 
    Corrigan-Curay J, McKee AE, Stein P 2018. Breakthrough-therapy designation—an FDA perspective. N. Engl. J. Med. 378:151457–58
     [Google Scholar]
  149. 149. 
    Siu LL, Lawler M, Haussler D, Knoppers BM, Lewin J et al. 2016. Facilitating a culture of responsible and effective sharing of cancer genome data. Nat. Med. 22:5464–71
     [Google Scholar]
  150. 150. 
    Raghavachari N, Garcia-Reyero N. 2018. Overview of gene expression analysis: transcriptomics. Methods in Molecular Biology, Vol. 1783: Gene Expression Analysis N Raghavachari, N Garcia-Reyero 1–6 New York: Humana Press
     [Google Scholar]
  151. 151. 
    Langhans SA. 2018. Three-dimensional in vitro cell culture models in drug discovery and drug repositioning. Front. Pharmacol. 9:6
     [Google Scholar]
  152. 152. 
    Maheswaran S, Haber DA. 2015. Ex vivo culture of CTCs: an emerging resource to guide cancer therapy. Cancer Res 75:122411–15
     [Google Scholar]
  153. 153. 
    Weeber F, Ooft SN, Dijkstra KK, Voest EE 2017. Tumor organoids as a pre-clinical cancer model for drug discovery. Cell Chem. Biol. 24:91092–100
     [Google Scholar]
  154. 154. 
    Vlachogiannis G, Hedayat S, Vatsiou A, Jamin Y, Fernández-Mateos J et al. 2018. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science 359:6378920–26
     [Google Scholar]
  155. 155. 
    Malaney P, Nicosia SV, Davé V 2014. One mouse, one patient paradigm: new avatars of personalized cancer therapy. Cancer Lett 344:11–12
     [Google Scholar]
  156. 156. 
    Meehan TF, Conte N, Goldstein T, Inghirami G, Murakami MA et al. 2017. PDX-MI: minimal information for patient-derived tumor xenograft models. Cancer Res 77:21e62–66
     [Google Scholar]
  157. 157. 
    Bruna A, Rueda OM, Greenwood W, Batra AS, Callari M et al. 2016. A biobank of breast cancer explants with preserved intra-tumor heterogeneity to screen anticancer compounds. Cell 167:1260–74.e22
     [Google Scholar]
  158. 158. 
    Morton JJ, Bird G, Keysar SB, Astling DP, Lyons TR et al. 2016. XactMice: humanizing mouse bone marrow enables microenvironment reconstitution in a patient-derived xenograft model of head and neck cancer. Oncogene 35:3290–300
     [Google Scholar]
  159. 159. 
    Lee JS, Das A, Jerby-Arnon L, Arafeh R, Auslander N et al. 2018. Harnessing synthetic lethality to predict the response to cancer treatment. Nat. Commun. 9:12546
     [Google Scholar]
  160. 160. 
    Toledo CM, Ding Y, Hoellerbauer P, Davis RJ, Basom R et al. 2015. Genome-wide CRISPR-Cas9 screens reveal loss of redundancy between PKMYT1 and WEE1 in glioblastoma stem-like cells. Cell Rep 13:112425–39
     [Google Scholar]
  161. 161. 
    Ashworth A, Lord CJ. 2018. Synthetic lethal therapies for cancer: what's next after PARP inhibitors. ? Nat. Rev. Clin. Oncol. 15:564–76
     [Google Scholar]
  162. 162. 
    Jain M, Arvanitis C, Chu K, Dewey W, Leonhardt E et al. 2002. Sustained loss of a neoplastic phenotype by brief inactivation of MYC. . Science 297:5578102–4
     [Google Scholar]
  163. 163. 
    Jung M, Gelato KA, Fernández-Montalván A, Siegel S, Haendler B 2015. Targeting BET bromo-domains for cancer treatment. Epigenomics 7:3487–501
     [Google Scholar]
  164. 164. 
    Hugo W, Zaretsky JM, Sun L, Song C, Moreno BH et al. 2016. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165:135–44
     [Google Scholar]
  165. 165. 
    Molenaar RJ, Maciejewski JP, Wilmink JW, van Noorden CJF 2018. Wild-type and mutated IDH1/2 enzymes and therapy responses. Oncogene 37:151949–60
     [Google Scholar]
  166. 166. 
    Thon N, Kreth S, Kreth F-W 2013. Personalized treatment strategies in glioblastoma: MGMT promoter methylation status. Onco Targets Ther 6:1363–72
     [Google Scholar]
  167. 167. 
    Warburg O. 1925. The metabolism of carcinoma cells. J. Cancer Res. 9:1148–63
     [Google Scholar]
  168. 168. 
    Martinez-Outschoorn UE, Peiris-Pagés M, Pestell RG, Sotgia F, Lisanti MP 2017. Cancer metabolism: a therapeutic perspective. Nat. Rev. Clin. Oncol. 14:111–31
     [Google Scholar]
  169. 169. 
    Yu L, Li K, Zhang X 2017. Next-generation metabolomics in lung cancer diagnosis, treatment and precision medicine: mini review. Oncotarget 8:70115774–86
     [Google Scholar]
  170. 170. 
    Cappelletti V, Iorio E, Miodini P, Silvestri M, Dugo M, Daidone MG 2017. Metabolic footprints and molecular subtypes in breast cancer. Dis. Markers 2017:7687851
     [Google Scholar]
  171. 171. 
    Zecchini V, Frezza C. 2017. Metabolic synthetic lethality in cancer therapy. Biochim. Biophys. Acta Bioenerg. 1858:8723–31
     [Google Scholar]
  172. 172. 
    Yugi K, Kubota H, Hatano A, Kuroda S 2016. Trans-omics: how to reconstruct biochemical networks across multiple ‘omic’ layers. Trends Biotechnol 34:4276–90
     [Google Scholar]
  173. 173. 
    Archer TC, Fertig EJ, Gosline SJC, Hafner M, Hughes SK et al. 2016. Systems approaches to cancer biology. Cancer Res 76:236774–77
     [Google Scholar]
  174. 174. 
    Karimzadeh M, Jandaghi P, Papadakis AI, Trainor S, Rung J et al. 2018. Aberration hubs in protein interaction networks highlight actionable targets in cancer. Oncotarget 9:3825166–80
     [Google Scholar]
  175. 175. 
    Goodman AM, Piccioni D, Kato S, Boichard A, Wang H-Y et al. 2018. Prevalence of PDL1 amplification and preliminary response to immune checkpoint blockade in solid tumors. JAMA Oncol 4:1237–44
     [Google Scholar]
  176. 176. 
    Ali HR, Glont S-E, Blows FM, Provenzano E, Dawson S-J et al. 2015. PD-L1 protein expression in breast cancer is rare, enriched in basal-like tumours and associated with infiltrating lymphocytes. Ann. Oncol. 26:71488–93
     [Google Scholar]
  177. 177. 
    Zaretsky JM, Garcia-Diaz A, Shin DS, Escuin-Ordinas H, Hugo W et al. 2016. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375:9819–29
     [Google Scholar]
  178. 178. 
    Motter AE, Gulbahce N, Almaas E, Barabási A-L 2008. Predicting synthetic rescues in metabolic networks. Mol. Syst. Biol. 4:168
     [Google Scholar]
  179. 179. 
    Oudard S, Rixe O, Beuselinck B, Linassier C, Banu E et al. 2011. A phase II study of the cancer vaccine TG4010 alone and in combination with cytokines in patients with metastatic renal clear-cell carcinoma: clinical and immunological findings. Cancer Immunol. Immunother. 60:2261–71
     [Google Scholar]
  180. 180. 
    Durant ST, Zheng L, Wang Y, Chen K, Zhang L et al. 2018. The brain-penetrant clinical ATM inhibitor AZD1390 radiosensitizes and improves survival of preclinical brain tumor models. Sci. Adv. 4:6eaat1719
     [Google Scholar]
  181. 181. 
    Eschrich SA, Fulp WJ, Pawitan Y, Foekens JA, Smid M et al. 2012. Validation of a radiosensitivity molecular signature in breast cancer. Clin. Cancer Res. 18:185134–43
     [Google Scholar]
  182. 182. 
    Zhao SG, Shilkrut M, Speers C, Liu M, Wilder-Romans K et al. 2015. Development and validation of a novel platform-independent metastasis signature in human breast cancer. PLOS ONE 10:5e0126631
     [Google Scholar]
  183. 183. 
    Williams NL, Dan T, Zaorsky NG, Garg S, Den RB 2017. The role of genomic techniques in predicting response to radiation therapy. Oncology 31:7562–70
     [Google Scholar]
  184. 184. 
    Bonner JA, Harari PM, Giralt J, Azarnia N, Shin DM et al. 2006. Radiotherapy plus cetuximab for squamous-cell carcinoma of the head and neck. N. Engl. J. Med. 354:6567–78
     [Google Scholar]
/content/journals/10.1146/annurev-biochem-062917-011840
Loading
Cancer Treatment in the Genomic Era
Annual Review of Biochemistry 88, 247 (2019); https://doi.org/10.1146/annurev-biochem-062917-011840
/content/journals/10.1146/annurev-biochem-062917-011840
/content/journals/10.1146/annurev-biochem-062917-011840
Loading

Data & Media loading...

Supplemental Material

Supplementary Data

  • Download Supplemental Text 1: Case study: the power of basket trials - neratinib for HER2-mutant cancers (PDF).

    Download Supplemental Text 2: Case study: targeting ALK fusions in lung cancer (PDF).

    Download Supplemental Text 3: Approaches to data curation and sharing to deliver broad patient benefit (PDF).

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error