Review Article | Published:

The emerging clinical relevance of genomics in cancer medicine

Nature Reviews Clinical Oncologyvolume 15pages353365 (2018) | Download Citation


The combination of next-generation sequencing and advanced computational data analysis approaches has revolutionized our understanding of the genomic underpinnings of cancer development and progression. The coincident development of targeted small molecule and antibody-based therapies that target a cancer’s genomic dependencies has fuelled the transition of genomic assays into clinical use in patients with cancer. Beyond the identification of individual targetable alterations, genomic methods can gauge mutational load, which might predict a therapeutic response to immune-checkpoint inhibitors or identify cancer-specific proteins that inform the design of personalized anticancer vaccines. Emerging clinical applications of cancer genomics include monitoring treatment responses and characterizing mechanisms of resistance. The increasing relevance of genomics to clinical cancer care also highlights several considerable challenges, including the need to promote equal access to genomic testing.

Key points

  • Genomic assays that enable the characterization of the somatic and germline defects in individual tumour samples are increasingly being used in clinical diagnostics as a means of identifying therapeutic options.

  • Many technical and cost-associated considerations have a role in decision-making processes regarding the implementation of cancer genomics assays into clinical practice.

  • Genomic methods can reveal individual targetable alterations, mutational load, complex mutation signatures, and tumour-specific antigens, which might inform the utilization of targeted therapies, immune-checkpoint inhibitors, and personalized anticancer vaccines.

  • The occurrence of shared targetable alterations across diverse tumour types has prompted new paradigms in the application of genomic profiling and the design of clinical trials.

  • These assays increasingly provide information that is pertinent to clinical cancer care, although several important attendant challenges surround their implementation.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


IEDB Analysis Resource, Epitope Prediction and Analysis Tools:

NCI-MATCH Trial (Molecular Analysis for Therapy Choice):

NIH Genomic Data Commons:

The Global Alliance for Genomics and Health:

The Novartis Signature trial programme:


  1. 1.

    Garraway, L. A. & Lander, E. S. Lessons from the cancer genome. Cell 153, 17–37 (2013).

  2. 2.

    Hyman, D. M., Taylor, B. S. & Baselga, J. Implementing genome-driven oncology. Cell 168, 584–599 (2017).

  3. 3.

    Meyerson, M., Gabriel, S. & Getz, G. Advances in understanding cancer genomes through second-generation sequencing. Nat. Rev. Genet. 11, 685–696 (2010).

  4. 4.

    Griffith, M. et al. Optimizing cancer genome sequencing and analysis. Cell Syst. 1, 210–223 (2015).

  5. 5.

    Do, H. & Dobrovic, A. Sequence artifacts in DNA from formalin-fixed tissues: causes and strategies for minimization. Clin. Chem. 61, 64–71 (2015).

  6. 6.

    Williams, C. et al. A high frequency of sequence alterations is due to formalin fixation of archival specimens. Am. J. Pathol. 155, 1467–1471 (1999).

  7. 7.

    Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008).

  8. 8.

    Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 474, 609–615 (2011).

  9. 9.

    Wagle, N. et al. High-throughput detection of actionable genomic alterations in clinical tumor samples by targeted, massively parallel sequencing. Cancer Discov. 2, 82–93 (2012).

  10. 10.

    Van Allen, E. M. et al. Whole-exome sequencing and clinical interpretation of formalin-fixed, paraffin-embedded tumor samples to guide precision cancer medicine. Nat. Med. 20, 682–688 (2014).

  11. 11.

    Al-Kateb, H., Nguyen, T. T., Steger-May, K. & Pfeifer, J. D. Identification of major factors associated with failed clinical molecular oncology testing performed by next generation sequencing (NGS). Mol. Oncol. 9, 1737–1743 (2015).

  12. 12.

    Goswami, R. S. et al. Identification of factors affecting the success of next-generation sequencing testing in solid tumors. Am. J. Clin. Pathol. 145, 222–237 (2016).

  13. 13.

    Cottrell, C. E. et al. Validation of a next-generation sequencing assay for clinical molecular oncology. J. Mol. Diagn. 16, 89–105 (2014).

  14. 14.

    Lih, C.-J. et al. Analytical validation of the next-generation sequencing assay for a nationwide signal-finding clinical trial: molecular analysis for therapy choice clinical trial. J. Mol. Diagn. 19, 313–327 (2017).

  15. 15.

    Cheng, D. T. et al. Memorial Sloan Kettering-integrated mutation profiling of actionable cancer targets (MSK-IMPACT): a hybridization capture-based next-generation sequencing clinical assay for solid tumor molecular oncology. J. Mol. Diagn. 17, 251–264 (2015).

  16. 16.

    Singh, R. R. et al. Clinical validation of a next-generation sequencing screen for mutational hotspots in 46 cancer-related genes. J. Mol. Diagn. 15, 607–622 (2013).

  17. 17.

    Jennings, L. J. et al. Guidelines for validation of next-generation sequencing-based oncology panels: a joint consensus recommendation of the Association for Molecular Pathology and College of American Pathologists. J. Mol. Diagn. 19, 341–365 (2017).

  18. 18.

    Laskin, J. et al. Lessons learned from the application of whole-genome analysis to the treatment of patients with advanced cancers. Cold Spring Harb. Mol. Case Stud. 1, a000570 (2015).

  19. 19.

    Roychowdhury, S. et al. Personalized oncology through integrative high-throughput sequencing: a pilot study. Sci. Transl Med. 3, 111ra121 (2011).

  20. 20.

    Frampton, G. M. et al. Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nat. Biotechnol. 31, 1023–1031 (2013).

  21. 21.

    Garofalo, A. et al. The impact of tumor profiling approaches and genomic data strategies for cancer precision medicine. Genome Med. 8, 79 (2016).

  22. 22.

    van Rooij, N. et al. Tumor exome analysis reveals neoantigen-specific T-cell reactivity in an ipilimumab-responsive melanoma. J. Clin. Oncol. 31, e439–e442 (2013).

  23. 23.

    Levin, J. Z. et al. Targeted next-generation sequencing of a cancer transcriptome enhances detection of sequence variants and novel fusion transcripts. Genome Biol. 10, R115 (2009).

  24. 24.

    Gnirke, A. et al. Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing. Nat. Biotechnol. 27, 182–189 (2009).

  25. 25.

    Albert, T. J. et al. Direct selection of human genomic loci by microarray hybridization. Nat. Methods 4, 903–905 (2007).

  26. 26.

    Hodges, E. et al. Genome-wide in situ exon capture for selective resequencing. Nat. Genet. 39, 1522–1527 (2007).

  27. 27.

    Jones, S. et al. Personalized genomic analyses for cancer mutation discovery and interpretation. Sci. Transl Med. 7, 283ra53 (2015).

  28. 28.

    Schrader, K. A. et al. Germline variants in targeted tumor sequencing using matched normal DNA. JAMA Oncol. 2, 104–111 (2016).

  29. 29.

    Zhang, J. et al. Germline mutations in predisposition genes in pediatric cancer. N. Engl. J. Med. 373, 2336–2346 (2015).

  30. 30.

    Lin, K. Y. & Kraus, W. L. PARP inhibitors for cancer therapy. Cell 169, 183 (2017).

  31. 31.

    Susswein, L. R. et al. Pathogenic and likely pathogenic variant prevalence among the first 10,000 patients referred for next-generation cancer panel testing. Genet. Med. 18, 823–832 (2016).

  32. 32.

    Mandelker, D. et al. Mutation detection in patients with advanced cancer by universal sequencing of cancer-related genes in tumor and normal DNA versus guideline-based germline testing. JAMA 318, 825–835 (2017).

  33. 33.

    Green, R. C. et al. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet. Med. 15, 565–574 (2013).

  34. 34.

    Johns, A. L. et al. Lost in translation: returning germline genetic results in genome-scale cancer research. Genome Med. 9, 41 (2017).

  35. 35.

    Gray, S. W. et al. Oncologists’ and cancer patients’ views on whole-exome sequencing and incidental findings: results from the CanSeq study. Genet. Med. 18, 1011–1019 (2016).

  36. 36.

    Genovese, G. et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N. Engl. J. Med. 371, 2477–2487 (2014).

  37. 37.

    Jaiswal, S. et al. Age-related clonal hematopoiesis associated with adverse outcomes. N. Engl. J. Med. 371, 2488–2498 (2014).

  38. 38.

    Xie, M. et al. Age-related mutations associated with clonal hematopoietic expansion and malignancies. Nat. Med. 20, 1472–1478 (2014).

  39. 39.

    Young, A. L., Challen, G. A., Birmann, B. M. & Druley, T. E. Clonal haematopoiesis harbouring AML-associated mutations is ubiquitous in healthy adults. Nat. Commun. 7, 12484 (2016).

  40. 40.

    Coombs, C. C. et al. Therapy-related clonal hematopoiesis in patients with non-hematologic cancers is common and associated with adverse clinical outcomes. Cell Stem Cell 21, 374–382.e4 (2017).

  41. 41.

    AACR Project GENIE Consortium. AACR Project GENIE: powering precision medicine through an international consortium. Cancer Discov. (2017).

  42. 42.

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

  43. 43.

    Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. (2017).

  44. 44.

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

  45. 45.

    Rizvi, N. A. et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015).

  46. 46.

    Van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015).

  47. 47.

    Slamon, D. J. et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N. Engl. J. Med. 344, 783–792 (2001).

  48. 48.

    Druker, B. J. et al. Efficacy and safety of a specific inhibitor of the BCR-ABL tyrosine kinase in chronic myeloid leukemia. N. Engl. J. Med. 344, 1031–1037 (2001).

  49. 49.

    Mok, T. S. et al. Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N. Engl. J. Med. 361, 947–957 (2009).

  50. 50.

    Kwak, E. L. et al. Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer. N. Engl. J. Med. 363, 1693–1703 (2010).

  51. 51.

    Shaw, A. T. et al. Crizotinib in ROS1-rearranged non-small-cell lung cancer. N. Engl. J. Med. 371, 1963–1971 (2014).

  52. 52.

    Chapman, P. B. et al. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N. Engl. J. Med. 364, 2507–2516 (2011).

  53. 53.

    Larkin, J. et al. Combined vemurafenib and cobimetinib in BRAF-mutated melanoma. N. Engl. J. Med. 371, 1867–1876 (2014).

  54. 54.

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

  55. 55.

    De Roock, W. et al. Effects of KRAS, BRAF, NRAS, and PIK3CA mutations on the efficacy of cetuximab plus chemotherapy in chemotherapy-refractory metastatic colorectal cancer: a retrospective consortium analysis. Lancet Oncol. 11, 753–762 (2010).

  56. 56.

    Zehir, A. et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat. Med. 23, 703–713 (2017).

  57. 57.

    Shaw, A. T., Hsu, P. P., Awad, M. M. & Engelman, J. A. Tyrosine kinase gene rearrangements in epithelial malignancies. Nat. Rev. Cancer 13, 772–787 (2013).

  58. 58.

    Stransky, N., Cerami, E., Schalm, S., Kim, J. L. & Lengauer, C. The landscape of kinase fusions in cancer. Nat. Commun. 5, 4846 (2014).

  59. 59.

    Ross, J. S. et al. The distribution of BRAF gene fusions in solid tumors and response to targeted therapy. Int. J. Cancer. 138, 881–890 (2016).

  60. 60.

    Paik, P. K. et al. Response to MET inhibitors in patients with stage IV lung adenocarcinomas harboring MET mutations causing exon 14 skipping. Cancer Discov. 5, 842–849 (2015).

  61. 61.

    Frampton, G. M. et al. Activation of MET via diverse exon 14 splicing alterations occurs in multiple tumor types and confers clinical sensitivity to MET inhibitors. Cancer Discov. 5, 850–859 (2015).

  62. 62.

    Downing, J. R. et al. The Pediatric Cancer Genome Project. Nat. Genet. 44, 619–622 (2012).

  63. 63.

    Oberg, J. A. et al. Implementation of next generation sequencing into pediatric hematology-oncology practice: moving beyond actionable alterations. Genome Med. 8, 133 (2016).

  64. 64.

    Kandoth, C. et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333–339 (2013).

  65. 65.

    Thomas, R. K. et al. High-throughput oncogene mutation profiling in human cancer. Nat. Genet. 39, 347–351 (2007).

  66. 66.

    Amatu, A., Sartore-Bianchi, A. & Siena, S. NTRK gene fusions as novel targets of cancer therapy across multiple tumour types. ESMO Open 1, e000023 (2016).

  67. 67.

    Cunanan, K. M. et al. Basket trials in oncology: a trade-off between complexity and efficiency. J. Clin. Oncol. 35, 271–273 (2017).

  68. 68.

    Hyman, D. M. et al. Vemurafenib in multiple nonmelanoma cancers with BRAF V600 mutations. N. Engl. J. Med. 373, 726–736 (2015).

  69. 69.

    Hyman, D. M. et al. AKT inhibition in solid tumors with AKT1 mutations. J. Clin. Oncol. 35, 2251–2259 (2017).

  70. 70.

    Govindan, R. et al. ALCHEMIST trials: a golden opportunity to transform outcomes in early-stage non-small cell lung cancer. Clin. Cancer Res. 21, 5439–5444 (2015).

  71. 71.

    Papadimitrakopoulou, V. et al. The BATTLE-2 study: a biomarker-integrated targeted therapy study in previously treated patients with advanced non-small-cell lung cancer J. Clin. Oncol. 34, 3638–3647 (2016).

  72. 72.

    Herbst, R. S. et al. Lung Master Protocol (Lung-MAP)-a biomarker-driven protocol for accelerating development of therapies for squamous cell lung cancer: SWOG S1400. Clin. Cancer Res. 21, 1514–1524 (2015).

  73. 73.

    Abrams, J. et al. National Cancer Institute’s Precision Medicine Initiatives for the new National Clinical Trials Network. Am. Soc. Clin. Oncol. Educ. Book 2014, 71–76 (2014).

  74. 74.

    Garraway, L. A. & Jänne, P. A. Circumventing cancer drug resistance in the era of personalized medicine. Cancer Discov. 2, 214–226 (2012).

  75. 75.

    Saglio, G. et al. Nilotinib versus imatinib for newly diagnosed chronic myeloid leukemia. N. Engl. J. Med. 362, 2251–2259 (2010).

  76. 76.

    Cortes, J. E. et al. Ponatinib in refractory Philadelphia chromosome-positive leukemias. N. Engl. J. Med. 367, 2075–2088 (2012).

  77. 77.

    Jänne, P. A. et al. AZD9291 in EGFR inhibitor-resistant non-small-cell lung cancer. N. Engl. J. Med. 372, 1689–1699 (2015).

  78. 78.

    Toy, W. et al. ESR1 ligand-binding domain mutations in hormone-resistant breast cancer. Nat. Genet. 45, 1439–1445 (2013).

  79. 79.

    Robinson, D. R. et al. Activating ESR1 mutations in hormone-resistant metastatic breast cancer. Nat. Genet. 45, 1446–1451 (2013).

  80. 80.

    Wagle, N. et al. Dissecting therapeutic resistance to RAF inhibition in melanoma by tumor genomic profiling. J. Clin. Oncol. 29, 3085–3096 (2011).

  81. 81.

    Van Allen, E. M. et al. The genetic landscape of clinical resistance to RAF inhibition in metastatic melanoma. Cancer Discov. 4, 94–109 (2014).

  82. 82.

    Shi, H. et al. Acquired resistance and clonal evolution in melanoma during BRAF inhibitor therapy. Cancer Discov. 4, 80–93 (2014).

  83. 83.

    Juric, D. et al. Convergent loss of PTEN leads to clinical resistance to a PI(3)Kα inhibitor. Nature 518, 240–244 (2015).

  84. 84.

    LoRusso, P. M. et al. Pilot trial of selecting molecularly guided therapy for patients with non-V600 BRAF-mutant metastatic melanoma: experience of the SU2C/MRA Melanoma Dream Team. Mol. Cancer Ther. 14, 1962–1971 (2015).

  85. 85.

    Landrum, M. J. et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 44, D862–868 (2016).

  86. 86.

    Damodaran, S. et al. Cancer Driver Log (CanDL): catalog of potentially actionable cancer mutations. J. Mol. Diagn. 17, 554–559 (2015).

  87. 87.

    Griffith, M. et al. CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nat. Genet. 49, 170–174 (2017).

  88. 88.

    Meric-Bernstam, F. et al. A decision support framework for genomically informed investigational cancer therapy. J. Natl Cancer Inst. 107, djv098 (2015).

  89. 89.

    Sholl, L. M. et al. Institutional implementation of clinical tumor profiling on an unselected cancer population. JCI Insight 1, e87062 (2016).

  90. 90.

    Wheler, J. J. et al. Cancer therapy directed by comprehensive genomic profiling: a single center study. Cancer Res. 76, 3690–3701 (2016).

  91. 91.

    Beltran, H. et al. Whole-exome sequencing of metastatic cancer and biomarkers of treatment response. JAMA Oncol. 1, 466–474 (2015).

  92. 92.

    Meric-Bernstam, F. et al. Feasibility of large-scale genomic testing to facilitate enrollment onto genomically matched clinical trials. J. Clin. Oncol. 33, 2753–2762 (2015).

  93. 93.

    Schwaederle, M. et al. On the road to precision cancer medicine: analysis of genomic biomarker actionability in 439 patients. Mol. Cancer Ther. 14, 1488–1494 (2015).

  94. 94.

    Stockley, T. L. et al. Molecular profiling of advanced solid tumors and patient outcomes with genotype-matched clinical trials: the Princess Margaret IMPACT/COMPACT trial. Genome Med. 8, 109 (2016).

  95. 95.

    Schwaederle, M. et al. Precision oncology: the UC San Diego Moores Cancer Center PREDICT experience. Mol. Cancer Ther. 15, 743–752 (2016).

  96. 96.

    Tsimberidou, A.-M. et al. Initiative for molecular profiling and advanced cancer therapy (IMPACT): an MD Anderson Precision Medicine Study. JCO Precis. Oncol. (2017).

  97. 97.

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

  98. 98.

    Hause, R. J., Pritchard, C. C., Shendure, J. & Salipante, S. J. Classification and characterization of microsatellite instability across 18 cancer types. Nat. Med. 22, 1342–1350 (2016).

  99. 99.

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

  100. 100.

    Le, D. T. et al. Mismatch-repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357, 409–413 (2017).

  101. 101.

    Bryant, H. E. et al. Specific killing of BRCA2-deficient tumours with inhibitors of poly(ADP-ribose) polymerase. Nature 434, 913–917 (2005).

  102. 102.

    Farmer, H. et al. Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature 434, 917–921 (2005).

  103. 103.

    Robson, M. et al. Olaparib for metastatic breast cancer in patients with a germline BRCA mutation. N. Engl. J. Med. 377, 523–533 (2017).

  104. 104.

    De Plaen, E. et al. Immunogenic (tum-) variants of mouse tumor P815: cloning of the gene of tum- antigen P91A and identification of the tum- mutation. Proc. Natl Acad. Sci. USA 85, 2274–2278 (1988).

  105. 105.

    Monach, P. A., Meredith, S. C., Siegel, C. T. & Schreiber, H. A unique tumor antigen produced by a single amino acid substitution. Immunity 2, 45–59 (1995).

  106. 106.

    van der Bruggen, P. et al. A gene encoding an antigen recognized by cytolytic T lymphocytes on a human melanoma. Science 254, 1643–1647 (1991).

  107. 107.

    Segal, N. H. et al. Epitope landscape in breast and colorectal cancer. Cancer Res. 68, 889–892 (2008).

  108. 108.

    Mardis, E. R. DNA sequencing technologies: 2006–2016. Nat. Protoc. 12, 213–218 (2017).

  109. 109.

    Lundegaard, C. et al. NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11. Nucleic Acids Res. 36, W509–W512 (2008).

  110. 110.

    Nielsen, M. et al. Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan. PLoS Comput. Biol. 4, e1000107 (2008).

  111. 111.

    Rasmussen, M. et al. Pan-specific prediction of peptide-MHC class I complex stability, a correlate of T cell immunogenicity. J. Immunol. 197, 1517–1524 (2016).

  112. 112.

    Peters, B. & Sette, A. Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method. BMC Bioinformatics 6, 132 (2005).

  113. 113.

    Shukla, S. A. et al. Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes. Nat. Biotechnol. 33, 1152–1158 (2015).

  114. 114.

    Warren, R. L. et al. Derivation of HLA types from shotgun sequence datasets. Genome Med. 4, 95 (2012).

  115. 115.

    Szolek, A. et al. OptiType: precision HLA typing from next-generation sequencing data. Bioinformatics 30, 3310–3316 (2014).

  116. 116.

    Hundal, J. et al. pVAC-Seq: a genome-guided in silico approach to identifying tumor neoantigens. Genome Med. 8, 11 (2016).

  117. 117.

    Rubinsteyn, A., Hodes, I., Kodysh, J. & Hammerbacher, J. Vaxrank: a computational tool for designing personalized cancer vaccines. Preprint at bioRxiv (2017).

  118. 118.

    Carreno, B. M. et al. Cancer immunotherapy. A dendritic cell vaccine increases the breadth and diversity of melanoma neoantigen-specific T cells. Science 348, 803–808 (2015).

  119. 119.

    Snyder, A. et al. Contribution of systemic and somatic factors to clinical response and resistance to PD-L1 blockade in urothelial cancer: an exploratory multi-omic analysis. PLoS Med. 14, e1002309 (2017).

  120. 120.

    Diggs, L. P. & Hsueh, E. C. Utility of PD-L1 immunohistochemistry assays for predicting PD-1/PD-L1 inhibitor response. Biomark. Res. 5, 12 (2017).

  121. 121.

    Ott, P. A. et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217–221 (2017).

  122. 122.

    Sahin, U. et al. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 547, 222–226 (2017).

  123. 123.

    Gubin, M. M. et al. Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens. Nature 515, 577–581 (2014).

  124. 124.

    Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G. & Hacohen, N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160, 48–61 (2015).

  125. 125.

    Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

  126. 126.

    Mose, L. E. et al. Assembly-based inference of B-cell receptor repertoires from short read RNA sequencing data with V’DJer. Bioinformatics. 32, 3729–3734 (2016).

  127. 127.

    Cole, C., Volden, R., Dharmadhikari, S., Scelfo-Dalbey, C. & Vollmers, C. Highly accurate sequencing of full-length immune repertoire amplicons using Tn5-enabled and molecular identifier-guided amplicon assembly. J. Immunol. 196, 2902–2907 (2016).

  128. 128.

    Robins, H. Immunosequencing: applications of immune repertoire deep sequencing. Curr. Opin. Immunol. 25, 646–652 (2013).

  129. 129.

    Li, B. et al. Landscape of tumor-infiltrating T cell repertoire of human cancers. Nat. Genet. 48, 725–732 (2016).

  130. 130.

    Diehl, F. et al. Circulating mutant DNA to assess tumor dynamics. Nat. Med. 14, 985–990 (2008).

  131. 131.

    Alix-Panabières, C. & Pantel, K. Clinical applications of circulating tumor cells and circulating tumor DNA as liquid biopsy. Cancer Discov. 6, 479–491 (2016).

  132. 132.

    Schwarzenbach, H., Hoon, D. S. B. & Pantel, K. Cell-free nucleic acids as biomarkers in cancer patients. Nat. Rev. Cancer 11, 426–437 (2011).

  133. 133.

    Tie, J. et al. Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer. Sci. Transl Med. 8, 346ra92 (2016).

  134. 134.

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

  135. 135.

    Chaudhuri, A. A. et al. Early detection of molecular residual disease in localized lung cancer by circulating tumor DNA profiling. Cancer Discov. 7, 1394–1403 (2017).

  136. 136.

    Bianchi, D. W. et al. Noninvasive prenatal testing and incidental detection of occult maternal malignancies. JAMA 314, 162–169 (2015).

  137. 137.

    Phallen, J. et al. Direct detection of early-stage cancers using circulating tumor DNA. Sci. Transl Med. 9, eaan2415 (2017).

  138. 138.

    Newman, A. M. et al. Integrated digital error suppression for improved detection of circulating tumor DNA. Nat. Biotechnol. 34, 547–555 (2016).

  139. 139.

    Kennedy, S. R. et al. Detecting ultralow-frequency mutations by Duplex Sequencing. Nat. Protoc. 9, 2586–2606 (2014).

  140. 140.

    [No authors listed.] Panel’s ‘Moonshot’ goals released. Cancer Discov. 6, 1202–1203 (2016).

  141. 141.

    Siu, L. L. et al. Facilitating a culture of responsible and effective sharing of cancer genome data. Nat. Med. 22, 464–471 (2016).

  142. 142.

    Jensen, M. A., Ferretti, V., Grossman, R. L. & Staudt, L. M. The NCI Genomic Data Commons as an engine for precision medicine. Blood 130, 453–459 (2017).

  143. 143.

    Gagan, J. & van Allen, E. M. Next-generation sequencing to guide cancer therapy. Genome Med. 7, 80 (2015).

Download references

Author information


  1. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    • Michael F. Berger
  2. Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH, USA

    • Elaine R. Mardis
  3. Department of Pediatrics, Ohio State University College of Medicine, Columbus, OH, USA

    • Elaine R. Mardis


  1. Search for Michael F. Berger in:

  2. Search for Elaine R. Mardis in:


Both authors made a substantial contribution to all aspects of the preparation of this manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Elaine R. Mardis.

About this article

Publication history



Further reading