Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Circulating tumor DNA and liquid biopsy in oncology

Abstract

Techniques for analyzing circulating tumor DNA (ctDNA) to detect, characterize and monitor cancer have matured rapidly. An increasing body of clinical evidence is demonstrating the capabilities of this technology as a diagnostic test. The full potential of ctDNA liquid biopsy in the diagnosis, characterization and management of solid and hematological malignancies will be uncovered through interventional clinical trials evaluating clinical utility. In this Review, we discuss the current landscape of ctDNA liquid-biopsy applications across the cancer continuum and highlight opportunities for clinical investigation.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Features of ctDNA and potential issues for liquid-biopsy testing in oncology.
Fig. 2: Applications of ctDNA liquid biopsy in the recurrent and/or metastatic setting.
Fig. 3: Evaluation of ctDNA in the setting of MRD.
Fig. 4: ctDNA screening for pre-cancer diagnosis and monitoring.

References

  1. 1.

    La Thangue, N. B. & Kerr, D. J. Predictive biomarkers: a paradigm shift towards personalized cancer medicine. Nat. Rev. Clin. Oncol. 8, 587–596 (2011).

    PubMed  Google Scholar 

  2. 2.

    Basik, M. et al. Biopsies: next-generation biospecimens for tailoring therapy. Nat. Rev. Clin. Oncol. 10, 437–450 (2013).

    CAS  PubMed  Google Scholar 

  3. 3.

    Wan, J. C. M. et al. Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat. Rev. Cancer 17, 223–238 (2017).

    CAS  PubMed  Google Scholar 

  4. 4.

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

    CAS  PubMed  Google Scholar 

  5. 5.

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

    CAS  PubMed  Google Scholar 

  6. 6.

    Thierry, A. R. et al. Clinical validation of the detection of KRAS and BRAF mutations from circulating tumor DNA. Nat. Med. 20, 430–435 (2014).

    CAS  PubMed  Google Scholar 

  7. 7.

    Odegaard, J. I. et al. Validation of a plasma-based comprehensive cancer genotyping assay utilizing orthogonal tissue- and plasma-based methodologies. Clin. Cancer Res. 24, 3539–3549 (2018).

    CAS  PubMed  Google Scholar 

  8. 8.

    Zill, O. A. et al. The landscape of actionable genomic alterations in cell-free circulating tumor DNA from 21,807 advanced cancer patients. Clin. Cancer Res. 24, 3528–3538 (2018).

    CAS  PubMed  Google Scholar 

  9. 9.

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

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Anagnostou, V. et al. Evolution of neoantigen landscape during immune checkpoint blockade in non-small cell lung cancer. Cancer Discov. 7, 264–276 (2017).

    CAS  PubMed  Google Scholar 

  11. 11.

    Newman, A. M. et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat. Med. 20, 548–554 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Dawson, S.-J. et al. Analysis of circulating tumor DNA to monitor metastatic breast cancer. N. Engl. J. Med. 368, 1199–1209 (2013).

    CAS  PubMed  Google Scholar 

  13. 13.

    Li, B. T. et al. Ultra-deep next-generation sequencing of plasma cell-free DNA in patients with advanced lung cancers: results from the Actionable Genome Consortium. Ann. Oncol. 30, 597–603 (2019).

    CAS  PubMed  Google Scholar 

  14. 14.

    Aggarwal, C. et al. Clinical implications of plasma-based genotyping with the delivery of personalized therapy in metastatic non-small cell lung cancer. JAMA Oncol. 5, 173–180 (2019).

    PubMed  Google Scholar 

  15. 15.

    Clark, T. A. et al. Analytical validation of a hybrid capture-based next-generation sequencing clinical assay for genomic profiling of cell-free circulating tumor DNA. J. Mol. Diagn. 20, 686–702 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Wyatt, A. W. et al. Concordance of circulating tumor DNA and matched metastatic tissue biopsy in prostate cancer. J. Natl. Cancer Inst. 109, djx118 (2017).

    PubMed Central  Google Scholar 

  17. 17.

    Adalsteinsson, V. A. et al. Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors. Nat. Commun. 8, 1324 (2017).

    PubMed  PubMed Central  Google Scholar 

  18. 18.

    Grasselli, J. et al. Concordance of blood- and tumor-based detection of RAS mutations to guide anti-EGFR therapy in metastatic colorectal cancer. Ann. Oncol. 28, 1294–1301 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Wang, Z. et al. Assessment of blood tumor mutational burden as a potential biomarker for immunotherapy in patients with non-small cell lung cancer with use of a next-generation sequencing cancer gene panel. JAMA Oncol. 5, 696–702 (2019).

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Willis, J. et al. Validation of microsatellite instability detection using a comprehensive plasma-based genotyping panel. Clin. Cancer Res. 25, 7035–7045 (2019).

    PubMed  Google Scholar 

  22. 22.

    Gandara, D. R. et al. Blood-based tumor mutational burden as a predictor of clinical benefit in non-small-cell lung cancer patients treated with atezolizumab. Nat. Med. 24, 1441–1448 (2018).

    CAS  PubMed  Google Scholar 

  23. 23.

    Baselga, J. et al. Buparlisib plus fulvestrant versus placebo plus fulvestrant in postmenopausal, hormone receptor-positive, HER2-negative, advanced breast cancer (BELLE-2): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol. 18, 904–916 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    André, F. et al. SOLAR-1 study group. Alpelisib for PIK3CA-mutated, hormone receptor-positive advanced breast cancer. N. Engl. J. Med. 380, 1929–1940 (2019).

    PubMed  Google Scholar 

  25. 25.

    US Food and Drug Administration. Premarket Approval (PMA). https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfPMA/pma.cfm?id=P190004 (2019).

  26. 26.

    Kalinsky, K., Heguy, A., Bhanot, U. K., Patil, S. & Moynahan, M. E. PIK3CA mutations rarely demonstrate genotypic intratumoral heterogeneity and are selected for in breast cancer progression. Breast Cancer Res. Treat. 129, 635–643 (2011).

    PubMed  Google Scholar 

  27. 27.

    Meador, C. B. & Oxnard, G. R. Effective cancer genotyping—many means to one end. Clin. Cancer Res. 25, 4583–4585 (2019).

    PubMed  Google Scholar 

  28. 28.

    O’Leary, B. et al. Early circulating tumor DNA dynamics and clonal selection with palbociclib and fulvestrant for breast cancer. Nat. Commun. 9, 896 (2018).

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Chabon, J. J. et al. Circulating tumour DNA profiling reveals heterogeneity of EGFR inhibitor resistance mechanisms in lung cancer patients. Nat. Commun. 7, 11815 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Clouthier, D. L. et al. A technical feasibility report on correlative studies from the investigator-initiated phase II study of pembrolizumab (Pembro) immunological response evaluation (INSPIRE). J. Clin. Orthod. 35, 11607–11607 (2017).

    Google Scholar 

  31. 31.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Anagnostou, V. et al. Dynamics of tumor and immune responses during immune checkpoint blockade in non-small cell lung cancer. Cancer Res. 79, 1214–1225 (2019).

    CAS  PubMed  Google Scholar 

  33. 33.

    Iafolla, M. A. J. et al. Bespoke circulating tumor DNA (ctDNA) analysis as a predictive biomarker in solid tumor patients (pts) treated with single-agent pembrolizumab (P). J. Clin. Orthod. 37, 2542–2542 (2019).

    Google Scholar 

  34. 34.

    Kim, S. T. et al. Comprehensive molecular characterization of clinical responses to PD-1 inhibition in metastatic gastric cancer. Nat. Med. 24, 1449–1458 (2018).

    CAS  PubMed  Google Scholar 

  35. 35.

    Cabel, L. et al. Circulating tumor DNA changes for early monitoring of anti-PD1 immunotherapy: a proof-of-concept study. Ann. Oncol. 28, 1996–2001 (2017).

    CAS  PubMed  Google Scholar 

  36. 36.

    Lee, J. H. et al. Circulating tumour DNA predicts response to anti-PD1 antibodies in metastatic melanoma. Ann. Oncol. 28, 1130–1136 (2017).

    CAS  PubMed  Google Scholar 

  37. 37.

    Kruger, S. et al. Repeated mutKRAS ctDNA measurements represent a novel and promising tool for early response prediction and therapy monitoring in advanced pancreatic cancer. Ann. Oncol. 29, 2348–2355 (2018).

    CAS  PubMed  Google Scholar 

  38. 38.

    Goldberg, S. B. et al. Early assessment of lung cancer immunotherapy response via circulating tumor DNA. Clin. Cancer Res. 24, 1872–1880 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Raja, R. et al. Early reduction in ctDNA predicts survival in patients with lung and bladder cancer treated with durvalumab. Clin. Cancer Res. 24, 6212–6222 (2018).

    PubMed  Google Scholar 

  40. 40.

    Lee, J. H. et al. Association between circulating tumor DNA and pseudoprogression in patients with metastatic melanoma treated with anti-programmed cell death 1 antibodies. JAMA Oncol. 4, 717–721 (2018).

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Tie, J. et al. Circulating tumor DNA as an early marker of therapeutic response in patients with metastatic colorectal cancer. Ann. Oncol. 26, 1715–1722 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Riediger, A. L. et al. Mutation analysis of circulating plasma DNA to determine response to EGFR tyrosine kinase inhibitor therapy of lung adenocarcinoma patients. Sci. Rep. 6, 33505 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Xi, L. et al. Circulating tumor DNA as an early indicator of response to T-cell transfer immunotherapy in metastatic melanoma. Clin. Cancer Res. 22, 5480–5486 (2016).

    CAS  PubMed  Google Scholar 

  44. 44.

    Stover, D. G. et al. Association of cell-free DNA tumor fraction and somatic copy number alterations with survival in metastatic triple-negative breast cancer. J. Clin. Oncol. 36, 543–553 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Parikh, A. R. et al. Liquid versus tissue biopsy for detecting acquired resistance and tumor heterogeneity in gastrointestinal cancers. Nat. Med. 25, 1415–1421 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Parseghian, C. M. et al. Anti-EGFR-resistant clones decay exponentially after progression: implications for anti-EGFR re-challenge. Ann. Oncol. 30, 243–249 (2019).

    CAS  PubMed  Google Scholar 

  47. 47.

    Cremolini, C. et al. Rechallenge for patients with RAS and BRAF wild-type metastatic colorectal cancer with acquired resistance to first-line cetuximab and irinotecan: a phase 2 single-arm clinical trial. JAMA Oncol. 5, 343–350 (2019).

    PubMed  Google Scholar 

  48. 48.

    Blackburn, E. H. Cancer interception. Cancer Prev. Res. 4, 787–792 (2011).

    CAS  Google Scholar 

  49. 49.

    Tan, L. et al. Prediction and monitoring of relapse in stage III melanoma using circulating tumor DNA. Ann. Oncol. 30, 804–814 (2019).

    CAS  PubMed  Google Scholar 

  50. 50.

    Tie, J. et al. Serial circulating tumour DNA analysis during multimodality treatment of locally advanced rectal cancer: a prospective biomarker study. Gut 68, 663–671 (2019).

    CAS  PubMed  Google Scholar 

  51. 51.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    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).

    PubMed  PubMed Central  Google Scholar 

  53. 53.

    Reinert, T. et al. Analysis of plasma cell-free DNA by ultradeep sequencing in patients with stages I to III colorectal cancer. JAMA Oncol. 5, 1124–1131 (2019).

    PubMed Central  Google Scholar 

  54. 54.

    Abbosh, C., Birkbak, N. J. & Swanton, C. Early stage NSCLC - challenges to implementing ctDNA-based screening and MRD detection. Nat. Rev. Clin. Oncol. 15, 577–586 (2018).

    CAS  PubMed  Google Scholar 

  55. 55.

    Coakley, M., Garcia-Murillas, I. & Turner, N. C. Molecular residual disease and adjuvant trials design in solid tumors. Clin. Cancer Res. 25, 6026–6034 (2019).

    PubMed  Google Scholar 

  56. 56.

    Abbosh, C. et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature 545, 446–451 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Chan, A. T. C. et al. Analysis of plasma Epstein-Barr virus DNA in nasopharyngeal cancer after chemoradiation to identify high-risk patients for adjuvant chemotherapy: a randomized controlled trial. J. Clin. Oncol. 36, 3091–3100 (2018). Others.

    CAS  Google Scholar 

  59. 59.

    Mailankody, S. et al. Minimal residual disease in multiple myeloma: bringing the bench to the bedside. Nat. Rev. Clin. Oncol. 12, 286–295 (2015).

    PubMed  Google Scholar 

  60. 60.

    Berry, D. A. et al. Association of minimal residual disease with clinical outcome in pediatric and adult acute lymphoblastic leukemia: a meta-analysis. JAMA Oncol. 3, e170580 (2017).

    PubMed  PubMed Central  Google Scholar 

  61. 61.

    Schuurhuis, G. J. et al. Minimal/measurable residual disease in AML: a consensus document from the European LeukemiaNet MRD Working Party. Blood 131, 1275–1291 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Yin, J. A. L. et al. Minimal residual disease monitoring by quantitative RT-PCR in core binding factor AML allows risk stratification and predicts relapse: results of the United Kingdom MRC AML-15 trial. Blood 120, 2826–2835 (2012).

    CAS  PubMed  Google Scholar 

  63. 63.

    Ivey, A. et al. Assessment of minimal residual disease in standard-risk AML. N. Engl. J. Med. 374, 422–433 (2016).

    CAS  PubMed  Google Scholar 

  64. 64.

    Venditti, A. et al. GIMEMA AML1310 trial of risk-adapted, MRD-directed therapy for young adults with newly diagnosed acute myeloid leukemia. Blood 134, 935–945 (2019).

    CAS  PubMed  Google Scholar 

  65. 65.

    Platzbecker, U. et al. Measurable residual disease-guided treatment with azacitidine to prevent haematological relapse in patients with myelodysplastic syndrome and acute myeloid leukaemia (RELAZA2): an open-label, multicentre, phase 2 trial. Lancet Oncol. 19, 1668–1679 (2018).

    CAS  PubMed  Google Scholar 

  66. 66.

    Takagi, S. & Tanaka, O. Magnetic resonance imaging of femoral marrow predicts outcome in adult patients with acute myeloid leukaemia in complete remission. Br. J. Haematol. 117, 70–75 (2002).

    PubMed  Google Scholar 

  67. 67.

    Kis, O. et al. Circulating tumour DNA sequence analysis as an alternative to multiple myeloma bone marrow aspirates. Nat. Commun. 8, 15086 (2017).

    PubMed  PubMed Central  Google Scholar 

  68. 68.

    Nakamura, S. et al. Prognostic impact of circulating tumor DNA status post-allogeneic hematopoietic stem cell transplantation in AML and MDS. Blood 133, 2682–2695 (2019).

    CAS  PubMed  Google Scholar 

  69. 69.

    Albitar, A. et al. Prevalence of somatic mutations in patients with aplastic anemia using peripheral blood cfDNA as compared with BM. Leukemia 32, 227–229 (2018).

    CAS  PubMed  Google Scholar 

  70. 70.

    Curry, S. J. et al. Screening for cervical cancer: US Preventive Services Task Force recommendation statement. J. Am. Med. Assoc. 320, 674–686 (2018).

    Google Scholar 

  71. 71.

    Bibbins-Domingo, K. et al. Screening for colorectal cancer: US Preventive Services Task Force recommendation statement. J. Am. Med. Assoc. 315, 2564–2575 (2016).

    CAS  Google Scholar 

  72. 72.

    Melnikow, J. et al. Screening for cervical cancer with high-risk human papillomavirus testing: updated evidence report and systematic review for the US Preventive Services Task Force. J. Am. Med. Assoc. 320, 687–705 (2018).

    Google Scholar 

  73. 73.

    Nelson, H. D. et al. Effectiveness of breast cancer screening: systematic review and meta-analysis to update the 2009 US Preventive Services Task Force recommendation. Ann. Intern. Med. 164, 244–255 (2016).

    PubMed  Google Scholar 

  74. 74.

    Henderson, J. T., Webber, E. M. & Sawaya, G. F. Screening for ovarian cancer: updated evidence report and systematic review for the US Preventive Services Task Force. J. Am. Med. Assoc. 319, 595–606 (2018).

    Google Scholar 

  75. 75.

    Fenton, J. J. et al. Prostate-specific antigen-based screening for prostate cancer: evidence report and systematic review for the US Preventive Services Task Force. J. Am. Med. Assoc. 319, 1914–1931 (2018).

    Google Scholar 

  76. 76.

    Lin, J. S., Bowles, E. J. A., Williams, S. B. & Morrison, C. C. Screening for thyroid cancer: updated evidence report and systematic review for the US Preventive Services Task Force. J. Am. Med. Assoc. 317, 1888–1903 (2017).

    Google Scholar 

  77. 77.

    Lin, K. & Sharangpani, R. Screening for testicular cancer: an evidence review for the U.S. Preventive Services Task Force. Ann. Intern. Med. 153, 396–399 (2010).

    PubMed  Google Scholar 

  78. 78.

    Notta, F. et al. A renewed model of pancreatic cancer evolution based on genomic rearrangement patterns. Nature 538, 378–382 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 79.

    Fearon, E. R. & Vogelstein, B. A genetic model for colorectal tumorigenesis. Cell 61, 759–767 (1990).

    CAS  Google Scholar 

  80. 80.

    Nelson, H. D., Pappas, M., Cantor, A. & Griffin, J. Harms of breast cancer screening: systematic review to update the 2009 US Preventive Services Task Force recommendation. Ann. Intern. Med. 164, 256–267 (2016).

    Google Scholar 

  81. 81.

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

    PubMed  Google Scholar 

  82. 82.

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

    PubMed  PubMed Central  Google Scholar 

  83. 83.

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

    PubMed  PubMed Central  Google Scholar 

  84. 84.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Abbosh, C., Swanton, C. & Birkbak, N. J. Clonal haematopoiesis: a source of biological noise in cell-free DNA analyses. Ann. Oncol. 30, 358–359 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. 86.

    Cohen, J. D. et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 359, 926–930 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. 87.

    Lo, Y. M. et al. Quantitative analysis of cell-free Epstein-Barr virus DNA in plasma of patients with nasopharyngeal carcinoma. Cancer Res. 59, 1188–1191 (1999).

    CAS  PubMed  Google Scholar 

  88. 88.

    Lam, W. K. J., Chan, K. C. A. & Lo, Y. M. D. Plasma Epstein-Barr virus DNA as an archetypal circulating tumour DNA marker. J. Pathol. 247, 641–649 (2019).

    PubMed  PubMed Central  Google Scholar 

  89. 89.

    Chan, K. C. A. et al. Analysis of plasma Epstein–Barr virus DNA to screen for nasopharyngeal cancer. N. Engl. J. Med. 377, 513–522 (2017). Others.

    CAS  PubMed  Google Scholar 

  90. 90.

    Lam, W. K. J. et al. Methylation analysis of plasma DNA informs etiologies of Epstein-Barr virus-associated diseases. Nat. Commun. 10, 3256 (2019).

    PubMed  PubMed Central  Google Scholar 

  91. 91.

    Lam, W. K. J. et al. Sequencing-based counting and size profiling of plasma Epstein-Barr virus DNA enhance population screening of nasopharyngeal carcinoma. Proc. Natl Acad. Sci. USA 115, E5115–E5124 (2018).

    CAS  PubMed  Google Scholar 

  92. 92.

    Song, L., Yu, H., Jia, J. & Li, Y. A systematic review of the performance of the SEPT9 gene methylation assay in colorectal cancer screening, monitoring, diagnosis and prognosis. Cancer Biomark. 18, 425–432 (2017).

    CAS  PubMed  Google Scholar 

  93. 93.

    Powrózek, T., Krawczyk, P., Kucharczyk, T. & Milanowski, J. Septin 9 promoter region methylation in free circulating DNA-potential role in noninvasive diagnosis of lung cancer: preliminary report. Med. Oncol. 31, 917 (2014).

    PubMed  PubMed Central  Google Scholar 

  94. 94.

    Church, T. R. et al. Prospective evaluation of methylated SEPT9 in plasma for detection of asymptomatic colorectal cancer. Gut 63, 317–325 (2014).

    CAS  PubMed  Google Scholar 

  95. 95.

    Lamb, Y. N. & Dhillon, S. Epi proColon 2.0 CE: a blood-based screening test for colorectal cancer. Mol. Diagn. Ther. 21, 225–232 (2017).

    PubMed  Google Scholar 

  96. 96.

    Shen, S. Y. et al. Sensitive tumour detection and classification using plasma cell-free DNA methylomes. Nature 563, 579–583 (2018).

    CAS  PubMed  Google Scholar 

  97. 97.

    Guo, S. et al. Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tumor tissue-of-origin mapping from plasma DNA. Nat. Genet. 49, 635–642 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. 98.

    Sun, K. et al. Plasma DNA tissue mapping by genome-wide methylation sequencing for noninvasive prenatal, cancer, and transplantation assessments. Proc. Natl Acad. Sci. USA 112, E5503–E5512 (2015).

    CAS  PubMed  Google Scholar 

  99. 99.

    Hoadley, K. A. et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 158, 929–944 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  100. 100.

    Hoadley, K. A. et al. Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell 173, 291–304.e6 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. 101.

    Moss, J. et al. Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease. Nat. Commun. 9, 5068 (2018).

    PubMed  PubMed Central  Google Scholar 

  102. 102.

    Song, C.-X. et al. 5-Hydroxymethylcytosine signatures in cell-free DNA provide information about tumor types and stages. Cell Res. 27, 1231–1242 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. 103.

    Li, W. et al. 5-Hydroxymethylcytosine signatures in circulating cell-free DNA as diagnostic biomarkers for human cancers. Cell Res. 27, 1243–1257 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. 104.

    Snyder, M. W., Kircher, M., Hill, A. J., Daza, R. M. & Shendure, J. Cell-free DNA comprises an in vivo nucleosome footprint that informs its tissues-of-origin. Cell 164, 57–68 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  105. 105.

    Ulz, P. et al. Inferring expressed genes by whole-genome sequencing of plasma DNA. Nat. Genet. 48, 1273–1278 (2016).

    CAS  PubMed  Google Scholar 

  106. 106.

    Mouliere, F. et al. Enhanced detection of circulating tumor DNA by fragment size analysis. Sci. Transl. Med. 10, eaat4921 (2018).

    PubMed  PubMed Central  Google Scholar 

  107. 107.

    Cristiano, S. et al. Genome-wide cell-free DNA fragmentation in patients with cancer. Nature 570, 385–389 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. 108.

    Jiang, P. et al. Lengthening and shortening of plasma DNA in hepatocellular carcinoma patients. Proc. Natl Acad. Sci. USA 17, E1317–1325 (2015).

    Google Scholar 

  109. 109.

    Aravanis, A. M., Lee, M. & Klausner, R. D. Next-generation sequencing of circulating tumor DNA for early cancer detection. Cell 168, 571–574 (2017).

    CAS  PubMed  Google Scholar 

  110. 110.

    Rey, C. M. Betting on blood: liquid biopsy companies pursuing both early and late-stage cancer detection. Clinical OMICs 6, 15 (2019). 10–2, 14.

    Google Scholar 

  111. 111.

    Klein, E. A. et al. Development of a comprehensive cell-free DNA (cfDNA) assay for early detection of multiple tumor types: the Circulating Cell-free Genome Atlas (CCGA) study. J. Clin. Oncol. 36, 12021 (2018).

    Google Scholar 

  112. 112.

    Kratz, C. P. et al. Cancer screening recommendations for individuals with Li-Fraumeni syndrome. Clin. Cancer Res. 23, e38–e45 (2017).

    CAS  PubMed  Google Scholar 

  113. 113.

    Humphrey, L. L. et al. Screening for lung cancer with low-dose computed tomography: a systematic review to update the US Preventive Services Task Force recommendation. Ann. Intern. Med. 159, 411–420 (2013).

    PubMed  Google Scholar 

  114. 114.

    Black, W. C. et al. Cost-effectiveness of CT screening in the National Lung Screening Trial. N. Engl. J. Med. 371, 1793–1802 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. 115.

    Cescon, D. & Siu, L. L. Cancer clinical trials: the rear-view mirror and the crystal ball. Cell 168, 575–578 (2017).

    CAS  PubMed  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Lillian L. Siu.

Ethics declarations

Competing interests

D.W.C. provides consultation for Agendia, AstraZeneca, GlaxoSmithKline, Merck, Novartis, Pfizer, Puma, Roche and Dynamo Therapeutics; receives research support (institutional) from GlaxoSmithKline, Merck and Pfizer; and (intellectual property) is a co-inventor on a patent related to biomarkers for TTK inhibitors (assigned to University Health Network). S.V.B. provides consultation for Bristol-Myers Squibb; receives research support from Nektar Therapeutics; and (intellectual property) is a co-inventor on a patent relating to circulating tumor DNA-mutation detection technology (PCT/US2014/02502; licensed to Roche Molecular Diagnostics) and on a patent application relating to ctDNA methylation analysis technology (PCT/CA2018/000203). S.M.C. receives honoraria from Celgene and Agios; and receives research support from Celgene, Agios and AbbVie Pharmaceuticals. L.L.S. provides consultation for Merck (compensated), Pfizer (compensated), Celgene (compensated), AstraZeneca/Medimmune (compensated), Morphosys (compensated), Roche (compensated), GeneSeeq (compensated), Loxo (compensated), Oncorus (compensated), Symphogen (compensated), Seattle Genetics (compensated), GlaxoSmithKline (compensated), Voronoi (compensated), Treadwell Therapeutics (compensated), Arvinas (compensated), Tessa (compensated), Navire (compensated); receives research support (institutional) from Novartis, Bristol-Myers Squibb, Pfizer, Boerhinger-Ingelheim, GlaxoSmithKline, Roche/Genentech, Karyopharm, AstraZeneca/Medimmune, Merck, Celgene, Astellas, Bayer, Abbvie, Amgen, Symphogen, Intensity Therapeutics, Mirati, Shattucks and Avid; and is a stockholder in Agios (spouse), Treadwell Therapeutics (spouse).

Additional information

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Cescon, D.W., Bratman, S., Chan, S.M. et al. Circulating tumor DNA and liquid biopsy in oncology. Nat Cancer 1, 276–290 (2020). https://doi.org/10.1038/s43018-020-0043-5

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing