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  • Perspective
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Liquid biopsy enters the clinic — implementation issues and future challenges

Nature Reviews Clinical Oncology volume 18, pages 297–312 (2021)Cite this article

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Abstract

Historically, studies of disseminated tumour cells in bone marrow and circulating tumour cells in peripheral blood have provided crucial insights into cancer biology and the metastatic process. More recently, advances in the detection and characterization of circulating tumour DNA (ctDNA) have finally enabled the introduction of liquid biopsy assays into clinical practice. The FDA has already approved several single-gene assays and, more recently, multigene assays to detect genetic alterations in plasma cell-free DNA (cfDNA) for use as companion diagnostics matched to specific molecularly targeted therapies for cancer. These approvals mark a tipping point for the widespread use of liquid biopsy in the clinic, and mostly in patients with advanced-stage cancer. The next frontier for the clinical application of liquid biopsy is likely to be the systemic treatment of patients with ‘ctDNA relapse’, a term we introduce for ctDNA detection prior to imaging-detected relapse after curative-intent therapy for early stage disease. Cancer screening and diagnosis are other potential future applications. In this Perspective, we discuss key issues and gaps in technology, clinical trial methodologies and logistics for the eventual integration of liquid biopsy into the clinical workflow.

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Fig. 1: Various clinical applications of liquid biopsy using CTCs, circulating nucleic acids or other tumour-derived materials in the bloodstream.
Fig. 2: Roadmap for integration of a liquid biopsy assay into clinical practice.
Fig. 3: Possible designs of clinical studies of treatments to improve the outcomes in patients with ctDNA relapse after treatment of early stage disease.

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References

  1. McGranahan, N. & Swanton, C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell 168, 613–628 (2017).

    CAS  PubMed  Google Scholar 

  2. López, S. et al. Interplay between whole-genome doubling and the accumulation of deleterious alterations in cancer evolution. Nat. Genet. 52, 283–293 (2020).

    PubMed  PubMed Central  Google Scholar 

  3. Yates, L. R. & Campbell, P. J. Evolution of the cancer genome. Nat. Rev. Genet. 13, 795–806 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Russo, M. et al. Adaptive mutability of colorectal cancers in response to targeted therapies. Science 366, 1473–1480 (2019).

    CAS  PubMed  Google Scholar 

  5. Heitzer, E., Haque, I. S., Roberts, C. E. S. & Speicher, M. R. Current and future perspectives of liquid biopsies in genomics-driven oncology. Nat. Rev. Genet. 20, 71–88 (2019).

    CAS  PubMed  Google Scholar 

  6. Hudson, T. J. et al. International network of cancer genome projects. Nature 464, 993–998 (2010).

    CAS  PubMed  Google Scholar 

  7. 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 

  8. ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).

    Google Scholar 

  9. Rozenblatt-Rosen, O. et al. The Human Tumor Atlas Network: charting tumor transitions across space and time at single-cell resolution. Cell 181, 236–249 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Ramalingam, N. & Jeffrey, S. S. Future of liquid biopsies with growing technological and bioinformatics studies: opportunities and challenges in discovering tumor heterogeneity with single-cell level analysis. Cancer J. 24, 104–108 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Bertucci, F. et al. Genomic characterization of metastatic breast cancers. Nature 569, 560–564 (2019).

    CAS  PubMed  Google Scholar 

  13. Reiter, J. G. et al. An analysis of genetic heterogeneity in untreated cancers. Nat. Rev. Cancer 19, 639–650 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Hanahan, D. & Coussens, L. M. Accessories to the crime: functions of cells recruited to the tumor microenvironment. Cancer Cell 21, 309–322 (2012).

    CAS  PubMed  Google Scholar 

  15. Pantel, K. & Alix-Panabières, C. Circulating tumour cells in cancer patients: challenges and perspectives. Trends Mol. Med. 16, 398–406 (2010).

    PubMed  Google Scholar 

  16. 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 

  17. Cherry, S. R. et al. Total-body PET: maximizing sensitivity to create new opportunities for clinical research and patient care. J. Nucl. Med. 59, 3–12 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Ignatiadis, M., Lee, M. & Jeffrey, S. S. Circulating tumor cells and circulating tumor DNA: challenges and opportunities on the path to clinical utility. Clin. Cancer Res. 21, 4786–4800 (2015).

    CAS  PubMed  Google Scholar 

  19. Siravegna, G., Marsoni, S., Siena, S. & Bardelli, A. Integrating liquid biopsies into the management of cancer. Nat. Rev. Clin. Oncol. 14, 531–548 (2017).

    CAS  PubMed  Google Scholar 

  20. Pantel, K. & Alix-Panabières, C. Liquid biopsy and minimal residual disease — latest advances and implications for cure. Nat. Rev. Clin. Oncol. 16, 409–424 (2019).

    CAS  PubMed  Google Scholar 

  21. Cescon, D. W., Bratman, S. V., Chan, S. M. & Siu, L. L. Circulating tumor DNA and liquid biopsy in oncology. Nat. Cancer 1, 276–290 (2020).

    PubMed  Google Scholar 

  22. Lee, J. S., Park, S. S., Lee, Y. K., Norton, J. A. & Jeffrey, S. S. Liquid biopsy in pancreatic ductal adenocarcinoma: current status of circulating tumor cells and circulating tumor DNA. Mol. Oncol. 13, 1623–1650 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Teutsch, S. M. et al. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) initiative: methods of the EGAPP working group. Genet. Med. 11, 3–14 (2009).

    PubMed  PubMed Central  Google Scholar 

  24. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Samstein, R. M. et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat. Genet. 51, 202–206 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. US Food and Drug Administration. MSK-IMPACT (Integrated Mutation Profiling of Actionable Cancer Targets). FDA https://www.accessdata.fda.gov/cdrh_docs/pdf17/DEN170058.pdf (2017).

  28. Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).

    PubMed  PubMed Central  Google Scholar 

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

    PubMed  Google Scholar 

  30. Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. 2017, PO.17.00011 (2017).

    Google Scholar 

  31. New York State Department of Health, Wadsworth Center. Memorial Hosp For Cancer and Allied Diseases Dept of Pathology. New York State https://www.wadsworth.org/memorial-hosp-for-cancer-and-allied-diseases-dept-of-pathology-115 (2020).

  32. Razavi, P. et al. High-intensity sequencing reveals the sources of plasma circulating cell-free DNA variants. Nat. Med. 25, 1928–1937 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Mateo, J. et al. A framework to rank genomic alterations as targets for cancer precision medicine: the ESMO Scale for Clinical Actionability of Molecular Targets (ESCAT). Ann. Oncol. 29, 1895–1902 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Katsoulakis, E., Duffy, J. E., Hintze, B., Spector, N. L. & Kelley, M. J. Comparison of annotation services for next-generation sequencing in a large-scale precision oncology program. JCO Precis. Oncol. 4, 212–221 (2020).

    Google Scholar 

  35. Wagner, A. H. et al. A harmonized meta-knowledgebase of clinical interpretations of somatic genomic variants in cancer. Nat. Genet. 52, 448–457 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Van Norman, G. A. Drugs and devices: comparison of European and US approval processes. JACC Basic. Transl. Sci. 1, 399–412 (2016).

    PubMed  PubMed Central  Google Scholar 

  37. Pittella-Silva, F. et al. Plasma or serum: which is preferable for mutation detection in liquid biopsy? Clin. Chem. 66, 946–957 (2020).

    PubMed  Google Scholar 

  38. Parpart-Li, S. et al. The effect of preservative and temperature on the analysis of circulating tumor DNA. Clin. Cancer Res. 23, 2471–2477 (2017).

    CAS  PubMed  Google Scholar 

  39. Gerber, T. et al. Assessment of pre-analytical sample handling conditions for comprehensive liquid biopsy analysis. J. Mol. Diagn. 22, 1070–1086 (2020).

    CAS  PubMed  Google Scholar 

  40. Salvianti, F. et al. The pre-analytical phase of the liquid biopsy. New Biotechnol. 55, 19–29 (2020).

    CAS  Google Scholar 

  41. Grölz, D. et al. Liquid biopsy preservation solutions for standardized pre-analytical workflows — venous whole blood and plasma. Curr. Pathobiol. Rep. 6, 275–286 (2018).

    PubMed  PubMed Central  Google Scholar 

  42. Van Paemel, R. et al. Genome-wide study of the effect of blood collection tubes on the cell-free DNA methylome. Epigenetics https://doi.org/10.1080/15592294.2020.1827714 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Greytak, S. R. et al. Harmonizing cell-free DNA collection and processing practices through evidence-based guidance. Clin. Cancer Res. 26, 3104–3109 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Merker, J. D. et al. Circulating tumor DNA analysis in patients with cancer: American Society of Clinical Oncology and College of American Pathologists joint review. J. Clin. Oncol. 36, 1631–1641 (2018).

    CAS  PubMed  Google Scholar 

  45. Andree, K. C., van Dalum, G. & Terstappen, L. W. M. M. Challenges in circulating tumor cell detection by the CellSearch system. Mol. Oncol. 10, 395–407 (2016).

    CAS  PubMed  Google Scholar 

  46. Wong, K. H. K. et al. Whole blood stabilization for the microfluidic isolation and molecular characterization of circulating tumor cells. Nat. Commun. 8, 1733 (2017).

    PubMed  PubMed Central  Google Scholar 

  47. Hodgkinson, C. L. et al. Tumorigenicity and genetic profiling of circulating tumor cells in small-cell lung cancer. Nat. Med. 20, 897–903 (2014).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Fehm, T. N. et al. Diagnostic leukapheresis for CTC analysis in breast cancer patients: CTC frequency, clinical experiences and recommendations for standardized reporting. Cytometry A 93, 1213–1219 (2018).

    PubMed  Google Scholar 

  50. Keller, L. & Pantel, K. Unravelling tumour heterogeneity by single-cell profiling of circulating tumour cells. Nat. Rev. Cancer 19, 553–567 (2019).

    CAS  PubMed  Google Scholar 

  51. Cook, L. et al. Does size matter? Comparison of extraction yields for different-sized DNA fragments by seven different routine and four new circulating cell-free extraction methods. J. Clin. Microbiol. 56, e01061-18 (2018).

    PubMed  PubMed Central  Google Scholar 

  52. Meddeb, R., Pisareva, E. & Thierry, A. Guidelines for the preanalytical conditions for analyzing circulating cell-free DNA. Clin. Chem. 65, 623–633 (2019).

    CAS  PubMed  Google Scholar 

  53. Lampignano, R. et al. Multicenter evaluation of circulating cell-free DNA extraction and downstream analyses for the development of standardized (pre)analytical work flows. Clin. Chem. 66, 149–160 (2020).

    PubMed  Google Scholar 

  54. Febbo, P. G. et al. Minimum technical data elements for liquid biopsy data submitted to public databases. Clin. Pharmacol. Ther. 107, 730–734 (2020).

    PubMed  PubMed Central  Google Scholar 

  55. Foundation for the National Institutes of Health. Biomarkers Consortium – Identification and validation of ctDNA quality control materials. FNIH https://fnih.org/ctdna (2020).

  56. Hao, Y. X. et al. Effectiveness of circulating tumor DNA for detection of KRAS gene mutations in colorectal cancer patients: a meta-analysis. Onco. Targets Ther. 10, 945–953 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Oxnard, G. R. et al. Noninvasive detection of response and resistance in EGFR-mutant lung cancer using quantitative next-generation genotyping of cell-free plasma DNA. Clin. Cancer Res. 20, 1698–1705 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Bidard, F.-C. et al. Circulating tumor cells and circulating tumor DNA detection in potentially resectable metastatic colorectal cancer: a prospective ancillary study to the Unicancer PRODIGE-14 trial. Cells 8, 516 (2019).

    CAS  PubMed Central  Google Scholar 

  59. US Food and Drug Administration. cobas® EGFR Mutation Test v2. FDA https://www.accessdata.fda.gov/cdrh_docs/pdf15/P150047A.pdf (2016).

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

    PubMed  Google Scholar 

  61. US Food and Drug Administration. therascreen PIK3CA RGQ PCR kit. FDA https://www.accessdata.fda.gov/cdrh_docs/pdf19/P190004A.pdf (2019).

  62. Kumar, S. et al. Tracking plasma DNA mutation dynamics in estrogen receptor positive metastatic breast cancer with dPCR-SEQ. NPJ Breast Cancer 4, 39 (2018).

    PubMed  PubMed Central  Google Scholar 

  63. Sabari, J. K. et al. A prospective study of circulating tumor DNA to guide matched targeted therapy in lung cancers. J. Natl Cancer Inst. 111, 575–583 (2019).

    PubMed  Google Scholar 

  64. 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 

  65. 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 

  66. 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 

  67. Mack, P. C. et al. Spectrum of driver mutations and clinical impact of circulating tumor DNA analysis in non-small cell lung cancer: analysis of over 8000 cases. Cancer 126, 3219–3228 (2020).

    CAS  PubMed  Google Scholar 

  68. Pritchett, M. A. et al. Prospective clinical validation of the InVisionFirst-Lung circulating tumor DNA assay for molecular profiling of patients with advanced nonsquamous non-small-cell lung cancer. JCO Precis. Oncol. 3, PO.18.00299 (2019).

    PubMed  PubMed Central  Google Scholar 

  69. Finzel, A., Sadik, H., Ghitti, G. & Laes, J.-F. The combined analysis of solid and liquid biopsies provides additional clinical information to improve patient care. J. Cancer Metastasis Treat. 4, 21 (2018).

    Google Scholar 

  70. Owonikoko, T. K. et al. Randomized phase II study of paclitaxel plus alisertib versus paclitaxel plus placebo as second-line therapy for SCLC: primary and correlative biomarker analyses. J. Thorac. Oncol. 15, 274–287 (2020).

    CAS  PubMed  Google Scholar 

  71. Leighl, N. B. et al. Clinical utility of comprehensive cell-free DNA analysis to identify genomic biomarkers in patients with newly diagnosed metastatic non-small cell lung cancer. Clin. Cancer Res. 25, 4691–4700 (2019).

    CAS  PubMed  Google Scholar 

  72. Nakamura, Y. et al. Clinical utility of circulating tumor DNA sequencing in advanced gastrointestinal cancer: SCRUM-Japan GI-SCREEN and GOZILA studies. Nat. Med. 26, 1859–1864 (2020).

    PubMed  Google Scholar 

  73. Razavi, P. et al. Alterations in PTEN and ESR1 promote clinical resistance to alpelisib plus aromatase inhibitors. Nat. Cancer 1, 382–393 (2020).

    PubMed  PubMed Central  Google Scholar 

  74. Kilgour, E., Rothwell, D. G., Brady, G. & Dive, C. Liquid biopsy-based biomarkers of treatment response and resistance. Cancer Cell 37, 485–495 (2020).

    CAS  PubMed  Google Scholar 

  75. Drilon, A. et al. Antitumor activity of crizotinib in lung cancers harboring a MET exon 14 alteration. Nat. Med. 26, 47–51 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Rothé, F. et al. Plasma circulating tumor DNA as an alternative to metastatic biopsies for mutational analysis in breast cancer. Ann. Oncol. 25, 1959–1965 (2014).

    PubMed  Google Scholar 

  77. Bachet, J. B. et al. RAS mutation analysis in circulating tumor DNA from patients with metastatic colorectal cancer: the AGEO RASANC prospective multicenter study. Ann. Oncol. 29, 1211–1219 (2018).

    CAS  PubMed  Google Scholar 

  78. Torga, G. & Pienta, K. J. Patient-paired sample congruence between 2 commercial liquid biopsy tests. JAMA Oncol. 4, 868–870 (2018).

    PubMed  Google Scholar 

  79. Stetson, D. et al. Orthogonal comparison of four plasma NGS tests with tumor suggests technical factors are a major source of assay discordance. JCO Precis. Oncol. 3, 1–9 (2019).

    PubMed  Google Scholar 

  80. US Food and Drug Administration. Guardant360® CDx. FDA https://www.accessdata.fda.gov/cdrh_docs/pdf20/P200010A.pdf (2020).

  81. Scheerens, H. et al. Current status of companion and complementary diagnostics: strategic considerations for development and launch. Clin. Transl. Sci. 10, 84–92 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. US Food and Drug Administration. FoundationOne® Liquid CDx (F1 Liquid CDx). FDA https://www.accessdata.fda.gov/cdrh_docs/pdf20/P200016A.pdf (2020).

  83. André, F. et al. Comparative genomic hybridisation array and DNA sequencing to direct treatment of metastatic breast cancer: a multicentre, prospective trial (SAFIR01/UNICANCER). Lancet Oncol. 15, 267–274 (2014).

    PubMed  Google Scholar 

  84. 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 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Shlush, L. I. Age-related clonal hematopoiesis. Blood 131, 496–504 (2018).

    CAS  PubMed  Google Scholar 

  87. Steensma, D. P. et al. Clonal hematopoiesis of indeterminate potential and its distinction from myelodysplastic syndromes. Blood 126, 9–16 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Steensma, D. P. & Ebert, B. L. Clonal hematopoiesis as a model for premalignant changes during aging. Exp. Hematol. 83, 48–56 (2020).

    PubMed  Google Scholar 

  89. Acuna-Hidalgo, R. et al. Ultra-sensitive sequencing identifies high prevalence of clonal hematopoiesis-associated mutations throughout adult life. Am. J. Hum. Genet. 101, 50–64 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Hu, Y. et al. False-positive plasma genotyping due to clonal hematopoiesis. Clin. Cancer Res. 24, 4437–4443 (2018).

    CAS  PubMed  Google Scholar 

  91. Chan, H. T., Chin, Y. M., Nakamura, Y. & Low, S. K. Clonal hematopoiesis in liquid biopsy: from biological noise to valuable clinical implications. Cancers 12, 2277 (2020).

    CAS  PubMed Central  Google Scholar 

  92. Jensen, K. et al. Association of clonal hematopoiesis in DNA repair genes with prostate cancer plasma cell-free DNA testing interference. JAMA Oncol. https://doi.org/10.1001/jamaoncol.2020.5161 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  93. Rothwell, D. G. et al. Utility of ctDNA to support patient selection for early phase clinical trials: the TARGET study. Nat. Med. 25, 738–743 (2019).

    CAS  PubMed  Google Scholar 

  94. Juric, D. et al. Alpelisib + fulvestrant for advanced breast cancer: subgroup analyses from the phase III SOLAR-1 trial [abstract]. Cancer Res 79 (Suppl. 4), GS3-08 (2019).

    Google Scholar 

  95. Turner, N. C. et al. Circulating tumour DNA analysis to direct therapy in advanced breast cancer (plasmaMATCH): a multicentre, multicohort, phase 2a, platform trial. Lancet Oncol. 21, 1296–1308 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Li, S. et al. Coexistence of EGFR with KRAS, or BRAF, or PIK3CA somatic mutations in lung cancer: a comprehensive mutation profiling from 5125 Chinese cohorts. Br. J. Cancer. 110, 2812–2820 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. De Roock, W., De Vriendt, V., Normanno, N., Ciardiello, F. & Tejpar, S. KRAS, BRAF, PIK3CA, and PTEN mutations: implications for targeted therapies in metastatic colorectal cancer. Lancet Oncol. 12, 594–603 (2011).

    PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  100. Mok, T. S. et al. Osimertinib or platinum-pemetrexed in EGFR T790M-positive lung cancer. N. Engl. J. Med. 376, 629–640 (2017).

    CAS  PubMed  Google Scholar 

  101. 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  PubMed Central  Google Scholar 

  102. Heitzer, E. et al. Tumor-associated copy number changes in the circulation of patients with prostate cancer identified through whole-genome sequencing. Genome Med. 5, 30 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Bourrier, C. et al. Shallow whole-genome sequencing from plasma identifies FGFR1 amplified breast cancers and predicts overall survival. Cancers 12, 1481 (2020).

    CAS  PubMed Central  Google Scholar 

  104. Murtaza, M. et al. Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA. Nature 497, 108–112 (2013).

    CAS  PubMed  Google Scholar 

  105. O’Leary, B. et al. The genetic landscape and clonal evolution of breast cancer resistance to palbociclib plus fulvestrant in the PALOMA-3 trial. Cancer Discov. 8, 1390–1403 (2018).

    PubMed  PubMed Central  Google Scholar 

  106. Antonarakis, E. S. et al. AR-V7 and resistance to enzalutamide and abiraterone in prostate cancer. N. Engl. J. Med. 371, 1028–1038 (2014).

    PubMed  PubMed Central  Google Scholar 

  107. Scher, H. I. et al. Assessment of the validity of nuclear-localized androgen receptor splice variant 7 in circulating tumor cells as a predictive biomarker for castration-resistant prostate cancer. JAMA Oncol. 4, 1179–1186 (2018).

    PubMed  PubMed Central  Google Scholar 

  108. Armstrong, A. J. et al. Prospective multicenter validation of androgen receptor splice variant 7 and hormone therapy resistance in high-risk castration-resistant prostate cancer: the PROPHECY study. in. J. Clin. Oncol. 37, 1120–1129 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. 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 

  110. Pairawan, S. et al. Cell-free circulating tumor DNA variant allele frequency associates with survival in metastatic cancer. Clin. Cancer Res. 26, 1924–1931 (2020).

    CAS  PubMed  Google Scholar 

  111. Beau-Faller, M. et al. Independent prognostic value of ultra-sensitive quantification of tumor pre-treatment T790M subclones in EGFR mutated non-small cell lung cancer (NSCLC) treated by first/second generation TKI, depends on variant allele frequency (VAF): results of the French Cooperative Thoracic Intergroup (IFCT) Biomarkers France project. Lung Cancer 140, 19–26 (2020).

    PubMed  Google Scholar 

  112. Ma, N. & Jeffrey, S. S. Deciphering cancer clues from blood. Science 367, 1424–1425 (2020).

    CAS  PubMed  Google Scholar 

  113. Ignatiadis, M. et al. Prognostic value of the molecular detection of circulating tumor cells using a multimarker reverse transcription-PCR assay for cytokeratin 19, mammaglobin A, and HER2 in early breast cancer. Clin. Cancer Res. 14, 2593–2600 (2008).

    CAS  PubMed  Google Scholar 

  114. Powell, A. A. et al. Single cell profiling of circulating tumor cells: transcriptional heterogeneity and diversity from breast cancer cell lines. PLoS ONE 7, e33788 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Lim, S. et al. Liquid biopsy: one cell at a time. NPJ Precis. Oncol. 3, 23 (2019).

    PubMed  PubMed Central  Google Scholar 

  116. Ebright, R. Y. et al. Deregulation of ribosomal protein expression and translation promotes breast cancer metastasis. Science 367, 1468–1473 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  117. Cabel, L. et al. Clinical potential of circulating tumour DNA in patients receiving anticancer immunotherapy. Nat. Rev. Clin. Oncol. 15, 639–650 (2018).

    CAS  PubMed  Google Scholar 

  118. Hofman, P., Heeke, S., Alix-Panabières, C. & Pantel, K. Liquid biopsy in the era of immuno-oncology: is it ready for prime-time use for cancer patients? Ann. Oncol. 30, 1448–1459 (2019).

    CAS  PubMed  Google Scholar 

  119. Heller, G. et al. Circulating tumor cell number as a response measure of prolonged survival for metastatic castration-resistant prostate cancer: a comparison with prostate-specific antigen across five randomized phase III clinical trials. J. Clin. Oncol. 36, 572–580 (2018).

    CAS  PubMed  Google Scholar 

  120. Cristofanilli, M. et al. Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N. Engl. J. Med. 351, 781–791 (2004).

    CAS  PubMed  Google Scholar 

  121. Bidard, F.-C. et al. Clinical validity of circulating tumour cells in patients with metastatic breast cancer: a pooled analysis of individual patient data. Lancet Oncol. 15, 406–414 (2014).

    PubMed  Google Scholar 

  122. Moreno, J. G. et al. Circulating tumor cells predict survival in patients with metastatic prostate cancer. Urology 65, 713–718 (2005).

    PubMed  Google Scholar 

  123. Cohen, S. J. et al. Prognostic significance of circulating tumor cells in patients with metastatic colorectal cancer. Ann. Oncol. 20, 1223–1229 (2009).

    CAS  PubMed  Google Scholar 

  124. Smerage, J. B. et al. Circulating tumor cells and response to chemotherapy in metastatic breast cancer: SWOG S0500. J. Clin. Oncol. 32, 3483–3489 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. Markou, A. et al. PIK3CA mutational status in circulating tumor cells can change during disease recurrence or progression in patients with breast cancer. Clin. Cancer Res. 20, 5823–5834 (2014).

    CAS  PubMed  Google Scholar 

  126. Gasch, C. et al. Frequent detection of PIK3CA mutations in single circulating tumor cells of patients suffering from HER2-negative metastatic breast cancer. Mol. Oncol. 10, 1330–1343 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  127. 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 

  128. Ignatiadis, M. et al. Different prognostic value of cytokeratin-19 mRNA-positive circulating tumor cells according to estrogen receptor and HER2 status in early-stage breast cancer. J. Clin. Oncol. 25, 5194–5202 (2007).

    PubMed  Google Scholar 

  129. Riethdorf, S. et al. Prognostic impact of circulating tumor cells for breast cancer patients treated in the neoadjuvant “Geparquattro” trial. Clin. Cancer Res. 23, 5384–5393 (2017).

    CAS  PubMed  Google Scholar 

  130. Bidard, F.-C. et al. Circulating tumor cells in breast cancer patients treated by neoadjuvant chemotherapy: a meta-analysis. J. Natl Cancer Inst. 110, 560–567 (2018).

    PubMed  Google Scholar 

  131. Sparano, J. et al. Association of circulating tumor cells with late recurrence of estrogen receptor-positive breast cancer: a secondary analysis of a randomized clinical trial. JAMA Oncol. 4, 1700–1706 (2018).

    PubMed  PubMed Central  Google Scholar 

  132. Thery, L. et al. Circulating tumor cells in early breast cancer. JNCI Cancer Spectr. 3, pkz026 (2019).

    PubMed  PubMed Central  Google Scholar 

  133. Trapp, E. et al. Presence of circulating tumor cells in high-risk early breast cancer during follow-up and prognosis. J. Natl Cancer Inst. 111, 380–387 (2019).

    PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  135. Garcia-Murillas, I. et al. Mutation tracking in circulating tumor DNA predicts relapse in early breast cancer. Sci. Transl. Med. 7, 302ra133 (2015).

    PubMed  Google Scholar 

  136. Garcia-Murillas, I. et al. Assessment of molecular relapse detection in early-stage breast cancer. JAMA Oncol. 5, 1473–1478 (2019).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  138. Berger, M. F. & Mardis, E. R. The emerging clinical relevance of genomics in cancer medicine. Nat. Rev. Clin. Oncol. 15, 353–365 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  139. 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 

  140. Wang, T. T. et al. High efficiency error suppression for accurate detection of low-frequency variants. Nucleic Acids Res. 47, e87 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  141. Zviran, A. et al. Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring. Nat. Med. 26, 1114–1124 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  142. Haque, I. S. & Elemento, O. Challenges in using ctDNA to achieve early detection of cancer. Preprint at bioRxiv https://doi.org/10.1101/237578 (2017).

    Article  Google Scholar 

  143. McDonald, B. R. et al. Personalized circulating tumor DNA analysis to detect residual disease after neoadjuvant therapy in breast cancer. Sci. Transl. Med. 11, eaax7392 (2019).

    PubMed  PubMed Central  Google Scholar 

  144. Coombes, R. C. et al. Personalized detection of circulating tumor DNA antedates breast cancer metastatic recurrence. Clin. Cancer Res. 25, 4255–4263 (2019).

    CAS  PubMed  Google Scholar 

  145. 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).

    CAS  PubMed  Google Scholar 

  146. Piccart-Gebhart, M. J. et al. Trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer. N. Engl. J. Med. 353, 1659–1672 (2005).

    CAS  PubMed  Google Scholar 

  147. Romond, E. H. et al. Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N. Engl. J. Med. 353, 1673–1684 (2005).

    CAS  PubMed  Google Scholar 

  148. Gianni, L. et al. Neoadjuvant chemotherapy with trastuzumab followed by adjuvant trastuzumab versus neoadjuvant chemotherapy alone, in patients with HER2-positive locally advanced breast cancer (the NOAH trial): a randomised controlled superiority trial with a parallel HER-negative cohort. Lancet 375, 377–384 (2010).

    CAS  PubMed  Google Scholar 

  149. Von Minckwitz, G. et al. Trastuzumab emtansine for residual invasive HER2-positive breast cancer. N. Engl. J. Med. 380, 617–628 (2019).

    Google Scholar 

  150. Cortazar, P. et al. Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. Lancet 384, 164–172 (2014).

    PubMed  Google Scholar 

  151. Pan, H. et al. 20-year risks of breast-cancer recurrence after stopping endocrine therapy at 5 years. N. Engl. J. Med. 377, 1836–1846 (2017).

    PubMed  PubMed Central  Google Scholar 

  152. US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT04567420 (2020).

  153. US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT04089631 (2020).

  154. US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT03826758 (2020).

  155. Pantel, K. & Hayes, D. F. Disseminated breast tumour cells: biological and clinical meaning. Nat. Rev. Clin. Oncol. 15, 129–131 (2018).

    PubMed  Google Scholar 

  156. Naume, B. et al. Clinical outcome with correlation to disseminated tumor cell (DTC) status after DTC-guided secondary adjuvant treatment with docetaxel in early breast cancer. J. Clin. Oncol. 32, 3848–3857 (2014).

    PubMed  Google Scholar 

  157. Ignatiadis, M. et al. Trastuzumab versus observation for HER2 nonamplified early breast cancer with circulating tumor cells (EORTC 90091-10093, BIG 1-12, Treat CTC): a randomized phase II trial. Ann. Oncol. 29, 1777–1783 (2018).

    CAS  PubMed  Google Scholar 

  158. Smith, M. R. et al. Apalutamide treatment and metastasis-free survival in prostate cancer. N. Engl. J. Med. 378, 1408–1418 (2018).

    CAS  PubMed  Google Scholar 

  159. Hussain, M. et al. Enzalutamide in men with nonmetastatic, castration-resistant prostate cancer. N. Engl. J. Med. 378, 2465–2474 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  160. Fizazi, K. et al. Darolutamide in nonmetastatic, castration-resistant prostate cancer. N. Engl. J. Med. 380, 1235–1246 (2019).

    CAS  PubMed  Google Scholar 

  161. Gökbuget, N. et al. Blinatumomab for minimal residual disease in adults with B-cell precursor acute lymphoblastic leukemia. Blood 131, 1522–1531 (2018).

    PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  163. Bick, A. G. et al. Inherited causes of clonal haematopoiesis in 97,691 whole genomes. Nature 586, 763–768 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  164. 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 

  165. 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 

  166. Hao, X. et al. DNA methylation markers for diagnosis and prognosis of common cancers. Proc. Natl Acad. Sci. USA 114, 7414–7419 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  167. 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 

  168. 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 

  169. Ulz, P. et al. Inference of transcription factor binding from cell-free DNA enables tumor subtype prediction and early detection. Nat. Commun. 10, 4666 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  170. Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020).

    CAS  PubMed  Google Scholar 

  171. Lennon, A. M. et al. Feasibility of blood testing combined with PET-CT to screen for cancer and guide intervention. Science 369, eabb9601 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  172. Keller, L., Belloum, Y., Wikman, H. & Pantel, K. Clinical relevance of blood-based ctDNA analysis: mutation detection and beyond. Br. J. Cancer https://doi.org/10.1038/s41416-020-01047-5 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  173. US Preventive Services Task Force. Prostate cancer screening. USPSTF https://www.uspreventiveservicestaskforce.org/uspstf/recommendation/prostate-cancer-screening (2018).

  174. Paik, S. et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N. Engl. J. Med. 351, 2817–2826 (2004).

    CAS  PubMed  Google Scholar 

  175. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  176. Van De Haar, J., Hoes, L. & Voest, E. Advancing molecular tumour boards: highly needed to maximise the impact of precision medicine. ESMO Open 4, e000516 (2019).

    PubMed  PubMed Central  Google Scholar 

  177. Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44–56 (2019).

    CAS  PubMed  Google Scholar 

  178. Zeune, L. L. et al. How to agree on a CTC: evaluating the consensus in circulating tumor cell scoring. Cytometry A 93, 1202–1206 (2018).

    PubMed  PubMed Central  Google Scholar 

  179. Iyer, A. et al. Integrative analysis and machine learning based characterization of single circulating tumor cells. J. Clin. Med. 9, 1206 (2020).

    CAS  PubMed Central  Google Scholar 

  180. Ota, S. et al. Ghost cytometry. Science 360, 1246–1251 (2018).

    CAS  PubMed  Google Scholar 

  181. Wan, N. et al. Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA. BMC Cancer 19, 832 (2019).

    PubMed  PubMed Central  Google Scholar 

  182. Camacho, D. M., Collins, K. M., Powers, R. K., Costello, J. C. & Collins, J. J. Next-generation machine learning for biological networks. Cell 173, 1581–1592 (2018).

    CAS  PubMed  Google Scholar 

  183. Chabon, J. J. et al. Integrating genomic features for non-invasive early lung cancer detection. Nature 580, 245–251 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  184. Menetski, J. P. et al. The Foundation for the National Institutes of Health Biomarkers Consortium: past accomplishments and new strategic direction. Clin. Pharmacol. Ther. 105, 829–843 (2019).

    PubMed  PubMed Central  Google Scholar 

  185. Xu, H. et al. A comparison of EGFR mutation status in tissue and plasma cell-free DNA detected by ADx-ARMS in advanced lung adenocarcinoma patients. Transl. Lung Cancer Res. 8, 135–143 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  186. Budd, G. T. et al. Circulating tumor cells versus imaging–predicting overall survival in metastatic breast cancer. Clin. Cancer Res. 12, 6403–6409 (2006).

    CAS  PubMed  Google Scholar 

  187. Eisenhauer, E. A. et al. New Response Evaluation Criteria in Solid Tumours: revised RECIST guideline (version 1.1). Eur. J. Cancer 45, 228–247 (2009).

    CAS  PubMed  Google Scholar 

  188. 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 

  189. Ilic, D. et al. Prostate cancer screening with prostate-specific antigen (PSA) test: a systematic review and meta-analysis. BMJ 362, k3519 (2018).

    PubMed  PubMed Central  Google Scholar 

  190. Olsson, E. et al. Serial monitoring of circulating tumor DNA in patients with primary breast cancer for detection of occult metastatic disease. EMBO Mol. Med. 7, 1034–1047 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  191. Riva, F. et al. Patient-specific circulating tumor DNA detection during neoadjuvant chemotherapy in triple-negative breast cancer. Clin. Chem. 63, 691–699 (2017).

    CAS  PubMed  Google Scholar 

  192. Chen, Y. H. et al. Next-generation sequencing of circulating tumor DNA to predict recurrence in triple-negative breast cancer patients with residual disease after neoadjuvant chemotherapy. NPJ Breast Cancer 3, 24 (2017).

    PubMed  PubMed Central  Google Scholar 

  193. Rothé, F. et al. Circulating tumor DNA in HER2-amplified breast cancer: a translational research substudy of the NeoALTTO phase III trial. Clin. Cancer Res. 25, 3581–3588 (2019).

    PubMed  Google Scholar 

  194. Zhang, X. et al. Parallel analyses of somatic mutations in plasma circulating tumor DNA (ctDNA) and matched tumor tissues in early-stage breast cancer. Clin. Cancer Res. 25, 6546–6553 (2019).

    CAS  PubMed  Google Scholar 

  195. Radovich, M. et al. Association of circulating tumor DNA and circulating tumor cells after neoadjuvant chemotherapy with disease recurrence in patients with triple-negative breast cancer: preplanned secondary analysis of the BRE12-158 randomized clinical trial. JAMA Oncol. 6, 1410–1415 (2020).

    PubMed  Google Scholar 

  196. Scher, H. I. et al. Association of AR-V7 on circulating tumor cells as a treatment-specific biomarker with outcomes and survival in castration-resistant prostate cancer. JAMA Oncol. 2, 1441–1449 (2016).

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The work of M.I. is supported by Les Amis de Bordet and Fondation Contre le Cancer. The work of G.W.S. is supported in part by the Susan G. Komen Foundation. The work of S.S.J. is supported in part by the John and Marva Warnock Research Fund, Natalie and Vladimir Ermakoff, and the Stanford Catalyst for Collaborative Solutions, Stanford School of Engineering.

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  1. Department of Medical Oncology, Jules Bordet Institute, Université Libre de Bruxelles, Brussels, Belgium

    Michail Ignatiadis

  2. Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA

    George W. Sledge

  3. Department of Surgery, Stanford University School of Medicine, Stanford, CA, USA

    Stefanie S. Jeffrey

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Correspondence to Michail Ignatiadis or Stefanie S. Jeffrey.

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Competing interests

M.I. has received consultancy fees from Celgene, Novartis, Pfizer, Seattle Genetics and Tesaro, and travel grants from Amgen and Pfizer. The institution of M.I. has received research grants from Menarini Silicon Biosystems and Natera. G.W.S. is a member of the Board of Directors of Tessa Therapeutics and of the Scientific Advisory Boards of Syndax and Verseau Therapeutics. S.S.J. serves as a scientific advisor for Quantumcyte and Ravel Biotechnology.

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Nature Reviews Clinical Oncology thanks R. Rosell; F.-C. Bidard, who co-reviewed with L. Cabel; and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Blood Profiling Atlas in Cancer (BloodPAC): https://www.bloodpac.org/

CANCER-ID: https://www.cancer-id.eu/

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European Liquid Biopsy Society (ELBS): http://www.elbs.eu

OncoKB: https://www.oncokb.org/

OncoPrint: https://bioc.ism.ac.jp/packages/3.2/bioc/vignettes/ComplexHeatmap/inst/doc/s8.oncoprint.html#toc_0

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Ignatiadis, M., Sledge, G.W. & Jeffrey, S.S. Liquid biopsy enters the clinic — implementation issues and future challenges. Nat Rev Clin Oncol 18, 297–312 (2021). https://doi.org/10.1038/s41571-020-00457-x

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