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Genetics and Genomics

Integrative analysis of multi-omics data for liquid biopsy

British Journal of Cancer volume 128, pages 505–518 (2023)Cite this article

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A Correction to this article was published on 24 November 2022

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Abstract

The innovation of liquid biopsy holds great potential to revolutionise cancer management through early diagnosis and timely treatment of cancer. Integrative analysis of different tumour-derived omics data (such as genomics, epigenetics, fragmentomics, and proteomics) from body fluids for cancer detection and monitoring could outperform the analysis of single modality data alone. In this review, we focussed on the discussion of early cancer detection and molecular residual disease surveillance based on multi-omics data of blood. We summarised diverse types of tumour-derived components, current popular platforms for profiling cancer-associated signals, machine learning approaches for joint analysis of liquid biopsy data, as well as multi-omics-based early detection of cancers, molecular residual disease monitoring, and treatment response surveillance. We also discussed the challenges and future directions of multi-omics-based liquid biopsy. With the development of both experimental protocols and computational methods dedicated to liquid biopsy, the implementation of multi-omics strategies into the clinical workflow will likely benefit the clinical management of cancers including decision-making guidance and patient outcome improvement.

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Fig. 1: Overview of diverse tumour-associated components in the blood of cancer patients.
Fig. 2: Schematic plot of different applications for liquid biopsy based on multi-omics strategies.

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References

  1. Wan JCM, Massie C, Garcia-Corbacho J, Mouliere F, Brenton JD, Caldas C, et al. Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat Rev Cancer. 2017;17:223–38.

    Article  CAS  PubMed  Google Scholar 

  2. Cescon DW, Bratman SV, Chan SM, Siu LL. Circulating tumor DNA and liquid biopsy in oncology. Nat Cancer. 2020;1:276–90.

    Article  CAS  PubMed  Google Scholar 

  3. Babayan A, Pantel K. Advances in liquid biopsy approaches for early detection and monitoring of cancer. Genome Med. 2018;10:4–6.

    Article  Google Scholar 

  4. Bedard PL, Hansen AR, Ratain MJ, Siu LL. Tumour heterogeneity in the clinic. Nature. 2013;501:355–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Abbosh C, Birkbak NJ, Wilson GA, Jamal-Hanjani M, Constantin T, Salari R, et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature. 2017;545:446–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Heitzer E, Haque IS, Roberts CES, Speicher MR. Current and future perspectives of liquid biopsies in genomics-driven oncology. Nat Rev Genet. 2019;20:71–88.

    Article  CAS  PubMed  Google Scholar 

  7. Alix-Panabières C, Pantel K. Liquid biopsy: from discovery to clinical application. Cancer Discov. 2021;11:858–73.

    Article  PubMed  Google Scholar 

  8. Im YR, Tsui DWY, Diaz LA, Wan JCM. Next-generation liquid biopsies: embracing data science in oncology. Trends Cancer. 2021;7:283–92.

    Article  CAS  PubMed  Google Scholar 

  9. Diaz LA, Bardelli A. Liquid biopsies: genotyping circulating tumor DNA. J Clin Oncol. 2014;32:579–86.

  10. Thierry AR, El Messaoudi S, Gahan PB, Anker P, Stroun M. Origins, structures, and functions of circulating DNA in oncology. Cancer Metastasis Rev. 2016;35:347–76.

  11. Han X, Wang J, Sun Y. Circulating tumor DNA as biomarkers for cancer detection. Genomics Proteomics Bioinformatics. 2017;15:59–72.

  12. Fu Y, Yang Z, Hu Z, Yang Z, Pan Y, Chen J, et al. Preoperative serum ctDNA predicts early hepatocellular carcinoma recurrence and response to systemic therapies. Hepatol Int. 2022;16:868–78.

  13. Baselga J, Im SA, Iwata H, Cortés J, De Laurentiis M, Jiang Z, 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. 2017;18:904–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Jia J, Morse MA, Nagy RJ, Lanman RB, Strickler JH. Cell-free DNA profiling to discover mechanisms of exceptional response to cabozantinib plus panitumumab in a patient with treatment refractory metastatic colorectal cancer. Front Oncol. 2018;8:305.

  15. Georgiadis A, Durham JN, Keefer LA, Bartlett BR, Zielonka M, Murphy D, et al. Noninvasive detection of microsatellite instability and high tumor mutation burden in cancer patients treated with PD-1 blockade. Clin Cancer Res. 2019;25:7024–34.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Lennon AM, Buchanan AH, Kinde I, Warren A, Honushefsky A, Cohain AT, et al. Feasibility of blood testing combined with PET-CT to screen for cancer and guide intervention. Science. 2020;369:eabb9601.

  17. Liu MC, Oxnard GR, Klein EA, Swanton C, Seiden MV, Liu MC, et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann Oncol. 2020;31:745–59.

    Article  CAS  PubMed  Google Scholar 

  18. Chen L, Abou-Alfa GK, Zheng B, Liu JF, Bai J, Du LT, et al. Genome-scale profiling of circulating cell-free DNA signatures for early detection of hepatocellular carcinoma in cirrhotic patients. Cell Res. 2021;31:589–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Chaudhuri AA, Chabon JJ, Lovejoy AF, Newman AM, Stehr H, Azad TD, et al. Early detection of molecular residual disease in localized lung cancer by circulating tumor DNA profiling. Cancer Discov. 2017;7:1394–403.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Reinert T, Henriksen TV, Christensen E, Sharma S, Salari R, Sethi H, et al. Analysis of plasma cell-free DNA by ultradeep sequencing in patients with stages I to III colorectal cancer. JAMA Oncol. 2019;5:1124–31.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Chen G, Peng J, Xiao Q, Wu HX, Wu X, Wang F, et al. Postoperative circulating tumor DNA as markers of recurrence risk in stages II to III colorectal cancer. J Hematol Oncol. 2021;14:80.

  22. Kilgour E, Rothwell DG, Brady G, Dive C. Liquid biopsy-based biomarkers of treatment response and resistance. Cancer Cell. 2020;37:485–95.

    Article  CAS  PubMed  Google Scholar 

  23. De Rubis G, Rajeev Krishnan S, Bebawy M. Liquid biopsies in cancer diagnosis, monitoring, and prognosis. Trends Pharmacol Sci. 2019;40:172–86.

    Article  PubMed  Google Scholar 

  24. Ignatiadis M, Sledge GW, Jeffrey, SS. Liquid biopsy enters the clinic—implementation issues and future challenges. Nat Rev Clin Oncol. 2021;18:297–31.

  25. Snyder MW, Kircher M, Hill AJ, Daza RM, Correspondence JS. Cell-free DNA comprises an in vivo nucleosome footprint that informs its tissues-of-origin. Cell. 2016;164:57–68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Costello JF, Frühwald MC, Smiraglia DJ, Rush LJ, Robertson GP, Gao X, et al. Aberrant CpG-island methylation has non-random and tumour-type-specific patterns. Nat Genet. 2000;24:132–8.

    Article  CAS  PubMed  Google Scholar 

  27. Shen SY, Singhania R, Fehringer G, Chakravarthy A, Roehrl MHA, Chadwick D, et al. Sensitive tumour detection and classification using plasma cell-free DNA methylomes. Nature. 2018;563:579–83.

    Article  CAS  PubMed  Google Scholar 

  28. Garrigou S, Perkins G, Garlan F, Normand C, Didelot A, Le Corre D, et al. A study of hypermethylated circulating tumor DNA as a universal colorectal cancer biomarker. Clin Chem. 2016;62:1129–39.

    Article  CAS  PubMed  Google Scholar 

  29. Pietrasz D, Wang-Renault S, Taieb J, Dahan L, Postel M, Durand-Labrunie J, et al. Prognostic value of circulating tumour DNA in metastatic pancreatic cancer patients: post-hoc analyses of two clinical trials. Br J Cancer. 2021;126:440–8.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Beinse G, Borghese B, Métairie M, Just P-A, Poulet G, Garinet S, et al. Highly specific droplet-digital PCR detection of universally methylated circulating tumor DNA in endometrial carcinoma. Clin Chem. 2022. https://doi.org/10.1093/CLINCHEM/HVAC020.

  31. Tan HT, Low J, Lim SG, Chung MCM. Serum autoantibodies as biomarkers for early cancer detection. FEBS J. 2009;276:6880–904.

    Article  CAS  PubMed  Google Scholar 

  32. Cristiano S, Leal A, Phallen J, Fiksel J, Adleff V, Bruhm DC, et al. Genome-wide cell-free DNA fragmentation in patients with cancer. Nature. 2019;570:385–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Heider K, Wan JCM, Hall J, Belic J, Boyle S, Hudecova I, et al. Detection of ctDNA from dried blood spots after DNA size selection. Clin Chem. 2020;66:697–705.

    Article  PubMed  Google Scholar 

  34. Mouliere F, Chandrananda D, Piskorz AM, Moore EK, Morris J, Barlebo Ahlborn L, et al. Enhanced detection of circulating tumor DNA by fragment size analysis. Sci Transl Med. 2018;10:4921.

    Article  Google Scholar 

  35. Jiang P, Sun K, Peng W, Cheng SH, Ni M, Yeung PC, et al. Plasma DNA end-motif profiling as a fragmentomic marker in cancer, pregnancy, and transplantation. Cancer Discov. 2020;10:664–73.

    Article  CAS  PubMed  Google Scholar 

  36. Larson MH, Pan W, Kim HJ, Mauntz RE, Stuart SM, Pimentel M, et al. A comprehensive characterization of the cell-free transcriptome reveals tissue- and subtype-specific biomarkers for cancer detection. Nat Commun. 2021;12:2357.

  37. Poore GD, Kopylova E, Zhu Q, Carpenter C, Fraraccio S, Wandro S, et al. Microbiome analyses of blood and tissues suggest cancer diagnostic approach. Nature. 2020;579:567–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Mathios D, Johansen JS, Cristiano S, Medina JE, Phallen J, Larsen KR, et al. Detection and characterization of lung cancer using cell-free DNA fragmentomes. Nat Commun. 2021;12:5060.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Li Y, Ma L, Wu D, Chen G. Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine. Brief Bioinform. 2021;22:1–18.

    Google Scholar 

  40. Amelio I, Bertolo R, Bove P, Buonomo OC, Candi E, Chiocchi M, et al. Liquid biopsies and cancer omics. Cell Death Discov. 2020;6:131.

  41. Greenberg MVC, Bourc’his D. The diverse roles of DNA methylation in mammalian development and disease. Nat Rev Mol Cell Biol. 2019;20:590–607.

    Article  CAS  PubMed  Google Scholar 

  42. Zaporozhchenko IA, Ponomaryova AA, Rykova EY, Laktionov PP. The potential of circulating cell-free RNA as a cancer biomarker: challenges and opportunities. Expert Rev Mol Diagn. 2018;18:133–45.

    Article  CAS  PubMed  Google Scholar 

  43. Geary B, Walker MJ, Snow JT, Lee DCH, Pernemalm M, Maleki-Dizaji S, et al. Identification of a biomarker panel for early detection of lung cancer patients. J Proteome Res. 2019;18:3369–82.

    Article  CAS  PubMed  Google Scholar 

  44. Peng L, Cantor DI, Huang C, Wang K, Baker MS, Nice EC. Tissue and plasma proteomics for early stage cancer detection. Mol Omics. 2018;14:405–23.

    Article  CAS  PubMed  Google Scholar 

  45. Chen F, Dai X, Zhou C-C, Li K, Zhang Y, Lou X-Y, et al. Integrated analysis of the faecal metagenome and serum metabolome reveals the role of gut microbiome-associated metabolites in the detection of colorectal cancer and adenoma. Gut. 2021. https://doi.org/10.1136/gutjnl-2020-323476.

  46. Dawson S-J, Tsui DWY, Murtaza M, Biggs H, Rueda OM, Chin S-F, et al. Analysis of circulating tumor DNA to monitor metastatic breast cancer. N Engl J Med. 2013;368:1199–209.

    Article  CAS  PubMed  Google Scholar 

  47. Garcia-Murillas I, Schiavon G, Weigelt B, Ng C, Hrebien S, Cutts RJ, et al. Mutation tracking in circulating tumor DNA predicts relapse in early breast cancer. Sci Transl Med. 2015;7:1–12.

    Article  Google Scholar 

  48. Diehl F, Li M, Dressman D, He Y, Shen D, Szabo S, et al. Detection and quantification of mutations in the plasma of patients with colorectal tumors. Proc Natl Acad Sci USA. 2005;102:16368–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Newman AM, Bratman SV, To J, Wynne JF, Eclov NCW, Modlin LA, et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat Med. 2014;20:548–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Phallen J, Sausen M, Adleff V, Leal A, Hruban C, White J, et al. Direct detection of early-stage cancers using circulating tumor DNA. Sci Transl Med. 2017;9:eaan2415.

  51. Razavi P, Li BT, Brown DN, Jung B, Hubbell E, Shen R, et al. High-intensity sequencing reveals the sources of plasma circulating cell-free DNA variants. Nat Med. 2019;25:1928–37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Lianidou E. Detection and relevance of epigenetic markers on ctDNA: recent advances and future outlook. Mol Oncol. 2021;15:1683–1700.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Dor Y, Cedar H. Principles of DNA methylation and their implications for biology and medicine. Lancet. 2018;392:777–86.

    Article  CAS  PubMed  Google Scholar 

  54. Venegas V, Halberg MC. Quantification of mtDNA mutation heteroplasmy (ARMS qPCR). Methods Mol Biol. 2012;837:313–26.

    Article  CAS  PubMed  Google Scholar 

  55. Thierry AR. A targeted Q-PCR-based method for point mutation testing by analyzing circulating DNA for cancer management care. Methods Mol Biol. 2016;1392:1–16.

    Article  CAS  PubMed  Google Scholar 

  56. Milbury CA, Li J, Liu P, Makrigiorgos GM. COLD-PCR: improving the sensitivity of molecular diagnostics assays. Expert Rev Mol Diagn. 2011;11:159–69.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Perkins G, Lu H, Garlan F, Taly V. Droplet-based digital PCR: application in cancer research. Adv Clin Chem. 2017;79:43–91.

    Article  CAS  PubMed  Google Scholar 

  58. Diehl F, Li M, He Y, Kinzler KW, Vogelstein B, Dressman D. BEAMing: single-molecule PCR on microparticles in water-in-oil emulsions. Nat Methods. 2006;3:551–9.

    Article  CAS  PubMed  Google Scholar 

  59. Tran NH, Kisiel J, Roberts LR. Using cell-free DNA for HCC surveillance and prognosis. JHEP Rep. 2021;3:100304.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Masfarré L, Vidal J, Fernández-Rodríguez C, Montagut C. ctDNA to guide adjuvant therapy in localized colorectal cancer (CRC). Cancers. 2021;13:2869.

  61. Takemasa I, Hamabe A, Ishii M. Perspectives for circulating tumor DNA in clinical management of colorectal cancer. Int J Clin Oncol. 2021;26:1420–30.

    Article  CAS  PubMed  Google Scholar 

  62. Chen M, Zhao H. Next-generation sequencing in liquid biopsy: cancer screening and early detection. Hum Genomics. 2019;13:34.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Oxnard GR, Klein EA, Seiden MV, Hubbell E, Venn O, Jamshidi A, et al. Simultaneous multi-cancer detection and tissue of origin (TOO) localization using targeted bisulfite sequencing of plasma cell-free DNA (cfDNA). Ann Oncol. 2019;30:v912.

    Article  Google Scholar 

  64. Liu MC, Klein E, Hubbell E, Maddala T, Aravanis AM, Beausang JF, et al. Plasma cell-free DNA (cfDNA) assays for early multi-cancer detection: the circulating cell-free genome atlas (CCGA) study. Ann Oncol. 2018;29:viii14–viii57.

    Article  Google Scholar 

  65. Liu L, Chen X, Petinrin OO, Zhang W, Rahaman S, Tang ZR, et al. Machine learning protocols in early cancer detection based on liquid biopsy: a survey. Life. 2021;11:1–39.

    Article  Google Scholar 

  66. Nalepa J, Kawulok M. Selecting training sets for support vector machines: a review. Artif Intell Rev. 2019;52:857–900.

    Article  Google Scholar 

  67. Leo B. Random forests. Mach Learn. 2001;45:5–32.

    Article  Google Scholar 

  68. Chalasani NP, Ramasubramanian TS, Bhattacharya A, Olson MC, Edwards V DK, Roberts LR, et al. A novel blood-based panel of methylated DNA and protein markers for detection of early-stage hepatocellular carcinoma. Clin Gastroenterol Hepatol. 2021;19:2597.e4–605.e4.

  69. Uehiro N, Sato F, Pu F, Tanaka S, Kawashima M, Kawaguchi K, et al. Circulating cell-free DNA-based epigenetic assay can detect early breast cancer. Breast Cancer Res. 2016;18:129.

  70. Qu C, Wang Y, Wang P, Chen K, Wang M, Zeng H, et al. Detection of early-stage hepatocellular carcinoma in asymptomatic HBsAg-seropositive individuals by liquid biopsy. Proc Natl Acad Sci USA 2019;116:6308–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Cohen JD, Li L, Wang Y, Thoburn C, Afsari B, Danilova L, et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science. 2018;17:20.

    Google Scholar 

  72. Zhong G, Wang LN, Ling X, Dong J. An overview on data representation learning: from traditional feature learning to recent deep learning. J Financ Data Sci. 2016;2:265–78.

    Article  Google Scholar 

  73. Dong X, Yu Z, Cao W, Shi Y, Ma Q. A survey on ensemble learning. Front Comput Sci. 2020;14:241–58.

    Article  Google Scholar 

  74. Bello M, Nápoles G, Sánchez R, Bello R, Vanhoof K. Deep neural network to extract high-level features and labels in multi-label classification problems. Neurocomputing. 2020;413:259–70.

    Article  Google Scholar 

  75. Le QV. Building high-level features using large scale unsupervised learning. ICASSP, IEEE Int Conf Acoust Speech Signal Process Proc. 2013. https://doi.org/10.1109/ICASSP.2013.6639343.

  76. Lones MA. How to avoid machine learning pitfalls: a guide for academic researchers. arXiv:2108.02497v2 [Preprint]. 2021 [cited 2021 Aug 5]: [19 p.]. Available from https://arxiv.org/abs/2108.02497

  77. Kann BH, Hosny A, Aerts HJWL. Artificial intelligence for clinical oncology. Cancer Cell. 2021;39:916–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Aravanis AM, Lee M, Klausner RD. Next-generation sequencing of circulating tumor DNA for early cancer detection. Cell. 2017;168:571–4.

    Article  CAS  PubMed  Google Scholar 

  79. Campos-Carrillo A, Weitzel JN, Sahoo P, Rockne R, Mokhnatkin JV, Murtaza M, et al. Circulating tumor DNA as an early cancer detection tool. Pharmacol Ther. 2020;207:107458.

  80. Clarke CA, Hubbell E, Ofman JJ. Multi-cancer early detection: a new paradigm for reducing cancer-specific and all-cause mortality. Cancer Cell. 2021;39:447–8.

    Article  CAS  PubMed  Google Scholar 

  81. Ahlquist DA. Universal cancer screening: revolutionary, rational, and realizable. npj Precis Oncol. 2018;2:1–5.

    Google Scholar 

  82. Chabon JJ, Hamilton EG, Kurtz DM, Esfahani MS, Moding EJ, Stehr H, et al. Integrating genomic features for non-invasive early lung cancer detection. Nature. 2020;580:245–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Putcha G, Liu T-Y, Ariazi E, Bertin M, Drake A, Dzamba M, et al. Blood-based detection of early-stage colorectal cancer using multiomics and machine learning. J Clin Oncol. 2020;38:66.

    Article  Google Scholar 

  84. Hematol J, Ma X, Chen Y, Tang W, Bao H, Mo S, et al. Multi‑dimensional fragmentomic assay for ultrasensitive early detection of colorectal advanced adenoma and adenocarcinoma. J Hematol Oncol. 2021. https://doi.org/10.1186/s13045-021-01189-w.

  85. Braunstein GD, Ofman JJ. Criteria for evaluating multi-cancer early detection tests. touchREVIEWS Oncol Haematol. 2021;17:3–6.

  86. Putcha G, Gutierrez A, Skates, S. Multicancer screening: one size does not fit all. JCO Precis Oncol. 2021;5:574–6.

  87. Liu L, Toung JM, Jassowicz AF, Vijayaraghavan R, Kang H, Zhang R, et al. Targeted methylation sequencing of plasma cell-free DNA for cancer detection and classification. Ann Oncol. 2018;29:1445–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Adashek JJ, Janku F, Kurzrock R. Signed in blood: circulating tumor DNA in cancer diagnosis, treatment and screening. Cancers. 2021;13:3600.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. Ca Cancer J Clin. 2020;70:7–30.

    Article  PubMed  Google Scholar 

  90. Abbosh C, Swanton C, Birkbak NJ. Clonal haematopoiesis: a source of biological noise in cell-free DNA analyses. Ann Oncol. 2019;30:358–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Hu Y, Ulrich BC, Supplee J, Kuang Y, Lizotte PH, Feeney NB, et al. False-positive plasma genotyping due to clonal hematopoiesis. Clin Cancer Res. 2018;24:4437–43.

    Article  CAS  PubMed  Google Scholar 

  92. Bettegowda C, Sausen M, Leary RJ, Kinde I, Wang Y, Agrawal N, et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci Transl Med. 2014;6:224ra24.

  93. Chen K, Zhao H, Shi Y, Yang F, Wang LT, Kang G, et al. Perioperative dynamic changes in circulating tumor DNA in patients with lung cancer (Dynamic). Clin Cancer Res. 2019;25:7058–67.

    Article  CAS  PubMed  Google Scholar 

  94. Xia L, Mei J, Kang R, Deng S, Chen Y, Yang Y, et al. Perioperative ctDNA-based molecular residual disease detection for non-small cell lung cancer: a prospective multicenter cohort study (LUNGCA-1). Clin Cancer Res. 2021. https://doi.org/10.1158/1078-0432.CCR-21-3044.

  95. Henriksen TV, Tarazona N, Frydendahl A, Reinert T, Gimeno-Valiente F, Carbonell-Asins JA, et al. Circulating tumor DNA in stage III colorectal cancer, beyond minimal residual disease detection, towards assessment of adjuvant therapy efficacy and clinical behavior of recurrences. Clin Cancer Res. 2021. https://doi.org/10.1158/1078-0432.ccr-21-2404.

  96. Chin RI, Chen K, Usmani A, Chua C, Harris PK, Binkley MS, et al. Detection of solid tumor molecular residual disease (MRD) using circulating tumor DNA (ctDNA). Mol Diagn Ther. 2019. https://doi.org/10.1007/s40291-019-00390-5.

  97. Pantel K, Alix-Panabières C. Liquid biopsy and minimal residual disease — latest advances and implications for cure. Nat Rev Clin Oncol. 2019;16:409–24.

    Article  CAS  PubMed  Google Scholar 

  98. Cai Z, Chen G, Zeng Y, Dong X, Li Z, Huang Y, et al. Comprehensive liquid profiling of circulating tumor DNA and protein biomarkers in long-term follow-up patients with hepatocellular carcinoma. Clin Cancer Res. 2019;25:5284–94.

    Article  CAS  PubMed  Google Scholar 

  99. Radovich M, Jiang G, Hancock, BA, Chitambar C, Nanda R, Falkson C, 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. 2020;6:1410–5.

  100. Przybyl J, Chabon JJ, Spans L, Ganjoo KN, Vennam S, Varma S, et al. Combination approach for detecting different types of alterations in circulating tumor DNA in leiomyosarcoma. Clin Cancer Res. 2018;24:2688–99.

  101. Parikh AR, Van Seventer EE, Siravegna G, Hartwig AV, Jaimovich A, He Y, et al. Minimal residual disease detection using a plasma-only circulating tumor DNA assay in patients with colorectal cancer. Clin Cancer Res. 2021. https://doi.org/10.1158/1078-0432.ccr-21-0410.

  102. Burgener JM, Zou J, Zhao Z, Zheng Y, Shen SY, Huang SH, et al. Tumor-naïve multimodal profiling of circulating tumor DNA in head and neck squamous cell carcinoma. Clin Cancer Res. 2021;27:4230–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Zehir A, Benayed R, Shah RH, Syed A, Middha S, Kim HR, et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat Med. 2017;23:703–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Signatera T. A comparison of tumor-informed and tumor-naive approaches for early-stage molecular residual disease (MRD) detection two different approaches: to personalize or not to personalize. 2021. https://www.natera.com/wp-content/uploads/2021/05/SGN_WP_Solar_20210503_NAT-9000052_FINAL_DWNLD.pdf.

  105. Abbosh C, Birkbak NJ, Swanton C. Early stage NSCLC — challenges to implementing ctDNA-based screening and MRD detection. Nat Rev Clin Oncol. 2018;15:577–86.

    Article  CAS  PubMed  Google Scholar 

  106. Newman AM, Lovejoy AF, Klass DM, Kurtz DM, Chabon JJ, Scherer F, et al. Integrated digital error suppression for improved detection of circulating tumor DNA. Nat Biotechnol. 2016;34:547–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Pécuchet N, Rozenholc Y, Zonta E, Pietraz D, Didelot A, Combe P, et al. Analysis of base-position error rate of next-generation sequencing to detect tumor mutations in circulating DNA. Clin Chem. 2016;62:1492–503.

    Article  PubMed  Google Scholar 

  108. Dai P, Wu LR, Chen SX, Wang MX, Cheng LY, Zhang JX, et al. Calibration-free NGS quantitation of mutations below 0.01% VAF. Nat Commun. 2021;12:1–9.

    Article  Google Scholar 

  109. Xie H, Mahoney DW, Foote PH, Burger K, Doering KA, Taylor WR, et al. Novel methylated DNA markers in plasma detect distant recurrence of colorectal cancer. J Clin Oncol. 2020;38:4088.

    Article  Google Scholar 

  110. Shoukry M, Broccard S, Kaplan J, Gabriel E. The emerging role of circulating tumor DNA in the management of breast cancer. Cancers. 2021;13:3813.

  111. Parseghian CM, Loree JM, Morris VK, Liu X, Clifton KK, Napolitano S, et al. Anti-EGFR-resistant clones decay exponentially after progression: implications for anti-EGFR re-challenge. Ann Oncol. 2019;30:243–9.

    Article  CAS  PubMed  Google Scholar 

  112. Siravegna G, Mussolin B, Buscarino M, Corti G, Cassingena A, Crisafulli G, et al. Clonal evolution and resistance to EGFR blockade in the blood of colorectal cancer patients. Nat Med. 2015;21:795–801.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Murtaza M, Dawson SJ, Tsui DWY, Gale D, Forshew T, Piskorz AM, et al. Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA. Nature. 2013;497:108–12.

    Article  CAS  PubMed  Google Scholar 

  114. Benhaim L, Bouché O, Normand C, Didelot A, Mulot C, Le Corre D, et al. Circulating tumor DNA is a prognostic marker of tumor recurrence in stage II and III colorectal cancer: multicentric, prospective cohort study (ALGECOLS). Eur J Cancer. 2021;159:24–33.

    Article  CAS  PubMed  Google Scholar 

  115. Darrigues L, Pierga JY, Bernard-Tessier A, Bièche I, Silveira AB, Michel M, et al. Circulating tumor DNA as a dynamic biomarker of response to palbociclib and fulvestrant in metastatic breast cancer patients. Breast Cancer Res. 2021;23:1–10.

    Article  Google Scholar 

  116. Nakagomi H, Hirotsu Y, Amemiya K, Nakada H, Inoue M, Mochizuki H, et al. Rapid changes in circulating tumor DNA in serially sampled plasma during treatment of breast cancer: a case report. Am J Case Rep. 2017;18:26–32.

    Article  PubMed  PubMed Central  Google Scholar 

  117. Garlan F, Laurent-Puig P, Sefrioui D, Siauve N, Didelot A, Sarafan-Vasseur N, et al. Early evaluation of circulating tumor DNA as marker of therapeutic efficacy in metastatic colorectal cancer patients (PLACOL Study). Clin Cancer Res. 2017;23:5416–25.

  118. Qiu B, Guo W, Zhang F, Lv F, Ji Y, Peng Y, et al. Dynamic recurrence risk and adjuvant chemotherapy benefit prediction by ctDNA in resected NSCLC. Nat Commun. 2021;12:6770.

  119. Lapin M, Oltedal S, Tjensvoll K, Buhl T, Smaaland R, Garresori H, et al. Fragment size and level of cell-free DNA provide prognostic information in patients with advanced pancreatic cancer. J Transl Med. 2018;16:300.

  120. Nabet BY, Esfahani MS, Moding EJ, Hamilton EG, Chabon JJ, Rizvi H, et al. Noninvasive early identification of therapeutic benefit from immune checkpoint inhibition. Cell. 2020;183:363.e13–76.e13.

    Article  Google Scholar 

  121. Anagnostou V, Forde PM, White JR, Niknafs N, Hruban C, Naidoo J, et al. Dynamics of tumor and immune responses during immune checkpoint blockade in Non-small cell lung cancer. Cancer Res. 2019;79:1214–25.

    Article  CAS  PubMed  Google Scholar 

  122. Paoletti C, Schiavon G, Dolce EM, Darga EP, Hedley Carr T, Geradts J, et al. Circulating biomarkers and resistance to endocrine therapy in metastatic breast cancers: correlative results from AZD9496 Oral SERD phase I trial. Clin Cancer Res. 2018;24:5860–72.

    Article  CAS  PubMed  Google Scholar 

  123. Pessoa LS, Heringer M, Ferrer VP. ctDNA as a cancer biomarker: a broad overview. Crit Rev Oncol Hematol. 2020;155:103109.

  124. Zhu G, Guo YA, Ho D, Poon P, Poh ZW, Wong PM, et al. Tissue-specific cell-free DNA degradation quantifies circulating tumor DNA burden. Nat Commun. 2021;12:2229.

  125. Swanton C, Frs F, Venn O, Aravanis A, Hubbell E, Maddala T, et al. Prevalence of clonal hematopoiesis of indeterminate potential (CHIP) measured by an ultra-sensitive sequencing assay: exploratory analysis of the Circulating Cell-free Genome Atlas (CCGA) study. J Clin Oncol. 2018;36:12003.

  126. Young AL, Challen GA, Birmann BM, Druley TE. Clonal haematopoiesis harbouring AML-associated mutations is ubiquitous in healthy adults. Nat Commun. 2016;7:12484.

  127. Jaiswal S, Fontanillas P, Flannick J, Manning A, Grauman PV, Mar BG, et al. Age-related clonal hematopoiesis associated with adverse outcomes. N. Engl J Med. 2014;371:2488–98.

    Article  PubMed  PubMed Central  Google Scholar 

  128. Zink F, Stacey SN, Norddahl GL, Frigge ML, Magnusson OT, Jonsdottir I, et al. Clonal hematopoiesis, with and without candidate driver mutations, is common in the elderly. Blood. 2017;130:742–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Steensma DP, Bejar R, Jaiswal S, Lindsley RC, Sekeres MA, Hasserjian RP, et al. Clonal hematopoiesis of indeterminate potential and its distinction from myelodysplastic syndromes. Blood. 2015;126:9–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Merker JD, Oxnard GR, Compton C, Diehn M, Hurley P, Lazar AJ, et al. Circulating tumor DNA analysis in patients with cancer: American Society of Clinical Oncology and College of American Pathologists joint review. Arch Pathol Lab Med. 2018;142:1242–53.

    Article  CAS  PubMed  Google Scholar 

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Funding

This work was supported by the Shanghai Municipal Health Commission (2020YJZX0108) and the Scientific Research Project of Education Department of Anhui Province (YJS20210323). VT acknowledge the support of the Ministère de l’Enseignement Supérieur et de la Recherche, the Université de Paris Cité, the Centre National de la Recherche Scientifique (CNRS), the Institut National de la Santé et de la Recherche Médicale (INSERM), the SIRIC CARPEM, and the ligue nationale contre le cancer (LNCC).

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Authors and Affiliations

  1. Shanghai Skin Disease Hospital, School of Medicine, Tongji University, 200443, Shanghai, China

    Geng Chen, Jing Zhang & Fei Tan

  2. Center for Bioinformatics and Computational Biology, School of Life Sciences, East China Normal University, 200241, Shanghai, China

    Geng Chen

  3. Shanghai Skin Disease Clinical College, The Fifth Clinical Medical College, Anhui Medical University, 200443, Shanghai, China

    Qiaoting Fu & Fei Tan

  4. Université de Paris, UMR-S1138, CNRS SNC5096, Équipe labélisée Ligue Nationale contre le cancer, Centre de Recherche des Cordeliers, Paris, France

    Valerie Taly

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  1. Geng Chen
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GC, JZ, QF, VT, and FT wrote the manuscript. GC and FT revised and finalised the manuscript. All authors read and approved the final manuscript.

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Correspondence to Geng Chen, Valerie Taly or Fei Tan.

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The original online version of this article was revised: The third and fourth rows of Table 2 in page 9 were duplicated with the first and second rows of page 9. The third and fourth rows of Table 2 in page 9 were removed.

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Chen, G., Zhang, J., Fu, Q. et al. Integrative analysis of multi-omics data for liquid biopsy. Br J Cancer 128, 505–518 (2023). https://doi.org/10.1038/s41416-022-02048-2

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