1932

Abstract

Liquid biopsy is the analysis of materials shed by tumors into circulation, such as circulating tumor cells, nucleic acids, and extracellular vesicles (EVs), for the diagnosis and management of cancer. These assays have rapidly evolved with recent FDA approvals of single biomarkers in patients with advanced metastatic disease. However, they have lacked sensitivity or specificity as a diagnostic in early-stage cancer, primarily due to low concentrations in circulating plasma. EVs, membrane-enclosed nanoscale vesicles shed by tumor and other cells into circulation, are a promising liquid biopsy analyte owing to their protein and nucleic acid cargoes carried from their mother cells, their surface proteins specific to their cells of origin, and their higher concentrations over other noninvasive biomarkers across disease stages. Recently, the combination of EVs with non-EV biomarkers has driven improvements in sensitivity and accuracy; this has been fueled by the use of machine learning (ML) to algorithmically identify and combine multiple biomarkers into a composite biomarker for clinical prediction. This review presents an analysis of EV isolation methods, surveys approaches for and issues with using ML in multianalyte EV datasets, and describes best practices for bringing multianalyte liquid biopsy to clinical implementation.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-biodatasci-122120-113218
2022-08-10
2024-05-25
Loading full text...

Full text loading...

/deliver/fulltext/biodatasci/5/1/annurev-biodatasci-122120-113218.html?itemId=/content/journals/10.1146/annurev-biodatasci-122120-113218&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Yap TA, Johnson A, Meric-Bernstam F. 2021. Precision medicine in oncology—toward the integrated targeting of somatic and germline genomic aberrations. JAMA Oncol 7:507–9
    [Google Scholar]
  2. 2.
    de Ruiter EJ, Mulder FJ, Koomen MB, Speel EJ, van den Hout MCFM et al. 2021. Comparison of three PD-L1 immunohistochemical assays in head and neck squamous cell carcinoma (HNSCC). Mod. Pathol. 34:1125–32
    [Google Scholar]
  3. 3.
    Diaz LA, Bardelli A 2014. Liquid biopsies: genotyping circulating tumor DNA. J. Clin. Oncol. 32:579–86
    [Google Scholar]
  4. 4.
    Distler M, Aust D, Weitz J, Pilarsky C, Grützmann R. 2014. Precursor lesions for sporadic pancreatic cancer: PanIN, IPMN, and MCN. BioMed. Res. Int. 2014:474905
    [Google Scholar]
  5. 5.
    Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D et al. 2012. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366:883–92
    [Google Scholar]
  6. 6.
    Cowling T, Loshak H. 2019. An overview of liquid biopsy for screening and early detection of cancer. CADTH Issues in Emerging Health Technologies Pap. 179 Ottawa, Can.: Can. Agency Drugs Technol. Health
    [Google Scholar]
  7. 7.
    Pereira SP, Oldfield L, Ney A, Hart PA, Keane MG et al. 2020. Early detection of pancreatic cancer. Lancet Gastroenterol. Hepatol. 5:7698–710
    [Google Scholar]
  8. 8.
    Figueroa JM, Carter BS. 2017. Detection of glioblastoma in biofluids. J. Neurosurg. 129:334–40
    [Google Scholar]
  9. 9.
    Kilgour E, Rothwell DG, Brady G, Dive C. 2020. Liquid biopsy-based biomarkers of treatment response and resistance. Cancer Cell 37:485–495
    [Google Scholar]
  10. 10.
    Zhao Z, Fan J, Hsu YS, Lyon CJ, Ning B, Hu TY. 2019. Extracellular vesicles as cancer liquid biopsies: from discovery, validation, to clinical application. Lab Chip 19:71114–40
    [Google Scholar]
  11. 11.
    Diamantopoulou Z, Castro-Giner F, Aceto N. 2020. Circulating tumor cells: ready for translation?. J. Exp. Med. 217:8e20200356
    [Google Scholar]
  12. 12.
    Millner LM, Linder MW, Valdes R. 2013. Circulating tumor cells: a review of present methods and the need to identify heterogeneous phenotypes. Ann. Clin. Lab. Sci. 43:295–304
    [Google Scholar]
  13. 13.
    Cristofanilli M, Budd GT, Ellis MJ, Stopeck A, Matera J et al. 2004. Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N. Engl. J. Med. 351:8781–91
    [Google Scholar]
  14. 14.
    Cohen SJ, Punt CJ, Iannotti N, Saidman BH, Sabbath KD et al. 2008. Relationship of circulating tumor cells to tumor response, progression-free survival, and overall survival in patients with metastatic colorectal cancer. J. Clin. Oncol. 26:193213–21
    [Google Scholar]
  15. 15.
    de Bono JS, Scher HI, Montgomery RB, Parker C, Miller MC et al. 2008. Circulating tumor cells predict survival benefit from treatment in metastatic castration-resistant prostate cancer. Clin. Cancer Res. 14:196302–9
    [Google Scholar]
  16. 16.
    Alvarez Cubero MJ, Lorente JA, Robles-Fernandez I, Rodriguez-Martinez A, Puche JL, Serrano MJ 2017. Circulating tumor cells: markers and methodologies for enrichment and detection. Circulating Tumor Cells (Methods in Molecular Biology, Vol. 1634 MJM Magbanua, JW Park 283–303 New York: Humana Press
    [Google Scholar]
  17. 17.
    Andree KC, van Dalum G, Terstappen LWMM. 2016. Challenges in circulating tumor cell detection by the CellSearch system. Mol. Oncol. 10:395–407
    [Google Scholar]
  18. 18.
    Kwapisz D. 2017. The first liquid biopsy test approved. Is it a new era of mutation testing for non-small cell lung cancer?. Ann. Transl. Med. 5:346
    [Google Scholar]
  19. 19.
    FDA (US Food Drug Admin.) 2020. FDA approves liquid biopsy NGS companion diagnostic test for multiple cancers and biomarkers Approv. Announc., Oct. 26 Food Drug Admin. Silver Spring, MD: https://www.fda.gov/drugs/resources-information-approved-drugs/fda-approves-liquid-biopsy-ngs-companion-diagnostic-test-multiple-cancers-and-biomarkers
  20. 20.
    Corcoran RB, Chabner BA. 2018. Application of cell-free DNA analysis to cancer treatment. N. Engl. J. Med. 379:181754–65
    [Google Scholar]
  21. 21.
    Liu MC, Oxnard GR, Klein EA, Swanton C, Seiden MV. 2020. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31:745–59
    [Google Scholar]
  22. 22.
    Anderson HC. 1969. Vesicles associated with calcification in the matrix of epiphyseal cartilage. J. Cell Biol. 41:59–72
    [Google Scholar]
  23. 23.
    Dvorak HF, Quay SC, Orenstein NS, Dvorak AM, Hahn P et al. 1981. Tumor shedding and coagulation. Science 212:4497923–24
    [Google Scholar]
  24. 24.
    van Niel G, D'Angelo G, Raposo G 2018. Shedding light on the cell biology of extracellular vesicles. Nat. Rev. Mol. Cell Biol. 19:213–28
    [Google Scholar]
  25. 25.
    Pisitkun T, Shen RF, Knepper MA. 2004. Identification and proteomic profiling of exosomes in human urine. PNAS 101:13368–73
    [Google Scholar]
  26. 26.
    Zlotogorski-Hurvitz A, Dayan D, Chaushu G, Korvala J, Salo T et al. 2015. Human saliva-derived exosomes: comparing methods of isolation. J. Histochem. Cytochem. 63:3181–89
    [Google Scholar]
  27. 27.
    Chiasserini D, van Weering JR, Piersma SR, Pham TV, Malekzadeh A et al. 2014. Proteomic analysis of cerebrospinal fluid extracellular vesicles: a comprehensive dataset. J. Proteom. 106:191–204
    [Google Scholar]
  28. 28.
    Yu W, Hurley J, Roberts D, Chakrabortty SK, Enderle D et al. 2021. Exosome-based liquid biopsies in cancer: opportunities and challenges. Ann. Oncol. 32:4466–77
    [Google Scholar]
  29. 29.
    Johnstone RM, Adam M, Hammond JR, Orr L, Turbide C. 1987. Vesicle formation during reticulocyte maturation. Association of plasma membrane activities with released vesicles (exosomes). J. Biol. Chem. 262:199412–20
    [Google Scholar]
  30. 30.
    Pan BT, Johnstone RM. 1983. Fate of the transferrin receptor during maturation of sheep reticulocytes in vitro: selective externalization of the receptor. Cell 33:3967–78
    [Google Scholar]
  31. 31.
    Harding C, Heuser J, Stahl P. 1983. Receptor-mediated endocytosis of transferrin and recycling of the transferrin receptor in rat reticulocytes. J. Cell. Biol. 97:2329–39
    [Google Scholar]
  32. 32.
    Xie F, Zhou X, Fang M, Li H, Su P et al. 2019. Extracellular vesicles in cancer immune microenvironment and cancer immunotherapy. Adv. Sci. 6:241901779
    [Google Scholar]
  33. 33.
    Tao SC, Guo SC. 2020. Role of extracellular vesicles in tumour microenvironment. Cell. Commun. Signal. 18:163
    [Google Scholar]
  34. 34.
    Zhao H, Achreja A, Iessi E, Logozzi M, Mizzoni D et al. 2018. The key role of extracellular vesicles in the metastatic process. Biochim. Biophys. Acta Rev. Cancer 1869:64–77
    [Google Scholar]
  35. 35.
    Johnsen KB, Gudbergsson JM, Andresen TL, Simonsen JB. 2019. What is the blood concentration of extracellular vesicles? Implications for the use of extracellular vesicles as blood-borne biomarkers of cancer. Biochim. Biophys. Acta Rev. Cancer 1871:1109–16
    [Google Scholar]
  36. 36.
    Sharma R, Huang X, Brekken RA, Schroit AJ. 2017. Detection of phosphatidylserine-positive exosomes for the diagnosis of early-stage malignancies. Br. J. Cancer 117:4545–52
    [Google Scholar]
  37. 37.
    Allenson K, Castillo J, San Lucas FA, Scelo G, Kim DU et al. 2017. High prevalence of mutant KRAS in circulating exosome-derived DNA from early-stage pancreatic cancer patients. Ann. Oncol. 28:4741–47
    [Google Scholar]
  38. 38.
    Akers JC, Ramakrishnan V, Kim R, Skog J, Nakano I et al. MiR-21 in the extracellular vesicles (EVs) of cerebrospinal fluid (CSF): a platform for glioblastoma biomarker development. PLOS ONE 8:10e78115
    [Google Scholar]
  39. 39.
    Lee SJ, Lee J, Jung JH, Park HY, Moon PG et al. 2021. Exosomal Del-1 as a potent diagnostic marker for breast cancer: prospective cohort study. Clin. Breast Cancer 21:6e748–56
    [Google Scholar]
  40. 40.
    Bio-Techne 2019. FDA grants breakthrough device designation to Bio-Techne's ExoDx Prostate (IntelliScore) (EPI) test Press Release, Jun. 17 Bio-Techne Waltham, MA: https://www.exosomedx.com/news-events/fda-grants-breakthrough-device-designation-bio-technes-exodx-prostate-intelliscore-epi
  41. 41.
    McKiernan J, Donovan MJ, O'Neill V, Bentink S, Noerholm M et al. 2016. A novel urine exosome gene expression assay to predict high-grade prostate cancer at initial biopsy. JAMA Oncol 2:7882–89
    [Google Scholar]
  42. 42.
    McKiernan J, Donovan MJ, Margolis E, Partin A, Carter B et al. 2018. A prospective adaptive utility trial to validate performance of a novel urine exosome gene expression assay to predict high-grade prostate cancer in patients with prostate-specific antigen 2–10 ng/ml at initial biopsy. Eur. Urol. 74:6731–38
    [Google Scholar]
  43. 43.
    NCCN (Natl. Compr. Cancer Netw.) 2019. Prostate cancer early detection Clin. Pract. Guidel., May 31 NCCN https://www2.tri-kobe.org/nccn/guideline/urological/english/prostate_detection.pdf
  44. 44.
    Ko J, Baldassano SN, Loh PL, Kording K, Litt B, Issadore D. 2018. Machine learning to detect signatures of disease in liquid biopsies—a user's guide. Lab Chip 18:3395–405
    [Google Scholar]
  45. 45.
    Albanese M, Chen YF, Hüls C, Gärtner K, Tagawa T et al. 2021. MicroRNAs are minor constituents of extracellular vesicles that are rarely delivered to target cells. PLOS Genet 17:12e1009951
    [Google Scholar]
  46. 46.
    O'Brien K, Breyne K, Ughetto S, Laurent LC, Breakefield XO. 2020. RNA delivery by extracellular vesicles in mammalian cells and its applications. Nat. Rev. Mol. Cell Biol. 21:585–606
    [Google Scholar]
  47. 47.
    Bojarski M, Del Testa D, Dworakowski D, Firner B, Flepp B et al. 2016. End to end learning for self-driving cars. arXiv.1604.07316 [cs.CV]
  48. 48.
    van Liebergen B. 2017. Machine learning: a revolution in risk management and compliance?. J. Financ. Transform. 45:60–67
    [Google Scholar]
  49. 49.
    Bartlett MS, Littlewort G, Lainscsek C, Fasel I, Movellan J. 2004. Machine learning methods for fully automatic recognition of facial expressions and facial actions. 2004 IEEE International Conference on Systems, Man and Cybernetics592–97 New York: IEEE
    [Google Scholar]
  50. 50.
    Yang Z, LaRiviere MJ, Ko J, Till JE, Christensen T et al. 2020. A multianalyte panel consisting of extracellular vesicle miRNAs and mRNAs, cfDNA, and CA19-9 shows utility for diagnosis and staging of pancreatic ductal adenocarcinoma. Clin. Cancer Res. 26:133248–58
    [Google Scholar]
  51. 51.
    Brennan K, Martin K, FitzGerald SP, O'Sullivan J, Wu Y et al. 2020. A comparison of methods for the isolation and separation of extracellular vesicles from protein and lipid particles in human serum. . Sci. Rep. 10:1039
    [Google Scholar]
  52. 52.
    Ko J, Bhagwat N, Yee SS, Ortiz N, Sahmoud A et al. 2017. Combining machine learning and nanofluidic technology to diagnose pancreatic cancer using exosomes. ACS Nano 11:1111182–93
    [Google Scholar]
  53. 53.
    Contreras-Naranjo JC, Wu HJ, Ugaz VM. 2017. Microfluidics for exosome isolation and analysis: enabling liquid biopsy for personalized medicine. Lab Chip 17:213558–77
    [Google Scholar]
  54. 54.
    Zhou B, Xu K, Zheng X, Chen T, Wang J et al. 2020. Application of exosomes as liquid biopsy in clinical diagnosis. Signal Transduct. Target Ther. 5:144
    [Google Scholar]
  55. 55.
    Coumans FAW, Brisson AR, Buzas EI, Dignat-George F, Drees EEE et al. 2017. Methodological guidelines to study extracellular vesicles. Circ. Res. 120:101632–48
    [Google Scholar]
  56. 56.
    Donovan MJ, Noerholm M, Bentink S, Belzer S, Skog J et al. 2015. A molecular signature of PCA3 and ERG exosomal RNA from non-DRE urine is predictive of initial prostate biopsy result. Prostate Cancer Prostat. Dis. 18:4370–75
    [Google Scholar]
  57. 57.
    Castellanos-Rizaldos E, Grimm DG, Tadigotla V, Hurley J, Healy J et al. 2018. Exosome-based detection of EGFR T790M in plasma from non-small cell lung cancer patients. Clin. Cancer Res. 24:122944–50
    [Google Scholar]
  58. 58.
    Paolini L, Zendrini A, Di Noto G, Busatto S, Lottini E et al. 2016. Residual matrix from different separation techniques impacts exosome biological activity. Sci. Rep. 6:23550
    [Google Scholar]
  59. 59.
    Heath N, Grant L, De Oliveira TM, Rowlinson R, Osteikoetxea X et al. 2018. Rapid isolation and enrichment of extracellular vesicle preparations using anion exchange chromatography. Sci. Rep. 8:5730
    [Google Scholar]
  60. 60.
    Ko J, Carpenter E, Issadore D. 2016. Detection and isolation of circulating exosomes and microvesicles for cancer monitoring and diagnostics using micro-/nano-based devices. Analyst 141:2450–60
    [Google Scholar]
  61. 61.
    Singh K, Nalabotala R, Koo KM, Bose S, Nayak R, Shiddiky MJA. 2021. Separation of distinct exosome subpopulations: isolation and characterization approaches and their associated challenges. Analyst 146:123731–49
    [Google Scholar]
  62. 62.
    Willms E, Cabañas C, Mäger I, Wood MJA, Vader P. 2018. Extracellular vesicle heterogeneity: subpopulations, isolation techniques, and diverse functions in cancer progression. Front. Immunol. 9:738
    [Google Scholar]
  63. 63.
    Wang Z, Wu HJ, Fine D, Schmulen J, Hu Y et al. 2013. Ciliated micropillars for the microfluidic-based isolation of nanoscale lipid vesicles. Lab Chip 13:152879–82
    [Google Scholar]
  64. 64.
    Smith JT, Wunsch BH, Dogra N, Ahsen ME, Lee K et al. 2018. Integrated nanoscale deterministic lateral displacement arrays for separation of extracellular vesicles from clinically-relevant volumes of biological samples. Lab Chip 18:243913–25
    [Google Scholar]
  65. 65.
    Hochstetter A, Vernekar R, Austin RH, Becker H, Beech JP et al. Deterministic lateral displacement: challenges and perspectives. ACS Nano 14:910784–95
    [Google Scholar]
  66. 66.
    Liu C, Guo J, Tian F, Yang N, Yan F et al. 2017. Field-free isolation of exosomes from extracellular vesicles by microfluidic viscoelastic flows. ACS Nano 11:76968–76
    [Google Scholar]
  67. 67.
    Davies RT, Kim J, Jang SC, Choi EJ, Gho YS, Park J. 2012. Microfluidic filtration system to isolate extracellular vesicles from blood. Lab Chip 12:245202–10
    [Google Scholar]
  68. 68.
    Cho S, Jo W, Heo Y, Kang JY, Kwak R, Park J 2016. Isolation of extracellular vesicle from blood plasma using electrophoretic migration through porous membrane. Sens. Actuators B 233:289–97
    [Google Scholar]
  69. 69.
    Liang LG, Kong MQ, Zhou S, Sheng YF, Wang P et al. 2017. An integrated double-filtration microfluidic device for isolation, enrichment and quantification of urinary extracellular vesicles for detection of bladder cancer. Sci. Rep. 7:46224
    [Google Scholar]
  70. 70.
    Park J, Lee C, Eom JS, Kim MH, Cho YK. 2020. Detection of EGFR mutations using bronchial washing-derived extracellular vesicles in patients with non-small-cell lung carcinoma. Cancers 12:102822
    [Google Scholar]
  71. 71.
    Zhang H, Freitas D, Kim HS, Fabijanic K, Li Z et al. 2018. Identification of distinct nanoparticles and subsets of extracellular vesicles by asymmetric flow field-flow fractionation. Nat. Cell Biol. 20:3332–43
    [Google Scholar]
  72. 72.
    Sunkara V, Kim CJ, Park J, Woo HK, Kim D et al. 2019. Fully automated, label-free isolation of extracellular vesicles from whole blood for cancer diagnosis and monitoring. Theranostics 9:71851–63
    [Google Scholar]
  73. 73.
    Zhang H, Lyden D. 2019. Asymmetric flow field-flow fractionation technology for exomere and small extracellular vesicle separation and characterization. Nat. Protoc. 14:1027–53
    [Google Scholar]
  74. 74.
    Chen Z, Yang Y, Yamaguchi H, Hung MC, Kameoka J 2020. Isolation of cancer-derived extracellular vesicle subpopulations by a size-selective microfluidic platform. Biomicrofluidics 14:034113
    [Google Scholar]
  75. 75.
    Kanwar SS, Dunlay CJ, Simeone DM, Nagrath S. 2014. Microfluidic device (ExoChip) for on-chip isolation, quantification and characterization of circulating exosomes. Lab Chip 14:111891–900
    [Google Scholar]
  76. 76.
    Shao H, Chung J, Lee K, Balaj L, Min C et al. 2015. Chip-based analysis of exosomal mRNA mediating drug resistance in glioblastoma. Nat. Commun. 6:6999
    [Google Scholar]
  77. 77.
    Park J, Lin HY, Assaker JP, Jeong S, Huang CH et al. 2017. Integrated kidney exosome analysis for the detection of kidney transplant rejection. ACS Nano 11:1111041–46
    [Google Scholar]
  78. 78.
    Ko J, Hemphill M, Yang Z, Beard K, Sewell E et al. 2020. Multi-dimensional mapping of brain-derived extracellular vesicle microRNA biomarker for traumatic brain injury diagnostics. J. Neurotrauma 37:222424–34
    [Google Scholar]
  79. 79.
    Ko J, Bhagwat N, Black T, Yee SS, Na YJ 2018. miRNA profiling of magnetic nanopore-isolated extracellular vesicles for the diagnosis of pancreatic cancer. Cancer Res 78:133688–97
    [Google Scholar]
  80. 80.
    Liu C, Xu X, Li B, Situ B, Pan W et al. 2018. Single-exosome-counting immunoassays for cancer diagnostics. Nano Lett 18:74226–32
    [Google Scholar]
  81. 81.
    Bordanaba-Florit G, Royo F, Kruglik SG, Falcón-Pérez JM. 2021. Using single-vesicle technologies to unravel the heterogeneity of extracellular vesicles. Nat. Protoc. 16:3163–85
    [Google Scholar]
  82. 82.
    Fernando MR, Jiang C, Krzyzanowski GD, Ryan WL. 2017. New evidence that a large proportion of human blood plasma cell-free DNA is localized in exosomes. PLOS ONE 12:e0183915
    [Google Scholar]
  83. 83.
    Kibria G, Ramos EK, Lee KE, Bedoyan S, Huang S et al. 2016. A rapid, automated surface protein profiling of single circulating exosomes in human blood. Sci. Rep. 6:36502
    [Google Scholar]
  84. 84.
    Koliha N, Wiencek Y, Heider U, Jüngst C, Kladt N et al. 2016. A novel multiplex bead-based platform highlights the diversity of extracellular vesicles. J. Extracell. Vesicles 5:29975
    [Google Scholar]
  85. 85.
    Chevillet JR, Kang Q, Ruf IK, Briggs HA, Vojtech LN et al. 2014. Quantitative and stoichiometric analysis of the microRNA content of exosomes. PNAS 111:4114888–93
    [Google Scholar]
  86. 86.
    Zhou J, Wu Z, Hu J, Yang D, Chen X et al. 2020. High-throughput single-EV liquid biopsy: rapid, simultaneous, and multiplexed detection of nucleic acids, proteins, and their combinations. Sci. Adv. 6:47eabc1204
    [Google Scholar]
  87. 87.
    Ko J, Wang Y, Sheng K, Weitz DA, Weissleder R. 2021. Sequencing-based protein analysis of single extracellular vesicles. ACS Nano 15:5631–38
    [Google Scholar]
  88. 88.
    Agranoff D, Fernandez-Reyes D, Papadopoulos MC, Rojas SA, Herbster M et al. 2006. Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum. Lancet 368:95401012–21
    [Google Scholar]
  89. 89.
    Lucien F, Lac V, Billadeau DD, Borgida A, Gallinger S, Leong HS. 2019. Glypican-1 and glycoprotein 2 bearing extracellular vesicles do not discern pancreatic cancer from benign pancreatic diseases. Oncotarget 10:101045–55
    [Google Scholar]
  90. 90.
    Qiu J, Xu J, Zhang K, Gu W, Nie L et al. 2020. Refining cancer management using integrated liquid biopsy. Theranostics 10:52374–84
    [Google Scholar]
  91. 91.
    Schutte E, Gansevoort RT, Benner J, Lutgers HL, Lambers Heerspink HJ. 2015. Will the future lie in multitude? A critical appraisal of biomarker panel studies on prediction of diabetic kidney disease progression. Nephrol. Dial. Transplant. 30:Suppl. 4iv96–104
    [Google Scholar]
  92. 92.
    Im YR, Tsui DWY, Diaz LA Jr., Wan JCM. 2021. Next-generation liquid biopsies: embracing data science in oncology. Trends Cancer 7:4283–92
    [Google Scholar]
  93. 93.
    Cohen JD, Javed AA, Thoburn C, Wong F, Tie J et al. 2017. Combined circulating tumor DNA and protein biomarker-based liquid biopsy for the earlier detection of pancreatic cancers. PNAS 114:3810202–207
    [Google Scholar]
  94. 94.
    Cohen JD, Li L, Wang Y, Thoburn C, Afsari B et al. 2018. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 359:6378926–30
    [Google Scholar]
  95. 95.
    Yelleswarapu V, Buser JR, Haber M, Baron J, Inapuri E, Issadore D. 2019. Mobile platform for rapid sub-picogram-per-milliliter, multiplexed, digital droplet detection of proteins. PNAS 116:104489–95
    [Google Scholar]
  96. 96.
    Chen C, Zong S, Liu Y, Wang Z, Zhang Y et al. 2019. Profiling of exosomal biomarkers for accurate cancer identification: combining DNA-PAINT with machine-learning-based classification. Small 15:43e1901014
    [Google Scholar]
  97. 97.
    Ebrahimkhani S, Vafaee F, Hallal S, Wei H, Lee MYT et al. 2018. Deep sequencing of circulating exosomal microRNA allows non-invasive glioblastoma diagnosis. NPJ Precis. Oncol. 2:28
    [Google Scholar]
  98. 98.
    Keup C, Suryaprakash V, Hauch S, Storbeck M, Hahn P et al. 2021. Integrative statistical analyses of multiple liquid biopsy analytes in metastatic breast cancer. Genome Med 13:85
    [Google Scholar]
  99. 99.
    Krug AK, Enderle D, Karlovich C, Priewasser T, Bentink S et al. 2018. Improved EGFR mutation detection using combined exosomal RNA and circulating tumor DNA in NSCLC patient plasma. . Ann. Oncol. 29:3700–6
    [Google Scholar]
  100. 100.
    Wu CX, Liu ZF. 2018. Proteomic profiling of sweat exosome suggests its involvement in skin immunity. J. Investig. Dermatol. 138:189–97
    [Google Scholar]
  101. 101.
    Fleischhacker M, Schmidt B. 2020. Pre-analytical issues in liquid biopsy—Where do we stand?. J. Lab. Med. 44:117–42
    [Google Scholar]
  102. 102.
    Trigg RM, Martinson LJ, Parpart-Li S, Shaw JA. 2018. Factors that influence quality and yield of circulating-free DNA: a systematic review of the methodology literature. Heliyon 4:7e00699
    [Google Scholar]
  103. 103.
    Till JE, Black TA, Gentile C, Abdalla A, Wang Z et al. 2021. Optimization of sources of circulating cell-free DNA variability for downstream molecular analysis. J. Mol. Diagn. 23:111545–52
    [Google Scholar]
  104. 104.
    Bæk R, Søndergaard EKL, Varming K, Jørgensen MM. 2016. The impact of various preanalytical treatments on the phenotype of small extracellular vesicles in blood analyzed by protein microarray. J. Immunol. Methods 438:11–20
    [Google Scholar]
  105. 105.
    Kondratov K, Kurapeev D, Popov M, Sidorova M, Minasian S et al. 2016. Heparinase treatment of heparin-contaminated plasma from coronary artery bypass grafting patients enables reliable quantification of microRNAs. Biomol. Detect. Quantif. 8:9–14
    [Google Scholar]
  106. 106.
    Alidousty C, Brandes D, Heydt C, Wagener S, Wittersheim M et al. 2017. Comparison of blood collection tubes from three different manufacturers for the collection of cell-free DNA for liquid biopsy mutation testing. J. Mol. Diagn. 19:5801–4
    [Google Scholar]
  107. 107.
    Salvianti F, Gelmini S, Costanza F, Mancini I, Sonnati G. 2020. The pre-analytical phase of the liquid biopsy. N. Biotechnol. 55:19–29
    [Google Scholar]
  108. 108.
    Gidlöf O, Evander M, Rezeli M, Marko-Varga G, Laurell T, Erlinge D. 2019. Proteomic profiling of extracellular vesicles reveals additional diagnostic biomarkers for myocardial infarction compared to plasma alone. Sci. Rep. 9:8991
    [Google Scholar]
  109. 109.
    Lugli G, Cohen AM, Bennett DA, Shah RC, Fields CJ et al. 2015. Plasma exosomal miRNAs in persons with and without Alzheimer disease: altered expression and prospects for biomarkers. PLOS ONE 10:10e0139233
    [Google Scholar]
  110. 110.
    Cazzoli R, Buttitta F, Di Nicola M, Malatesta S, Marchetti A et al. 2013. microRNAs derived from circulating exosomes as noninvasive biomarkers for screening and diagnosing lung cancer. J. Thorac. Oncol. 8:91156–62
    [Google Scholar]
  111. 111.
    Perakis S, Speicher MR. 2017. Emerging concepts in liquid biopsies. BMC Med 15:75
    [Google Scholar]
  112. 112.
    Margolis L, Sadovsky Y. 2019. The biology of extracellular vesicles: the known unknowns. PLOS Biol 17:e3000363
    [Google Scholar]
  113. 113.
    Pinto JV, Passos IC, Gomes F, Reckziegel R, Kapczinski F et al. 2017. Peripheral biomarker signatures of bipolar disorder and schizophrenia: a machine learning approach. Schizophr. Res. 188:182–84
    [Google Scholar]
  114. 114.
    Uddin S, Khan A, Hossain ME, Moni MA. 2019. Comparing different supervised machine learning algorithms for disease prediction. BMC Med. Inform. Decis. Mak. 19:281
    [Google Scholar]
  115. 115.
    Paeglis A, Strumfs B, Mezale D, Fridrihsone I 2018. A review on machine learning and deep learning techniques applied to liquid biopsy. Liquid Biopsy I Strumfa, J Gardovskis, Pap. 4 London: IntechOpen
    [Google Scholar]
  116. 116.
    Faul F, Erdfelder E, Buchner A, Lang AG. 2009. Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav. Res. Methods 41:41149–60
    [Google Scholar]
  117. 117.
    Figueroa RL, Zeng-Treitler Q, Kandula S, Ngo LH. 2012. Predicting sample size required for classification performance. BMC Med. Inform. Decis. Mak. 12:8
    [Google Scholar]
  118. 118.
    Liu L, Chen X, Petinrin OO, Zhang W, Rahaman S et al. 2021. Machine learning protocols in early cancer detection based on liquid biopsy: a survey. Life 11:7638
    [Google Scholar]
  119. 119.
    Stoltzfus JC. Logistic regression: a brief primer. 2011. Acad. Emerg. Med. 18:101099–104
    [Google Scholar]
  120. 120.
    Pochet NL, Suykens JA. 2006. Support vector machines versus logistic regression: improving prospective performance in clinical decision-making. Ultrasound Obstet. Gynecol. 27:6607–8
    [Google Scholar]
  121. 121.
    Kouiroukidis N, Evangelidis G. 2011. The effects of dimensionality curse in high dimensional kNN search. 2011 15th Panhellenic Conference on Informatics41–45 New York: IEEE
    [Google Scholar]
  122. 122.
    Zhou ZH 2009. Ensemble learning. Encyclopedia of Biometrics SZ Li, A Jain Boston: Springer https://doi.org/10.1007/978-0-387-73003-5_293
    [Crossref] [Google Scholar]
  123. 123.
    Tang B, Pan Z, Yin K, Khateeb A. 2019. Recent advances of deep learning in bioinformatics and computational biology. Front. Genet. 10:214
    [Google Scholar]
  124. 124.
    D'souza RN, Huang PY, Yeh FC 2020. Structural analysis and optimization of convolutional neural networks with a small sample size. Sci. Rep. 10:834
    [Google Scholar]
  125. 125.
    Shin H, Oh S, Hong S, Kang M, Kang D et al. 2020. Early-stage lung cancer diagnosis by deep learning-based spectroscopic analysis of circulating exosomes. ACS Nano 14:55435–44
    [Google Scholar]
  126. 126.
    Schmidhuber J. 2015. Deep learning in neural networks: an overview. Neural Netw 61:85–117
    [Google Scholar]
  127. 127.
    Zhang Y, Yang Y. 2015. Cross-validation for selecting a model selection procedure. J. Econom. 187:95–112
    [Google Scholar]
  128. 128.
    Francois-Lavet V, Rabusseau G, Pineau J, Ernst D, Fonteneau R. 2020. On overfitting and asymptotic bias in batch reinforcement learning with partial observability. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence5055–59 https://doi.org/10.24963/ijcai.2020/706
    [Crossref] [Google Scholar]
  129. 129.
    Marcot BG, Hanea AM. 2021. What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?. Comput. Stat. 36:2009–31
    [Google Scholar]
  130. 130.
    Flach P, Hernández-Orallo J, Ferri C 2011. A coherent interpretation of AUC as a measure of aggregated classification performance. Proceedings of the 28th International Conference on Machine Learning L Getoor, T Scheffer 657–64 New York: Assoc. Comput. Mach.
    [Google Scholar]
  131. 131.
    Shen H, Liu T, Cui J, Borole P, Benjamin A et al. 2020. A web-based automated machine learning platform to analyze liquid biopsy data. Lab Chip 20:122166–74
    [Google Scholar]
  132. 132.
    Šimundić AM. 2009. Measures of diagnostic accuracy: basic definitions. eJIFCC 19:203–11
    [Google Scholar]
  133. 133.
    Tenny S, Hoffman MR. 2021. Prevalence. StatPearls Treasure Island, Fla.: StatPearls Publ https://www.ncbi.nlm.nih.gov/books/NBK430867/
    [Google Scholar]
  134. 134.
    Lakshminarayan K, Harp SA, Goldman R, Samad T. 1996. Imputation of missing data using machine learning techniques. KDD-96 Proceedings140–45 Palo Alto, CA: AAAI
    [Google Scholar]
  135. 135.
    Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T et al. 2001. Missing value estimation methods for DNA microarrays. Bioinformatics 17:6520–25
    [Google Scholar]
  136. 136.
    Stekhoven DJ, Bühlmann P. 2012. MissForest—non-parametric missing value imputation for mixed-type data. Bioinformatics 28:1112–18
    [Google Scholar]
  137. 137.
    Wu X, Akbarzadeh Khorshidi H, Aickelin U, Edib Z, Peate M 2019. Imputation techniques on missing values in breast cancer treatment and fertility data. Health Inf. Sci. Syst. 7:119
    [Google Scholar]
  138. 138.
    Yan Q, Hu D, Li M, Chen Y, Wu X et al. 2020. The serum microRNA signatures for pancreatic cancer detection and operability evaluation. Front. Bioeng. Biotechnol. 8:379
    [Google Scholar]
  139. 139.
    Lim M, Park J, Lowe AC, Jeong HO, Lee S et al. 2020. A lab-on-a-disc platform enables serial monitoring of individual CTCs associated with tumor progression during EGFR-targeted therapy for patients with NSCLC. Theranostics 10:125181–94
    [Google Scholar]
  140. 140.
    Beaulieu-Jones BK, Moore JH. 2017. Missing data imputation in the electronic health record using deeply learned autoencoders. Pac. Symp. Biocomput. 22:207–18
    [Google Scholar]
  141. 141.
    Rantalainen M, Holmes CC. 2011. Accounting for control mislabeling in case-control biomarker studies. J. Proteome Res. 10:5562–67
    [Google Scholar]
  142. 142.
    Yuan W, Han G, Guan D. 2021. Learning from mislabeled training data through ambiguous learning for in-home health monitoring. IEEE J. Sel. Areas Commun. 39:549–61
    [Google Scholar]
  143. 143.
    Mirylenka K, Giannakopoulos G, Do LM, Palpanas T. 2017. On classifier behavior in the presence of mislabeling noise. Data Min. Knowl. Discov. 31:661–701
    [Google Scholar]
  144. 144.
    Beam AL, Manrai AK, Ghassemi M. 2020. Challenges to the reproducibility of machine learning models in health care. JAMA 323:305–6
    [Google Scholar]
  145. 145.
    McDermott MBA, Wang S, Marinsek N, Ranganath R, Foschini L, Ghassemi M. Reproducibility in machine learning for health research: still a ways to go. Sci. Transl. Med. 13:586eabb1655
    [Google Scholar]
  146. 146.
    Svensson CM, Hübler R, Figge MT. 2015. Automated classification of circulating tumor cells and the impact of interobserver variability on classifier training and performance. J. Immunol. Res. 2015:573165
    [Google Scholar]
  147. 147.
    Karimi D, Dou H, Warfield SK, Gholipour A. 2020. Deep learning with noisy labels: exploring techniques and remedies in medical image analysis. Med. Image Anal. 65:101759
    [Google Scholar]
  148. 148.
    Kompa B, Snoek J, Beam AL. 2021. Second opinion needed: communicating uncertainty in medical machine learning. NPJ Digit. Med. 4:14
    [Google Scholar]
  149. 149.
    Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. 2018. Potential biases in machine learning algorithms using electronic health record data. JAMA Intern. Med. 178:1544–47
    [Google Scholar]
/content/journals/10.1146/annurev-biodatasci-122120-113218
Loading
/content/journals/10.1146/annurev-biodatasci-122120-113218
Loading

Data & Media loading...

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