Use of Circulating Cell-Free DNA to Guide Precision Medicine in Patients with Colorectal Cancer

Literature Cited

1. 1. 

   Illei PB, Wong W, Wu N et al. 2018. ALK testing trends and patterns among community practices in the United States. JCO Precision Oncol 2: https://ascopubs.org/doi/10.1200/PO.18.00159
   [Google Scholar]
2. 2. 

   Meric-Bernstam F, Brusco L, Shaw K et al. 2015. Feasibility of large-scale genomic testing to facilitate enrollment onto genomically matched clinical trials. J. Clin. Oncol. 33:2753–62
   [Google Scholar]
3. 3. 

   Gerlinger M, Rowan AJ, Horswell S et al. 2012. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366:883–92
   [Google Scholar]
4. 4. 

   De Mattos-Arruda L, Weigelt B, Cortes J et al. 2014. Capturing intra-tumor genetic heterogeneity by de novo mutation profiling of circulating cell-free tumor DNA: a proof-of-principle. Ann. Oncol. 25:1729–35
   [Google Scholar]
5. 5. 

   Yates LR, Gerstung M, Knappskog S et al. 2015. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat. Med. 21:751–59
   [Google Scholar]
6. 6. 

   Khan KH, Cunningham D, Werner B et al. 2018. Longitudinal liquid biopsy and mathematical modeling of clonal evolution forecast time to treatment failure in the PROSPECT-C phase II colorectal cancer clinical trial. Cancer Discov 8:1270–85
   [Google Scholar]
7. 7. 

   Siravegna G, Mussolin B, Buscarino M et al. 2015. Clonal evolution and resistance to EGFR blockade in the blood of colorectal cancer patients. Nat. Med. 21:795–801Identifies dynamic changes in RAS mutations in blood in response to anti-EGFR therapy.
   [Google Scholar]
8. 8. 

   Bettegowda C, Sausen M, Leary RJ et al. 2014. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci. Transl. Med. 6:224ra24
   [Google Scholar]
9. 9. 

   Strickler JH, Loree JM, Ahronian LG et al. 2018. Genomic landscape of cell-free DNA in patients with colorectal cancer. Cancer Discov 8:164–73
   [Google Scholar]
10. 10. 

    Husain H, Melnikova VO, Kosco K et al. 2017. Monitoring daily dynamics of early tumor response to targeted therapy by detecting circulating tumor DNA in urine. Clin. Cancer Res. 23:4716–23
    [Google Scholar]
11. 11. 

    Siravegna G, Marsoni S, Siena S, Bardelli A 2017. Integrating liquid biopsies into the management of cancer. Nat. Rev. Clin. Oncol. 14:531–48
    [Google Scholar]
12. 12. 

    Wang Y, Springer S, Mulvey CL et al. 2015. Detection of somatic mutations and HPV in the saliva and plasma of patients with head and neck squamous cell carcinomas. Sci. Transl. Med. 7:293ra104
    [Google Scholar]
13. 13. 

    Bachet JB, Bouche O, Taieb J et al. 2018. RAS mutation analysis in circulating tumor DNA from patients with metastatic colorectal cancer: the AGEO RASANC prospective multicenter study. Ann. Oncol. 29:1211–19
    [Google Scholar]
14. 14. 

    Sacher AG, Paweletz C, Dahlberg SE et al. 2016. Prospective validation of rapid plasma genotyping for the detection of EGFR and KRAS mutations in advanced lung cancer. JAMA Oncol 2:1014–22
    [Google Scholar]
15. 15. 

    Russo M, Siravegna G, Blaszkowsky LS et al. 2016. Tumor heterogeneity and lesion-specific response to targeted therapy in colorectal cancer. Cancer Discov 6:147–53
    [Google Scholar]
16. 16. 

    Gremel G, Lee RJ, Girotti MR et al. 2016. Distinct subclonal tumour responses to therapy revealed by circulating cell-free DNA. Ann. Oncol. 27:1959–65
    [Google Scholar]
17. 17. 

    Spindler KG, Boysen AK, Pallisgard N et al. 2017. Cell-free DNA in metastatic colorectal cancer: a systematic review and meta-analysis. Oncologist 22:1049–55
    [Google Scholar]
18. 18. 

    Kuderer NM, Burton KA, Blau S et al. 2017. Comparison of 2 commercially available next-generation sequencing platforms in oncology. JAMA Oncol 3:996–98
    [Google Scholar]
19. 19. 

    Chae YK, Davis AA, Carneiro BA et al. 2016. Concordance between genomic alterations assessed by next-generation sequencing in tumor tissue or circulating cell-free DNA. Oncotarget 7:65364–73
    [Google Scholar]
20. 20. 

    Bando H, Kagawa Y, Kato T et al. 2019. A multicentre, prospective study of plasma circulating tumour DNA test for detecting RAS mutation in patients with metastatic colorectal cancer. Br. J. Cancer 120:982–86
    [Google Scholar]
21. 21. 

    Gupta R, Othman T, Chen C et al. 2020. Guardant360 circulating tumor DNA assay is concordant with FoundationOne next-generation sequencing in detecting actionable driver mutations in anti-EGFR naive metastatic colorectal cancer. Oncologist 25:235–43
    [Google Scholar]
22. 22. 

    Diehl F, Li M, He Y et al. 2006. BEAMing: single-molecule PCR on microparticles in water-in-oil emulsions. Nat. Methods 3:551–59
    [Google Scholar]
23. 23. 

    Hindson BJ, Ness KD, Masquelier DA et al. 2011. High-throughput droplet digital PCR system for absolute quantitation of DNA copy number. Anal. Chem. 83:8604–10
    [Google Scholar]
24. 24. 

    Lanman RB, Mortimer SA, Zill OA et al. 2015. Analytical and clinical validation of a digital sequencing panel for quantitative, highly accurate evaluation of cell-free circulating tumor DNA. PLOS ONE 10:e0140712
    [Google Scholar]
25. 25. 

    Reinert T, Henriksen TV, Christensen E et al. 2019. Analysis of plasma cell-free DNA by ultradeep sequencing in patients with stages I to III colorectal cancer. JAMA Oncol 5:1124–31
    [Google Scholar]
26. 26. 

    Tie J, Wang Y, Tomasetti C et al. 2016. Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer. Sci. Transl. Med. 8:346ra92Detection of ctDNA after surgical resection is prognostically associated with a high likelihood of recurrence.
    [Google Scholar]
27. 27. 

    Reinert T, Scholer LV, Thomsen R et al. 2016. Analysis of circulating tumour DNA to monitor disease burden following colorectal cancer surgery. Gut 65:625–34
    [Google Scholar]
28. 28. 

    Corcoran RB, Chabner BA. 2018. Application of cell-free DNA analysis to cancer treatment. N. Engl. J. Med. 379:1754–65
    [Google Scholar]
29. 29. 

    Osumi H, Shinozaki E, Yamaguchi K, Zembutsu H 2019. Clinical utility of circulating tumor DNA for colorectal cancer. Cancer Sci 110:1148–55
    [Google Scholar]
30. 30. 

    Wolmark N, Rockette H, Mamounas E et al. 1999. Clinical trial to assess the relative efficacy of fluorouracil and leucovorin, fluorouracil and levamisole, and fluorouracil, leucovorin, and levamisole in patients with Dukes’ B and C carcinoma of the colon: results from National Surgical Adjuvant Breast and Bowel Project C-04. J. Clin. Oncol. 17:3553–59
    [Google Scholar]
31. 31. 

    Boland GM, Chang GJ, Haynes AB et al. 2013. Association between adherence to National Comprehensive Cancer Network treatment guidelines and improved survival in patients with colon cancer. Cancer 119:1593–601
    [Google Scholar]
32. 32. 

    Hines RB, Barrett A, Twumasi-Ankrah P et al. 2015. Predictors of guideline treatment nonadherence and the impact on survival in patients with colorectal cancer. J. Natl. Compr. Canc. Netw. 13:51–60
    [Google Scholar]
33. 33. 

    Gray RG, Quirke P, Handley K et al. 2011. Validation study of a quantitative multigene reverse transcriptase-polymerase chain reaction assay for assessment of recurrence risk in patients with stage II colon cancer. J. Clin. Oncol. 29:4611–19
    [Google Scholar]
34. 34. 

    Niedzwiecki D, Frankel WL, Venook AP et al. 2016. Association between results of a gene expression signature assay and recurrence-free interval in patients with stage II colon cancer in Cancer and Leukemia Group B 9581 (Alliance). J. Clin. Oncol. 34:3047–53
    [Google Scholar]
35. 35. 

    Kopetz S, Tabernero J, Rosenberg R et al. 2015. Genomic classifier ColoPrint predicts recurrence in stage II colorectal cancer patients more accurately than clinical factors. Oncologist 20:127–33
    [Google Scholar]
36. 36. 

    Garcia-Murillas I, Schiavon G, Weigelt B et al. 2015. Mutation tracking in circulating tumor DNA predicts relapse in early breast cancer. Sci. Transl. Med. 7:302ra133
    [Google Scholar]
37. 37. 

    Abbosh C, Birkbak NJ, Wilson GA et al. 2017. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature 545:446–51
    [Google Scholar]
38. 38. 

    Chaudhuri AA, Chabon JJ, Lovejoy AF et al. 2017. Early detection of molecular residual disease in localized lung cancer by circulating tumor DNA profiling. Cancer Discov 7:1394–403
    [Google Scholar]
39. 39. 

    Abbosh C, Birkbak NJ, Swanton C 2018. Early stage NSCLC—challenges to implementing ctDNA-based screening and MRD detection. Nat. Rev. Clin. Oncol. 15:577–86
    [Google Scholar]
40. 40. 

    Parikh AR, Leshchiner I, Elagina L et al. 2019. Liquid versus tissue biopsy for detecting acquired resistance and tumor heterogeneity in gastrointestinal cancers. Nat. Med. 25:1415–21Paired analysis of tissue and blood after progression demonstrates that cfDNA better identifies the molecular heterogeneity of acquired resistance.
    [Google Scholar]
41. 41. 

    Kato S, Schwaederle MC, Fanta PT et al. 2019. Genomic assessment of blood-derived circulating tumor DNA in patients with colorectal cancers: correlation with tissue sequencing, therapeutic response, and survival. JCO Precis. Oncol. 3: https://ascopubs.org/doi/10.1200/PO.18.00158
    [Google Scholar]
42. 42. 

    Diaz LA Jr., Bardelli A. 2014. Liquid biopsies: genotyping circulating tumor DNA. J. Clin. Oncol. 32:579–86
    [Google Scholar]
43. 43. 

    Scholer LV, Reinert T, Orntoft MW et al. 2017. Clinical implications of monitoring circulating tumor DNA in patients with colorectal cancer. Clin. Cancer Res. 23:5437–45
    [Google Scholar]
44. 44. 

    Grothey A, Sobrero AF, Shields AF et al. 2018. Duration of adjuvant chemotherapy for stage III colon cancer. N. Engl. J. Med. 378:1177–88
    [Google Scholar]
45. 45. 

    Corcoran RB. 2019. Circulating tumor DNA: clinical monitoring and early detection. Annu. Rev. Cancer Biol. 3:187–201
    [Google Scholar]
46. 46. 

    Tie J, Cohen JD, Wang Y et al. 2019. Circulating tumor DNA analyses as markers of recurrence risk and benefit of adjuvant therapy for stage III colon cancer. JAMA Oncol 5:1710–17Absence of ctDNA after completion of adjuvant chemotherapy is linked to improved survival outcomes.
    [Google Scholar]
47. 47. 

    Overman MJ, Vauthey J-N, Aloia TA et al. 2017. Circulating tumor DNA (ctDNA) utilizing a high-sensitivity panel to detect minimal residual disease post liver hepatectomy and predict disease recurrence. J. Clin. Oncol. 35:Suppl.3522
    [Google Scholar]
48. 48. 

    Wang Y, Li L, Cohen JD et al. 2019. Prognostic potential of circulating tumor DNA measurement in postoperative surveillance of nonmetastatic colorectal cancer. JAMA Oncol 5:1118–23
    [Google Scholar]
49. 49. 

    Hoadley KA, Yau C, Wolf DM et al. 2014. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 158:929–44
    [Google Scholar]
50. 50. 

    Shen SY, Singhania R, Fehringer G et al. 2018. Sensitive tumour detection and classification using plasma cell-free DNA methylomes. Nature 563:579–83Methylation patterns can be recognized in ctDNA distinct from nonmalignant tissue and by cancer-specific type.
    [Google Scholar]
51. 51. 

    Pedersen SK, Baker RT, McEvoy A et al. 2015. A two-gene blood test for methylated DNA sensitive for colorectal cancer. PLOS ONE 10:e0125041
    [Google Scholar]
52. 52. 

    Murray DH, Symonds EL, Young GP et al. 2018. Relationship between post-surgery detection of methylated circulating tumor DNA with risk of residual disease and recurrence-free survival. J. Cancer Res. Clin. Oncol. 144:1741–50
    [Google Scholar]
53. 53. 

    Pereira AAL, Morelli MP, Overman M et al. 2017. Clinical utility of circulating cell-free DNA in advanced colorectal cancer. PLOS ONE 12:e0183949
    [Google Scholar]
54. 54. 

    Hsu HC, Lapke N, Wang CW et al. 2018. Targeted sequencing of circulating tumor DNA to monitor genetic variants and therapeutic response in metastatic colorectal cancer. Mol. Cancer Ther. 17:2238–47
    [Google Scholar]
55. 55. 

    Vandeputte C, Kehagias P, El Housni H et al. 2018. Circulating tumor DNA in early response assessment and monitoring of advanced colorectal cancer treated with a multi-kinase inhibitor. Oncotarget 9:17756–69
    [Google Scholar]
56. 56. 

    Osumi H, Shinozaki E, Takeda Y et al. 2019. Clinical relevance of circulating tumor DNA assessed through deep sequencing in patients with metastatic colorectal cancer. Cancer Med 8:408–17
    [Google Scholar]
57. 57. 

    Vidal J, Muinelo L, Dalmases A et al. 2017. Plasma ctDNA RAS mutation analysis for the diagnosis and treatment monitoring of metastatic colorectal cancer patients. Ann. Oncol. 28:1325–32
    [Google Scholar]
58. 58. 

    Davies H, Bignell GR, Cox C et al. 2002. Mutations of the BRAF gene in human cancer. Nature 417:949–54
    [Google Scholar]
59. 59. 

    Weisenberger DJ, Siegmund KD, Campan M et al. 2006. CpG island methylator phenotype underlies sporadic microsatellite instability and is tightly associated with BRAF mutation in colorectal cancer. Nat. Genet. 38:787–93
    [Google Scholar]
60. 60. 

    Hong DS, Morris VK, El Osta B et al. 2016. Phase IB study of vemurafenib in combination with irinotecan and cetuximab in patients with metastatic colorectal cancer with BRAFV600E mutation. Cancer Discov 6:1352–65
    [Google Scholar]
61. 61. 

    Corcoran RB, Andre T, Atreya CE et al. 2018. Combined BRAF, EGFR, and MEK inhibition in patients with BRAF(V600E)-mutant colorectal cancer. Cancer Discov 8:428–43
    [Google Scholar]
62. 62. 

    Cancer Genome Atlas Netw 2012. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487:330–37
    [Google Scholar]
63. 63. 

    Tie J, Kinde I, Wang Y et al. 2015. Circulating tumor DNA as an early marker of therapeutic response in patients with metastatic colorectal cancer. Ann. Oncol. 26:1715–22
    [Google Scholar]
64. 64. 

    Parikh AR, Mojtahed A, Schneider JL et al. 2020. Serial ctDNA monitoring to predict response to systemic therapy in metastatic gastrointestinal cancers. Clin. Cancer Res. 26:1877–85Changes in ctDNA levels after systemic treatment for metastatic colorectal cancer precede radiographic response.
    [Google Scholar]
65. 65. 

    Misale S, Yaeger R, Hobor S et al. 2012. Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer. Nature 486:532–36
    [Google Scholar]
66. 66. 

    Diaz LA Jr., Williams RT, Wu J et al. 2012. The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers. Nature 486:537–40
    [Google Scholar]
67. 67. 

    Morelli MP, Overman MJ, Dasari A et al. 2015. Characterizing the patterns of clonal selection in circulating tumor DNA from patients with colorectal cancer refractory to anti-EGFR treatment. Ann. Oncol. 26:731–36
    [Google Scholar]
68. 68. 

    Murtaza M, Dawson SJ, Tsui DW et al. 2013. Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA. Nature 497:108–12
    [Google Scholar]
69. 69. 

    Mok T, Wu YL, Lee JS et al. 2015. Detection and dynamic changes of EGFR mutations from circulating tumor DNA as a predictor of survival outcomes in NSCLC patients treated with first-line intercalated erlotinib and chemotherapy. Clin. Cancer Res. 21:3196–203
    [Google Scholar]
70. 70. 

    Yu HA, Arcila ME, Rekhtman N et al. 2013. Analysis of tumor specimens at the time of acquired resistance to EGFR-TKI therapy in 155 patients with EGFR-mutant lung cancers. Clin. Cancer Res. 19:2240–47
    [Google Scholar]
71. 71. 

    Douillard JY, Oliner KS, Siena S et al. 2013. Panitumumab-FOLFOX4 treatment and RAS mutations in colorectal cancer. N. Engl. J. Med. 369:1023–34
    [Google Scholar]
72. 72. 

    Van Cutsem E, Lenz HJ, Kohne CH et al. 2015. Fluorouracil, leucovorin, and irinotecan plus cetuximab treatment and RAS mutations in colorectal cancer. J. Clin. Oncol. 33:692–700
    [Google Scholar]
73. 73. 

    Bokemeyer C, Kohne CH, Ciardiello F et al. 2015. FOLFOX4 plus cetuximab treatment and RAS mutations in colorectal cancer. Eur. J. Cancer 51:1243–52
    [Google Scholar]
74. 74. 

    Van Emburgh BO, Arena S, Siravegna G et al. 2016. Acquired RAS or EGFR mutations and duration of response to EGFR blockade in colorectal cancer. Nat. Commun. 7:13665
    [Google Scholar]
75. 75. 

    Misale S, Arena S, Lamba S et al. 2014. Blockade of EGFR and MEK intercepts heterogeneous mechanisms of acquired resistance to anti-EGFR therapies in colorectal cancer. Sci. Transl. Med. 6:224ra26
    [Google Scholar]
76. 76. 

    Pietrantonio F, Vernieri C, Siravegna G et al. 2017. Heterogeneity of acquired resistance to anti-EGFR monoclonal antibodies in patients with metastatic colorectal cancer. Clin. Cancer Res. 23:2414–22
    [Google Scholar]
77. 77. 

    Montagut C, Dalmases A, Bellosillo B et al. 2012. Identification of a mutation in the extracellular domain of the epidermal growth factor receptor conferring cetuximab resistance in colorectal cancer. Nat. Med 18:221–23EGFR^S492R confers resistance to cetuximab but not panitumumab.
    [Google Scholar]
78. 78. 

    Newhall K, Price T, Peeters M et al. 2014. Frequency of S492R mutations in the epidermal growth factor receptor: analysis of plasma DNA from metastatic colorectal cancer patients treated with panitumumab or cetuximab monotherapy. Ann. Oncol. 25:ii109
    [Google Scholar]
79. 79. 

    Arena S, Bellosillo B, Siravegna G et al. 2015. Emergence of multiple EGFR extracellular mutations during cetuximab treatment in colorectal cancer. Clin. Cancer Res. 21:2157–66
    [Google Scholar]
80. 80. 

    Yaeger R, Chatila WK, Lipsyc MD et al. 2018. Clinical sequencing defines the genomic landscape of metastatic colorectal cancer. Cancer Cell 33:125–36.e3
    [Google Scholar]
81. 81. 

    Bardelli A, Corso S, Bertotti A et al. 2013. Amplification of the MET receptor drives resistance to anti-EGFR therapies in colorectal cancer. Cancer Discov 3:658–73
    [Google Scholar]
82. 82. 

    Raghav K, Morris V, Tang C et al. 2016. MET amplification in metastatic colorectal cancer: an acquired response to EGFR inhibition, not a de novo phenomenon. Oncotarget 7:54627–31
    [Google Scholar]
83. 83. 

    Bertotti A, Migliardi G, Galimi F et al. 2011. A molecularly annotated platform of patient-derived xenografts (“xenopatients”) identifies HER2 as an effective therapeutic target in cetuximab-resistant colorectal cancer. Cancer Discov 1:508–23
    [Google Scholar]
84. 84. 

    Raghav KP, Overman MJ, Yu R et al. 2016. HER2 amplification as a negative predictive biomarker for anti-epidermal growth factor receptor antibody therapy in metastatic colorectal cancer. J. Clin. Oncol. 34:3517
    [Google Scholar]
85. 85. 

    Kim SR, Srinivasan A, Allegra CJ et al. 2017. NSABP FC-7 correlative study: HER2 amplification (amp) in circulating cell-free DNA (cfDNA) in metastatic colorectal cancer (mCRC) resistant to anti-EGFR therapy (tx). Cancer Res 77:5684 Abstr .)
    [Google Scholar]
86. 86. 

    Valtorta E, Misale S, Sartore-Bianchi A et al. 2013. KRAS gene amplification in colorectal cancer and impact on response to EGFR-targeted therapy. Int. J. Cancer 133:1259–65
    [Google Scholar]
87. 87. 

    Parseghian CM, Loree JM, Morris VK et al. 2019. Anti-EGFR-resistant clones decay exponentially after progression: implications for anti-EGFR re-challenge. Ann. Oncol. 30:243–49Describes the decay half-life of acquired RAS and EGFR mutations after withdrawal of anti-EGFR therapy.
    [Google Scholar]
88. 88. 

    Cremolini C, Rossini D, Dell'Aquila E et al. 2019. Rechallenge for patients with RAS and BRAF wild-type metastatic colorectal cancer with acquired resistance to first-line cetuximab and irinotecan: a phase 2 single-arm clinical trial. JAMA Oncol 5:343–50Phase II study demonstrates how cfDNA can identify patients who benefit from anti-EGFR rechallenge.
    [Google Scholar]
89. 89. 

    Siena S, Bardelli A, Sartore-Bianchi A et al. 2018. Exploiting clonal evolution and liquid biopsy to overcome resistance to anti-EGFR treatment in metastatic colorectal cancer: the CHRONOS trial. Mol. Cancer Ther. 17:A089-A (Abstr.)
    [Google Scholar]
90. 90. 

    Mauri G, Pizzutilo EG, Amatu A et al. 2019. Retreatment with anti-EGFR monoclonal antibodies in metastatic colorectal cancer: systematic review of different strategies. Cancer Treat. Rev. 73:41–53
    [Google Scholar]
91. 91. 

    Arena S, Siravegna G, Mussolin B et al. 2016. MM-151 overcomes acquired resistance to cetuximab and panitumumab in colorectal cancers harboring EGFR extracellular domain mutations. Sci. Transl. Med. 8:324ra14
    [Google Scholar]
92. 92. 

    Montagut C, Argiles G, Ciardiello F et al. 2018. Efficacy of Sym004 in patients with metastatic colorectal cancer with acquired resistance to anti-EGFR therapy and molecularly selected by circulating tumor DNA analyses: a phase 2 randomized clinical trial. JAMA Oncol 4:e175245
    [Google Scholar]
93. 93. 

    Troiani T, Napolitano S, Vitagliano D et al. 2014. Primary and acquired resistance of colorectal cancer cells to anti-EGFR antibodies converge on MEK/ERK pathway activation and can be overcome by combined MEK/EGFR inhibition. Clin. Cancer Res. 20:3775–86
    [Google Scholar]
94. 94. 

    Gandara DR, Paul SM, Kowanetz M et al. 2018. Blood-based tumor mutational burden as a predictor of clinical benefit in non-small-cell lung cancer patients treated with atezolizumab. Nat. Med. 24:1441–48
    [Google Scholar]
95. 95. 

    Willis J, Lefterova MI, Artyomenko A et al. 2019. Validation of microsatellite instability detection using a comprehensive plasma-based genotyping panel. Clin. Cancer Res. 25:7035–45Assessment of microsatellite status corresponds between matched tissue and ctDNA in patients with colorectal cancer.
    [Google Scholar]
96. 96. 

    Liu MC, Oxnard GR, Klein EA et al. 2020. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol 31:745–59
    [Google Scholar]
97. 97. 

    Tie J, Cohen JD, Wang Y et al. 2019. Serial circulating tumour DNA analysis during multimodality treatment of locally advanced rectal cancer: a prospective biomarker study. Gut 68:663–71
    [Google Scholar]