Abstract
Background
Evaluating the tumor RAS/BRAF status is important for treatment selection and prognosis assessment in metastatic colorectal cancer (mCRC) patients. Correction of artifacts from library preparation and sequencing is essential for accurately analyzing circulating tumor DNA (ctDNA) mutations. Here, we assessed the analytical and clinical performance of a novel amplicon-based next-generation sequencing (NGS) assay, Firefly™, which employs a concatemer-based error correction strategy.
Methods
Firefly assay targeting KRAS/NRAS/BRAF/PIK3CA was evaluated using cell-free DNA (cfDNA) reference standards and cfDNA samples from 184 mCRC patients. Plasma results were compared to the mutation status determined by ARMS-based PCR from matched tissue. Samples with a mutation abundance below the limit of detection (LOD) were retested again by droplet digital polymerase chain reaction (ddPCR) or NGS.
Results
The Firefly assay demonstrated superior sensitivity and specificity with a 98.89% detection rate at an allele frequency (AF) of 0.2% for 20 ng cfDNA. Generally, 40.76% and 48.37% of the patients were reported to be positive by NGS of plasma cfDNA and ARMS of FFPE tissue, respectively. The concordance rate between the two platforms was 80.11%. In the pre-treatment cohort, the concordance rate between plasma and tissue was 93.33%, based on the 17 common exons that Firefly™ and ARMS genotyped, and the positive percent agreement (PPA) and negative percent agreement (NPA) for KRAS/NRAS/BRAF/PIK3CA were 100% and 99.60%, respectively.
Conclusions
Total plasma cfDNA detected by Firefly offers a viable complement for mutation profiling in CRC patients, given the high agreement with matched tumor samples. Together, these data demonstrate that Firefly could be routinely applied for clinical applications in mCRC patients.
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 81572064
Award Identifier / Grant number: 81772263
Award Identifier / Grant number: 81772511
Award Identifier / Grant number: 81602038
Funding source: Key Developing Disciplines of Shanghai Municipal Commission of Health and Family Planning
Award Identifier / Grant number: 2015ZB0201
Award Identifier / Grant number: 201440389
Funding source: Shanghai Science and Technology Commission
Award Identifier / Grant number: 16411952100
Funding source: Zhongshan Hospital
Award Identifier / Grant number: 2018ZSLC05
Funding statement: This study was supported by grants from the National Natural Science Foundation of China (no. 81572064, 81772263, 81772511, and 81602038, funder Id: http://dx.doi.org/10.13039/501100001809), the Key Developing Disciplines of Shanghai Municipal Commission of Health and Family Planning (2015ZB0201, 201440389), the Projects from the Shanghai Science and Technology Commission (16411952100, funder Id: http://dx.doi.org/10.13039/501100003399) and the Project from Zhongshan Hospital, Fudan university (2018ZSLC05).
Acknowledgments
We thank the team of Laboratory Medicine of Zhongshan Hospital who provided facilities, insight and expertise that greatly assisted the research.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
References
1. Bhandari A, Woodhouse M, Gupta S. Colorectal cancer is a leading cause of cancer incidence and mortality among adults younger than 50 years in the USA: a SEER-based analysis with comparison to other young-onset cancers. J Invest Med 2016:jim-2016-000229.10.1136/jim-2016-000229Search in Google Scholar
2. Baltruskeviciene E, Mickys U, Zvirblis T, Stulpinas R, Pipiriene Zelviene T, Aleknavicius E. Significance of KRAS, NRAS, BRAF and PIK3CA mutations in metastatic colorectal cancer patients receiving Bevacizumab: a single institution experience. Acta Med Litu 2016;23:24–34.Search in Google Scholar
3. Benson AB, Venook AP, Cederquist L, Chan E, Chen Y-J, Cooper HS, et al. Colon cancer, version 1.2017, NCCN clinical practice guidelines in oncology. J Natl Comprehens Cancer Netw 2017;15:370–98.10.6004/jnccn.2017.0036Search in Google Scholar
4. Douillard JY, Oliner KS, Siena S, Tabernero J, Burkes R, Barugel M, et al. Panitumumab-FOLFOX4 treatment and RAS mutations in colorectal cancer. N Engl J Med 2013;369:1023–34.10.1056/NEJMoa1305275Search in Google Scholar
5. Sorich MJ, Wiese MD, Rowland A, Kichenadasse G, McKinnon RA, Karapetis CS. Extended RAS mutations and anti-EGFR monoclonal antibody survival benefit in metastatic colorectal cancer: a meta-analysis of randomized, controlled trials. Ann Oncol 2015;26:13–21.10.1093/annonc/mdu378Search in Google Scholar
6. Van Cutsem E, Kohne CH, Lang I, Folprecht G, Nowacki MP, Cascinu S, et al. Cetuximab plus irinotecan, fluorouracil, and leucovorin as first-line treatment for metastatic colorectal cancer: updated analysis of overall survival according to tumor KRAS and BRAF mutation status. J Clin Oncol 2011;29:2011–9.10.1200/JCO.2010.33.5091Search in Google Scholar
7. Laurent-Puig P, Cayre A, Manceau G, Buc E, Bachet JB, Lecomte T, et al. Analysis of PTEN, BRAF, and EGFR status in determining benefit from cetuximab therapy in wild-type KRAS metastatic colon cancer. J Clin Oncol 2009;27:5924–30.10.1200/JCO.2008.21.6796Search in Google Scholar
8. Huang L, Liu Z, Deng D, Tan A, Liao M, Mo Z, et al. Anti-epidermal growth factor receptor monoclonal antibody-based therapy for metastatic colorectal cancer: a meta-analysis of the effect of PIK3CA mutations in KRAS wild-type patients. Arch Med Sci 2014;10:1.10.5114/aoms.2014.40728Search in Google Scholar
9. De Roock W, Claes B, Bernasconi D, De Schutter J, Biesmans B, Fountzilas G, et al. Effects of KRAS, BRAF, NRAS, and PIK3CA mutations on the efficacy of cetuximab plus chemotherapy in chemotherapy-refractory metastatic colorectal cancer: a retrospective consortium analysis. Lancet Oncol 2010;11:753–62.10.1016/S1470-2045(10)70130-3Search in Google Scholar
10. Yoshino T, Arnold D, Taniguchi H, Pentheroudakis G, Yamazaki K, Xu R-H, et al. Pan-Asian adapted ESMO consensus guidelines for the management of patients with metastatic colorectal cancer: a JSMO–ESMO initiative endorsed by CSCO, KACO, MOS, SSO and TOS. Ann Oncol 2017;29:44–70.10.1093/annonc/mdx738Search in Google Scholar PubMed
11. Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012;366:883–92.10.1056/NEJMoa1113205Search in Google Scholar PubMed PubMed Central
12. Symonds EL, Pedersen SK, Murray DH, Jedi M, Byrne SE, Rabbitt P, et al. Circulating tumour DNA for monitoring colorectal cancer – a prospective cohort study to assess relationship to tissue methylation, cancer characteristics and surgical resection. Clin Epigenet 2018;10:63.10.1186/s13148-018-0500-5Search in Google Scholar PubMed PubMed Central
13. Burrell RA, McGranahan N, Bartek J, Swanton C. The causes and consequences of genetic heterogeneity in cancer evolution. Nature 2013;501:338–45.10.1038/nature12625Search in Google Scholar PubMed
14. Siravegna G, Bardelli A. Blood circulating tumor DNA for non-invasive genotyping of colon cancer patients. Mol Oncol 2016;10:475–80.10.1016/j.molonc.2015.12.005Search in Google Scholar PubMed PubMed Central
15. 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;21:795.10.1038/nm.3870Search in Google Scholar PubMed PubMed Central
16. Han X, Wang J, Sun Y. Circulating tumor DNA as biomarkers for cancer detection. Genom Proteom Bioinf 2017;15:59–72.10.1016/j.gpb.2016.12.004Search in Google Scholar PubMed PubMed Central
17. 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 U S A 2005;102:16368–73.10.1073/pnas.0507904102Search in Google Scholar PubMed PubMed Central
18. Zhu L, Zhang S, Xun Y, Jiang Y, Xia B, Chen X, et al. Comparison of the amplification refractory mutation system, super amplification refractory mutation system, and droplet digital PCR for T790 M mutation detection in non-small cell lung cancer after failure of tyrosine kinase inhibitor treatment. Pathol Oncol Res 2018;24:843–51.10.1007/s12253-017-0286-3Search in Google Scholar PubMed
19. Denis JA, Guillerm E, Coulet F, Larsen AK, Lacorte J-M. The role of BEAMing and digital PCR for multiplexed analysis in molecular oncology in the era of next-generation sequencing. Mol Diagn Ther 2017;21:587–600.10.1007/s40291-017-0287-7Search in Google Scholar PubMed
20. Zhang W, Xia W, Lv Z, Xin Y, Ni C, Yang L. Liquid biopsy for cancer: circulating tumor cells, circulating free DNA or exosomes? Cell Physiol Biochem 2017;41:755–68.10.1159/000458736Search in Google Scholar PubMed
21. Haber DA, Velculescu VE. Blood-based analyses of cancer: circulating tumor cells and circulating tumor DNA. Cancer Discov 2014;4:650–61.10.1158/2159-8290.CD-13-1014Search in Google Scholar PubMed PubMed Central
22. Forshew T, Murtaza M, Parkinson C, Gale D, Tsui DW, Kaper F, et al. Noninvasive identification and monitoring of cancer mutations by targeted deep sequencing of plasma DNA. Sci Transl Med 2012;4:136–68.10.1126/scitranslmed.3003726Search in Google Scholar PubMed
23. Kinde I, Wu J, Papadopoulos N, Kinzler KW, Vogelstein B. Detection and quantification of rare mutations with massively parallel sequencing. Proc Natl Acad Sci U S A 2011;108:9530–5.10.1073/pnas.1105422108Search in Google Scholar PubMed PubMed Central
24. 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;34:547–55.10.1038/nbt.3520Search in Google Scholar PubMed PubMed Central
25. Chaudhuri AA, Binkley MS, Osmundson EC, Alizadeh AA, Diehn M. Predicting radiotherapy responses and treatment outcomes through analysis of circulating tumor DNA. Semin Radiat Oncol 2015;25:305–12.10.1016/j.semradonc.2015.05.001Search in Google Scholar PubMed PubMed Central
26. Xu T, Kang X, You X, Dai L, Tian D, Yan W, et al. Cross-platform comparison of four leading technologies for detecting EGFR mutations in circulating tumor DNA from non-small cell lung carcinoma patient plasma. Theranostics 2017;7:1437–46.10.7150/thno.16558Search in Google Scholar PubMed PubMed Central
27. Stojanovic M, Andjelkovic Apostolovic M, Stojanovic D, Milosevic Z, Ignjatovic A, Lakusic VM, et al. Understanding sensitivity, specificity and predictive values. Vojnosanit Pregl 2014;71:1062–5.10.2298/VSP1411062SSearch in Google Scholar
28. Obermeier P, Muehlhans S, Hoppe C, Karsch K, Tief F, Seeber L, et al. Enabling precision medicine with digital case classification at the point-of-care. EBioMedicine 2016;4:191–6.10.1016/j.ebiom.2016.01.008Search in Google Scholar PubMed PubMed Central
29. Guibert N, Hu Y, Feeney N, Kuang Y, Plagnol V, Jones G, et al. Amplicon-based next-generation sequencing of plasma cell-free DNA for detection of driver and resistance mutations in advanced non-small cell lung cancer. Ann Oncol 2018;29:1049–55.10.1093/annonc/mdy005Search in Google Scholar PubMed PubMed Central
30. Lee RJ, Gremel G, Marshall A, Myers KA, Fisher N, Dunn JA, et al. Circulating tumor DNA predicts survival in patients with resected high-risk stage II/III melanoma. Ann Oncol 2018;29:490–6.10.1093/annonc/mdx717Search in Google Scholar PubMed PubMed Central
31. Van Emburgh BO, Arena S, Siravegna G, Lazzari L, Crisafulli G, Corti G, et al. Acquired RAS or EGFR mutations and duration of response to EGFR blockade in colorectal cancer. Nat Commun 2016;7:13665.10.1038/ncomms13665Search in Google Scholar PubMed PubMed Central
32. Vidal J, Muinelo L, Dalmases A, Jones F, Edelstein D, Iglesias M, et al. Plasma ctDNA RAS mutation analysis for the diagnosis and treatment monitoring of metastatic colorectal cancer patients. Ann Oncol 2017;28:1325–32.10.1093/annonc/mdx125Search in Google Scholar
33. Jenkins S, Yang JC, Ramalingam SS, Yu K, Patel S, Weston S, et al. Plasma ctDNA analysis for detection of the EGFR T790M mutation in patients with advanced non-small cell lung cancer. J Thorac Oncol 2017;12:1061–70.10.1016/j.jtho.2017.04.003Search in Google Scholar
34. Demuth C, Spindler KG, Johansen JS, Pallisgaard N, Nielsen D, Hogdall E, et al. Measuring KRAS mutations in circulating tumor DNA by droplet digital PCR and next-generation sequencing. Transl Oncol 2018;11:1220–4.10.1016/j.tranon.2018.07.013Search in Google Scholar
35. Gale D, Arj L, Howarth K, Madi M, Durham B, Smalley S, et al. Development of a highly sensitive liquid biopsy platform to detect clinically-relevant cancer mutations at low allele fractions in cell-free DNA. PLoS One 2018;13:e0194630.10.1371/journal.pone.0194630Search in Google Scholar
36. Gandara D, Paul S, Kowanetz M, Schleifman E, Zou W, Li Y, et al. Blood-based tumor mutational burden as a predictor of clinical benefit in non-small-cell lung cancer patients treated with atezolizumab. Nat Med 2018;24:1441–8.10.1038/s41591-018-0134-3Search in Google Scholar
37. Das M. Blood-based tumour mutational burden analysis in NSCLC. Lancet Oncol 2018;19:e446.10.1016/S1470-2045(18)30615-6Search in Google Scholar
Supplementary Material
The online version of this article offers supplementary material (https://doi.org/10.1515/cclm-2019-0142).
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