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Recurrence Prediction by Circulating Tumor DNA in the Patient with Colorectal Liver Metastases After Hepatectomy: A Prospective Biomarker Study

  • Hepatobiliary Tumors
  • Published:
Annals of Surgical Oncology Aims and scope Submit manuscript

Abstract

Background

The recurrence rate after hepatic resection of colorectal liver metastases (CRLM) remains high. This study aimed to investigate postoperative circulating tumor DNA (ctDNA) based on ultra-deep next-generation sequencing (NGS) to predict patient recurrence and survival.

Methods

Using the high-throughput NGS method tagged with a dual-indexed unique molecular identifier, named the CRLM-specific 25-gene panel (J25), this study sequenced ctDNA in peripheral blood samples collected from 134 CRLM patients who underwent hepatectomy after postoperative day 6.

Results

Of 134 samples, 42 (31.3%) were shown to be ctDNA-positive, and 37 resulted in recurrence. Kaplan-Meier survival analysis showed that disease-free survival (DFS) in the ctDNA-positive subgroup was significantly shorter than in the ctDNA-negative subgroup (hazard ratio [HR], 2.96; 95% confidence interval [CI], 1.91–4.6; p < 0.05). When the 42 ctDNA-positive samples were further divided by the median of the mean allele frequency (AF, 0.1034%), the subgroup with higher AFs showed a significantly shorter DFS than the subgroup with lower AFs (HR, 1.98; 95% CI, 1.02–3.85; p < 0.05). The ctDNA-positive patients who received adjuvant chemotherapy longer than 2 months showed a significantly longer DFS than those who received treatment for 2 months or less (HR, 0.377; 95% CI, 0.189–0.751; p < 0.05). Uni- and multivariable Cox regression indicated two factors independently correlated with prognosis: ctDNA positivity and no preoperative chemotherapy.

Conclusion

The study demonstrated that ctDNA status 6 days postoperatively could sensitively and accurately predict recurrence for patients with CRLM using the J25 panel.

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References

  1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424.

    Article  PubMed  Google Scholar 

  2. Adam R, Kitano Y. Multidisciplinary approach of liver metastases from colorectal cancer. Ann Gastroenterol Surg. 2019;3:50–6.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Margonis GA, Buettner S, Andreatos N, et al. Prognostic factors change over time after hepatectomy for colorectal liver metastases: a multi-institutional, international analysis of 1099 patients. Ann Surg. 2019;269:1129–37.

    Article  PubMed  Google Scholar 

  4. Imai K, Allard MA, Benitez CC, et al. Early recurrence after hepatectomy for colorectal liver metastases: what optimal definition and what predictive factors? Oncologist. 2016;21:887–94.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Naidoo M, Gibbs P, Tie J. ctDNA and adjuvant therapy for colorectal cancer: time to re-invent our treatment paradigm. Cancers. 2021;13:346.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. To YH, Lee B, Wong HL, Gibbs P, Tie J. Circulating tumour DNA to guide treatment of gastrointestinal malignancies. Visc Med. 2020;36:388–96.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Alimirzaie S, Bagherzadeh M, Akbari MR. Liquid biopsy in breast cancer: a comprehensive review. Clin Genet. 2019;95:643–60.

    Article  CAS  PubMed  Google Scholar 

  8. Chabon JJ, Hamilton EG, Kurtz DM, et al. Integrating genomic features for non-invasive early lung cancer detection. Nature. 2020;580:245–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Kobayashi S, Nakamura Y, Taniguchi H, et al. Impact of preoperative circulating tumor DNA status on survival outcomes after hepatectomy for resectable colorectal liver metastases. Ann Surg Oncol. 2021;28:4744–55.

    Article  PubMed  Google Scholar 

  10. Mason MC, Tzeng CD, Tran Cao HS, et al. Preliminary analysis of liquid biopsy after hepatectomy for colorectal liver metastases. J Am Coll Surg. 2021;233:82–9.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Sing T, Sander O, Beerenwinkel N, Thomas L. ROCR: visualizing classifier performance in R. Bioinformatics. 2005; 21(20):3940–1. https://doi.org/10.1093/bioinformatics/bti623.

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  13. Fong Y, Fortner J, Sun RL, Brennan MF, Blumgart LH. Clinical score for predicting recurrence after hepatic resection for metastatic colorectal cancer: analysis of 1001 consecutive cases. Ann Surg. 1999;230:309–18 (discussion 318–21).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Tosi F, Magni E, Amatu A, et al. Effect of KRAS and BRAF mutations on survival of metastatic colorectal cancer after liver resection: a systematic review and meta-analysis. Clin Colorectal Cancer. 2017;16:e153–63.

    Article  PubMed  Google Scholar 

  15. Liu APY, Smith KS, Kumar R, et al. Serial assessment of measurable residual disease in medulloblastoma liquid biopsies. Cancer Cell. 2021;39:1519–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Diehl F, Schmidt K, Choti MA, et al. Circulating mutant DNA to assess tumor dynamics. Nat Med. 2008;14:985–90.

    Article  CAS  PubMed  Google Scholar 

  17. Chen K, Zhao H, Shi Y, 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 

  18. Guo N, Lou F, Ma Y, et al. Circulating tumor DNA detection in lung cancer patients before and after surgery. Sci Rep. 2016;6:33519.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Loupakis F, Sharma S, Derouazi M, et al. Detection of molecular residual disease using personalized circulating tumor DNA assay in patients with colorectal cancer undergoing resection of metastases. JCO Precis Oncol. 2021;5:1166–77.

    Article  Google Scholar 

  20. Warton K, Mahon KL, Samimi G. Methylated circulating tumor DNA in blood: power in cancer prognosis and response. Endocr Relat Cancer. 2016;23:R157–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Reinert T, Henriksen TV, Christensen E, 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 

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

    Article  PubMed  PubMed Central  Google Scholar 

  23. Bidard FC, Kiavue N, Ychou M, et al. Circulating tumor cells and circulating tumor DNA detection in potentially resectable metastatic colorectal cancer: a prospective ancillary study to the Unicancer Prodige-14 Trial. Cells. 2019;8:516.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Narayan RR, Goldman DA, Gonen M, et al. Peripheral circulating tumor DNA detection predicts poor outcomes after liver resection for metastatic colorectal cancer. Ann Surg Oncol. 2019;26:1824–32.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Tie J, Wang Y, Tomasetti C, et al. Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer. Sci Transl Med. 2016;8:346–92.

    Article  Google Scholar 

  26. Tie J, Cohen JD, Wang Y, et al. Circulating tumor DNA analyses as markers of recurrence risk and benefit of adjuvant therapy for stage III colon cancer. JAMA Oncol. 2019;5:1710–7.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Gorgannezhad L, Umer M, Islam MN, Nguyen NT, Shiddiky MJA. Circulating tumor DNA and liquid biopsy: opportunities, challenges, and recent advances in detection technologies. Lab Chip. 2018;18:1174–96.

    Article  CAS  PubMed  Google Scholar 

  28. Mouliere F, Chandrananda D, Piskorz AM, et al. Enhanced detection of circulating tumor DNA by fragment size analysis. Sci Transl Med. 2018;10:eaat4921.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Chakrabarti S, Xie H, Urrutia R, Mahipal A. The promise of circulating tumor DNA (ctDNA) in the management of early-stage colon cancer: a critical review. Cancers. 2020;12:2808.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Samorodnitsky E, Jewell BM, Hagopian R, et al. Evaluation of hybridization capture versus amplicon-based methods for whole-exome sequencing. Hum Mutat. 2015;36:903–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Garcia-Garcia G, Baux D, Faugere V, et al. Assessment of the latest NGS enrichment capture methods in clinical context. Sci Rep. 2016;6:20948.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgment

We thank Hong-Wei Wang and Juan Li for their helpful suggestion in data analyzing. This study was supported by grants (nos. 81874143 and 31971192) from the National Nature Science Foundation of China and the Beijing Hospitals Authority Youth Program (code: QMS20201105).

Author information

Author notes

  1. Meng-Huan Zhang PhD

    Present address: Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, Frontier Science Center for Stem Cell Research, Tongji University, Shanghai, People’s Republic of China

  2. Wei Liu, Ke-Min Jin and Meng-Huan Zhang have contributed equally.

Authors and Affiliations

  1. Key Laboratory of Carcinogenesis and Translational Research, Hepatopancreatobiliary Surgery Department I, Ministry of Education, Peking University School of Oncology, Beijing Cancer Hospital and Institute, Beijing, People’s Republic of China

    Wei Liu MD, Ke-Min Jin MD, Quan Bao MD, Ming Liu MD, Da Xu MD, Kun Wang MD & Bao-Cai Xing MD

  2. GloriousMed Clinical Laboratory (Shanghai) Co., Ltd., Shanghai, People’s Republic of China

    Meng-Huan Zhang PhD

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Correspondence to Kun Wang MD or Bao-Cai Xing MD.

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There are no conflicts of interest.

Ethical Approval

The study was approved by the Ethics Committee of Beijing Cancer Hospital. The study was performed in accordance with the Declaration of Helsinki

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Supplementary Information

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Supplementary file1 (DOCX 138 kb)

Time-dependent ROC curve at 3 years point (PDF 92 kb)

The mean mutation AFs in the 43 paired ctDNA in blood sampled at median 6 days and 31 days after surgery (PDF 389 kb)

10434_2023_13362_MOESM4_ESM.pdf

The mutation oncoplot of tumor tissue sequenced by 642-gene panel in 134 patients. Genes in the J25 panel were highlighted in red. All tissue samples had at least one mutation detected in the genes of the J25 panel (PDF 26 kb)

The Kaplan-Meier survival curves for OS grouped by the median allele frequency in ctDNA-positive group (PDF 116 kb)

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Liu, W., Jin, KM., Zhang, MH. et al. Recurrence Prediction by Circulating Tumor DNA in the Patient with Colorectal Liver Metastases After Hepatectomy: A Prospective Biomarker Study. Ann Surg Oncol 30, 4916–4926 (2023). https://doi.org/10.1245/s10434-023-13362-1

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  • DOI: https://doi.org/10.1245/s10434-023-13362-1

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