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Dynamic recurrence risk and adjuvant chemotherapy benefit prediction by ctDNA in resected NSCLC

Author

Listed:
  • Bin Qiu

    (Chinese Academy of Medical Sciences and Peking Union Medical College
    Chinese Academy of Medical Sciences)

  • Wei Guo

    (Chinese Academy of Medical Sciences and Peking Union Medical College
    Chinese Academy of Medical Sciences)

  • Fan Zhang

    (Chinese Academy of Medical Sciences and Peking Union Medical College)

  • Fang Lv

    (Chinese Academy of Medical Sciences and Peking Union Medical College)

  • Ying Ji

    (Chinese Academy of Medical Sciences and Peking Union Medical College)

  • Yue Peng

    (Chinese Academy of Medical Sciences and Peking Union Medical College)

  • Xiaoxi Chen

    (Nanjing Geneseeq Technology Inc)

  • Hua Bao

    (Nanjing Geneseeq Technology Inc)

  • Yang Xu

    (Nanjing Geneseeq Technology Inc)

  • Yang Shao

    (Nanjing Geneseeq Technology Inc)

  • Fengwei Tan

    (Chinese Academy of Medical Sciences and Peking Union Medical College
    Chinese Academy of Medical Sciences)

  • Qi Xue

    (Chinese Academy of Medical Sciences and Peking Union Medical College
    Chinese Academy of Medical Sciences)

  • Shugeng Gao

    (Chinese Academy of Medical Sciences and Peking Union Medical College
    Chinese Academy of Medical Sciences)

  • Jie He

    (Chinese Academy of Medical Sciences and Peking Union Medical College)

Abstract

Accurately evaluating minimal residual disease (MRD) could facilitate early intervention and personalized adjuvant therapies. Here, using ultradeep targeted next-generation sequencing (NGS), we evaluate the clinical utility of circulating tumor DNA (ctDNA) for dynamic recurrence risk and adjuvant chemotherapy (ACT) benefit prediction in resected non-small cell lung cancer (NSCLC). Both postsurgical and post-ACT ctDNA positivity are significantly associated with worse recurrence-free survival. In stage II-III patients, the postsurgical ctDNA positive group benefit from ACT, while ctDNA negative patients have a low risk of relapse regardless of whether or not ACT is administered. During disease surveillance, ctDNA positivity precedes radiological recurrence by a median of 88 days. Using joint modeling of longitudinal ctDNA analysis and time-to-recurrence, we accurately predict patients’ postsurgical 12-month and 15-month recurrence status. Our findings reveal longitudinal ctDNA analysis as a promising tool to detect MRD in NSCLC, and we show pioneering work of using postsurgical ctDNA status to guide ACT and applying joint modeling to dynamically predict recurrence risk, although the results need to be further confirmed in future studies.

Suggested Citation

  • Bin Qiu & Wei Guo & Fan Zhang & Fang Lv & Ying Ji & Yue Peng & Xiaoxi Chen & Hua Bao & Yang Xu & Yang Shao & Fengwei Tan & Qi Xue & Shugeng Gao & Jie He, 2021. "Dynamic recurrence risk and adjuvant chemotherapy benefit prediction by ctDNA in resected NSCLC," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-27022-z
    DOI: 10.1038/s41467-021-27022-z
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    1. Xu-Chao Zhang & Jun Wang & Guo-Guang Shao & Qun Wang & Xiaotao Qu & Bo Wang & Christopher Moy & Yue Fan & Zayed Albertyn & Xiayu Huang & Jingyu Zhang & Yang Qiu & Suso Platero & Matthew V. Lorenzi & E, 2019. "Comprehensive genomic and immunological characterization of Chinese non-small cell lung cancer patients," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
    2. Christopher Abbosh & Nicolai J. Birkbak & Gareth A. Wilson & Mariam Jamal-Hanjani & Tudor Constantin & Raheleh Salari & John Le Quesne & David A. Moore & Selvaraju Veeriah & Rachel Rosenthal & Teresa , 2017. "Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution," Nature, Nature, vol. 545(7655), pages 446-451, May.
    3. Hans C. Van Houwelingen, 2007. "Dynamic Prediction by Landmarking in Event History Analysis," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(1), pages 70-85, March.
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