Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Circulating tumor DNA dynamics predict benefit from consolidation immunotherapy in locally advanced non-small-cell lung cancer

Abstract

Circulating tumor DNA (ctDNA) molecular residual disease (MRD) following curative-intent treatment strongly predicts recurrence in multiple tumor types, but whether further treatment can improve outcomes in patients with MRD is unclear. We applied cancer personalized profiling by deep sequencing (CAPP-Seq) ctDNA analysis to 218 samples from 65 patients receiving chemoradiation therapy for locally advanced non-small-cell lung cancer, including 28 patients receiving consolidation immune checkpoint inhibition (ICI). Patients with undetectable ctDNA after chemoradiation therapy had excellent outcomes whether or not they received consolidation ICI. Among such patients, one died from consolidation ICI-related pneumonitis, highlighting the potential utility of only treating patients with MRD. In contrast, patients with MRD after chemoradiation therapy who received consolidation ICI had significantly better outcomes than patients who did not receive consolidation ICI. Furthermore, the ctDNA response pattern early during consolidation ICI identified patients responding to consolidation therapy. Our results suggest that consolidation ICI improves outcomes for non-small-cell lung cancer patients with MRD and that ctDNA analysis may facilitate personalization of consolidation therapy.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Study schema and pretreatment genotypes for patients with locoregionally advanced NSCLC treated with chemoradiation therapy with or without consolidation immunotherapy.
Fig. 2: ctDNA changes during therapy are associated with outcomes in NSCLC patients treated with CRT and consolidation ICI.
Fig. 3: Patients with ctDNA not detected after CRT may not benefit from consolidation ICI.
Fig. 4: ctDNA dynamics predict benefit from consolidation ICI after CRT.
Fig. 5: Decreasing ctDNA concentration during consolidation ICI identifies MRD-positive patients with improved outcomes.

Similar content being viewed by others

Data availability

The sequencing results supporting the findings are available upon request from the lead corresponding author (M.Diehn). The raw data are not publicly available because they contain information that could compromise participant privacy and/or consent. The supporting variant level data for all figures are available in Supplementary Tables 25. The source data for Figs. 25 and Extended Data Fig. 13 have been provided as Source Data files.

Code availability

The computer code used for the analysis was described previously26 and is available at http://cappseq.stanford.edu.

References

  1. Bray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68, 394–424 (2018).

    PubMed  Google Scholar 

  2. Ettinger, D. S. et al. Non-small cell lung cancer, version 5.2017, NCCN Clinical Practice Guidelines in Oncology. J. Natl Compr. Canc. Netw. 15, 504–535 (2017).

    Article  PubMed  Google Scholar 

  3. Goldstraw, P. et al. The IASLC Lung Cancer Staging Project: proposals for revision of the TNM stage groupings in the forthcoming (eighth) edition of the TNM Classification for lung cancer. J. Thorac. Oncol. 11, 39–51 (2016).

    Article  PubMed  Google Scholar 

  4. Antonia, S. J. et al. Overall survival with durvalumab after chemoradiotherapy in stage III NSCLC. N. Engl. J. Med. 379, 2342–2350 (2018).

    Article  CAS  PubMed  Google Scholar 

  5. Bradley, J. D. et al. Long-term results of RTOG 0617: a randomized phase 3 comparison of standard dose versus high dose conformal chemoradiation therapy ± cetuximab for stage III NSCLC. Int. J. Radiat. Oncol. Biol. Phys. 99, S105 (2017).

    Article  Google Scholar 

  6. Moding, E. J., Diehn, M. & Wakelee, H. A. Circulating tumor DNA testing in advanced non-small cell lung cancer. Lung Cancer 119, 42–47 (2018).

    Article  PubMed  Google Scholar 

  7. Garcia-Murillas, I. et al. Mutation tracking in circulating tumor DNA predicts relapse in early breast cancer. Sci. Transl. Med. 7, 302ra133 (2015).

    Article  PubMed  Google Scholar 

  8. Sausen, M. et al. Clinical implications of genomic alterations in the tumour and circulation of pancreatic cancer patients. Nat. Commun. 6, 7686 (2015).

    Article  PubMed  Google Scholar 

  9. Tie, J. et al. Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer. Sci. Transl. Med. 8, 346ra92 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Tie, J. et al. Serial circulating tumour DNA analysis during multimodality treatment of locally advanced rectal cancer: a prospective biomarker study. Gut 68, 663–671 (2019).

    Article  CAS  PubMed  Google Scholar 

  11. Abbosh, C. et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature 545, 446–451 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Chaudhuri, A. A. et al. Early detection of molecular residual disease in localized lung cancer by circulating tumor DNA profiling. Cancer Discov. 7, 1394–1403 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Hammerman, P. S. et al. Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519–525 (2012).

    Article  CAS  Google Scholar 

  14. Collisson, E. A. et al. Comprehensive molecular profiling of lung adenocarcinoma. Nature 511, 543–550 (2014).

    Article  CAS  Google Scholar 

  15. Larsen, B. T. et al. Clinical and histopathologic features of immune checkpoint inhibitor-related pneumonitis. Am. J. Surg. Pathol. 43, 1331–1340 (2019).

    Article  PubMed  Google Scholar 

  16. Reinert, T. et al. Analysis of plasma cell-free DNA by ultradeep sequencing in patients with stages I to III colorectal cancer. JAMA Oncol. 5, 1124–1131 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Christensen, E. et al. Early detection of metastatic relapse and monitoring of therapeutic efficacy by ultra-deep sequencing of plasma cell-free DNA in patients with urothelial bladder carcinoma. J. Clin. Oncol. 37, 1547–1557 (2019).

    Article  CAS  PubMed  Google Scholar 

  18. Raja, R. et al. Early reduction in ctDNA predicts survival in patients with lung and bladder cancer treated with durvalumab. Clin. Cancer Res. 24, 6212–6222 (2018).

    Article  PubMed  Google Scholar 

  19. Anagnostou, V. et al. Dynamics of tumor and immune responses during immune checkpoint blockade in non-small cell lung cancer. Cancer Res. 79, 1214–1225 (2019).

    Article  CAS  PubMed  Google Scholar 

  20. Goldberg, S. B. et al. Early assessment of lung cancer immunotherapy response via circulating tumor DNA. Clin. Cancer Res. 24, 1872–1880 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Wang, D. Y. et al. Fatal toxic effects associated with immune checkpoint inhibitors: a systematic review and meta-analysis. JAMA Oncol. 4, 1721–1728 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Coombes, R. C. et al. Personalized detection of circulating tumor DNA antedates breast cancer metastatic recurrence. Clin. Cancer Res. 25, 4255–4263 (2019).

    Article  PubMed  Google Scholar 

  23. Chan, T. A. et al. Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Ann. Oncol. 30, 44–56 (2019).

    Article  CAS  PubMed  Google Scholar 

  24. Dupont, W. D. & Plummer, W. D. Jr. Power and sample size calculations: a review and computer program. Control. Clin. Trials 11, 116–128 (1990).

    Article  CAS  PubMed  Google Scholar 

  25. Newman, A. M. et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat. Med. 20, 548–554 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Newman, A. M. et al. Integrated digital error suppression for improved detection of circulating tumor DNA. Nat. Biotechnol. 34, 547–555 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Mouliere, F. et al. Enhanced detection of circulating tumor DNA by fragment size analysis. Sci. Transl. Med. 10, eaat4921 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the patients and families who participated in this study. We thank R. Tibshirani for biostatistics advice. This work was supported by the Stanford Radiation Oncology Kaplan Fellowship (E.J.M.) and grants from the American Society for Radiation Oncology (E.J.M.), the Radiological Society of North America (E.J.M.), the National Cancer Institute (no. R01CA188298 and R01CA233975 to M. Diehn and A.A.A., U01CA194389 and R01CA229766 to A.A.A.), the US National Institutes of Health Director’s New Innovator Award Program (no. 1-DP2-CA186569 to M. Diehn), the Virginia and D.K. Ludwig Fund for Cancer Research (M. Diehn and A.A.A.), Bakewell Foundation (to M. Diehn and A.A.A.), the SDW/DT and Shanahan Family Foundations (to A.A.A.). and the CRK Faculty Scholar Fund (M. Diehn). A.A.A. is a Scholar of the Leukemia & Lymphoma Society. The schematic in Fig. 1a was produced using Servier Medical Art (https://smart.servier.com). Servier Medical Art by Servier is licensed under a Creative Commons Attribution 3.0 Unported License (https://creativecommons.org/licenses/by/3.0/).

Author information

Authors and Affiliations

Authors

Contributions

E.J.M., S.H.L., A.A.A. and M.Diehn conceived the project, designed the experiments, analyzed the data and wrote the manuscript. Y.L. collected the clinical data and scored progression. E.J.M., B.Y.N., A.A.C. and A.B.H. processed the samples for sequencing. J.J.C. assisted with data analysis. R.F.B., R.B.K. and C.H.Y. banked the samples. L.G., C.D.J., J.H., Y.Q. and T.X. maintained the sample banks. J.V.H., A.T., Z.L., D.R.G., M.Das, S.K.P., K.J.R., J.W.N., H.A.W., B.W.L., S.H.L. and M.Diehn provided the patient samples. All authors reviewed the manuscript at all stages.

Corresponding authors

Correspondence to Steven H. Lin, Ash A. Alizadeh or Maximilian Diehn.

Ethics declarations

Competing interests

E.J.M., Y.L., B.Y.N., J.J.C., A.B.H., R.F.B, R.B.K., C.H.Y., L.G., C.D.J., J.H., Y.Q., T.X., J.V.H., A.T., Z.L., D.R.G., S.K.P., K.J.R. and B.W.L. declare no competing interests. A.A.C. has served as an advisor/consultant for Roche, Tempus Labs, Geneoscopy and Oscar Health, and has received speaker honoraria from Roche, Varian Medical Systems and Foundation Medicine. M.Das has received research funding from Verily, AbbVie, United Therapeutics and Celgene and has served as a consultant and received honoraria from Bristol-Myers Squibb and AstraZeneca. J.W.N. has served as an advisor/consultant for ARIAD Pharmaceuticals/Takeda, AstraZeneca, Genentech/Roche, Eli Lilly, Exelixis, Loxo Oncology and Jounce. J.W.N. has received research funding from Genentech/Roche, Merck, Novartis, Boehringer Ingelheim, Exelixis, ARIAD Pharmaceuticals/Takeda and Nektar. H.A.W. has served on the advisory board for AstraZeneca, Xcovery, Janssen, Mirati Therapeutics, Merck, Takeda and Genentech/Roche and has received compensation from AstraZeneca, Xcovery, Janssen and Mirati. H.A.W. has received research funding from ACEA Biosciences, Arrys Therapeutics, AstraZeneca/MedImmune, BMS, Celgene, Clovis Oncology, Exelixis, Genentech/Roche, Gilead Sciences, Eli Lilly, Merck, Novartis, Pfizer, Pharmacyclics and Xcovery. S.H.L. receives grant funding from Hitachi Chemical Diagnostics, Genentech and BeyondSpring Pharmaceuticals, an honorarium from Varian Medical Systems and has served on the advisory board for AstraZeneca and BeyondSpring Pharmaceuticals. A.A.A. and M.Diehn are coinventors on patent applications related to CAPP-Seq. A.A.A. has equity in Forty Seven and CiberMed and has served as a consultant for Roche, Genentech, Chugai Pharma and Pharmacyclics. M.Diehn has equity in CiberMed, has received research funding from Varian Medical Systems and has served as a paid consultant for Roche, AstraZeneca, BioNTech, Illumina and RefleXion and as an unpaid consultant for Genentech.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Validation of predictive value of ctDNA MRD after chemoradiation therapy (CRT) alone.

a, Event chart showing timing of therapy, progression based on RECIST 1.1 evaluation of imaging, and results of ctDNA testing for each patient. b, Kaplan-Meier analysis of freedom from progression stratified by ctDNA detection within 4 months of completing CRT (n=6 not detected, n=6 detected). P values were calculated using a two-sided log-rank test.

Source data

Extended Data Fig. 2 ctDNA dynamics during consolidation immune checkpoint inhibition (CICI) after chemoradiation therapy (CRT) predict disease progression.

Kaplan-Meier analysis of (a) freedom from progression and (b) freedom from distant progression in patients with ctDNA decreasing early on-CICI (n=5) and ctDNA increasing early on-CICI (n=5). P values were calculated using two-sided log-rank tests.

Source data

Extended Data Fig. 3 Pretreatment tumor mutational burden is not significantly correlated with response to chemoradiation or consolidation immune checkpoint inhibition (CICI).

Pretreatment tumor mutational burden in non-synonymous mutations per megabase extrapolated from CAPP-Seq in (a) patients with ctDNA detected (n=26) and not detected (n=25) after chemoradiation therapy and (b) patients with ctDNA increasing early on-CICI (n=5) and decreasing early on-CICI (n=5). P values were calculated using two-sided Mann-Whitney tests. Bars represent medians.

Source data

Supplementary information

Source data

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Moding, E.J., Liu, Y., Nabet, B.Y. et al. Circulating tumor DNA dynamics predict benefit from consolidation immunotherapy in locally advanced non-small-cell lung cancer. Nat Cancer 1, 176–183 (2020). https://doi.org/10.1038/s43018-019-0011-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43018-019-0011-0

This article is cited by

Search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer