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.

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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.

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.

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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

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.

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

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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.

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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

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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

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