A longitudinal circulating tumor DNA-based model associated with survival in metastatic non-small-cell lung cancer.
Journal
Nature medicine
ISSN: 1546-170X
Titre abrégé: Nat Med
Pays: United States
ID NLM: 9502015
Informations de publication
Date de publication:
04 2023
04 2023
Historique:
received:
29
06
2022
accepted:
23
01
2023
medline:
21
4
2023
pubmed:
18
3
2023
entrez:
17
3
2023
Statut:
ppublish
Résumé
One of the great challenges in therapeutic oncology is determining who might achieve survival benefits from a particular therapy. Studies on longitudinal circulating tumor DNA (ctDNA) dynamics for the prediction of survival have generally been small or nonrandomized. We assessed ctDNA across 5 time points in 466 non-small-cell lung cancer (NSCLC) patients from the randomized phase 3 IMpower150 study comparing chemotherapy-immune checkpoint inhibitor (chemo-ICI) combinations and used machine learning to jointly model multiple ctDNA metrics to predict overall survival (OS). ctDNA assessments through cycle 3 day 1 of treatment enabled risk stratification of patients with stable disease (hazard ratio (HR) = 3.2 (2.0-5.3), P < 0.001; median 7.1 versus 22.3 months for high- versus low-intermediate risk) and with partial response (HR = 3.3 (1.7-6.4), P < 0.001; median 8.8 versus 28.6 months). The model also identified high-risk patients in an external validation cohort from the randomized phase 3 OAK study of ICI versus chemo in NSCLC (OS HR = 3.73 (1.83-7.60), P = 0.00012). Simulations of clinical trial scenarios employing our ctDNA model suggested that early ctDNA testing outperforms early radiographic imaging for predicting trial outcomes. Overall, measuring ctDNA dynamics during treatment can improve patient risk stratification and may allow early differentiation between competing therapies during clinical trials.
Identifiants
pubmed: 36928816
doi: 10.1038/s41591-023-02226-6
pii: 10.1038/s41591-023-02226-6
pmc: PMC10115641
doi:
Substances chimiques
Circulating Tumor DNA
0
Biomarkers, Tumor
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
859-868Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2023. The Author(s).
Références
Eisenhauer, E. A. et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur. J. Cancer 45, 228–247 (2009).
pubmed: 19097774
doi: 10.1016/j.ejca.2008.10.026
Fojo, A. T. & Noonan, A. Why RECIST works and why it should stay—counterpoint. Cancer Res. 72, 5151–5157 (2012).
pubmed: 22952221
doi: 10.1158/0008-5472.CAN-12-0733
Chiou, V. L. & Burotto, M. Pseudoprogression and immune-related response in solid tumors. J. Clin. Oncol. 33, 3541–3543 (2015).
pubmed: 26261262
pmcid: 4622096
doi: 10.1200/JCO.2015.61.6870
Tazdait, M. et al. Patterns of responses in metastatic NSCLC during PD-1 or PDL-1 inhibitor therapy: comparison of RECIST 1.1, irRECIST and iRECIST criteria. Eur. J. Cancer 88, 38–47 (2018).
pubmed: 29182990
doi: 10.1016/j.ejca.2017.10.017
Petrelli, F. et al. Surrogate endpoints in immunotherapy trials for solid tumors. Ann. Transl. Med. 7, 154–154 (2019).
pubmed: 31157275
pmcid: 6511564
doi: 10.21037/atm.2019.03.20
Nie, R.-C. et al. Evaluation of objective response, disease control and progression-free survival as surrogate end-points for overall survival in anti-programmed death-1 and anti-programmed death ligand 1 trials. Eur. J. Cancer 106, 1–11 (2019).
pubmed: 30453169
doi: 10.1016/j.ejca.2018.10.011
Corcoran, R. B. & Chabner, B. A. Application of cell-free DNA analysis to cancer treatment. N. Engl. J. Med. 379, 1754–1765 (2018).
pubmed: 30380390
doi: 10.1056/NEJMra1706174
Wan, J. C. M. et al. Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat. Rev. Cancer 17, 223–238 (2017).
pubmed: 28233803
doi: 10.1038/nrc.2017.7
Tie, J. et al. Circulating tumor DNA analyses as markers of recurrence risk and benefit of adjuvant therapy for stage III colon cancer. JAMA Oncol. 5, 1710 (2019).
pubmed: 31621801
pmcid: 6802034
doi: 10.1001/jamaoncol.2019.3616
Garcia-Murillas, I. et al. Assessment of molecular relapse detection in early-stage breast cancer. JAMA Oncol. 5, 1473 (2019).
pubmed: 31369045
pmcid: 6681568
doi: 10.1001/jamaoncol.2019.1838
Abbosh, C. et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature 545, 446–451 (2017).
pubmed: 28445469
pmcid: 5812436
doi: 10.1038/nature22364
Lau, E. et al. Detection of ctDNA in plasma of patients with clinically localised prostate cancer is associated with rapid disease progression. Genome Med. 12, 72 (2020).
pubmed: 32807235
pmcid: 7430029
doi: 10.1186/s13073-020-00770-1
Cullinane, C. et al. Association of circulating tumor DNA with disease-free survival in breast cancer. JAMA Netw. Open 3, e2026921 (2020).
pubmed: 33211112
pmcid: 7677763
doi: 10.1001/jamanetworkopen.2020.26921
Kuang, P.-P. et al. Circulating tumor DNA analyses as a potential marker of recurrence and effectiveness of adjuvant chemotherapy for resected non-small-cell lung cancer. Front. Oncol. 10, 595650 (2021).
pubmed: 33659207
pmcid: 7919598
doi: 10.3389/fonc.2020.595650
Powles, T. et al. ctDNA guiding adjuvant immunotherapy in urothelial carcinoma. Nature 595, 432–437 (2021).
pubmed: 34135506
doi: 10.1038/s41586-021-03642-9
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).
pubmed: 31059311
doi: 10.1200/JCO.18.02052
Magbanua, M. J. M. et al. Circulating tumor DNA in neoadjuvant-treated breast cancer reflects response and survival. Ann. Oncol. 32, 229–239 (2021).
pubmed: 33232761
doi: 10.1016/j.annonc.2020.11.007
Osumi, H., Shinozaki, E., Yamaguchi, K. & Zembutsu, H. Early change in circulating tumor DNA as a potential predictor of response to chemotherapy in patients with metastatic colorectal cancer. Sci. Rep. 9, 17358 (2019).
pubmed: 31758080
pmcid: 6874682
doi: 10.1038/s41598-019-53711-3
Zou, W. et al. ctDNA predicts overall survival in patients with NSCLC treated with PD-L1 blockade or with chemotherapy. JCO Precis. Oncol. 5, 827–838.
Buder, A., Hochmair, M. J., Setinek, U., Pirker, R. & Filipits, M. EGFR mutation tracking predicts survival in advanced EGFR-mutated non-small cell lung cancer patients treated with osimertinib. Transl. Lung Cancer Res. 9, 239–245 (2020).
pubmed: 32420063
pmcid: 7225165
doi: 10.21037/tlcr.2020.03.02
Cheng, M. L. et al. Plasma ctDNA response is an early marker of treatment effect in advanced NSCLC. JCO Precis. Oncol. 16, 393–402 (2021).
doi: 10.1200/PO.20.00419
Bratman, S. V. et al. Personalized circulating tumor DNA analysis as a predictive biomarker in solid tumor patients treated with pembrolizumab. Nat. Cancer 1, 873–881 (2020).
pubmed: 35121950
doi: 10.1038/s43018-020-0096-5
Ricciuti, B. et al. Early plasma circulating tumor DNA (ctDNA) changes predict response to first-line pembrolizumab-based therapy in non-small-cell lung cancer (NSCLC). J. Immunother. Cancer 9, e001504 (2021).
pubmed: 33771889
pmcid: 7996662
doi: 10.1136/jitc-2020-001504
Bettegowda, C. et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci. Transl. Med. 6, 224ra24 (2014).
pubmed: 24553385
pmcid: 4017867
doi: 10.1126/scitranslmed.3007094
Ferrara, R. et al. Do immune checkpoint inhibitors need new studies methodology? J. Thorac. Dis. 10, S1564–S1580 (2018).
pubmed: 29951307
pmcid: 5994495
doi: 10.21037/jtd.2018.01.131
Chen, T.-T. Statistical issues and challenges in immuno-oncology. J. Immunother. Cancer 1, 18 (2013).
pubmed: 24829754
pmcid: 4019889
doi: 10.1186/2051-1426-1-18
Socinski, M. A. et al. Atezolizumab for first-line treatment of metastatic nonsquamous NSCLC. N. Engl. J. Med. 378, 2288–2301 (2018).
pubmed: 29863955
doi: 10.1056/NEJMoa1716948
Socinski, M. A. et al. IMpower150 final overall survival analyses for atezolizumab plus bevacizumab and chemotherapy in first-line metastatic nonsquamous NSCLC. J. Thorac. Oncol. 16, 1909–1924 (2021).
pubmed: 34311108
doi: 10.1016/j.jtho.2021.07.009
Felip, E. et al. Adjuvant atezolizumab after adjuvant chemotherapy in resected stage IB–IIIA non-small-cell lung cancer (IMpower010): a randomised, multicentre, open-label, phase 3 trial. Lancet 398, 1344–1357 (2021).
pubmed: 34555333
doi: 10.1016/S0140-6736(21)02098-5
Herbst, R. S. et al. Atezolizumab for first-line treatment of PD-L1-selected patients with NSCLC. N. Engl. J. Med. 383, 1328–1339 (2020).
pubmed: 32997907
doi: 10.1056/NEJMoa1917346
Balar, A. V. et al. Atezolizumab as first-line treatment in cisplatin-ineligible patients with locally advanced and metastatic urothelial carcinoma: a single-arm, multicentre, phase 2 trial. Lancet 389, 67–76 (2017).
pubmed: 27939400
doi: 10.1016/S0140-6736(16)32455-2
Horn, L. et al. First-line atezolizumab plus chemotherapy in extensive-stage small-cell lung cancer. N. Engl. J. Med. 379, 2220–2229 (2018).
pubmed: 30280641
doi: 10.1056/NEJMoa1809064
Gutzmer, R. et al. Atezolizumab, vemurafenib, and cobimetinib as first-line treatment for unresectable advanced BRAF
pubmed: 32534646
doi: 10.1016/S0140-6736(20)30934-X
Finn, R. S. et al. Atezolizumab plus bevacizumab in unresectable hepatocellular carcinoma. N. Engl. J. Med. 382, 1894–1905 (2020).
pubmed: 32402160
doi: 10.1056/NEJMoa1915745
Gandara, D. R. 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. 24, 1441–1448 (2018).
pubmed: 30082870
doi: 10.1038/s41591-018-0134-3
Yaung, S. J. et al. Clonal hematopoiesis in late-stage non-small-cell lung cancer and its impact on targeted panel next-generation sequencing. JCO Precis. Oncol. 4, 1271–1279 (2020).
pubmed: 35050787
doi: 10.1200/PO.20.00046
Razavi, P. et al. High-intensity sequencing reveals the sources of plasma circulating cell-free DNA variants. Nat. Med. 25, 1928–1937 (2019).
pubmed: 31768066
pmcid: 7061455
doi: 10.1038/s41591-019-0652-7
Friends of Cancer Research. Assessing the use of ctDNA as an early endpoint in early-stage disease. https://friendsofcancerresearch.org/wp-content/uploads/Assessing_Use_of_ctDNA_Early_Endpoint_Early-Stage_Disease-1.pdf (2021).
Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Statist. Soc. Ser. B 67, 301–320 (2005).
doi: 10.1111/j.1467-9868.2005.00503.x
Guan, L. & Tibshirani, R. Post model‐fitting exploration via a ‘next‐door’ analysis. Can. J. Statist. 48, 447–470 (2020).
doi: 10.1002/cjs.11542
FDA. FDA approves atezolizumab with chemotherapy and bevacizumab for first-line treatment of metastatic non-squamous NSCLC. https://www.fda.gov/drugs/fda-approves-atezolizumab-chemotherapy-and-bevacizumab-first-line-treatment-metastatic-non-squamous (2018).
Fridlyand, J., Kaiser, L. D. & Fyfe, G. Analysis of tumor burden versus progression-free survival for phase II decision making. Contemp. Clin. Trials 32, 446–452 (2011).
pubmed: 21266203
doi: 10.1016/j.cct.2011.01.010
Nabet, B. Y. et al. Noninvasive early identification of therapeutic benefit from immune checkpoint inhibition. Cell 183, 363–376 (2020).
pubmed: 33007267
pmcid: 7572899
doi: 10.1016/j.cell.2020.09.001
Sanz-Garcia, E., Zhao, E., Bratman, S. V. & Siu, L. L. Monitoring and adapting cancer treatment using circulating tumor DNA kinetics: current research, opportunities, and challenges. Sci. Adv. 8, eabi8618 (2022).
pubmed: 35080978
pmcid: 8791609
doi: 10.1126/sciadv.abi8618
Clark, T. A. et al. Analytical validation of a hybrid capture-based next-generation sequencing clinical assay for genomic profiling of cell-free circulating tumor DNA. J. Mol. Diagn. 20, 686–702 (2018).
pubmed: 29936259
pmcid: 6593250
doi: 10.1016/j.jmoldx.2018.05.004
Woodhouse, R. et al. Clinical and analytical validation of FoundationOne Liquid CDx, a novel 324-Gene cfDNA-based comprehensive genomic profiling assay for cancers of solid tumor origin. PLoS ONE 15, e0237802 (2020).
pubmed: 32976510
pmcid: 7518588
doi: 10.1371/journal.pone.0237802
Beyersmann, J., Gastmeier, P., Wolkewitz, M. & Schumacher, M. An easy mathematical proof showed that time-dependent bias inevitably leads to biased effect estimation. J. Clin. Epidemiol. 61, 1216–1221 (2008).
pubmed: 18619803
doi: 10.1016/j.jclinepi.2008.02.008
Penciana, M. J. & D’Agostino, R. B. Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat. Med. 23, 2109–2123 (2004).
doi: 10.1002/sim.1802
Schröder, M. S., Culhane, A. C., Quackenbush, J. & Haibe-Kains, B. survcomp: an R/bioconductor package for performance assessment and comparison of survival models. Bioinformatics 27, 3206–3208 (2011).
pubmed: 21903630
pmcid: 3208391
doi: 10.1093/bioinformatics/btr511