The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Circulating tumor DNA dynamics predict benefit from consolidation immunotherapy in locally advanced non-small-cell lung cancer
Open accessAbstract
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. Diehn and colleagues report that assaying circulating DNA in patients receiving chemoradiation therapy for non-small-cell lung cancer could identify the patients most likely to benefit from consolidation immunotherapy.
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Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21492 · Cited by 84,109
Comprehensive molecular profiling of lung adenocarcinoma
The Cancer Genome Atlas Research Network (2014) · Nature · doi:10.1038/nature13385 · Cited by 5,732
Goldstraw, P., Chansky, K., Crowley, J., Rami-Porta, R., Asamura, H., Eberhardt, W. E. E., Nicholson, A. G., Groome, P., Mitchell, A., Bolejack, V., et al. (2016) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2015.09.009 · Cited by 4,607
Comprehensive genomic characterization of squamous cell lung cancers
The Cancer Genome Atlas Research Network (2012) · Nature · doi:10.1038/nature11404 · Cited by 3,994
Overall Survival with Durvalumab after Chemoradiotherapy in Stage III NSCLC
Antonia, S. J., Villegas, A., Daniel, D., Vicente, D., Murakami, S., Hui, R., Kurata, T., Chiappori, A., Lee, K. H., de Wit, M., et al. (2018) · New England Journal of Medicine · doi:10.1056/nejmoa1809697 · Cited by 2,798
Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic
Chan, T. A., Yarchoan, M., Jaffee, E., Swanton, C., Quezada, S. A., Stenzinger, A., & Peters, S. (2019) · Annals of Oncology · doi:10.1093/annonc/mdy495 · Cited by 2,683
Wang, D. Y., Salem, J.-E., Cohen, J. V., Chandra, S., Menzer, C., Ye, F., Zhao, S., Das, S., Beckermann, K. E., Ha, L., et al. (2018) · JAMA Oncology · doi:10.1001/jamaoncol.2018.3923 · Cited by 2,674
An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage
Newman, A. M., Bratman, S. V., To, J., Wynne, J. F., Eclov, N. C. W., Modlin, L. A., Liu, C. L., Neal, J. W., Wakelee, H. A., Merritt, R. E., et al. (2014) · Nature Medicine · doi:10.1038/nm.3519 · Cited by 2,220
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Dupont, W. D., & Plummer, W. D. (1990) · Controlled Clinical Trials · doi:10.1016/0197-2456(90)90005-m · Cited by 2,092
Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution
The TRACERx consortium, Abbosh, C., The PEACE consortium, Birkbak, N. J., Wilson, G. A., Jamal-Hanjani, M., Constantin, T., Salari, R., Le Quesne, J., Moore, D. A., et al. (2017) · Nature · doi:10.1038/nature22364 · Cited by 1,874
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Tie, J., Cohen, J. D., Lahouel, K., Lo, S. N., Wang, Y., Kosmider, S., Wong, R., Shapiro, J., Lee, M., Harris, S., et al. (2022) · New England Journal of Medicine · doi:10.1056/nejmoa2200075 · Cited by 1,091
Pascual, J., Attard, G., Bidard, F.-C., Curigliano, G., De Mattos-Arruda, L., Diehn, M., Italiano, A., Lindberg, J., Merker, J. D., Montagut, C., et al. (2022) · Annals of Oncology · doi:10.1016/j.annonc.2022.05.520 · Cited by 783
Self-citationRolfo, C., Mack, P., Scagliotti, G. V., Aggarwal, C., Arcila, M. E., Barlesi, F., Bivona, T., Diehn, M., Dive, C., Dziadziuszko, R., et al. (2021) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2021.06.017 · Cited by 683
Self-citationBratman, S. V., Yang, S. Y. C., Iafolla, M. A. J., Liu, Z., Hansen, A. R., Bedard, P. L., Lheureux, S., Spreafico, A., Razak, A. A., Shchegrova, S., et al. (2020) · Nature Cancer · doi:10.1038/s43018-020-0096-5 · Cited by 487
Tracking early lung cancer metastatic dissemination in TRACERx using ctDNA
Abbosh, C., Frankell, A. M., Harrison, T., Kisistok, J., Garnett, A., Johnson, L., Veeriah, S., Moreau, M., Chesh, A., Chaunzwa, T. L., et al. (2023) · Nature · doi:10.1038/s41586-023-05776-4 · Cited by 382
Metadata
- Authors
- Moding, E. J., Liu, Y., Nabet, B. Y., Chabon, J. J., Chaudhuri, A. A., Hui, A. B., Bonilla, R. F., Ko, R. B., Yoo, C. H., Gojenola, L., Jones, C. D., He, J., Qiao, Y., Xu, T., Heymach, J. V., Tsao, A., Liao, Z., Gomez, D. R., Das, M., Padda, S. K., Ramchandran, K. J., Neal, J. W., Wakelee, H. A., Loo, B. W., Lin, S. H., Alizadeh, A. A., & Diehn, M.
- Venue
- Nature Cancer
- Published
- 2020-01-20
- Volume
- 1
- Issue
- 2
- Pages
- 176-183
- ISSN
- 2662-1347 (ISSN-L)
- OpenAlex
- W3000712054
- Cited by
- 365
- References
- 27
- FWCI
- 15.17
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Moding, E. J., Liu, Y., Nabet, B. Y., Chabon, J. J., Chaudhuri, A. A., Hui, A. B., Bonilla, R. F., Ko, R. B., Yoo, C. H., Gojenola, L., Jones, C. D., He, J., Qiao, Y., Xu, T., Heymach, J. V., Tsao, A., Liao, Z., Gomez, D. R., Das, M., . . . Diehn, M. (2020). Circulating tumor DNA dynamics predict benefit from consolidation immunotherapy in locally advanced non-small-cell lung cancer. Nature Cancer. https://doi.org/10.1038/s43018-019-0011-0
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