journal article·2025-12Open access

Tumor‐Informed ctDNA in Guiding First‐Line Immunochemotherapy in Advanced Non‐Small Cell Lung Cancers

Kailun Fei, Jie Zhao, Jiachen Xu, Jianchun Duan, Chengcheng Li, Wenchuan Xie, Jia Zhong, Guoqiang Wang, Yu Xu, Boyang Sun, Rui Wan, Hua Bai, Qinxiang Guo, Wei Guo, Shangli Cai, Zhijie Wang, Jie Wang

Advanced Science· Volume 12· Issue 48

Abstract

Abstract Predictive biomarkers are urgently needed for first‐line immune checkpoint inhibitors plus chemotherapy (ICI‐chemo) in advanced non‐small cell lung cancer (NSCLC). Circulating tumor DNA (ctDNA) reflects tumor burden and immunogenicity, potentially identifyinging of patients suitable for ICI‐chemo. In this study, pre‐ and on ‐ treatment tissue and plasma samples are prospectively collected and analyzed from the randomized phase III CHOICE‐01 trial comparing ICI ‐ chemo versus chemotherapy alone in advanced NSCLC. Pre‐treatment tissue and plasma samples, as well as on‐treatment plasma samples, are prospectively collected. Tumor‐informed ctDNA detection is based on tissue‐identified mutations. Among patients with tumor‐informed ctDNA positivity, those receiving ICI‐chemo experienced significantly improved‐free survival (PFS) and overall survival (OS) compared to those receiving chemotherapy alone (PFS: HR 0.45, 95% CI 0.34–0.60; OS: HR 0.66, 95% CI 0.49–0.88; p = 0.0045). In contrast, no significant differences in PFS or OS are observed between treatment arms in the ctDNA‐negative subgroup. The predictive value of tumor‐informed ctDNA is independent of other immune biomarkers and superior to other ctDNA metrics. Validation in a combined cohort from RATIONALE 304/307 trials shows similar results. Furthermore, ctDNA clearance during treatment correlates with better clinical outcomes (log‐rank p = 0.0004 for OS and p = 0.044 for PFS).
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Cancer Genomics and Diagnostics · Cancer Immunotherapy and Biomarkers · Lung Cancer Treatments and Mutations

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

State Key Laboratory of Molecular Oncology CAMS Key Laboratory of Translational Research on Lung Cancer Department of Medical Oncology National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing P. R. China

Department of Respiratory Medicine Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University ShanXi P. R. China

Burning Rock Biotech Guangdong P. R. China

Funding

Fundamental Research Funds for the Central Universities · Peking Union Medical College

References (54)

Show source-provided references
1. 10.1016/S2213-2600(20)30365-910.1016/S2213-2600(20)30365-9 ↗
2. 10.1056/NEJMoa180100510.1056/NEJMoa1801005 ↗
3. 10.1200/JCO.22.0072710.1200/JCO.22.00727 ↗
4. 10.1016/S1470-2045(16)30498-310.1016/S1470-2045(16)30498-3 ↗
5. 10.1016/j.annonc.2021.04.00810.1016/j.annonc.2021.04.008 ↗
6. 10.3322/caac.2165010.3322/caac.21650 ↗
7. 10.1038/s41571-019-0187-310.1038/s41571-019-0187-3 ↗
8. 10.1016/j.annonc.2022.09.16310.1016/j.annonc.2022.09.163 ↗
9. 10.1038/s41591-023-02226-610.1038/s41591-023-02226-6 ↗
10. 10.1038/s43018-020-0096-510.1038/s43018-020-0096-5 ↗
11. 10.1158/1078-0432.CCR-23-157810.1158/1078-0432.CCR-23-1578 ↗
12. 10.1038/s41586-023-05776-410.1038/s41586-023-05776-4 ↗
13. 10.1038/s41586-021-03642-910.1038/s41586-021-03642-9 ↗
14. 10.1038/s43018-019-0011-010.1038/s43018-019-0011-0 ↗
15. 10.1038/s41571-020-0413-z10.1038/s41571-020-0413-z ↗
16. 10.1158/2159-8290.CD-21-063410.1158/2159-8290.CD-21-0634 ↗
17. 10.1016/j.ccell.2023.08.01010.1016/j.ccell.2023.08.010 ↗
18. 10.15252/emmm.20221650510.15252/emmm.202216505 ↗
19. 10.3390/ijms24121023010.3390/ijms241210230 ↗
20. 10.1136/jitc-2022-00628410.1136/jitc-2022-006284 ↗
21. 10.1038/s41591-019-0652-710.1038/s41591-019-0652-7 ↗
22. 10.1001/jamaoncol.2018.229710.1001/jamaoncol.2018.2297 ↗
23. 10.1016/S2468-1253(23)00146-210.1016/S2468-1253(23)00146-2 ↗
24. 10.1126/science.aau387910.1126/science.aau3879 ↗
25. 10.1016/j.jtho.2021.05.00510.1016/j.jtho.2021.05.005 ↗
26. 10.1001/jamaoncol.2021.036610.1001/jamaoncol.2021.0366 ↗
27. 10.1200/JCO.2023.41.16_suppl.900310.1200/JCO.2023.41.16_suppl.9003 ↗
28. 10.1038/nature2236410.1038/nature22364 ↗
29. 10.1038/s41591-022-01933-w10.1038/s41591-022-01933-w ↗
30. 10.1038/s41586-023-05776-410.1038/s41586-023-05776-4 ↗
31. 10.1136/jitc-2020-00150410.1136/jitc-2020-001504 ↗
32. 10.1038/s41591-022-02047-z10.1038/s41591-022-02047-z ↗
33. 10.1016/j.jtho.2018.10.00810.1016/j.jtho.2018.10.008 ↗
34. 10.1038/s41591-023-02598-910.1038/s41591-023-02598-9 ↗
35. 10.1158/1078-0432.CCR-22-314610.1158/1078-0432.CCR-22-3146 ↗
36. 10.1038/s41586-020-2140-010.1038/s41586-020-2140-0 ↗
37. 10.1016/j.stem.2017.07.01010.1016/j.stem.2017.07.010 ↗
38. 10.1038/s41587-021-00857-z10.1038/s41587-021-00857-z ↗
39. 10.1038/ncomms1181510.1038/ncomms11815 ↗
40. 10.1007/s00262-021-02943-210.1007/s00262-021-02943-2 ↗
41. 10.1186/s12916-022-02444-810.1186/s12916-022-02444-8 ↗
42. 10.1101/gr.107524.11010.1101/gr.107524.110 ↗
43. 10.1093/bioinformatics/btp37310.1093/bioinformatics/btp373 ↗
44. 10.1093/nar/gkw22710.1093/nar/gkw227 ↗
45. 10.1038/nbt.334410.1038/nbt.3344 ↗
46. 10.1007/s00251-008-0341-z10.1007/s00251-008-0341-z ↗
47. 10.4049/jimmunol.170089310.4049/jimmunol.1700893 ↗
48. 10.1093/annonc/mdu47910.1093/annonc/mdu479 ↗
49. 10.1186/gb-2011-12-4-r4110.1186/gb-2011-12-4-r41 ↗
50. 10.1038/nature1193510.1038/nature11935 ↗
51. 10.1038/bjc.2012.45110.1038/bjc.2012.451 ↗
52. 10.1073/pnas.100984310710.1073/pnas.1009843107 ↗
53. 10.1093/nar/gkw52010.1093/nar/gkw520 ↗
54. 10.1016/j.xcrm.2023.10130210.1016/j.xcrm.2023.101302 ↗