18F-FDG PET/CT metrics-based stratification of large B-cell lymphoma receiving CAR-T cell therapy: immunosuppressive tumor microenvironment as a negative prognostic indicator in patients with high tumor burden.

18F-FDG PET/CT CAR-T cell therapy Large B-cell lymphoma Microenvironment

Journal

Biomarker research
ISSN: 2050-7771
Titre abrégé: Biomark Res
Pays: England
ID NLM: 101607860

Informations de publication

Date de publication:
14 Sep 2024
Historique:
received: 15 07 2024
accepted: 05 09 2024
medline: 14 9 2024
pubmed: 14 9 2024
entrez: 13 9 2024
Statut: epublish

Résumé

Chimeric antigen receptor T (CAR-T) cell therapy has greatly improved the prognosis of relapsed and refractory patients with large B-cell lymphoma (LBCL). Early identification and intervention of patients who may respond poorly to CAR-T cell therapy will help to improve the efficacy. Ninety patients from a Chinese cohort who received CAR-T cell therapy and underwent 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) scans at the screening stage (median time to infusion 53.5 days, range 27-176 days), 1 month and 3 months after CAR-T cell infusion were analyzed, with RNA-sequencing conducted on 47 patients at the screening stage. Patients with maximum diameter of the largest lesion (Dmax) < 6 cm (N = 60) at screening stage showed significantly higher 3-month complete response rate (85.0% vs. 33.3%, P < 0.001), progression-free survival (HR 0.17; 95% CI 0.08-0.35, P < 0.001) and overall survival (HR 0.18; 95% CI 0.08-0.40, P < 0.001) than those with Dmax ≥ 6 cm (N = 30). Besides, at the screening stage, Dmax combined with extranodal involvement was more efficient in distinguishing patient outcomes. The best cut-off values for total metabolic tumor volume (tMTV) and total lesion glycolysis (tTLG) at the screening stage were 50cm

Identifiants

pubmed: 39272132
doi: 10.1186/s40364-024-00650-5
pii: 10.1186/s40364-024-00650-5
doi:

Types de publication

Letter

Langues

eng

Pagination

104

Informations de copyright

© 2024. The Author(s).

Références

Neelapu SS, Jacobson CA, Ghobadi A, Miklos DB, Lekakis LJ, Oluwole OO, et al. Five-year follow-up of ZUMA-1 supports the curative potential of axicabtagene ciloleucel in refractory large B-cell lymphoma. Blood. 2023;141(19):2307–15.
pubmed: 36821768 pmcid: 10646788
Leithner D, Flynn JR, Devlin SM, Mauguen A, Fei T, Zeng S, et al. Conventional and novel [(18)F]FDG PET/CT features as predictors of CAR-T cell therapy outcome in large B-cell lymphoma. J Hematol Oncol. 2024;17(1):21.
doi: 10.1186/s13045-024-01540-x pubmed: 38649972 pmcid: 11035117
Gui J, Li M, Xu J, Zhang X, Mei H, Lan X. [18F]FDG PET/CT for prognosis and toxicity prediction of diffuse large B-cell lymphoma patients with chimeric antigen receptor T-cell therapy. Eur J Nucl Med Mol Imaging. 2024;51:2308–19. 
Sesques P, Tordo J, Ferrant E, Safar V, Wallet F, Dhomps A, et al. Prognostic Impact of 18F-FDG PET/CT in Patients With Aggressive B-Cell Lymphoma Treated With Anti-CD19 Chimeric Antigen Receptor T Cells. Clin Nucl Med. 2021;46(8):627–34.
doi: 10.1097/RLU.0000000000003756 pubmed: 34115706
Breen WG, Hathcock MA, Young JR, Kowalchuk RO, Bansal R, Khurana A, et al. Metabolic characteristics and prognostic differentiation of aggressive lymphoma using one-month post-CAR-T FDG PET/CT. J Hematol Oncol. 2022;15(1):36.
doi: 10.1186/s13045-022-01256-w pubmed: 35346315 pmcid: 8962609
Breen WG, Young JR, Hathcock MA, Kowalchuk RO, Thorpe MP, Bansal R, et al. Metabolic PET/CT analysis of aggressive Non-Hodgkin lymphoma prior to Axicabtagene Ciloleucel CAR-T infusion: predictors of progressive disease, survival, and toxicity. Blood Cancer J. 2023;13(1):127.
doi: 10.1038/s41408-023-00895-7 pubmed: 37591834 pmcid: 10435575
Ababneh HS, Ng AK, Abramson JS, Soumerai JD, Takvorian RW, Frigault MJ, et al. Metabolic parameters predict survival and toxicity in chimeric antigen receptor T-cell therapy-treated relapsed/refractory large B-cell lymphoma. Hematol Oncol. 2024;42(1):e3231.
doi: 10.1002/hon.3231 pubmed: 37795759
Wang J, Hu Y, Yang S, Wei G, Zhao X, Wu W, et al. Role of Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Predicting the Adverse Effects of Chimeric Antigen Receptor T Cell Therapy in Patients with Non-Hodgkin Lymphoma. Biol Blood Marrow Transplant. 2019;25(6):1092–8.
doi: 10.1016/j.bbmt.2019.02.008 pubmed: 30769193
Cerchietti L. Genetic mechanisms underlying tumor microenvironment composition and function in diffuse large B-cell lymphoma. Blood. 2024;143(12):1101–11.
doi: 10.1182/blood.2023021002 pubmed: 38211334
Yan Z, Li L, Fu D, Wu W, Qiao N, Huang Y, et al. Immunosuppressive tumor microenvironment contributes to tumor progression in diffuse large B-cell lymphoma upon anti-CD19 chimeric antigen receptor T therapy. Front Med. 2023;17(4):699–713.
doi: 10.1007/s11684-022-0972-8 pubmed: 37060525

Auteurs

Ling-Shuang Sheng (LS)

Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Rong Shen (R)

Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Zi-Xun Yan (ZX)

Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Chao Wang (C)

Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Xin Zheng (X)

Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Yi-Lun Zhang (YL)

Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Hao-Xu Yang (HX)

Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Wen Wu (W)

Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Peng-Peng Xu (PP)

Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Shu Cheng (S)

Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Emmanuel Bachy (E)

Department of Haematology, Hospices Civils de Lyon, Lyon, France.

Pierre Sesques (P)

Department of Haematology, Hospices Civils de Lyon, Lyon, France.

Nicolas Jacquet-Francillon (N)

Department of Nuclear Medicine, Hospices Civils de Lyon, Lyon, France.

Xu-Feng Jiang (XF)

Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Wei-Li Zhao (WL)

Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. zhao.weili@yahoo.com.

Li Wang (L)

Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. wl11194@rjh.com.cn.

Classifications MeSH