
==== Front
Biomark Res
Biomark Res
Biomarker Research
2050-7771
BioMed Central London

39272132
650
10.1186/s40364-024-00650-5
Correspondence
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
Sheng Ling-Shuang 1
Shen Rong 1
Yan Zi-Xun 1
Wang Chao 2
Zheng Xin 1
Zhang Yi-Lun 1
Yang Hao-Xu 1
Wu Wen 1
Xu Peng-Peng 1
Cheng Shu 1
Bachy Emmanuel 3
Sesques Pierre 3
Jacquet-Francillon Nicolas 4
Jiang Xu-Feng 2
Zhao Wei-Li zhao.weili@yahoo.com

1
Wang Li wl11194@rjh.com.cn

1
1 grid.412277.5 0000 0004 1760 6738 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
2 grid.412277.5 0000 0004 1760 6738 Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
3 https://ror.org/01502ca60 grid.413852.9 0000 0001 2163 3825 Department of Haematology, Hospices Civils de Lyon, Lyon, France
4 https://ror.org/01502ca60 grid.413852.9 0000 0001 2163 3825 Department of Nuclear Medicine, Hospices Civils de Lyon, Lyon, France
14 9 2024
14 9 2024
2024
12 10415 7 2024
5 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
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 50cm3 and 500 g, respectively. A prediction model combining maximum standardized uptake value (SUVmax) at 1 month after CAR-T cell therapy (M1) and tTLG clearance rate was established to predict early progression for partial response/stable disease patients evaluated at M1 after CAR-T cell therapy and validated in Lyon cohort. Relevant association of the distance separating the two farthest lesions, standardized by body surface area to the severity of neurotoxicity (AUC = 0.74; P = 0.034; 95% CI, 0.578–0.899) after CAR-T cell therapy was found in patients received axicabtagene ciloleucel. In patients with Dmax ≥ 6 cm, RNA-sequencing analysis conducted at the screening stage showed enrichment of immunosuppressive-related biological processes, as well as increased M2 macrophages, cancer-associated fibroblasts, myeloid-derived suppressor cells, and intermediate exhausted T cells. Collectively, immunosuppressive tumor microenvironment may serve as a negative prognostic indicator in patients with high tumor burden who respond poorly to CAR-T cell therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40364-024-00650-5.

Keywords

Large B-cell lymphoma
CAR-T cell therapy
18F-FDG PET/CT
Microenvironment
issue-copyright-statement© Yumed Inc. and BioMed Central Ltd., part of Springer Nature 2024
==== Body
pmcTo the editor

Chimeric Antigen Receptor T (CAR-T) cell therapy has greatly improved the prognosis of relapsed and refractory patients with large B-cell lymphoma (LBCL), with objective remission rate (ORR) as 83%, complete response (CR) as 58%, and 5-year overall survival (OS) as 42% [1]. Despite its efficacy, a subset of patients still experiences disease progression. Recently, it has been demonstrated that pre-apheresis and pre-infusion total metabolic tumor volume (tMTV) could predict survival and that higher pre-apheresis or pre-infusion tMTV values were associated with shorter progression-free survival (PFS) and OS. Furthermore, at pre-infusion, tMTV was associated with grade ≥ 2 cytokine release syndrome (CRS), and maximum standardized uptake value (SUVmax) was associated with failure to achieve CR. A predictive model using pre-infusion tMTV combined with lactate dehydrogenase (LDH) was established to predict patient outcomes after CAR-T cell therapy [2].

Here we reviewed 90 Chinese LBCL patients (53 males, 37 females; median age 56.5 years) received CAR-T cell therapy at our institution from January 1, 2018 to March 31, 2023 (baseline characteristics showed in Table 1). Key radiomic metrics included SUVmax, tMTV, total lesion glycolysis (tTLG), maximum diameter of the largest lesion (Dmax), and distance separating the two farthest lesions, standardized by body surface area (hereafter referred to as distance of the farthest lesions), at screening or 1-month after CAR-T cell infusion. By analyzing these radiomics metrics in conjunction with the M3 response and survival of patients, patients at screening with Dmax < 6cm (N = 60) had higher 3-month CR rates (85.0% vs. 33.3%, P < 0.001), PFS (HR 0.17; 95% CI 0.08–0.35, P < 0.001), and OS (HR 0.18; 95% CI 0.08–0.40, P < 0.001). Similarly, patients with tMTV < 50cm3 and tTLG < 500g had higher 3-month CR rates, PFS and OS (Fig. 1). Based on the areas under curve (AUC) of the receiver operating characteristic (ROC) curves for each metric and multivariate analysis, Dmax had the largest AUC and the most significant P-value, therefore, it was the optimal screening metric for predicting prognosis (Supplementary Fig. 1A-C and Table 2). Next, we validated previously reported predictor model [2] using our cohort and found that Dmax combined with extranodal involvement was more efficient in distinguishing patient outcome than the combination of tMTV or tTLG with extranodal involvement or LDH (Fig. 1B-D and Table 2).Table 1 Baseline characteristics of RJ cohort and Lyon cohort

		RJ cohort (n = 90)	Lyon cohort (N = 72)	
Age	 > 60	31 (34.4%)	33 (45.8%)	
 ≤ 60	59 (65.6%)	39 (54.2%)	
Gender	Male	53 (58.9%)	44 (61.1%)	
Female	37 (41.1%)	28 (38.9%)	
ECOG score	0–1	63 (70.0%)	53 (73.6%)	
 ≥ 2	27 (30.0%)	19 (26.4%)	
Ann Arbor stage	I-II	21 (23.3%)	18 (25.0%)	
III-IV	69 (76.7%)	54 (75.0%)	
LDH level	Normal	19 (21.1%)	16 (22.2%)	
Elevated	71 (78.9%)	56 (77.8%)	
Extranodal sites	0–1	36 (40.0%)	NA	
 ≥ 2	54 (60.0%)	NA	
IPI score	0–2	33 (36.7%)	NA	
3–5	57 (63.3%)	NA	
Disease type	DLBCL	72 (80.0%)	45 (62.5%)	
PBMCL	4 (4.4%)	5 (6.9%)	
PCNSL	1 (1.1%)	0	
Transformed low-grade lymphoma	13 (14.4%)	22 (30.6%)	
Cell of origin	GCB	35 (38.9%)	36/63 (57.1%)	
Non-GCB	55 (61.1%)	27/63 (42.9%)	
Prior lines of therapy	1–2	56 (62.2%)	17 (23.6%)	
 ≥ 3	34 (37.8%)	55 (76.4%)	
Primary refractory	No	26 (28.9%)	25 (34.7%)	
Yes	64 (71.1%)	47 (65.3%)	
Response to last line	PR	19 (21.1%)	NA	
SD/PD	71 (78.9%)	NA	
Prior ASCT	No	83 (92.2%)	52 (72.2%)	
Yes	7 (7.8%)	20 (27.8%)	
Bulky disease ≥ 6cm	No	60 (66.7%)	37/59 (62.7%)	
Yes	30 (33.3%)	22/59 (37.3%)	
Double expressor	No	52 (57.8%)	20/64 (31.2%)	
Yes	38 (42.2%)	44/64 (68.8%)	
Double/triple-hit	No	79 (87.8%)	26/31 (83.9%)	
Yes	11 (12.2%)	5/31 (16.1%)	
TP53 mutation	No	59 (65.6%)	NA	
Yes	31 (34.4%)	NA	
CAR-T products	Axi-cel	61 (67.8%)	33 (45.8%)	
Relma-cel	29 (32.2%)	0	
Kymriah	0	39 (54.2%)	

Fig. 1 The value of screening-phase 18F-FDG PET/CT metrics in predicting response, prognosis, and death. A Examination time points during CAR-T cell therapy process. B-D PFS and OS of patients stratified with whether extranodal involvement and screening-phase Dmax (B), tMTV (C), and tTLG (D). E 3-, 6- and 12-month responses after CAR-T cell therapy stratified with screening-phase 18F-FDG PET/CT metrics. 18F-FDG PET/CT, 18F-fluorodeoxyglucose positron emission tomography/computed tomography; CAR-T, chimeric antigen receptor T; PFS, progression-free survival; OS, overall survival; Dmax, the maximum diameters of the largest lesion; tMTV, total metabolic tumor volume; tTLG, total lesion glycolysis

Table 2 Univariate and multivariate logistic regression of the predictive factors for progression-free survival

Characteristics	Univariate analysis	Multivariate analysis	
OR (95%CI)	P value	OR (95%CI)	P value	
Age > 60	1.053 (0.436–2.546)	0.908	0.535 (0.115–2.496)	0.426	
Male	1.041 (0.443–2.445)	0.927	0.485 (0.116–2.031)	0.322	
ECOG ≥ 2	1.509 (0.607–3.749)	0.376	0.918 (0.197–4.283)	0.913	
Ann Arbor stage ≥ 3	1.538 (0.552–4.286)	0.410	0.931 (0.185–4.681)	0.931	
LDH elevated	0.949 (0.340–2.650)	0.921	0.351 (0.076–1.627)	0.181	
Extranodal involvement	1.679 (0.573–4.920)	0.345	4.376 (0.805–23.792)	0.087*	
GCB subtype	0.511 (0.210–1.243)	0.511	0.376 (0.094–1.506)	0.167	
Prior lines of therapy ≥ 3	1.220 (0.514–2.894)	0.652	1.543 (0.370–6.438)	0.551	
Primary refractory	1.168 (0.459–2.968)	0.745	1.104 (0.265–4.596)	0.892	
Response to last line: SD/PD	2.297 (0.747–7.063)	0.147	2.473 (0.531–11.527)	0.249	
Prior ASCT	1.081 (0.227–5.141)	0.922	0.726 (0.070–7.558)	0.789	
Double expressor	1.074 (0.459–2.510)	0.870	1.545 (0.410–5.830)	0.521	
Product: Axi-cel	0.648 (0.265–1.585)	0.342	0.556 (0.130–2.384)	0.429	
Dmax ≥ 6cm	20.000 (6.334–63.163)	0.000**	25.178 (4.958–127.861)	0.000**	
tMTV ≥ 50cm3	6.308 (2.483–16.025)	0.000**	2.085 (0.247–17.630)	0.500	
tTLG ≥ 500g	4.020 (1.615–10.007)	0.003**	1.265 (0.145–11.0.28)	0.832	
Abbreviations: ECOG Eastern Cooperative Oncology Group, LDH lactate dehydrogenase, IPI international prognostic index, DLBCL diffuse large B-cell lymphoma, PMBCL primary mediastinal B-cell lymphoma, PCNSL primary central nervous system lymphoma, GCB germinal center B cell, PR partial response, SD stable disease, PD progressive disease, ASCT autologous stem-cell transplantation, CAR-T chimeric antigen receptor T, Axi-cel axicabtagene ciloleucel, Relma-cel relmacabtagene autoleucel, PFS progression-free survival, OR odds ratio, CI confidential interval

*P < 0.1

**P < 0.05

Identifying patients who will experience early progression is critical to implementing preemptive treatment strategies. Therefore, we developed a prediction model for early progression using these radiomic data for partial response (PR)/ stable disease (SD) patients 1 month after CAR-T cell therapy. Recently, growing evidence has demonstrated that high SUVmax in M1 is strongly associated with poor prognosis [3–5]. Of note, the tTLG index is derived in conjunction with lesion volume and spatial distribution to measure the metabolic activity of the lesion and treatment response. We found that the prediction model combining the SUVmax and the tTLG clearance rate (ΔtTLG / tTLGpre = [tTLG at screening stage]-[tTLG at M1]) / tTLG at screening stage) of M1 was able to accurately predict the patients without progression after CAR-T cell therapy. M1 SUVmax < 8 and ΔtTLG / tTLGpre ≥ 0.9 (N = 11, median PFS not reached) predicted a constant state of remission. However, SUVmax ≥ 8 indicted progression within 6 months, regardless of tTLG clearance rate (tTLG clearance rate ≥ 0.9, N = 6, median PFS 4.2 months, HR 95.0, 95%CI 13.6–665.6 months; tTLG clearance rate < 0.9, N = 7, median PFS 3.7 months, HR 65.2, 95%CI 11.3–376.0 months), while M1 SUVmax < 8 and tTLG clearance rate < 0.9 (N = 5, median PFS 4.4 months, HR 69.6, 95%CI 8.8–551.2 months) indicated progression in 12 months (Supplementary Fig. 2A, 2E). Furthermore, this model was validated by Lyon cohort (Supplementary Fig. 2B) [4], indicating that the model is robust and reliable, as the genetic background and ethnicity of patients do not affect the predictive power of the model.

As for the predictive value of adverse events during CAR-T cell therapy, a correlation between baseline MTV with CRS/neurotoxicity (NT) grades [6–8], and baseline SUVmax with CRS [3] has been revealed. In our cohort, for axicabtagene ciloleucel (axi-cel), distance of the farthest lesions was associated with NT (AUC = 0.74) (Supplementary Fig. 3). No NT occurred in patients with distance of the farthest lesions < 0.15m−1, while 34.8% of patients with distance of the farthest lesion ≥ 0.15m−1 experienced NT. However, for relmacabtagene autoleucel (relma-cel), no strong correlations were observed. Imaging metrics did not correlate significantly with CRS/NT duration or onset, or with CAR-T cell expansion metrics (Supplementary Fig. 4). Therefore, incidence of CRS and NT could vary from different CAR-T cells, probably due to differences in CAR-T cell co-stimulatory molecules and could also be due to the small sample size.

Gene Set Enrichment Analysis (GSEA) of RNA-sequencing data showed that immunosuppressive-related biological processes were enriched in patients with Dmax ≥ 6cm (Fig. 2). Tumor microenvironment (TME) analysis revealed higher levels of M2 macrophages, cancer-associated fibroblasts (CAF), myeloid-derived suppressor cell (MDSC), and intermediate exhausted T cells in these patients, suggesting an immunosuppressive microenvironment and a possible reason for CAR-T cell therapy failure in patients with high tumor burden. Accompanied by the increasing immunosuppressive cells within TME, the lipid metabolic, iron/copper ion transport, macrophage, granulocyte, monocyte chemotaxis, and autophagy pathways were significantly activated (Dmax ≥ 6cm). Under high tumor burden, tumor cells tend to recruit and activate more immunosuppressive cells, through metabolism alterations and subsequent induction of T-cell exhaustions [9, 10].Fig. 2 Biological process and tumor microenvironment characteristics in patients stratified with Dmax. A Enriched BP terms in patients with Dmax ≥ 6 cm, as compared to patients with Dmax < 6 cm in CAR-T screening phase. The size of points indicates the number of genes included in each gene set. B Volcano plot image of characterized gene expression from enriched categories in patients with Dmax ≥ 6 cm, as compared to patients with Dmax < 6 cm in CAR-T screening phase. The background color in the box represents which categories the gene belongs to. The black dashed line corresponds to p = 0.05. C Normalized expression of M2 macrophage, CAF, MDSC, and intermediate Tex of patients with Dmax ≥ 6 cm and Dmax < 6 cm in the CAR-T screening phase. BP, biological process; TME, tumor microenvironment; CAF, cancer-associated fibroblasts; MDSC, myeloid-derived suppressor cell; Tex, exhausted T cell

In summary, imaging metrics of 18F-FDG PET/CT, especially Dmax at the screening stage had the predictive value of clinical efficacy, progression, and death of CAR-T cell therapy, while the distance of the farthest lesions was associated with the occurrence of NT. Furthermore, we developed a prediction model combining M1 SUVmax and tTLG clearance rate to predict early progression for patients evaluated as PR/SD at M1 after CAR-T cell therapy. Immunosuppressive TME may serve as a possible mechanism for those patients who respond poorly to CAR-T cell therapy with high tumor burden.

Supplementary Information

Supplementary Material 1.

Supplementary Material 2: Figure S1. ROC curves of screening-phase 18F-FDG PET/CT metrics with the 3-month response, PFS, and OS. (A-C) ROC curves of screening-phase 18F-FDG PET/CT metrics (Dmax, tMTV, tTLG, SUVmax, and sDmax) with PFS (A); OS (B), and 3-month response (C). (D) ROC curves of M1 18F-FDG PET/CT metrics after CAR-T cell therapy (M1 tMTV, M1 tTLG, and M1 SUVmax), (E–F) Δvalue (ΔtMTV, ΔtTLG, and ΔSUVmax) (E) and Δvalue / valuepre (ΔtMTV/tMTVpre, ΔtTLG/tTLGpre and ΔSUVmax/SUVmaxpre) (F) with PFS. ROC, receiver operating characteristic; SUVmax, maximum standardized uptake value; sDmax, the distance separating the two farthest lesions, standardized according to the body surface area.

Supplementary Material 3: Figure S2. The value of 18F-FDG PET/CT metrics in predicting early progression in PR/SD patients on M1 after CAR-T cell therapy. (A-B) PFS of patients evaluated as PR/SD on M1 after CAR-T cell therapy stratified with M1 SUVmax and ΔtTLG/tTLGpre from RJ cohort (A) and Lyon cohort (B). (C-D) OS of patients evaluated as PR/SD on M1 after CAR-T cell therapy stratified with M1 SUVmax and ΔtTLG/tTLGpre from RJ cohort (C) and Lyon cohort (D). (E) A prediction model combining M1 SUVmax and tTLG clearance rate to predict early progression for patients evaluated as PR/SD at M1 after CAR-T cell therapy. PR, partial response; SD, stable disease.

Supplementary Material 4:  Figure S3. Correlation of screening-phase 18F-FDG PET/CT metrics with CAR-T toxicity. (A-B) ROC curves of screening-phase 18F-FDG PET/CT metrics (Dmax, tMTV, tTLG, SUVmax, and sDmax) withCRS grade < 2 or CRS grade ≥ 2 (A) and no NT grade or any NT grade (B) in patients received axi-cel treatment. ROC curves of screening-phase 18F-FDG PET/CT metrics (Dmax, tMTV, tTLG, SUVmax and sDmax) with CRS grade < 2 or CRS grade ≥ 2 (C) and no NT grade or any NT grade (D) in patients received relma-cel treatment. (E) Occurrence of NT in patients stratified with screening-phase sDmax. (F) Correlation of screening-phase 18F-FDG PET/CT metrics with duration of CRS and NT. (G) Correlation of screening-phase 18F-FDG PET/CT metrics with onset day of CRS and NT. CRS, cytokine release syndrome; NT, neurotoxicity; Axi-cel, axicabtagene ciloleucel; relma-cel, relmacabtagene autoleucel.

Supplementary Material 5: Figure S4. Correlation of screening-phase 18F-FDG PET/CT metrics with CAR-T cell expansion. Correlation of screening-phase 18F-FDG PET/CT metrics with the duration of CAR-T Cmax (A), Tmax (B), and AUC0-28d (C). Cmax, the peak CAR-T cell expansion value; Tmax, the days to peak expansion; AUC0-28d, expansion area under curve of day 0–28 after CAR-T cell therapy.

Abbreviations

CAR-T Chimeric antigen receptor T

LBCL Large B-cell lymphoma

18F-FDG PET/CT 18F-fluorodeoxyglucose positron emission tomography/computed tomography

tMTV Total metabolic tumor volume

tTLG Total lesion glycolysis

SUVmax Maximum standardized uptake value

ORR Objective remission rate

CR Complete response

OS Overall survival

PFS Progression-free survival

CRS Cytokine release syndrome

LDH Lactate dehydrogenase

PR Partial response

SD Stable disease

NT Neurotoxicity

Axi-cel Axicabtagene ciloleucel

AUC Area under curve

Relma-cel Relmacabtagene autoleucel

GSEA Gene Set Enrichment Analysis

TME Tumor microenvironment

CAF Cancer-associated fibroblasts

MDSC Myeloid-derived suppressor cell

Acknowledgements

We thank Emmanuel Bachy at Lyon Sud Hospital, for kindly providing the raw data of Lyon cohort. We thank all the patients involved for allowing us to analyze their clinical data.

Authors’ contributions

L.W. and W.Z. conceived the study and designed the experiments; L.S. and R.S. performed the experiments or analysis; H.Y performed a statistical analysis of the data; all the authors analyzed the data; L.S., L.W. and W.Z. wrote the manuscript with the help from all the authors.

Funding

This study was supported, in part, by research funding from the National Key R&D Program of China (2022YFC2502600), National Natural Science Foundation of China (82130004, 82170178 and 82300210), Chang Jiang Scholars Program, Shanghai Municipal Education Commission Gaofeng Clinical Medicine Grant Support (20152206 and 20152208), Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR1032B and SHDC2022CRD033), Shanghai Rising-Star Program (19QA145600), Municipal Human Resources Development Program for Outstanding Young Talents in Medical and Health Sciences in Shanghai (2017YQ075), Talent (Class A) of Guangci Excellence Youth Plan (GCQN-2019-A16), Clinical research project by Shanghai Jiao Tong University School of Medicine (CARTFR-05, KY2023727, YW20220022), Samuel Waxman Cancer Research Foundation, and Innovation Technology Launch Plan of Guangci.

Availability of data and materials

The data used in this study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

The study was approved by the Shanghai Ruijin Hospital Review Board, and informed consent was obtained in accordance with the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Ling-Shuang Sheng, Rong Shen, Zi-Xun Yan and Chao Wang contributed equally to this work.
==== Refs
References

1. Neelapu SS Jacobson CA Ghobadi A Miklos DB Lekakis LJ Oluwole OO 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 2315 36821768
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):2307–15.36821768
2. Leithner D Flynn JR Devlin SM Mauguen A Fei T Zeng S 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 10.1186/s13045-024-01540-x 38649972
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):21.38649972 10.1186/s13045-024-01540-x
3. 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. 
4. Sesques P Tordo J Ferrant E Safar V Wallet F Dhomps A 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 8 627 634 10.1097/RLU.0000000000003756 34115706
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):627–34.34115706 10.1097/RLU.0000000000003756
5. Breen WG Hathcock MA Young JR Kowalchuk RO Bansal R Khurana A Metabolic characteristics and prognostic differentiation of aggressive lymphoma using one-month post-CAR-T FDG PET/CT J Hematol Oncol 2022 15 1 36 10.1186/s13045-022-01256-w 35346315
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):36.35346315 10.1186/s13045-022-01256-w
6. Breen WG Young JR Hathcock MA Kowalchuk RO Thorpe MP Bansal R 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 10.1038/s41408-023-00895-7 37591834
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):127.37591834 10.1038/s41408-023-00895-7
7. Ababneh HS Ng AK Abramson JS Soumerai JD Takvorian RW Frigault MJ 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 10.1002/hon.3231 37795759
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):e3231.37795759 10.1002/hon.3231
8. Wang J Hu Y Yang S Wei G Zhao X Wu W 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 1098 10.1016/j.bbmt.2019.02.008 30769193
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):1092–8.30769193 10.1016/j.bbmt.2019.02.008
9. Cerchietti L Genetic mechanisms underlying tumor microenvironment composition and function in diffuse large B-cell lymphoma Blood 2024 143 12 1101 1111 10.1182/blood.2023021002 38211334
Cerchietti L. Genetic mechanisms underlying tumor microenvironment composition and function in diffuse large B-cell lymphoma. Blood. 2024;143(12):1101–11.38211334 10.1182/blood.2023021002
10. Yan Z Li L Fu D Wu W Qiao N Huang Y 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 10.1007/s11684-022-0972-8 37060525
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):699–713.37060525 10.1007/s11684-022-0972-8
