Increasing evidence suggests that the gut microbiome may modulate the efficacy of cancer immunotherapy. In a B cell lymphoma patient cohort from five centers in Germany and the United States (Germany, n = 66; United States, n = 106; total, n = 172), we demonstrate that wide-spectrum antibiotics treatment (‘high-risk antibiotics’) prior to CD19-targeted chimeric antigen receptor (CAR)-T cell therapy is associated with adverse outcomes, but this effect is likely to be confounded by an increased pretreatment tumor burden and systemic inflammation in patients pretreated with high-risk antibiotics. To resolve this confounding effect and gain insights into antibiotics-masked microbiome signals impacting CAR-T efficacy, we focused on the high-risk antibiotics non-exposed patient population. Indeed, in these patients, significant correlations were noted between pre-CAR-T infusion Bifidobacterium longum and microbiome-encoded peptidoglycan biosynthesis, and CAR-T treatment-associated 6-month survival or lymphoma progression. Furthermore, predictive pre-CAR-T treatment microbiome-based machine learning algorithms trained on the high-risk antibiotics non-exposed German cohort and validated by the respective US cohort robustly segregated long-term responders from non-responders. Bacteroides, Ruminococcus, Eubacterium and Akkermansia were most important in determining CAR-T responsiveness, with Akkermansia also being associated with pre-infusion peripheral T cell levels in these patients. Collectively, we identify conserved microbiome features across clinical and geographical variations, which may enable cross-cohort microbiome-based predictions of outcomes in CAR-T cell immunotherapy.
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All data are available in the main text or the extended materials. The sequencing data generated during this study are available at European Nucleotide Archive (ENA) (project accession no. PRJEB54704) including basic phenotypes.
Code used in the present study is available at https://github.com/Elinav-Lab-DKFZ/CART-Microbiome and upon request.
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We thank the members of the DKFZ Microbiome & Cancer Division and the Elinav laboratory, Weizmann Institute of Science for insightful discussions; and the participants, investigators, collaborators and study site personnel engaging in the trials. We also thank the REG allo-SCT/CAR team, especially H. Bremm, T. Schifferstein and Y. Schumann for their help in collecting and cryopreserving stool samples, and S. Gleich for data management. We are grateful for the partnership with the Mark Foundation (Endeavor Award 2021 to C.K.S.-T., E.E., R.R.J., M.D.J., M.L.D.), which funded key aspects of this study. C.K.S.-T. is supported by the German José Carreras Leukemia Foundation (01 R/2020), the German Research Foundation (DFG; STE 2964/5-1), the Baden-Württemberg Stiftung (MiCART19), the DFG Excellence Cluster EXC-2124 (Controlling Microbes to Fight Infections, CMFI) and the NCT Heidelberg Funds against Cancer. V.B. is supported by the Else-Kröner Fresenius-Stiftung and the German Cancer Consortium (DKTK) and the Bavarian Cancer for Cancer Research (BZKF). M.-L.S. is supported by the Olympia Morata Program of the University of Heidelberg (OM 09/2019). H.P. is supported by the DFG (Projektnummer 360372040 [SFB1335], 395357507 [SFB1371], 324392634 [TRR221]), German Cancer Aid (70114547), the Wilhelm Sander Foundation (2021.040.1), and by the EMBO Young Investigator Program. This study was supported by the generous philanthropic contributions to The University of Texas MD Anderson Cancer Center Moon Shots Program, the Plaform for Innovative Microbiome and Translational Research (PRIME-TR) at the Department of Genomic Medicine at The University of Texas MD Anderson Cancer Center. This work was supported in part by a Cancer Center Support Grant (P30CA016672) from the National Cancer Institute and the Microbiome Core Facility at MD Anderson Cancer Center, and by a Cancer Center Support Grant (P30 CA076292) from the National Cancer Institute to Moffitt Cancer Center and the Moffitt Flow Cytometry Core Facility. R.R.J. is supported by R01HL124112 from the National Institutes of Health. E.E. is supported by the European Research Council, Israel Science Foundation, Israel Ministry of Science and Technology, Israel Ministry of Health, Helmholtz Foundation, Garvan Institute of Medical Research, European Crohn’s and Colitis Organization, Deutsch-Israelische Projektkooperation, IDSA Foundation and Wellcome Trust, and the Charlie Teo Foundation. E.E. is the incumbent of the Sir Marc and Lady Tania Feldmann Professorial Chair, a senior fellow of the Canadian Institute of Advanced Research (CIFAR) and an international scholar of the Bill & Melinda Gates Foundation and Howard Hughes Medical Institute (HHMI).
V.B. received research funding from Bristol Myers-Sqibb (BMS)/Celgene, Gilead, Janssen, Novartis, Roche and Takeda, and honoraria from Gilead, Janssen and Novartis. M.-L.S. is a consultant for Novartis, Gilead and Janssen. H.P. is a consultant for Gilead, Abbvie, Pfizer, Novartis, Servier and BMS, and has received research funding from BMS, and honoraria from Novartis, Gilead, Abbvie, BMS, Servier and Janssen-Cilag. M.L.D. reports consultancy/advisory/honoraria for Kite/Gilead, Novartis, Atara, Precision Biosciences, Celyad, Bellicum, GSK, Adaptive Biotech and Anixa Biosciences, and research funding from Kite/Gilead, Novartis and Atara. F.L.L. reports consultancy/advisory fees from Allogene, Amgen, Bluebird Bio, BMS/Celgene, Calibr, Cellular Biomedicine Group, Cowen, EcoR1, Emerging Therapy Solutions, GammaDelta Therapeutics, Gerson Lehrman Groupt, Iovance, Kite Pharma, Janssen, Legend Biotech, Novartis, Sana, Takeda, Wugen and Umoja, research funding from Kite/Gilead, Allogene, Novartis, BlueBird Bio, BMS, NCI, Leukemia and Lymphoma Society, and education or editorial activity for Aptitude Health, ASH, BioPharma Communications CARE Education, Clinical Care Options Oncology, Imedex and Society for Immunotherapy of Cancer. M.D.J. reports consultancy/advisory fees from Kite/Gilead, Novartis, BMS and MyeloidTx, and research funding from Incyte and Kite/Gilead. R.R.J. has served as a consultant or advisory board member for Merck, Microbiome DX, Karius, MaaT Pharma, LisCure, Seres, Kaleido, and Prolacta and has received patent licence fee or stock options from Seres and Kaleido. N.Y.S., C.-C.C., S.S.N., and R.R.J. are inventors on patent applications by the University of Texas MD Anderson Cancer Center, related to the results of the current study entitled "Serum Metabolomics Related to Chimeric Antigen Receptor (CAR) T-Cell Therapy" and "Gut Microbiome as a Predictive Biomarker of Outcomes for Chimeric Antigen Receptor T-Cell Therapy and its Modulation to Enhance Efficacy and Reduce Toxicity". E.E. is a scientific cofounder of DayTwo and BiomX and an advisor to Hello Inside, Igen and Aposense in topics unrelated to this work. The other authors have no competing interests.
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Extended Data Fig. 1 Effects of antibiotic exposure on survival outcomes and on development of toxicities in CD19-targeted CAR-T cell-treated lymphoma patients.
(a) Kaplan–Meier curves for progression-free survival (PFS) in the German and US cohorts displayed separately according to exposure to any antibiotic within 3 weeks before CAR-T cell infusion. (b) Incidence of progression and (c) overall survival in the combined cohort stratified by pre-infusion antibiotic exposure. (d) PFS for the US and the German patient cohorts displayed according to antibiotic risk strata (no antibiotic exposure vs. exposures to low- or to high-risk antibiotics in the three-weeks pre-infusion time window). (e) Histograms of the frequencies of CRS or ICANS grades according to antibiotic risk stratification; statistics by log-rank tests (for survival analyses) and Fisher’s exact tests.
Extended Data Fig. 2 Antibiotic classes administered to CAR-T cell-treated lymphoma patients.
Frequency of individual antibiotic drugs administered to patients within three weeks before (a) and four weeks after (b) CAR-T cell infusion. Histogram of patients receiving individual antibiotics over the course of CAR-T cell therapy for the 8 most frequently prescribed antibiotics in the combined cohort (c) and stratified by country (d). Note that cefepime was only administered to US patients and is therefore not shown in D.
Extended Data Fig. 3 Associations of pre-CAR-T cell infusion antibiotic exposure, tumor burden, performance status, CAR-T cell product and peripheral blood phenotypes.
Serum levels of LDH (a) in German and US patients at the time point of lymphodepletion chemotherapy stratified by whether patients received none or low-risk antibiotics (US: n = 83, Germany: n = 49) versus high-risk antibiotics (US: n = 20: Germany: n = 16). (b) Serum levels of CRP in German and US patients at the time point of lymphodepletion chemotherapy stratified by whether patients received none or low-risk antibiotics versus high-risk antibiotics. (c) IL-6 serum levels in patients at the day of CAR-T cell infusion (none | LR antibiotics: n = 67; HR antibiotics: n = 18). (d) Histograms of the frequencies of bulky lymphomas (>10 cm by radiological measurement), or (e) ECOG performance status according to antibiotic risk stratification. (f) Histograms of the frequencies of antibiotic exposures, or (g) ECOG performance status (left), or ICANS grades (right) according to type of CAR-T cell product administered to the patients. (h) Peripheral blood CD3, CD4 and CD8 T cell counts at the time point of leukapheresis displayed separately for US and German patients. (i) Blood T cell counts stratified by the number of treatment lines prior to CD19-directed CAR-T cell therapy (≤ 3: n = 53; ≥ 4: n = 39). (j and k) FACS analyses of peripheral blood T cell subsets of CAR-T cell-treated patients from Moffitt Cancer Center at the time of leukocyte apheresis and stratified by antibiotic exposure (none | LR antibiotics: n = 39; HR antibiotics: n = 12). CD4 and CD8 stem central memory (SCM; CCR7+, CD45RO-), central memory (CM; CCR7+, CD45RO+), effector memory (EM; CCR7-, CD45RO+), and effector (E; CCR7-CD45RO-) subsets are shown. Statistics for A-C, H, I, K by Mann–Whitney tests, for E-G by Fisher’s exact tests.
Extended Data Fig. 4 Multivariate analysis of antibiotic exposure prior to CD19-targeted CAR-T cell infusion and PFS or OS.
(a) The multivariate Cox model was adjusted for age (stratified by 65 years of age), ECOG (grade 0 and 1 vs. higher), country (Germany vs. US), LDH (normal vs. higher than ULN), CRP (normal vs. higher than 5 mg/dl), bulky disease (> 10 cm) and number of prior therapies (1-3 vs. 4-9). (b) The multivariate Cox model was adjusted for age (stratified by 65 years of age), ECOG (grade 0 and 1 vs. higher), country (Germany vs. US), LDH (normal vs. higher than ULN), CRP (normal vs. higher than 5 mg/dl), bulky disease (> 10 cm) and number of prior therapies (1-3 vs. 4-9). Error bars represent the low and high 95% confidence intervals (CI) of HR; *P < 0.05; **P < 0.01.
Extended Data Fig. 5 Gut microbiome diversity metrics grouped by clinical variables and outcomes.
(a) Number of stool samples collected over the course of CAR-T cell therapy by center and country (n = 351 in total). (b) Shannon indices for alpha diversity of the basal gut microbiome (that is, samples collected between days -21 and 0 relative to CAR-T cell infusion and species composition averaged by mean in case of multiple samples per patient) and grouped for CR vs. no CR at day 180 (left; CR: n = 38, no CR: n = 41 patients), or early progression at day 180 (right; ≤ 180 days: progression within 6 months after infusion (n = 37); no | > 180 days: no progression within follow-up or progression after 6 months after infusion (n = 42 patients) after excluding specimens collected while or less than two weeks after high-risk antibiotics exposure. (c and d) Shannon’s diversity of the basal gut microbiome, after the same exclusion of HR antibiotic samples, grouped for CRS (n = 7, 40, 28, 4 patients [grade 0, 1, 2, 3 + 4]) and ICANS grades (n = 45, 12, 11, 11 patients [grade 0, 1, 2, 3 + 4]). (e) PCoA plots based on Bray–Curtis dissimilarity metrics of the microbiome species and metabolic pathways beta diversity in all samples color-coded for German vs. US patients. (f–h) PCoA plots as in (E) for species composition color-coded for ECOG levels (F), number of prior therapy lines (PT; prior to CAR-T cell therapy) (G), and day of specimen collection relative to the day of CAR-T cell infusion (that is, day 0) (H). Group comparisons carried out by Mann–Whitney tests or Kruskal–Wallis rank tests.
Extended Data Fig. 6 Analyzing microbiome features associated with major cohort characteristics.
(a) Differential relative abundance analyses by log-fold changes and statistical testing by Mann–Whitney tests illustrated with volcano plots for species and metabolic pathways encoded in the fecal metagenomes: the log2 abundance in none | LR antibiotic samples (n = 79) / abundance in HR antibiotic samples (n = 16), versus the significance of the abundance difference. Metabolic pathways that have both p-value < 0.05 and absolute log2 fold-change > 1 are shown in red. (b) Bar-plots indicating the fold-change of the top features revealed by the differential abundance analyses described in (A). (c and d) Differential abundance analyses for species and metabolic pathway composition comparing the German (n = 50 patients) and the US (n = 45 patients) cohort. Here, comparisons of fecal metagenomes for samples collected in the 3-weeks pre-CAR-T cell infusion window are shown. Group comparisons carried out by Mann–Whitney tests.
Extended Data Fig. 7 Analyzing microbiome features associated with major outcomes of CAR-T cell therapy.
Differential abundance analyses were done as described in Extended Data Figure 7 with different grouping variables. (a) Left, volcano plots showing species and metabolic pathway compositions of the abundance in patients with survival > 180 day (n = 77) / abundance in patients with survival ≤ 180 day (n = 18) versus the significance of the abundance difference by Mann–Whitney tests. All samples collected in the pre-infusion period were included here. Features with a p-value < 0.05 and absolute log2 change > 1 are shown in red. Middle, bar-plots indicating the fold-change of the top species found in (A). Right, comparison of the relative abundances of the indicated species between patients with survival shorter or longer than 180 days. (b) As in (A), but samples collected during and less than two weeks after exposure to high-risk antibiotics were excluded (alive: n = 66; dead: n = 13). (c and d) As in (B), for metabolic pathways encoded in fecal metagenomes comparing 6-months survival (alive vs. dead at day 180) and early progression (≤180 days: progression within 6 months after infusion vs. no progression within follow-up or progression after day 180; n = 37 vs. n = 42). Group comparisons were computed by Mann–Whitney tests.
Extended Data Fig. 8 Summary of differentially abundant microbiome features by clinical variables and outcomes including all specimens collected in the 3-weeks pre-infusion period.
Heatmaps showing the associations between the species (a) and metabolic pathways (b) of the baseline gut microbiome and clinical parameters and major outcomes (Mann–Whitney tests, p-value < 0.05). Color indicates the Log10 of the following ratios: age (>= 65 / < 65 years), country (Germany / US), ECOG (grades 0-1 / 2-3), LDH (LDH at lymphodepletion chemotherapy: >= 280 / < 280 U ml-1), HR antibiotic exposures (no / yes), response (CR / no CR at day 180), survival (alive / dead at day 180), early progression (lymphoma progression until day 180 / progression > 180 days or no progression during follow-up) and CRS (grades < =1 / >1). Associations with P-value > 0.05 or with absolute log2 fold-change < 1 were set on the heatmap to have a log10 fold-change of zero.
Extended Data Fig. 9 Summary of differentially abundant microbiome features by clinical variables and outcomes excluding baseline specimens collected while or within 2 weeks after exposure to high-risk antibiotics.
Heatmaps showing the associations between the species (a) and metabolic pathways (b) of the baseline gut microbiome and clinical parameters and major outcomes (Mann–Whitney tests, p-value < 0.05). Color indicates the Log10 of the following ratios: age (>= 65 / < 65 years), country (Germany / US), ECOG (grades 0-1 / 2-3), LDH (LDH at lymphodepletion chemotherapy: >= 280 / < 280 U ml-1), response (CR / no CR at day 180), survival (alive / dead at day 180), early progression (lymphoma progression until day 180 / progression > 180 days or no progression during follow-up) and CRS (grades < =1 / >1). Associations with P-value > 0.05 or with absolute log2 fold-change < 1 were set on the heatmap to have a log10 fold-change of zero.
Extended Data Fig. 10 Interaction of antibiotic exposures, survival outcomes and response prediction modeling.
(a) Incidence curves for disease progression in patients were stratified according to high-risk antibiotics exposure in the 3-weeks pre-CAR-T cell infusion time window and according to presence of the peptidoglycan biosynthesis V - pathway in the patients’ stool metagenomes. (b) Deterministic logistic regression models were trained on clinical variables and pre-CAR-T cell infusion gut microbial species composition (mean per patient) of the German cohort and their performance was assessed on the US cohort (with all baseline samples: n = 45 patients; after excluding high-risk antibiotics samples: n = 36 patients). Area under the curve (AUC) receiver operating characteristic (ROC) curves indicating the false-positive and true-positive rates for predicting CR vs. non-CR at day 180 based on an analysis including all patient samples. (c) PCA computed for the whole dataset (that is, training and validation data including high-risk antibiotics samples) based on the features selected during the training process of response at day 180 after CAR-T cell infusion. (d) AUROC curves indicating the false-positive and true-positive rates for predicting CR vs. non-CR at day 180 after training the probabilistic model on the German dataset that excluded high-risk antibiotics exposed samples. (e) AUROC curves indicating the false-positive and true-positive rates for predicting CR vs. non-CR at day 180 based on a deterministic model analysis that excluded samples collected on or less than two weeks after exposure to high-risk antibiotics; data displayed for the US validation cohort. (f) PFS curve of patients after CAR-T cell infusion stratified by the median baseline relative abundance of Bacteroides stercoris as top feature for non-CR as revealed in the machine learning model for CR; only pre-infusion samples without high-risk antibiotic exposure were included. (g) Differences in relative abundances for the four most important species predicting non-response (no CR) grouped by response and high-risk antibiotic exposures; group comparisons carried out by pairwise Mann–Whitney tests with FDR correction of P-values (n = 38 / 41 / 4 / 12 patients [from left to right]); FDR-corrected P-values for B. stercoris are 0.089 (for all three comparisons), for B. fragilis are 0.0725 (for all four comparisons), and for E. sp. CAG38 are 0.0037 (for both comparisons). Statistics in (A) and (F) performed by log-rank tests.
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Stein-Thoeringer, C.K., Saini, N.Y., Zamir, E. et al. A non-antibiotic-disrupted gut microbiome is associated with clinical responses to CD19-CAR-T cell cancer immunotherapy. Nat Med (2023). https://doi.org/10.1038/s41591-023-02234-6
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