CAR-T cell therapy-related cytokine release syndrome and therapeutic response is modulated by the gut microbiome in hematologic malignancies -…

Clinical trial outcomes

Previously we reported the safety and efficacy of interim results of the trial (61 patients)24. Here after completion of the trial, 99 patients with relapsed/refractory multiple myeloma (r/r MM) were included (Fig.1a). The primary outcome was to evaluate the safety of BCMA CAR-T cells in the treatment of r/r MM. All patients were evaluated for safety analysis. Cytokine release syndrome (CRS) was observed in 97% (96/99) patients, including 50 (52.1%) patients with grades 12 CRS, 42 (43.8%) and 4 (4.1%) with grades 3 and 4 CRS. None grade 5 CRS occurred. The neurotoxicities were reported for 11 patients (11.1%), of whom 10 (10.1%) and 1 (1.0%) had grade 1 and grade 2 events, no grade 3 or higher neurotoxic effect was observed. After treatment, all episodes of CRS and neurotoxicity were resolved. The secondary outcome was to evaluate the efficacy and characterization of BCMA CAR-T cells in the treatment of r/r MM. Within 1 month after BCMA CAR-T cell infusion, 1 patient died of cerebral hemorrhage and 3 died of severe infections. Of the 95 remained patients, 91 (95.8%) had an overall response. In all, 55.8% (53/95), 15.8% (15/95), and 24.2% (23/95) of patients achieved a complete remission (CR), very good partial response (VGPR), or partial response (PR), respectively. With a median follow-up time of 21.2 months (95% CI, 18.432.1), the median progression free survival (PFS) was 12.2 (95% CI, 9.115.7) months. The 1-year OS and PFS rates were 0.71 (95% CI, 0.620.81) and 0.51 (95% CI, 0.420.62), respectively. BCMA CAR-T cells expanded dramatically in vivo. The BCMA CAR-T/CD3+ T-cell percentages in peripheral blood (PB) peaked on day 11 (range: 531) after CAR-T cell infusion. The median BCMA CAR-T/CD3+ T-cell percentages was 81.95% (range: 6.0797.30%).

a Patient enrollment. b AntiBCMA single-chain variable fragment (scFv), a hinge and transmembrane regions, and 4-1BB costimulatory moiety, and CD3 T-cell activation domain. c Blood and fecal sample collection. d Clinical response; CRS grade distribution in 43 r/r MM patients. e Numbers of BCMA CAR-T cell percentages in PB assessed by FACS in different therapy stages after CAR-T cell infusion and serum concentrations of IL-10 and IFN- in different therapy stages among the CR (n=24 biologically independent patients), VGPR (n=6 biologically independent patients), and PR (n=11 biologically independent patients) groups. Blue, green, and red colors indicate CR, VGPR, and PR group, respectively. Data are presented as mean valuesSEM. Significance determined by two-sided Kruskal-Wallis test and adjustments were made for multiple comparison. P values for CAR-T percent in PB, serum IL-10 and IFN- between CR and PR groups in CRSb stage were 0.004, 0.048, 0.085, respectively. *p<0.05, **p<0.01. f Body temperature and serum concentrations of IL-6 and IFN- in different therapy stages among CRS grade groups. (Grade 1 CRS group: n=8 biologically independent patients, Grade 2 CRS group: n=16 biologically independent patients, and Grade 3 CRS group: n=19 biologically independent patients). Data are presented as mean valuesSEM. Significance determined by two-sided Kruskal-Wallis test and adjustments were made for multiple comparison. P values for serum IL-6 and IFN- between Grade 1 CRS and Grade 3 CRS were 0.002 and 0.006, respectively. *p<0.05, **p<0.01. g Representative MM patients with impressive antimyeloma response. Positron emission tomography-computed tomography scans before and 5 months after CAR-T cell treatment showing complete elimination of large number of MM bone metastases. Before receiving CAR-T cell infusion, 43.5% of bone marrow cells of the patient were plasma cells, but after 1.5 months of infusion, dramatic eradication of MM from the bone marrow was observed; and MM cells became undetectable by flow cytometry. The bar indicates a length of 5m.

Microbiome samples were not available from 12 patients and 16S sequencing depth was not sufficient for analysis on 6 patients. Finally, a total of 81 patients with r/r MM was included for gut microbiome analysis, which included 43 patients for experiment group and 38 patients for validation group (Fig.1a). Number of samples collected, and sequencing depth were summarized in Supplementary Data12. Clinical and sequencing information of patients used in the study are presented in Supplementary Table3 and Supplementary Data3.

The median age of the MM patients was 59 (range 3975) years, and 55.8% were male (Table1). The median number of prior lines of therapy was 4 (range 28), with all receiving proteasome inhibitor therapy and 95.3% immunomodulatory agents. At enrollment, 39.5% had received autologous stem cell transplantation, and 55.8% had extramedullary disease(s).

Three months after infusion of a median dose of 4.4106/kg (range 1.26.9106/kg) of BCMA CAR-T cells, 55.8%, 14%, and 25.5% of patients had a CR, VGPR, or PR, respectively. All 43 MM patients showed CRS, grade 1 in 8 patients (18.6%), grade 2 in 16 (37.2%), and grade 3 in 19 (44.2%). No higher grade was observed (Fig.1d). The CRS was fully controlled and managed for all patients. Of these patients, 24 received only supportive care, 6 received supportive care plus tocilizumab treatment (IL-6 receptor-blocking monoclonal antibody), 10 received supportive care and corticosteroid treatment, and 3 received supportive care accompanied with tocilizumab and corticosteroids treatment. The antibiotics used before or during treatment were -lactam (41 patients), Carbapenems (26 patients), Quinolone (26 patients), Aminoglycosides (1 patient), Macrolide (1 patient), Tetracyclines (4 patients), Cephalosporins (3 patients), and Glycopeptides (6 patients). Although we included age, gender, number of prior lines of therapy, CAR-T cell dose, autologous stem cell transplantation, antibiotic use before or during treatment as covariates into our analyses, no significant differences were observed among different efficacy groups or CRS grade groups (Supplementary Tables12). Two patients died: one from sepsis caused by Pseudomonas aeruginosa and the other from intracranial hemorrhage (Fig.1d). Both the BCMA CAR-T/CD3+ T-cell percentages in peripheral blood (PB) and serum concentrations of interleukin (IL)10 increased during CRS and differed significantly in the CR and PR groups (Fig.1e). Patients temperature and C-reactive protein (CRP), ferritin, and lactic dehydrogenase (LDH) concentrations were elevated, and IL-6 and IFN- concentrations were significantly different in grade 3 vs grade 1 CRS (Fig.1f and Supplementary Fig.1ac). The serum immunoglobulins (IgG, IgA) and immunoglobulin and light chain concentrations decreased dramatically after CAR-T (Supplementary Fig.1df). Figure1g shows the differences of positron emission tomographycomputed tomography (PET-CT) scans and plasma cells detected by Wrights stain of a bone marrow smear (43.5% vs. 0), as well as flow cytometry (68.9% vs. 0) of bone marrow cells before and after CAR-T infusion for a representative subject.

To detect changes in the gut microbiota during CAR-Ttherapy, we collected fecal samples from each patient at five times (FCa, FCb, CRSa, CRSb, and CRSc; Fig.1c), where FCa denotes the baseline before chemotherapy; FCb after chemotherapy; CRSa after CAR T-cell infusion but before the onset of CRS; and CRSb and CRScdenote the peak and during the recovery phase of CRS, respectively. The median date of FCa was 4 days(range 27) before CAR-T cell infusion in MM patients, the median date of FCb was 0 days(range 07) before CAR-T cell infusion, and the median dates of CRSa, CRSb, CRSc after CAR-T cell infusion were 2days (range 15.3), 6days (range 2.517.4), and 14days (range 837.5), respectively.

We first evaluated the diversity of the gut microbiota in all subjects during CAR-T cell therapy in MM patients. Compared with early stage, there was a significant decrease in diversity (measured by the Shannon index) after the CAR-T therapy (Fig.2a). This decrease was observed in the microbiome of patients receiving CAR-T therapy for r/r ALL (Supplementary Fig.4a) or r/r NHL (Supplementary Fig.4b). Refer to Supplementary Table3 for details on the characteristics of r/r B-ALL and B-NHL patients. In addition, we analyzed diversity change in an independent MM sample with 38 patients included and found a decreased Shannon index along different therapy stages (Supplementary Fig.4c). To further assess the similarity of composition between different therapy stages, we performed pairwise Spearman correlation analysis of operational taxonomic unit (OTU) level bacterial abundance (Fig.2b) and found that stronger correlations emerged during the early stages with a value of 0.71, 0.73, and 0.68, respectively, at FCa, FCb, and CRSa. Correlations between late stages (CRSb and CRSc) and early stages were weaker, suggesting that changes in microbiome composition might be related to CRS.

a Shannon diversity indices of gut microbiome across CAR-T stages in all myeloma patients. Differential tests by Friedmans tests and two-tailed Wilcoxon rank-sum tests for 10 pairwise comparisons of the five timepoints (n=14). Bonferroni correction was applied for multiple testing; *FDR<0.05, **FDR<0.01. For FCa versus CRSc, adjusted p=0.023; FCb versus CRSc, adjusted p=0.009; CRSa versus CRSc, adjusted p=0.017. Boxplots indicate the median (thick bar), first and third quartiles (lower and upper bounds of the box, respectively), lowest and highest data value within 1.5 times the interquartile range (lower and upper bounds of the whisker). b Pairwise Spearman correlation of OTU-level bacterial abundance across different timepoints. Rho value for each significant correlation is labeled inside box. c Stacked bar plot of mean phylum-level phylogenetic composition of bacterial taxa in myeloma patients across different therapy stages. d Significant features identified by longitudinal analysis in Qiime2 feature-volatility plugin to identify taxonomic features associated with therapy stages. Scatter plot shows importance and average change of each important features by the longitudinal analysis. Genus-level features are labeled in the figure. Genus identified by both longitudinal analysis in Qiime2 and maSigPro are bolded and underlined. e Bar plot in the left shows significantly changed genera across the therapy identified by Friedmans tests (FDR<0.05, n=14). Effect size was estimated by Kendalls W Test. Heatmap in the right side denotes difference of each genus between two therapy stages. Red represents significant enrichment while blue represents significant depletion of the genus in the posterior stage comprising with the anterior stage. Significant p values were labeled in the boxes. Significances by two-tailed Wilcoxon rank-sum tests with FDR correction.

We next explored community structure and temporal shift of bacterial abundance at multiple taxonomic levels during CAR-T therapy. In these myeloma patients, bacterial communities were dominated by Firmicutes and Bacteroidetes at the phylum level (Fig.2c). Abundance of Firmicutes increased but that of Bacteroidetes decreased at later stages compared with the baseline (Wilcoxon rank-sum test, p<0.05, Supplementary Fig.4d). By applying the longitudinal analysis in the Qiime2 microbiome analysis platform, we detected changes in the gut microbial communities at taxonomic levels from phylum to genus (Fig.2d and Supplementary Data3). We further employed a negative binominal (NB) regression model-based time-course analysis to identify genera with significant temporal changes (Supplementary Data4). Five genera were detected by both Qiime2 and maSigPro procedures, which included increases in Enterococcus, Lactobacillus, and Actinomyces and decreases in Bifidobacterium and Lachnospira (bolded genera in Fig.2d). Most changes were aggravated during the late stages (Supplementary Fig.4e). Additionally, for repeated measure data (Subjects=10), we applied Friedmans test and found nine genera affected significantly by CAR-T therapy among which the genus Enterococcus had the largest difference between stages (Fig.2e).

Moreover, by checking changes in the five genera in ALL and NHL patients, we observed consistent shift trends in NHL (two genera; Supplementary Fig.4f) and ALL (four genera; Supplementary Fig.4g), respectively. These results were further verified in another independent MM sample, showing that CAR-T therapy correlated significantly with decreased Shannon diversity (Supplementary Fig.4c) and increased abundance of genus Enterococcus and Actinomyces (Supplementary Fig.4h).

We next determined whether microbial compositions or changes were associated with the response to CAR-Ttherapy. Because we wanted to identify maximum differences and only six subjects presented in the VGPR group, we performed comparisons only between the CR and PR groups.

In MM patients, notable differences in microbial alpha and within-sample diversity were observed in patients with CR and PR at CRSb stage (Fig.3a, b). Although no differences were detected at baseline, PR patients descended more dramatically in alpha diversity and had significantly lower Shannon indices than CR patients after CAR-T infusion (Fig.3a). As the degree of differences between CR and PR groups changed across therapeutic stages, we characterized the periods with greater differences by summarizing the amount of CR/PR-enriched OTU at each timepoint. The most pronounced differences occurred at CRSb (Fig.3c).

a Shannon diversity indices of gut microbiome differed between CR and PR groups across CAR-T stages. Significances were assessed by two-sided Wilcoxon rank-sum test (n=35). P values were 0.077, 0.040, 0.036 for FCb, CRSa, and CRSb, respectively. Boxplots indicate the median (thick bar), first and third quartiles (lower and upper bounds of the box, respectively), lowest and highest data value within 1.5 times the interquartile range (lower and upper bounds of the whisker). b Principal coordinate analysis of fecal samples in CRSb stage by response (CR versus PR) using Canberra distance. P value was calculated by PERMANOVA (n=35). c Summary of number of PR or CR-enriched OTUs in different therapy stages. Difference between CR and PR groups was assessed by two-sided Wilcoxon rank-sum test. P value significant cutoff was 0.05 (n=35). d Heatmap for abundance of OTUs with significant temporal differences between CR and PR groups identified by maSigPro (FDR<0.05). Rows denote bacterial OTUs grouped into three sets according to regression coefficients and sorted by mean abundance within each set. Individual fecal samples were organized in columns and grouped by therapy stages. Columns in the blue and red dashed box show abundance and longitudinal changes of these OTUs in CR and PR groups across the five timepoints. Color of the heatmap is proportional to OTU abundance (red indicates higher abundance and blue indicates lower abundance). e Profiles of significant gene clusters correspond to d. Solid lines denote median profile of abundance of OTUs within cluster for each experimental group through time. Fitted curve of each group is displayed as dotted line. f Phylogenetic composition of OTUs within each cluster in d at phylum and order levels.

To explore longitudinal differences between CR and PR across all therapeutic stages, we identified OTU features with differential dynamic profiles by applying negative binominal regression-based time-course differential analysis with the maSigPro package. In total, 125 OTUs were found to have differential time-course patterns between CR and PR patients (Fig.3d and Supplementary Data5). The significant OTUs were further grouped into three clusters according to profiles of their abundance. Most of these OTUs were in clusters 1 and 2 (Fig.3e). Cluster 1, characterized by enrichment in the CR group, was comprised mainly of OTUs, which belong to the phyla Firmicutes and Bacteroidetes and the orders Clostridiales and Bacteroidales. Cluster 2 was comprised of OTUs from a broader taxonomy, which included the orders Clostridiales, Bacteroidales, Lactobacillales, and Actinomycetales (Fig.3f).

In genus level, we identified 30 genera with differential time-course patterns in MM patients with CR and PR (Fig.4a left panel, Supplementary Data6). To explore these differences further, we divided the therapeutic period into before and after CAR-T infusion and performed genus-level class comparisons using linear discriminant analysis (LDA) of effect size (LEfSe)25 and generalized linear-mixed model (Fig.4a middle and right panel). Consistent with the results from OTU-level pattern analysis, most of the significant genera such as Faecalibacterium, Roseburia, and Ruminococcus were enriched in CR patients after CAR-T. The genera Bifidobacterium, Prevotella, Sutterella, Oscillospira, Paraprevotella, and Collinsella had a higher abundance in CR versus PR patients both before and after CAR-T (Fig.4a and Supplementary Fig.5a). We also took patients with VGPR into consideration and analyzed the above-mentioned genera before and after CAR-T infusion. The bacterial abundance in VGPR patients fell somewhere between CR and PR patients, but no statistical significance was evident for most of genera (Fig.4b and Supplementary Fig.5b).

a Differentially abundant genera between CR and PR group. Bubble plot in the left represents p values by maSigPro. Bar plots in the middle and right show significances and coefficients by generalized linear-mixed models (GLMMs) before and after CAR-T infusion (n=35). Blue bars indicate significant enrichment in CR group while red bars indicate significant enrichment in PR group (FDR<0.05). Red stars marked genera that was identified to be differentially abundant by linear discriminant analysis (p<0.05 for KruskalWallis H statistic and LDA score >2). P values by linear discriminant analysis for Sutterella, Collinsella, Paraprevotella, Bifidobacterium, Anaerotruncus, Prevotella, and Oscillospira before CAR-T were 0.0017, 0.0014, 0.038, 0.0015, 0.0064, 0.030, and 0.006, respectively; P values by linear discriminant analysis for Sutterella, Collinsella, Paraprevotella, Bifidobacterium, Anaerotruncus, Prevotella, Oscillospira, Faecalibacterium, Gemmiger, Clostridium, Odoribacter, Roseburia, Dialister, Enhydrobacter, Ruminococcus, and Dorea after CAR-T were 0.00012, 0.00076, 0.0060, 0.0.0067, 0.042, 0.0049, 0.011, 0.00017, 0.0035, 0.0058, 0.0073, 0.0013, 0.000038, 0.021, 0.0056, and 0.017, respectively. b Mean bacterial abundance [log2 (percentage+1)] of CR, VGPR, and PR myeloma patents before and after CAR-T cell infusion (n=43). Red stars indicate significant difference between CR and PR group by all three methods in panel a. P values for Sutterella by maSigPro were 1.17e-06, by generalized linear-mixed model were 7.86e-12 and 1.51e-14 before and after CAR-T, by linear discriminant analysis were 0.0017 and 0.00012 before and after CAR-T, respectively; P values for Faecalibacterium by maSigPro were 0.0093, by generalized linear-mixed model and linear discriminant analysis were 1.22e-10 and 0.00017 after CAR-T, respectively; P values for Bifidobacterium by maSigPro were 2.19e-06, by generalized linear-mixed model were 5.67e-08 and 1.51e-08 before and after CAR-T, by linear discriminant analysis were 0.0015 and 0.0067 before and after CAR-T, respectively; P values for Ruminococcus by maSigPro were 1.49e-08, by generalized linear-mixed model and linear discriminant analysis were 0.00031 and 0.0056 after CAR-T, respectively. c Relative abundance [log2 (percentage+1)] of top discriminative signatures at baseline (FCa) timepoint identified by RF feature selection procedure (n=35). Genera with highest scores of mean decreases in Gini were selected. Importance scores in RF classification model and fold-change levels in log2 scale are noted below plot for each genus. Blue and red colors indicate CR and PR group, respectively. d Same as panel c for post-chemotherapy (FCb) timepoint (n=35). Only signatures enriched in CR patents are displayed. Those depleted in CR patents are displayed in Fig. S2C. e Receiver operating characteristic (ROC) curve of RF model using discriminatory genera as predictors for baseline timepoint. f Same as panel e for post-chemotherapy timepoint. g KaplanMeier (KM) plot of PFS curves by log-rank test for patients with high (dark blue), median (green), or low (red) abundance of Sutterella. Abundance of genus Sutterella was in terms of median abundance of all timepoints. Boxplots indicate the median (thick bar), first and third quartiles (lower and upper bounds of the box, respectively), lowest and highest data value within 1.5 times the interquartile range (lower and upper bounds of the whisker).

To explore whether early bacterial abundance was indicative of therapeutic response, we used RF feature selection to identify key discriminatory genera for responses26. By defining the stages before CAR-T infusion as early, we applied feature selection procedures individually at both baseline (FCa) and post-chemotherapy (FCb) and identified gut microbiome signatures comprising 8 and 14 discriminatory genera separately for baseline and post-chemotherapy (Fig.4c, d and Supplementary Fig.5c). The area under the receiver operating characteristic curve (ROC) of the two RF models using these discriminatory features was 0.73 and 0.85, respectively (Fig.4e, f). Prevotella, Collinsella, Bifidobacterium, and Sutterella were enriched in CR versus PR both before and after CAR-T infusion and were identified by RF analysis as significant at baseline and post-chemotherapy. This indicates potential associations between these genera and the response to CAR-T.

We also checked the abundance of these genera in r/r NHL and ALL patients. In NHL, Faecalibacterium, Bifidobacterium, and Ruminococcus were significantly (or almost significantly) enriched in CR versus PR and in patients not having a remission (NR), consistent with our results in myeloma (Supplementary Fig.5e). However, for ALL, we observed enrichment of Bifidobacterium, Roseburia, and Collinsella in NR (Supplementary Fig.5f), which differed from the results for MM and NHL but might be determined by the small NR sample.

In the independent 38 validation MM patients, no significance of Shannon diversity was observed between CR and PR (Supplementary Fig.5g). Given that genus Sutterella, Prevotella, Collinsella, and Bifidobacterium were detected to be significant by both differential analysis and RF analysis at baseline and post-chemotherapy, we then examined abundance of these significantly changed bacteria of interest in an independent 38 MM validation sample. We found that abundance of genera Sutterella and Prevotella were higher in CR group than that in non-CR group at multiple stages. No significance was observed for Collinsella and Bifidobacterium (Supplementary Fig.5d).

To further demonstrate the association between these taxa and outcome, we assessed PFS following CAR-T therapy. By stratifying patients by tertile of bacterial abundance, we observed that for Sutterella, patients in the highest-abundance tertile had significantly prolonged PFS (Fig.4g). Even after stratification by timepoints, this association remained significant (Supplementary Fig.6a). However, for genus Faecalibacterium, which was reported to be significantly associated with PFS and anti-PD-1 therapy19, we did not observe an association (Supplementary Fig.6b, c).

Manifestations of severe CRS, namely high fever and greater amounts of cytokines, typically develop within several days after CAR-T cell infusion and may cause death if untreated27. We scaled CRS from level 1 to 528. To analyze associations between bacterial communities associated with CRS, we compared patients with severe (level 3) versus mild (level 1) CRS and severe and moderate CRS (level 2) in MM patients. We found 146 OTUs with different time patterns in the severe and mild groups (Supplementary Fig.7 and Supplementary Data7), and 99 OTUs with different patterns in the severe and moderate CRS groups (Supplementary Fig.8 and Supplementary Data8). The profiles of the OTU clusters for the comparisons were similar, with OTUs in clusters 1 and 3 having a higher abundance during late therapy in patients with severe versus mild CRS (Supplementary Figs.7b and 8b).

By analyzing associations between CRS grade and taxa at the genus level, we identified signatures discriminating severe from mild CRS, including decreases in amount of Bifidobacterium and Leuconostoc in patients with severe CRS (Fig.5a and Supplementary Data9). Bifidobacterium was increased in patients with worse CRS, not only during the window of CRS, but also at early stages (Fig.5a, b). Leuconostoc was significantly enriched during the window in patients with high CRS grade (Fig.5a, b). In the 38 validation MM patients, no significance was observed for Bifidobacterium or Leuconostoc among different CRS grade groups (Supplementary Fig.9).

a Correlation of differentially abundant genera with CRSgrade. Bubble plot in the left shows significant genera between severe and mild CRS groups by maSigPro (n=27). Bar plots in the middle and right show significances and coefficients by generalized linear-mixed models (GLMMs) before and during CRS. Orange bars indicate positive correlation with CRS. Green bars indicate negative correlation. Red stars marked genera that was identified to be differentially abundant by linear discriminant analysis (p<0.05 for Kruskal-Wallis H statistic and LDA score >2). P values by linear discriminant analysis for Bifidobacterium and Butyricicoccus before CAR-T were 0.003 and 0.027, respectively; P values by linear discriminant analysis for Leuconostoc, Bifidobacterium, Lactococcus, and Enhydrobacter after CAR-T were 0.016, 0.029, 0.0029, and 0.037, respectively. b Mean bacterial abundance in MM patients with different CRS grades before and during occurrence of CRS (n=43). Red stars indicate significant difference between Grade 1 CRS and Grade 3 CRS group by all three methods in panel a. P values for Bifidobacterium by maSigPro was 8.9e-08, by generalized linear-mixed model were 9.75e-06 and 1.42e-08 before and after CAR-T, by linear discriminant analysis were 0.003 and 0.029 before and after CAR-T, respectively; P values for Leuconostoc by maSigPro was 1.29e-14, by generalized linear-mixed model and linear discriminant analysis were 3.14e-11 and 0.016 after CAR-T, respectively. Boxplots indicate the median (thick bar), first and third quartiles (lower and upper bounds of the box, respectively), lowest and highest data value within 1.5 times the interquartile range (lower and upper bounds of the whisker). c Network representing correlations between gut microbes (gray nodes), immune cells and inflammatory markers (green nodes) at FDR<0.05. Correlations were measured by repeated measure correlation analysis (rmcorr). Red edges indicate positive correlations and blue edges negative correlations. Edge width is proportional to correlation coefficient () calculated by Spearman correlation test. Only genera identified as associated with clinical response and CRS grade were included in correlation analysis. d Top 2 positive and negative correlations in repeated measure correlation analysis. Data are presented as meanSEM.

To determine if gut microbial functions correlated with CAR-T therapy, we first inferred community function of MM patients using Phylogenetic Investigation of Communities by Reconstruction of Unobserved State (PICRUSt2). By applying time-course differential analysis, we identified differential pathways related to fatty acid metabolism, glutathione metabolism, quinone biosynthesis and glycan degradation (Supplementary Fig.10) in the MM cohort. Further, we compared pathways across different CRS groups. Microbial function of fecal samples from patients with severe CRS had high metabolism or biosynthesis related to inflammatory compounds, including several pathways associated with phosphonate and its metabolism, amino acid metabolism, lipoic acid metabolism, amino sugar, and nucleotide sugar metabolism and antibiotic synthesis (Supplementary Fig.11).

Likewise, we performed differential analysis of PICRUSt2 predicted functions in the 38 validation MM cohort. Comparing PR with CR, differential pathways concerning glutamate (d-Glutamine and d-glutamate metabolism), glycan (Glycan biosynthesis and metabolism), arginine, proline (d-Arginine and d-ornithine metabolism, Arginine and proline metabolism) and phenylalanine (phenylpropanoid biosynthesis) were revealed (Supplementary Fig.12a), among which the pathways related to glutamate and phenylalanine metabolism were endorsed in differential analysis of predicted KEGG pathways between PR and CR groups in the discovery MM sample (Supplementary Fig.10a). Lipopolysaccharide and steroid biosynthesis pathways were also consistently found to be differ between the CR and PR group by differential analysis of predicted pathways (Supplementary Fig.10a) and metabolites (Supplementary Fig.12a). Referring to the CRS grade-related pathway, difference in glycerolipid metabolism pathway was reproducible detected in both the discovery (Supplementary Fig.11a) and validation MM samples (Supplementary Fig.12c).

In addition, we applied metabolic Liquid Chromatography Mass Spectrometry (LC-MS) to quantify concentration of fecal metabolites during CRS. Intermediates (Choline, l-Cysteine, S-Sulfo-l-cysteine, Rosmarinic acid, l-Phenylalanine, and 2-Phenylacetamide) involved in multiple amino acid metabolism pathways were differentially abundant between CP and PR group when during CRS (p-value<0.05). We also identified metabolites concerning phosphonate and phosphonate metabolism (Bialaphos) and steroid biosynthesis (Desoxycortone) to be differ between CR and PR (Supplementary Fig.13). In differential analysis between CRS groups, we identified phosphocreatine which annotated to arginine and proline metabolism (Supplementary Fig.14). Moreover, three abovementioned pathways (i.e., tyrosine metabolism, phenylalanine metabolism, phosphonate, and phosphonate metabolism) were also indicated to have differentially abundances between the CR and PR group in the predicted pathway analysis (Supplementary Fig.10). Two pathways (tyrosine metabolism and phenylalanine metabolism) were also differed among patients with different CRS grades (Supplementary Fig.11). Additionally, we performed pathway enrichment analysis of differentially abundant metabolites between the CR and PR subjects to reveal distinction on metabolic functions (Supplementary Fig.15a). Two pathways (Phenylalanine, tyrosine and tryptophan biosynthesis; Riboflavin metabolism) reached marginal significance (p=0.07). These concordant findings strengthened the results of functional prediction analysis and highlighted the importance of amino acid metabolism during the CAR-T therapy.

Primary inflammatory markers of CRS are cytokines, such as IL-6, IL-2, IL-10, interferon gamma (IFN-), and tumor necrosis factor- (TNF-). Various cytokines are elevated in the serum of patients experiencing CRS after CAR-T cell infusion29. By assessing serum cytokine concentrations and immune cell numbers during CAR-T, we observed significantly increased amounts of serum inflammatory cytokines (IL-6, CRP, IFN-, D-dimer, ferritin) but low numbers of immune cells (monocytes, lymphocytes, neutrophils, leukocytes) in severe CRS (Fig.5c). We also compared serum cytokine concentrations and immune cell numbers in CR and PR, observing significant differences for many of them (see Supplementary Fig.16).

To explore further associations between the gut microbiome and CRS during CAR-T therapy, we determined whether serum cytokine concentrations and numbers of PB immune cells correlated with the abundance of gut microorganisms (Fig.5d). By assessing common within-individual correlation for repeated measures30, we constructed correlation network between gut microbes, cytokines, and immune cells (Fig.5c). The top significant correlation pairs were MCP-1 and Lactobacillus, lymphocyte and Clostridium, IL-15 and Lactobacillus, leukocyte and Veillonella (Fig.5d). In addition, serum level of lymphocyte was negatively correlated with 11 genera, including multiple genera related to CRS level such as Bifidobacterium, Butyricimona and Oscillospira. M1 and M2 macrophages, which play a key role in CRS initiation, did not show significant correlation with any microbes.

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