Patient cohorts
A total of 92 patients with r/r B-cell-derived hematologic malignancies were screened. Ten patients were not eligible for inclusion. Another four patients were excluded because of lack of sufficient 16S sequencing depth. Thus, MM (n = 43), B-ALL (n = 23), and B-NHL (n = 12) patients were included (Fig. 1A).
The median age of the MM patients was 59 (range 39–75) years, and 55.8% were male (Table 1). The median number of prior lines of therapy was 4 (range 2–8), 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).
Table 1
Baseline characteristics of multiple myeloma patients included in final fecal microbiome analyses cohorts.
| Total N = 43(%) |
Age Median Range | 59 39–75 |
Gender Male Female | 24 (55.8) 19 (44.2) |
Number of prior lines of therapy Median Range | 4 2–8 |
CAR-T cell dose(×106/kg) Median Range | 4.4 1.2–6.9 |
Autologous stem cell transplantation No Yes | 26 (60.5) 17 (39.5) |
Extramedullary disease No Yes | 19 (44.2) 24 (55.8) |
Prior PI therapy No Yes | 0 43 (100) |
Prior IMiD therapy No Yes | 2 (4.7) 41 (95.3) |
PI, Proteasome inhibitors (Bortezomib/Carfilzomib/Ixazomib). |
IMiD, immunomodulatory agent (Lenalidomide/Thalidomid/Pomalidomide). |
Three months after infusion of a median dose of 4.4 × 106/kg (range 1.2–6.9 × 106/kg) of BCMA CAR-T cells, 55.8%, 14%, and 25.5% of patients had a complete remission (CR), very good partial response (VGPR), or partial response (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). 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 and interferon (IFN)-γ 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-γ concentrations were significantly different in grade 3 vs grade 1 CRS (Fig. 1F and Supplementary Fig. 1A-C). The serum immunoglobulins (IgG, IgA) and immunoglobulin κ and λ light chain concentrations decreased dramatically after CAR-T (Supplementary Fig. 1D-F). Figure 1G shows the differences of positron emission tomography–computed tomography (PET-CT) scans and plasma cells detected by Wright’s 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.
Changes in the intestinal microbiome during CAR-T cell therapy
To detect changes in the gut microbiota during CAR-T, we collected fecal samples from each patient at five times (FCa, FCb, CRSa, CRSb, and CRSc; Fig. 1C), where FCa denotes the baseline when patients were first enrolled; FCb after chemotherapy; CRSa after CAR T-cell infusion but before the onset of CRS; and CRSb and CRSc the peak and during the recovery phase of CRS, respectively.
We first evaluated the diversity of the gut microbiota in all subjects during CAR-T cell therapy. There was a significant decrease in diversity (measured by the Simpson index) during and after CRS (at CRSb and CRSc) compared with baseline (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 Table 1 for details on the characteristics of r/r B-ALL and B-NHL patients. 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 ρ 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.
We next explored community structure and temporal shift of bacterial abundance at multiple taxonomic levels during CAR-T therapy. In myeloma, bacterial communities were dominated by Firmicutes and Bacteroidetes at the phylum level (Fig. 2C) and characterized by significant enrichment of Firmicutes and depletion of Bacteroidetes at the last two timepoints (Fig. 2D, E and Supplementary Fig. 4C). 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. 2F and Supplementary Table 2). We further employed a negative binominal (NB) regression model-based time-course analysis to identify genera with significant temporal changes (Supplementary Table 3). Five genera were detected by both Qiime2 and maSigPro procedures, which included increases in Enterococcus, Lactobacillus, and Actinomyces and decreases in Bifidobacterium and Lachnospira (Supplementary Fig. 4D). Most changes were aggravated during the late stages. Moreover, by checking changes in the five genera in ALL and NHL patients, we observed consistent shift trends in NHL (four genera; Supplementary Fig. 4E) and ALL (two genera; Supplementary Fig. 4F), respectively.
Association between microbial communities and clinical response to CAR-T therapy
We next determined whether microbial compositions or changes were associated with the response to CAR-T. 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.
Notable differences in microbial alpha and within-sample diversity were observed in patients with CR and PR (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).
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 Table 4). 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).
We identified 30 genera with differential time-course patterns in patients with CR and PR after CAR-T (Supplementary Table 5). 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) 24. We detected 34 genera with differences in abundance in the CR and PR groups (Fig. 4A). Eighteen genera were detected by both procedures (Supplementary Fig. 5A). 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. 5B). 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. 5D).
To explore whether early bacterial abundance was indicative of therapeutic response, we used RF feature selection to identify key discriminatory genera for responses 25. 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.
To further demonstrate the association between these taxa and outcome, we assessed progression-free survival (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 therapy 19, we did not observe an association (Supplementary Fig. 6B, C).
We performed pathway analysis using Phylogenetic Investigation of Communities by Reconstruction of Unobserved State (PICRUSt) and identified significant changes in amino acid metabolism (Fig. 4H), important for immune function 26. For example, CR patients had higher lysine biosynthesis, whereas PR patients had higher lysine degradation. Glutathione metabolism, which can have different effects on functional immunity 27, was increased in PR patients. Peptidoglycans biosynthesis was increased in CR versus PR patients. Bacteria-derived peptidoglycans are an important pathogen-associated molecular pattern (PAMP) that can activate inflammatory signaling pathways and stimulate immune responses 28.
Associations between gut microbiome and CRS
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 untreated 29. We scaled CRS from level 1 to 5 30. 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). We found 146 OTUs with different time patterns in the severe and mild groups (Supplementary Fig. 7 and Supplementary Table 6), and 99 OTUs with different patterns in the severe and moderate CRS groups (Supplementary Fig. 8 and Supplementary Table 7). 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 Fig. 7B and Supplementary Fig. 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 Table 8). 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 addition, the abundance of Stenotrophomonas and Staphylococcus differed severe vs moderate CRS during the window (Supplementary Fig. 8D and Supplementary Table 9).
Comparisons of KEGG pathways across CRS groups showed that the gut microbiome of patients with severe CRS had high metabolism or biosynthesis related to inflammatory compounds, including several pathways associated with amino acid synthesis and metabolism, purine metabolism, lipoic acid metabolism, and biosynthesis of lipopolysaccharide and peptidoglycan (Supplementary Fig. 9 and Supplementary Fig. 10).
Primary inflammatory markers of CRS are cytokines, such as IL-6, IL-2, IL-10, interferon gamma (IFN-γ), and tumor necrosis factor-α (TNF-α). Various cytokines are elevated in the serum of patients experiencing CRS after CAR-T cell infusion 31. 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-γ, 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. 11A).
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). The abundance of the genus Leuconostoc, previously linked to CRS grade, correlated positively with ferritin and D-dimer concentrations. The abundance Bifidobacterium correlated significantly negatively with PB monocytes (Fig. 5E). We also found a correlation between inflammatory markers and bacteria associated with the clinical response and PFS. For example, Sutterella correlated negatively with serum concentrations of CRP and D-dimer (Supplementary Fig. 11B). Prevotella correlated negatively with the number of multiple PB immune cells but positively with the serum D-dimer concentration (Supplementary Fig. 11B). Faecalibacterium correlated negatively with the serum concentrations of D-dimer and IFN-γ (Supplementary Fig. 11B).