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. 2025 Jul 22;9(14):3429-3440.
doi: 10.1182/bloodadvances.2024014476.

Longitudinal analysis of gut microbiome and metabolome correlates of response and toxicity with idecabtagene vicleucel

Satabdi Saha  1 , Lubna Rehman  2 , Abdur Rehman  2 , Faezeh Darbaniyan  3 , Donna M Weber  2 , Melody Becnel  2 , Mahmoud Gaballa  2 , Sheeba K Thomas  2 , Hans C Lee  2 , Chia-Chi Chang  4 , Reetakshi Arora  2 , Meghan Menges  5 , Salvatore Corallo  5 , Marco L Davila  6 , Frederick L Locke  5 , Mark R Tanner  7 , Sattva S Neelapu  2 , Elizabeth J Shpall  7 , Christopher R Flowers  2 , Robert Z Orlowski  2   8 , Robert R Jenq  4   7 , Michael D Jain  5 , Christine Peterson  1 , Doris K Hansen  5 , Neeraj Y Saini  2   7 , Krina K Patel  2
Affiliations

Longitudinal analysis of gut microbiome and metabolome correlates of response and toxicity with idecabtagene vicleucel

Satabdi Saha et al. Blood Adv. .

Abstract

Increasing evidence suggests that the gut microbiome may influence the responses and toxicities associated with chimeric antigen receptor T-cell (CAR-T) therapy. We conducted whole-genome shotgun sequencing on stool samples (N = 117) collected at various times from patients with multiple myeloma (n = 33) who underwent idecabtagene vicleucel (ide-cel) anti-B-cell maturation antigen CAR-T therapy. We observed a significant decrease in bacterial diversity after ide-cel infusion, along with significant differences in the bacterial composition linked to therapy response and toxicities. Specifically, we found significant enrichment of Flavonifractor plautii, Bacteroides thetaiotaomicron, Blautia fecis, and Dysosmobacter species in ide-cel responders. A notable finding was the link of major microbiome disruption, defined as the presence of dominant specific taxa (>35% prevalence), and increased facultative pathobionts, like Enterococcus, with ide-cel toxicities, especially cytokine release syndrome (CRS). Patients with genus dominance in baseline samples had a higher incidence of grade 2 or higher CRS at 46.2% than those without genus dominance (11.1%; P = .043). In addition, network analysis and mass spectrometric assessment of stool metabolites revealed important associations and pathways, such as F plautii being linked to increased indole metabolites and pathways in responders. Our findings uncovered novel microbiome associations between ide-cel responses and toxicities that may be useful for developing modalities to improve CAR-T outcomes.

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Conflict of interest statement

Conflict-of-interest disclosure: M.D.J. reports consultancy/advisory fees from Kite/Gilead, Novartis, Bristol Myers Squibb (BMS), and Myeloid Therapeutics, and research funding from Incyte and Kite/Gilead. S.S.N. reports research support from Kite/Gilead, BMS, Allogene, Precision Biosciences, Adicet Bio, Sana Biotechnology, and Cargo Therapeutics; serving as an advisory board member/consultant for Kite/Gilead, Merck, Sellas Life Sciences, Athenex, Allogene, Incyte, Adicet Bio, BMS, bluebird bio, Fosun Kite, Sana Biotechnology, Caribou, Astellas Pharma, MorphoSys, Janssen, Chimagen, ImmunoACT, Orna Therapeutics, Takeda, Synthekine, CARsgen, Appia Bio, and GlaxoSmithKline; having stock options from Longbow Immunotherapy, Inc; and having intellectual property related to cell therapy. C.R.F. reports serving as a consultant for AbbVie, Bayer, BeiGene, Celgene, Denovo Biopharma, Foresight Diagnostics, Genentech/Roche, Genmab, Gilead, Karyopharm, N-Power Medicine, Pharmacyclics/Janssen, Seagen, and Spectrum; having stock or stock options in Foresight Diagnostics and N-Power Medicine; and receiving research funding from 4D, AbbVie, Acerta, Adaptimmune, Allogene, Amgen, Bayer, BostonGene, Celgene, Cellectis EMD, Gilead, Genentech/Roche, Guardant, Iovance, Janssen Pharmaceutical, Kite, MorphoSys, Nektar, Novartis, Pfizer, Pharmacyclics, Sanofi, Takeda, TG Therapeutics, Xencor, Ziopharm, Burroughs Wellcome Fund, Eastern Cooperative Oncology Group, National Cancer Institute, V Foundation, and the Cancer Prevention and Research Institute of Texas: CPRIT Scholar in Cancer Research. R.Z.O. reports research funding unrelated to this work from Heidelberg Pharma AG, Asylia Therapeutics, and Biotheryx; serving on advisory boards for Amgen, Inc, BMS, Celgene, EcoR1 Capital LLC, Forma Therapeutics, Genzyme, GSK Biologicals, Ionis Pharmaceuticals, Inc, Janssen Biotech, Juno Therapeutics, Kite Pharma, Legend Biotech USA, Molecular Partners, Sanofi-Aventis, Servier, and Takeda Pharmaceuticals North America, Inc; and being a Founder of Asylia Therapeutics, Inc, with an equity interest. N.Y.S. reports research funding from Panbela Therapeutics. R.R.J. reports serving as a consultant or an advisory board member for Merck, Microbiome DX, Karius, MaaT Pharma, LisCure, Seres, Kaleido, and Prolacta, and having received patent license fees or stock options from Seres and Kaleido. K.K.P. reports research funding from and/or has served on advisory boards for AbbVie, Arcellx, AstraZeneca, BMS, Caribou Biosciences, Celgene, Genentech, Janssen, Kite, Merck, Oricel, Novartis, Legend Biotech, Pfizer, Regeneron, and Sanofi. N.Y.S., C.-C.C., S.S.N., and R.R.J. are inventors on patent applications through 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.” The remaining authors declare no competing financial interests.

Figures

None
Graphical abstract
Figure 1.
Figure 1.
Changes in the gut microbiome diversity during ide-cel therapy. (A) Bacterial diversity at different time points relative to CAR-T therapy, calculated using the Inverse Simpson index. This plot demonstrates the changes in bacterial diversity over time, highlighting how microbial communities evolve before, during, and after CAR-T therapy. (B) Relative abundance of different bacterial phyla in stool samples collected at various time points relative to ide-cel therapy. This bar plot shows the distribution of bacterial phyla, thereby enabling the comparison of microbial community composition at different stages of the treatment. (C) Phylum-level diversity of the baseline stool samples. This plot illustrates the diversity and abundance of bacterial phyla present in the baseline stool samples, providing a snapshot of the microbial community during initiation of therapy. (D) Box plots exploring the relationship between bacterial alpha diversity, measured at baseline, and the clinical outcomes of patients who underwent ide-cel therapy with the aim of identifying potential microbial predictors of treatment response and toxicities. PD, partial disease.
Figure 2.
Figure 2.
Association of the gut microbiome with ide-cel responses. (A) Significant associations detected using MaAsLin2 for the disease status (DS) outcome. All hits were filtered for 40% prevalence, an LFC >1, and multiplicity corrected with an FDR <0.05. This plot highlights the taxa that were significantly associated with DS, indicating potential microbial markers of the condition. (B) Representative survival plots for differentially abundant species identified by MaAsLin2 showcasing the significant differences in survival probabilities associated with changes in DS. These plots illustrate how the presence and abundance of specific microbial species can influence survival outcomes, thereby emphasizing the clinical relevance of microbial composition in disease prognosis. PD, partial disease.
Figure 3.
Figure 3.
Association of the gut microbiome community dominance with ide-cel toxicities. (A) Significant associations detected by MaAsLin2 for toxicity outcomes. CRS and ICANS were filtered to include only those with at least 25% prevalence, an LFC >1, and corrected for multiplicity with an FDR of <0.05. (B) Community dominance analysis illustrating the differences in the presence of dominant vs nondominant taxa in terms of toxicity outcomes.
Figure 4.
Figure 4.
Association between gut microbial and stool metabolomics. (A) Significant correlations between taxa hits identified by MaAsLin2 for disease status outcome and indoles. (B) Significant correlations between taxa hits identified by MaAsLin2 for disease status outcome and SCFAs. (C) Representative significant associations between metagenomic (DNA) pathways and taxa hits identified for disease status outcome. PD, partial disease.
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