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Gut microbiome correlates of response and toxicity following anti-CD19 CAR T cell therapy

Nature Medicine volume 28, pages 713–723 (2022)Cite this article

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An Author Correction to this article was published on 17 October 2022

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Abstract

Anti-CD19 chimeric antigen receptor (CAR) T cell therapy has led to unprecedented responses in patients with high-risk hematologic malignancies. However, up to 60% of patients still experience disease relapse and up to 80% of patients experience CAR-mediated toxicities, such as cytokine release syndrome or immune effector cell-associated neurotoxicity syndrome. We investigated the role of the intestinal microbiome on these outcomes in a multicenter study of patients with B cell lymphoma and leukemia. We found in a retrospective cohort (n = 228) that exposure to antibiotics, in particular piperacillin/tazobactam, meropenem and imipenem/cilastatin (P-I-M), in the 4 weeks before therapy was associated with worse survival and increased neurotoxicity. In stool samples from a prospective cohort of CAR T cell recipients (n = 48), the fecal microbiome was altered at baseline compared to healthy controls. Stool sample profiling by 16S ribosomal RNA and metagenomic shotgun sequencing revealed that clinical outcomes were associated with differences in specific bacterial taxa and metabolic pathways. Through both untargeted and hypothesis-driven analysis of 16S sequencing data, we identified species within the class Clostridia that were associated with day 100 complete response. We concluded that changes in the intestinal microbiome are associated with clinical outcomes after anti-CD19 CAR T cell therapy in patients with B cell malignancies.

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Fig. 1: Impact of antibiotic exposure in patients with hematologic malignancies treated with anti-CD19 CAR T cell therapy.
Fig. 2: Impact of P-I-M antibiotic exposure in patients with NHL treated with anti-CD19 CAR T cells according to the CAR costimulatory domain.
Fig. 3: Association of baseline fecal microbiota with clinical response in recipients of CD19 CAR T cells.

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Data availability

We utilized the BMTagger database, which was built with human genome assembly GRCh38, to remove human contamination from the metagenomic shotgun sequencing samples. Data requests will be reviewed by the corresponding authors at MSK and the University of Pennsylvania. Patient-related data not included in the paper were generated as part of clinical trials and may be subject to patient confidentiality. We will review requests for stool sequence data. Any data and materials that can be shared will be released via a material transfer agreement. Raw ASV counts and annotation, as well as shotgun pathway counts and accompanying clinical annotation, are provided in the Supplementary Data files.

Code availability

The code and the corresponding figures can be accessed at GitHub (https://vdblab.github.io/CART_and_microbiome/). Additionally, we have created an open license with a DOI of the code available at https://doi.org/10.5281/zenodo.5701510.

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Acknowledgements

We acknowledge the Integrated Genomics Operation at MSK, which performed all the 16S and metagenomic shotgun sequencing for the fecal microbiome cohort and healthy controls. This research was supported by the following funding sources: Damon Runyon Physician-Scientist Award (M.Sm.); Burroughs Wellcome Fund Postdoctoral Enrichment Program (M.Sm.); American Society of Hematology-Robert Wood Johnson Foundation and Harold Amos Medical Faculty Development Program (M.Sm.); Fauci Fellowships — National Italian American Foundation and Mario Luvini fellowship grant – Fondazione Ticinese per la Ricerca sul Cancro (G.G.); National Cancer Institute (NCI) grant no. K08CA194256 (S.G.); Lymphoma Research Foundation (LRF) Career Development Award (M.R.); Gilead Research Scholar Award (M.R.); Gabrielle’s Angel Foundation (M.R.); Emerson Collective Award (M.R.); Laffey-McHugh Foundation (M.R.); Berman and Maguire Funds for Lymphoma Research at the University of Pennsylvania (M.R.); NCI grant no. 1K99CA212302 (M.R.); grant no. R00CA212302 (M.R.); Center for Precision Medicine Accelerator Award (M.R. and A.F.); grant no. 1R01CA219871-01A1 (A.F.); University of Pennsylvania-Novartis Alliance (S.G. and C.H.J.); grant no. 1P01CA214278 (C.H.J.); grant no. R01CA226983 (C.H.J.); Scholar in Clinical Research award from the Leukemia and Lymphoma Society (A.G.); American-Italian Cancer Foundation Postdoctoral Research Fellowship and Associazione Italiana contro le Leucemie-Linfomi e Mieloma Milano e Provincia Organizzazione Non Lucrativa di Utilità Sociale (M.P.); grant no. P01 CA23766 (M-A.P.); grant no. P30 CA008748 National Institutes of Health/NCI MSK Cancer Center Support Grant (M-A.P., S.M.D., J.U.P., M.Sm. and M.R.M.B.); LRF Postdoctoral Fellowship Grant (E.A.C.); grant no. K08HL143189 (J.U.P); Parker Institute for Cancer Immunotherapy (J.U.P.); grant nos. R01-CA228358, R01-CA228308, R01-HL147584, P01-CA023766, R01-HL125571, R01-HL123340 and P01-AG052359 (M.R.M.B.); Starr Cancer Consortium (M.R.M.B.); Tri-Institutional Stem Cell Initiative (M.R.M.B.); the Lymphoma Foundation (M.R.M.B.); the Susan and Peter Solomon Divisional Genomics Program (M.R.M.B.); Cycle for Survival (M.R.M.B.); and the Parker Institute for Cancer Immunotherapy (M.R.M.B.).

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Author notes

  1. These authors contributed equally: Anqi Dai, Andrea Facciabene, Guido Ghilardi, Marcel R. M. van den Brink, Marco Ruella.

  2. These authors jointly supervised this work: Andrea Facciabene, Marcel R. M. van den Brink, Marco Ruella.

Authors and Affiliations

  1. Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Melody Smith, Anqi Dai, John B. Slingerland, Pamela S. Herrera, Paul Giardina, Annelie Clurman, Emmanuel Dwomoh, Gabriel Armijo, Antonio L. C. Gomes, Josel Ruiz, Aishat Olaide Afuye, Miguel-Angel Perales, Sergio Giralt, Jonathan U. Peled & Marcel R. M. van den Brink

  2. Department of Medicine, Weill Cornell Medical College, New York, NY, USA

    Melody Smith, Jae H. Park, Maria Lia Palomba, Miguel-Angel Perales, Sergio Giralt, Renier J. Brentjens & Jonathan U. Peled

  3. Cellular Therapeutics Center, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Melody Smith, Jae H. Park, Maria Lia Palomba, Elizabeth Halton, Isabelle Riviere & Renier J. Brentjens

  4. Division of Blood and Marrow Transplantation and Cellular Therapy, Stanford University School of Medicine, Stanford, CA, USA

    Melody Smith

  5. Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, PA, USA

    Guido Ghilardi, Kimberly V. Amelsberg, Raymone Pajarillo, Alfred Garfall, Shannon Gier, Sunita Nasta, Daniel Landsburg, James Gerson, Jakub Svoboda, Elise A. Chong, Saar I. Gill, David L. Porter, Stephen J. Schuster, Noelle Frey, Carl H. June, Andrea Facciabene & Marco Ruella

  6. Division of Hematology-Oncology, Department of Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA

    Guido Ghilardi, Kimberly V. Amelsberg, Raymone Pajarillo, Elise A. Chong, Saar I. Gill, David L. Porter, Stephen J. Schuster, Noelle Frey & Marco Ruella

  7. Department of Biostatistics and Epidemiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Sean M. Devlin

  8. Department of Radiation Oncology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA

    Silvia Beghi & Andrea Facciabene

  9. Weill Cornell Medical College, New York, NY, USA

    Pamela S. Herrera

  10. The Duchossois Family Institute, University of Chicago, Chicago, IL, USA

    Eric R. Littmann & Eric G. Pamer

  11. Institute for Computational Medicine, New York University Langone Health, New York, NY, USA

    Jonas Schluter

  12. Molecular Microbiology Core Facility, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Emily Fontana

  13. Infectious Disease Service, Department of Medicine, and Immunology Program, Sloan Kettering Institute, New York, NY, USA

    Ying Taur

  14. Leukemia Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Jae H. Park & Renier J. Brentjens

  15. Lymphoma Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Maria Lia Palomba

  16. Department of Nursing, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Elizabeth Halton

  17. Division of Hematologic Malignancies and Bone Marrow Transplantation, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, MD, USA

    Tania Jain

  18. Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy

    Martina Pennisi

  19. Department of Pharmacy, Hospital of the University of Pennsylvania, Philadelphia, PA, USA

    Craig W. Freyer

  20. Lymphoma Program, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, USA

    Sunita Nasta, Daniel Landsburg, James Gerson, Jakub Svoboda, Elise A. Chong, Stephen J. Schuster & Marco Ruella

  21. The Donald B. and Catherine C. Marron Cancer Metabolism Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Justin Cross

  22. Center for Cell Engineering, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Isabelle Riviere, Michel Sadelain & Renier J. Brentjens

  23. Department of Pathology and Laboratory Medicine, Perelman School of Medicine University of Pennsylvania, Philadelphia, PA, USA

    Carl H. June

  24. Ovarian Cancer Research Center, University of Pennsylvania, Philadelphia, PA, USA

    Andrea Facciabene

  25. Department of Immunology, Sloan Kettering Institute, New York, NY, USA

    Marcel R. M. van den Brink

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  1. Melody Smith
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  2. Anqi Dai
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Contributions

M.Sm., M.R., M.R.M.B. and A.F. designed, performed and supervised the research. A.D. performed the bioinformatics analysis on 16S and shotgun sequencing. S.M.D. and G.G. performed the analysis of the antibiotic cohort. J.Sl., A.C. and P.G. coordinated the fecal microbiome collection at MSK, while R.P., G.G., K.V.A., M.R., S.B., S.G., A.G. and A.F. coordinated collection at the University of Pennsylvania. G.A., E.F., E.G.P. aided in the processing of the fecal samples at MSK. P.S.H., E.D., E.R.L., Y.T., and J.C. contributed to data analysis. J.R., T.J., M.P., and A.O.A. assisted in the collection of clinical data. J.H.P., M.L.P., E.H., M-A.P., R.J.B., M.Sa., and I.R. were the principal investigators or provided oversight of the CD19 CAR T cell clinical trials at MSK. S.J.S., D.L.P., E.A.C., A.L.C.G., S.N., J.Sv., D.L.P., M.R., A.G., C.W.F., J.G., S.I.G., C.H.J. and N.F. were the principal investigators or provided oversight of the CD19 CAR T cell clinical trials at the University of Pennsylvania. M.Sm., M.R., M.R.M.B., A.F., A.D., G.G., A.L.C.G. and J.Sc. provided significant intellectual contribution to the study design and research. M.Sm., M.R., M.R.M.B., A.F., J.U.P. and G.G. wrote the manuscript. All authors reviewed and approved the manuscript.

Corresponding authors

Correspondence to Andrea Facciabene, Marcel R. M. van den Brink or Marco Ruella.

Ethics declarations

Competing interests

M.Sm. has served as a consultant for Janssen and has a patent application related to the microbiome (PCT/US2019/056137). A.L.C.G. is currently employed by Xbiome. E.G.P. serves on the advisory board of Diversigen and has received speaker honoraria from Bristol Myers Squibb, Celgene, Seres Therapeutics, MedImmune, Novartis and Ferring Pharmaceuticals and is an inventor on patent application nos. WPO2015179437A1 and WO2017091753A1; he holds patents that receive royalties from Seres Therapeutics. M.A.P. reports honoraria from AbbVie, Astellas Pharma, Bristol Myers Squibb, Celgene, Equilium, Incyte, Karyopharm, Kite Pharma/Gilead, Merck, Miltenyi Biotec, MorphoSys, Novartis, Nektar Therapeutics, Omeros, Takeda and VectivBio. M.A.P. serves on the data and safety monitoring boards of Cidara Therapeutics, Medigene, Sellas Life Sciences and Servier, and the scientific advisory board of NexImmune. M.A.P. has ownership interests in NexImmune and Omeros. M.A.P. has received research support for clinical trials from Incyte, Kite Pharma/Gilead, Miltenyi Biotec and Novartis. J.H.P. has received consulting fees from Amgen, Novartis, Autolus, Kite Pharma, Bristol Myers Squibb, Takeda, Servier, Innate Pharma, Kura Oncology, AstraZeneca, Curocell and Intellia Therapeutics and serves on the scientific advisory board of Artiva. E.A.C. serves on the advisory boards for Novartis, Bristol Myers Squibb and Kite Pharma. J.U.P. reports research funding, intellectual property fees and travel reimbursement from Seres Therapeutics and consulting fees from DaVolterra and from MaaT Pharma. J.U.P. has filed intellectual property applications related to the microbiome (reference nos. 62/843,849, 62/977,908 and 15/756,845). M.R.M.B. has received research support and stock options from Seres Therapeutics and stock options from Notch Therapeutics and Pluto Therapeutics; he has received royalties from Wolters Kluwer, has consulted, received honoraria from or participated in advisory boards for Seres Therapeutics, WindMIL Therapeutics, Merck, Magenta Therapeutics, Frazier Healthcare Partners, Nektar Therapeutics, Notch Therapeutics, Forty Seven, Priothera, Ceramedix, LyGenesis, Pluto Therapeutics, Novartis (spouse), Kite Pharma (spouse), BeiGene (spouse); he has intellectual property licensing with Seres Therapeutics and Juno Therapeutics and holds a fiduciary role on the Foundation Board of DKMS (a nonprofit organization). J.Sv. has served as a consultant for Adaptive, AstraZeneca, Atara, Bristol Myers Squibb, Seattle Genetics, Imbrium and Genmab. J.Sv. has received research funding from AstraZeneca, Bristol Myers Squibb, Incyte, Merck, Seattle Genetics, TG Therapeutics and Pharmacyclics. J.Sc. is cofounder of Postbiotics Plus. J.G. reports research funding from Loxo and serves on the advisory boards of Kite Pharma, Genentech, AbbVie and TG Therapeutics. M.Sa. and R.B. hold patents related to CD19 CAR T cells. M.R., S.I.G. and S.J.S. hold patents related to CD19 CAR T cells. M.R. has served as a consultant for NanoString, Bristol Myers Squibb, GlaxoSmithKline, Bayer and AbClon. M.R. receives research funding from AbClon, NanoString and Beckman Coulter. M.R. is the scientific founder of ViTToria Biotherapeutics. C.H.J. has received grant support from Novartis and has patents related to CAR therapy with royalties paid from Novartis to the University of Pennsylvania. He is also a scientific cofounder and holds equity in DeCART Therapeutics and Tmunity Therapeutics. He serves on the board of AC Immune and is a scientific advisor to Cabaletta Bio, Celldex Therapeutics, Carisma Therapeutics, Viracta Therapeutics and WIRB-Copernicus Group. A.G. has research funding from Novartis, Janssen, Tmunity Therapeutics and CRISPR Therapeutics and honoraria from Janssen and GlaxoSmithKline. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Impact of antibiotic exposure in patients with hematologic malignancies treated with anti-CD19 CAR T cell therapy according to institution.

(A) Frequency of antibiotic exposure in the four weeks prior to CD19 CAR T cell infusion in patients with NHL and ALL treated at MSK (upper panel, n = 127) and Penn (bottom panel, n = 101). Purple denotes patients with ALL, while orange denotes patients with NHL. (B and C) Kaplan-Meier curves of overall survival (OS) by log-rank test according to the exposure to P-I-M antibiotics within 4 weeks before CD19 CAR T cell infusion in patients with ALL and NHL treated at MSK (B, n = 127) and Penn (C, n = 101). The dark gray line is estimated Kaplan-Meier survival probability for patients not exposed to P-I-M antibiotics, while the dark yellow line is the estimated probability for patients exposed to P-I-M antibiotics. The shading is the estimated pointwise 95% confidence interval, and the tick marks indicate censored events. Abbreviations: Trimeth./Sulfameth.: trimethoprim/sulfamethoxazole; IV: intravenous; NHL: non-Hodgkin lymphoma; ALL: acute lymphoblastic leukemia; MSK: Memorial Sloan Kettering Cancer Center; Penn: University of Pennsylvania; P-I-M: exposure to either piperacillin/tazobactam, imipenem/cilastatin or meropenem within the 4 weeks before CD19 CAR T cell infusion; Not exposed: patients exposed to non-P-I-M plus patients who did not receive any antibiotics; IV: intravenous; p: p-value.

Extended Data Fig. 2 Impact of any antibiotic exposure in patients with non-Hodgkin lymphoma treated with anti-CD19 CAR T cell therapy.

(A and B) Kaplan-Meier (A) progression-free (PFS) and (B) overall survival (OS) curves by log-rank test in NHL populations according to exposure to any antibiotic within 4 weeks before CD19 CAR T cell infusion (n = 137). The dark gray line is the estimated Kaplan-Meier survival estimates for patients not exposed to any antibiotic treatment, while the dark yellow line is the estimated probability for patients exposed to any antibiotic treatment. The shading indicates the pointwise 95% confidence interval, and the tick marks indicate censored events. (C) Histograms of the frequencies of any grade CRS and ICANS by two-sided Wilcoxon rank-sum test according to the exposure to any antibiotic within the 4 weeks before CD19 CAR T cell infusion in patients with NHL (n = 137). Blue indicates the presence of CRS or ICANS of any grade, while red indicates the absence of CRS or ICANS of any grade. Abbreviations: NHL: non-Hodgkin lymphoma; p: p-value; CRS: cytokine releasing syndrome; ICANS: immune effector cell-associated neurotoxicity.

Extended Data Fig. 3 Survival analysis comparison of different antibiotics exposure on non-Hodgkin lymphoma patients treated with CD19 CAR T cells.

(A and B) Kaplan-Meier curves of (A) progression-free survival (PFS) and (B) overall survival (OS) by log-rank test. Data shows the combined NHL population (n = 137) treated with different antibiotics in the 4 weeks before CD19 CAR T cell infusion. The dark gray line is the estimated Kaplan-Meier survival probability for patients not exposed to P-I-M antibiotics or cefepime (n = 107), the dark yellow line is the estimated probability for patients exposed to P-I-M antibiotics (n = 21), and the dark green line is the estimated probability for patients not exposed to P-I-M antibiotics and exposed to cefepime (n = 9). The shading is the estimated pointwise 95% confidence interval, and the tick marks indicate censored events. P values are shown (log-rank test). Abbreviations: NHL: non-Hodgkin lymphoma; P-I-M: exposure to either piperacillin/tazobactam, imipenem/cilastatin or meropenem within the 4 weeks before CD19 CAR T cell infusion; No P-I-M antibiotic exposure: patients exposed to non-P-I-M plus patients who did not receive any antibiotics within 4 weeks before CD19 CAR T cell infusion; p: p-value.

Extended Data Fig. 4 Survival analysis comparison of piperacillin/tazobactam compared to cefepime exposure in non-Hodgkin lymphoma patients treated with CD19 CAR T cells.

(A and B) Kaplan-Meier curves of (A) progression-free survival (PFS) and (B) overall survival (OS) by log-rank test. Data shows patients from the combined NHL population treated with piperacillin/tazobactam or cefepime in the 4 weeks before CD19 CAR T cell infusion. The dark blue line is the estimated Kaplan-Meier survival probability for patients exposed to piperacillin/tazobactam (n = 18) and the dark green line is the estimated probability for patients exposed to cefepime (n = 12). The shading is the estimated pointwise 95% confidence interval, and the tick marks indicate censored events. P values are shown (log-rank test). P values are shown (log-rank analysis). The p-values are not stratified by Center. Abbreviations: NHL: non-Hodgkin lymphoma; p: p-value.

Extended Data Fig. 5 Survival analysis comparison of P-I-M versus non-P-I-M exposure in non-Hodgkin lymphoma patients receiving at least one antibiotic before treatment with CD19 CAR T cells.

(A and B) Kaplan-Meier curves of (A) progression-free survival (PFS) and (B) overall survival (OS) by log-rank test. Data shows patients from the combined NHL population treated with P-I-M or non-P-I-M antibiotics in the 4 weeks before CD19 CAR T cell infusion. Patients who did not receive any antibiotic in the four weeks prior to CAR T cell infusion are excluded from this analysis. The dark gray line is the estimated Kaplan-Meier survival probability for patients exposed to P-I-M (n = 21) and the dark yellow line is the estimated probability for patients exposed to non-P-I-M (n = 60). The shading is the estimated pointwise 95% confidence interval, and the tick marks indicate censored events. P values are shown (log-rank test). The p-values are not stratified by Center. Abbreviations: NHL: non-Hodgkin lymphoma; p: p-value.

Extended Data Fig. 6 Timing of fecal sample collection relative to the start of conditioning chemotherapy and CD19 CAR T cell infusion.

Forty-eight patients were evaluated in the fecal microbiome cohort. Of the forty-eight patients, the fecal samples of fourteen were collected before the start of conditioning chemotherapy, whereas thirty-four fecal samples were collected after the start of conditioning chemotherapy. All the baseline fecal microbiome samples were collected prior to CD19 CAR T cell infusion. The red square denotes the start of conditioning chemotherapy. The black circle denotes the collection of the baseline fecal sample prior to CAR T cell infusion. Day 0 denotes the day of CD19 CAR T cell infusion.

Extended Data Fig. 7 Flow diagram of the fecal microbiome sample collection.

Fifty-one unique patients were collected upon informed consent. Of the fifty-one patients, one patient did not have sufficient fecal material for sequencing and two patients failed during the amplification or quality control step. Following these exclusions, there were forty-eight patients in the fecal microbiome cohort. Of these patients, we successfully amplified and sequenced the 16S ribosomal RNA gene with ≥ 200 reads per sample from forty-five patients. Forty-five patients passed quality control measures for metagenomic shotgun sequencing. There were three non-overlapping patients in the 16S and metagenomic shotgun sequencing cohorts. Hence, there were 48 unique patients in the fecal microbiome cohort.

Extended Data Fig. 8 The association of intestinal microbiota and clinical response in recipients of CD19 CAR T cells, including subset analysis institution.

(A to E) All data reported in this figure are based on 16S rRNA gene sequencing data. (A) Inverse Simpson diversity index of the fecal microbiome in the baseline fecal samples by institution, MSK (n = 26) and Penn (n = 19), compared to healthy volunteers (n = 30) by two-sided Wilcoxon rank-sum test. The middle line is the median, the box limits represent the upper and lower quartiles, the whiskers note 1.5x the interquartile range, and the dots represent the individual data points. (B to C) Beta-diversity was calculated using the Bray-Curtis dissimilarity between a reference point defined by the average of healthy volunteers and each of 30 samples from healthy volunteers. Healthy volunteers were compared to the 45 baseline patient samples (B) and by institution (MSK n = 26; Penn n = 19) (C) by two-sided Wilcoxon rank-sum test. This healthy volunteer cohort has been investigated in a prior study32. The middle line is the median, the box limits represent the upper and lower quartiles, the whiskers note 1.5x the interquartile range, and the dots represent the individual data points. (D to E) Patient samples with higher (one standard deviation above the mean) (red) or lower (one standard deviation below the mean) (blue) Inverse Simpson diversity index. The coefficients for the predicted probability of (C) Day 100 CR and (D) toxicity by Inverse Simpson diversity index. The coefficients correspond to the Bayesian models for Day 100 CR and toxicity, respectively, in Fig. 3e.

Extended Data Fig. 9 Principal Coordinates Analysis (PCoA) visualization of beta-diversity of fecal samples of CAR T cell patients and healthy volunteers.

All data reported in this figure are based on 16S rRNA gene sequencing data. Fecal microbiome composition of the CAR T cell patients (n = 45) and healthy volunteers (n = 30) was displayed in a PCoA. Composition was assessed using beta-diversity calculated with Bray-Curtis dissimilarity. Data visualized at the genus level. Red dots indicate CAR T cells patients and green dots indicate healthy volunteers.

Extended Data Fig. 10 Boxplots of the relative abundance of selected taxa from LEfSe of Day 100 CR.

All data reported in this figure are based on 16S rRNA gene sequencing data from patients (n = 45). The relative abundance of Bacteroides, Bifidobacterium, Blautia, Faecalibacterium, Longicatena, and Ruminococcus are presented. Data is categorized by patients who did not achieve a Day 100 CR (No), and patients who achieved a Day 100 CR (Yes). Dots indicate relative abundance of the baseline fecal sample from a CAR T cell patient. Two-sided Wilcoxon rank-sum test was used to calculate the p-values, and the p-values were adjusted for multiple hypothesis testing. The middle line is the median, the box limits represent the upper and lower quartiles, the whiskers note 1.5x the interquartile range, and the dots represent the individual data points.

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2 and Supplementary Figs. 1–3.

Reporting Summary

Supplementary Data 1

Sample ID (sid), clinical outcomes (day 100 CR and toxicity, center (MSK (M) or University of Pennsylvania (P)) and inverse Simpson diversity index of baseline fecal samples by 16S rRNA gene sequencing.

Supplementary Data 2

Annotation of the bacterial taxa identified by each ASV.

Supplementary Data 3

ASV counts by 16S rRNA gene sequencing for baseline fecal samples.

Supplementary Data 4

Shotgun pathway counts by metagenomic shotgun sequencing for baseline fecal samples.

Supplementary Data 5

Sample ID (sid), center (MSK (M) or University of Pennsylvania (P)) and clinical outcomes (day 100 CR and toxicity).

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Smith, M., Dai, A., Ghilardi, G. et al. Gut microbiome correlates of response and toxicity following anti-CD19 CAR T cell therapy. Nat Med 28, 713–723 (2022). https://doi.org/10.1038/s41591-022-01702-9

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