{"database":"biostudies-arrayexpress","file_versions":[],"scores":null,"additional":{"submitter":["Sheila Zúñiga Trejos"],"organism":["human gut metagenome"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/E-MTAB-17540"],"description":["The interplay between the commensal microbiota and the mammalian immune system may influence the outcomes of T cell‐driven cancer immunotherapies. However, clinical studies supporting microbiota‐based interventions in chimeric antigen receptor T‐cell (CAR‐T) therapy remain scarce. This study included 30 adult patients with B‐cell lymphoma treated with axicabtagene ciloleucel (axi‐cel) or 4‐1BB investigational product. Shotgun metagenomics sequencing (SMS) of fecal samples, collected before lymphodepletion and 1 month post infusion, enabled species‐level resolution. We also trained 25 microbiome‐based machine‐learning (ML) models for response prediction. Neither prior “high‐risk” antibiotics exposure nor alpha diversity influenced toxicity, response, or survival. However, dysbiosis was observed between 11 healthy controls and patients, particularly in those treated with axi‐cel. SMS identified species associated with clinical outcomes. Increased abundance of Alistipes senegalensis and Alistipes onderdonkii correlated with lower neurotoxicity and improved survival, respectively. Bifidobacterium longum was associated with reduced cytokine release syndrome, whereas Bifidobacterium adolescentis , Bifidobacterium bifidum , and Bifidobacterium breve correlated with poorer survival. ML models demonstrated strong predictive performance, with some identifying non‐responders using only six species selected by the Boruta method ( Bacteroides xylanisolvens , Bifidobacterium bifidum , Bifidobacterium breve , Eubacteriaceae bacterium Marseille‐Q4139, Negativibacillus massiliensis, and Sellimonas intestinalis). These findings deepen current knowledge and support prospective microbiota‐based strategies in CAR‐T therapy."],"repository":["biostudies-arrayexpress"],"sample_protocol":["Sample Collection - Fecal samples were collected using a DNA stabilization buffer kit (Canvax Stool Sample Collection & Stabilization Kit), which was stored at −20°C in the Microbiology Department.","Nucleic Acid Extraction - DNA was initially extracted from faecal samples using the EZ1&2™ Virus Mini Kit V2.0 (Qiagen, cat. no. 509955134). Samples were resuspended in ASL buffer (Qiagen, cat. no. 50919082) at a 1:10 ratio and loaded onto the EZ2 automated instrument (Qiagen) in 1 mL suspensions. DNA extraction and purification were performed in a single step using paramagnetic particles, following the automated EZ1&2 Virus protocol. DNA was eluted in a final volume of 90 µL.","Sequencing - Library quality and success were assessed using capillary electrophoresis on the TapeStation 4200 system (Agilent Technologies) with the HSD5000 ScreenTape kit (Agilent Technologies, cat. no. 5067-5592). Final libraries were pooled in equimolar amounts and sequenced on the Illumina NextSeq 550 platform, generating approximately 18–25 million paired-end reads of 150 bp (2x150 bp).","Library Construction - DNA concentration was quantified using the QuantiFluor dsDNA System (Promega, cat. no. E2671). Metagenomic library preparation was performed through SMS using samples normalized to 0.2 ng/µL, with a total input of 1 ng of DNA. Library preparation was carried out with the Illumina DNA Prep Kit (Illumina, cat. no. 20060060), following the manufacturer’s instructions. Briefly, samples underwent enzymatic fragmentation via tagmentation, followed by purification and amplification using random primers to amplify microbial genomes. Sample-specific adapters were added to enable post-sequencing identification."],"figure_sub":["Organization","MINSEQE Score","Assays and Data","MAGE-TAB Files"],"omics_type":["Metabolomics","Unknown","Transcriptomics","Genomics","Proteomics"],"instrument_platform":["NextSeq 550"],"pubmed_abstract":["The interplay between the commensal microbiota and the mammalian immune system may influence the outcomes of T cell-driven cancer immunotherapies. However, clinical studies supporting microbiota-based interventions in chimeric antigen receptor T-cell (CAR-T) therapy remain scarce. This study included 30 adult patients with B-cell lymphoma treated with axicabtagene ciloleucel (axi-cel) or 4-1BB investigational product. Shotgun metagenomics sequencing (SMS) of fecal samples, collected before lymphodepletion and 1 month post infusion, enabled species-level resolution. We also trained 25 microbiome-based machine-learning (ML) models for response prediction. Neither prior \"high-risk\" antibiotics exposure nor alpha diversity influenced toxicity, response, or survival. However, dysbiosis was observed between 11 healthy controls and patients, particularly in those treated with axi-cel. SMS identified species associated with clinical outcomes. Increased abundance of Alistipes senegalensis and Alistipes onderdonkii correlated with lower neurotoxicity and improved survival, respectively. Bifidobacterium longum was associated with reduced cytokine release syndrome, whereas Bifidobacterium adolescentis , Bifidobacterium bifidum , and Bifidobacterium breve correlated with poorer survival. ML models demonstrated strong predictive performance, with some identifying non-responders using only six species selected by the Boruta method ( Bacteroides xylanisolvens , Bifidobacterium bifidum , Bifidobacterium breve , Eubacteriaceae bacterium Marseille-Q4139, Negativibacillus massiliensis, and Sellimonas intestinalis). These findings deepen current knowledge and support prospective microbiota-based strategies in CAR-T therapy."],"study_type":["DNA-seq"],"species":["human gut metagenome"],"pubmed_title":["Microbiome‐Based Modeling of CAR‐T Therapy Response in Lymphoma: Insights From Shotgun Metagenomics Sequencing"],"pubmed_authors":["Rafael Hernani, Eliseo Albert, Carlos Hernani‐Morales, Sheila Zúñiga, Ana Benzaquén, Laura González‐Castillo, Ester Colomer, Júlia Morell, José Francisco Català‐Senent, José Luis Piñana, Estela Giménez, Ariadna Pérez, Juan Carlos Hernández‐Boluda, Ignacio Arroyo, Marcos Rivada, Teresa Barber, Teresa Alemany, Enric Santacatalina, Pilar Rentero‐Garrido, María José Terol, Rafael Díaz, David Navarro, Carlos Solano.","Sheila Zúñiga Trejos","Rafael Hernani"],"additional_accession":[]},"is_claimable":false,"name":"Microbiome‐Based Modeling of CAR‐T Therapy Response in Lymphoma: Insights From Shotgun Metagenomics Sequencing","description":"The interplay between the commensal microbiota and the mammalian immune system may influence the outcomes of T cell‐driven cancer immunotherapies. However, clinical studies supporting microbiota‐based interventions in chimeric antigen receptor T‐cell (CAR‐T) therapy remain scarce. This study included 30 adult patients with B‐cell lymphoma treated with axicabtagene ciloleucel (axi‐cel) or 4‐1BB investigational product. Shotgun metagenomics sequencing (SMS) of fecal samples, collected before lymphodepletion and 1 month post infusion, enabled species‐level resolution. We also trained 25 microbiome‐based machine‐learning (ML) models for response prediction. Neither prior “high‐risk” antibiotics exposure nor alpha diversity influenced toxicity, response, or survival. However, dysbiosis was observed between 11 healthy controls and patients, particularly in those treated with axi‐cel. SMS identified species associated with clinical outcomes. Increased abundance of Alistipes senegalensis and Alistipes onderdonkii correlated with lower neurotoxicity and improved survival, respectively. Bifidobacterium longum was associated with reduced cytokine release syndrome, whereas Bifidobacterium adolescentis , Bifidobacterium bifidum , and Bifidobacterium breve correlated with poorer survival. ML models demonstrated strong predictive performance, with some identifying non‐responders using only six species selected by the Boruta method ( Bacteroides xylanisolvens , Bifidobacterium bifidum , Bifidobacterium breve , Eubacteriaceae bacterium Marseille‐Q4139, Negativibacillus massiliensis, and Sellimonas intestinalis). These findings deepen current knowledge and support prospective microbiota‐based strategies in CAR‐T therapy.","dates":{"release":"2026-08-26T00:00:00Z","modification":"2026-08-27T15:22:20.05Z","creation":"2026-08-17T08:50:30.097Z"},"accession":"E-MTAB-17540","cross_references":{"pubmed":["41582602"],"ENA":["ERP203769"],"EFO":["EFO_0002944","EFO_0004170","EFO_0002693","EFO_0005518","EFO_0004184"],"doi":["10.1111/ejh.70121"]}}