Evidence map›Paper›PMID 41582602›Full record

ArticleEuropean journal of haematology2026

Microbiome-Based Modeling of CAR-T Therapy Response in Lymphoma: Insights From Shotgun Metagenomics Sequencing.

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 and 13 more

Abstract read
In one paragraph

Article in European journal of haematology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors.

Rafael HernaniHaematology Department, Hospital Clínico Universitario, INCLIVA Research Institute, Valencia, Spain.ORCID https://orcid.org/0000-0003-1043-8076
Eliseo AlbertMicrobiology Department, Hospital Clínico Universitario, INCLIVA Research Institute, Valencia, Spain.ORCID https://orcid.org/0000-0002-5037-6095
Carlos Hernani-MoralesQuantum Spain, Universitat de València, Valencia, Spain.
Sheila ZúñigaBioinformatics Unit, INCLIVA Research Institute, Valencia, Spain.
Ana BenzaquénHaematology Department, Hospital Clínico Universitario, INCLIVA Research Institute, Valencia, Spain.
Laura González-CastilloPrecision Medicine Unit, INCLIVA Research Institute, Valencia, Spain.
Ester ColomerMicrobiology Department, Hospital Clínico Universitario, INCLIVA Research Institute, Valencia, Spain.
Júlia MorellHaematology Department, Hospital Clínico Universitario, INCLIVA Research Institute, Valencia, Spain.
José Francisco Català-SenentBioinformatics Unit, INCLIVA Research Institute, Valencia, Spain.
José Luis PiñanaHaematology Department, Hospital Clínico Universitario, INCLIVA Research Institute, Valencia, Spain.
Estela GiménezMicrobiology Department, Hospital Clínico Universitario, INCLIVA Research Institute, Valencia, Spain.
Ariadna PérezHaematology Department, Hospital Clínico Universitario, INCLIVA Research Institute, Valencia, Spain.
Juan Carlos Hernández-BoludaHaematology Department, Hospital Clínico Universitario, INCLIVA Research Institute, Valencia, Spain.
Ignacio ArroyoHaematology Department, Hospital Clínico Universitario, INCLIVA Research Institute, Valencia, Spain.
Marcos RivadaHaematology Department, Hospital Clínico Universitario, INCLIVA Research Institute, Valencia, Spain.
Teresa BarberHaematology Department, Hospital Clínico Universitario, INCLIVA Research Institute, Valencia, Spain.
Teresa AlemanyHaematology Department, Hospital Clínico Universitario, INCLIVA Research Institute, Valencia, Spain.
Enric SantacatalinaHaematology Department, Hospital Clínico Universitario, INCLIVA Research Institute, Valencia, Spain.
Pilar Rentero-GarridoPrecision Medicine Unit, INCLIVA Research Institute, Valencia, Spain.
María José TerolHaematology Department, Hospital Clínico Universitario, INCLIVA Research Institute, Valencia, Spain.
Rafael DíazNuclear Medicine Department, Hospital Clínico Universitario, INCLIVA Research Institute, Valencia, Spain.
David NavarroMicrobiology Department, Hospital Clínico Universitario, INCLIVA Research Institute, Valencia, Spain.ORCID https://orcid.org/0000-0003-3010-4110
Carlos SolanoHaematology Department, Hospital Clínico Universitario, INCLIVA Research Institute, Valencia, Spain.ORCID https://orcid.org/0000-0003-3702-0817

Funding

2023 Strategic Action in Healthbioinformatics technician CA23/00007European UnionEuropean Union through the Operational Program of the European Regional Development FundFundación FERO and the Fundación para la Promoción de Acciones SolidariasInstituto de Salud Carlos III
6 · The paper itself

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.

Indexed as

Immunotherapy, AdoptiveLymphomaMetagenomicsMicrobiotaReceptors, Chimeric AntigenAdultAgedFemaleHumansMachine LearningMaleMiddle AgedShotgun SequencingTreatment OutcomeReceptors, Chimeric AntigenCAR‐T (chimeric antigen receptor T‐cell)gut microbiomemachine learningshotgun metagenomics sequencing

Identifiers

PMID41582602
PMCPMC13050679

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.