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Record W4413812423 · doi:10.1016/j.immuni.2025.08.001

Anchored screening identifies transcription factor blueprints underlying dendritic cell diversity and subset-specific anti-tumor immunity

2025· article· en· W4413812423 on OpenAlexaff
Luís Henriques‐Oliveira, Abigail Altman, Ilia Kurochkin, Ervin Ascic, Evelyn Halitzki, Diogo Pértiga-Cabral, Isabel Ulmert, Signe Holst, M. Nair, Pedro P. Cunha, Sun‐Mi Park, Stefano Vergani, Michael G. Kharas, Joan Yuan, Katharina Lahl, Fábio F. Rosa, Cristiana F. Pires, Carlos‐Filipe Pereira

Bibliographic record

VenueImmunity · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsUniversity of Calgary
FundersEuropean Research CouncilUniversity at BuffaloHorizon 2020 Framework ProgrammeNovo Nordisk FondenRegion SkåneVetenskapsrådetScience for Life LaboratoryLund University Medical Faculty FoundationH2020 European Research CouncilCancerfondenEuropean CommissionKnut och Alice Wallenbergs StiftelseLunds UniversitetFundação para a Ciência e a TecnologiaFrancis Crick InstituteCancer Research Institute
KeywordsBiologyTranscription factorImmunityDiversity (politics)Dendritic cellBlueprintImmunologyCancer researchGeneticsImmune systemGene

Abstract

fetched live from OpenAlex

Transcription factor cooperation is essential for specifying the heterogeneous dendritic cell (DC) lineages that orchestrate adaptive immunity, yet how it drives subset diversification remains poorly understood. Here, we employed a sequential anchored screen of 70 transcription factors using direct cellular reprogramming to identify regulators that specify type 2 conventional DCs (cDC2s) and plasmacytoid DCs (pDCs). We identified PU.1, IRF4, and PRDM1 as inducers of a pro-inflammatory cDC2B-like fate and SPIB, IRF8, and IKZF2 as mediators of an immature lymphoid DC program. Transcriptomic profiling linked these triads to lineage-specific signatures and demonstrated their requirement for subset identity. Mechanistically, lineage divergence was driven by chromatin co-engagement at subset-specific sites early in reprogramming. Functionally, reprogrammed DCs employed distinct immune mechanisms to elicit orthogonal anti-tumor responses in different tumor models. Collectively, our findings uncover transcriptional circuits that control DC diversification and pave the way to generate patient-tailored DC subsets for cancer immunotherapy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.265
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2025
Admission routes1
Has abstractyes

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