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Record W3186005391 · doi:10.1084/jem.20210108

Anti-tumor immunity in mismatch repair-deficient colorectal cancers requires type I IFN–driven CCL5 and CXCL10

2021· article· en· W3186005391 on OpenAlexafffund
Courtney Mowat, Shayla R. Mosley, Afshin Namdar, Dan Schiller, Kristi Baker

Bibliographic record

VenueThe Journal of Experimental Medicine · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsRoyal Alexandra HospitalUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity Hospital FoundationCanadian Institutes of Health ResearchCancer Research Society
KeywordsCCL5CXCL10Cancer researchCD8Priming (agriculture)Cytotoxic T cellImmunotherapyColorectal cancerImmunologyBiologyImmunityChemokineT cellImmune systemCancerIL-2 receptorGenetics

Abstract

fetched live from OpenAlex

Colorectal cancers (CRCs) deficient in DNA mismatch repair (dMMR) contain abundant CD8+ tumor-infiltrating lymphocytes (TILs) responding to the abundant neoantigens from their unstable genomes. Priming of such tumor-targeted TILs first requires recruitment of CD8+ T cells into the tumors, implying that this is an essential prerequisite of successful dMMR anti-tumor immunity. We have discovered that selective recruitment and activation of systemic CD8+ T cells into dMMR CRCs strictly depend on overexpression of CCL5 and CXCL10 due to endogenous activation of cGAS/STING and type I IFN signaling by damaged DNA. TIL infiltration into orthotopic dMMR CRCs is neoantigen-independent and followed by induction of a resident memory-like phenotype key to the anti-tumor response. CCL5 and CXCL10 could be up-regulated by common chemotherapies in all CRCs, indicating that facilitating CD8+ T cell recruitment underlies their efficacy. Induction of CCL5 and CXCL10 thus represents a tractable therapeutic strategy to induce TIL recruitment into CRCs, where local priming can be maximized even in neoantigen-poor CRCs.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.018
GPT teacher head0.286
Teacher spread0.268 · 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

Citations130
Published2021
Admission routes2
Has abstractyes

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