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302 Characterization of TIGIT and PVR expression in colorectal liver metastases

2021· article· en· W3212495819 on OpenAlexafffundabout
Antoine Bernard, David Henault, Sandy Pelletier, Paméla Thébault, Benoit Barrette, Simon Turcotte

Bibliographic record

VenueRegular and Young Investigator Award Abstracts · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersInstitute of Cancer ResearchInstitut Du Cancer de MontréalBristol-Myers Squibb
KeywordsTIGITImmune systemCancer researchImmune checkpointImmunotherapyMedicineMetastasisCancerImmunologyInternal medicine

Abstract

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<h3>Background</h3> Metastatic colorectal cancer (CRC) is common and lethal and generally not responsive to current immunotherapies. We hypothesize that efficacious T cell-based immunotherapy can be developed for this malignancy, provided that immune checkpoints relevant to liver metastasis, the first site of disease progression, are targeted. Here, we characterized CRC liver metastases by RNAseq, FACS and in vitro functional assays to identify candidate immune checkpoints. <h3>Methods</h3> We performed deep RNAseq clustering and differential gene expression analysis on bulk RNA extracted from 52 mismatch repair gene proficient CRC liver metastases. By multiparameter FACS, we analyzed the expression of candidate immune checkpoints in cell suspensions derived from 18 liver metastases, matched non-tumoral livers, and pre-operative PBMCs. We evaluated IFN-γ (ELISA) secretion and tumor lysis (Incucyte) of tumor-infiltrating T lymphocytes (TILs) expanded from liver metastases stimulated by autologous cancer cells with or without monoclonal antibodies blocking candidate immune checkpoints. <h3>Results</h3> Out of 52 metastases, 21 (40.3%) clustered as immune reactive (IR) defined by concurrent high expression of transcripts related to antigen processing, immune cell lineage, immune checkpoints, interferon-gamma response, cytokines, and chemokines, whereas 25 (48.1%) were classified as non-IR. Of all inhibitory ligands assessed, PVR and PVRL2 had the highest expression, both in IR and non-IR metastases, and higher than PD-L1 and PD-L2 expression. The expression of corresponding receptors TIGIT and CD226 was significantly higher in IR compared to non-IR metastases, at absolute levels higher than PD-1. By FACS analysis, PVR and PVRL2 expression by tumor-infiltrating myeloid and tumor cells was higher than PD-L1 and PD-L2 expression. High PVR expression was also found in hepatocytes, liver macrophages and circulating monocytes in the same patients. In TILs, TIGIT was significantly overexpressed in activated CD4+CD25+ (74.8 ±3.0%) and CD8+CD25+ (68.7 ± 8.4%) compared to resting CD25neg T cells, an expression pattern that was not seen for PD-1 or in T cells infiltrating the liver or circulating in the blood. The majority of cancer cell lines derived from liver metastases expressed PVR, but low levels of PD-L1. TIL clones expanded from liver metastases expressed TIGIT at various levels inducible by TCR stimulation. Upon co-culture with autologous cancer cell lines, TIL clones were more lytic and secreted more IFN-γ in presence of anti-TIGIT blocking antibody. <h3>Conclusions</h3> By expression and functional data, the TIGIT/PVR immune suppressive axis appears as a biologically promising target for the development of immunotherapy in patients with CRC metastatic to the liver. <h3>Acknowledgements</h3> This work is supported by Bristol Myers Squibb and by the Quebec Cancer Consortium. A.B. holds a postdoctoral scholarship award from the Institut du cancer de Montréal. S.T. holds a Junior 2 clinical-scientist salary award from the Fond de recherche Santé-Québec. The University of Montreal Roger des Groseillers Research Chair in hepatopancreatobiliary surgical oncology supports the biobanking and clinicopathological database associated with this project. <h3>Ethics Approval</h3> Institutional review board approvals were obtain to conduct this project (16.262) and all patients provided informed consent to contribute to this project with biospecimens and clinicopathological data (09.237).

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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 categoriesnone
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 score0.424
Threshold uncertainty score0.420

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)0.0000.000

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 head0.014
GPT teacher head0.236
Teacher spread0.222 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2021
Admission routes3
Has abstractyes

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