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Abstract A009: CD103+CD56+ innate lymphoid cells are associated with an immunosuppressive microenvironment in ovarian cancer

2023· article· en· W4389241714 on OpenAlexaffabout
Douglas C. Chung, Jehan Vakharia, Kathrin Warner, Nicolas Jacquelot, Azin Sayad, SeongJun Han, Maryam Ghaedi, Carlos R. Garcia-Batres, Alisha R. Elford, Ben X. Wang, Linh T. Nguyen, Patricia A. Shaw, Blaise Clarke, Marcus Q. Bernardini, Sarah E. Ferguson, Pamela S. Ohashi

Bibliographic record

VenueCancer Immunology Research · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicIL-33, ST2, and ILC Pathways
Canadian institutionsPrincess Margaret Cancer CentrePublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsCD8BiologyOvarian cancerTumor microenvironmentCancer researchCD16ImmunologyAntigenCancerCD3Immune system

Abstract

fetched live from OpenAlex

Abstract Background: Regulatory innate lymphoid cells (ILCregs) are an emerging heterogenous subset of innate lymphoid cells that suppress immunity in several healthy and diseased contexts, including cancer. Previously, we identified CD56+ ILCregs from in vitro expanded tumour-infiltrating lymphocyte (TIL) cultures in patients with epithelial ovarian carcinoma (EOC). However, lineage-defining surface markers or transcription factors that distinguishes these regulatory populations from other conventional NK cells have not been identified. The objective of this study is to characterize markers that define ILCregs directly from primary tumours and investigate its role within the tumour microenvironment (TME). Methods: Women with suspected abdominal mass were recruited and consented at Gynecology Cancer Clinic at Princess Margaret Hospital. Surgically resected EOC tumours were processed and analyzed by flow cytometry and single-cell RNA-sequencing. Suppression assays were performed by co-culture of tumour-infiltrating CD103+CD56+ ILCregs with autologous TILs for 4 days in vitro with soluble αCD3. Results: We found that CD103 expressing CD56+ ILCregs were associated with poor clinical prognosis in patients with ovarian carcinoma. CD103+CD56+ ILCregs displayed reduced expression of cytolytic related markers (i.e., GZMB, CD107a, CD16) compared to CD103-CD56+ NK cells and had a distinct transcriptomic profile from NK cells. Interestingly, CD103+CD56+ ILCregs displayed distinct surface markers (i.e., CD49a, CD69, GITR) and transcription factor networks that were associated with Treg development, differentiation, and suppressive function. Intratumoural CD103+CD56+ ILCregs suppressed autologous CD8+ T cells by downregulation of granzyme B in vitro. Concordantly, the abundance of CD103+CD56+ ILCregs in patient tumours were associated with reduced activated CD8+ T cells within the TME. Conclusion: This study identified CD103+CD56+ ILCregs in the TME of patients with EOC that are associated with poor clinical prognosis and plays a key role in regulating T cells. Further investigations into underlying immunoregulatory networks, including ILCregs, within the TME may provide novel targets to improve prognosis for patients with EOC. Citation Format: Douglas C Chung, Jehan Vakharia, Kathrin Warner, Nicolas Jacquelot, Azin Sayad, SeongJun Han, Maryam Ghaedi, Carlos R Garcia-Batres, Alisha Elford, Ben X Wang, Linh T Nguyen, Patricia A Shaw, Blaise A Clarke, Marcus Q Bernardini, Sarah E Ferguson, Pamela S Ohashi. CD103+CD56+ innate lymphoid cells are associated with an immunosuppressive microenvironment in ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr A009.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

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)0.0050.000

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 head0.049
GPT teacher head0.323
Teacher spread0.273 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2023
Admission routes2
Has abstractyes

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