A novel population of CD103-expressing CD56+ regulatory ILCs suppresses intratumoural T cells and are associated with poor prognosis in patients with ovarian carcinoma
Bibliographic record
Abstract
Abstract Regulatory innate lymphoid cells (ILCregs) are an emerging family of immunoregulatory cells that suppress innate and adaptive immunity in the context of diseases, including cancer. However, there is currently a lack of lineage-defining surface markers or transcription factors (TFs) that distinguishes ILCregs from other subsets of ILCs. Moreover, the functional role of ILCregs within the tumour microenvironment (TME) has not been well established. In this study, we uncovered a novel population of CD103-expressing CD56+ ILCregs within the TME that correlated with poor recurrent-free survival (RFS) in patients with epithelial ovarian carcinoma (EOC). CD103+ ILCregs expressed distinct surface markers and TFs that distinguishes them from canonical CD56+ natural killer (NK) cells. Moreover, intratumoural CD103+ ILCregs expressed markers (i.e., CD39, CD69, CD101, TIGIT) and transcriptomic profiles that are shared with Tregs, suggesting that they may have immunoregulatory properties. We found that ascites-derived TGF-β drives NK cell conversion into CD103+ ILCs that can suppress autologous CD4+ T cells in vitro. Furthermore, intratumoural CD103+ ILCregs suppressed autologous CD8+ T cells in vitro. Finally, we found that the abundance of CD103+ ILCregs in the TME were associated with reduced levels of both activated CD4+ T cells and CD8+ T cells. Overall, we have identified CD103+ ILCregs as distinct immunoregulatory cells that suppresses T cells within the TME of patients with EOC. Further untangling of the complex networks of immune regulation, including ILCregs, may give rise to novel therapeutic targets to enhance anti-tumour immunity and improve clinical response rates to immunotherapies. This work was supported by a CIHR foundation award (CIHR FDN #143220), a TRANSCAN CIHR grant (CIHR – TRN #184713), a TRI grant from the Ontario Institute for Cancer Research (TRI OICR GCS #108796), and the Terry Fox Research Institute (TFRI-iTNT #1060). This project was also generously funded in memory of Mr. JIM JoTak under the Canadian Cancer Society (CCSRI #706152).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".