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999 Investigating the immunomodulatory role of innate lymphoid cells in epithelial ovarian carcinoma

2022· article· en· W4308398250 on OpenAlexaff
Douglas C. Chung, Kathrin Warner, Jehan Vakharia, SeongJun Han, Maryam Ghaedi, Carlos R. Garcia-Batres, Nicolas Jacquelot, Azin Sayad, S. W. Ferguson, Pamela S. Ohashi

Bibliographic record

VenueRegular and Young Investigator Award Abstracts · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicIL-33, ST2, and ILC Pathways
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsInnate lymphoid cellPopulationBiologyImmunologyFlow cytometryOvarian cancerCancerInnate immune systemTumor microenvironmentCancer researchImmune systemMedicineGenetics

Abstract

fetched live from OpenAlex

<h3>Background</h3> Innate lymphoid cells (ILCs) are an emerging family of effector cells that mostly reside within non-lymphoid peripheral tissues and orchestrate innate and adaptive immunity in response to infections.<sup>1</sup> ILCs play an important role in cancer including its ability to directly kill cancer cells and promote anti-tumour immunity within the tumour microenvironment (TME).<sup>2</sup> In addition to these classical pro-inflammatory functions of ILCs, our group and others have identified a subset of immunoregulatory ILCs (ILCregs) in various diseases including cancer.<sup>3</sup> We previously found a CD56<sup>+</sup> ILCreg population that suppressed T cells in slow growing ex vivo tumour-infiltrating lymphocyte (TIL) cultures.<sup>4</sup> The objective of this study is to identify markers that distinguish immunoregulatory and non-immunoregulatory ILCs straight from primary tumours and uncover its role within the TME of epithelial ovarian carcinoma (EOC). <h3>Methods</h3> Women with suspected EOC were recruited and consented pre-operatively at the Gynecology Cancer Clinic at Princess Margaret Hospital. Surgical resections were processed and analyzed by flow cytometry and single-cell RNA sequencing (scRNA-seq). In vitro stimulation of peripheral blood CD56<sup>+</sup> ILCs were performed over 7 days in IL-15 with 50% ascites supernatant. <h3>Results</h3> We identified subsets of intratumoural ILCs including ILC1s, ILC2s, ILC3s, and CD56<sup>+</sup> ILCs within lineage negative populations. Interestingly, a population of CD56<sup>+</sup> GZMB<sup>-</sup> ILCs exhibited distinct tissue-resident-like properties including expression of tissue-retention marker CD69 and reduced expression of tissue-egress marker CD49e. Interestingly, transcriptomic profile of CD56<sup>+</sup>GZMB<sup>-</sup>CD49e<sup>-</sup> ILCs from our scRNA-seq dataset (n=3) had similar gene expression as intraepithelial ILC1s (ieILC1) from other studies.<sup>5</sup> These ieILC1-like cells were associated with poor recurrent free survival and reduced granzyme B expression in CD8+ TILs. Moreover, ieILC1-like phenotypes can be induced from peripheral blood CD56+ cells using ascites supernatant from patients with EOC. Finally, ieILC1-like cells from primary tumours expressed gene signatures that have been previously upregulated in ILCregs and regulatory T cells (Tregs), suggesting that these populations may have immunoregulatory properties. Ongoing work is being done to identify whether ieILC1-like cells directly suppress T cells in vitro, and uncover mechanisms of immunosuppression. <h3>Conclusions</h3> Our findings suggest that ieILC1-like CD56+ cells are negatively associated with prognosis of EOC and may play a unique role in modulating the tumour microenvironment. Further investigation into the biology of ILCs in human tumours may provide novel therapeutic targets for ovarian carcinoma and beyond. <h3>References</h3> Sonnenberg GF, Artis D. Innate lymphoid cells in the initiation, regulation and resolution of inflammation. <i>Nat Med</i> 2015;<b>21</b>:698–708. Jacquelot N, Seillet C, Vivier E, Belz GT. Innate lymphoid cells and cancer. <i>Nat Immunol</i>. 2022; <b>23</b>: 371–379. Chung DC, Jacquelot N, Ghaedi M, Warner K, Ohashi PS. Innate lymphoid cells: Role in immune regulation and cancer. <i>Cancer</i>. 2022; <b>14</b>(9): 2071. Crome SQ, Nguyen LT, Lopez-Verges S, Yang SYCC, Martin B, Yam JY, Johnson DJ, Nie J, Pniak M, Yen PH, <i>et al</i>. A distinct innate lymphoid cell population regulates tumor-associated T cells. <i>Nat Med</i> 2017; <b>23</b>: 368–375. Collins PL, Cella M, Porter SI, Li S, Gurewitz GL, Hong HS, Johnson P, Oltz EM, Colonna M. Gene regulatory programs conferring phenotypic identities to human NK cells. <i>Cell</i>. 2019;<b>176</b>: 348–360. <h3>Ethics Approval</h3> This study was conducted according to principles in the Declaration of Helsinki. The Research Ethics Board (REB) of the University Health Network (UHN) approved of this study. Fresh tissue was prepared from pre-operatively consented patients with EOC who were undergoing standard-of-care surgical procedures (UHN REB 10-0335).

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)
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 score0.177
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.009
GPT teacher head0.189
Teacher spread0.180 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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