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Record W4310459994 · doi:10.1007/s00432-022-04440-0

Nutrient deprivation and hypoxia alter T cell immune checkpoint expression: potential impact for immunotherapy

2022· article· en· W4310459994 on OpenAlexaff
Maria Davern, Noel E. Donlon, Fiona O’Connell, Caoimhe Gaughan, Cillian O’Donovan, Jason McGrath, Andrew D. Sheppard, Conall Hayes, Ross King, Hugo C. Temperley, Michael MacLean, Christine Bulter, Anshul Bhardwaj, Jenny Moore, Claire Donohoe, Narayanasamy Ravi, Melissa J. Conroy, John V. Reynolds, Joanne Lysaght

Bibliographic record

VenueJournal of Cancer Research and Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsTrinity College
FundersIrish Research CouncilIrish Research Council for Science, Engineering and Technology
KeywordsImmune systemImmune checkpointNivolumabCancer researchDownregulation and upregulationTumor microenvironmentImmunotherapyCytotoxic T cellTIGITT cellStromal cellImmunologyBiologyMedicineIn vitro

Abstract

fetched live from OpenAlex

Abstract Aim Use of immune checkpoint blockade to enhance T cell-mediated immunity within the hostile tumour microenvironment (TME) is an attractive approach in oesophageal adenocarcinoma (OAC). This study explored the effects of the hostile TME, including nutrient deprivation and hypoxia, on immune checkpoint (IC) expression and T cell phenotypes, and the potential use of nivolumab to enhance T cell function under such conditions. Methods and Results ICs were upregulated on stromal immune cells within the tumour including PD-L2, CTLA-4 and TIGIT. OAC patient-derived PBMCs co-cultured with OE33 OAC cells upregulated LAG-3 and downregulated the co-stimulatory marker CD27 on T cells, highlighting the direct immunosuppressive effects of tumour cells on T cells. Hypoxia and nutrient deprivation altered the secretome of OAC patient-derived PBMCs, which induced upregulation of PD-L1 and PD-L2 on OE33 OAC cells thus enhancing an immune-resistant phenotype. Importantly, culturing OAC patient-derived PBMCs under dual hypoxia and glucose deprivation, reflective of the conditions within the hostile TME, upregulated an array of ICs on the surface of T cells including PD-1, CTLA-4, A2aR, PD-L1 and PD-L2 and decreased expression of IFN-γ by T cells. Addition of nivolumab under these hostile conditions decreased the production of pro-tumorigenic cytokine IL-10. Conclusion Collectively, these findings highlight the immunosuppressive crosstalk between tumour cells and T cells within the OAC TME. The ability of nivolumab to suppress pro-tumorigenic T cell phenotypes within the hostile TME supports a rationale for the use of immune checkpoint blockade to promote anti-tumour immunity in OAC. Graphical abstract Study schematic: (A) IC expression profiles were assessed on CD45 + cells in peripheral whole blood and infiltrating tumour tissue from OAC patients in the treatment-naïve setting. (B) PBMCs were isolated from OAC patients and expanded ex vivo for 5 days using anti-CD3/28 + IL-2 T cell activation protocol and then co-cultured for 48 h with OE33 cells. T cell phenotypes were then assessed by flow cytometry. (C) PBMCs were isolated from OAC patients and expanded ex vivo for 5 days using anti-CD3/28 + IL-2 T cell activation protocol and then further cultured under conditions of nutrient deprivation or hypoxia for 48 h and T cell phenotypes were then assessed by flow cytometry. Key findings: (A) TIGIT, CTLA-4 and PD-L2 were upregulated on CD45 + immune cells and CTLA-4 expression on CD45 + cells correlated with a subsequent decreased response to neoadjuvant regimen. (B) Following a 48 h co-culture with OE33 cells, T cells upregulated LAG-3 and decreased CD27 co-stimulatory marker. (C) Nutrient deprivation and hypoxia upregulated a range of ICs on T cells and decreased IFN-γ production by T cells. Nivolumab decreased IL-10 production by T cells under nutrient deprivation-hypoxic conditions.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.538
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.090
GPT teacher head0.494
Teacher spread0.405 · 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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Citations22
Published2022
Admission routes1
Has abstractyes

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