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544 Coordinated blockade of TGF-β and PD-L1 by bintrafusp alfa promotes survival in preclinical ovarian cancer models by promoting T effector memory responses

2023· article· en· W4388047842 on OpenAlexafffund
Jacob Kment, Daniel Newsted, Stephanie Young, Michael Vermeulen, Andrew W. Craig

Bibliographic record

VenueRegular and Young Investigator Award Abstracts · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsQueen's University
FundersCanadian Institutes of Health ResearchEMD Serono
KeywordsOvarian cancerCancer researchImmune systemImmunotherapyTumor microenvironmentCytotoxic T cellCancerMedicineImmunologyCytokineBiologyInternal medicine

Abstract

fetched live from OpenAlex

Background Ovarian cancer is a leading cause of mortality in women due to late detection and lack of durable therapy responses. The most aggressive subtype is high grade serous ovarian cancer (HGSC). Within the ascites, high levels of the immunosuppressive cytokine Transforming Growth Factor-β (TGF-β) is linked to poor prognosis. We hypothesize that aberrant TGF-β signaling in the tumor microenvironment promotes resistance to immunotherapies, including immune checkpoint inhibitors targeting PD-1/PD-L1. Here, we test whether dual blockade of TGF-β and PD-L1 with bintrafusp alfa provokes durable anti-tumor immune responses in preclinical HGSC models. Methods Two murine HGSC syngeneic engraftment models were used to model advanced metastatic HGSC. Mouse ID8-Trp53-/-/Brca2-/--Luc HGSC cells were injected intraperitoneally into female B cell-deficient μMT- mice (C57BL/6 background). Control IgG or bintrafusp alfa treatments were administered twice weekly, and HGSC was monitored using IVIS. Treatment effects on survival, tumor burden, ascites volume, cytokines, and immunophenotypes were compared. Mouse BR5-Luc (Trp53-/-/Brca1-/-;MycO/E/AktO/E) HGSC cells were engrafted into female FVB mice. During control IgG or bintrafusp alfa treatments, B cells were depleted using anti-CD20 injections every 3 weeks to avoid neutralization of the humanized proteins. Survivor mice were rechallenged to test memory responses and immunophenotypes compared to naïve FVB control mice. A simplified co-culture model of HGSC killing by cytotoxic T cells was established using T cells from OT-1 transgenic mice and BR5-Luc cells transduced with chicken ovalbumin antigen and a Granzyme B-cleavable FRET reporter. Results In the ID8 syngeneic HGSC model, bintrafusp alfa treatments reduced ascites development and HGSC tumor burden. Analysis of the ascites revealed depletion of TGF-β and VEGF, with increased CD8 T cell activation and M1 tumor-associated macrophages. In the BR5 syngeneic model, bintrafusp alfa treatments led to HGSC rejection and ∼ 50% survivors. Upon rechallenge, 75% of survivor mice were protected from HGSC without further treatments. These mice had increased peritoneal CD4 and CD8 T effector memory cells and NK cells compared to naïve mice. Testing bintrafusp alfa in an HGSC/T cell co-culture model, we found increased expression of Granzyme B and improved Granzyme B-mediated killing of BR5-Luc cells compared to controls. Bintrafusp alfa also reduced the HGSC-targeting integrin CD103 compared to controls. Conclusions These results show that coordinated blockade of TGF-β and PD-L1 by bintrafusp alfa leads to acquired anti-tumor immunity in HGSC models. Since bintrafusp alfa has advanced to clinical trials in other cancers, this project has potential to inform new immunotherapy regimes for ovarian cancer patients. Acknowledgements We acknowledge funding and in-kind contributions from CIHR and EMD Serono.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.302
Teacher spread0.270 · 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 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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Citations1
Published2023
Admission routes2
Has abstractyes

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