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Abstract 1449: MG1 Maraba boost following adenovirus prime generates tumor antigen-specific T cells which are potentiated by anti-PD-1 antibody combination

2019· article· en· W4245501240 on OpenAlexaff
Kyle B. Stephenson, Kwame Twumasi‐Boateng, Amy Patrick, Caroline J. Breitbach, Michael Burgess, Brian D. Lichty

Bibliographic record

VenueImmunology · 2019
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsInterface Biologics (Canada)
Fundersnot available
KeywordsAntigenAntibodyPrime (order theory)Cancer researchMolecular biologyVirologyChemistryImmunologyMedicineBiologyMathematics

Abstract

fetched live from OpenAlex

Immune checkpoint inhibitors, such as antibodies blocking PD-1 and PD-L1, have been shown to potentiate pre-existing immune responses and improve patient survival. MG1 Maraba is a novel oncolytic virus that we bioselected and engineered to cause cancer cell death through two distinct and complementary mechanisms-of-action, direct cancer lysis and tumor-antigen specific T cell generation. MG1 Maraba encoding tumor antigens has been demonstrated to boost pre-existing immune responses. We are currently using a non-replicating adenovirus as the priming entity in our ongoing preclinical and clinical studies for our MG1 Maraba product candidates. This therapeutic platform is able to generate a large number of highly-functional antigen-specific T cells, in addition to its oncolytic activity, in mice, non-human primates (NHP) and patients treated with Adenovirus and oncolytic MG1 Maraba, expressing the same tumour associated antigen. This study investigates the ability of αPD-1 to potentiate immune responses generated by Ad/MG1 prime/boosting, and whether the timing of αPD-1 administration impacts the immune responses and therapeutic outcome. The combination of αPD-1 and Ad/MG1 treatment was investigated in a challenging murine model of metastatic melanoma. In three independent experiments, mice bearing advanced B16F10 lung metastases treated with Ad/MG1-hDCT in combination with αPD1 antibody, overall survival was increased to greater than 90%, compared to survival rates of less than 40% in mice treated with Ad/MG1-hDCT alone. The increased efficacy was correlated with improved anti-tumour immune responses in the αPD-1 combination group. The strongest effects of αPD-1 were observed when αPD-1 treatment was initiated immediately following Ad-hDCT immunization leading to significantly increased anti-tumour immune responses at all timepoints analyzed. When delayed until 1 week after MG1 treatment, αPD-1 was unable to improve the anti-tumour immune responses, or therapeutic efficacy, elicited by Ad/MG1 treatment. Similar effects were observed using an αPD-L1 targeted antibody. Therefore, the timing of PD-1/L1 blockade during Ad/MG1 treatment was determined to be a critical parameter for successful therapeutic outcomes. In addition, a non-human primate study is underway to assess the combination of αPD-1 with Ad/MG1-E6E7 (expressing HPV E6 and E7) when delivered concurrent with adenovirus immunization. Moreover, these data highlight the timing of checkpoint inhibitor treatment as a critical parameter for consideration when administering immune checkpoint inhibitors with other agents, including oncolytic viral immunotherapies.Citation Format: Kyle Stephenson, Kwame Twumasi-Boateng, Amy Patrick, Caroline Breitbach, Michael Burgess, Brian Lichty. MG1 Maraba boost following adenovirus prime generates tumor antigen-specific T cells which are potentiated by anti-PD-1 antibody combination [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1449.

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.314
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.014
GPT teacher head0.284
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; both teacher heads agree on what is shown here.

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
Published2019
Admission routes1
Has abstractyes

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