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Abstract B49: Development and characterization of a novel CA9 targeting dual-antibody T-cell engager for renal cell carcinoma

2020· article· en· W3036965359 on OpenAlexaff
Jason Moffat, Xiaoyu Zhang, Keith A. Lawson, Sunandan Banerjee, Xiaowei Wang, Jarrett Adams, James Pan, Laurie Ailles, Antonio Finelli, Sachdev S. Sidhu

Bibliographic record

VenueCancer Immunology Research · 2020
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsCancer researchAntigenFlow cytometryT cellImmunotherapyBiologyAntibodyClear cell renal cell carcinomaImmunologyMedicineImmune systemRenal cell carcinomaPathology

Abstract

fetched live from OpenAlex

Abstract The survival benefit of checkpoint immunotherapies is currently limited, with de novo and acquired resistance to therapy occurring in over 75% of patients. A major mechanism of resistance accounting for these poor responses is a lack of tumor immunogenicity, resulting from loss of function mutations in antigen presentation pathways (e.g., B2M, MHC1), or a lack of tumor expressed neoantigens. Dual-antibody T cell engagers (DATE) represent a promising immunotherapeutic approach for poorly immunogenic tumors as these agents engage CD3 on T cells while binding a tumor-associated antigen, resulting in intratumoral T-cell activation, independent of antigen recognition or co-stimulation. To harness the therapeutic potential of this approach for treatment of clear cell renal cell carcinoma (ccRCC), a carbonic anhydrase 9 (CA9) targeting DATE was developed, given the near ubiquitous and selective expression of this antigen on ccRCC tumors. To engineer the CA9-DATE, a panel of CA9 antigen binding fragments (Fabs) was developed through phage display and characterized for CA9-membrane fragment esterase inhibition. A CA9-inhibiting Fab was subsequently cloned into a mammalian expression system containing the anti-CD3 single chain variable fragment. CA9-DATE binding specificity was measured by flow cytometry utilizing a panel of CA9-expressing and -knockout cell lines. Functionally, T-cell activation was assessed through CD25 levels using flow cytometry and IFN-γ secretion using ELISA. In vitro T-cell mediated cytotoxicity was tested utilizing luciferase expressing ccRCC cells and LDH release assays. Finally, humanized NOG mice bearing patient-derived RCC tumors were employed for in vivo efficacy studies. CA9-DATE showed antigen-specific binding towards both CD3- and CA9 in the low-nanomolar ranges. CA9-DATE treatment of patient-derived ccRCC cell lines co-cultured with healthy isolated CD3+ lymphocytes or peripheral blood mononuclear cells resulted in rapid CD4+ and CD8+ lymphocyte activation characterized by a proliferative response, increased CD25 expression, and IFN-γ production. In addition, CA9-DATE resulted in CA9 specific-lysis across 5 patient-derived ccRCC cell lines in in vitro cytotoxicity assays. Preliminary in vivo results demonstrate significant reductions in tumor burden following CA9-DATE therapy relative to vehicle control-treated mice. Collectively, these data provide proof of principle to support the clinical study of CA9-DATE targeting strategies in clear cell RCC. Citation Format: Jason Moffat, Xiaoyu Zhang, Keith Lawson, Sunandan Banerjee, Xiaowei Wang, Jarrett Adams, James Pan, Laurie Ailles, Antonio Finelli, Sachdev Sidhu. Development and characterization of a novel CA9 targeting dual-antibody T-cell engager for renal cell carcinoma [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2018 Nov 27-30; Miami Beach, FL. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(4 Suppl):Abstract nr B49.

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 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 score0.169
Threshold uncertainty score0.594

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 studies0.0000.000
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 head0.107
GPT teacher head0.380
Teacher spread0.272 · 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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Citations0
Published2020
Admission routes1
Has abstractyes

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