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Record W2740810776 · doi:10.1158/1538-7445.am2017-3770

Abstract 3770: CAR-T cell harboring a camelid single domain antibody as a targeting agent to kill tumors expressing VEGFR2

2017· article· en· W2740810776 on OpenAlexaff
Heman Chao, Baomin Tian, Marni D. Uger, Wah Y. Wong

Bibliographic record

VenueCancer Research · 2017
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsHelix Biopharma (Canada)
Fundersnot available
KeywordsAntibodyAngiogenesisCancer researchKinase insert domain receptorVascular endothelial growth factorBiologyAntigenMolecular biologyImmunologyVascular endothelial growth factor AVEGF receptors

Abstract

fetched live from OpenAlex

Abstract Modulation of the immune system is showing tremendous promise in the treatment of malignancies. In addition to checkpoint inhibitors that re-activate T cells present in the tumor microenvironment, exogenously transduced chimeric antigen receptor (CAR) T cells are providing excellent responses in clinical trials for the treatment of leukemias. In this study, we describe CAR-T cells that target VEGFR2-expressing tumors. Angiogenesis is the process of new blood vessel formation and is essential for a tumor to grow beyond a certain size. Tumors secrete the pro-angiogenic factor vascular endothelial growth factor (VEGF), which acts upon local endothelial cells by binding to vascular endothelial growth factor receptors (VEGFR). As VEGFR2 is also expressed by a variety of tumors, we investigated the utility of anti-VEGFR2 CAR-T cells as a method to treat VEGFR2-expressing tumors. Camelid antibodies are small (14 kD) single chain antibodies. To generate a camelid antibody targeting the extracellular domain of VEGFR2, a llama was immunized with recombinant VEGFR2/Fc. A phage display library was generated and screened to identify an antibody with high binding affinity to VEGFR2. The selected antibody was expressed in the E. coli. BL21 (DE3) pT7 system. The purified antibody was characterized by SEC, LC-MS peptide mapping and ELISA. CAR-T cells were engineered to express the camelid anti-VEGFR2 antibody in combination with the CD28 and 4-1BB costimulatory molecules and the CD3 zeta chain. Tumor cells were screened for expression of VEGFR2, and the HL-60 acute promyelocytic leukemia, ZR-75-30 breast ductal carcinoma and NCI-H23 non-small cell lung adenocarcinoma were identified. Co-incubation of anti-VEGFR2 CAR-T cells with the VEGFR-2-expressing cell lines resulted in dose-dependent target cell toxicity as measured by LDH release. In addition, T cell activity was confirmed, as high levels of IL-2 and IFN-γ were detected in the cell culture media. These results suggest that anti-VEGFR2 CAR-T may be useful in directly targeting VEGFR2-expressing tumors. We previously showed the utility of camelid antibodies in CAR-T constructs as anti-CEACAM6 CAR-T cells show both in vitro and in vitro efficacy against the pancreatic tumor Bx-PC3. The use of the a camelid V21 antibody to target VEGFR2-expressing tumors provides hope that camelid single domain antibodies can be developed for CAR-T therapies. Citation Format: Heman Chao, Baomin Tian, Marni Uger, Wah Wong. CAR-T cell harboring a camelid single domain antibody as a targeting agent to kill tumors expressing VEGFR2 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3770. doi:10.1158/1538-7445.AM2017-3770

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 score0.003
Threshold uncertainty score0.009

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.097
GPT teacher head0.447
Teacher spread0.350 · 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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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