Abstract A08: Development of FGFR4-specific chimeric antibody receptor (CAR) T cell and bispecific T cell engager (BiTE) for rhabdomyosarcoma (RMS) immunotherapy
Bibliographic record
Abstract
Abstract Background: Despite decades of multimodule therapies, RMS remains incurable once it has metastasized; thus, new therapeutic strategies are warranted. FGFR4 is a developmentally regulated cell surface receptor tyrosine kinase, overexpressed in virtually all, mutationally activated in about 7.5% of RMS, and directly activated by PAX3-FOXO1 fusion protein, which makes it a tractable target for immunotherapy. Material and Methods: Using monoclonal antibody technologies and a yeast display B-cell library, we generated 15 human or mouse binders specific to human FGFR4 and engineered into human scFvFc. All binders were successfully produced in vitro, and we further characterized them using FACS and ELISA for their specificity. Octet was used to measure the binding affinity against human FGFR4. For those lead hits, they were made into different formats of therapeutic including CAR and BiTE. We then performed in vitro killing assays and/or in vivo xenograft model to determine the efficacy of those therapeutics in killing RMS cells. Results: m410 and m412 were two lead hits and scFvFcs of these two binders were successfully produced in vitro and showed FGFR4 specificity with a binding affinity at nanomolar concentration. By ELISA, these binders showed dose-dependent binding to FGFR4 protein but not to other FGFR family members. We then made m410 and m412 into CAR and BiTE format, respectively. T cells transduced with m410 CAR construct were found highly potent in inducing gamma interferon, TNF alpha, and cytotoxicity when the FGFR4-CART are cocultured with RMS cells. Our in vivo testing found them to be effective in eliminating RMS cells in murine xenograft models. When T cells were cocultured with RMS cells in the presence of m412 BiTE in vitro, potent selective antitumor effect was observed, suggesting this can be another promising strategy for RMS immunotherapy. Conclusions: Here our data demonstrated that we had successfully generated binders specific to human FGFR4. The CAR and BiTE developed from these binders were able to kill FGFR4-positive target cells. Our data suggest that these FGFR4 CARs and FGFR4 BiTEs could provide effective immune therapies for rhabdomyosarcoma and other FGFR4-expressing cancers. Citation Format: Adam Cheuk, Nityashree Shivaprasad, Dina Schneider, Marielle Yohe, Meijie Tan, Peter Azorsa, Ronald Sams, Silvia Pomella, Berkley Gryder, Rossella Rota, Ben Stanton, Jun Wei, Young Song, Xinyu Wen, Sivasish Sindiri, Jeetendra Kumar, Robert Hawley, Joon-Yong Chung, Doncho Zhelev, Zhongyu Zhu, Dimiter Dimitrov, Stephen Hewitt, Boro Dropulic, Rimas Orentas, Javed Khan. Development of FGFR4-specific chimeric antibody receptor (CAR) T cell and bispecific T cell engager (BiTE) for rhabdomyosarcoma (RMS) immunotherapy [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A08.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".