442-A Optimization and validation of novel chimeric antigen receptor (CARs) architectures for the treatment of leukemia and beyond
Bibliographic record
Abstract
Background One avenue of recent research has focused on harnessing the patient‘s immune system to eradicate tumors called immunotherapies. To do so, T cells from a cancer patient are harvested and genetically modified to express a chimeric antigen receptor (CAR), capable of recognizing tumor cells, then reinfused into the patient. CARs are synthetic transmembrane proteins composed of a tumor-targeting domain, a membrane anchoring domain and a complex signaling domain to activate T cells promoting tumor cell killing. These therapies have shown exceptional results in the treatment of young patients with leukemia and lymphoma. We hypothesize that CAR-based immunotherapy complications arise from faulty CAR architecture resulting in an aberrant immune response. Methods To mitigate this, we have developed a new CAR architecture through functional screening, which mimics natural immune receptor assembly and surface expression. These new modular CARs (mCARs) show better efficacy in signaling and killing tumor cells compared to the standard of care (SOC-CAR). More so, mCARs are compatible for hematopoietic stem cell-based treatment avenue as mCAR-HSC are able to differentiate into a myriad of immune cells enable the deployment of an effector cell armada against target cells. Results We are currently optimizing the signaling cues provided by mCARs by introducing new signaling motifs and benchmarking them to SOC-CAR in clinically relevant mouse tumor models. Additionally, we are screening novel signaling domains to improve tumor cell killing, functionality and longevity. Finally, these results will allow us to pick the best combination of signaling motif provided by the mCAR that result in an increase treatment outcomes and decrease in complications. Conclusions Together, the findings made here will help determine the efficacy of the newly optimized mCARs in maintaining cell survival and functionality and set the framework for their clinical implementation.
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 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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".