Engineering Hematopoietic Cells for Cancer Immunotherapy: Strategies to Address Safety and Toxicity Concerns
Bibliographic record
Abstract
Advances in cancer immunotherapies utilizing engineered hematopoietic cells have recently generated significant clinical successes. Of great promise are immunotherapies based on chimeric antigen receptor-engineered T (CAR-T) cells that are targeted toward malignant cells expressing defined tumor-associated antigens. CAR-T cells harness the effector function of the adaptive arm of the immune system and redirect it against cancer cells, overcoming the major challenges of immunotherapy, such as breaking tolerance to self-antigens and beating cancer immune system-evasion mechanisms. In early clinical trials, CAR-T cell-based therapies achieved complete and durable responses in a significant proportion of patients. Despite clinical successes and given the side effect profiles of immunotherapies based on engineered cells, potential concerns with the safety and toxicity of various therapeutic modalities remain. We discuss the concerns associated with the safety and stability of the gene delivery vehicles for cell engineering and with toxicities due to off-target and on-target, off-tumor effector functions of the engineered cells. We then overview the various strategies aimed at improving the safety of and resolving toxicities associated with cell-based immunotherapies. Integrating failsafe switches based on different suicide gene therapy systems into engineered cells engenders promising strategies toward ensuring the safety of cancer immunotherapies in the clinic.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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.
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".