Multiplex engineering and multifunction T cells for precise and effective immunotherapies
Bibliographic record
Abstract
Adoptive T cell transfer has emerged as a pillar of modern cancer immunotherapy. Propelled by viral and non-viral-based technologies, such as CRISPR-Cas9, genetic engineering offers novel opportunities for both emerging cellular therapies and the improvement of more established approaches such as chimeric antigen receptor (CAR) modified T cells. First-generation genetically modified T-cell therapeutics remain limited by the intrinsic constraints imposed by T-cell biology, such as T-cell exhaustion, poor trafficking into hostile tumor beds, toxicity, and challenges associated with tumor antigenic escape. Several of such limitations can be addressed by further engineering, expanding significantly the potential of cell therapy. This review focuses on the promise of using currently available cellular engineering technologies to genetically engineer single T cells at multiple different loci and/or confer several novel functions to circumvent the shortcomings of adoptive immunotherapy to treat cancer. Various methodologies and rationales for the design of these advanced engineered cellular products are described, along with emerging clinical data supporting the use of multiplex-engineered T cells. The limitations of advanced cell engineering and the remaining gaps that need to be filled to optimize the efficacy of adoptive T-cell immunotherapies are also discussed.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".