CRISPR/Cas9 Applications in T Cell Modifications for Tumor Immunotherapy
Bibliographic record
Abstract
Cancer is a serious global health issue that has a high fatality rate. Therefore, immunotherapy as a promising tumor therapy has long-term, specific targets, et.al, benefits. The powerful gene editing tool CRISPR/Cas9 is commonly used for tumor immunotherapy in modifying immune cells such as T cells. The T cell modification research primarily focused on antigen editing, checkpoint knockout, and cytokine regulation to enhance cell targeting and immune functions. However, current studies justified the T cell immunotherapy feasibility for treating cancer, but the underlying mechanism in CAR and TCR and T cell function regulation is still not clear. This article concentrated on analyzing the CRISPR/Cas9 applications for editing T cell mechanisms for tumor cell targeting. For example, design new CARs or TCRs for either directly or indirectly bound to the tumor cell surface to target tumor cells and activate T cells. Also, analyzed tumor escape-related immune checkpoints (ICs) and the T cell function improvement method of cytokine regulations. Furthermore, this article also summarizes the drawbacks and possible improvement methods for T cell modification in tumor immunotherapy. This article provided the basic understanding and concepts of research and development for T cell immunotherapy for tumor treatment by CRISPR/Cas9 editing. However, the underlying mechanisms and more editing target genes for T cell enhancing need to be studied and discovered in further research to achieve a complete and thorough cure for cancers.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".