CRISPR/Cas9 screening: unraveling cancer immunotherapy’s ‘Rosetta Stone’
Bibliographic record
Abstract
Clustered regularly interspaced palindromic repeats (CRISPR)-based technology, a powerful toolset for the unbiased functional genomic screening of biological processes, has facilitated several scientific breakthroughs in the biomedical field. Cancer immunotherapy has advanced the treatment of numerous malignancies that previously had restricted treatment options or unfavorable outcomes. In the realm of cancer immunotherapy, the application of CRISPR/CRISPR-associated protein 9 (Cas9)-based genetic perturbation screening has enabled the identification of genes, biomarkers, and signaling pathways that govern various cancer immunoreactivities, as well as the development of effective immunotherapeutic targets. In this review, we summarize the advances in CRISPR/Cas9-based screening for cancer immunotherapy and outline the immunotherapeutic targets identified via CRISPR screening based on cancer-type classification.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".