Applying CRISPR-Cas9 screens to dissect hematological malignancies
Bibliographic record
Abstract
Bit by bit, over the last few decades, functional genomic tools have been piecing together the molecular puzzle driving tumorigenesis in human patients. Nevertheless, our understanding of the role of several genes and regulatory elements that drive critical cancer-associated physiological processes from disease development to progression to spread is very limited, which significantly affects our ability of applying these insights in the context of improved disease management. The recent advent of clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9)-based technology and its application in cancer genomics has, however, allowed the generation of a wealth of knowledge that has helped decipher several critical questions associated with translational cancer research. Precisely, the high-throughput capability coupled with a high level of technological plasticity associated with the CRISPR-Cas9 screens have expanded our horizons from a mere struggle to appreciate cancer as a genetic disease to observing the integrated genomic/epigenomic network of numerous malignancies and correlating it with our present knowledge of drugging strategies to develop innovative approaches for next-generation precision cancer medicine. Specifically, within blood cancers, current CRISPR screens have specifically focused on improving our understanding of drug resistance mechanisms, disease biology, the development of novel therapeutic approaches, and identifying the molecular mechanisms of current therapies, with an underlying aim of improving disease outcomes. Here, we review the development of the CRISPR-Cas9 genome-editing strategy, explicitly focusing on the recent advances in the CRISPR-Cas9-based screening approaches, its current capabilities, limitations, and future applications in the context of hematological malignancies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".