In vivo genome-wide CRISPR screening in murine acute myeloid leukemia uncovers microenvironmental dependencies
Bibliographic record
Abstract
Genome-wide CRISPR screens have been extremely useful in identifying therapeutic targets in diverse cancers by defining genes that are essential for malignant growth. However, most CRISPR screens were performed in vitro and thus cannot identify genes that are essential for interactions with the microenvironment in vivo. Here, we report genome-wide CRISPR screens in 2 in vivo murine models of acute myeloid leukemia (AML) driven by the KMT2A/MLLT3 fusion or by the constitutive coexpression of Hoxa9 and Meis1. Secondary validation using a focused library identified 72 genes specifically essential for leukemic growth in vivo, including components of the major histocompatibility complex class I complex, Cd47, complement receptor Cr1l, and the β-4-galactosylation pathway. Importantly, several of these in vivo-specific hits have a prognostic effect or are inferred to be master regulators of protein activity in human AML cases. For instance, we identified Fermt3, a master regulator of integrin signaling, as having in vivo-specific dependency with high prognostic relevance. Overall, we show an experimental and computational pipeline for genome-wide functional screens in vivo in AML and provide a genome-wide resource of essential drivers of leukemic growth in vivo.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".