<i>In vivo</i> CRISPR screens reveal SCAF1 and USP15 as novel drivers of pancreatic cancer
Bibliographic record
Abstract
Abstract Functionally characterizing the genetic alterations that drive pancreatic cancer progression is a prerequisite for Precision Medicine. Here, we developed a somatic CRISPR/Cas9 mutagenesis screen to assess the transforming potential of 125 recurrently mutated ‘long-tail’ pancreatic cancer genes, which revealed USP15 and SCAF1 as novel and potent Pancreatic ductal adenocarcinoma PDAC tumor suppressors, with USP15 functioning in a haplo-insufficient manner. Mechanistically, we found that loss of USP15 leads to reduced inflammatory responses associated with TNFα, TGF-β and IL6 signaling and sensitizes pancreatic cancer cells to PARP inhibition and gemcitabine. Similarly, genetic ablation of SCAF1 reduced inflammatory responses linked to TNFα, TGF-β and mTOR signaling and increased sensitivity to PARP inhibition. Furthermore, we identified that loss of SCAF1 resulted in the formation of a truncated inactive USP15 isoform at the expense of full length USP15, functionally coupling SACF1 and USP15. Notably, USP15 and SCAF1 mutations or copy number losses are observed in 31% of PDAC patients. Together, our results demonstrate the utility of in vivo CRISPR to integrate human cancer genomics with mouse modeling to delineate novel cancer driver genes USP15 and SCAF1 such as with potential prognostic and therapeutic implications.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".