RAS Synthetic Lethal Screens Revisited: Still Seeking the Elusive Prize?
Bibliographic record
Abstract
The RAS genes are critical oncogenic drivers activated by point mutation in some 20% of human malignancies. However, no pharmacologic approaches to targeting RAS proteins directly have yet succeeded, leading to suggestions that these proteins may be "undruggable." This has led to two alternative indirect approaches to targeting RAS function in cancer. One has been to target RAS signaling pathways downstream at tractable enzymes such as kinases, particularly in combination. The other, which is the focus of this review, has been to seek targets that are essential in cells bearing an activated RAS oncogene, but not those without. This synthetic lethal approach, while rooted in ideas from invertebrate genetics, has been inspired most strongly by the successful use of PARP inhibitors, such as olaparib, in the clinic to treat BRCA defective cancers. Several large-scale screens have been carried out using RNA interference-mediated expression silencing to find genes that are uniquely essential to RAS-mutant but not wild-type cells. These screens have been notable for the low degree of overlap between their results, with the possible exception of proteasome components, and have yet to lead to successful new clinical approaches to the treatment of RAS-mutant cancers. Possible reasons for these disappointing results are discussed here, along with a reevaluation of the approaches taken. On the basis of experience to date, RAS synthetic lethality has so far fallen some way short of its original promise and remains unproven as an approach to finding effective new ways of tackling RAS-mutant cancers. Clin Cancer Res; 21(8); 1802-9. ©2015 AACR. See all articles in this CCR Focus section, "Targeting RAS-Driven Cancers."
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.007 |
| 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".