Molecular basis and therapeutic implications of binary YAPOn/YAPOff cancer classes
Bibliographic record
Abstract
Cancers have traditionally been classified based on their tissue of origin. However, with advances in sophisticated genome sequencing techniques and progression toward an era of precision medicine, it has become increasingly clear that classifying tumors based on unifying molecular features instead of tissue of origin may hold the key to improving patient outcomes. Various efforts have been undertaken to address this critical aspect of cancer biology, but it is still unclear as to the best approach to stratify tumors into different molecular classes. One approach is to define many small subclasses based on complex molecular signatures, while another option is to divide cancers into larger groups based on higher-order features of cancer behavior. This latter approach holds appeal as it may provide opportunities to identify broadly relevant therapeutics. However, our understanding of these fundamental 'rules' of cancer biology and how they can be used to better classify and treat cancers is in its infancy. We recently demonstrated that cancers can be functionally stratified into binary YAPon and YAPoff super-classes with unique therapeutic vulnerabilities based on distinct expression and function of the transcriptional coactivators, YAP and TAZ. In YAPon cancers, YAP and TAZ drive oncogenesis, whereas in YAPoff cancers, YAP and TAZ are instead tumor suppressors. In this review, we discuss our understanding of these distinct cancer classes with a focus on the mechanisms that underlie the opposite function of YAP/TAZ in YAPon and YAPoff cancers, as well as the potential therapeutic implications of these findings.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".