NOTCH Signaling Limits the Response of Low-Grade Serous Ovarian Cancers to MEK Inhibition
Bibliographic record
Abstract
Low-grade serous ovarian cancer (LGSOC) is a rare subtype of epithelial ovarian cancer with high fatality rates in advanced stages due to its chemoresistant properties. LGSOC is characterized by activation of MAPK signaling, and recent clinical trials indicate that the MEK inhibitor (MEKi) trametinib may be a good treatment option for a subset of patients. Understanding MEKi-resistance mechanisms and subsequent identification of rational drug combinations to suppress resistance may greatly improve LGSOC treatment strategies. Both gain-of-function and loss-of-function CRISPR-Cas9 genome-wide libraries were used to screen LGSOC cell lines to identify genes that modulate the response to MEKi. Overexpression of MAML2 and loss of MAP3K1 were identified, both leading to overexpression of the NOTCH target HES1, which has a causal role in this process as its knockdown reversed MEKi resistance. Interestingly, increased HES1 expression was also observed in selected spontaneous trametinib-resistant clones, next to activating MAP2K1 (MEK1) mutations. Subsequent trametinib synthetic lethality screens identified SHOC2 downregulation as being synthetic lethal with MEKis. Targeting SHOC2 with pan-RAF inhibitors (pan-RAFis) in combination with MEKi was effective in parental LGSOC cell lines, in MEKi-resistant derivatives, in primary ascites cultures from patients with LGSOC, and in LGSOC (cell line-derived and patient-derived) xenograft mouse models. We found that the combination of pan-RAFi with MEKi downregulated HES1 levels in trametinib-resistant cells, providing an explanation for the synergy that was observed. Combining MEKis with pan-RAFis may provide a promising treatment strategy for patients with LGSOC, which warrants further clinical validation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".