Abstract B23: CDK4/6 and MEK inhibitor combination in low-grade serous ovarian cancer cell lines
Bibliographic record
Abstract
Abstract Background: Low-grade serous ovarian cancer (LGSC) is a rare tumor type that requires effective therapies. CDKN2A/B (p16/p15) loss and oncogenic mutations in MAPK genes are common genetic aberrations found in these tumors. We aimed to evaluate CDK4/6 inhibitor (palbociclib) activity, with and without MEK inhibitor (trametinib) treatment, in LGSC cell lines. Methods: Nine LGSC cell lines (from advanced/recurrent cases) were used to evaluate the effects of palbociclib treatment, with and without trametinib, on cell proliferation (Incucyte) and viability (MTS). Cell cycle gene and protein expression status (p16, CDK4, CDK6, Rb, p-Rb, CCDN1 and E2F) were characterized using whole-exome sequencing and Western blot analysis. CDKN2A and RB1 gene copy-number (CN) data and P16 immunohistochemistry staining were obtained from 70 LGSC FFPE tumors and correlated with patient overall survival (OS). Results: Palbociclib treatment had cytotoxic effects on one LGSC cell line (11%). Presence of CDK4/CDK6 protein expression was observed in 100% of the lines. Absence of p16 and Rb1 expression was detected in 78% (7/9) and 44% (4/9) of lines, respectively, and did not correlate with gene copy number status. Interestingly, none of the lines had detectable mutations in the cell cycle genes screened. Trametinib-resistant lines expressed total and phosphorylated Rb1 but palbociclib treatment did not result in drug synergistic effects. CDKN2A and RB1 copy number loss was detected in 21% and 7% LGSC tumors (all stages), respectively. The prognostic value of CDKN2A/RB1 expression in LGSC tumors is under evaluation. Conclusions: Palbociclib (with and without trametinib) showed poor activity in LGSC in vitro, and its activity did not correlate with either p16 or Rb1 expression. Our data suggest that other factors in addition to Rb-proficiency determine palbociclib efficacy in LGSC. Therefore, future translational research to define predictive biomarkers of palbociclib efficacy is needed. Citation Format: Joshua Hoenisch, Hannah Kim, Amy Dawson, Nicole Lam, Dane A. Cheasley, Kylie Gorringe, Ian Campbell, David G. Huntsman, Gabriel DiMattia, Martin Kobel, Marta Llaurado Fernandez, Mark S. Carey. CDK4/6 and MEK inhibitor combination in low-grade serous ovarian cancer cell lines [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr B23.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 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".