Enhanced <i>GAB2</i> Expression Is Associated with Improved Survival in High-Grade Serous Ovarian Cancer and Sensitivity to PI3K Inhibition
Bibliographic record
Abstract
Identification of genomic alterations defining ovarian carcinoma subtypes may aid the stratification of patients to receive targeted therapies. We characterized high-grade serous ovarian carcinoma (HGSC) for the association of amplified and overexpressed genes with clinical outcome using gene expression data from 499 HGSC patients in the Ovarian Tumor Tissue Analysis cohort for 11 copy number amplified genes: ATP13A4, BMP8B, CACNA1C, CCNE1, DYRK1B, GAB2, PAK4, RAD21, TPX2, ZFP36, and URI. The Australian Ovarian Cancer Study and The Cancer Genome Atlas datasets were also used to assess the correlation between gene expression, patient survival, and tumor classification. In a multivariate analysis, high GAB2 expression was associated with improved overall and progression-free survival (P = 0.03 and 0.02), whereas high BMP8B and ATP13A4 were associated with improved progression-free survival (P = 0.004 and P = 0.02). GAB2 overexpression and copy number gain were enriched in the AOCS C4 subgroup. High GAB2 expression correlated with enhanced sensitivity in vitro to the dual PI3K/mTOR inhibitor PF-04691502 and could be used as a genomic marker for identifying patients who will respond to treatments inhibiting PI3K signaling.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".