Prognostic value of INPP4B protein immunohistochemistry in ovarian cancer.
Bibliographic record
Abstract
PURPOSE OF INVESTIGATION: Ovarian cancer is associated with poor prognosis and altered protein expression patterns may be useful for identifying patients likely to have poor disease outcomes. The impact of altered INPP4B protein expression on prognosis is unclear. The aim of this study was to evaluate the implication of INPP4B expression changes in a large series of ovarian cancer tissue samples. MATERIALS AND METHODS: Tissue microarrays were constructed from 599 epithelial ovarian tumors and stained with antibodies for INPP4B, p53, and PTEN. Proportional hazard models were used to estimate survival hazard ratios (HRs) associated with altered protein expression. RESULTS: Seventy-nine percent of the ovarian cancers demonstrated loss of INPP4B, whereas 53% showed aberrant p53 expression (i.e., complete loss of p53 or over-expression of p53) and 8% showed loss of PTEN. INPP4B was frequently lost in serous and endometrioid cancer subtypes, aberrant p53 expression was most common among serous subtype, and loss of PTEN was most common among endometrioid tumors (p for all three proteins across histologic subtypes ≤ 0.0001). INPP4B loss or aberrant p53 expression were both associated with increased mortality (HR = 1.84; 95% CI 1.27 - 2.68 and HR = 3.10; 95% CI 2.33 - 4.11, respectively); however, in multivariate models, only the relationship with p53 achieved statistical significance (HR = 1.20; 95% CI 0.82 - 1.76 for INPP4B and HR = 1.73; 95% CI 1.28 - 2.34 for p53). Conclusion: The INPP4B protein is frequently lost in serous and endometrioid subtypes of ovarian cancer. A possible prognostic role of INPP4B for endometrioid ovarian tumors requires further evaluation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".