Functional Proteomic Analysis of Advanced Serous Ovarian Cancer Using Reverse Phase Protein Array: TGF-β Pathway Signaling Indicates Response to Primary Chemotherapy
Bibliographic record
Abstract
PURPOSE: Using reverse phase protein array, we measured protein expression associated with response to primary chemotherapy in patients with advanced-stage, high-grade serous ovarian cancer. EXPERIMENTAL DESIGN: Tumor samples were obtained from 45 patients with advanced high-grade serous cancers from the Gynecology Tumor Bank at the British Columbia Cancer Agency. Treatment consisted of platinum-based chemotherapy following debulking surgery. Protein lysates were prepared from fresh frozen tumor samples, and 80 validated proteins from signaling pathways implicated in ovarian carcinogenesis were measured by reverse phase protein array. Normalization of Ca-125 by the 3rd cycle of chemotherapy was chosen as the primary outcome measure of chemotherapy response. Logistic regression was used for multivariate analysis to identify protein predictors of Ca-125 normalization and Cox regression to test for the association between protein expression and progression-free survival. A significance level of P<or=0.05 was used. RESULTS: The mean age at diagnosis was 56.8 years. epidermal growth factor receptor, YKL-40, and several transforming growth factor beta (TGF-beta) pathway proteins [c-jun-NH2-kinase (JNK), JNK phosphorylated at residues 183 and 185, plasminogen activator inhibitor 1, Smad3, TAZ] showed significant associations with Ca-125 normalization on univariate testing. On multivariate analysis, epidermal growth factor receptor (P<0.02), JNK (P<0.01), and Smad3 (P<0.04) were significantly associated with normalization of Ca-125. Contingency table analysis of pathway-classified proteins revealed that the selection of TGF-beta pathway proteins was unlikely because of false discovery (P<0.007; Bonferroni adjusted). CONCLUSION: TGF-beta pathway signaling likely plays an important role as a marker or mediator of chemoresistance in advanced serous ovarian cancer. On this basis, future studies to develop and validate a useful predictor of treatment failure are warranted.
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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".