IL6-STAT3-HIF Signaling and Therapeutic Response to the Angiogenesis Inhibitor Sunitinib in Ovarian Clear Cell Cancer
Bibliographic record
Abstract
PURPOSE: Ovarian clear cell adenocarcinoma (OCCA) is an uncommon histotype that is generally refractory to platinum-based chemotherapy. We analyze here the most comprehensive gene expression and copy number data sets, to date, to identify potential therapeutic targets of OCCA. EXPERIMENTAL DESIGN: Gene expression and DNA copy number were carried out using primary human OCCA tumor samples, and findings were confirmed by immunohistochemistry on tissue microarrays. Circulating interleukin (IL) 6 levels were measured in serum from patients with OCCA or high-grade serous cancers and related to progression-free and overall survival. Two patients were treated with sunitinib, and their therapeutic responses were measured clinically and by positron emission tomography. RESULTS: We find specific overexpression of the IL6-STAT3-HIF (interleukin 6-signal transducer and activator of transcription 3-hypoxia induced factor) pathway in OCCA tumors compared with high-grade serous cancers. Expression of PTHLH and high levels of circulating IL6 in OCCA patients may explain the frequent occurrence of hypercalcemia of malignancy and thromboembolic events in OCCA. We describe amplification of several receptor tyrosine kinases, most notably MET, suggesting other potential therapeutic targets. We report sustained clinical and functional imaging responses in two OCCA patients with chemotherapy-resistant disease who were treated with sunitinib, thus showing significant parallels with renal clear cell cancer. CONCLUSIONS: Our findings highlight important therapeutic targets in OCCA, suggest that more extensive clinical trials with sunitinib in OCCA are warranted, and provide significant impetus to the growing realization that OCCA is molecularly and clinically distinct to other forms of ovarian cancer.
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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 | 0.001 |
| 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) | 0.000 | 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 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".