Toward Proteomic-Based Prediction of Ex Vivo Platinum Sensitivity in Ovarian Cancer Ascitic Cellular Aggregates: A Pilot Study
Bibliographic record
Abstract
The accumulation of malignant ascites in the peritoneal cavity is a hallmark of high-grade serous ovarian cancer (HGSC). This fluid contains three-dimensional multicellular aggregates known as spheroids, which contribute to chemoresistance and are an accessible source of tumor material for proteomic-based biomarker discovery studies. Although heterogeneous ascitic spheroids can be generated from primary cell suspensions for ex vivo applications, they suffer from long generation times and reduced biological relevance. Here, we compare their ex vivo chemotherapy responses and proteomes to native spheroids that are collected directly from HGSC ascites, with the aim of assessing their suitability for proteomic-based chemoresponse prediction strategies that yield results within a clinically relevant time frame. We demonstrate that the chemoresponses of native spheroids better correlate with patients’ clinical treatment responses in 4 of 5 cases and that their proteomes uniquely segregate according to ex vivo carboplatin response along the first component. This pilot study suggests key proteins and biological pathways that may facilitate a global proteomic-based screening strategy for personalized HGSC treatment, with particular emphasis on extracellular matrix proteins. As such, native spheroids have the potential to progress the personalized treatment of HGSC patients with malignant ascites.
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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 | 0.001 | 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".