Proteomic analysis of plasma exosomes as biomarkers of response to MVP-S based immunotherapy
Bibliographic record
Abstract
Abstract Ovarian cancer is a deadly gynaecological disease owing to its late-stage diagnosis. Recurrence and chemo-resistance develop despite initial treatment success. Maveropepimut-S (MVP-S), formerly DPX-Survivac, is a DPX-based immune educating therapy that elicits robust, targeted and, sustained specific anti-tumor T- and B-cell responses in advanced ovarian cancer patients. Exosomes are a subtype of extracellular vesicles (EVs) secreted by cells involved in intercellular communication. They contain bioactive molecules that can influence the tumor, the immune system, and the extracellular environment. Hence, circulating exosomes could provide real time assessment of disease evolution making them ideal minimally invasive biomarkers for monitoring response to immunotherapies. Plasma exosomes were isolated from advanced ovarian cancer patients (n=22) at pre- and on-treatment with MVP-S based therapy (DeCidE1 trial, NCT02785250). Subjects with clinical benefit (>10% tumor shrinkage) to therapy displayed a lower overall total EV protein concentration. Untargeted proteomic profiling by liquid chromatography tandem mass spectrometry identified 227 proteins in exosomes. Of these, 95 (42%) were likely from non-tumor exosomes and involved in immune modulation such as regulation of humoral response and B-cell immunity. When compared to the plasma proteome, 128 (56%) proteins were found to be exclusive to exosomes. Functional annotations of these proteins suggest their role in modulating immune pathways related to Fc receptor mediated signaling and T-cell migration. The results highlight the potential value of plasma exosome assays for monitoring disease and as a potential surrogate response biomarker. Supported by -IMV inc. -NRC-IRAP R&D Certificate Program.
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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 | 0.001 | 0.000 |
| 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".