Abstract A043 Plasma circulating proteomic markers are associated with metastatic spread and outcome in osteosarcoma
Bibliographic record
Abstract
Abstract Background: Osteosarcoma is the most frequent bone cancer in children and young adults. Histological response and control of primary metastases are the main prognostic clinical factors, but are insufficient to identify the ∼30% of hard-to-treat patients and are not predictive of the response to combinations of chemotherapies or biological treatments. Our aim was to evaluate if circulating proteomic analyses could predict tumor progression. Methods: 88 plasma samples from newly diagnosed patients with osteosarcoma in the OS2006 trial were sequenced using the OLINK Explore panel (3072 proteins) and 88 more coming from the SARCOME13 trial will be used as a validation cohort. Results: Principal component analysis defined co-circulating proteins associated to critical biological pathways involved in osteosarcoma oncogenesis, such as cell adhesion, cell survival/growth, bone formation and extracellular matrix organization. Cox survival analysis associated circulating bone formation pathway with OS(HR=1.10 [1.03;1.17], p=0.006) and PFS(HR=1.12 [1.05;1.19], p=<0.001). These circulating pathways were also associated with presence of metastases at diagnosis (odd ratio = 1.33 [1.12;1.72], p=0.006). These results are being validated on SARCOME13 data. Conclusion: Proteomic plasma markers quantification in patients with osteosarcoma at diagnosis capture a biological signal significantly associated to prognosis and the presence of metastasis at diagnosis. Citation Format: Baptiste Audinot, Damien Drubay, Maria Eugenia Da Costa, Gael Moquin Beaudry, Marta Jimenez, Birgit Geoerger, Nathalie Gaspar, Samuel Abbou, Antonin Marchais. Plasma circulating proteomic markers are associated with metastatic spread and outcome in osteosarcoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr A043.
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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 | 0.001 |
| 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) | 0.003 | 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".