Organelle resolved proteomics reveals new chordoma cell surface markers required for proliferation and association with outcome
Bibliographic record
Abstract
Abstract BackgroundChordomas are rare, clinically aggressive tumors with a median survival of 6-7 years and a high rate of disease progression despite maximal surgery and radiotherapy. Given the limited options available to prevent and treat progression and relatively poor prognosis, there remains an urgent need for the development of novel therapies to improve clinical outcomes. Cell-surface proteins are attractive therapeutic targets due to their accessible subcellular localization.MethodsHere, we used a proteomics approach to identify novel chordoma-specific cell-surface protein markers. Four established chordoma cell lines were analyzed by quantitative proteomics using a comprehensive differential ultracentrifugation organellar fractionation approach. A subtractive proteomics strategy was applied to select proteins that are plasma membrane enriched. Using commercially available antibodies, the expression profiles of these cell-surface proteins were validated across chordoma cell lines, patient surgical tissue samples, and normal tissue lysates via Western blotting. The candidates were further validated by immunohistochemical analysis in a 25-patient tissue cohort. Finally, the essentiality of these candidates for in vitro chordoma growth was evaluated.ResultsMass spectrometry-based proteomics identified 120 high-confidence cell-surface proteins in four established chordoma cell line models. Systematic data integration prioritized two chordoma-specific cell surface proteins for further interrogation. Immunohistochemistry in a richly annotated cohort of chordoma tumor tissues revealed that PLA2R1 and SLC6A12 are broadly expressed in chordoma patient samples. Higher expression of PLA2R1 correlated with poor prognosis whereas SLC6A12 expression was significantly enriched in skull-base chordomas compared to those arising in the spine. Using a siRNA-mediated knockdown of PLA2R1, we demonstrated significant inhibition of cell growth and colony-forming ability, suggesting these proteins play an essential role in chordoma biology.ConclusionWe have comprehensively elucidated the proteome of four established chordoma cell lines. Subtractive proteomics and integrative data mining revealed novel cell-surface proteins required for chordoma cell survival and associated with clinical parameters in a small chordoma tissue sample cohort.
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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 | 0.001 | 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".