Quantitative Profiling of Single Formalin Fixed Tumour Sections: proteomics for translational research
Bibliographic record
Abstract
Although re-sequencing of gene panels and mRNA expression profiling are now firmly established in clinical laboratories, in-depth proteome analysis has remained a niche technology, better suited for studying model systems rather than challenging materials such as clinical trial samples. To address this limitation, we have developed a novel and optimized platform called SP3-Clinical Tissue Proteomics (SP3-CTP) for in-depth proteome profiling of practical quantities of tumour tissues, including formalin fixed and paraffin embedded (FFPE). Using single 10 μm scrolls of clinical tumour blocks, we performed in-depth quantitative analyses of individual sections from ovarian tumours covering the high-grade serous, clear cell, and endometrioid histotypes. This examination enabled the generation of a novel high-resolution proteome map of ovarian cancer histotypes from clinical tissues. Comparison of the obtained proteome data with large-scale genome and transcriptome analyses validated the observed proteome biology for previously validated hallmarks of this disease, and also identified novel protein features. A tissue microarray analysis validated cystathionine gamma-lyase (CTH) as a novel clear cell carcinoma feature with potential clinical relevance. In addition to providing a milestone in the understanding of ovarian cancer biology, these results show that in-depth proteomic analysis of clinically annotated FFPE materials can be effectively used as a biomarker discovery tool and perhaps ultimately as a diagnostic approach.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".