Combined Mass Spectrometry Imaging and Top-down Microproteomics Reveals Evidence of a Hidden Proteome in Ovarian Cancer
Bibliographic record
Abstract
Background: Recently, it was demonstrated that proteins can be translated from alternative open reading frames (altORFs), increasing the size of the actual proteome. Top-down mass spectrometry-based proteomics allows the identification of intact proteins containing post-translational modifications (PTMs) as well as truncated forms translated from reference ORFs or altORFs. Methods: Top-down tissue microproteomics was applied on benign, tumor and necrotic-fibrotic regions of serous ovarian cancer biopsies, identifying proteins exhibiting region-specific cellular localization and PTMs. The regions of interest (ROIs) were determined by MALDI mass spectrometry imaging and spatial segmentation. Findings: Analysis with a customized protein sequence database containing reference and alternative proteins (altprots) identified 15 altprots, including alternative G protein nucleolar 1 (AltGNL1) found in the tumor, and translated from an altORF nested within the GNL1 canonical coding sequence. Co-expression of GNL1 and altGNL1 was validated by transfection in HEK293 and HeLa cells with an expression plasmid containing a GNL1-FLAG (V5) construct. Western blot and immunofluorescence experiments confirmed constitutive co-expression of altGNL1-V5 with GNL1-FLAG. Conclusions: Taken together, our approach provides means to evaluate protein changes in the case of serous ovarian cancer, allowing the detection of potential markers that have never been considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".