Deciphering the ghost proteome in ovarian cancer cells by deep proteogenomic characterization
Bibliographic record
Abstract
Proteogenomics is becoming a powerful tool in personalized medicine by linking genomics, transcriptomics and mass spectrometry (MS)-based proteomics. Due to increasing evidence of alternative open reading frame-encoded proteins (AltProts), proteogenomics has a high potential to unravel the characteristics, variants, expression levels of the alternative proteome, in addition to already annotated proteins (RefProts). To obtain a broader view of the proteome of ovarian cancer cells compared to ovarian epithelial cells, cell-specific total RNA-sequencing profiles and customized protein databases were generated. In total, 128 RefProts and 30 AltProts were identified exclusively in SKOV-3 and PEO-4 cells. Among them, an AltProt variant of IP_715944, translated from DHX8, was found mutated (p.Leu44Pro). We show high variation in protein expression levels of RefProts and AltProts in different subcellular compartments. The presence of 117 RefProt and two AltProt variants was described, along with their possible implications in the different physiological/pathological characteristics. To identify the possible involvement of AltProts in cellular processes, cross-linking-MS (XL-MS) was performed in each cell line to identify AltProt-RefProt interactions. This approach revealed an interaction between POLD3 and the AltProt IP_183088, which after molecular docking, was placed between POLD3-POLD2 binding sites, highlighting its possibility of the involvement in DNA replication and repair.
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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".