Variant peptide detection utilizing mass spectrometry: laying the foundations for proteogenomic identification and validation
Bibliographic record
Abstract
BACKGROUND: Proteogenomics is an emerging field at the intersection of genomics and proteomics. Many variant peptides corresponding to single nucleotide variations (SNVs) are associated with specific diseases. The aim of this study was to demonstrate the feasibility of proteogenomic-based variant peptide detection in disease models and clinical specimens. METHODS: We sought to detect p53 single amino acid variant (SAAV) peptides in breast cancer tumor samples that have been previously subjected to sequencing analysis. Initially, two cancer cell lines having a cellular tumor antigen p53 (TP53) mutation and one wild type for TP53 were analyzed by selected reaction monitoring (SRM) assays as controls. One pool of wild type and one pool of mutated for TP53 cytosolic extracts were assayed with a shotgun proteogenomic workflow. Furthermore, 18 individual samples having a mutation in TP53 were assayed by SRM. RESULTS: Two mutant p53 peptides were successfully detected in two cancer cell lines as expected from their DNA sequence. Wild type p53 peptides were detected in both cytosolic pools, however, none of the mutant p53 peptides were identified. Mutations at the protein level were detected in two cytosolic extracts and whole tumor lysates from the same patients by SRM analysis. Six thousand and six hundred and twenty eight non-redundant proteins were identified in the two cytosolic pools, thus greatly improving a previously reported cytosolic proteome. CONCLUSIONS: In the current study we show the great potential of using proteogenomics for the direct identification of cancer-associated mutations in clinical samples and we discuss current limitations and future perspectives.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".