Characterization of 53 Multiplexed Targeted Proteomics Assays for Verification Studies in Cancer Cell Lines
Bibliographic record
Abstract
The National Cancer Institute's Clinical Proteomics Tumor Analysis Consortium (CPTAC) was established to address the need for improved design, standardization, and validation of proteomics assays to enable better translation of biomarkers from the analytical lab to the clinic. Here, we applied CPTAC guidelines to characterize quantitative mass spectrometry (MS) assays in a new multiple reaction monitoring (MRM) proteomics panel. The panel of 50 proteins was developed in response to a previous study that identified a proteomic profile of altered translational control associated with response to a new cancer drug. MRM-MS assays for 53 peptides of interest were developed, optimized, and characterized on a UPLC system coupled to a triple-quadrupole mass spectrometer (QQQ-MS) using synthetic proteotypic peptides and corresponding stable-isotope labeled internal standard (SIS) peptides. Most of the assays were found to be fit-for-purpose for biomarker verification in that they precisely and reproducibly quantify the peptides at levels corresponding to the endogenous concentration in the desired cancer cell lines. Of these, 28 peptide assays represent to proteins that previously had no associated assays published in the CPTAC database. The targeted proteins in this publicly deposited validated multiplexed panel may be of use for research applications in cancer, cellular stress, neurology, cardiology, and metabolism.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".