A multiplexed, automated immuno-matrix assisted laser desorption/ionization mass spectrometry assay for simultaneous and precise quantitation of PTEN and p110α in cell lines and tumor tissues
Bibliographic record
Abstract
The PI3-kinase/AKT/mTOR pathway plays a central role in cancer signaling. While p110α is the catalytic α-subunit of PI3-kinase and a major drug target, PTEN is the main negative regulator of the PI3-kinase/AKT/mTOR pathway. PTEN is often down-regulated in cancer, and there are conflicting data on PTEN's role as breast cancer biomarker. PTEN and p110α protein expression in tumors is commonly analyzed by immunohistochemistry, which suffers from poor multiplexing capacity, poor standardization, and antibody crossreactivity, and which provides only semi-quantitative data. Here, we present an automated, and standardized immuno-matrix-assisted laser desorption/ionization mass spectrometry (iMALDI) assay that allows precise and multiplexed quantitation of PTEN and p110α concentrations, without the limitations of immunohistochemistry. Our iMALDI assay only requires a low-cost benchtop MALDI-TOF mass spectrometer, which simplifies clinical translation. We validated our assay's precision and accuracy, with simultaneous enrichment of both target proteins not significantly affecting the precision and accuracy of the quantitation when compared to the PTEN- and p110α-singleplex iMALDI assays (<15% difference). The multiplexed assay's linear range is from 0.6-20 fmol with accuracies of 90-112% for both target proteins, and the assay is free of matrix-related interferences. The inter-day reproducibility over 5-days was high, with an overall CV of 9%. PTEN and p110α protein concentrations can be quantified down to 1.4 fmol and 0.6 fmol per 10 μg of total tumor protein, respectively, in various tumor tissue samples, including fresh-frozen breast tumors and colorectal cancer liver metastases, and patient-derived xenograft (PDX) tumors.
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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".