Immuno-MALDI (iMALDI) mass spectrometry for the analysis of proteins in signaling pathways
Bibliographic record
Abstract
INTRODUCTION: Biomarkers are commonly used to stratify cancer patients and guide targeted therapies, but most biomarkers are of a genomic nature. Discrepancies between the genome and proteome and the high rates of drug resistance indicate that proteomic analyses may provide additional critically important information. Here we present immuno-Matrix-Assisted Laser Desorption/Ionization (iMALDI), the combination of immuno-affinity enrichment of peptides followed by direct MALDI-mass spectrometry analysis. iMALDI is a highly sensitive, targeted protein-quantitation technique with the potential to measure clinically relevant signaling-pathway proteins using minimal sample amounts, thus improving upon existing methodologies. Areas covered: We provide a brief overview of the current state of biomarker analysis technologies for modern cancer treatment. We also show the advantages of iMALDI for translating potential new biomarkers into the clinic, factors to consider for iMALDI assay development, and the utility of iMALDI for the quantitation of cell-signaling proteins. Expert commentary: We see targeted mass spectrometry approaches such as iMALDI as an important part of improving patient responses to targeted therapies by providing highly sensitive, accurate, precise, and specific measurements of signaling-pathway proteins, both in tumor cells and in cells from the tumor microenvironment. iMALDI results can be integrated with other -omics data to aid in tumor-targeting therapies and immuno-oncology.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".