Immuno-Matrix-Assisted Laser Desorption/Ionization Assays for Quantifying AKT1 and AKT2 in Breast and Colorectal Cancer Cell Lines and Tumors
Bibliographic record
Abstract
The phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT)/mechanistic target of rapamycin (mTOR) pathway is one of the most commonly dysregulated signaling pathways that is linked to cancer development and progression, and its quantitative protein analysis holds the promise to facilitate patient stratification for targeted therapies. Whereas immunohistochemistry (IHC) and immunoassays are routinely used for clinical analysis of signaling pathways, mass spectrometry-based approaches such as liquid chromatography/electrospray ionization multiple reaction monitoring mass spectrometry (LC/ESI-MRM-MS) are more commonly used in clinical research. Both technologies have certain disadvantages, namely, the nonspecificity of IHC and immunoassays, and potentially long analysis times per sample of LC/ESI-MRM-MS. To create a robust, fast, and sensitive protein quantification tool, we developed immuno-matrix-assisted laser desorption/ionization (iMALDI) assays with automated liquid handling. The assays are able to quantify AKT1 and AKT2 from breast cancer and colon cancer cell lines and flash-frozen tumor lysates with a linear range of 0.05-2.0 fmol/μg of total lysate protein and with coefficients of variation < 15%. Compared to other mass spectrometric methods, the developed assays require less sample per analysis-only 25 μg of total protein-and are therefore suitable for analysis of needle biopsies. Furthermore, the presented iMALDI technique is the first MS-based method for absolute quantitation of AKT peptides from cancer tissues. This study demonstrates the suitability of iMALDI for low limit-of-detection and reproducible quantitation of signaling pathway members using a benchtop MALDI mass spectrometer within approximately 6-7 h.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".