Immuno-multiple reaction monitoring (iMRM) for quantitation of PD-L1 and PD-1-signaling proteins in non-small cell lung carcinoma (NSCLC).
Bibliographic record
Abstract
2627 Background: More accurate predictive biomarkers of response to checkpoint inhibitors (CPIs) still is a major unmet need in oncology. PD-L1 immunohistochemistry (IHC) limitations include its analytical variability and the post-translational modifications of PD-1 signaling-associated proteins like glycosylation. Moreover, PD-L1 IHC is an imperfect surrogate of the tumor immune microenvironment, and immunoscoring is important but difficult to assess in a clinical setting. Proteomic based technologies can overcome these challenges, but the low concentration of these proteins and the presence of high background noise in formalin-fixed paraffin embedded (FFPE) tumors were limiting obstacles. In this study, we evaluate the benefit of a new approach we used with anti-peptide antibodies to purify surrogate peptides, while liquid chromatography (LC) was coupled to multiple reaction monitoring mass spectrometry (iMRM) to improve specificity and precision of protein quantitation. Methods: To determine the concentration of PD-L1, PD-1, PD-L2, NT5E, LCK and ZAP70, we used unique and well detectable proteolytic peptides as surrogates. In a refined protocol, we optimized protein extraction and digestion, peptide immuno-enrichment, LC and MRM parameters to maximize recovery, increase target-specific signal and reduce noise. Plus, we assessed the glycosylation status of PD-L1, PD-L2, and PD-1. The entire workflow was fully validated using 31 NSCLC FFPE tumors. PD-L1 quantitation by iMRM was compared to PD-L1 IHC clone 22C3. Results: On average, 71±29 µg (n = 52) of protein could be extracted from each 1–3 mm 3 NSCLC tumor FFPE core. The optimized iMRM method allowed the quantitation of PD-L1 and PD-1 down to 21 amol on-column. Inter- and intra-day repeatability were well below FDA guidelines (coefficients of variation [CV] < 20%) with average CVs of 5.2±4.0% (intra-day) and 4.5±2.6% (inter-day). Sample storage had no significant effect on peptide quantitation. The final multiplexed iMRM assay enables quantitation all targets and glycosylation states for > 40 samples in only 3 days (including external calibration and quality controls) and was used to quantify the PD-1/PD-L1 axis proteins successfully in all 31 NSCLC FFPE tumors. PD-L1 expression ranged from 2 amol/μg to 61 amol/μg of total protein. As expected, iMRM results correlated moderately (R = 0.56, ρ < 0.001) with PD-L1 IHC. PD-L1 glycosylation status ranged from 99.9±0.2%, and therefore did not explain the discrepancies between IHC and iMRM for these samples. Conclusions: Herein a robust iMRM workflow was developed for the quantitation of the PD-1/PD-L1 axis in FFPE. This proof-of-concept supports that MS-based assay can provide otherwise unavailable data (e.g., PD-L1 concentration, glycosylation status). CPI treated patient tumors are being currently processed to validate the predictive value of the assay.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".