Liquid chromatography coupled to multiple reaction monitoring (LC-MRM) for quantification of PD-L1 and PD1-signaling proteins in non-small cell lung carcinoma (NSCLC).
Bibliographic record
Abstract
e21040 Background: Improving the predictive biomarkers arena of checkpoint inhibitors (CPIs) beyond PD-L1 immunohistochemistry (IHC) is one of the most important unmet need in NSCLC. Up to 50% of patients that show positive PD-L1 expression by IHC do not respond to anti-PD-L1 treatments, and patients with low/undetectable PD-L1 have significantly improved survival with CPI. Moreover, PD-L1 IHC is heterogeneous, can be affected by tissue fixation time and post-translational modifications like glycosylation. Also, multiple studies indicated that PD-L1 expression alone does not reliably reflect the immune status of the tumor, thus requiring the measurement of other members of the PD1 signalling pathway. Methods: To address these issues, we developed a multiplexed targeted mass spectrometry-based (MS) assay for the quantitation of protein members of the PD-1/PD-L1 axis in formalin fixed paraffin-embedded (FFPE) tissue.The effect of fixation time on protein recovery was determined using differentially fixed H1915 cells. Liquid chromatography (LC) coupled to MRM was used to develop a targeted assay for PD-L1, PD-1, PD-L2, NT5E, LCK and ZAP70. Results: After 30 minutes fixation 33.6 µg of protein were extracted per mg of FFPE H1915 cells, while protein recovery after 7 days was 55.8 µg/mg, with greater variability (18% and 28% CV, p-value = ns). The optimized LC-MRM method allows the quantitation of PD-L1 and PD-1 down to 23 amol on-column. We evaluated the utility of our MRM assays using the H1915 FFPE cells (PD-L1 3+ by IHC) and determined an endogenous concentration of PD-L1 and NT5E as being 33.2±0.1 amol/µg of total protein and 4.8±0.5 fmol/µg respectively. Noteworthy, a known glycosylation site of PD-L1 was quantified at 10.9±0.3 amol/µg of total protein (30% of total). As increases sensitivity was required, anti-peptide antibodies were generated against the 15 best peptides. We then evaluated this LC-MRM method using MDA-MB-436 cells with lower levels of PD-L1 protein assessed by IHC. Determination of endogenous PD-L1 concentration (3.0 amol/µg of total protein) was achieved using anti-peptide immuno-enrichment followed by MRM. Conclusions: We developed a fixation time independent extraction technique for FFPE and optimized a highly sensitive LC-MRM method that allows the absolute quantification of our targets. This proteomic workflow allows absolute quantification of the PD-1/PD-L1 axis from FFPE tissue using immuno-enrichment and a multiplexed LC-MRM method.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".