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Immuno-multiple reaction monitoring (iMRM) for quantitation of PD-L1 and PD-1-signaling proteins in non-small cell lung carcinoma (NSCLC).

2022· article· en· W4281761203 on OpenAlexafffund
Vincent Lacasse, Vincent R. Richard, Hangjun Wang, Georgia Mitsa, Olivier Poetz, Andreas I. Papadakis, Mounib Elchebly, Victor Cohen, Jason Agulnik, Gerald Batist, René P. Zahedi, Christoph H. Borchers, Alan Spatz

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicPeptidase Inhibition and Analysis
Canadian institutionsJewish General HospitalMcGill University
FundersGenome Canada
KeywordsImmunohistochemistryPD-L1MedicineCancer researchLung cancerAntibodySelected reaction monitoringPeptideBiomarkerImmunotherapyComputational biologyOncologyCancerPathologyInternal medicineChemistryBiologyMass spectrometryImmunologyChromatographyTandem mass spectrometryBiochemistry

Abstract

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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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.106
GPT teacher head0.422
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2022
Admission routes2
Has abstractyes

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