MétaCan
Menu
Back to cohort

Quantitation of the PD-1/PD-L1 axis in non-small cell lung cancer by immuno-multiple reaction monitoring.

2023· article· en· W4379283594 on OpenAlexaff
Vincent Lacasse, René P. Zahedi, Vincent R. Richard, Hangjun Wang, Georgia Mitsa, Olivier Poetz, Margaret Redpath, Andreas I. Papadakis, Mounib Elchebly, Victor Cohen, Jason Agulnik, Gerald Batist, Christoph H. Borchers, Alan Spatz

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsJewish General HospitalResearch ManitobaMcGill University
Fundersnot available
KeywordsPD-L1MedicineGlycosylationLung cancerImmunohistochemistryTumor microenvironmentAntibodyCancer researchCancerInternal medicineOncologyImmunotherapyImmunologyChemistryBiochemistry

Abstract

fetched live from OpenAlex

e14656 Background: Immuno-oncology revolutionized lung cancer treatment, but predictive biomarkers of response to checkpoint inhibitors (CPI) are still lacking. Only 50% of PD-L1 positive tumors by immunohistochemistry (IHC) respond to anti-PD-L1 treatment. Analytical variability and post-translational modifications (PTM) of the PD-1 signaling associated proteins (e.g.: glycosylation), can explain some of this discrepancy. Plus, the tumor immune microenvironment (TME) is complex and PD-L1 IHC alone is a flawed surrogate its status. Mass spectrometry based technologies have the potential to overcome these challenges by integrating sensitivity, specificity and absolute quantitation of proteins and PTMs in a standardized fashion. Here, we demonstrate the advantages of using anti-peptide antibodies to purify surrogate peptides followed by liquid-chromatography (LC) and multiple reaction monitoring (MRM), herby termed as iMRM, to gain new insight into the TME. 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. Our previously described protocol (ASCO 2022, #377181) allows robust quantitation of 13 peptides and monitors the glycosylation status of PD-L1, PD-L2, and PD-1. NSCLC samples were either fresh frozen (n = 42) or FFPE (n = 77) and sometimes both (n = 17). PD-L1 quantitation by iMRM was compared to PD-L1 IHC 22C3. Results: This multiplexed iMRM assay successfully quantified the PD-1/PD-L1 axis proteins in 96% of NSCLC patients (61/63). PD-L1 glycosylation levels ranged from 82 to 100% (n = 69, median = 100%, SD = 3.7%). Only PD-1 was significantly different between the fresh frozen (mean = 11±6 amol/µg total protein) and FFPE group (mean = 5±2 amol/µg total protein, ρ = 0.0001), all other peptides showed comparable levels. In our 17 matched FF/FFPE samples, PD-L1 moderately correlated (R = 0.45, ρ = 0.045) and the other peptides did not correlate (R < 0.03). Intra/intertumoral heterogeneity, differences in cellularity and tumor origin likely contributed to this discrepancy. IMRM results correlated moderately (R = 0.56, ρ < 0.01) with PD-L1 IHC. Most of these patients were not CPI-treated, but survival data was available. A trend was noted between the concentration of certain targets and patient survival. Linear regression was used to establish a cut-off for each peptide from which an immunoscore was calculated. The immunoscore could predict long-term survival (accuracy 75.4%) irrespective of tumor staging, grade or subtype and survival was significantly associated with the immunoscore (log-rank ρ = 0.005). Conclusions: Our iMRM workflow provides a new understanding of the TME in NSCLC through the PD-1/PD-L1 axis with an easy-to-read immunoscore. A set of 60 tumors from CPI-treated patients is currently being processed to validate the clinical utility of the assay in relation with CPI-response.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.074
GPT teacher head0.468
Teacher spread0.394 · 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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical OncologySame topicCancer Research and TreatmentsFrench-language works237,207