Quantification of the PD-1/PD-L1 Axis in Various Cancer Types by Immuno-Multiple Reaction Monitoring
Bibliographic record
Abstract
III tests), and iii.correlations between peptide levels and survival time after immunotherapy with Kaplan-Meier methods and Cox regression. ResultsWe examined samples from 175 patients with different cancer types.The proportions of samples with detectable amounts of each peptide varied substantially: 92% had detectable NIIQ (PD-L1), 80% had detectable LQDA (PD-L1), 60% had detectable LFDV (PD-L1), 54% had detectable ATLL (PD-L2), 76% had detectable GPLA (NT5E), 89% had detectable LAAF (PD-1), 68% had detectable ITFP (LCK), and 53% had detectable LIAT (ZAP70).Correlations between each pair of the PD-L1 peptides (LQDA, NIIQ, and LFDV) were high at R=0.70 between (LQDA and NIIQ), R=0.65 between (NIIQ and LFDV), and R=0.92 (between LFDV and LQDA), with pvalues for these correlations of less than 0.001.Of the 175 samples, 83 were linked to clinical data.Of those samples, 51 patients received immunotherapy as at least one of their lines of therapy and 26 of those 51 patients benefited from immunotherapy.P-values from t-tests comparing single peptide concentrations between patients who experienced clinical benefit and those who did not were all greater than 0.05.P-values from the Cox regression comparing patients above vs below cut-offs based on individual peptide concentrations were also not statistically significant. ConclusionWe were able to determine the concentrations of multiple peptides that contributed to proteins in the PD-1/PD-L1 signaling axis by using anti-peptide antibodies.The strong correlation between the PD-L1-associated peptides and the strong correlations between those peptides and the peptide IV associated with PD-1 both suggest the assay functioned well.That being said, the lack of significant differences between peptide levels in patients who benefited from immunotherapy and those who did not indicates that single peptide concentrations may not be sufficient to predict clinical outcomes in patients receiving immunotherapy.Research into algorithms for combining information on peptide concentrations may demonstrate the potential of optimized iMRM and LC-MS.Multiplexed mass spectrometry-based assays like optimized iMRM may prove an effective alternative to PD-L1 IHC.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".