Abstract C003: A quantitative mass spectrometry workflow for highly multiplexed measurement of immunomodulatory proteins to support immunotherapy clinical trials
Bibliographic record
Abstract
Abstract Background: Since the approval of the first immune checkpoint-directed immunotherapies, it has been well-known that the efficacy of these therapies has been limited to a subset of cancer patients. As the number of new potential immunotherapy targets has grown, including novel combination therapy strategies, improved immune-related biomarker measurement has played a key role in understanding novel mechanisms of action and response to drugs. To support these efforts, we have developed an analytically validated assay for the multiplexed quantitation of up to 113 immunomodulatory proteins in various matrices (e.g., human plasma, PBMC, cells, or tissue) based on anti-peptide immunoaffinity capture followed by LC-MRM mass spectrometry analysis. Methods: Remnant samples from commercial biobanks were acquired in a variety of solid tumor indications. Briefly, matrices were denatured, reduced, alkylated, and digested using trypsin. Immunoaffinity capture of the peptide targets consisted of overnight incubation of the digested sample with anti-peptide antibodies coupled to protein G magnetic beads, followed by automated washing and elution of the peptides from the antibodies using the KingFisher platform. The peptides were analyzed in a multiplexed method by LC-MRM mass spectrometry. Endogenous levels for each target were measured relative to a stable isotopically labeled peptide standard. Results: In panel experiments, we found that the targets can be quantified precisely and accurately over 3 orders of magnitude with an intra-/inter-assay precision and accuracy of less than 20%. In total, 107 peptide targets were detected endogenously in cell lines. We expand on these data with analysis of solid tumor FFPE specimens where the results will be compared to immunohistochemical staining for selected targets. Conclusions: This study shows how multiplex immunoaffinity methods can complement classical methods of tissue-based protein expression for simultaneous and quantitative interpretation of protein levels. Citation Format: Ons Ousji, Anne Jang, Luca Genovesi, Nicholas Dupuis, Timon Geib, Gwenael Pottiez, Michael Schirm. A quantitative mass spectrometry workflow for highly multiplexed measurement of immunomodulatory proteins to support immunotherapy clinical trials [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C003.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".