Abstract PR03: Immuno-mass spectrometric identification of serum biomarkers of response and toxicity to pembrolizumab
Bibliographic record
Abstract
Abstract Immune checkpoint blockade (ICB) is a breakthrough form of cancer immunotherapy that employs antibody targeting of specific inhibitory receptors and ligands, such as cytotoxic T-lymphocyte associated antigen 4 (CTLA-4), programmed cell death protein 1 (PD-1), and programmed cell death ligand 1 (PD-L1). The major limitations of ICB are high cost, limited success rate (10-40%), and potential severe toxicity due to immune-related adverse effects (IRAEs), which resemble autoimmune disease. Predictive biomarkers of ICB are not currently widespread in clinical use, despite the growing need for a personalized approach to cancer treatment. Effective immunotherapy causes tumor cell death, which releases tumor-associated antigens (TAAs) into circulation. This results in abnormal presentation of these antigens to immune cells, which leads to B-cell autoantibody production against them. Autoantibodies are effective biomarkers of some autoimmune diseases and may be present before disease onset. We hypothesized that patients who develop immune-related toxicity from immunotherapy will produce specific autoantibodies that are indicative of an autoimmune-like response. Furthermore, we hypothesized that responders to pembrolizumab will develop high titers of serum autoantibodies against TAAs, indicative of a strong humoral immune response to TAAs released during immunotherapy. Likewise, nonresponders will have low levels of these autoantibodies, due to a weaker or nonexistent antitumor and humoral immune response. We used a novel immuno-mass spectrometry method to screen for autoantibodies in the sera of patients with various tumors treated with PD-1 inhibition in the clinical trial called INSPIRE (INvestigator-initiated Phase II Study of Pembrolizumab Immunological Response Evaluation; NCT02644369) at Princess Margaret Cancer Centre. Our methodology involves immunoprecipitation of proteome-wide target antigens of autoantibodies in patient sera with the use of protein G magnetic beads, followed by shotgun mass spectrometry analysis. We analyzed autoantibody responses in the sera before and after immunotherapy initiation in a total of 24 patients, subdivided into 4 patient groups based on their objective response and toxicity status. Candidate autoantibody target antigens, including thyroglobulin, thyroid peroxidase, and ficolin-2, were identified by our pilot study. Validation with additional datasets is planned. Furthermore, we identified PD-1 as an antibody target exclusively in the post-immunotherapy patient sera of all 4 patient groups. This finding confirms the efficacy of our method since pembrolizumab is a humanized antibody targeting PD-1. Predictive biomarkers of cancer immunotherapy will save significant resources, ensure proper patient selection for cancer treatment, and spare certain patients from the toxic effects of immunotherapy. This abstract is also being presented as Poster B05. Citation Format: Milena Music, Marco Iafolla, Antoninus Soosaipillai, Ihor Batruch, Ioannis Prassas, Lillian L. Siu, Eleftherios P. Diamandis. Immuno-mass spectrometric identification of serum biomarkers of response and toxicity to pembrolizumab [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2018 Nov 27-30; Miami Beach, FL. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(4 Suppl):Abstract nr PR03.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".