Abstract PR03: Immuno-mass spectrometric identification of serum biomarkers of response and toxicity to pembrolizumab
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».