Abstract CT006: Clinical study of a personalized neoantigen cancer vaccine in treating patients with advanced malignant tumor
Notice bibliographique
Résumé
Abstract Despite recent advances in cancer treatment by immune checkpoint blockade therapies, the overall response rate is still low in patients with advanced malignancies. Therapeutic cancer vaccines designed to reactivate T cells against tumor cells has achieved great progress. Neoantigens arisen from tumor somatic mutations provide tumor specific targets for developing personalized cancer vaccines, which are able to elicit strong T cell mediated immune responses due to the immunogenicity and the overcoming immune tolerance. Therefore, we developed a peptide-based personalized cancer vaccines (iNeo-Vac-P01) to mobilize immunity against a spectrum of tumor neoantigens, and initiated a clinical trial (NCT03662815). This study was a single-arm, open-label, Phase I trial, carried out in Sir Run Run Shaw Hospital, Zhejiang University School of Medicine in China with approval by the institutional review board and independent ethics committee. The main objectives were to assess safety, progression-free survival (PFS) and overall survival (OS). Patients with advanced malignancies were enrolled. For each patient, Exome-seq and RNA-seq was conducted. The analyses of somatic mutations, tumor neoantigens, as well as neoantigen prioritization and long peptide design, were performed by in-house pipeline (iNeo). The clinical-grade long peptides incorporating multiple neo-epitopes of both HLA class I and II were synthesized. The iNeo-Vac-P01 consisting of 10~20 long peptides per patient (grouped into 3~4 pools with 0.1mg of each peptide) was administered subcutaneously on day 1, 4, 8, 15, and 22 (priming phase) and day 78 and 162 (booster phase) with GM-CSF as adjuvant. The Cox regression model was used to assess the risk factors for OS and PFS. Total 30 patients were enrolled with different cancer types, including non-small cell lung cancer, pancreatic cancer, colorectal cancer, melanoma, ovarian cancer and others. The iNeo-Vac-P01 vaccines were successfully manufactured and administered for all patients. None of them had a grade 3 or 4 adverse event. 18 of them completed priming phase were included in the efficacy evaluation. The disease control rate (percentage of patients whose best response was not PD) was 75%. The median PFS was 5.37 months, and the median OS was not available yet. Overall, T cell responses were detected against 80% of peptide pools by ELISpot assays using post-vaccination blood samples. No response was detected in pre-vaccination blood samples, except for 2 patients who underwent pretreatment of radiofrequency ablation (RFA), which might cause tumor antigens release to trigger immune response. The preliminary results of this study demonstrated that iNeo-Vac-P01 was feasible, safe, able to elicit T cell mediated immune response specifically targeting tumor cells, and beneficial to patients with advanced diseases. The pre-existing responses against vaccine peptides in two patients with RFA pretreatment suggested that neoantigens incorporated in iNeo-Vac-P01 were accurately predicted. Citation Format: Yong Fang, Jiawei Shou, Fan Mo, Huimin Wang, Shanshan Zhang, Hongsen Li, Xiaoyun Zhou, Hongming Pan, Shuqing Chen. Clinical study of a personalized neoantigen cancer vaccine in treating patients with advanced malignant tumor [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr CT006.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».