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2.
Front Immunol ; 14: 1234912, 2023.
Article in English | MEDLINE | ID: mdl-37720215

ABSTRACT

Introduction: Tumor-specific mutations generate neoepitopes unique to the cancer that can be recognized by the immune system, making them appealing targets for therapeutic cancer vaccines. Since the vast majority of tumor mutations are patient-specific, it is crucial for cancer vaccine designs to be compatible with individualized treatment strategies. Plasmid DNA vaccines have substantiated the immunogenicity and tumor eradication capacity of cancer neoepitopes in preclinical models. Moreover, early clinical trials evaluating personalized neoepitope vaccines have indicated favorable safety profiles and demonstrated their ability to elicit specific immune responses toward the vaccine neoepitopes. Methods: By fusing in silico predicted neoepitopes to molecules with affinity for receptors on the surface of APCs, such as chemokine (C-C motif) ligand 19 (CCL19), we designed an APC-targeting cancer vaccine and evaluated their ability to induce T-cell responses and anti-tumor efficacy in the BALB/c syngeneic preclinical tumor model. Results: In this study, we demonstrate how the addition of an antigen-presenting cell (APC) binding molecule to DNA-encoded cancer neoepitopes improves neoepitope-specific T-cell responses and the anti-tumor efficacy of plasmid DNA vaccines. Dose-response evaluation and longitudinal analysis of neoepitope-specific T-cell responses indicate that combining APC-binding molecules with the delivery of personalized tumor antigens holds the potential to improve the clinical efficacy of therapeutic DNA cancer vaccines. Discussion: Our findings indicate the potential of the APC-targeting strategy to enhance personalized DNA cancer vaccines while acknowledging the need for further research to investigate its molecular mechanism of action and to translate the preclinical results into effective treatments for cancer patients.


Subject(s)
Cancer Vaccines , Neoplasms , Vaccines, DNA , Humans , Neoplasms/genetics , Neoplasms/therapy , Antigen-Presenting Cells , Mutation
3.
Oncoimmunology ; 11(1): 2023255, 2022.
Article in English | MEDLINE | ID: mdl-35036074

ABSTRACT

The majority of neoantigens arise from unique mutations that are not shared between individual patients, making neoantigen-directed immunotherapy a fully personalized treatment approach. Novel technical advances in next-generation sequencing of tumor samples and artificial intelligence (AI) allow fast and systematic prediction of tumor neoantigens. This study investigates feasibility, safety, immunity, and anti-tumor potential of the personalized peptide-based neoantigen vaccine, EVX-01, including the novel CD8+ T-cell inducing adjuvant, CAF®09b, in patients with metastatic melanoma (NTC03715985). The AI platform PIONEERTM was used for identification of tumor-derived neoantigens to be included in a peptide-based personalized therapeutic cancer vaccine. EVX-01 immunotherapy consisted of 6 administrations with 5-10 PIONEERTM-predicted neoantigens as synthetic peptides combined with the novel liposome-based Cationic Adjuvant Formulation 09b (CAF®09b) to strengthen T-cell responses. EVX-01 was combined with immune checkpoint inhibitors to augment the activity of EVX-01-induced immune responses. The primary endpoint was safety, exploratory endpoints included feasibility, immunologic and objective responses. This interim analysis reports the results from the first dose-level cohort of five patients. We documented a short vaccine manufacturing time of 48-55 days which enabled the initiation of EVX-01 treatment within 60 days from baseline biopsy. No severe adverse events were observed. EVX-01 elicited long-lasting EVX-01-specific T-cell responses in all patients. Competitive manufacturing time was demonstrated. EVX-01 was shown to be safe and able to elicit immune responses targeting tumor neoantigens with encouraging early indications of a clinical and meaningful antitumor efficacy, warranting further study.


Subject(s)
Cancer Vaccines , Melanoma , Antigens, Neoplasm/genetics , Artificial Intelligence , Humans , Melanoma/drug therapy , Peptides
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