AIMC Topic: Cancer Vaccines

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pVACtools: A Computational Toolkit to Identify and Visualize Cancer Neoantigens.

Cancer immunology research Jan 6, 2020
Identification of neoantigens is a critical step in predicting response to checkpoint blockade therapy and design of personalized cancer vaccines. This is a cross-disciplinary challenge, involving genomics, proteomics, immunology, and computational a...
Cancer Vaccines Antigens, Neoplasm Mutation Data Mining Humans Neural Networks, Computer Artificial Intelligence Software Algorithms Neoplasms Computational Biology Immunotherapy
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High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets.

Cancer immunology research Dec 23, 2019
Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins can be used to predict patient response to cancer immunotherapy. Current neoantigen predictors focus on estimation of MHC binding aff...
Algorithms Neoplasms Protein Binding Computational Biology Humans Neural Networks, Computer Artificial Intelligence Software Histocompatibility Antigens Class I Data Mining Mutation, Missense Histocompatibility Antigens Class II Cancer Vaccines Antigens, Neoplasm CD8-Positive T-Lymphocytes Predictive Value of Tests
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Novel approaches for glioblastoma treatment: Focus on tumor heterogeneity, treatment resistance, and computational tools.

Cancer reports (Hoboken, N.J.) Nov 11, 2019
BACKGROUND: Glioblastoma (GBM) is a highly aggressive primary brain tumor. Currently, the suggested line of action is the surgical resection followed by radiotherapy and treatment with the adjuvant temozolomide, a DNA alkylating agent. However, the a...
DNA Repair Enzymes Drug Resistance, Neoplasm Microtubules Tumor Suppressor Protein p53 Chemoradiotherapy, Adjuvant Immune Checkpoint Inhibitors Precision Medicine Clonal Evolution Computational Biology Humans Prognosis DNA Modification Methylases Cancer Vaccines Machine Learning Tumor Microenvironment DNA Mutational Analysis Models, Genetic Brain Brain Neoplasms Tumor Suppressor Proteins Antineoplastic Combined Chemotherapy Protocols Glioblastoma Mutation Genetic Heterogeneity
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Machine-Learning Prediction of Tumor Antigen Immunogenicity in the Selection of Therapeutic Epitopes.

Cancer immunology research Sep 12, 2019
Current tumor neoantigen calling algorithms primarily rely on epitope/major histocompatibility complex (MHC) binding affinity predictions to rank and select for potential epitope targets. These algorithms do not predict for epitope immunogenicity usi...
Peptide Fragments Histocompatibility Antigens Class I Histocompatibility Antigens Class II T-Lymphocytes, Cytotoxic Cancer Vaccines Antigens, Neoplasm Mice, Inbred BALB C Mice, Inbred C57BL Algorithms Female Animals Male Neoplasms Epitopes, T-Lymphocyte Mice Computational Biology Humans Machine Learning
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and cell-based analyses reveal strong divergence between prediction and observation of T-cell-recognized tumor antigen T-cell epitopes.

The Journal of biological chemistry May 23, 2017
Tumor exomes provide comprehensive information on mutated, overexpressed genes and aberrant splicing, which can be exploited for personalized cancer immunotherapy. Of particular interest are mutated tumor antigen T-cell epitopes, because neoepitope-s...
Animals Computational Biology Cancer Vaccines Antigens, Neoplasm Vaccines, Synthetic Membrane Proteins Neoplasm Proteins Mice, Knockout Artificial Intelligence Cells, Cultured Melanoma Reproducibility of Results Models, Immunological Humans Expert Systems Protein Refolding Peptide Fragments T-Lymphocytes, Cytotoxic Oligopeptides Epitopes Immunogenicity, Vaccine HLA-A2 Antigen Mice, Transgenic Protein Stability
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Extracellular vesicles as nature's nano carriers in cancer therapy: Insights toward preclinical studies and clinical applications.

Pharmacological research Jul 1, 2025
Extracellular vesicles (EVs), which are secreted by various cell types, hold significant potential for cancer therapy. However, there are several challenges and difficulties that limit their application in clinical settings. This review, which integr...
Animals Cancer Vaccines Neoplasms Antineoplastic Agents Nanoparticles Extracellular Vesicles Drug Carriers Humans Tumor Microenvironment Drug Delivery Systems
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An Integrated Approach to Develop a Potent Vaccine Candidate Construct Against Prostate Cancer by Utilizing Machine Learning and Bioinformatics.

Cancer reports (Hoboken, N.J.) Dec 1, 2024
BACKGROUND: Prostate cancer is the most common malignancy among males. Prostaglandin G/H synthase (PGHS) is an essential enzyme in the synthesis of prostaglandins, and its activation has been linked to many malignancies, including colorectal cancer.
Humans Machine Learning Male Epitopes, T-Lymphocyte Prostatic Neoplasms Molecular Dynamics Simulation Epitopes, B-Lymphocyte Vaccines, Subunit Molecular Docking Simulation Computational Biology Cancer Vaccines Vaccine Development
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New tools for MHC research from machine learning and predictive algorithms to the tumour immunopeptidome.

Immunology Jul 1, 2018
At a time when immunology seeks to progress ever more rapidly from characterization of a microbial or tumour antigen to the immune correlates that may define protective T-cell immunity, there is a need for robust tools to enable accurate predictions ...
Immunotherapy, Adoptive Major Histocompatibility Complex Epitopes, T-Lymphocyte Peptides Cancer Vaccines Humans Machine Learning Research T-Lymphocytes Neoplasms
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