This paper describes the sequencing protocol and computational pipeline for the PGV-001 personalized vaccine trial. PGV-001 is a therapeutic peptide vaccine targeting neoantigens identified from patient tumor samples. Peptides are selected by a computational pipeline that identifies mutations from tumor/normal exome sequencing and ranks mutant sequences by a combination of predicted Class I MHC affinity and abundance estimated from tumor RNA. The personalized genomic vaccine (PGV) pipeline is modular and consists of independently usable tools and software libraries. We hope that the functionality of these tools may extend beyond the specifics of the PGV-001 trial and enable other research groups in their own neoantigen investigations.
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http://dx.doi.org/10.3389/fimmu.2017.01807 | DOI Listing |
Biochim Biophys Acta Mol Basis Dis
January 2025
MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-infective Medicine, Department of Bioinformatics and Computational Biology, School of Life Sciences, Suzhou Medical College of Soochow University, Suzhou 215123, China; Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University, Suzhou 215123, China; Key Laboratory of Alkene-carbon Fibres-based Technology & Application for Detection of Major Infectious Diseases, Soochow University, Suzhou 215123, China; Jiangsu Key Laboratory of Infection and Immunity, Soochow University, Suzhou 215123, China. Electronic address:
Cancer, a heterogeneous disease, presents significant challenges for drug development due to its complex etiology. Drug repurposing, particularly through network medicine approaches, offers a promising avenue for cancer treatment by analyzing how drugs influence cellular networks on a systemic scale. The advent of large-scale proteomics data provides new opportunities to elucidate regulatory mechanisms specific to cancer subtypes.
View Article and Find Full Text PDFCell
January 2025
Program in Bioinformatics, Boston University, Boston, MA 02215, USA; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; Center for Network Systems Biology, Boston University, Boston, MA 02218, USA; Department of Chemistry, Boston University, Boston, MA 02215, USA; Department of Chemical Physiology and Biochemistry, Division of Oncological Sciences, Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA. Electronic address:
Knowledge of protein-metabolite interactions can enhance mechanistic understanding and chemical probing of biochemical processes, but the discovery of endogenous ligands remains challenging. Here, we combined rapid affinity purification with precision mass spectrometry and high-resolution molecular docking to precisely map the physical associations of 296 chemically diverse small-molecule metabolite ligands with 69 distinct essential enzymes and 45 transcription factors in the gram-negative bacterium Escherichia coli. We then conducted systematic metabolic pathway integration, pan-microbial evolutionary projections, and independent in-depth biophysical characterization experiments to define the functional significance of ligand interfaces.
View Article and Find Full Text PDFExpert Opin Drug Discov
January 2025
Department of Radiology, Molecular Imaging Innovations Institute (MI3), Weill Cornell Medicine, New York, NY, USA.
Introduction: Technological advancements in virtual screening (VS) have rapidly accelerated its application in drug discovery, as reflected by the exponential growth in VS-related publications. However, a significant gap remains between the volume of computational predictions and their experimental validation. This discrepancy has led to a rise in the number of unverified 'claimed' hits which impedes the drug discovery efforts.
View Article and Find Full Text PDFPharmaceuticals (Basel)
December 2024
Faculty of Pharmacy, "Carol Davila" University of Medicine and Pharmacy, Traian Vuia 6, 020956 Bucharest, Romania.
Aurora kinase B (AurB) is a pivotal regulator of mitosis, making it a compelling target for cancer therapy. Despite significant advances in protein kinase inhibitor development, there are currently no AurB inhibitors readily available for therapeutic use. This study introduces a machine learning-assisted drug repurposing framework integrating quantitative structure-activity relationship (QSAR) modeling, molecular fingerprints-based classification, molecular docking, and molecular dynamics (MD) simulations.
View Article and Find Full Text PDFClin Transl Med
February 2025
Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York, USA.
Background: The goal of precision oncology is to find effective therapeutics for every patient. Through the inclusion of emerging therapeutics in a high-throughput drug screening platform, our functional genomics pipeline inverts the common paradigm to identify patient populations that are likely to benefit from novel therapeutic strategies.
Approach: Utilizing drug screening data across a panel of 46 cancer cell lines from 11 tumor lineages, we identified an ovarian cancer-specific sensitivity to the first-in-class CRL4 inhibitors KH-4-43 and 33-11.
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