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pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens | Genome Medicine
Description:
Cancer immunotherapy has gained significant momentum from recent clinical successes of checkpoint blockade inhibition. Massively parallel sequence analysis suggests a connection between mutational load and response to this class of therapy. Methods to identify which tumor-specific mutant peptides (neoantigens) can elicit anti-tumor T cell immunity are needed to improve predictions of checkpoint therapy response and to identify targets for vaccines and adoptive T cell therapies. Here, we present a flexible, streamlined computational workflow for identification of personalized Variant Antigens by Cancer Sequencing (pVAC-Seq) that integrates tumor mutation and expression data (DNA- and RNA-Seq). pVAC-Seq is available at https://github.com/griffithlab/pVAC-Seq .
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Keywords {๐}
pubmed, article, google, scholar, data, binding, prediction, variant, central, tumor, sequencing, cas, hla, mutations, pvacseq, pipeline, neoantigens, cancer, epitope, sequence, vaccine, peptides, cells, genome, class, somatic, normal, candidates, exome, information, expression, amino, neoantigen, variants, reads, vaf, mutant, affinity, input, analysis, methods, gene, rna, patients, file, filter, epitopes, alleles, based, silico,
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max-bundle-lengthโ=โ10000000 --num-threads 4 4 --bamreadcount-min-base-quality 15 ๏ฟฝ--library-type fr-firststrand --bowtie-versionโ=โ2 related subjects full size image variant effect predictor snp effect predictor false-positive filter v1 efficacious patient-oriented immunotherapy adoptively transferred tumor-reactive article download pdf checkpoint blockade therapies checkpoint blockade inhibition checkpoint blockade drugs checkpoint therapy response personal tumor-specific neoantigens pan-specific mhc class rna-seq fragment population gms โsnp-filter v1 12p70-producing dendritic cells pan-specific prediction methods cell-mediated tumor rejection checkpoint blockade response tumor-specific mutant peptides align rna-seq data targeted assembly adapter processing open source code evaluate tumor-specific neoepitopes full access t-cell-dependent mechanism rna prep kit sequence alignment/map format leverages rna-seq data patient-specific vaccines patient-specific hla alleles hamming distance-based score integrating sequencing-based information personalized cancer vaccines somatic variant-calling pipeline silico pvac-seq pipeline cancer vaccines rests national cancer institute high-confidence list melanoma neoantigen-specific pvac-seq pipeline requires mca-induced sarcomas [3 epitope prediction software privacy choices/manage cookies identifying tumor neoantigens
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headline:pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens
description:Cancer immunotherapy has gained significant momentum from recent clinical successes of checkpoint blockade inhibition. Massively parallel sequence analysis suggests a connection between mutational load and response to this class of therapy. Methods to identify which tumor-specific mutant peptides (neoantigens) can elicit anti-tumor T cell immunity are needed to improve predictions of checkpoint therapy response and to identify targets for vaccines and adoptive T cell therapies. Here, we present a flexible, streamlined computational workflow for identification of personalized Variant Antigens by Cancer Sequencing (pVAC-Seq) that integrates tumor mutation and expression data (DNA- and RNA-Seq). pVAC-Seq is available at
https://github.com/griffithlab/pVAC-Seq
.
datePublished:2016-01-29T00:00:00Z
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Exome Sequence Data
Variant Effect Predictor
Variant Allele Fraction
Checkpoint Blockade Therapy
Human Genetics
Metabolomics
Bioinformatics
Medicine/Public Health
general
Cancer Research
Systems Biology
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headline:pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens
description:Cancer immunotherapy has gained significant momentum from recent clinical successes of checkpoint blockade inhibition. Massively parallel sequence analysis suggests a connection between mutational load and response to this class of therapy. Methods to identify which tumor-specific mutant peptides (neoantigens) can elicit anti-tumor T cell immunity are needed to improve predictions of checkpoint therapy response and to identify targets for vaccines and adoptive T cell therapies. Here, we present a flexible, streamlined computational workflow for identification of personalized Variant Antigens by Cancer Sequencing (pVAC-Seq) that integrates tumor mutation and expression data (DNA- and RNA-Seq). pVAC-Seq is available at
https://github.com/griffithlab/pVAC-Seq
.
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Exome Sequence Data
Variant Effect Predictor
Variant Allele Fraction
Checkpoint Blockade Therapy
Human Genetics
Metabolomics
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general
Cancer Research
Systems Biology
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