Month: July 2017

RegNetDriver

[PMID: 28750683] [Genome Biology]

Identification of novel prostate cancer drivers using RegNetDriver: a framework for integration of genetic and epigenetic alterations with tissue-specific regulatory network

“to identify tumorigenic drivers using the combined effects of coding and non-coding single nucleotide variants, structural variants, and DNA methylation changes in the DNase I hypersensitivity based regulatory network. “

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Genomic Metrics and Immune Infiltration in TNBC

[PMID: 28750120] [JAMA Oncology]

Association Between Genomic Metrics and Immune Infiltration in Triple-Negative Breast Cancer

“calculate previously described immune metagene expression values and histologic lymphocyte counts to quantify immune infiltration and assign prognostic categories to TNBCs” “compared clonal heterogeneity, somatic total mutational load, neoantigen load, and somatic copy number alteration levels between immune-rich TNBC cohorts with good prognosis and immune-poor TNBC cohorts with poor prognosis.”

HIT’nDRIVE

[PMID: 28720581] [Genome Research]

HIT’nDRIVE: Patient-specific multi-driver gene prioritization for precision oncology

“a computational method that integrates genomic and transcriptomic data to identify a set of patient-specific, sequence-altered genes, with sufficient collective influence over dysregulated transcripts. HIT’nDRIVE aims to solve the “random walk facility location” (RWFL) problem in a gene (or protein) interaction network, which differs from the standard facility location problem by its use of an alternative distance measure: “multi-hitting time”, the expected length of the shortest random walk from any one of the set of sequence-altered genes to an expression-altered target gene.”

TCGA Neoantigens

[PMID: 28694034] [Lancet Oncology]

Insertion-and-deletion-derived tumour-specific neoantigens and the immunogenic phenotype: a pan-cancer analysis

“analysed whole-exome sequencing data from 5777 solid tumours, spanning 19 cancer types from The Cancer Genome Atlas.” “Analysis of tumour-specific neoantigens showed that enrichment of indel mutations for high-affinity binders was three times that of non-synonymous SNV mutations. Furthermore, neoantigens derived from indel mutations were nine times enriched for mutant specific binding, as compared with non-synonymous SNV derived neoantigens.”