Month: April 2017

Clonality of Metastasis Breast Cancer

[PMID:28424200] [Clinical Cancer Research]

Whole exome sequencing of metaplastic breast carcinoma indicates monoclonality with associated ductal carcinoma component

In eight patients. “In each case, the tumor components have nearly identical landscapes of somatic mutation, implying that the differing histologies do not derive from genetic clonal divergence.”

[PMID:28429735] [Nature Communications]

Phylogenetic analysis of metastatic progression in breast cancer using somatic mutations and copy number aberrations

“using whole-exome sequencing and copy number profiling of primary and multiple-matched metastatic tumours from ten autopsied patients” “two modes of disease progression. In some patients, all distant metastases cluster on a branch separate from their primary lesion. Clonal frequency analyses of somatic mutations show that the metastases have a monoclonal origin and descend from a common ‘metastatic precursor’. Alternatively, multiple metastatic lesions are seeded from different clones present within the primary tumour.”

Energy Landscapes

[PMID:28346445] [Nature Genetics]

Potential energy landscapes identify the information-theoretic nature of the epigenome

“Using principles from statistical physics and information theory, we derive epigenetic energy landscapes from whole-genome bisulfite sequencing (WGBS) data that enable us to quantify methylation stochasticity genome-wide using Shannon’s entropy, associating it with chromatin structure. Moreover, we consider the Jensen–Shannon distance between sample-specific energy landscapes as a measure of epigenetic dissimilarity and demonstrate its effectiveness for discerning epigenetic differences. By viewing methylation maintenance as a communications system, we introduce methylation channels and show that higher-order chromatin organization can be predicted from their informational properties.”

PVP

[] [PLoS Computational Biology]

Semantic prioritization of novel causative genomic variants

“Computational approaches for variant prioritization include machine learning methods utilizing a large number of features, including molecular information, interaction networks, or phenotypes. Here, we demonstrate the PhenomeNET Variant Predictor (PVP) system that exploits semantic technologies and automated reasoning over genotype-phenotype relations to filter and prioritize variants in whole exome and whole genome sequencing datasets.”