PREDICTING SIGNIFICANCE OF UNKNOWN VARIANTS IN GLIAL TUMORS THROUGH SUB-CLASS ENRICHMENT

Pac Symp Biocomput. 2016;21:297-308.

Abstract

Glial tumors have been heavily studied and sequenced, leading to scores of findings about altered genes. This explosion in knowledge has not been matched with clinical success, but efforts to understand the synergies between drivers of glial tumors may alleviate the situation. We present a novel molecular classification system that captures the combinatorial nature of relationships between alterations in these diseases. We use this classification to mine for enrichment of variants of unknown significance, and demonstrate a method for segregating unknown variants with functional importance from passengers and SNPs.

MeSH terms

  • Astrocytoma / classification
  • Astrocytoma / genetics
  • Biomarkers, Tumor / genetics
  • Brain Neoplasms / classification*
  • Brain Neoplasms / genetics*
  • Computational Biology / methods*
  • DNA, Neoplasm / genetics
  • Databases, Genetic / statistics & numerical data
  • Genetic Variation
  • Glioblastoma / classification
  • Glioblastoma / genetics
  • Glioma / classification*
  • Glioma / genetics*
  • Humans
  • Models, Genetic
  • Models, Statistical
  • Mutation
  • Oligodendroglioma / classification
  • Oligodendroglioma / genetics
  • Polymorphism, Single Nucleotide

Substances

  • Biomarkers, Tumor
  • DNA, Neoplasm