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Biology subjects

Jones, D. C.

Publications and source records attributed to Jones, D. C..

2 recordsLinked to original sources

Analysis of Copy Number Loss of the ErbB4 Receptor Tyrosine Kinase in Glioblastoma

Current treatments for glioblastoma multiforme (GBM)--an aggressive form of brain cancer--are minimally effective and yield a median survival of 14.6 months and a two-year survival rate of 30%. Given the severity of GBM and the limitations of its treatment, there is a need for the discovery of novel drug targets for GBM and more personalized treatment approaches based on the characteristics of an individuals tumor. Most receptor tyrosine kinases--such as EGFR--act as oncogenes, but publicly available data from the Cancer Cell Line Encyclopedia (CCLE) indicates copy number loss in the ERBB4 RTK gene across dozens of GBM cell lines, suggesting a potential tumor suppressor role. This loss is mutually exclusive with loss of its cognate ligand NRG1 in CCLE as well, more strongly suggesting a functional role. The availability of higher resolution copy number data from clinical GBM patients in The Cancer Genome Atlas (TCGA) revealed that a region in Intron 1 of the ERBB4 gene was deleted in 69.1% of tumor samples harboring ERBB4 copy number loss; however, it was also found to be deleted in the matched normal tissue samples from these GBM patients (n = 81). Using the DECIPHER Genome Browser, we also discovered that this mutation occurs at approximately the same frequency in the general population as it does in the disease population. We conclude from these results that this loss in Intron 1 of the ERBB4 gene is neither a de novo driver mutation nor a predisposing factor to GBM, despite the indications from CCLE. A biological role of this significantly occurring genetic alteration is still unknown. While this is a negative result, the broader conclusion is that while copy number data from large cell line-based data repositories may yield compelling hypotheses, careful follow up with higher resolution copy number assays, patient data, and general population analyses are essential to codify initial hypotheses.

systems biology

Isolator: accurate and stable analysis of isoform-level expression in RNA-Seq experiments

While RNA-Seq has enabled great progress towards the goal of wide-scale isoform-level mRNA quantification, short reads have limitations when resolving complex or similar sets of isoforms. As a result, estimates of isoform abundance carry far more uncertainty than those made at the gene level. When confronted with this uncertainty, commonly used methods produce estimates that are often high-variance--small perturbations in the data often produce dramatically different results, confounding downstream analysis. We introduce a new method, Isolator, which analyzes all samples in an experiment in unison using a simple Bayesian hierarchical model. Combined with aggressive bias correction, it produces estimates that are simultaneously accurate and show high agreement between samples. In a comprehensive comparison of accuracy and variance, we show that this property is unique to Isolator. We further demonstrate that the approach of modeling an entire experiment enables new analyses, which we demonstrate by examining splicing monotonicity across several time points in the development of human cardiomyocyte cells.

bioinformatics