Scientific Overview: Computational Biology

Research Interests

As high-throughput technologies such as DNA sequencing, gene and protein expression profiling, DNA copy number analysis, and single nucleotide polymorphism genotyping become more available to researchers, extracting the most significant biological information from the large amount of data produced by these technologies becomes increasingly difficult. Computational disciplines such as bioinformatics and computational biology have emerged to develop methods that assist in the storage, distribution, integration, and analysis of these large data sets. The Computational Biology laboratory at VARI currently focuses on using mathematical and computer science approaches to analyze and integrate complex data sets in order to develop a better understanding of how cancer cells differ from normal cells at the molecular level. In addition, members of the lab provide assistance in data analysis and other computational projects on a collaborative and/or fee-for-service basis.

In the past year the laboratory has taken part in many projects to further the research efforts at VARI. We have worked closely with the Laboratory of Mass Spectrometry and Proteomics in developing computational infrastructure to support new protein profiling instrumentation and analysis. We have contributed to several gene expression microarray analysis projects ranging from mechanisms of oncogene transformation to the identification of genes that are associated with drug sensitivity. We also work closely with the Laboratory of Cancer Genetics in the development of gene expression–based models for diagnosis and prognosis of renal cell carcinoma. Moreover, we and other groups have demonstrated that several types of biological information, in addition to relative transcript abundance, can be derived from high-density gene expression profiling data. Taking advantage of this additional information can lead to the rapid development of plausible computational models of disease development and progression.

Changes in DNA copy number result in dramatic changes in gene expression within the abnormal region and are detectable through examination of the population of mRNAs generated from the genes that map to each chromosome. Additionally, activation of certain oncogenes or inactivation of certain tumor suppressor genes can produce context-independent gene signatures that can be detected in a gene expression profile. For example, genes that are up-regulated by overexpression of RAS in breast epithelial cells also tend to be overexpressed in other samples containing activated RAS signaling, such as lung tumors that contain activating RAS mutations. We have invested a reasonable portion of the past several years developing and evaluating computational methods to predict deregulated signal transduction pathways and chromosomal abnormalities using gene expression data. We have worked closely with the Laboratory of Cancer Genetics on computational models to describe the development and progression of renal cell carcinoma. An example of the successful application of this analytic approach is in the examination of gene expression profiling data derived from papillary renal cell carcinoma (RCC).

Computational analysis of gene expression data derived from papillary RCC revealed that a transcriptional signature indicative of MYC pathway activation was present in high-grade papillary RCC, but not other high-grade RCCs. Predictions of chromosomal gains and losses were also generated from the gene expression data, and it was demonstrated that the presence of the MYC signature was coincident with a predicted amplification of chromosome 8q. Because the c-MYC gene maps to chromosome 8q, a computational model was developed such that amplification of chromosome 8q occurs in the high-grade papillary tumors, which leads to c-MYC overexpression and activation of the MYC pathway. The importance of MYC activation was confirmed by both pharmacological and siRNA inhibition of active MYC signaling in a cell line model of high-grade papillary RCC. These results highlight the effectiveness of using gene expression profiling data to build integrative computational models of tumor development and progression.