The generation of high-throughput data from sequencing and gene expression profiling experiments has resulted in an overabundance of information, making analysis quite overwhelming.
Pathway analysis offers an approach that involves grouping thousands of molecules based on similarities, interactions, and components, thus simplifying the task of finding meaning in all the gathered information.
Pathway analysis also allows identification of minor changes that might occur within biological systems at varying conditions, which may help in devising an explanation for a particular response to a stimulus. At the recent “BIO-IT World Conference and Expo”, several computational scientists and researchers shared their analytical tools, experiences, and expertise in using pathway analysis in their investigations.
An enrichment analysis platform allows the enumeration and interpretation of genes that have been identified from genomics and gene-ChIP studies, according to Gary Bader, Ph.D., associate professor at The Donnelly Centre, University of Toronto.
“Genomics researchers often generate massive gene expression data, so it’s important to be able to identify which genes are differentially expressed, know which ones are highly expressed, and those that are least expressed. In addition, these experimental data should also be compared to that of the control.”
Dr. Bader also explained that there is currently so much information from genomics studies, resulting in a lot of redundancy in terms of raw information. Thus it is important to further analyze these datasets to delineate smaller groups among a huge network of genes.
“We have developed the Enrichment Map, which is a visualization method that allows the user to identify functional themes within gene expression data. These gene sets, or pathways, reduce the complexity of the analysis because they simplify the interpretation of the dataset,” discussed Dr. Bader.
“This approach thus generates a map of biological processes that one can view, together with p-values based on pathway (gene set) enrichment. This network-based approach is interactive, allowing the user to see nodes, edges, and overlaps between gene sets. For example, within the cell cycle alone, genes could be further grouped into specific mechanisms within this biological event, such as anaphase, metaphase, and DNA replication, and thus the user is provided with a ranked pathway list, which helps in prioritizing target genes for further analysis.”
Dr. Bader’s group plans to further zoom in to mechanistic details of biological events, including mutations from cancer genomics. “We would like to look into ChIP-seq data and mutation data to identify specific mechanisms for particular types of cancers. It would be interesting to determine which regulators are responsible for a certain gene expression profile in a specific cancer type.”
Pathway analysis has helped Novartis perform research in two major aspects: patient selection and identification of drug combinations that may be useful for addressing unmet clinical needs, especially relating to drug resistance, according to Joseph Lehar, Ph.D., director of bioinformatics, oncology translational research.
“In patient selection, we identify gene sets from networks of association data, and these sets of genes often belong to the same functional unit,” Dr. Lehar explained. “The approach allows us to have a predictive advantage in terms of drug response and reduces the noise that is usually observed when examining individual genetic features. It may also be possible that based on relationships established using pathway analysis, we may know whether one gene affects the expression of another.”
Furthermore, Dr. Lehar said that pathway analysis has facilitated their work in assessing drug resistance and identifying useful drug combinations. “It is possible that one drug works on a specific mechanism, and pathway analysis can help us find that a second drug works on an escape mechanism, and perhaps find ways to pulse the treatment—that is, treating cells with the second drug before the cells develop resistance to the first.”
Dr. Lehar is also a member of a research consortium that has developed the Cancer Cell Line Encyclopedia, which is a compilation of cell line-specific data on gene expression, sequencing, and copy number that could be utilized in identifying various predictors for drug resistance and sensitivity. His group envisions that this unique dataset will enhance the design and development of personalized treatment schemes in cancer.
“One challenge is that drug research in cancer involves the identification of mechanisms that could be targeted during therapy. Some cancers are dependent on a simple dominant mechanism of activation and progression, but other cancers are very heterogeneous, meaning that any one type of drug may not be effective for all patients.
“We are now figuring out how to select patients that would respond to specific drugs based on markers in pathways of the targeted proteins. We and most drug companies no longer rely on the classical approach of treating large undifferentiated cancer populations and instead are using a personalized approach, identifying groups of patients with likely drug responses based on their specific cancer genotypes.”