Fitting Datasets Together
With the advent of new high-throughput approaches to generate vast datasets, one of the challenges is how to integrate different types of information that are sometimes generated by using distinct techniques. For example, various types of genome-scale data are currently available, particularly from cancer patients, and include microarray analyses and specific amplifications or deletions on the chromosome.
“The question is how these datasets fit together, how one can make sense out of it. That is really important, because if we find the connections between these data types, we may in fact identify the genes that drive certain diseases,” says Gábor Balázsi, Ph.D., assistant professor in the department of systems biology at the University of Texas MD Anderson Cancer Center.
At the recent CHI “Molecular Medicine Tri Conference” in San Francisco, Dr. Balázsi talked about work that he and collaborators conducted to integrate mRNA and gene amplification/deletion datasets in an effort to identify genes and sets of genes that drive specific subtypes of breast cancer.
“We designed a method to put together the two types of data and, also, used existing information on protein-protein interaction and gene regulation,” he explains. Based on this approach, Dr. Balázsi and collaborators, involving postdoctoral fellow Bhaskar Dutta, identified gene networks that are important in breast cancer and unveiled specific subnetworks that they termed “driver networks” to illustrate the putative importance of the participating genes in the appearance of different breast cancer subtypes.
In breast cancer, triple negative tumors pose some of the most significant therapeutic challenges. This is in contrast to estrogen-receptor positive tumors, which often respond to tamoxifen or estrogen receptor antagonists, and to Her2 positive tumors, which usually respond to herceptin.
“One of the most interesting aspects of our study is that we identified, in collaboration with Dr. Lajos Pusztai’s laboratory, the gene sets for the triple negative subset of breast cancers,” explains Dr. Balázsi. Furthermore, by knocking down the genes from this network in triple negative cell lines established from patients, Dr. Pusztai and colleagues experimentally confirmed that genes identified by computational analysis play a role in the survival of triple negative breast cancer cells. “The driver networks we defined from gene expression and CGH data of human breast cancer patients provided directly testable therapeutic hypotheses that suggest treatment strategies and in particular combination therapies that could and should be tested in the clinic,” concluded Dr. Balázsi.
One of the most important and clinically relevant aspects related to gene expression is the need to visualize it as a highly dynamic process. “Overall, gene expression changes as tumors progress. The tumor microenvironment affects gene expression, and epigenetic modifications bring a significant contribution to this,” says David S. Hoon, Ph.D., director of the department of molecular oncology at the John Wayne Cancer Institute. One of the research efforts in Dr. Hoon’s laboratory, particularly over the past eight years, has focused on studying gene-expression changes that occur during cancer progression and metastasis, particularly from an epigenetic perspective.
Dr. Hoon and colleagues recently reported that RUNX3, a gene that exists in most cell types and appears to be important for development and cell differentiation, shows abnormal expression in primary and metastatic cutaneous melanoma. This work revealed that two epigenetic mechanisms, miRNA and promoter CpG hypermethylation, suppress RUNX3 mRNA levels in primary tumors as compared to untransformed cells, and an intriguing finding was that this suppression was even stronger in metastatic melanoma.
“An important aspect to remember is that we often assume that the gene-expression profile of a metastatic tumor will be the same as in the primary tumor, and we often treat based on the primary tumor, but this is not always correct,” emphasizes Dr. Hoon. The existence of gene-expression differences between primary and metastatic tumors represents a clinically relevant aspect that underscores the necessity to explore the epigenetic and gene-expression profile for both the primary and metastatic tumor, because they may be different in the same patient.
The interplay between genetic and epigenetic changes is emerging as one of the most exciting and thought-provoking developments from recent years. Increasingly, findings from multiple biomedical fields have converged to illustrate the concerted contribution of genetic and epigenetic factors as they shape gene expression. The reversible nature of epigenetic changes opens the possibility to monitor or modulate their impact on gene expression and promises attractive prophylactic, diagnostic, and therapeutic applications.