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.