At Indiana University, Milan Radovich uses Ingenuity Pathway Analysis from QIAGEN to analyze breast cancer networks based on RNA-seq data. So far, his results promise to improve studies of genetic changes in tumors and may lead to new treatment options for triple-negative patients — and he’s just getting started.
Radovich uses next-generation sequencing and genomic studies to learn more about triple-negative breast cancer, which in reality is a catch-all for several different forms of cancer. With RNA and DNA sequencing, he aims to unravel the subtypes included in the triple-negative breast cancer category, and then determine which treatments and therapies will give patients the best possible outcome.
Still early in his career, Radovich has already reported findings that are likely to upend the way breast cancer research is conducted. His groundbreaking discovery that breast tissue taken from healthy patients serves as a better control than adjacent normal tissue from the cancer patient is challenging conventional wisdom in the cancer community.
That critical finding, just one of many coming out of his lab, was powered by IPA. “For so many years, IPA has been the go-to tool for network analysis,” Radovich says. “We primarily use it to do pathway, network, and upstream regulator analysis.” More recently, Radovich and his team have begun to use Ingenuity Variant Analysis as well, and will soon be launching studies designed to take advantage of both applications.
In a recent effort, Radovich and his team made real strides toward identifying genetic elements that are common across the various types of cancer bundled into the triple-negative group. “Out of 14,000 or so expressed genes, we were able to find with Ingenuity about 146 that acted as the common denominator — and a large proportion of them belong to a single network,” he says. With this new information, his lab is now moving ahead with mouse testing of drug targets that might influence that network.
IPA has played a pivotal role in these studies, says Radovich, who uses several bioinformatics tools, many of them developed at universities. “A company can invest in people who can really debug software and commit to quality control,” he says. “What you’re really buying with IPA is a high-quality database and a really well done tool that gives you faster time to results. You’ve got the confidence that the information it generates is right.” IPA features such as Upstream Regulator Analysis, Causal Network Analysis, and Molecular Activity Predictor go well beyond what publicly available tools can offer, he adds.
For more on Milan Radovich’s work on triple-negative cancer, check out this case study.