When Leland Dunwoodie, a biochemistry student, contacted his PI (Principal Investigator) in the spring of 2016 that he wanted to start “some human things” in the spring of 2016, he would not have thought that this would lead to the discovery of 22 genes involved in glioblastoma, the most aggressive type of brain tumor.

“I definitely didn’t come to Clemson to think about brain cancer research,” Dunwoodie said. “I was working on a project with grapes and other plants. I told Dr. (Alex) Feltus that I wanted to do some human things, and he said, ‘That’s cool – take an organ.’ ”

After consultation with his family – should he study the brain or the heart? – Dunwoodie decided to study the brain and especially brain tumors. An earlier summer internship at the Van Andel Institute had awakened his interest in cancer research.

Dunwoodie’s study, which two years later led to a publication in the journal Oncotarget in January 2018, was the first to describe glioblastoma-specific gene coexpression relationships between a group of 22 specific genes.

Heard in the news as the disease Senator John McCain and Beau Biden, the late son of U.S. Vice President Joe Biden, glioblastoma is very malignant and characterized by its lethality. Patients with glioblastoma have an average survival time of only 14, 6 months after diagnosis.

“Like many other tumors, diseases and complex characteristics, glioblastoma is controlled by a variety of genetic and epigenetic factors,” said Dunwoodie. “If there was a major regulator for these cancers, we would say, ‘We will use the drugs and we will save millions of lives every year,’ but there are more things in glioblastoma than we can identify now. ”

However, the complexity of glioblastoma is suitable for research in Professor Feltus’ Systems Genetics Lab in the Department of Genetics and Biochemistry, where Dunwoodie is a student. As the laboratory’s name suggests, Systems Genetics uses computer and mathematics based approaches to analyze biological systems such as genes and regulatory pathways.

To make this discovery, Dunwoodie first compiled data from two public online databases of genomic information: The Cancer Genome Atlas (TCGA) and the National Centre for Biotechnology Information (NCBI).

TCGA downloaded more than 2,000 tumor expression datasets, each detailing how tumor cells differ from normal cells at the genetic level. Five different types of tumors, including those from bladder, ovarian, thyroid, glioma and glioblastoma cancer, were included in the data to provide a well-rounded case study.

More than 2000 data sets, each containing approximately 75,000 genes, were then organized into a gene expression matrix (GEM), a table that quantifies the level of expression of each gene over each sample. For example, one of the genes derived from TCGA, called LAPTM5, encodes a protein involved in the formation of blood cells. In the gene expression matrix, LAPTM5 was examined for each tumor type to determine whether it was overactive (overexpressed) or underactive (underexpressed) in a tumor type, as indicated by a numerical ranking. The same evaluation process was then performed for the 74999 remaining genes across the five tumor types in the TCGA data.

A separate GEM containing 210,000 genes from 204 records in the NCBI database – including normal brain samples, glioblastoma brain samples, and brain samples from patients with Parkinson’s disease – was created independently for comparison. Will Poehlman, a PhD student at the Systems Genetics Lab, helped Dunwoodie prepare these GEMs.

Using Feltus’ novel computer software and former PhD student Stephen Ficklin, now Assistant Professor at Washington State University, Dunwoodie was able to translate the GEMs into two different gene coexpression networks (GCNs), providing insight into how the genes interact with each other.

The software package, known as Knowledge Independent Network Construction (KINC), is new in the sense that it finds expression relationships between genes without the need for researchers to perform prior analysis. This knowledge-independent method reduces the amount of “noise” from laboratory protocols or from natural variations between cells that can prevent genetic interactions from being discovered.

“Through the two GCNs, we found a group of 22 genes expressed in a single module in both the cancer genomatlas network and the NCBI brain network,” said Dunwoodie. “Only about 70 genes overlapped between the two networks, and 22 of them were in the same module – the same group of coexpressed genes. The overlap was really easy to see.”

While it is tempting to think that the genes, many of which function in the immune system feed on each other to influence glioblastoma, Dunwoodie says that this is not exactly the case.

“It’s hard to say they’re working together because they’re correlations. So if person A runs eight miles on the same day that person B runs eight miles, that doesn’t necessarily mean they run together.” Dunwoodie said. “These genes are more likely to be regulated in the same way, and there are probably several things that regulate them that we cannot currently identify.”

In addition, these 22 genes were found to have much higher co-expression in glioblastoma compared to glioblastoma and non-cancerous samples, suggesting a disease-specific regulatory mechanism. The same finding was found when comparing glioblastoma with less severe glioma, a less aggressive brain tumor, indicating glioblastoma-specific activity of the 22 genes. The other remarkable result of the study showed that the 22 genes are more associated with mesenchymal glioblastoma, a distinct subtype of cancer, and that when the genes are strongly expressed, they reduce survival time for patients in the mesenchymal group.

As in research, where answering a question leads to a wealth of new questions, the team’s study is only a small step towards understanding glioblastoma pathogenesis.

“It would be nice to find out what the 22 genes specifically do,” Dunwoodie said. “Are they expressed in the surrounding immune cells? Are they a cause of cancer or are they an effect of cancer? Does cancer spread its expression? Why these genes are expressed there together and what they do are questions that have been answered.”

Dunwoodie – who plans to attend a medical school to become a Cancer Doctor Informatist – says the tools and methods he learned in the Systems Genetics Lab will stay with him long in his career.

“Cancer research is interesting because there are so many amazing people who do so many amazing things – but that’s just a drop in the ocean,” Dunwoodie said. “For me, the true purpose is that patients are cured. The publication of a paper is great, but no one was cured immediately, and that’s the ultimate goal.”

Source: The News Stand