The AGCTs of cancer

Data tsunami

The origin of TCGA may, in part, be traced to John Weinstein, M.D., Ph.D., chair of Bioinformatics and Computational Biology at MD Anderson. But it almost didn’t happen.

It began back in 1991 when Weinstein, who was then working at the NCI, was planning to take the day off. Then he remembered his boss was speaking at Grand Rounds that day. Going to work was a fortuitous decision that would lead Weinstein to pursue the development of vital data-related programs that would ultimately influence the creation of TCGA.

“During his talk, I had an idea that led to my laboratory group spearheading a comprehensive molecular profiling of the NCI-60, a set of 60 human cancer cell lines used by the NCI to screen more than 100,000 chemical compounds, plus natural products, for anti-cancer activity,” says Weinstein.

He calls the supernova explosion of data resulting from these earlier efforts a “data tsunami,” and likens the TCGA to a “12,000-square chess board of patient samples.”

“It provides us a context within which to work out the rules of the genomic game and generate useful, potentially significant results, benefiting cancer patients and their families,” he says.

Those results can provide researchers with a fresh look at how tumors are developing, and point the way to new therapies. Weinstein often is referred to as a “pioneer of postgenomic biology” in part for another one of his inventions: clustered heat maps. These maps allow researchers to visualize patterns in the huge masses of data and more quickly take advantage of them. Clustered heat maps have appeared in many thousands of publications, and Weinstein’s group has now developed “Next-Generation Clustered Heat Maps (NG-CHMs),” which can be zoomed in on and navigated like a Google map. NG-CHMs are routinely used for data from many different cancer types in TCGA.

Today, TCGA is a massive effort that is studying genomic changes in more than 30 different cancer types and, according to Rehan Akbani, Ph.D., assistant professor of Bioinformatics and Computational Biology, MD Anderson is a “heavy hitter and proud participant in every one of TCGA’s disease working groups.”

“The MD Anderson Genome Data Analysis Center (GDAC) has established itself as the premiere provider of reverse-phase protein array (RPPA)-based TCGA proteomics data and analysis as well as batch effects analysis and data quality control,” says Akbani.

Batch effects are the findings and results that occur due to technical artifacts when aggregating data from multiple institutions with varied computer systems, lab procedures and data collection methods. MD Anderson’s work with assessing batch effects has helped assure that the data being studied by research institutions worldwide via TCGA are accurate and consistent. RPPAs involve performing protein assays on thousands of samples simultaneously, allowing measurements of protein expression, as well as protein modifications such as phosphorylation, which turn protein enzymes on and off.

Finding the treasure

Sifting through enormous amounts of data has often been referred to as “mining,” and scientists are the treasure-seekers in search of valuable nuggets that will lead to a better understanding of cancer’s molecular workings.

MD Anderson investigators have had significant success in mining TCGA data and, working with multiple partners at cancer research institutions globally, have made important discoveries about the molecular nuances of cancer cells.

“MD Anderson researchers not only have contributed at a leadership level to The Cancer Genome Atlas, helping to ensure its success, but we also have participated in and served as lead investigators for TCGA-based studies that have opened up new possibilities for cancer diagnosis and treatment,” says MD Anderson President Ronald DePinho, M.D. “I am proud of the effort that has gone into this pioneering program, and I know we will continue to benefit from the data for many years to come.”

Revelations about two aggressive cancers

Roeland Verhaak, Ph.D., associate professor of Bioinformatics and Computational Biology, has led research investigations based on TCGA data that have revealed startling findings for two aggressive forms of cancer.

Earlier this year, Verhaak published findings from a study co-led by the University of São Paulo’s Ribeirão Preto Medical School and Columbia University that revealed new information about diffuse glioma, which is found in some adult brain cancer patients.

Analyzing TCGA data, the team defined a complete set of glioma-associated genes from patient samples and used molecular profiling to improve disease classification. They were able to identify molecular correlations and provide insight into disease progression from low to high grades.

Another study, led by Verhaak and colleagues at the University of Michigan, revealed significant new findings about adrenocortical carcinoma (ACC), a rare cancer typically associated with poor prognosis.

The researchers — including those from 39 international institutions — examined 91 ACC tumor specimens from four continents and observed “massive” DNA loss followed by whole genome doubling (WGD). WGD occurs when tumor cells acquire an extra copy of their entire genome. The researchers found that WGD was associated with an aggressive clinical course, suggesting that it could be a hallmark of disease progression. They speculated that tumor growth could be slowed if they could prohibit WGD in future pre-clinical studies.