What are ‘omics and how can they improve cancer treatment?

One gene can tell you a lot about your risk for developing cancer and how well you’re likely to respond to cancer treatment. But there’s only so much one gene can do, and in your body, it’s working alongside thousands of other genes, proteins and molecules to support everything your body needs to live.

Where ‘omics come in

That’s where ‘omics come in. The term comes from the Greek word “ome,” meaning group or whole, and in biology, it’s the study and characterization of all biological molecules of one type and how they interrelate in the body to produce the functions of life. So proteomics – the ‘omics of proteins – is the study of all proteins that work together to provide a specific function for a cell or organ. Genomics is looking at all of your genes – your genome – and how they interact.

It’s important to look at individual genes in detail to learn more about their function, says John Weinstein, M.D., Ph.D. But researchers can complement that work by using ‘omic approaches to look at a gene in context and see how things work and interact in the cellular environment. Since Weinstein first used the term in a publication in 1997, ‘omics has been applied to almost anything scientists can study: glycomics, lipidomics, metabolomics, pharmacogenomics and immunomics, among dozens of others.

Asking the right questions

Scientific research is based on hypotheses – and the clearer your hypothesis is before you begin your study, the better chance you have of finding a high-quality answer. Much of the ‘omics work done helps researchers craft better-formulated hypotheses.

“Using ‘omics, you won’t get the final answer to the cancer problem,” Weinstein says. “But you’ll get pointed in the right direction for your next study.”

The large sets of data can help generate hypotheses by helping prioritize a gene or set of genes over others for further investigation, and they can be used to validate the data from a clinical trial. They can also lead to more studies: Once you’ve found something that’s works for patients, can you find something else that’s similar? Can you connect structure and function, or find two proteins that play similar roles?

The best example of this is the search for drug targets. Once one gene that can be targeted by a specific drug is identified, can you find another that’s similar but may be more effectively targeted?

A roadmap to better cancer treatments

The Cancer Genome Atlas (TCGA) program, established by the National Cancer Institute and National Human Genome Research Institute, has generated data from more than 11,000 patients to map key molecular changes in 33 tumor types. Many MD Anderson doctors and researchers have contributed to TCGA, which has made data available to help researchers around the world.

Rehan Akbani, Ph.D., associate professor of Bioinformatics and Computational Biology, has been involved in the gynecologic/breast cancer subgroup of TCGA. The goal is to find similarities and differences across breast and gynecologic cancers.

“We want to identify common biomarkers between these cancers, in the hopes of being able to apply our current therapies more effectively across tumor types,” he says.

Another major aspect of his research is a novel analysis of the transforming growth factor beta signaling pathway, well-known to impact many cancers. Akbani and his colleagues are the first to analyze it in all of the 33 TCGA tumor types, and they’re looking to use what’s already known about the pathway in certain cancers to help us better understand how it works in other cancers.

“We’re looking for opportunities for novel applications of existing therapies,” he notes.

Big data requires big collaboration

Much of what’s being done in ‘omics research wasn’t possible a few decades ago. By definition, ‘omics research is looking at the whole. That means any one large-scale study, whether it be of genes, proteins, metabolic molecules or anything else, has a huge amount of data connected to it. (TCGA alone has produced more than 2 petabytes – that’s 2 million gigabytes – of data.) It requires huge amounts of computing power and data storage, as well as trained professionals who know how to manage, process, analyze and interpret all of the data we generate every day in our labs and clinics.

That’s why MD Anderson’s Bioinformatics and Computational Biology department has 10 software engineers to help handle computing needs and to create new software programs that implement specialized algorithms to help in analysis and interpretation. They and the department’s statistical analysts work collaboratively with doctors and researchers to make sense of what’s been happening in the lab or a clinical trial.

The way forward

‘Omics studies and big data will never fully replace the more traditional one gene/one protein studies, Weinstein notes, but the two are complementary. Researchers can learn more about cancer and how it forms by combining the two approaches.

Knowing more, MD Anderson can take further steps toward true precision medicine. This will allow us to customize treatments for our cancer patients by using the best therapies for their specific tumor characteristics.

“We’re looking for as many vulnerabilities in cancer as we can find,” Akbani says. “If we look at sets of genes instead of just one, look at all the molecular targets and the relationships among them, hopefully we’ll find more ways to target cancer. It’s not a one-size-fits-all problem, so we need to find the options that fit everyone in need.”

A longer version of this story originally appeared in Messenger, MD Anderson’s quarterly publication for employees, volunteers, retirees and their families.

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