Unlocking Cancer’s Secrets Using the Social Networks of Cells
Boston, MA – Can we analyze genetic networks in cancer cells to find new drug targets, just as marketers analyze social networks to target advertising? A new computer program uses the tools of social network analysis to track the chatter of genes, which pump out proteins in the same way tweets come across a smartphone’s screen.
Megha Padi, PhD, director of the UA Cancer Center Bioinformatics Shared Resource and assistant professor of molecular and cellular biology, developed a computer algorithm called ALPACA that reveals which gene networks are activated in a diseased cell — an approach that could lead to better treatments for various diseases. The results were published online April 19 in the open-access Nature Partner journal Systems Biology and Applications.
Padi is the first author on the study, which was conducted when she was a postdoctoral fellow at Dana-Farber Cancer Institute, in collaboration with John Quackenbush, PhD, director of the Center for Cancer Computational Biology at Dana-Farber.
Cancer researchers usually focus on specific genes when comparing healthy cells to tumor cells, an approach that doesn’t completely explain what goes on behind the scenes to cause cancer.
“You can get a list of the parts in your car, but you won’t understand what makes the car run until you understand how all the parts are connected to each other,” said Padi.
Likewise, it is essential to study how genes work together as part of a larger network. Dr. Padi is analyzing these gene communities in the same way one would examine a social network composed of connections between people who know each other.
Genes in a community, like people in a social network, talk to one another. In a healthy cell, gene communities function like factory workers, cooperating to process raw materials into goods the cell needs to thrive. In a diseased cell, miscommunications along the assembly line result in defective products.
Tracking how genes’ conversations change over time might provide clues about how cancer arises. These conversations can be analyzed using tools developed to study social networks.
“[A social network] is like the group of friends and family whom you call and exchange text messages with,” said Quackenbush. “While you don't call everyone on a given day, there are many more one-to-one connections within the group than you would expect by chance.”
Quackenbush added, “In the same way, we see that gene regulatory networks form communities. The pattern of ‘conversations’ within the communities change between healthy and diseased individuals. ALPACA is the first method to understand how the cell’s ‘social network’ is reorganized in disease, which might provide clues about how cancer forms.”
To uncover cancer’s causes, the challenge is finding the differences between gene communities in healthy cells compared to diseased cells, rather than the differences between individual genes. But comparing gene communities is easier said than done, as the genetics underlying cancer can have tens of thousands of interacting components to sort through. Drawing a diagram of these interactions results in what researchers call a “hairball.”
“To make a map that human beings can actually understand, scientists need computers to figure out the subtle ways in which this ‘hairball’ goes awry in tumor cells,” said Padi.
One of the next steps is to identify drug candidates that can be further investigated in the laboratory.
“We’d like to use what we’ve learned to develop new strategies that can help prevent or cure disease,” said Quackenbush.
Padi is particularly interested in using ALPACA to find novel treatments for people whose cancers don’t respond to currently available treatments. By contrasting cancers that cannot be treated with drugs, known as chemoresistant tumors, with chemosensitive tumors, those that can be treated with drugs, researchers may be able to home in on a community of “bad guys” — genetic pathways that might be targeted with customized drugs.
ALPACA’s incorporation of social network analysis is an innovative use of tools that are most commonly associated with marketing, not medical research.
“Network scientists usually ask questions like how information is being spread through Twitter or other communication channels,” Padi said. “We’re asking completely different questions, like how networks function in different types of tumors. This type of research is rare because not many people work on both those fields at the same time.”
Padi and Quackenbush used databases that were developed with information from patients who consented to having their anonymized data used for research. This research was supported by NIH grants K25 HG006031, R01 HL111759 and R35 CA197449.