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| HotNet2 helps define the terrain for complex genetic associations involved in cancer. “The next step,” says researcher Ben Raphael, “is translating all of this information from cancer sequencing into clinically actionable decisions.” |
Using a computer algorithm that can sift through mounds of genetic data,
researchers from Brown University have identified several networks of
genes that, when hit by a mutation, could play a role in the development
of multiple types of cancer.
The algorithm, called Hotnet2, was used to analyze genetic data from
12 different types of cancer assembled as part of the pan-cancer project
of The Cancer Genome Atlas (TCGA). The research looked at somatic
mutations -- those that occur in cells during one's lifetime -- and not
genetic variants inherited from parents. The study identified 16
subnetworks of genes -- several of which have not previously received
much attention for their potential role in cancer -- that are mutated
with surprising frequency in the 3,281 samples in the dataset.
The researchers hope the new findings, published in Nature Genetics,
will provide scientists with new leads in the search for somatic
mutations that drive cancer. Additional data from the project, along
with a downloadable version of the Hotnet2 software, is also available
online.
"Ultimately, there will need to be laboratory experiments that
confirm these findings," said Ben Raphael, associate professor of
computer science, director of the Center for Computational Molecular
Biology at Brown, and the paper's senior author. "But the hope is that
the computational analysis will help prioritize the experiments toward
those genes and mutations that are likely to be involved in cancer."
The research takes a different approach than many cancer genetics
studies, which often look for mutations in single genes that occur
frequently in cancer samples. Genes often do not work alone, but operate
together to form networks and pathways that govern cell functions. In
some cases, a mutation in any of the multiple genes in a pathway could
cause a malfunction that leads to cancer. Because damaging mutations can
be spread across multiple such networks of genes, it can be hard to
detect them in statistical tests.
"When looking at single genes, you typically find a small number that
you can confidently say are likely to be cancer genes," Raphael said.
"But you also see many other genes that, statistically, you cannot say
much about. You don't know if they're important or not."
The Hotnet2 algorithm analyzes genes at the network level, and that
helps to identify mutations that occur rarely but are nonetheless
important in cancer.
"For example, maybe there's a gene that's mutated in 80 percent of
samples, but the other 20 percent have rare mutations in multiple other
genes," Raphael said. "If we see that some of those rare mutations are
in the same pathway as the more common one, it helps to build the case
that those rare mutations are important."
The HotNet2 algorithm works by projecting mutation data from patients
onto a map of known gene interactions and looking for connected
networks that are mutated more often than would be expected by chance.
The program represents frequently mutated genes as heat sources. By
looking at the way heat is distributed and clustered across the map, the
program finds the "hot" networks involved in cancer.
The original version of Hotnet was used to identify networks
important in acute myeloid leukemia, ovarian cancer, and several other
types of cancer. Hotnet2 was modified from the original in order to deal
with the much larger and more complex pan-cancer dataset used in this
most recent study.
All told, the algorithm picked out 16 different networks that appear
to be important across cancer types. Several of those 16 were networks
associated with genes and pathways that are known cancer drivers, which
provides a validation of the algorithm, Raphael said. Examples in that
group include the p53 and NOTCH pathways.
But the algorithm also identified pathways that are not as well known
as being important in cancer. Those included protein complexes such as
cohesin and condensin, both of which play roles in cell division and
other cellular processes.
Raphael hopes that research like this could point the way toward new
laboratory investigations of these genes to confirm and better
understand the role they may play in cancer. Ultimately, Raphael and his
colleagues hope their network analysis will eventually help patients
more directly.
"The next step is translating all of this information from cancer
sequencing into clinically actionable decisions," he said. "For example,
there are now drugs that are used to treat patients who have mutations
in particular genes. However, perhaps patients who don't have a mutation
in the targeted gene, but have a mutation in the same pathway, might
respond to the same drug. This is the kind of analysis we would like to
perform next."
Max Leiserson, a student in Brown's computational biology Ph.D.
program and lead author of the study, is excited about the future of
computational approaches to genetics and biology. "This type of analysis
wouldn't have been possible without the recent technological advances
in both computing and DNA sequencing," he said. "It is a very exciting
time to be working in computational biology."
Additional contributors to the study include co-lead author Fabio
Vandin, and multiple postdoctoral fellows, graduate students and
undergraduates from Brown's Department of Computer Science, Center for
Computational Biology, and Department of Molecular Biology, Cell
Biology, and Biochemistry. Joining them were scientists from several
other institutions.
"It was really a great team effort," Raphael said
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