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A virtual encyclopedia
May 2012
EDIT CONNECT
SHARING OPTIONS:
BASEL, Switzerland—Armed with the knowledge that cancer
is a
genetic disease and that cell lines reflect the genetic disturbances which
drive it, Novartis and the Broad Institute have developed an online data
repository that catalogues the genetic and molecular profiles of nearly 1,000
human cancer
cell lines.
This resource, dubbed the Cancer Cell Line Encyclopedia
(CCLE), provides a powerful
tool for the design of cancer drug trials and will
help researchers identify patients who could benefit most from specific drugs
in development,
according to the two organizations, which published the results
of their collaboration March 28 in the journal Nature. The CCLE project was specifically a collaboration between
the Broad Institute, the Novartis Institutes for Biomedical Research (NIBR) and
the Genomics Institute of the Novartis Research Foundation to conduct a
detailed
genetic and pharmacologic characterization of a large panel of human
cancer models, develop integrated computational analyses that link distinct
pharmacologic vulnerabilities to genomic patterns and translate cell line
integrative genomics into cancer patient stratification.
"The systematic translation of cancer genomic data into
knowledge of tumor biology and therapeutic possibilities
remains challenging,"
the CCLE's creators wrote in their article, "The Cancer Cell Line Encyclopedia
enables predictive modeling of anticancer drug
sensitivity."
"Such efforts," the researchers added, "should be greatly
aided by robust
preclinical model systems that reflect the genomic diversity of
human cancers and for which detailed genetic and pharmacological annotation is
available. Here, we describe the Cancer Cell Line Encyclopedia (CCLE): a
compilation of gene expression, chromosomal copy number and massively parallel
sequencing data from 947 human cancer cell lines. When coupled with
pharmacological profiles for 24 anticancer drugs across 479 of the cell lines,
this collection allowed identification of genetic, lineage and
gene-expression-based predictors of drug sensitivity."
On the
website, http://www.broadinstitute.org/ccle—which
anyone can access—researchers can enter a keyword to search for genes, news
items and publications; search results for a gene, including links to
annotations and analyses; and browse, analyze and download studies and data
sets.
"The goals
of the CCLE project are twofold: one is to
assemble a resource of genomic characteristics of 1,000 cancer cell lines, and
the second is to develop
tools to predict the sensitivity of those cell lines
to cancer drugs based on genomic alterations," explains Dr. Nicolas Stransky, a
computational
biologist in the Cancer Program at the Broad and a co-first
author of the paper. "Certainly, people have been doing these kinds of things
in the past,
but on a much smaller scale. There are many applications here. One
is to better inform the clinical trials that are taking place in the
development of
drugs. The use of the CCLE here would be to better select which
patients are more likely to respond when they are given a specific drug,
because you
can tell which cancer cell lines are being killed by a drug."
The team purchased cell lines and
their associated
information directly from several commercial vendors in the United States,
Europe, Japan and Korea, says William Sellers, global head
of oncology at the
NIBR. Cell lines represent a diverse picture of cancer as a disease, as they
include many subtypes of both common and rare forms of
cancer.
"If someone buys a vial of cells that we characterized, it
should be very close with as
minimal drift as possible to what we used in our
genetic studies. It was expensive to do this, but it was done to make this a
publicly valuable
resource," he adds.
Each cell line was genetically characterized through a
series of high-
throughput analyses at the Broad Institute, including global RNA
expression patterns, changes in DNA copy number, as well as DNA sequence
variations in
about 1,600 genes associated with cancer, and pharmacologic
profiling for several drugs in about half of the cell lines. Algorithms were
developed to
predict drug responses based on the genetic and molecular makeup
of cancer cells.
The
collaboration was "exciting" for Novartis "because we
are in the drug discovery arena," says Sellers. "We are good at team-oriented
and project-
oriented science. It turned out to be a lot of fun because both
teams worked together as a single project team."
In fact, a number of Novartis' clinical trials have already
been influenced by the data generated during the collaboration,
he adds. For
example, Novartis used the data in the development of BYL719, a novel, oral,
targeted anticancer agent that selectively inhibits the
phosphatidylinositol-3-kinase (PI3K) pathway. The compound, which is being
investigated in advanced solid tumor patients, has shown significant cell
growth inhibition and induction of apoptosis in a variety of tumor cell lines
as well as in animal models. In addition, in preclinical models, it has
been
shown to possess antiangiogenic properties.
"While this result wasn't unexpected, the
power of the
encyclopedia results motivated a specific trial design," Sellers says. "The
strength of association was so compelling, suggesting that
not only was the
molecule a good molecule, but also that the best thing to do with respect to
its clinical development was to focus the trial on
patients who had a certain
mutation, so we had the best chance of seeing early efficacy in patients."
Sellers acknowledges that "there is a lot of debate about
whether cell lines are OK to use in cancer research." While human cancer cell
lines
represent a mainstay of tumor biology and drug discovery through facile
experimental manipulation, global and detailed mechanistic studies and various
high-throughput applications, many previous efforts have been limited in their depth of genetic
characterization and pharmacological
interrogation, he notes.
"It is important to remember that while cell lines are not
always the
best tools, they are the most widely used by cancer researchers," he
stresses. "People know about their limitations, but it is important to remember
that they are tremendously useful tools for studying cancer therapies and how
drugs work. In certain cases, cell lines don't fully represent the
genomic
heterogeneity of cancers. Again, in certain cases, looking at cell lines won't
give you a complete picture, but what we show in our paper is
that in many
cases, they are reasonable models."
"Our biggest hope for this project is that the
data will be
used by the community, but also that the biggest discoveries in the data are
yet to come. This is likely to generate a lot of enthusiasm
and be widely
used," says Sellers.
Stransky notes that the CCLE is still an ongoing project,
and its repository of data is neither final nor complete. In the second phase
of the project, "our goal is to perform a much deeper genomic
characterization
using several sequencing techniques including whole-genome, transcriptome,
exome sequencing, etc. We're also looking at other data
types such as
epigenetic alterations, phosphoproteomics and metabolomics," he says.
Pairing this
information with ways to rapidly genotype
patient tumor samples represents the next step in the effort to enable the
personalization of cancer
treatment, according to the researchers. Some major
research hospitals already genetically profile cancer patients' tumors
routinely, and many more
are likely to follow, says the CCLE team.
"What we're trying to do here," Stransky concludes, "is lay down the
basis of what personalized medicine would be in the future, which means we're
trying to have the best match between a particular drug and which tumors
are
likely to respond. This is going to be tremendously useful for drug development
in the future." Code: E051218 Back |
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