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 The life science DREAM team
August 2012
SHARING OPTIONS:
Three of the four challenges for this year’s DREAM 7
challenge call for the development of informatics tools and methods that will
support ongoing efforts to treat cancer and ALS. Stolovitzky, a Yale University
Ph.D. and manager of functional genomics and systems biology at IBM, notes that
for this year’s challenges, DREAM organizers are partnering with the National
Cancer Institute, Sage Bionetworks and Prize4Life, a not-for-profit
organization that supports efforts to discover treatments for ALS.
The NCI-DREAM Drug Sensitivity Prediction Challenge will
focus on using genomic information to build models that can estimate the
sensitivity of cancer cell lines to a set of small-molecule compounds—both
alone and in combination. The goal is to understand how well computational
analysis of ‘omics data—including proteomics data, SNP data, gene expression
data and drug dose response data—can be used to predict drug activity in cell
lines, and ultimately to select the best treatments for patients based on
genetic profiling of tumors, Stolovitzky explains. The best-performing team
will have the opportunity to publish its results in Nature Biotechnology.
The second challenge for the year, organized with
Prize4Life, will attempt to develop methods that can predict the future
progression of ALS. Prize4Life is collecting data from ALS patients and, using
this data, the DREAM team will use combinations of mathematical and statistical
models to find a combination that defines fast and slow progression of the
disease. Emerging from this data may be chemical differences that are at the
causative root of ALS. Finally, the team will try to determine what drugs would
work best in each patient's case, Stolovitzky says. Prize4Life is offering a
$25,000 prize for the winning submission in this category.
The third challenge, created in collaboration with Sage
Bionetworks, aims to develop algorithms for predicting breast cancer survival.
The best performer in this category will have a paper published in Science Translational Medicine.
The final challenge is “pure informatics” and focuses on
network topology and parameter inference. Participants are expected to develop
optimization methods that accurately estimate parameters, predict outcomes of
perturbations and rewire networks in systems biology network models. This challenge
is meant to help researchers “understand how to do experimental design when it
comes to reconstruction of gene regulatory networks, and understand how to
create mathematical models, including what parameters to use,” Stolovitzky
says.
Stolovitzky notes that in DREAM 1, he “created the
scaffolding and did all the scoring.” The contest has grown since, and now
includes a number of collaborators to help him with the various evaluations.
“In 7,” he observes, “results will be scrutinized as part of a peer-review
process.” For example, the journals that have agreed to publish results from
two of the challenges—Nature Biotech
and Science Translational Medicine—will
provide what Stolovitzky refers to as “challenge-assisted peer review.” He
notes that DREAM 7 has reached out to participants involved in previous
challenges as well as individuals who attend conferences on related subjects.
“We are as inclusive as possible,” he says. “The more the
merrier.”
As its centerpiece, the DREAM process relies upon the wisdom
of crowds, a process based on the
title of a book published in 2004 and written by James
Surowiecki about
the aggregation of information in groups and resulting in decisions that are
often better than could have been made by any single member of the group. Stolovitzky
is a believer, arguing that an aggregate solution is better than any single
solution.
He claims that
aggregating the best results from teams in each of the categories from previous
challenges has resulted in improved methods and better results.
While the timelines haven’t yet been finalized, it’s likely
that most submissions will be due by mid-October, although entries for some
challenges could be due as early as Oct. 1, Stolovitzky says. The winning
entries from each challenge will be presented at the DREAM 7 conference to be
held in San Francisco Nov. 12 to 14.
Prize4Life was founded in 2006 by a group of Harvard
Business School students when one of them, Avichai Kremer, then 29, was
diagnosed with ALS. After his diagnosis, Kremer and his colleagues decided to
pilot an innovative new way to accelerate ALS research.
Prize4Life is a results-oriented nonprofit organization founded to accelerate ALS research by offering substantial prizes to scientists who solve the most critical scientific problems preventing the discovery of an effective ALS treatment. In a statement to ddn, Dr. Neta Zach, the organization’s scientific director, said: “Prize4Life wants to generate new solutions and breakthroughs in ALS. One way to do this is to introduce new minds to the research of ALS. For this challenge, we are appealing to computational savants, many of whom don’t often have the opportunity to use their skills to generate meaningful change in human health, let alone a terrible disease like ALS. While the potential for Big Data to impact health is much discussed, ALS patients, with their short lifespan, need to see these benefits as soon as possible. We are proud to be one of the first initiatives to actually develop and launch such a challenge and hope that others will follow.” Ultimately, it is expected that the solution resulting from
this challenge will improve disease prediction and lead to more accurate
methods of forecasting progression earlier on in the course of the disease.
Based on initial promising clinical results, computational
approaches to infer molecular predictors of cancer clinical phenotypes are one
of the most active areas of research in both industrial and academic
institutions, leading to a flood of published reports of signatures predictive
of cancer phenotypes, notes Dr. Adam Margolin, director of computational
biology at Sage Bionetworks. To evaluate such predictors, the Sage/DREAM Breast
Cancer Prognosis Challenge will create a community-based effort to provide an
unbiased assessment of models and methodologies for the prediction of breast
cancer survival. A common dataset containing gene expression, copy number, and
clinical information for 2,000 breast cancer samples will be provided for
participants to build models to predict survival time (median 10 years, also
provided for all samples). A novel dataset of 350 samples will be generated at
the end of the challenge and used to provide a final, unbiased score for each
model. More than 200 participants from 20 different countries have already
signed up for the challenge.
The journal Science
Translational Medicine (STM) has agreed for the best performing individual
or team in the final evaluation (using the newly generated data) to publish
their results as a “prize” for best performance provided its score is better
than the score of a pre-defined baseline set of models. STM representatives
agreed that having an evaluation committee re-run and compare all models in a
transparent environment can serve the role of challenge-assisted peer review,
allowing the results from the winning individual or team to be published
without additional review.
Code: E081204 Back  |
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