Cancer Prediction Study by Yale Public Health Researcher Funded by NCI

Annette Molinaro A three-year $500,000 National Cancer Institute grant to Yale School of Medicine Public Health and Biostatistics scientist Annette Molinaro will advance the use of statistics for predicting outcomes in cancer patients.
Annette Molinaro

A three-year $500,000 National Cancer Institute grant to Yale School of Medicine Public Health and Biostatistics scientist Annette Molinaro will advance the use of statistics for predicting outcomes in cancer patients.

Molinaro, an assistant professor of biostatistics in the Department of Epidemiology and Public Health (EPH) at Yale will use the grant to partially fund a project that pairs large disease-related data sets with clinical information to find variables that are significantly linked to disease outcomes.

“One of the promises of biomedical research in the genomics era is that we will have powerful tools for predicting the outcomes for people with a given disease, based on their genetic profiles,” said Molinaro. “In cancer care, we would like to know which tumors are likely to spread and recur and which patients will respond to certain treatments. We currently have a lot of genomic, proteomic and expression data as well as clinical connections, but we lack tools to tell us consistently how to treat patients to minimize ineffective therapies and maximize benefits.”

Molinaro said a challenge in using genomics for prediction is that there is often information missing in real patient data files—and it contributes to the normal variability in biomedical experimentation. For example, patients may be lost to follow-up after receiving treatment, or spots on a genomic array may not provide useful data. Part of Molinaro’s project is to develop ways to calculate the information that will yield valid results despite these information gaps. She will also develop tools for testing which methods and models are appropriate for different types of disease.

“We need to verify that the predictive models we develop reflect the actual disease as a whole and not a bias or random occurrences in the population that we used to build the model,” said Molinaro. “For common diseases, models can often be tested with data from an independent population––but for rare diseases, we need to be able to confirm our results directly.”

Much of Molinaro’s initial work will be accomplished as co-director of the Bioinformatics and Biostatistics Core for the Yale University Skin Cancer Specialized Program of Research Excellence (SPORE) from the National Cancer Institute of the National Institutes of Health. She will collaborate with Yale Cancer Center members David Rimm, M.D., associate professor in the Pathology Department and Ruth Halaban, M.D., senior research scientist in the Dermatology Department.

Molinaro received her Ph.D. in Biostatistics at the University of California, Berkeley and completed a fellowship in cancer prevention at the National Cancer Institute prior to joining the EPH faculty in July 2005.

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Karen N. Peart: karen.peart@yale.edu, 203-980-2222