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Molinaro Receives Transition Career Development Award to Study Cancer Prediction

Annette Molinaro, Ph.D., first-year assistant professor in the Division of Biostatistics at the Yale School of Public Health, has received a three-year, $500,000 grant from the National Cancer Institute to study cancer prediction based on genetic profiles.

Annette Molinaro, Ph.D., assistant professor in the Division of Biostatistics at the Yale School of Public Health, has been awarded a three-year, $500,000 grant by the National Cancer Institute to develop statistical methods for searching within large sets of genomic, epidemiologic, and pathological data for variables that predict cancer outcomes. The purpose of the career grant is to fund salary and additional training while a junior investigator develops an independent research program.

Molinaro's project 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 of different people with a given disease, based on their genetic profiles,” said Molinaro.

“In cancer care, we want to have a good idea of whose tumors are likely to metastasize or recur and whose won't, who will respond to a particular treatment and who won't,” she said. “Currently, we have lots of genomic, proteomic, and expression data, as well as clinical correlates. What we lack are tools to tell us consistently how to treat patients to minimize ineffective therapies and maximize benefits.”

One of the challenges in using genomics for prediction is that there is often missing information in real-world data sets--part of the normal variability in biomedical experimentation. Patients may be lost to follow-up after receiving treatment, or spots on a genomic array may not yield useful data.  Part of Molinaro's project is to develop algorithms that will yield valid results despite these gaps in the information. Furthermore, she will also develop tools for testing which methods and models are appropriate for each particular type of disease.

“We also need to verify that the predictive models that emerge generalize to the disease as a whole and not based on a bias or random occurrences in the population that we used to build the model,” asserts Molinaro. “For common diseases, models can often be tested against data from an independent population, but for rare diseases, we need to be able to confirm our results without finding a whole new cohort of patients.”

Much of Molinaro's initial work will be accomplished as the co-director of the Bioinformatics and Biostatistics Core for the new Yale University SPORE in Skin Cancer and as a collaborator of Heping Zhang, Ph.D., Hongyu Zhao, Ph.D., and Theodore Holford, Ph.D. all of the Biostatistics Division at the Yale School of Public Health as well as David Rimm, of the Department of Pathology and Ruth Halaban, of the Department of Dermatology.

Molinaro completed her Ph.D. in Biostatistics at the University of California Berkeley and was awarded a one year fellowship in Cancer Prevention at the National Cancer Institute prior to joining the Yale School of Public Health faculty in July 2005.