Sylvester Receives NCI Grant to Advance Proteogenomics
Despite significant progress in understanding cancer at the molecular level, the complexity of the disease remains a daunting barrier to developing interventions that are needed to diagnose, treat, and prevent cancer in the era of precision medicine.
Steven Chen, Ph.D., who directs the Biostatistics and Bioinformatics Shared Resource at Sylvester Comprehensive Cancer Center at the University of Miami Miller School of Medicine, and colleagues from Dan L. Duncan Comprehensive Cancer Center at Baylor College of Medicine, Gladstone Institutes, and other institutions recently received a five-year grant of more than $5 million from the National Cancer Institute (NCI) to study proteogenomics.
“We need to understand the linkage between proteomics and genomics to find the relevant protein markers or the combined protein-genomic markers that will guide treatment,” said Chen.
In the hierarchy of cellular molecules, DNA is transcribed into RNAs, which are translated into proteins, which do most of the work in a cell. The genome is a blueprint but it doesn’t always predict what a protein is going to do, just as an architectural blueprint may not perfectly reflect a house’s structure. The builder might add modifications during construction, or they might go out of business and the house is never built. Similarly, to fully understand genes and proteins, researchers must look at both.
That’s the purpose behind the Clinical Proteomic Tumor Analysis Consortium (CPTAC) at the NCI, which has spent the last few years cataloguing how proteins are expressed in tumors. In this new phase, CPTAC researchers will use proteomic and proteogenomic data to conduct translational research on clinically relevant problems, such as the ability to predict which treatments are likely to be effective against a specific patient’s tumor.
“We want to get a more comprehensive understanding of cancer biology,” said Chen. “Ultimately, our goal is to create prognosis and prediction models using biomarkers for cancer treatment — precision oncology.”
Chen’s team will be part of the Proteogenomic Data Analysis Center, which will develop computational tools to elucidate the molecular complexity of cancer by integrating proteomic and genomic data from diverse types of samples, including clinical specimens with treatment, cultured cells, and patient-derived xenografts. By integrating data from preclinical models and human tumors to enable effective translation of experimental findings to the clinic, the group aims to gradually tease out biomarkers that will help oncologists prescribe the best possible combination of treatments.
These findings could have an enormous impact on cancer care. Better markers could identify which patients respond to different chemotherapies, immunotherapies, and other treatments, determine a tumor’s aggressiveness and even boost clinical trials. To further accelerate the process, CPTAC data will be publicly available, informing cancer research around the world.
“So far, we have comprehensive genomic profiles for 31 types of cancer,” said Chen. “We’ve made tremendous progress, but there’s a lot to do to make the enormous amount of information useful in the clinic.”