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Immense Proteogenomic Dataset Provides New Resource For Pan-Cancer Analysis

Researchers at BYU perform a crucial role in the organization and sharing of an innovative cancer dataset

Using an innovative approach to tumor research, the Clinical Proteomic Tumor Analysis Consortium (CPTAC), founded by the National Cancer Institute, studied over 1000 tumor samples from 10 different cancer types. The results generated a vast amount of data that will improve our understanding of cancer’s mechanisms, thereby paving the way for advancements in pan-cancer treatment strategies.

The CPTAC investigates tumors through three primary lenses—genomic, transcriptomic, and proteomic. Each of these represents successive layers of biological information:

  • Genomics provides the DNA blueprint of all genes.
  • Transcriptomics captures gene expression patterns by quantifying RNA levels transcribed from the DNA.
  • Proteomics examines the resulting proteins made from RNA transcripts.

Together, these perspectives provide a comprehensive overview of the intricate molecular process within tumors, bridging the gap between the genetic code and functional behavior.

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Photo by Nicholas Rex

Dr. Sam Payne, a bioinformatics professor in BYU’s Department of Biology, has been a leader in CPTAC’s data organization and dissemination effort. He is the senior author on one of the three seminal papers published in Cancer Cell. In working with the consortium, Payne’s role was to ensure the dataset was coherent, consistent, and shared effectively.

“We gathered details about each research team’s data in order to present it in a single application programming interface with the same normalizations and corrections,” says Caleb Lindgren ('23), who worked in the Payne Lab as an undergraduate student and is currently a PhD candidate at Harvard. “We made the data consistent in such a way that you could take your analysis of one type of cancer and then use the same analysis to ask the same questions of another cancer type.”

Payne and his research team combined the data into an organized dataset to perform a pan-cancer analysis—grouping different cancer types and looking for trends between cancer and non-cancer tissue. Typically, cancer analyses focus on one specific type. However, by searching for common behavior trends across cancer types, researchers will discover common mutations that drive cancer progression which can then be targeted for treatments. Researchers can also find potential FDA approved therapeutics that have been used effectively for one type of cancer and use it off-label for the treatment of other types of cancer. This may reduce the time that it takes for new treatments to reach patients.

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Photo by Spencer Hall

The second focus of Payne’s lab was to make the data accessible to other researchers. Generating an expansive amount of data requires significant time, money, and effort. Making the data publicly available provides a valuable resource for researchers who have interesting questions but don’t have the funding or connections to address them independently. CPTAC’s data sharing effort, facilitated with the help of the Payne Lab, is a compelling effort to improve the collective understanding of cancer with the goal of improving treatments.

“One of the most rewarding things for me is when other researchers outside the CPTAC contact me,” Payne said. “I like knowing that people across the globe are finding and utilizing the data to advance the understanding of cancer.”

View this invaluable cancer research resource at: