Researchers discovered an alternative way to classify distinct types of colon cancer, making the information more valuable to patients and their doctors as they consider treatment.
A team at Wilmot Cancer Institute collaborated with a German company, Indivumed Therapeutics, on the project. Currently, colon cancers can be classified into four subtypes based on gene expression patterns, yet this method can be unreliable and is very costly, scientists said. In the new proof-of-concept study, researchers found that using RNA splicing events rather than gene-expression analysis offers more precise and lower cost tumor-type identification. When a patient is diagnosed, this step — identifying the unique characteristics and molecular properties of tumors — is crucial to determining prognosis and what medications may work best to attack the disease.
The journal Gastroenterology reported the research today. Hucky Land, PhD, deputy director at Wilmot and chair of the University of Rochester Medical Center Department of Biomedical Genetics, is corresponding author for the publication. He credits Aslihan Ambeskovic, PhD, lead bioinformatics analyst in the Land lab, for conducting most of the work using RNA sequencing data from hundreds of human colon cancer tissue samples. Matthew N. McCall, PhD, associate professor of Biostatistics, is also a co-author.
Calling the discovery “a significant advance based on biological principles that is highly translational,” Land noted that the next step is to develop a diagnostic test suitable for the clinic.
Colorectal cancers have a complex landscape of genetic and epigenetic alterations. Some subtypes, for example, may respond better to immunotherapy while certain chemotherapy regimens may be the correct approach for other subtypes.
Researchers believe their newly discovered subtype identifier is accurate and reliable because variation in RNA splicing holds more relevant information in each cancer specimen.
Indivumed funded the study. Acknowledgements went to Wilmot’s Genomics and Biostatistics and Bioinformatics shared resources, and the Center for Integrated Research Computing at the University of Rochester.
Source – University of Rochester
RNA sequencing reveals a more precise and cost-effective way to classify colon cancer subtypes
