A graph representing the sequence of genomic alterations in chronic lymphocytic leukemia (CLL). Each node represents a mutation, with arrows indicating temporal relationships between them. The size of the nodes indicates the number of patients in the study who exhibited the alteration, while the thickness of the lines shows how often the temporal relationships between nodes were seen. The method the researchers use enabled them to identify multiple, distinct evolutionary patterns in CLL.
As biologists have gained a better understanding of cancer, it has become clear that tumors are often driven not by a single mutation, but by a series of genetic changes that correspond to particular stages of cancer progression. In this sense, a tumor is constantly evolving, with different groups of cells that harbor distinctive mutations multiplying at different rates, depending on their fitness for particular disease states. As the search for more effective cancer diagnostics and therapies continues, one key question is how to disentangle the order in which mutations occur in order to understand how tumors change over time. Being able to predict how a tumor will behave based on signs seen early in the course of disease could enable the development of new diagnostics that could better inform treatment planning.
In a paper just published in the journal eLife, a team of investigators led by Department of Systems Biology Associate Professor Raul Rabadan reports on a new computational strategy for addressing this challenge. Their framework, called tumor evolutionary directed graphs (TEDG), considers next-generation sequencing data from tumor samples from a large number of patients. Using TEDG to analyze cancer cells in patients with chronic lymphocytic leukemia (CLL), they were able to develop a model of how the disease’s mutational landscape changes from its initial onset to its late stages. Their findings suggest that CLL may not be just the result of a single evolutionary path, but can evolve in alternative ways.