Analyzing how cancer mutations interact may improve targeted therapies

Cancer is a disease where unchecked, improper cell growth occurs due to numerous mutations. However, these mutations do not function independently. They can influence each other and affect the progression of cancer. Researchers have been trying to understand these interactions for a while but often oversimplify the complex correlations. However, a recent method by the Yale School of Public Health (YSPH) addresses this complication and will provide insights into how these mutations interact and affect tumor growth. This could assist in creating targeted treatments that could eradicate cancer.

The new method allows us to determine where, along its genetic path, a particular patient’s cancer is. Jeffrey P. Townsend, the lead author of the study and the Elihu Professor of Biostatistics at Yale, reveals that this could help in choosing the suitable treatment, particularly with the increase in precision treatment options. The findings have been published in Mathematical Biosciences.

Cancerous cells mutate and evolve specific characteristics known as hallmarks of cancer, such as the ability to metastasize, grow or dodge immune cells, among others. They proliferate by differing mutations to acquire these capabilities. This persistence in adapting to the environment coupled with the problem of targeted medicine evolving into ineffectiveness owing to the survival and dominance of cells, makes cancer challenging to treat. Therefore, predicting future mutations could help avoid resistance.

Townsend, along with colleagues, earlier developed a method to estimate the influence of each mutation by examining the frequency of single mutations in various tumors. This was a significant advancement because it quantified the contribution of each mutation.

This recent study aimed to not only examine individual mutations but determine how they interact with subsequent mutations. Jorge Alfaro Murillo, co-author and associate research scientist in biostatistics at YSPH, outlined a new mathematical approach that could estimate these interactions with ample data.

Previously, researchers observed that certain mutations always coexisted, while others were mutually exclusive, thus presuming cooperation or antagonism between mutations. However, coexistence may not always indicate a biological interaction. Therefore this new method provides improved answers about gene interactions, taking into consideration mutation rates and sequence of mutation.

Despite this advancement, Alfaro Murillo and Townsend focused only on untreated tumors, and the next step is to study treated tumors and consider mutations resulting in more significant changes.

These analytical methods should improve cancer trials, making treatments involving multiple drugs more efficient. Alfaro Murillo suggests the potential for immediate application of specific drugs if we can predict subsequent mutations based on a tumor's mutation composition and therapy.