Cancer Mutation Interaction Analysis Holds Promise for Targeted Therapies

Cancer, characterized by uncontrolled cell growth due to mutations, poses challenges in understanding the complex interactions among these mutations. Yale School of Public Health (YSPH) introduces a method to analyze mutation interactions, aiding in predicting cancer evolution and developing targeted therapies. 

Researchers often oversimplify interactions between mutations, hindering comprehensive understanding. YSPH’s innovative approach aims to unravel how mutations influence tumor development, providing crucial insights for precision cancer treatment. 

Lead author Jeffrey P. Townsend explains the method’s significance in characterizing a cancer’s genetic trajectory within a patient. This information is valuable for tailoring treatments, especially in the evolving landscape of precision tumor therapy options. 

Cancer cells acquire hallmarks such as unregulated growth, metastasis, and immune evasion through mutations. The continual adaptation of cancer cells makes targeted treatment challenging. Predicting the likely mutations aids in developing strategies to combat resistance, a common issue with targeted drugs. 

Years ago, Townsend and colleagues pioneered a method to estimate the importance of each mutation to cancer. This breakthrough quantifies each mutation’s contribution, moving beyond simplistic categorizations. The focus then shifted to understanding interactions (epistasis) between mutations. 

The current study introduces a mathematical approach to estimate epistasis for pairs of point mutations. Jorge Alfaro-Murillo, the study’s first author, extends this approach to estimate interactions among three, four, or more mutations with sufficient data. 

Researchers observed certain mutations co-occurring in cancers, while others appeared mutually exclusive. Previous studies assumed either cooperation or antagonism between specific mutations. The new method challenges this binary view, considering that co-occurrences might not imply biological interactions. For instance, shared environmental exposures could lead to similar mutations independently. 

Townsend emphasizes that their method, unlike others focusing on mutual exclusivity and co-occurrence, provides a better understanding of gene interactions. It considers underlying mutation rates and the order in which mutations occur, a crucial factor. The sequence of mutations influences the biological consequences, challenging the oversimplified view of mutual exclusivity and co-occurrence. 

Alfaro-Murillo highlights the importance of understanding mutation order. For example, if a cell develops a mutation in a gene that promotes self-destruction before mutating a gene promoting cell multiplication, it safeguards against uncontrolled growth. However, if the order is reversed, the cell can evade self-destruction and multiply uncontrollably. 

The study’s limitation involves analyzing untreated tumors only. Future research will explore treated tumors and consider mutations resulting in larger changes, such as copy number alterations (CNAs) in the genome or chromosomal alterations. CNAs involve changes in the structure of chromosomes, influencing DNA copies and frequently occurring in various cancers. 

The analytical methods developed by YSPH aim to enhance the efficiency of cancer trials and treatments involving multiple drugs. Understanding a tumor’s mutation composition and predicting likely future mutations can streamline treatment decisions and preparations for potential developments. 

In conclusion, YSPH’s innovative method contributes to unraveling the intricate landscape of cancer evolution. By comprehensively assessing mutation interactions, the approach provides a nuanced understanding that goes beyond simplistic models. The implications extend to optimizing cancer treatments and preparing for evolving tumor landscapes, fostering advancements in precision medicine. 

Journal Reference  

Jorge A. Alfaro-Murillo et al, Pairwise and higher-order epistatic effects among somatic cancer mutations across oncogenesis, Mathematical Biosciences (2023). DOI: 10.1016/j.mbs.2023.109091.