By targeting proteins used to splice genes, UC San Diego researchers have unlocked a new approach to treating triple-negative breast cancer, the most aggressive and difficult to treat subtype of the disease.

A new machine-learning-based approach to mapping real-time tumor metabolism in brain cancer patients, developed at the University of Michigan, could help doctors discover which treatment strategies are most likely to be effective against individual cases of glioma. The team verified the accuracy of the model by comparing it against human patient data and running mouse experiments.

Mutations in the tumor suppressor TP53 are a common cause of cancer, making the altered protein an attractive target for therapeutics. Among them, the Y220C mutation is the ninth most frequent and it creates a small crevice in the mutant protein that is not present in the wild type conformation. This druggable cavity has led to the development of small molecules such as rezatapopt that are designed to restore p53 and reactivate its normal tumor suppressor function. Rezatapopt has shown promising efficacy in early studies, but as with most targeted therapies, patients can eventually develop resistance to treatment.