Scientists at St. Jude Children’s Research Hospital have found that genetics and type of cancer treatment contribute most to a survivor’s risk of a second cancer.

A lab-designed molecule developed and extensively studied by scientists with Virginia Tech’s Fralin Biomedical Research Institute at VTC could represent a breakthrough in slowing tumor recurrence in glioblastoma, an aggressive and deadly form of brain cancer.

Carnegie Mellon University researchers have developed a new way to help doctors make better, personalized decisions and predict how a disease or treatment might play out in the future. Researchers from CMU’s School of Computer Science developed a new approach to bridge the gap between available data and actionable insight, creating personalized models to help doctors better understand individual patients and improve their prognosis. The researchers published their work in the Proceedings of the National Academy of Sciences. The team introduced contextualized modeling, a family of ultra-personalized machine learning methods, to build individualized gene network models for nearly 8,000 tumors across 25 cancer types simultaneously. These networks helped identify new cancer biology, revealing hidden cancer subtypes and improving survival predictions, especially for rare cancers. This development opens the door to more precise, individualized cancer treatment.