Cancer remains a leading cause of death worldwide, with early and accurate diagnosis being paramount for effective treatment. However, traditional diagnostic methods like biopsies are invasive and carry risks, while non-invasive approaches often struggle to identify reliable biomarkers due to tumor heterogeneity and the complexity of biological data. Integrating information across different molecular layers—genomics, transcriptomics, proteomics, and metabolomics—holds promise but is technically challenging, particularly in capturing the dynamic metabolic state of tumors.

Radiotherapy (RT) is one of the most widely used cancer treatment modalities, applied in over half of all patients with cancer. In clinical oncology, positron emission tomography (PET) with ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) is widely used to noninvasively monitor tumor glucose metabolism and evaluate therapeutic responses, including those to RT. However, transient increases in ¹⁸F-FDG uptake—referred to as post-RT “metabolic flares”—are frequently observed in responding tumors and have traditionally been attributed to localized inflammatory reactions. Whether these flares reflect underlying immune cell dynamics, particularly tumor-infiltrating T cells, has remained poorly understood.

In proof-of-concept study, scientists at Cincinnati Children's discover a method to reduce heart damage risk for people with cancer taking immune checkpoint inhibitors.

A Japanese study of more than 7,400 patients has identified a genetic mismatch that sharply increases the risk of severe acute graft-versus-host disease (GVHD) following umbilical cord blood transplantation. The specific donor–recipient human leukocyte antigen mismatch triples the likelihood of life-threatening immune complications, and severe acute GVHD itself is associated with significantly worse survival. These findings may help refine donor selection and improve the safety and long-term outcomes of stem cell transplantation.