A new preclinical study has identified a signaling mechanism that drives metastasis in triple-negative breast cancer (TNBC). The experiments provide insight into why some patients with TNBC who receive drugs called beta-blockers might be at a lower risk of metastasis, and also identify a transcription factor that holds promise as a prognostic and therapeutic target. TNBC and other tumors feed on the body’s sympathetic nervous system to fuel their growth and metastasis. As a result, researchers have started to treat some cancers with beta-blockers, which inhibit the β2-adrenergic receptors that respond to adrenaline. Recent research has shown that the use of beta-blockers can reduce mortality in patients with TNBC by as much as 26%, and seems to extend the time between diagnosis and metastasis. However, much remains unknown about how β2-adrenergic receptors support metastasis, and why the receptors activate metastatic pathways in some tumor cells but not others. Terrance Lam and colleagues analyzed 11 lines of TNBC cells and focused on the role of a molecular loop that involves calcium and cAMP, crucial intracellular messengers. They discovered that in some of the cells, a transcription factor named Hox-C12 supported metastasis by connecting functional β2-adrenergic receptors to the cAMP-calcium loop, but in a manner that didn’t involve transcription. The team also found that knocking out the gene HOXC12 suppressed metastatic behavior in cultured TNBC cells, and confirmed that the expression of HOXC12 correlated with shorter survival times in patients with TNBC. “In future work, it will be important to determine whether HOXC12 can be used to identify patients who will benefit from [beta]-blocker therapy at the time of diagnosis,” Lam et al. conclude.

Catheryn Yashar, MD, has been named president-elect of the American Society of Radiation Oncology, which represents more than 10,000 members internationally.

A hybrid reading strategy for screening mammography, developed by Dutch researchers and deployed retrospectively to more than 40,000 exams, reduced radiologist workload by 38% without changing recall or cancer detection rates. The study, which emphasizes AI confidence, was published today.