An international team of researchers has discovered a new breast cancer gene, according to a study published online in the New England Journal of Medicine.
Multiple endocrine neoplasia type 1 (MEN1) is a rare hereditary disease in which a mutation of the MEN1 gene makes patients susceptible to developing both benign and malignant tumors on hormone-producing organs such as the parathyroid gland, pancreas, and pituitary gland. Since animal studies have suggested a link between the MEN1 gene and breast cancer, lead researchers Dr. Koen Dreijerink, PhD, from the Dana-Farber Cancer Institute in Boston, Massachusetts, U.S., and Dr. Gerlof Valk, PhD, from the University Medical Center (UMC) Utrecht, as well as colleagues from the University of Burgundy in Dijon, France, and the University of Tasmania in Australia, studied the risk of breast cancer in women with the syndrome (NEJM, 7 August, 2014).
The study included 190 Dutch female MEN1 patients. The researchers found that, among the women with a gene anomaly in the MEN1 gene, the probability of developing breast cancer was almost three times greater than for women without the gene anomaly, and they found that the disease occurs at a relatively young age (average age at diagnosis, 48). These results were confirmed in three studies among a total of 675 women with the MEN1 anomaly in Australia, the U.S., and France.
"Our study demonstrates for the first time that, in addition to the known risk of endocrine tumors, women with a mutation of the MEN1 gene also run a greater risk of developing breast cancer," Valk said in a statement released by UMC Utrecht. "Based on this finding, we will contact the Netherlands' clinical genetics association and screening organizations to discuss whether women with a mutation of the MEN1 gene should be regularly screened for breast cancer from an early age onwards."
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
