Scientists from the University of Manchester in the U.K. have identified a biological mechanism that may explain why women with dense breast tissue are at greater risk of developing breast cancer.
For the study, Dr. Michael Lisanti, PhD; Dr. Federica Sotgia, PhD; and colleagues worked with U.K. research organization Breakthrough Breast Cancer, as well as IBM researchers and academics in the U.S. and Cyprus. Results are published in the February 15 issue of Cell Cycle (Vol. 13:4, pp. 580-599).
The researchers used structural cells called fibroblasts from high-density breast tissue to generate a molecular signature. This signature showed that a cellular communication network called JNK1 was activated to a greater extent in fibroblasts from high-density breast tissue.
The JNK1 network is known to instruct cells to release chemicals that create an inflammatory environment, and inflammation is a driver of tumor formation, according to Lisanti and colleagues. Blocking the JNK1 network could reduce the risk of and potentially prevent breast cancer in women with high-density breast tissue.
The group also found that the molecular signature of the fibroblasts isolated from high-density tissue matched the fibroblasts found in breast tumors. This suggests that drugs that interfere with the JNK1 network could also be used to treat women who already have breast cancer.












![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&fit=crop&h=112&q=70&w=112)




