Breast cancer is a highly heterogeneous malignancy among women worldwide. Traditional prognostic models relying solely on clinicopathological features offer limited predictive accuracy and lack molecular-level insights. Unlike such conventional approaches, this study integrates proteomic and clinical data within an interpretable deep learning framework to improve prognostic precision and biological interpretability. We aimed to develop a more reliable model to accurately predict the 5-year survival status of patients with breast cancer using multi-omics data. The model integrating proteomics and clinical features demonstrated superior performance (AUC = 0.8136) compared to other feature combination models. The optimized model with 13 key features (4 clinical features and 9 proteins) achieved an AUC of 0.864 with the precision of 0.970, the recall of 0.810, and F1-score of 0.883. SHapley Additive exPlanations analysis identified MPHOSPH10, EGFR, ARL3, KRT18, lymph node status, and HER2 status as the most influential features, while Kolmogorov–Arnold Network analysis provided explicit mathematical relationships between key contributors and prediction outcomes. Collectively, our interpretable multi-modal model demonstrates robust performance in predicting 5-year survival in breast cancer patients and offers mechanistic insights, thereby enhancing its potential for clinical translation through the development of an accessible prediction tool.

A naturally occurring protein that tends to be expressed at higher levels in breast cancer cells boosts the effectiveness of some anticancer agents, including doxorubicin, one of the most widely used chemotherapies, and a preclinical drug known as ErSO, researchers report. The protein, FGD3, contributes to the rupture of cancer cells disrupted by these drugs, boosting their effectiveness and enhancing anticancer immunotherapies.

Obesity and cancer are two major health challenges of our time, yet the link between them remains only partially understood. New research now highlights a molecular chain of events in estrogen receptor-positive breast cancer, revealing how leptin, a hormone produced by fat cells, signals cancer cells to grow. This leads to increased activity of the enzyme stearoyl-CoA desaturase (SCD), which fuels the growth and motility of cancer cells. The findings from a study in The American Journal of Pathology, published by Elsevier, provides insights into the obesity–breast cancer link and identifies potential new therapeutic targets to counter obesity-driven breast cancer progression.

CSHL Professor David Spector and graduate student Disha Aggarwal have tracked levels of long non-coding RNA MALAT1 over the course of one woman’s experience with triple-negative breast cancer. Their findings could help inform clinical trials of a drug targeting MALAT1. They also suggest MALAT1 may play a role in determining whether and where the cancer will spread.