MRI-Based Deep Learning Guides Multi-Omics Discovery of NBPF4 as a Therapeutic Target for Breast Cancer Lymph Node Metastasis.
Authors
Affiliations (9)
Affiliations (9)
- Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China.
- Guangdong Provincial People's Hospital Ganzhou Hospital, Ganzhou Municipal Hospital, Ganzhou Innovation Center, National Regional Medical Center, Gannan Medical University, Ganzhou, China.
- The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China.
- Department of Urology, The Third Affiliated Hospital (the First Hosptial of Nanchang), Jiangxi Medical College, Nanchang University, Nanchang 330006, Jiangxi, China.
- Department of Clinical Laboratory, The First Affiliated Hospital of Nanchang University, Nanchang, China.
- Department of Breast Cancer, Foshan Women and Children Hospital Affiliated to Guangdong Medical University, Foshan, China.
- The First People's Hospital of Foshan (Foshan Hospital Affiliated to Southern University of Science and Technology), School of Medicine, Southern University of Science and Technology, Guangdong, China.
- Department of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, China.
- Wuxi College of Clinical Medicine, Nanjing Medical University, Wuxi, China.
Abstract
Deep learning models are increasingly used to analyze medical images, but their "black box" nature makes it hard to understand the underlying biology and slows down the development of targeted treatments. To tackle this, we built a multi-step approach that combines deep learning analysis of breast magnetic resonance imaging (MRI) with several types of molecular data, including gene activity, protein levels, and genetic information, along with laboratory experiments. Our MRI-based deep learning model accurately predicted whether breast cancer had spread to lymph nodes, and it performed consistently across 3 separate groups of patients. Causal inference using double least absolute shrinkage and selection operator (LASSO) and causal forest double machine learning established a significant effect of <i>NBPF4</i> expression on the imaging-defined high-risk phenotype, independent of genomic confounders. When we looked at which genes were linked to the imaging-defined high-risk pattern, one gene called <i>NBPF4</i> stood out because it was supported by all 4 kinds of evidence: imaging features, gene expression, protein data, and genetic association studies. Follow-up experiments in cells and animals showed that boosting <i>NBPF4</i> activity made tumor cells grow faster, move more, form new lymphatic vessels, and spread to lymph nodes. Mechanistically, <i>NBPF4</i> worked by activating the mitogen-activated protein kinase (MAPK) signaling pathway and triggering a process known as epithelial mesenchymal transition (EMT). Interestingly, tumors with high <i>NBPF4</i> were sensitive to drugs that block one part of the MAPK pathway (JNK/p38) but resistant to another part (ERK), suggesting that the pathway had been rewired. Using this insight, computer-based drug screening and further testing identified MK-886 as a promising compound that could suppress <i>NBPF4</i>-promoted MAPK activation and tumor growth. Together, this work traces a complete path from a noninvasive imaging finding to a specific gene (<i>NBPF4</i>) and a potential treatment (MK-886). It establishes the <i>NBPF4</i>-MAPK-EMT axis as a key player in breast cancer metastasis and provides a general framework for turning imaging-based risk predictions into biological understanding and possible therapies.