A multi-step approach that combines deep learning analysis of breast magnetic resonance imaging with several types of molecular data, including gene activity, protein levels, and genetic information, along with laboratory experiments establishes the NBPF4–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.
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 NBPF4 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 NBPF4 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 NBPF4 activity made tumor cells grow faster, move more, form new lymphatic vessels, and spread to lymph nodes. Mechanistically, NBPF4 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 NBPF4 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 NBPF4-promoted MAPK activation and tumor growth. Together, this work traces a complete path from a noninvasive imaging finding to a specific gene (NBPF4) and a potential treatment (MK-886). It establishes the NBPF4–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.
Accurate assessment of Ki-67 expression levels in breast cancer is crucial for determining prognosis and making informed treatment decisions. Current immunohistochemical methods relying on needle biopsy introduce sampling errors due to tumor spatial heterogeneity, making the development of non-invasive, precise preoperative prediction methods of significant clinical importance. This study aims to explore and compare advanced deep learning models based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for noninvasive assessment of Ki-67 expression. This retrospective study analyzed preoperative DCE-MRI data from 308 patients with histologically confirmed breast cancer. Adjacent slices centered on the tumor’s most significant cross-section were obtained to create a 2.5-dimensional (2·5D) dataset. We innovatively developed two deep learning models using the same dataset (1): a Multi-Instance Learning (MIL) model that combines slice-level predictive features with Predictive Likelihood Histogram (PLH) and Bag-of-Words (BoW) techniques (2); a Transformer-based fusion model that directly captures global contextual relationships between slices via self-attention mechanisms. The predictive performance of both models was systematically compared with traditional radiomics and clinical models. On an independent test set, the Transformer fusion model demonstrated optimal predictive performance with an area under the curve (AUC) of 0.875, achieving accuracy, sensitivity, and specificity of 0.839, 0.848, and 0.833, respectively. The MIL model ranked second (AUC = 0.825), with both models significantly outperforming traditional radiomics models (AUC = 0.698) and clinical models (AUC = 0.648). Deep learning models based on 2·5D DCE-MRI, especially Transformer models that achieve global feature fusion through self-attention mechanisms, can effectively and non-invasively predict Ki-67 expression status in breast cancer, surpassing traditional methods. This model shows potential as a reliable tool to help clinicians accurately assess tumor proliferation activity before surgery.
Yiying Cao, Mi Lin, Yanshan Ouyang et al.· Cancer Imaging· 0 citations
By embedding three-dimensional genome organization into deep-learning models, OMNIS nominates biologically coherent, context-specific drivers of progression and may guide future biomarker development and personalized therapy in precision oncology.
Junxian Li, Yuchen Xing, Ximin Gao et al.· Frontiers in Artificial Inte...· 0 citations
Metastasis remains the leading cause of cancer-related mortality, yet predicting future metastasis is a major clinical challenge due to the lack of validated biomarkers and effective assessment methods. Here, we present EmitGCL, a deep-learning framework that accurately predicts future metastasis and its corresponding biomarkers. Based on a comprehensive benchmarking comparison, EmitGCL outperformed other computational tools across six cancer types from seven cohorts of patients with superior sensitivity and specificity. It captured occult metastatic cells in a patient with a lymph node-negative breast cancer, who was declared to have no evidence of disease by conventional imaging methods but was later confirmed to have a metastatic disease. Notably, EmitGCL identified HSP90AA1 and HSP90AB1 as predictable biomarkers for future breast cancer metastasis, which was validated across five independent cohorts of patients (n=420). Furthermore, we demonstrated YY1 transcription factor as a key driver of breast cancer metastasis which was validated through in-silico and CRISPR-based migration assays, suggesting that YY1 is a potential therapeutic target for deterring metastasis.
Xiaoying Wang, Maoteng Duan, Po-Lan Su et al.· bioRxiv· 0 citations
Metastasis remains the leading cause of cancer-related mortality, yet predicting future metastasis is a major clinical challenge due to the lack of validated biomarkers and effective assessment methods. Here, we present EmitGCL, a deep-learning framework that accurately predicts future metastasis and its corresponding biomarkers. Based on a comprehensive benchmarking comparison, EmitGCL outperforms other computational tools across six cancer types from seven cohorts of patients with superior sensitivity and specificity. It captures occult metastatic cells in a patient with a lymph node-negative breast cancer, who was declared to have no evidence of disease by conventional imaging methods but was later confirmed to have metastatic disease. Notably, EmitGCL identifies HSP90AA1 and HSP90AB1 as predictable biomarkers for future breast cancer metastasis, which we validate by in-vitro pharmacological inhibition of HSP90 that reduced breast cancer cell migration and further support across five independent cohorts of patients (n = 420). Furthermore, we demonstrate YY1 transcription factor as a key driver of breast cancer metastasis, which we corroborate with in-silico, CRISPR-based migration assays, and in vivo mouse lung colonization experiments, suggesting that YY1 is a potential therapeutic target for further investigation. Predicting future metastases remains a major clinical challenge. Here, the authors develop EmitGCL, a deep-learning framework to predict metastasis and related biomarkers using cancer single-cell sequencing data, enabling and validating the discovery of occult metastases and breast cancer metastasis biomarkers.
Xiaoying Wang, Maoteng Duan, Anthony J. Snyder et al.· Nature Communications· 0 citations
ProphDR is an interpretable deep learning framework that integrates multiomics data and drug structural information using a hierarchical attention mechanism, and generates biologically interpretable attention maps that highlight key pharmacophores and resistance-related genes consistent with established mechanisms in NSCLC and BRCA.
Yundian Zeng, Qing Ye, Jike Wang et al.· Journal of Chemical Informat...· 0 citations
OBJECTIVE
Intratumoral heterogeneity may limit the representativeness of biopsy-based Ki-67 assessment in breast cancer. We therefore developed and validated a habitat-guided 2.5D deep learning (DL) model based on multiparametric MRI for noninvasive preoperative prediction of high versus low Ki-67 expression.
METHODS
This retrospective study enrolled 333 patients with invasive breast carcinoma from 2 distinct MRI vendor cohorts (Siemens, training set, n=233; United Imaging, independent test set, n=100). All patients underwent preoperative multiparametric MRI, including DCE-MRI and DWI. Hemodynamic parametric maps (wash-in, wash-out) and ADC maps were generated and subsequently clustered using a K-means algorithm (k=3) to create a functional habitat mask that quantitatively encodes intratumoral heterogeneity. A 7-channel 2.5D input tensor was then constructed by concatenating the central habitat-guided slice with its 6 adjacent anatomic slices. A ResNet18 backbone was trained to classify high (≥20%) versus low Ki-67 expression. The model's performance was rigorously evaluated against conventional 2D DL, clinical, and combined (DL+clinical) models using AUC, the DeLong test, and decision curve analysis (DCA).
RESULTS
In the challenging independent cross-vendor test set, our habitat-guided DL25D model demonstrated superior performance, achieving an AUC of 0.821 (95% CI: 0.736-0.906) and a sensitivity of 0.804. It significantly outperformed both the conventional DL2D model (AUC: 0.654, P=0.002) and the clinical model (AUC: 0.686, P=0.019). The incorporation of clinical variables failed to yield further improvement (combined model AUC: 0.837, P=0.483 vs. DL25D; NRI=0.021, P>0.05). DCA confirmed the superior net clinical benefit of our approach across a wide spectrum of threshold probabilities. Importantly, Grad-CAM visualizations revealed that the habitat-guided model strategically focused its attention on intratumoral core regions, whereas the conventional 2D model was distracted by tumor margins and background tissue.
CONCLUSIONS
The habitat-guided 2.5D deep learning model showed potential as a noninvasive imaging adjunct for preoperative Ki-67 status prediction in breast cancer. Multicenter prospective validation is required before clinical use.
Zeyang Miao, Run Xu, Meng-Yao Guo et al.· Journal of computer assisted...· 0 citations
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