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Approximately 75% of diagnosed breast cancer tumors are estrogen-receptor-positive tumors and are associated with a better prognosis due to response to hormonal therapies. However, around 40% of patients relapse after hormonal therapies. Genomic analysis of gene expression profiles in primary breast cancers and tamoxifen-resistant cell lines suggested the potential role of miR-489 in the regulation of estrogen signaling and development of tamoxifen resistance. Our in vitro analysis showed that loss of miR-489 expression promoted tamoxifen resistance, while overexpression of miR-489 in tamoxifen-resistant cells restored tamoxifen sensitivity. Mechanistically, we found that miR-489 is an estrogen-regulated miRNA that negatively regulates estrogen receptor signaling by using at least the following two mechanisms: (i) modulation of the ER phosphorylation status by inhibiting MAPK and AKT kinase activities; (ii) regulation of nuclear-to-cytosol translocation of estrogen receptor α (ERα) by decreasing p38 expression and consequently ER phosphorylation. In addition, miR-489 can break the positive feed-forward loop between the estrogen-Erα axis and p38 MAPK in breast cancer cells, which is necessary for its function as a transcription factor. Overall, our study unveiled the underlying molecular mechanism by which miR-489 regulates an estrogen signaling pathway through a negative feedback loop and uncovered its role in both the development of and overcoming of tamoxifen resistance in breast cancers.
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Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning
Abstract Breast carcinoma is the most common cancer among women worldwide that consists of a heterogeneous group of subtype diseases. The whole-slide images (WSIs) can capture the cell-level heterogeneity, and are routinely used for cancer diagnosis by pathologists. However, key driver genetic mutations related to targeted therapies are identified by genomic analysis like high-throughput molecular profiling. In this study, we develop a deep-learning model to predict the genetic mutations and biological pathway activities directly from WSIs. Our study offers unique insights into WSI visual interactions between mutation and its related pathway, enabling a head-to-head comparison to reinforce our major findings. Using the histopathology images from the Genomic Data Commons Database, our model can predict the point mutations of six important genes (AUC 0.68–0.85) and copy number alteration of another six genes (AUC 0.69–0.79). Additionally, the trained models can predict the activities of three out of ten canonical pathways (AUC 0.65–0.79). Next, we visualized the weight maps of tumor tiles in WSI to understand the decision-making process of deep-learning models via a self-attention mechanism. We further validated our models on liver and lung cancers that are related to metastatic breast cancer. Our results provide insights into the association between pathological image features, molecular outcomes, and targeted therapies for breast cancer patients.
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- Award ID(s):
- 1747778
- PAR ID:
- 10307467
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- npj Precision Oncology
- Volume:
- 5
- Issue:
- 1
- ISSN:
- 2397-768X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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