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Published on: March 20, 2020
A signal-based method for finding driver modules of breast cancer metastasis to the lung
Gaibo Yan1,2, Vicky Chen1,2, Xinghua Lu1,2
1Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
Abstract:
Tumor metastasis is mainly caused by somatic genomic alterations (SGAs) that perturb pathways regulating metastasis-relevant activities and thus help the primary tumor to adapt to the new microenvironment. Identifying drivers of metastasis, i.e. SGAs, sheds light on the metastasis mechanism and provides guidance for targeted therapy. In this paper, we introduce a novel method to search for SGAs driving breast cancer metastasis to the lung. First, we search for transcriptomic modules with genes that are differentially expressed in breast cell lines with strong metastatic activities to the lung and co-expressed in a large number of breast tumors. Then, for each transcriptomic module, we search for a set of SGA genes (driver modules) such that genes in each driver module carry a common signal regulating the transcriptomic module. Evaluations indicate that many genes in driver modules are indeed related to metastasis, and our methods have identified many new driver candidates. We further choose two novel metastatic driver genes, BCL2L11 and CDH9, for in vitro verification. The wound healing assay reveals that inhibiting either BCL2L11 or CDH9 will enhance the migration of cell lines, which provides evidence that these two genes are suppressors of tumor metastasis.
Insights
Identifying somatic genomic alterations (SGAs) driving breast cancer metastasis is key for targeted therapy. This study introduces a novel method to find these SGAs, uncovering new candidates like BCL2L11 and CDH9 that suppress tumor metastasis.
Area of Science:
- Oncology
- Genomics
- Molecular Biology
Background:
- Tumor metastasis, a complex process, is driven by somatic genomic alterations (SGAs).
- Understanding metastasis mechanisms is crucial for developing effective targeted therapies.
- Identifying specific SGAs that promote metastasis can guide treatment strategies.
Purpose of the Study:
- To introduce a novel computational method for identifying SGAs that drive breast cancer lung metastasis.
- To discover new candidate genes involved in the metastatic process.
- To provide experimental validation for identified metastatic driver genes.
Main Methods:
- Transcriptomic analysis to identify co-expressed gene modules in metastatic breast cancer cell lines and tumors.
- Integration of transcriptomic data with genomic alterations to identify driver modules.
- In vitro wound healing assays to validate the role of candidate genes (BCL2L11, CDH9) in cell migration.
Main Results:
- The novel method successfully identified several candidate SGAs associated with breast cancer lung metastasis.
- Many identified genes within driver modules showed a significant relationship with metastatic processes.
- BCL2L11 and CDH9 were validated as novel metastatic suppressor genes, as their inhibition enhanced cell migration.
Conclusions:
- The developed method is effective in discovering novel metastatic driver candidates in breast cancer.
- BCL2L11 and CDH9 represent promising therapeutic targets for inhibiting breast cancer metastasis.
- Further research into these identified SGAs can enhance our understanding of metastasis and inform targeted treatment approaches.
