A Functional Network Model of the Metastasis Suppressor PEBP1/RKIP and Its Regulators in Breast Cancer Cells
Mahmoud Ahmed1, Trang Huyen Lai1, Wanil Kim1
1Department of Biochemistry and Convergence Medical Science, Institute of Health Sciences, Gyeongsang National University College of Medicine, Jinju 527-27, Korea.
Abstract:
Drug screening strategies focus on quantifying the phenotypic effects of different compounds on biological systems. High-throughput technologies have the potential to understand further the mechanisms by which these drugs produce the desired outcome. Reverse causal reasoning integrates existing biological knowledge and measurements of gene and protein abundances to infer their function. This approach can be employed to appraise the existing biological knowledge and data to prioritize targets for cancer therapies. We applied text mining and a manual literature search to extract known interactions between several metastasis suppressors and their regulators. We then identified the relevant interactions in the breast cancer cell line MCF7 using a knockdown dataset. We finally adopted a reverse causal reasoning approach to evaluate and prioritize pathways that are most consistent and responsive to drugs that inhibit cell growth. We evaluated this model in terms of agreement with the observations under treatment of several drugs that produced growth inhibition of cancer cell lines. In particular, we suggested that the metastasis suppressor PEBP1/RKIP is on the receiving end of two significant regulatory mechanisms. One involves RELA (transcription factor p65) and SNAI1, which were previously reported to inhibit PEBP1. The other involves the estrogen receptor (ESR1), which induces PEBP1 through the kinase NME1. Our model was derived in the specific context of breast cancer, but the observed responses to drug treatments were consistent in other cell lines. We further validated some of the predicted regulatory links in the breast cancer cell line MCF7 experimentally and highlighted the points of uncertainty in our model. To summarize, our model was consistent with the observed changes in activity with drug perturbations. In particular, two pathways, including PEBP1, were highly responsive and would be likely targets for intervention.
Insights
This study uses reverse causal reasoning to identify key pathways regulating metastasis suppressors in breast cancer. Two pathways involving PEBP1/RKIP show high responsiveness to growth-inhibiting drugs, suggesting potential therapeutic targets.
Area of Science:
- Systems biology
- Computational biology
- Cancer research
Background:
- Drug screening quantifies compound effects on biological systems, with high-throughput technologies aiding mechanistic understanding.
- Reverse causal reasoning integrates biological knowledge and molecular abundance data to infer gene and protein function, useful for prioritizing cancer therapy targets.
Purpose of the Study:
- To apply reverse causal reasoning to appraise existing biological knowledge and data for prioritizing cancer therapy targets.
- To identify and evaluate pathways responsive to growth-inhibiting drugs in breast cancer.
Main Methods:
- Text mining and manual literature search to extract known interactions of metastasis suppressors and their regulators.
- Identification of relevant interactions in the MCF7 breast cancer cell line using a knockdown dataset.
- Application of reverse causal reasoning to prioritize drug-responsive pathways.
Main Results:
- The model identified two significant regulatory mechanisms for the metastasis suppressor PEBP1/RKIP.
- One mechanism involves RELA and SNAI1 inhibiting PEBP1, while the other involves ESR1 inducing PEBP1 via NME1.
- The model's predictions were consistent with observed drug perturbation responses across different cell lines and experimentally validated in MCF7 cells.
Conclusions:
- Two pathways, including PEBP1, were found to be highly responsive to drug perturbations and represent potential targets for cancer intervention.
- The reverse causal reasoning approach effectively evaluated and prioritized pathways for therapeutic targeting in breast cancer.
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