Project

Perturbing biological networks in cancer cell lines

Goal: Studying causal biological networks and their perturbation using existing biological knowledge and data.

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Mahmoud Ahmed
added 2 research items
Screening for potential cancer therapies using existing large datasets of drug perturbations requires expertise and resources not available to all. This is often a barrier for lab scientists to tap into these valuable resources. To address these issues, one can take advantage of prior knowledge especially those coded in standard formats such as causal biological networks (CBN). Large datasets can be converted into appropriate structures, analyzed once and the results made freely available in easy-to-use formats. In this three parts tutorial, we will give a full description of one large scale analysis of using this approach, one case study of building a network of metastasis suppressors from scratch, and a walkthrough example code to perform and adapt these tools for different use cases
Breast cancer is the most prevalent type of cancer among women, and the vast majority of deaths are due to metastasis. Effective cancer therapies require an understanding of the underlying mechanisms and regulatory pathways of genes involved in metastasis. 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. Here, we suggested two key pathways regulating the metastasis suppressor RKIP. One involves RELA (transcription factor p65) and SNAI1 to inhibit RKIP. The other involves the estrogen receptor (ESR1) inducing RKIP through the kinase NME1. We further validated some of the predicted regulatory links in the 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.
Mahmoud Ahmed
added a research item
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.
Mahmoud Ahmed
added a research item
Objectives • Integrate gene expression of cancer cell lines under drug perturbations and causal biological networks data • Evaluate the effect of drug treatments on perturbing the biological networks • Build a database and an interactive interface to make the output of the analysis accessible to all Summary Background. Screening for potential cancer therapies using existing large datasets of drug perturbations requires expertise and resources not available to all. This is often a barrier for lab scientists to tap into these valuable resources. To address these issues, one can take advantage of prior knowledge especially those coded in standard formats such as causal biological networks (CBN) [1]. Large datasets can be converted into appropriate structures, analyzed once and the results made freely available in easy-to-use formats. Methods. We used the library of integrated library signatures (LINCS) to model the cell-specific effect of hundreds of drug treatments on gene expression [2]. These signatures were then used to predict the effect of the treatments on several causal biological networks using the network perturbation amplitudes (NPA) analysis [4, 3]. Implementation. We packaged the pre-computed scores in a database with an interactive web interface. The intuitive user-friendly interface can be used to query the database for drug perturbations and quantify their effect on multiple key biological functions in cancer cell lines. In addition to describing the process of building the database and the interface, we provide a realistic use case to explain how to use and interpret the results. Conclusion. To sum, we pre-computed cancer cell-specific perturbation amplitudes of several biological networks and made the output available in a database with an interactive web interface.
Mahmoud Ahmed
added a research item
Screening for potential cancer therapies using existing large datasets of drug perturbations requires expertise and resources not available to all. This is often a barrier for lab scientists to tap into these valuable resources. To address these issues, one can take advantage of prior knowledge especially those coded in standard formats such as causal biological networks (CBN). Large datasets can be converted into appropriate structures, analyzed once and the results made freely available in easy-to-use formats. We used the Library of Integrated Cellular Signatures to model the cell-specific effect of hundreds of drug treatments on gene expression. These signatures were then used to predict the effect of the treatments on several CBN using the network perturbation amplitudes analysis. We packaged the pre-computed scores in a database with an interactive web interface. The intuitive user-friendly interface can be used to query the database for drug perturbations and quantify their effect on multiple key biological functions in cancer cell lines. In addition to describing the process of building the database and the interface, we provide a realistic use case to explain how to use and interpret the results. To sum, we pre-computed cancer-cell-specific perturbation amplitudes of several biological networks and made the output available in a database with an interactive web interface. Database URL https://mahshaaban.shinyapps.io/LINPSAPP/.
Mahmoud Ahmed
added a project goal
Studying causal biological networks and their perturbation using existing biological knowledge and data.