MEDICI: Mining Essentiality Data to Identify Critical Interactions for Cancer Drug Target Discovery and Development

The Use of Informed Priors in Biclustering of Gene Expression with the Hierarchical Dirichlet Process

We motivate and describe the application of Hierarchical Dirichlet Process (HDP) models to the "soft" biclustering of gene expression data, in which we obtain modules (biclusters) where the affiliation of genes and samples with the modules are weighted, instead of being hard memberships. As a distinct contribution, we propose a method which HDP is informed with prior beliefs, significantly increasing the quality of the biclustering in terms of both the correctness of the number of modules inferred, and the precision of these modules, especially when evidence is sparse. We outline two such informed priors; one based on co-expression relationships inherent in the data, the other based on an externally provided regulatory network. We validate these results and compare the performance of our approach to Weighted Gene Correlation Network Analysis (WGCNA), another model that features weighted modules. We have, to this end, performed experiments on semi-synthetic data. The results show that HDP, with the addition of a well-informed prior, is able to capture the correct number of modules with increased accuracy. Furthermore, the model becomes robust to changes in the strength of the prior. We conclude by discussing these results and the benefits provided by our approach for gene expression analysis and network validation.

Explore Protein-Protein Interactions for Cancer Target Discovery Using the OncoPPi Portal

Protein-protein interactions (PPIs) control all functions and physiological states of the cell. Identification and understanding of novel PPIs would facilitate the discovery of new biological models and therapeutic targets for clinical intervention. Numerous resources and PPI databases have been developed to define a global interactome through the PPI data mining, curation, and integration of different types of experimental evidence obtained with various methods in different model systems. On the other hand, the recent advances in cancer genomics and proteomics have revealed a critical role of genomic alterations in acquisition of cancer hallmarks through a dysregulated network of oncogenic PPIs. Deciphering of cancer-specific interactome would uncover new mechanisms of oncogenic signaling for therapeutic interrogation. Toward this goal our team has developed a high-throughput screening platform to detect PPIs between cancer-associated proteins in the context of cancer cells. The established network of oncogenic PPIs, termed the OncoPPi network, is available through the OncoPPi Portal, an interactive web resource that allows to access and interpret a high-quality cancer-focused network of PPIs experimentally detected in cancer cell lines integrated with the analysis of mutual exclusivity of genomic alterations, cellular co-localization of interacting proteins, domain-domain interactions, and therapeutic connectivity. This chapter presents a guide to explore the OncoPPi network using the OncoPPi Portal to facilitate cancer biology.

The OncoPPi Portal: an integrative resource to explore and prioritize protein-protein interactions for cancer target discovery

Motivation

As cancer genomics initiatives move toward comprehensive identification of genetic alterations in cancer, attention is now turning to understanding how interactions among these genes lead to the acquisition of tumor hallmarks. Emerging pharmacological and clinical data suggest a highly promising role of cancer-specific protein-protein interactions (PPIs) as druggable cancer targets. However, large-scale experimental identification of cancer-related PPIs remains challenging, and currently available resources to explore oncogenic PPI networks are limited.

Results

Recently, we have developed a PPI high-throughput screening platform to detect PPIs between cancer-associated proteins in the context of cancer cells. Here, we present the OncoPPi Portal, an interactive web resource that allows investigators to access, manipulate and interpret a high-quality cancer-focused network of PPIs experimentally detected in cancer cell lines. To facilitate prioritization of PPIs for further biological studies, this resource combines network connectivity analysis, mutual exclusivity analysis of genomic alterations, cellular co-localization of interacting proteins and domain-domain interactions. Estimates of PPI essentiality allow users to evaluate the functional impact of PPI disruption on cancer cell proliferation. Furthermore, connecting the OncoPPi network with the approved drugs and compounds in clinical trials enables discovery of new tumor dependencies to inform strategies to interrogate undruggable targets like tumor suppressors. The OncoPPi Portal serves as a resource for the cancer research community to facilitate discovery of cancer targets and therapeutic development.

Availability and implementation

The OncoPPi Portal is available at http://oncoppi.emory.edu.

Contact

andrey.ivanov@emory.edu or hfu@emory.edu.