Rod K. Nibbe

An integrative -omics approach to identify functional sub-networks in human colorectal cancer.

Emerging evidence indicates that gene products implicated in human cancers often cluster together in “hot spots” in protein-protein interaction (PPI) networks. Additionally, small sub-networks within PPI networks that demonstrate synergistic differential expression with respect to tumorigenic phenotypes were recently shown to be more accurate classifiers of disease progression when compared to single targets identified by traditional approaches. However, many of these studies rely exclusively on mRNA expression data, a useful but limited measure of cellular activity. Proteomic profiling experiments provide information at the post-translational level, yet they generally screen only a limited fraction of the proteome. Here, we demonstrate that integration of these complementary data sources with a “proteomics-first” approach can enhance the discovery of candidate sub-networks in cancer that are well-suited for mechanistic validation in disease. We propose that small changes in the mRNA expression of multiple genes in the neighborhood of a protein-hub can be synergistically associated with significant changes in the activity of that protein and its network neighbors. Further, we hypothesize that proteomic targets with significant fold change between phenotype and control may be used to “seed” a search for small PPI sub-networks that are functionally associated with these targets. To test this hypothesis, we select proteomic targets having significant expression changes in human colorectal cancer (CRC) from two independent 2-D gel-based screens. Then, we use random walk based models of network crosstalk and develop novel reference models to identify sub-networks that are statistically significant in terms of their functional association with these proteomic targets. Subsequently, using an information-theoretic measure, we evaluate synergistic changes in the activity of identified sub-networks based on genome-wide screens of mRNA expression in CRC. Cross-classification experiments to predict disease class show excellent performance using only a few sub-networks, underwriting the strength of the proposed approach in discovering relevant and reproducible sub-networks.

Discovery and Scoring of Protein Interaction Subnetworks Discriminative of Late Stage Human Colon Cancer

We used a systems biology approach to identify and score protein interaction subnetworks whose activity patterns are discriminative of late stage human colorectal cancer (CRC) versus control in colonic tissue. We conducted two gel-based proteomics experiments to identify significantly changing proteins between normal and late stage tumor tissues obtained from an adequately sized cohort of human patients. A total of 67 proteins identified by these experiments was used to seed a search for protein-protein interaction subnetworks. A scoring scheme based on mutual information, calculated using gene expression data as a proxy for subnetwork activity, was developed to score the targets in the subnetworks. Based on this scoring, the subnetwork was pruned to identify the specific protein combinations that were significantly discriminative of late stage cancer versus control. These combinations could not be discovered using only proteomics data or by merely clustering the gene expression data. We then analyzed the resultant pruned subnetwork for biological relevance to human CRC. A number of the proteins in these smaller subnetworks have been associated with the progression (CSNK2A2, PLK1, and IGFBP3) or metastatic potential (PDGFRB) of CRC. Others have been recently identified as potential markers of CRC (IFITM1), and the role of others is largely unknown in this disease (CCT3, CCT5, CCT7, and GNA12). The functional interactions represented by these signatures provide new experimental hypotheses that merit follow-on validation for biological significance in this disease. Overall the method outlines a quantitative approach for integrating proteomics data, gene expression data, and the wealth of accumulated legacy experimental data to discover significant protein subnetworks specific to disease.

Subnetwork state functions define dysregulated subnetworks in cancer

Emerging research demonstrates the potential of protein-protein interaction (PPI) networks in uncovering the mechanistic bases of cancers, through identification of interacting proteins that are coordinately dysregulated in tumorigenic and metastatic samples When used as features for classification, such coordinately dysregulated subnetworks improve diagnosis and prognosis of cancer considerably over single-gene markers However, existing methods formulate coordination between multiple genes through additive representation of their expression profiles and utilize greedy heuristics to identify dysregulated subnetworks, which may not be well suited to the potentially combinatorial nature of coordinate dysregulation Here, we propose a combinatorial formulation of coordinate dysregulation and decompose the resulting objective function to cast the problem as one of identifying subnetwork state functions that are indicative of phenotype Based on this formulation, we show that coordinate dysregulation of larger subnetworks can be bounded using simple statistics on smaller subnetworks We then use these bounds to devise an efficient algorithm, Crane, that can search the subnetwork space more effectively than simple greedy algorithms Comprehensive cross-classification experiments show that subnetworks identified by Crane significantly outperform those identified by greedy algorithms in predicting metastasis of colorectal cancer (CRC).

Protein-protein interaction networks and subnetworks in the biology of disease.

The main goal of systems medicine is to provide predictive models of the patho‐physiology of complex diseases as well as define healthy states. The reason is clear—we hope accurate models will ultimately lead to more specific and sensitive markers of disease that will help clinicians better stratify their patient populations and optimize treatment plans. In addition, we expect that these models will define novel targets for combating disease. However, for many complex diseases, particularly at the clinical level, it is becoming increasingly clear that one or a few genomic variations alone (e.g., simple models) cannot adequately explain the multiple phenotypes related to disease states, or the variable risks that attend disease progression. We suggest that models that account for the activities of many interacting proteins will explain a wider range of variability inherent in these phenotypes. These models, which encompass protein interaction networks dysregulated for specific diseases and specific patient sub‐populations, will be constructed by integrating protein interaction data with multiple types of other relevant cellular information. Protein interaction databases are thus playing an increasingly important role in systems biology approaches to the study of disease. They present us with a static, but highly functional view of the cellular state, and thus give us a better understanding of not only the normal phenotype, but also the overall disease phenotype at the level of the whole organism when certain interactions become dysregulated. WIREs Syst Biol Med 2011 3 357–367 DOI: 10.1002/wsbm.121

Subnetwork state functions define dysregulated subnetworks in cancer.

Emerging research demonstrates the potential of protein-protein interaction (PPI) networks in uncovering the mechanistic bases of cancers, through identification of interacting proteins that are coordinately dysregulated in tumorigenic and metastatic samples. When used as features for classification, such coordinately dysregulated subnetworks improve diagnosis and prognosis of cancer considerably over single-gene markers. However, existing methods formulate coordination between multiple genes through additive representation of their expression profiles and utilize fast heuristics to identify dysregulated subnetworks, which may not be well suited to the potentially combinatorial nature of coordinate dysregulation. Here, we propose a combinatorial formulation of coordinate dysregulation and decompose the resulting objective function to cast the problem as one of identifying subnetwork state functions that are indicative of phenotype. Based on this formulation, we show that coordinate dysregulation of larger subnetworks can be bounded using simple statistics on smaller subnetworks. We then use these bounds to devise an efficient algorithm, Crane, that can search the subnetwork space more effectively than existing algorithms. Comprehensive cross-classification experiments show that subnetworks identified by Crane outperform those identified by additive algorithms in predicting metastasis of colorectal cancer (CRC).

Omics and biomarkers development for intestinal tumorigenesis

While considerable research into colorectal cancer (CRC) has implicated many genetic alterations that trigger the disease and sustain its progression, there are few well-validated, clinically useful molecular biomarkers of CRC. The observation that cancer is highly diverse across individual tumors is manifested at the molecular level by concomitantly diverse patterns of gene expression. However, while analysis of gene expression has been used to identify candidate biomarkers of cancer, such biomarkers frequently do not cross validate well on independent datasets and this has raised legitimate concerns regarding the usefulness of gene expression based markers. It is has been postulated that by integrating the functional information of gene products into the approach, networks of mechanistically related gene products may be identified and used to develop more robust biomarkers. Many such approaches focus on established signaling pathways for this purpose; however, pathways consisting of a few proteins interacting in a serial fashion oversimplify, and provide inadequate models for, a complex phenotype (e.g. CRC) mediated by a constellation of interacting gene products. Here, we discuss several integrative techniques based on cellular networks (protein–protein interactions) and incorporation of lower-coverage, but functionally relevant proteomic data, and show the power these techniques hold for prioritizing disease genes for biomarker discovery and biological verification of function.

Molecular Networks and Complex Diseases

Many human diseases are based on a set of complex interactions among multiple genetic and environmental factors. Recent developments in biotechnology have enabled interrogation of the cell at various levels leading to many types of “omic” data that provide valuable information on these factors and their interactions. These data include (1) genomic data, which reveals possible genetic factors involved in disease, (2) transcriptomic data, which reveals changes in regulation of gene expression, and (3) proteomic data, which reveals irregularities in the amount of functional proteins in affected tissues. While these data are very useful in understanding differences between disease phenotypes, they provide information at the level of a single molecular type. To integrate these disparate data types, molecular network analysis is invaluable in uncovering the relations between disparate molecular targets and understanding disease development and progression at the systems level. This chapter provides an overview of current findings on the systems biology of human diseases in the context of molecular networks and outlines current computational approaches in network biology of human diseases.