Cancer networks and beyond: interpreting mutations using the human interactome and protein structure

DSBSO-Based XL-MS Analysis of Breast Cancer PDX Tissues to Delineate Protein Interaction Network in Clinical Samples

Protein-protein interactions (PPIs) are fundamental to understanding biological systems as protein complexes are the active molecular modules critical for carrying out cellular functions. Dysfunctional PPIs have been associated with various diseases including cancer. Systems-wide PPI analysis not only sheds light on pathological mechanisms, but also represents a paradigm in identifying potential therapeutic targets. In recent years, cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for defining endogenous PPIs of cellular networks. While proteome-wide studies have been performed in cell lysates, intact cells and tissues, applications of XL-MS in clinical samples have not been reported. In this study, we adopted a DSBSO-based in vivo XL-MS platform to map interaction landscapes from two breast cancer patient-derived xenograft (PDX) models. As a result, we have generated a PDX interaction network comprising 2,557 human proteins and identified interactions unique to breast cancer subtypes. Interestingly, most of the observed differences in PPIs correlated well with protein abundance changes determined by TMT-based proteome quantitation. Collectively, this work has demonstrated the feasibility of XL-MS analysis in clinical samples, and established an analytical workflow for tissue cross-linking that can be generalized for mapping PPIs from patient samples in the future to dissect disease-relevant cellular networks.

Exosomes-Promising Carriers for Regulatory Therapy in Oncology

Extracellular vesicles (EVs), including exosomes and microvesicles, together with apoptotic bodies form a diverse group of nanoparticles that play a crucial role in intercellular communication, participate in numerous physiological and pathological processes. In the context of cancer, they can allow the transfer of bioactive molecules and genetic material between cancer cells and the surrounding stromal cells, thus promoting such processes as angiogenesis, metastasis, and immune evasion. In this article, we review recent advances in understanding how EVs, especially exosomes, influence tumor progression and modulation of the microenvironment. The key mechanisms include exosomes inducing the epithelial-mesenchymal transition, polarizing macrophages toward protumoral phenotypes, and suppressing antitumor immunity. The therapeutic potential of engineered exosomes is highlighted, including their loading with drugs, RNA therapeutics, or tumor antigens to alter the tumor microenvironment. Current techniques for their isolation, characterization, and engineering are discussed. Ongoing challenges include improving exosome loading efficiency, optimizing biodistribution, and enhancing selective cell targeting. Overall, exosomes present promising opportunities to understand tumorigenesis and develop more targeted diagnostic and therapeutic strategies by exploiting the natural intercellular communication networks in tumors. In the context of oncology, regulatory therapy provides the possibility of reproducing the original conditions that are unfavorable for the existence of the cancer process and may thus be a feasible alternative to population treatments. We also review current access to the technology enabling regulatory intervention in the cancer process using exosomes.

The Emerging Roles of Protein Interactions with O-GlcNAc Cycling Enzymes in Cancer

The dynamic O-GlcNAc modification of intracellular proteins is an important nutrient sensor for integrating metabolic signals into vast networks of highly coordinated cellular activities. Dysregulation of the sole enzymes responsible for O-GlcNAc cycling, O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA), and the associated cellular O-GlcNAc profile is a common feature across nearly every cancer type. Many studies have investigated the effects of aberrant OGT/OGA expression on global O-GlcNAcylation activity in cancer cells. However, recent studies have begun to elucidate the roles of protein-protein interactions (PPIs), potentially through regions outside of the immediate catalytic site of OGT/OGA, that regulate greater protein networks to facilitate substrate-specific modification, protein translocalization, and the assembly of larger biomolecular complexes. Perturbation of OGT/OGA PPI networks makes profound changes in the cell and may directly contribute to cancer malignancies. Herein, we highlight recent studies on the structural features of OGT and OGA, as well as the emerging roles and molecular mechanisms of their aberrant PPIs in rewiring cancer networks. By integrating complementary approaches, the research in this area will aid in the identification of key protein contacts and functional modules derived from OGT/OGA that drive oncogenesis and will illuminate new directions for anti-cancer drug development.

Random Matrix Analysis for Gene Interaction Networks in Cancer Cells

Investigations of topological uniqueness of gene interaction networks in cancer cells are essential for understanding the disease. Although cancer is considered to originate from the topological alteration of a huge molecular interaction network in cellular systems, the theoretical study to investigate such complex networks is still insufficient. It is necessary to predict the behavior of a huge complex interaction network from the behavior of a finite size network. Based on the random matrix theory, we study the distribution of the nearest neighbor level spacings P(s) of interaction matrices of gene networks in human cancer cells. The interaction matrices are computed using the Cancer Network Galaxy (TCNG) database which is a repository of gene interactions inferred by a Bayesian network model. 256 NCBI GEO entries regarding gene expressions in human cancer cells have been used for the inference. We observe the Wigner distribution of P(s) when the gene networks are dense networks that have more than ~38,000 edges. In the opposite case, when the networks have smaller numbers of edges, the distribution P(s) becomes the Poisson distribution. We investigate relevance of P(s) both to the sparseness of the networks and to edge frequency factor which is the reliance (likelihood) of the inferred gene interactions.

Systems-level effects of ectopic galectin-7 reconstitution in cervical cancer and its microenvironment

Background

Galectin-7 (Gal-7) is negatively regulated in cervical cancer, and appears to be a link between the apoptotic response triggered by cancer and the anti-tumoral activity of the immune system. Our understanding of how cervical cancer cells and their molecular networks adapt in response to the expression of Gal-7 remains limited.

Methods

Meta-analysis of Gal-7 expression was conducted in three cervical cancer cohort studies and TCGA. In silico prediction and bisulfite sequencing were performed to inquire epigenetic alterations. To study the effect of Gal-7 on cervical cancer, we ectopically re-expressed it in the HeLa and SiHa cervical cancer cell lines, and analyzed their transcriptome and SILAC-based proteome. We also examined the tumor and microenvironment host cell transcriptomes after xenotransplantation into immunocompromised mice. Differences between samples were assessed with the Kruskall-Wallis, Dunn’s Multiple Comparison and T tests. Kaplan–Meier and log-rank tests were used to determine overall survival.

Results

Gal-7 was constantly downregulated in our meta-analysis (p < 0.0001). Tumors with combined high Gal-7 and low galectin-1 expression (p = 0.0001) presented significantly better prognoses (p = 0.005). In silico and bisulfite sequencing assays showed de novo methylation in the Gal-7 promoter and first intron. Cells re-expressing Gal-7 showed a high apoptosis ratio (p < 0.05) and their xenografts displayed strong growth retardation (p < 0.001). Multiple gene modules and transcriptional regulators were modulated in response to Gal-7 reconstitution, both in cervical cancer cells and their microenvironments (FDR < 0.05 %). Most of these genes and modules were associated with tissue morphogenesis, metabolism, transport, chemokine activity, and immune response. These functional modules could exert the same effects in vitro and in vivo, even despite different compositions between HeLa and SiHa samples.

Conclusions

Gal-7 re-expression affects the regulation of molecular networks in cervical cancer that are involved in diverse cancer hallmarks, such as metabolism, growth control, invasion and evasion of apoptosis. The effect of Gal-7 extends to the microenvironment, where networks involved in its configuration and in immune surveillance are particularly affected.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-016-2700-8) contains supplementary material, which is available to authorized users.

Integration of multiple biological features yields high confidence human protein interactome

The biological function of a protein is usually determined by its physical interaction with other proteins. Protein-protein interactions (PPIs) are identified through various experimental methods and are stored in curated databases. The noisiness of the existing PPI data is evident, and it is essential that a more reliable data is generated. Furthermore, the selection of a set of PPIs at different confidence levels might be necessary for many studies. Although different methodologies were introduced to evaluate the confidence scores for binary interactions, a highly reliable, almost complete PPI network of Homo sapiens is not proposed yet. The quality and coverage of human protein interactome need to be improved to be used in various disciplines, especially in biomedicine. In the present work, we propose an unsupervised statistical approach to assign confidence scores to PPIs of H. sapiens. To achieve this goal PPI data from six different databases were collected and a total of 295,288 non-redundant interactions between 15,950 proteins were acquired. The present scoring system included the context information that was assigned to PPIs derived from eight biological attributes. A high confidence network, which included 147,923 binary interactions between 13,213 proteins, had scores greater than the cutoff value of 0.80, for which sensitivity, specificity, and coverage were 94.5%, 80.9%, and 82.8%, respectively. We compared the present scoring method with others for evaluation. Reducing the noise inherent in experimental PPIs via our scoring scheme increased the accuracy significantly. As it was demonstrated through the assessment of process and cancer subnetworks, this study allows researchers to construct and analyze context-specific networks via valid PPI sets and one can easily achieve subnetworks around proteins of interest at a specified confidence level.

Discovering and understanding oncogenic gene fusions through data intensive computational approaches

Although gene fusions have been recognized as important drivers of cancer for decades, our understanding of the prevalence and function of gene fusions has been revolutionized by the rise of next-generation sequencing, advances in bioinformatics theory and an increasing capacity for large-scale computational biology. The computational work on gene fusions has been vastly diverse, and the present state of the literature is fragmented. It will be fruitful to merge three camps of gene fusion bioinformatics that appear to rarely cross over: (i) data-intensive computational work characterizing the molecular biology of gene fusions; (ii) development research on fusion detection tools, candidate fusion prioritization algorithms and dedicated fusion databases and (iii) clinical research that seeks to either therapeutically target fusion transcripts and proteins or leverages advances in detection tools to perform large-scale surveys of gene fusion landscapes in specific cancer types. In this review, we unify these different-yet highly complementary and symbiotic-approaches with the view that increased synergy will catalyze advancements in gene fusion identification, characterization and significance evaluation.

A Network-Based Model of Oncogenic Collaboration for Prediction of Drug Sensitivity

Tumorigenesis is a multi-step process, involving the acquisition of multiple oncogenic mutations that transform cells, resulting in systemic dysregulation that enables proliferation, invasion, and other cancer hallmarks. The goal of precision medicine is to identify therapeutically-actionable mutations from large-scale omic datasets. However, the multiplicity of oncogenes required for transformation, known as oncogenic collaboration, makes assigning effective treatments difficult. Motivated by this observation, we propose a new type of oncogenic collaboration where mutations in genes that interact with an oncogene may contribute to the oncogene's deleterious potential, a new genomic feature that we term "surrogate oncogenes." Surrogate oncogenes are representatives of these mutated subnetworks that interact with oncogenes. By mapping mutations to a protein-protein interaction network, we determine the significance of the observed distribution using permutation-based methods. For a panel of 38 breast cancer cell lines, we identified a significant number of surrogate oncogenes in known oncogenes such as BRCA1 and ESR1, lending credence to this approach. In addition, using Random Forest Classifiers, we show that these significant surrogate oncogenes predict drug sensitivity for 74 drugs in the breast cancer cell lines with a mean error rate of 30.9%. Additionally, we show that surrogate oncogenes are predictive of survival in patients. The surrogate oncogene framework incorporates unique or rare mutations from a single sample, and therefore has the potential to integrate patient-unique mutations into drug sensitivity predictions, suggesting a new direction in precision medicine and drug development. Additionally, we show the prevalence of significant surrogate oncogenes in multiple cancers from The Cancer Genome Atlas, suggesting that surrogate oncogenes may be a useful genomic feature for guiding pancancer analyses and assigning therapies across many tissue types.

How do oncoprotein mutations rewire protein-protein interaction networks?

The acquisition of mutations that activate oncogenes or inactivate tumor suppressors is a primary feature of most cancers. Mutations that directly alter protein sequence and structure drive the development of tumors through aberrant expression and modification of proteins, in many cases directly impacting components of signal transduction pathways and cellular architecture. Cancer-associated mutations may have direct or indirect effects on proteins and their interactions and while the effects of mutations on signaling pathways have been widely studied, how mutations alter underlying protein-protein interaction networks is much less well understood. Systematic mapping of oncoprotein protein interactions using proteomics techniques as well as computational network analyses is revealing how oncoprotein mutations perturb protein-protein interaction networks and drive the cancer phenotype.

Proteomics in cancer biomarkers discovery: challenges and applications

With the introduction of recent high-throughput technologies to various fields of science and medicine, it is becoming clear that obtaining large amounts of data is no longer a problem in modern research laboratories. However, coherent study designs, optimal conditions for obtaining high-quality data, and compelling interpretation, in accordance with the evidence-based systems biology, are critical factors in ensuring the emergence of good science out of these recent technologies. This review focuses on the proteomics field and its new perspectives on cancer research. Cornerstone publications that have tremendously helped scientists and clinicians to better understand cancer pathogenesis; to discover novel diagnostic and/or prognostic biomarkers; and to suggest novel therapeutic targets will be presented. The author of this review aims at presenting some of the relevant literature data that helped as a step forward in bridging the gap between bench work results and bedside potentials. Undeniably, this review cannot include all the work that is being produced by expert research groups all over the world.

Alternative RNA structure-coupled gene regulations in tumorigenesis

Alternative RNA structures (ARSs), or alternative transcript isoforms, are critical for regulating cellular phenotypes in humans. In addition to generating functionally diverse protein isoforms from a single gene, ARS can alter the sequence contents of 5'/3' untranslated regions (UTRs) and intronic regions, thus also affecting the regulatory effects of these regions. ARS may introduce premature stop codon(s) into a transcript, and render the transcript susceptible to nonsense-mediated decay, which in turn can influence the overall gene expression level. Meanwhile, ARS can regulate the presence/absence of upstream open reading frames and microRNA targeting sites in 5'UTRs and 3'UTRs, respectively, thus affecting translational efficiencies and protein expression levels. Furthermore, since ARS may alter exon-intron structures, it can influence the biogenesis of intronic microRNAs and indirectly affect the expression of the target genes of these microRNAs. The connections between ARS and multiple regulatory mechanisms underline the importance of ARS in determining cell fate. Accumulating evidence indicates that ARS-coupled regulations play important roles in tumorigenesis. Here I will review our current knowledge in this field, and discuss potential future directions.

Entropy, complexity, and Markov diagrams for random walk cancer models

The notion of entropy is used to compare the complexity associated with 12 common cancers based on metastatic tumor distribution autopsy data. We characterize power-law distributions, entropy, and Kullback-Liebler divergence associated with each primary cancer as compared with data for all cancer types aggregated. We then correlate entropy values with other measures of complexity associated with Markov chain dynamical systems models of progression. The Markov transition matrix associated with each cancer is associated with a directed graph model where nodes are anatomical locations where a metastatic tumor could develop, and edge weightings are transition probabilities of progression from site to site. The steady-state distribution corresponds to the autopsy data distribution. Entropy correlates well with the overall complexity of the reduced directed graph structure for each cancer and with a measure of systemic interconnectedness of the graph, called graph conductance. The models suggest that grouping cancers according to their entropy values, with skin, breast, kidney, and lung cancers being prototypical high entropy cancers, stomach, uterine, pancreatic and ovarian being mid-level entropy cancers, and colorectal, cervical, bladder, and prostate cancers being prototypical low entropy cancers, provides a potentially useful framework for viewing metastatic cancer in terms of predictability, complexity, and metastatic potential.

Structural bioinformatics of the interactome

The past decade has seen a dramatic expansion in the number and range of techniques available to obtain genome-wide information and to analyze this information so as to infer both the functions of individual molecules and how they interact to modulate the behavior of biological systems. Here, we review these techniques, focusing on the construction of physical protein-protein interaction networks, and highlighting approaches that incorporate protein structure, which is becoming an increasingly important component of systems-level computational techniques. We also discuss how network analyses are being applied to enhance our basic understanding of biological systems and their disregulation, as well as how these networks are being used in drug development.

Impact of subdomain D1 of the short form S1b of the human prolactin receptor on its inhibitory action on the function of the long form of the receptor induced by prolactin

BACKGROUND

Long-form (LF) homodimers of the human prolactin receptor (PRLR) mediate prolactin's diverse actions. Short form S1b inhibits the LF function through heterodimerization. Reduced S1b/LF-ratio in breast cancer could contribute to tumor development/progression. Current work defines the structural and functional relevance of the D1 domain of S1b on its inhibitory function on prolactin-induced LF function.

METHODS

Studies were conducted using mutagenesis, promoter/signaling analyses, bioluminescence resonance energy transfer (BRET) and molecular modeling approaches.

RESULTS

Mutation of E69 in D1 S1b or adjacent residues at the receptor surface near to the binding pocket (S) causes loss of its inhibitory effect while mutations away from this region (A) or in the D2 domain display inhibitory action as the wild-type. All S1b mutants preserved prolactin-induced Jak2 activation. BRET reveals an increased affinity in D1 mutated S1b (S) homodimers in transfected cells stably expressing LF. In contrast, affinity in S1b homodimers with either D1 (A) or D2 mutations remained unchanged. This favors LF mediated signaling induced by prolactin. Molecular dynamics simulations show that mutations (S) elicit major conformational changes that propagate downward to the D1/D2 interface and change their relative orientation in the dimers.

CONCLUSIONS

These findings demonstrate the essential role of D1 on the S1b structure and its inhibitory action on prolactin-induced LF-mediated function.

GENERAL SIGNIFICANCE

Major changes in receptor conformation and dimerization affinity are triggered by single mutations in critical regions of D1. Our structure-function/simulation studies provide a basis for modeling and design of small molecules to enhance inhibition of LF activation for potential use in breast cancer treatment.