Kelly Regan

Oligo- and Polymetastatic Progression in Lung Metastasis(es) Patients Is Associated with Specific MicroRNAs

Rationale Strategies to stage and treat cancer rely on a presumption of either localized or widespread metastatic disease. An intermediate state of metastasis termed oligometastasis(es) characterized by limited progression has been proposed. Oligometastases are amenable to treatment by surgical resection or radiotherapy. Methods We analyzed microRNA expression patterns from lung metastasis samples of patients with ≤5 initial metastases resected with curative intent. Results Patients were stratified into subgroups based on their rate of metastatic progression. We prioritized microRNAs between patients with the highest and lowest rates of recurrence. We designated these as high rate of progression (HRP) and low rate of progression (LRP); the latter group included patients with no recurrences. The prioritized microRNAs distinguished HRP from LRP and were associated with rate of metastatic progression and survival in an independent validation dataset. Conclusion Oligo- and poly- metastasis are distinct entities at the clinical and molecular level.

Single Sample Expression-Anchored Mechanisms Predict Survival in Head and Neck Cancer

Gene expression signatures that are predictive of therapeutic response or prognosis are increasingly useful in clinical care; however, mechanistic (and intuitive) interpretation of expression arrays remains an unmet challenge. Additionally, there is surprisingly little gene overlap among distinct clinically validated expression signatures. These “causality challenges” hinder the adoption of signatures as compared to functionally well-characterized single gene biomarkers. To increase the utility of multi-gene signatures in survival studies, we developed a novel approach to generate “personal mechanism signatures” of molecular pathways and functions from gene expression arrays. FAIME, the Functional Analysis of Individual Microarray Expression, computes mechanism scores using rank-weighted gene expression of an individual sample. By comparing head and neck squamous cell carcinoma (HNSCC) samples with non-tumor control tissues, the precision and recall of deregulated FAIME-derived mechanisms of pathways and molecular functions are comparable to those produced by conventional cohort-wide methods (e.g. GSEA). The overlap of “Oncogenic FAIME Features of HNSCC” (statistically significant and differentially regulated FAIME-derived genesets representing GO functions or KEGG pathways derived from HNSCC tissue) among three distinct HNSCC datasets (pathways:46%, p<0.001) is more significant than the gene overlap (genes:4%). These Oncogenic FAIME Features of HNSCC can accurately discriminate tumors from control tissues in two additional HNSCC datasets (n = 35 and 91, F-accuracy = 100% and 97%, empirical p<0.001, area under the receiver operating characteristic curves = 99% and 92%), and stratify recurrence-free survival in patients from two independent studies (p = 0.0018 and p = 0.032, log-rank). Previous approaches depending on group assignment of individual samples before selecting features or learning a classifier are limited by design to discrete-class prediction. In contrast, FAIME calculates mechanism profiles for individual patients without requiring group assignment in validation sets. FAIME is more amenable for clinical deployment since it translates the gene-level measurements of each given sample into pathways and molecular function profiles that can be applied to analyze continuous phenotypes in clinical outcome studies (e.g. survival time, tumor volume).

Network models of genome-wide association studies uncover the topological centrality of protein interactions in complex diseases

Background While genome-wide association studies (GWAS) of complex traits have revealed thousands of reproducible genetic associations to date, these loci collectively confer very little of the heritability of their respective diseases and, in general, have contributed little to our understanding the underlying disease biology. Physical protein interactions have been utilized to increase our understanding of human Mendelian disease loci but have yet to be fully exploited for complex traits. Methods We hypothesized that protein interaction modeling of GWAS findings could highlight important disease-associated loci and unveil the role of their network topology in the genetic architecture of diseases with complex inheritance. Results Network modeling of proteins associated with the intragenic single nucleotide polymorphisms of the National Human Genome Research Institute catalog of complex trait GWAS revealed that complex trait associated loci are more likely to be hub and bottleneck genes in available, albeit incomplete, networks (OR=1.59, Fishers exact test p<2.24×10−12). Network modeling also prioritized novel type 2 diabetes (T2D) genetic variations from the Finland–USA Investigation of Non-Insulin-Dependent Diabetes Mellitus Genetics and the Wellcome Trust GWAS data, and demonstrated the enrichment of hubs and bottlenecks in prioritized T2D GWAS genes. The potential biological relevance of the T2D hub and bottleneck genes was revealed by their increased number of first degree protein interactions with known T2D genes according to several independent sources (p<0.01, probability of being first interactors of known T2D genes). Conclusion Virtually all common diseases are complex human traits, and thus the topological centrality in protein networks of complex trait genes has implications in genetics, personal genomics, and therapy.

Translating Mendelian and complex inheritance of Alzheimer's disease genes for predicting unique personal genome variants.

Objective Although trait-associated genes identified as complex versus single-gene inheritance differ substantially in odds ratio, the authors nonetheless posit that their mechanistic concordance can reveal fundamental properties of the genetic architecture, allowing the automated interpretation of unique polymorphisms within a personal genome. Materials and methods An analytical method, SPADE-gen, spanning three biological scales was developed to demonstrate the mechanistic concordance between Mendelian and complex inheritance of Alzheimers disease (AD) genes: biological functions (BP), protein interaction modeling, and protein domain implicated in the disease-associated polymorphism. Results Among Gene Ontology (GO) biological processes (BP) enriched at a false detection rate <5% in 15 AD genes of Mendelian inheritance (Online Mendelian Inheritance in Man) and independently in those of complex inheritance (25 host genes of intragenic AD single-nucleotide polymorphisms confirmed in genome-wide association studies), 16 overlapped (empirical p=0.007) and 45 were similar (empirical p<0.009; information theory). SPAN network modeling extended the canonical pathway of AD (KEGG) with 26 new protein interactions (empirical p<0.0001). Discussion The study prioritized new AD-associated biological mechanisms and focused the analysis on previously unreported interactions associated with the biological processes of polymorphisms that affect specific protein domains within characterized AD genes and their direct interactors using (1) concordant GO-BP and (2) domain interactions within STRING protein–protein interactions corresponding to the genomic location of the AD polymorphism (eg, EPHA1, APOE, and CD2AP). Conclusion These results are in line with unique-event polymorphism theory, indicating how disease-associated polymorphisms of Mendelian or complex inheritance relate genetically to those observed as ‘unique personal variants’. They also provide insight for identifying novel targets, for repositioning drugs, and for personal therapeutics.

‘RE:fine drugs’: an interactive dashboard to access drug repurposing opportunities

The process of discovering new drugs has been extremely costly and slow in the last decades despite enormous investment in pharmaceutical research. Drug repurposing enables researchers to speed up the process of discovering other conditions that existing drugs can effectively treat, with low cost and fast FDA approval. Here, we introduce ‘RE:fine Drugs’, a freely available interactive website for integrated search and discovery of drug repurposing candidates from GWAS and PheWAS repurposing datasets constructed using previously reported methods in Nature Biotechnology. ‘RE:fine Drugs’ demonstrates the possibilities to identify and prioritize novelty of candidates for drug repurposing based on the theory of transitive Drug–Gene–Disease triads. This public website provides a starting point for research, industry, clinical and regulatory communities to accelerate the investigation and validation of new therapeutic use of old drugs. Database URL: http://drug-repurposing.nationwidechildrens.org

Gene expression profiling of the human natural killer cell response to Fc receptor activation: unique enhancement in the presence of interleukin-12

BackgroundTraditionally, the CD56dimCD16+ subset of Natural Killer (NK) cells has been thought to mediate cellular cytotoxicity with modest cytokine secretion capacity. However, studies have suggested that this subset may exert a more diverse array of immunological functions. There exists a lack of well-developed functional models to describe the behavior of activated NK cells, and the interactions between signaling pathways that facilitate effector functions are not well understood. In the present study, a combination of genome-wide microarray analyses and systems-level bioinformatics approaches were utilized to elucidate the transcriptional landscape of NK cells activated via interactions with antibody-coated targets in the presence of interleukin-12 (IL-12).MethodsWe conducted differential gene expression analysis of CD56dimCD16+ NK cells following FcR stimulation in the presence or absence of IL-12. Next, we functionally characterized gene sets according to patterns of gene expression and validated representative genes using RT-PCR. IPA was utilized for biological pathway analysis, and an enriched network of interacting genes was generated using GeneMANIA. Furthermore, PAJEK and the HITS algorithm were employed to identify important genes in the network according to betweeness centrality, hub, and authority node metrics.ResultsAnalyses revealed that CD56dimCD16+ NK cells co-stimulated via the Fc receptor (FcR) and IL-12R led to the expression of a unique set of genes, including genes encoding cytotoxicity receptors, apoptotic proteins, intracellular signaling molecules, and cytokines that may mediate enhanced cytotoxicity and interactions with other immune cells within inflammatory tissues. Network analyses identified a novel set of connected key players, BATF, IRF4, TBX21, and IFNG, within an integrated network composed of differentially expressed genes in NK cells stimulated by various conditions (immobilized IgG, IL-12, or the combination of IgG and IL-12).ConclusionsThese results are the first to address the global mechanisms by which NK cells mediate their biological functions when encountering antibody-coated targets within inflammatory sites. Moreover, this study has identified a set of high-priority targets for subsequent investigation into strategies to combat cancer by enhancing the anti-tumor activity of CD56dimCD16+ NK cells.

From Molecules to Patients: The Clinical Applications of Translational Bioinformatics

OBJECTIVE In order to realize the promise of personalized medicine, Translational Bioinformatics (TBI) research will need to continue to address implementation issues across the clinical spectrum. In this review, we aim to evaluate the expanding field of TBI towards clinical applications, and define common themes and current gaps in order to motivate future research. METHODS Here we present the state-of-the-art of clinical implementation of TBI-based tools and resources. Our thematic analyses of a targeted literature search of recent TBI-related articles ranged across topics in genomics, data management, hypothesis generation, molecular epidemiology, diagnostics, therapeutics and personalized medicine. RESULTS Open areas of clinically-relevant TBI research identified in this review include developing data standards and best practices, publicly available resources, integrative systemslevel approaches, user-friendly tools for clinical support, cloud computing solutions, emerging technologies and means to address pressing legal, ethical and social issues. CONCLUSIONS There is a need for further research bridging the gap from foundational TBI-based theories and methodologies to clinical implementation. We have organized the topic themes presented in this review into four conceptual foci - domain analyses, knowledge engineering, computational architectures and computation methods alongside three stages of knowledge development in order to orient future TBI efforts to accelerate the goals of personalized medicine.

Plasma microRNA levels following resection of metastatic melanoma

Melanoma remains the leading cause of skin cancer–related deaths. Surgical resection and adjuvant therapies can result in disease-free intervals for stage III and stage IV disease; however, recurrence is common. Understanding microRNA (miR) dynamics following surgical resection of melanomas is critical to accurately interpret miR changes suggestive of melanoma recurrence. Plasma of 6 patients with stage III (n = 2) and stage IV (n = 4) melanoma was evaluated using the NanoString platform to determine pre- and postsurgical miR expression profiles, enabling analysis of more than 800 miRs simultaneously in 12 samples. Principal component analysis detected underlying patterns of miR expression between pre- vs postsurgical patients. Group A contained 3 of 4 patients with stage IV disease (pre- and postsurgical samples) and 2 patients with stage III disease (postsurgical samples only). The corresponding preoperative samples to both individuals with stage III disease were contained in group B along with 1 individual with stage IV disease (pre- and postsurgical samples). Group A was distinguished from group B by statistically significant analysis of variance changes in miR expression (P < .0001). This analysis revealed that group A vs group B had downregulation of let-7b-5p, miR-520f, miR-720, miR-4454, miR-21-5p, miR-22-3p, miR-151a-3p, miR-378e, and miR-1283 and upregulation of miR-126-3p, miR-223-3p, miR-451a, let-7a-5p, let-7g-5p, miR-15b-5p, miR-16-5p, miR-20a-5p, miR-20b-5p, miR-23a-3p, miR-26a-5p, miR-106a-5p, miR-17-5p, miR-130a-3p, miR-142-3p, miR-150-5p, miR-191-5p, miR-199a-3p, miR-199b-3p, and miR-1976. Changes in miR expression were not readily evident in individuals with distant metastatic disease (stage IV) as these individuals may have prolonged inflammatory responses. Thus, inflammatory-driven miRs coinciding with tumor-derived miRs can blunt anticipated changes in expression profiles following surgical resection.

MD-Miner: a network-based approach for personalized drug repositioning

BackgroundDue to advances in next generation sequencing technologies and corresponding reductions in cost, it is now attainable to investigate genome-wide gene expression and variants at a patient-level, so as to better understand and anticipate heterogeneous responses to therapy. Consequently, it is feasible to inform personalized drug treatment decisions using personal genomics data. However, these efforts are limited due to a lack of reliable computational approaches for predicting effective drugs for individual patients. The reverse gene set enrichment analysis (i.e., connectivity mapping) approach and its variants have been widely and successfully used for drug prediction. However, the performance of these methods is limited by undefined mechanism of action (MoA) of drugs and reliance on cohorts of patients rather than personalized predictions for individual patients.ResultsIn this study, we have developed and evaluated a computational approach, known as Mechanism and Drug Miner (MD-Miner), using a network-based computational approach to predict effective drugs and reveal potential drug mechanisms of action at the level of signaling pathways. Specifically, the patient-specific signaling network is constructed by integrating known disease associated genes with patient-derived gene expression profiles. In parallel, a drug mechanism of action network is constructed by integrating drug targets and z-score profiles of drug-induced gene expression (pre vs. post-drug treatment). Potentially effective candidate drugs are prioritized according to the number of common genes between the patient-specific dysfunctional signaling network and drug MoA network. We evaluated the MD-Miner method on the PC-3 prostate cancer cell line, and showed that it significantly improved the success rate of discovering effective drugs compared with the random selection, and could provide insight into potential mechanisms of action.ConclusionsThis work provides a signaling network-based drug repositioning approach. Compared with the reverse gene signature based drug repositioning approaches, the proposed method can provide clues of mechanism of action in terms of signaling transduction networks.

DISCOVERY OF MOLECULARLY TARGETED THERAPIES

The delivery of personalized healthcare is predicated on the application of the best available scientific knowledge to the practice of medicine in order to promote health, improve outcomes and enhance patient safety [1-3]. Unfortunately, current approaches to basic science research and clinical care are poorly integrated, yielding clinical decision-making processes that do not take advantage of up-to-date scientific knowledge [2-4]. Basic scientists investigating the biological basis for a given disease may regularly encounter synergistic effects spanning two or more bio-molecular entities or processes that can contribute to our understanding of the mechanisms underlying phenomena such as the etiologic basis of the targeted disease state or potential response to therapeutic agents [5]. However, systematic approaches to the use of that knowledge in order to directly inform the selection of targeted molecular therapies for “real world” patients are extremely limited [1, 3, 6-9]. There are an increasing number of multi-modelling and in-silico knowledge synthesis techniques that can provide investigators with the tools to quickly generate hypotheses concerning the relationships between entities found in heterogeneous collections of scientific data — for example, exploring potential linkages among genes, phenotypes and molecularly targeted therapeutic agents, thus enabling the “forward engineering” of treatment strategies based on knowledge generated via basic science studies [1, 4, 6, 10, 11]. Ultimately, the goal of such methodologies is to accelerate the identification of actionable research questions that can make direct contributions to clinical practice. Given increasing concerns over the barriers to the timely translation of discoveries from the laboratory to the clinic or broader population settings, such high-throughput hypothesis generation and testing is highly desirable [1, 4, 6, 8, 12]. These needs are particularly critical in numerous disease areas where the availability of new therapeutic agents is constrained, thus calling for the re-use and repositioning of existing treatments [13, 14]. In response to the challenges and opportunities enumerated above, there exits an emerging body of research and development focusing on multi-modeling approaches to the discovery of molecularly targeted therapies, including experimental paradigms spanning a spectrum from the identification of molecular targets for drugs, to the repurposing or repositioning of existing agents that utilize such targets, to the systematic identification of novel combination therapy regimens that amplify or enhance the effectiveness of their constituent components. This focus is motivated by recent and significant advances in the state of systems biology and medicine that have demonstrated that the ability to generate and reason across complex and scalar models is essential to the discovery of high-impact biologically and clinically actionable knowledge [1, 4, 12]. Such approaches are designed to overcome the limitations of reductionist approaches to scientific discovery, replacing decomposition-focused problem-solving with integrative network-based modeling and analysis techniques [4, 8]. Systems-level analysis of complex problem domains ultimately enables the study of critical interactions that influence health and wellness across a scale from molecules to populations, and are not observable when such systems are broken down into constituent components. The use of systems-level analysis methodologies is well supported by the foundational theory of vertical reasoning first proposed by Blois [15]. This theory holds that effective decision-making in the biomedical domain is predicated on the vertical integration of multiple, scalar levels of reasoning. This fundamental premise is the basis for a correlative framework put forth by Tsafnat and colleagues, which states that the ability to replicate expert reasoning relative to complex biomedical problems using computational agents (e.g., in-silico knowledge synthesis) requires the replication of such multi-scalar and integrative decision-making [16]. In order to achieve such an outcome, Tsafnat posits that multi-scalar decision-making in an in-silico context requires both: 1) the generation of component decision-making models at multiple scales; and 2) the similar generation of interchange layers that define important pair-wise connections between entities situated in two or more component models, often referred to as vertical linkages [16]. When such component models and interchange layers are combined in a computationally actionable format, they yield what can be referred to as a multi-model for a given domain that is able to satisfy the premises of Blois’ vertical reasoning axiom, and therefore facilitate the replication of expert performance in a high-throughput manner [16]. Of note, this type of approach is extremely reliant upon graph-theoretic reasoning and representational models, using a network paradigm that allows for the application of logical reasoning operations spanning the entities and relationships that make up a multi-model [8]. Network paradigms have been regularly shown to be the ideal representational model for naturally occurring systems, such as the ‘scale-free’ networks encountered in biological and clinical phenomena [8]. At the most basic level, network-based multi-modeling across scales presents an elegant and computationally tractable approach to understanding and evaluating complex biological and clinical systems in order to discover the knowledge incumbent to such constructs. This type of approach benefits from a robust set of foundational theories and frameworks that can inform and shape the application of multi-modeling techniques to a variety of knowledge discovery use cases. As such, there is a growing body of evidence concerning the application of network-based approaches to multi-modeling with an emphasis on therapeutic agent discovery, re-positioning and molecular targeting. Examples of such evidence include reports and perspectives published by Hood and Perlmutter [1], Butcher and colleagues [12], and Lussier and Chen [13].