Zachary B. Abrams

Folate-conjugated immunoglobulin targets melanoma tumor cells for NK cell effector functions.

The folate receptor (FR) is overexpressed on the vascular side of cancerous cells including those of the breast, ovaries, testes, and cervix. We hypothesized that a folate-conjugated immunoglobulin (F-IgG) would bind to the FR that is overexpressed on melanoma tumor cells to target these cells for lysis by natural killer (NK) cells. Folate receptor expression was confirmed in the Mel-39 (human melanoma) cell line by flow cytometry and immunoblot analysis using KB (human oral epithelial) and F01 (human melanoma) as a positive and a negative control, respectively. FR-positive and FR-negative cell lines were treated with F-IgG or control immunoglobulin G in the presence or absence of cytokines to determine NK cell ability to lyse FR-positive cell lines. NK cell activation was significantly upregulated and lysis of Mel 39 tumor cells increased following treatment with F-IgG compared with control immunoglobulin G at all effector : target (E : T) ratios (P<0.01). This trend further increased by NK cell stimulation with the activating cytokine interleukin-12. NK cell production of cytokines such as interferon-gamma, macrophage inflammatory protein 1&agr;, and regulated on activation normal T-cell expressed and secreted (RANTES) was also significantly increased in response to costimulation with interleukin-12 stimulation and F-IgG-coated Mel 39 target cells compared with controls (P<0.01). In contrast, F-IgG did not bind to the FR-negative cell line F01 and had no significant effect on NK cell lysis or cytokine production. This research indicates the potential use of F-IgG for its ability to induce an immune response from NK cells against FR-positive melanoma tumor cells, which can be further increased by the addition of cytokines.

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.

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].

MicroRNA profiling of patient plasma for clinical trials using bioinformatics and biostatistical approaches

Background MicroRNAs (miRNAs) are short noncoding RNAs that function to repress translation of mRNA transcripts and contribute to the development of cancer. We hypothesized that miRNA array-based technologies work best for miRNA profiling of patient-derived plasma samples when the techniques and patient populations are precisely defined. Methods Plasma samples were obtained from five sources: melanoma clinical trial of interferon and bortezomib (12), purchased normal donor plasma samples (four), gastrointestinal tumor bank (nine), melanoma tumor bank (ten), or aged-matched normal donors (eight) for the tumor bank samples. Plasma samples were purified for miRNAs and quantified using NanoString® arrays or by the company Exiqon. Standard biostatistical array approaches were utilized for data analysis and compared to a rank-based analytical approach. Results With the prospectively collected samples, fewer plasma samples demonstrated visible hemolysis due to increased attention to eliminating factors, such as increased pressure during phlebotomy, small gauge needles, and multiple punctures. Cancer patients enrolled in a melanoma clinical study exhibited the clearest pattern of miRNA expression as compared to normal donors in both the rank-based analytical method and standard biostatistical array approaches. For the patients from the tumor banks, fewer miRNAs (<5) were found to be differentially expressed and the false positive rate was relatively high. Conclusion In order to obtain consistent results for NanoString miRNA arrays, it is imperative that patient cohorts have similar clinical characteristics with a uniform sample preparation procedure. A clinical workflow has been optimized to collect patient samples to study plasma miRNAs.

Thresher: Determining the Number of Clusters While Removing Outliers

BackgroundCluster analysis is the most common unsupervised method for finding hidden groups in data. Clustering presents two main challenges: (1) finding the optimal number of clusters, and (2) removing “outliers” among the objects being clustered. Few clustering algorithms currently deal directly with the outlier problem. Furthermore, existing methods for identifying the number of clusters still have some drawbacks. Thus, there is a need for a better algorithm to tackle both challenges.ResultsWe present a new approach, implemented in an R package called Thresher, to cluster objects in general datasets. Thresher combines ideas from principal component analysis, outlier filtering, and von Mises-Fisher mixture models in order to select the optimal number of clusters. We performed a large Monte Carlo simulation study to compare Thresher with other methods for detecting outliers and determining the number of clusters. We found that Thresher had good sensitivity and specificity for detecting and removing outliers. We also found that Thresher is the best method for estimating the optimal number of clusters when the number of objects being clustered is smaller than the number of variables used for clustering. Finally, we applied Thresher and eleven other methods to 25 sets of breast cancer data downloaded from the Gene Expression Omnibus; only Thresher consistently estimated the number of clusters to lie in the range of 4–7 that is consistent with the literature.ConclusionsThresher is effective at automatically detecting and removing outliers. By thus cleaning the data, it produces better estimates of the optimal number of clusters when there are more variables than objects. When we applied Thresher to a variety of breast cancer datasets, it produced estimates that were both self-consistent and consistent with the literature. We expect Thresher to be useful for studying a wide variety of biological datasets.

Polychrome: Creating and Assessing Qualitative Palettes With Many Colors

Although R includes numerous tools for creating color palettes to display continuous data, facilities for displaying categorical data primarily use the RColorBrewer package, which is, by default, limited to 12 colors. The colorspace package can produce more colors, but it is not immediately clear how to use it to produce colors that can be reliably distingushed in different kinds of plots. However, applications to genomics would be enhanced by the ability to display at least the 24 human chromosomes in distinct colors, as is common in technologies like spectral karyotyping. In this article, we describe the Polychrome package, which can be used to construct palettes with at least 24 colors that can be distinguished by most people with normal color vision. Polychrome includes a variety of visualization methods allowing users to evaluate the proposed palettes. In addition, we review the history of attempts to construct qualitative color palettes with many colors.

Alterations in patient plasma microRNA expression profiles following resection of metastatic melanoma: LATCHANA et al.

MicroRNAs (miRs) are noncoding RNAs that regulate protein translation and melanoma progression. Changes in plasma miR expression following surgical resection of metastatic melanoma are under‐investigated. We hypothesize differences in miR expression exist following complete surgical resection of metastatic melanoma.

Thirty biologically interpretable clusters of transcription factors distinguish cancer type

BackgroundTranscription factors are essential regulators of gene expression and play critical roles in development, differentiation, and in many cancers. To carry out their regulatory programs, they must cooperate in networks and bind simultaneously to sites in promoter or enhancer regions of genes. We hypothesize that the mRNA co-expression patterns of transcription factors can be used both to learn how they cooperate in networks and to distinguish between cancer types.ResultsWe recently developed a new algorithm, Thresher, that combines principal component analysis, outlier filtering, and von Mises-Fisher mixture models to cluster genes (in this case, transcription factors) based on expression, determining the optimal number of clusters in the process. We applied Thresher to the RNA-Seq expression data of 486 transcription factors from more than 10,000 samples of 33 kinds of cancer studied in The Cancer Genome Atlas (TCGA). We found that 30 clusters of transcription factors from a 29-dimensional principal component space were able to distinguish between most cancer types, and could separate tumor samples from normal controls. Moreover, each cluster of transcription factors could be either (i) linked to a tissue-specific expression pattern or (ii) associated with a fundamental biological process such as cell cycle, angiogenesis, apoptosis, or cytoskeleton. Clusters of the second type were more likely also to be associated with embryonically lethal mouse phenotypes.ConclusionsUsing our approach, we have shown that the mRNA expression patterns of transcription factors contain most of the information needed to distinguish different cancer types. The Thresher method is capable of discovering biologically interpretable clusters of genes. It can potentially be applied to other gene sets, such as signaling pathways, to decompose them into simpler, yet biologically meaningful, components.