Leaders in Pharmaceutical Business Intelligence (LPBI) Group

GEN Tech Focus: Rethinking Gene Expression Analysis

Larry H. Bernstein, MD, FCAP, Curator

LPBI

Quantitating gene expression is essential for researchers to answer important biological questions about basic cellular functions, as well as disease states. In the following articles you will discover the multitude of advances investigators have made to accurately measure and quantitate genetic transcripts within the cell.

Diverse Pathways to Drug Targets

A great deal of research on pathway analysis is currently focusing on RNA rather than proteins, and the complex RNA networks that regulate gene expression. With the realization that more than 90% of the genome that is transcribed into RNA is not translated into protein, and the growing numbers of naturally occurring microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) being identified and characterized, the important role these RNAs play in normal biological processes and across human diseases is becoming increasingly clear.

The Gene-Expression Undergrowth Have Been Well Trodden, but RNA Paths Want Wear, Too

• Vicki Glaser

• Hepatitis C virus depends on a functional interaction between its genome and miR-122 for viral stability and replication. Researchers recently used an antisense oligonucleotide that targets the liver-specific microRNA miR-122, blocking its function. [Bluebay2014/Fotolia]

  A great deal of research on pathway analysis is currently focusing on RNA rather than proteins, and the complex RNA networks that regulate gene expression.

  With the realization that more than 90% of the genome that is transcribed into RNA is not translated into protein, and the growing numbers of naturally occurring microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) being identified and characterized, the important role these RNAs play in normal biological processes and across human diseases is becoming increasingly clear.

  This knowledge—combined with the available technology and strategies to decipher RNA pathways and link alterations in the levels or activity of miRNAs or lncRNAs to gene expression, epigenetic mechanisms, and protein activity in normal and disease phenotypes—is driving the development and clinical testing of novel drug targets and therapeutics that target regulatory RNAs.

  For example, a microRNA was targeted in a Phase II clinical study that assessed the effect of miravirsen, an antisense oligonucleotide, in patients with hepatitis C. The study, which was described in 2013 in the New England Journal of Medicine, indicated that miravirsen sequesters the liver-specific microRNA miR-122 in a highly stable heteroduplex, thereby inhibiting its function.

  Hepatitis C virus (HCV) depends on a functional interaction between its genome and miR-122 for viral stability and replication. According to the study, inhibition of miR-122 in HCV-infected patients was associated with decreased levels of HCV RNA that continued beyond the treatment period, without evidence of viral resistance.

  The therapeutic potential of regulatory RNAs is also being assessed in other conditions such as cancer. Specifically, miRNAs and other ncRNAs in cancer initiation, progression, and metastasis are being studied by George Calin, M.D., Ph.D., a professor of experimental therapeutics, MD Anderson Cancer Center, University of Texas. Dr. Calin’s group is scouring the “microRNAome” to identify miRNAs of about 21–22 nucleotides that can serve as reliable biomarkers for cancer diagnosis and to guide decision-making in patient management, including as predictors of survival and response to drug therapy.

  miRNAs are involved in every aspect of tumorigenesis, cancer progression, and dissemination. Not only are they expressed in tumor cells, they are also stably expressed in exosomes and are present in various bodily fluids, where they can act like hormones and signaling molecules. Comparative profiling of these fluids for differences in miRNA levels between patients with and without cancer could identify relevant biomarkers.

   

  Analyzing RNA Pathways

• Using Qiagen’s Ingenuity Pathway Analysis, researchers can analyze relationships between molecules and diseases of interest by modeling how gene expression patterns affect functional outcomes or disease processes.

   

   

   

   

   

   

   

   

   

  Dr. Calin and colleagues have described the significance of miRNA signatures obtained in recent studies involving miRNA profiling of human tumors. An overview appeared 2014 in CA: A Cancer Journal for Clinicians (“MicroRNAome genome: a treasure for cancer diagnosis and therapy”). Also, last February, Dr. Calin gave an account of his group’s work at the Molecular Med Tri Conference in San Francisco.

  Technology is not holding back advances in the field of RNA pathway analysis according to Dr. Calin. The main bottleneck at present is in the design of prospective studies needed to confirm the predictive value of miRNA-based biomarkers.

  Dr. Calin points to two other key challenges that scientists currently face in translating research findings into diagnostic, prognostic, and therapeutic tools. One is the difficulty in selecting an miRNA target, mainly because an individual miRNA could have a role in regulating tens, hundreds, or even thousands of protein-coding genes. For drug discovery, the aim is to identify miRNAs that affect a single pathway of interest to help limit off-target effects. The need for novel delivery systems for RNA-targeted drugs is another key challenge.

  At the Molecular Med Tri Conference, Jean-Noel Billaud, Ph.D., principal scientist at Qiagen Bioinformatics, presented a case study demonstrating how the company’s Ingenuity Pathway Analysis technology can be used to conduct a systems biology analysis to identify the pathways, potential upstream regulators, and downstream outcomes involved in the host response to West Nile Virus (WNV) infection. Dr. Billaud also discussed how to interpret the results from a biological perspective.

  In his presentation, Dr. Billaud described the first step in this analytical process as the acquisition of RNA sequence data using next-generation sequencing techniques for the purpose of characterizing and quantifying differential gene expression between an infected and uninfected cell. The CLC Cancer Research Workbench tool is used to process the sequence data, and the results are imported directly into the IPA system.

  Analysis of differential gene expression aims to answer a series of key questions, including the following: What metabolic and/or signaling pathway(s) is activated or inhibited? Is there an overlap of the genes or pathways that are activated or inhibited? What are the potential upstream, downstream, functional, and phenotypic implications of this pathway activation or inhibition?

  Dr. Billaud described other questions researchers might attempt to answer through the use of IPA: What are the identifying the underlying transcriptional programs? Which biological processes are involved and in what way? Are there splice variants of interest? What type of regulation is involved?

  In the WNV case study, IPA predicted activation of the interferon signaling pathway and added statistically and functionally relevant biological processes to the WNV-related biochemical network the system developed. IPA is able to simulate the effects of interferon pathway activation on neighboring molecules and processes, which enables broader modeling of antiviral responses, prediction of the effects on viral replication, and identification of upstream transcriptional regulators of antiviral and related anti-inflammatory processes, for example.

  These data and analytical capabilities may allow researchers to propose new hypotheses that connect molecules in regulatory networks to disease-related pathways in a predictive way, leading to the identification of a “master regulator” that could serve as a disease-specific drug target, according to Dr. Billaud.

  In the WNV example, he described the use of the Molecule Activity Predictor (MAP) function in IPA to test the hypothesis that CLEC7A is a host susceptibility factor required by WNV to stimulate an immune response in the brains of infected patients, contributing to the development of life-threatening encephalitis. The MAP function simulates the inhibition or downregulation of CLEC7A, showing how it would likely reduce the risk of WNV-associated encephalitis. These types of hypotheses would then need to be tested and validated.

  Pathways Driving B-Cell Differentiation

  • Robert C. Rickert, Ph.D., professor and director of the Tumor Microenvironment and Metastasis Program at Sanford-Burnham Medical Research Institute, is using conditional gene targeting to identify the genes and biochemical pathways that play a role at specific stages of B-cell differentiation. With this approach, it is possible to knock out targeted genes in a mouse at different stages of B-cell development, and to do so in an inducible fashion, allowing you “to look at how it affects different signal transduction pathways in a context-specific manner,” says Dr. Rickert.

    When applied to a relevant mouse model of disease—such as a B-cell lymphoma—this inducible genetic system should yield effects similar to those that could be obtained with a drug capable of blocking the activity of the targeted gene product. Dr. Rickert and colleagues are exploring the similarity between the effects achieved with conditional gene targeting and those of recently approved drugs to treat chronic lymphocytic leukemia (CLL) and some forms of lymphoma such as idelalisib and ibrutinib, which are both inhibitors of the B-cell receptor pathway via blocking of PI3K or Bruton’s tyrosine kinase (BTK), respectively.

    Dr. Rickert presented his group’s latest research at a Keystone Symposium Conference, PI 3-Kinase Signaling Pathways in Disease, which took place last January in Vancouver. In his talk, Dr. Rickert emphasized that the phosphatidyl inositol-3 kinase (PI3K) pathway is a major regulator B lymphocyte differentiation and function.

    Dr. Rickert has also applied conditional gene targeting to compare the roles of the NFκB and PI3K pathways in B-cell maturation. He has shown that while both pathways are essential at some stages of B-cell differentiation, only one pathway may be necessary for B-cell maintenance and survival.

    “Ultimately we want to gain more insight at the biochemical level into single cells and the heterogeneity of the cell populations we’re interested in,” says Dr. Rickert. Tumors and cancer cell populations are quite heterogeneic, and better biochemical tools are needed to be able to sort through these populations of cells and “look at some of the more interesting, rogue cells, such as cancer stem cells,” he adds.

  An Evolutionary Approach

  In his laboratory at Hebrew University of Jerusalem, researcher Yuval Tabach, Ph.D., is using computational tools to analyze and compare the genomes and proteins of hundreds of species to identify evolutionary patterns of conservation and loss that point to connections between molecular pathways and disease.

  “The main power of this phylogenetic profiling approach is that if you look at proteins across evolution, some are lost at certain points in certain species,” says Dr. Tabach. For example, proteins involved in the tricarboxylic acid (TCA) cycle have been highly conserved across some species, but have disappeared in others because those species have lost their mitochondria.

  Dr. Tabach and colleagues have shown that sets of genes associated with particular diseases have similar phylogenetic profiles. They are also using this approach to identify genes associated with longevity, cancer resistance, and various extreme environmental conditions.

  Phylogenetic profiling to connect patterns of conservation and loss across millions of years of evolution can be applied to entire proteins, protein domains, and RNA molecules such as microRNAs. The potential applicability of this approach to drug discovery and development is multifaceted.

  For example, given a gene known to be related to a certain disease, the ability to identify other genes with a similar phylogenetic profile might reveal genetic factors that could explain incomplete penetrance or the variability of disease severity in different affected individuals. Alternatively, identification of a candidate gene in one patient could serve as the basis for identifying other key factors in other patients with the same disease using the phylogenetic profile.

  Compared to strategies such as gene expression analysis or protein-protein interaction mapping for identifying disease-related genes, phylogenetic profiling “is much faster” and will become an increasingly powerful tool as the genome sequences of more species become available, explains Dr. Tabach.

  The Israeli start-up company ReThink Pharmaceuticals is using the molecular networks generated through this phylogenetic profiling work for the purpose of drug repositioning. “If you know that a certain drug targets a gene, we can build a network to find other genes/proteins that interact with the drug target,” asserts Dr. Tabach, citing preliminary results that demonstrate the ability to predict additional effects of a drug candidate.

   

Measuring siRNA-mediated Knockdown of the IL-8 gene Using the QuantiGene Singleplex Assay

A critical component of RNA interference (RNAi) studies is the validation of gene expression inhibition. RNAi experiments have many sources of variation that make accurate quantitation of target mRNA difficult when qPCR is used. Variation in the potency and stability of short interfering RNA (siRNA), coupled with differences in transfection efficiency and protein turnover, results in varying gene knockdown efficiency.

The RNA World Expands

Over the past 10 years, scientists say new methods, including deep sequencing and DNA tiling arrays, have enabled the identification and characterization of the human transcriptome. These techniques completely changed our understanding of genome organization and content and revealed that a much larger part of the human genome is transcribed into RNA than was previously assumed—about 70%.

The RNA World Expands  

Long noncoding RNAs mean more than HOTAIR.

• Patricia Fitzpatrick Dimond, Ph.D.

Long noncoding RNA (lncRNAs) can regulate gene expression at epigenetic, transcriptional, and post-transcriptional levels. [© Alila Medicinal Media – Fotolia.com]

• Over the past 10 years, scientists say new methods, including deep sequencing and DNA tiling arrays, have enabled the identification and characterization of the human transcriptome. These techniques completely changed our understanding of genome organization and content and revealed that a much larger part of the human genome is transcribed into RNA than was previously assumed—about 70%.

  Last year researchers, including Tim Mercer, Ph.D., at the Institute for Molecular Bioscience-University of Queensland, Roche Nimblegen, and John Rinn, Ph.D., and his team in the department of stem cell and regenerative biology at Harvard, reported that “transcriptomic analyses have revealed an ‘unexpected complexity’ to the human transcriptome, the depth and breadth of which exceeds current RNA sequencing capability.”

  These scientists used these techniques to identify and characterize unannotated transcripts whose rare or transient expression is below the detection limits of conventional sequencing approaches. The data also show that intermittent sequenced reads observed in conventional RNA sequencing datasets, previously dismissed as noise, are indicative of unassembled rare transcripts. Collectively, they say these results reveal the range, depth, and complexity of a human transcriptome that is far from fully characterized.

  Noncoding transcripts are RNA molecules that include classical “housekeeping” RNAs such as transfer RNAs (tRNAs), ribosomal RNAs (rRNAs), small nuclear RNAs (snRNAs), and small nucleolar RNAs (snoRNAs), which are constitutively expressed and play critical roles in protein biosynthesis.

  Among these noncoding RNAs are numerous long noncoding RNAs (lncRNAs), which are defined as endogenous cellular RNAs of more than 200 nucleotides in length that lack an open reading frame of significant length (less than 100 amino acids). The RNA molecules constitute a heterogeneous group, allowing them, scientists point out, to cover a broad spectrum of molecular and cellular functions by implementing different modes of action. lncRNAs are roughly classified based on their position relative to protein-coding genes as intergenic (between genes), intragenic/intronic (within genes), and antisense. Initial efforts to characterize these molecules demonstrated that they function in cis, regulating their immediate genomic neighbors.

  Regulatory Levels

• lncRNAs can regulate gene expression at epigenetic, transcriptional, and post-transcriptional levels and take part in various physiological and pathological processes, such as cell development, immunity, oncogenesis, clinical disease processes, and more. A classic lncRNA, HOTAIR, was originally identified through work done by Howard Chang, M.D., Ph.D., at Stanford, and Dr. Rinn. Their research eventually led to the discovery of this 2.2 kilobase spliced RNA transcript that interacts with Polycomb group proteins to modify chromatin and repress transcription of the human HOX genes, which regulate development. It remains unclear as to exactly this is accomplished.

  HOTAIR, it was found, originates from the HOXC locus and represses transcription across 40 kb of that locus by altering the chromatin trimethylation state. Hox genes, a highly conserved subgroup of the homeobox superfamily, regulate numerous processes including apoptosis, receptor signaling, differentiation, motility, and angiogenesis. Aberrations in Hox gene expression have been reported in abnormal development and malignancy.

  HOTAIR works to repress Hox gene expression by directing the action of Polycomb chromatin remodeling complexes in trans to govern the cells’ epigenetic state and subsequent gene expression.HOTAIR expression is increased in primary breast tumors and metastases and its expression level in primary tumors can predict eventual metastasis and death. The recent discovery that lncRNA HOTAIRcan link chromatin changes to cancer metastasis furthers the relevance of lncRNAs to human disease.

  Dr. Chang and his colleagues say that the finding that several lncRNAs can control transcriptional alteration implies that the difference in lncRNA profiling between normal and cancer cells is not merely the secondary effect of cancer transformation, and that lncRNAs are strongly associated with cancer progression. The researchers showed that lncRNAs in the HOX loci become systematically dysregulated during breast cancer progression.

  They further demonstrated that enforced expression of HOTAIR in epithelial cancer cells induced genome-wide retargeting of polycomb repressive complex 2 (PRC2) to an occupancy pattern more resembling embryonic fibroblasts, leading to altered histone H3 lysine 27 methylation, gene expression, and increased cancer invasiveness and metastasis in a manner dependent on PRC2.

  On the other hand they noted loss of HOTAIR can inhibit cancer invasiveness, particularly in cells that possess excessive PRC2 activity. These findings indicate that lncRNAs have active roles in modulating the cancer epigenome and may be important targets for cancer diagnosis and therapy. Thus, the investigators say, differential expression of lncRNAs may be profiled to aid in cancer diagnosis and prognosis and in the selection of potential therapeutics.

  Two years ago the GENCODE consortium, within the framework of the ENCODE project, presented, and analyzed the most complete human lncRNA annotation to date. The data comprise 9,277 manually annotated genes producing 14,880 transcripts. The identification and annotation of this wealth of lncRNAs leaves scientists with a lot of research to do to fully characterize the varied functions of these unusual RNAs. Their identification also challenges technology developers to produce the tools to necessary for these analyses.

   

Transcript Regulation of 18 ADME Genes by Prototypical Inducers in Human Hepatocytes

Drug-drug interactions (DDIs) are of particular concern for regulatory agencies and the pharmaceutical industry for drug safety. Induction of drug metabolizing enzymes by pharmaceuticals, nutraceuticals, and lifestyle influences is one type of DDI in which the influence of a perpetrator molecule increases the enzyme capacity that can metabolize a victim molecule, rendering it ineffective as a therapy. To evaluate this potential, screening assays have been developed, such as the use…

Biomarkers Reshape Drug Development

Biomarkers defining specific phenotypes are becoming increasingly important for developing new drugs for specific patient subpopulations. The value of a new biomarker is measured by its ability to reduce risk. Ideally, the biomarker should be developed in parallel with the new drug, as nearly 50% of the projected development costs can be saved by…

Biomarkers Reshape Drug Development

• Kate Marusina, Ph.D.

• Imanova takes a structured approach to the development of imaging biomarkers, or i-biomarkers.

  Biomarkers defining specific phenotypes are becoming increasingly important for developing new drugs for specific patient subpopulations. The value of a new biomarker is measured by its ability to reduce risk.

  Ideally, the biomarker should be developed in parallel with the new drug, as nearly 50% of the projected development costs can be saved by shutting down a development program before it enters Phase II. A meaningful risk-benefit analysis of a biomarker requires estimates of its cost and accuracy, as well as the consequences of decisions that it will enable.

  For the biomarker to be of value, the cost of its development has to be less than the projected costs of development from Phase II onwards, discounted to present time. While multiple competing business considerations affect a pharmaceutical company’s decision to proceed with a biomarker program, the skyrocketing market for biomarker discovery underscores the pharmaceutical industry’s hope that biomarkers will bolster the success rates of pipeline products.

  “Imaging biomarkers have been Ideally, the biomarker should be developed in parallel with the new drug, as nearly 50% of the projected development costs can be saved by shutting down a development program before it enters Phase II. A meaningful risk-benefit analysis of a biomarker requires estimates of its cost and accuracy, as well as the consequences of decisions that it will enable.

  Ideally, the biomarker should be developed in parallel with the new drug, as nearly 50% of the projected development costs can be saved by shutting down a development program before it enters Phase II. A meaningful risk-benefit analysis of a biomarker requires estimates of its cost and accuracy, as well as the consequences of decisions that it will enable.

  For the biomarker to be of value, the cost of its development has to be less than the projected costs of development from Phase II onwards, discounted to present time. While multiple competing business considerations affect a pharmaceutical company’s decision to proceed with a biomarker program, the skyrocketing market for biomarker discovery underscores the pharmaceutical industry’s hope that biomarkers will bolster the success rates of pipeline products.

  “Imaging biomarkers have been largely underutilized in drug development,” says Kevin Cox, Ph.D., CEO of London-based Imanova. “But we believe that molecular imaging has the power to assist in successful translation of molecules by reducing the risk of several specific causes of failure in Phase II clinical studies. Imaging biomarkers, or i-biomarkers, are especially valuable in giving confidence of tissue delivery, determination of target engagement, and the evaluation of a drug’s pharmacodynamic effects.”

  While imaging is routinely used in clinical diagnostics for cancer, its acceptance in drug development has been slow. “This is a highly specialized area of knowledge,” Dr. Cox observes. “Designing imaging experiments to answer the right questions is not trivial. Combined with the perceived high costs and dearth of well-equipped facilities, this has slowed down the adoption of imaging as an integral step in drug development.”

  Imanova presents an innovative and highly integrated solution in reducing the barriers for use of molecular imaging. Located in the former GlaxoSmithKline imaging center, Imanova’s staff applies the knowledge needed for translational application of imaging science.

  “Another historical barrier for use of molecular imaging has been the lack of versatile PET tracers for key therapeutic targets,” remarks Dr. Cox. Together with its pharmaceutical clients, Imanova develops proprietary tracers that can answer critical questions about target engagement directly after drug administration. A structured approach for i-biomarker development takes the novel tracer from the candidate pool to clinical validation.

  Uniquely, Imanova utilizes in silico biomathematical modeling to predict a candidate with ideal physicohemical characteristics. “The i-biomarker development pipeline adheres to a strict quality system,” continues Dr. Cox. “We not only provide candidate selection and labeling, but also rigorous preclinical evaluation in several species, combined with blood chemistry or other physiological measurements.”

  The resulting biomarker provides quantitative information to make informed go/no-go decisions. Imanova hopes to develop an open innovation approach to i-biomarker research, and to encourage pharmaceutical companies to collaborate on tracer development.

  “By collaborating in this pre-competitive space, a pharma-academic consortium can de-risk i-biomarker development programs and generate new tools to eliminate costs associated with futile activities downstream,” concludes Dr. Cox. “Most tracers need to be utilized early in the drug development process. Used at the right time, imaging biomarkers are able to inform the design of Phase II studies, including dose ranging and possibly patient selection, saving many months in development and millions of dollars in costs.”

  Answers from Big Data

• “Clinical bioinformatics is the application of a data-driven, high-tech approach in clinical setting,” says Jerome Wojcik, Ph.D., CEO of Quartz Bio, a clinical bioinformatics service provider located in Plan-Les-Ouates, Switzerland. “We use clinical bioinformatics to adapt treatment to patients, that is, to identify cohorts that respond to the drug in a predictable manner,” says Dr. Wojcik.

  Pharmaceutical partners supply Quartz Bio with data collected in a course of clinical trials. The data (which may include information from protein and RNA expression, genotyping, molecular diagnostics, and flow cytometry studies) often exists in silos within a pharma company. To make sense of the data, Quartz Bio integrates heterogeneously formatted data, analyzes it for consistency, and identifies gaps and outliers.

  Dr. Wojcik’s team dedicates over 40% of the overall analysis time to the biomarker data management. This key step is crucial for the quality of the overall analysis. According to Quartz Bio, all the data-management processes are documented, auditable, and reproducible.

  Once the “Big Data” horde is adequately cleaned up, the team applies adaptive statistical methods to generate multiple hypotheses linking the drug action with subpopulations of patients. “Our challenge is to generate reliable hypotheses on a fairly small statistical patient sample, for example, a thousand patients, but using millions of biomarker datapoints,” continues Dr. Wojcik. “We do not rely on statistics alone. Graphical visualization adapted to the objectives of the study is necessary for interpretation of results.”

  In a recent project, Quartz Bio analyzed multiple oncology biomarkers, such as gene expression, circulating tumor cells, and immunohistochemistry, to identify patient cohorts that would most likely benefit from a novel treatment. Biomarker analysis revealed a subpopulation whose survival rate increased significantly over the population average, bringing a potential application of personalized medicine closer to reality.