Gene Expression-based Biomarkers Research Articles

Gene Expression-Based Biomarkers of Air Pollution Mixtures

Gene Expression-Based Biomarkers of Air Pollution Mixtures

Transcriptomics and micronutrient research

This review examines the extent to which transcriptomic methods have lived up to their promise in the context of nutrition research, placing particular emphasis on examples from micronutrient research. A case is made that the high quality platform technologies now available, together with established standards and systems for data storage and exchange and powerful new methods of data analysis, mean that microarrays have reached a level of technical maturity at which they can be exploited to their full potential. In the context of nutrition and micronutrient research, transcriptomic methods have already been widely applied, albeit primarily in studies using cell lines and animal models. Using this type of approach, a multitude of genes regulated at the mRNA level by dietary components has been identified and this, in turn, has provided new insights into the biological processes affected by nutritional parameters. Evidence from the very limited number of published transcriptomics-based nutritional studies performed in human volunteers suggests that, with appropriate study design, it is feasible to apply transcriptomic methods successfully in dietary intervention trials. On the other hand, gene expression-based biomarker development still poses a major challenge. Here the use of expression profile 'signatures', rather than single genes, may provide a solution. Approaches designed to identify such 'signatures' are being developed and tested widely, primarily in the context of medical research. The applicability and power of such approaches should also be evaluated in the context of nutrition.

Characteristics of Genomic Signatures Derived Using Univariate Methods and Mechanistically Anchored Functional Descriptors for Predicting Drug- and Xenobiotic-Induced Nephrotoxicity

ABSTRACTThe ideal toxicity biomarker is composed of the properties of prediction (is detected prior to traditional pathological signs of injury), accuracy (high sensitivity and specificity), and mechanistic relationships to the endpoint measured (biological relevance). Gene expression-based toxicity biomarkers (“signatures”) have shown good predictive power and accuracy, but are difficult to interpret biologically. We have compared different statistical methods of feature selection with knowledge-based approaches, using GeneGo's database of canonical pathway maps, to generate gene sets for the classification of renal tubule toxicity. The gene set selection algorithms include four univariate analyses: t-statistics, fold-change, B-statistics, and RankProd, and their combination and overlap for the identification of differentially expressed probes. Enrichment analysis following the results of the four univariate analyses, Hotelling T-square test, and, finally out-of-bag selection, a variant of cross-validation, were used to identify canonical pathway maps—sets of genes coordinately involved in key biological processes—with classification power. Differentially expressed genes identified by the different statistical univariate analyses all generated reasonably performing classifiers of tubule toxicity. Maps identified by enrichment analysis or Hotelling T-square had lower classification power, but highlighted perturbed lipid homeostasis as a common discriminator of nephrotoxic treatments. The out-of-bag method yielded the best functionally integrated classifier. The map “ephrins signaling” performed comparably to a classifier derived using sparse linear programming, a machine learning algorithm, and represents a signaling network specifically involved in renal tubule development and integrity. Such functional descriptors of toxicity promise to better integrate predictive toxicogenomics with mechanistic analysis, facilitating the interpretation and risk assessment of predictive genomic investigations.

Clinical-Biomarker Correlations in Adult AML Patients in a Phase I Trial of AVN944 Support Observations of Clinical Effect and Provide Hypotheses for Patient Selection Criteria for Further Clinical Trials.

Background: AVN944 is an uncompetitive inhibitor of inosine monophosphate dehydrogenase (IMPDH) that catalyzes the rate limiting step in the synthesis of guanine nucleotides. In vitro studies have demonstrated its efficacy in inducing apoptosis in leukemic cell lines. Between 01/06 and 7/07 a total of 117 cycles of AVN944 were administered to 50 patients (pts) with advanced hematologic malignancies (AML = 26, multiple myeloma = 15, CLL = 6, ALL = 2, and NHL = 1) at dose levels of 25 mg, 50 mg, 75 mg, 100 mg, 125 mg, 150 mg, 200 mg, and 250 mg bid po x 21d every 28 d. The study employed open label dose escalation with safety, pK, IMPDH activity, gene expression biomarkers, & efficacy endpoints. Biomarker evaluations in AML pts were most useful since AML blasts could be obtained from the peripheral blood in most subjects.Results: Pharmacokinetics corresponded to dose levels over the dose range tested with a Tmax = 1 hour, T1/2 of 1.5 hours, a mean Cmax=4060 ng/ml and AUC=12146hr*ng/ml at the 200 mg bid dose. Toxicities were generally mild-moderate and/or attributable to the underlying disease. Twenty-four SAEs were observed in 16 pts (10 w/AML; 4 w/CLL, 1w/MM, 1w/ALL). Twenty-two of the SAEs were felt to be unrelated to AVN944; 1 AVN944-related SAE/DLT was observed at both the 150 mg bid (dizziness and fever) and at the 200 mg bid (UGI mucositis w/bleeding) levels, prompting expansion of the accrual to those dose levels. Additional DLTs were not seen. Among AML pts, no protocol-defined responses were seen, but 9 of 26 pts had stable disease of 2 to 9 months' duration. Peripheral blood mononuclear cell (mean blast percentage 78%) were obtained from all pts before and after receiving AVN944 to determine drug effects on IMPDH enzyme activity (GTP pools) and modulation of 34-gene expression biomarkers that reflect cellular and metabolic effects of AVN944, including inhibition of IMPDH activity, depletion of GTP pools, G1/S phase cell-cycle block, and apoptosis. At the highest doses tested, Western blots show IMPDH-drug binding for at least 4 hours and depletion of GTP pools relative to baseline for at least 8 hours (last time point tested). Gene expression-based biomarkers show a dynamic response in patient samples with a strong correlation of changes in gene expression with GTP pool depletion and clinically stable disease. Specifically, genes involved in cell cycle and cell division (RRM2 & ZWINT), GTP function (GNAQ & PDE7A), and apoptotic signaling (PIM1) change 2–4 fold after drug exposure compared to baseline measurements. Further, in pts naïve to AVN944 therapy, expression of genes related to drug metabolism, drug resistance and apoptosis predict for biochemical activity of AVN944. Based on these data, algorithms were applied retrospectively to define a set of gene expression biomarkers for enrichment for patients more likely to benefit from drug. This set of biomarkers will be validated for their sensitivity and specificity in identifying the most appropriate patient population in upcoming phase II trials.

NSAID-Induced Acute Phase Response is Due to Increased Intestinal Permeability and Characterized by Early and Consistent Alterations in Hepatic Gene Expression

Toxicogenomics using a reference database can provide a better understanding and prediction of toxicity, largely by creating biomarkers that tie gene expression to actual pathology events. During the course of building a toxicogenomic database, an observation was made that a number of non-steroidal anti-inflammatory compounds (NSAIDs) at supra-pharmacologic doses induced an acute phase response (APR) and displayed hepatic gene expression patterns similar to that of intravenous lipopolysaccharide (LPS). Since NSAIDs are known to cause injury along the gastrointestinal tract, it has been suggested that NSAIDs increase intestinal permeability, allowing LPS and/or bacteria into the systemic circulation and stimulating an APR detectable in the liver. A short term study was subsequently conducted examining the effects of aspirin, indomethacin, ibuprofen, and rofecoxib to rats and a variety of endpoints were examined that included serum levels of inflammatory cytokines, histologic evaluation, and hepatic gene expression. Both indomethacin and ibuprofen injured the gastrointestinal tract, induced an APR, and increased serum levels of LPS, while rofecoxib and aspirin did not affect the GI tract or induce an APR. In treatments that eventually showed a systemic inflammatory response, hepatic expression of many inflammatory genes was noted as early as 6 hours after treatment well before alterations in traditional clinical pathology markers were detected. This finding led to the creation of a hepatic gene expression biomarker of APR that was effectively shown to be an early identifier of imminent inflammatory injury. In terms of the relative gastrointestinal safety and the NSAIDs studied, an important safety distinction can be made between the presumptive efficacious dose and the APR-inducing dose for indomethacin (1-2-fold), ibuprofen (5-fold), and rofecoxib (approximately 250-fold). Our data support the notion that NSAID-induced intestinal injury results in leakage of commensural bacteria and/or LPS into the circulation, provoking a systemic inflammatory response and that hepatic gene expression-based biomarkers can be used as early and sensitive biomarkers of APR onset.

Strategy for Gene Expression-Based Biomarker Discovery

INTRODUCTION A validated and effective gene expression-based, biomarker discovery process can be an incredibly valuable and often necessary tool in drug discovery, development, and diagnostic research. However, to successfully implement a biomarker signature discovery program and mine the most value out of such a program, one needs to have access to a broad range of expertise, both biological and statistical, as well as sophisticated tools for performing the laboratory and data analysis processes that enable research and commercial applications. Access to these skills and the tools necessary to support biomarker or signature discovery efforts may come from internally funded and constructed resources or through exterior collaborations, or in the case of the Signature DiscoveryTM Program developed by Althea Technologies (San Diego, CA, USA) and Expression Analysis (Durham, NC, USA), outsourced to a sole-source development provider. Whichever direction best suits your development and validation needs, several critical steps should be closely adhered to, from both biological and statistical perspectives. Our goal here is to provide an overview and some insights that identify these critical paths along your discovery activities.

Preclinical Drug Safety Analysis by Chemogenomic Profiling in the Liver

The economic hurdles of drug development and the emergence of genomic technologies such as chemogenomics are combining to shift the existing paradigms in preclinical drug development. Today, the information gleaned from high content molecular data has begun to augment traditional approaches to the assessment of drug safety. The optimal approach is a hybrid strategy employing chemogenomic data and gene expression-based biomarkers of drug efficacy and toxicity to supplement low content and insensitive methods for risk assessment and mechanistic evaluation of drug candidates. Large reference databases of chemogenomic data are essential to the derivation and validation of accurate and predictive gene expression biomarkers. An example of the development of a predictive biomarker for hepatic bile duct hyperplasia is described herein. As gene expression technologies improve, biomarkers will achieve higher throughput, and become more cost effective and increasingly accurate. This will elevate the value of chemogenomics in drug development, shift attrition to earlier in the process, and reduce the overall cost of drug development. Over the past 2 to 3 years, the transition of chemogenomics from a research tool to a decision-making tool has begun and regulatory agencies are anxiously awaiting implementation of this technology to make faster and more informed evaluations of potential drugs.