The next steps for genomic medicine: challenges and opportunities for the developing world.

This is a historical moment on the path to genomic medicine - the point at which theory is about to be translated into practice. We have previously described human genome variation studies taking place in Mexico, India, Thailand, and South Africa. Such investments into science and technology will enable these countries to embark on the path to the medical and health applications of genomics, and to benefit economically. Here we provide a perspective on the challenges and opportunities facing these and other countries in the developing world as they begin to harness genomics for the benefit of their populations.

A precision medicine framework using artificial intelligence for the identification and confirmation of genomic biomarkers of response to an Alzheimer's disease therapy: Analysis of the blarcamesine (ANAVEX2-73) Phase 2a clinical study.

INTRODUCTION: The search for drugs to treat Alzheimer's disease (AD) has failed to yield effective therapies. Here we report the first genome-wide search for biomarkers associated with therapeutic response in AD. Blarcamesine (ANAVEX2-73), a selective sigma-1 receptor (SIGMAR1) agonist, was studied in a 57-week Phase 2a trial (NCT02244541). The study was extended for a further 208 weeks (NCT02756858) after meeting its primary safety endpoint. METHODS: Safety, clinical features, pharmacokinetic, and efficacy, measured by changes in the Mini-Mental State Examination (MMSE) and the Alzheimer's Disease Cooperative Study-Activities of Daily Living scale (ADCS-ADL), were recorded. Whole exome and transcriptome sequences were obtained for 21 patients. The relationship between all available patient data and efficacy outcome measures was analyzed with unsupervised formal concept analysis (FCA), integrated in the Knowledge Extraction and Management (KEM) environment. RESULTS: Biomarkers with a significant impact on clinical outcomes were identified at week 57: mean plasma concentration of blarcamesine (slope MMSE:P < .041), genomic variants SIGMAR1 p.Gln2Pro (ΔMMSE:P < .039; ΔADCS-ADL:P < .063) and COMT p.Leu146fs (ΔMMSE:P < .039; ΔADCS-ADL:P < .063), and baseline MMSE score (slope MMSE:P < .015). Their combined impact on drug response was confirmed at week 148 with linear mixed effect models. DISCUSSION: Confirmatory Phase 2b/3 clinical studies of these patient selection markers are ongoing. This FCA/KEM analysis is a template for the identification of patient selection markers in early therapeutic development for neurologic disorders.

Rat toxicogenomic study reveals analytical consistency across microarray platforms.

To validate and extend the findings of the MicroArray Quality Control (MAQC) project, a biologically relevant toxicogenomics data set was generated using 36 RNA samples from rats treated with three chemicals (aristolochic acid, riddelliine and comfrey) and each sample was hybridized to four microarray platforms. The MAQC project assessed concordance in intersite and cross-platform comparisons and the impact of gene selection methods on the reproducibility of profiling data in terms of differentially expressed genes using distinct reference RNA samples. The real-world toxicogenomic data set reported here showed high concordance in intersite and cross-platform comparisons. Further, gene lists generated by fold-change ranking were more reproducible than those obtained by t-test P value or Significance Analysis of Microarrays. Finally, gene lists generated by fold-change ranking with a nonstringent P-value cutoff showed increased consistency in Gene Ontology terms and pathways, and hence the biological impact of chemical exposure could be reliably deduced from all platforms analyzed.

Evaluation of DNA microarray results with quantitative gene expression platforms.

We have evaluated the performance characteristics of three quantitative gene expression technologies and correlated their expression measurements to those of five commercial microarray platforms, based on the MicroArray Quality Control (MAQC) data set. The limit of detection, assay range, precision, accuracy and fold-change correlations were assessed for 997 TaqMan Gene Expression Assays, 205 Standardized RT (Sta)RT-PCR assays and 244 QuantiGene assays. TaqMan is a registered trademark of Roche Molecular Systems, Inc. We observed high correlation between quantitative gene expression values and microarray platform results and found few discordant measurements among all platforms. The main cause of variability was differences in probe sequence and thus target location. A second source of variability was the limited and variable sensitivity of the different microarray platforms for detecting weakly expressed genes, which affected interplatform and intersite reproducibility of differentially expressed genes. From this analysis, we conclude that the MAQC microarray data set has been validated by alternative quantitative gene expression platforms thus supporting the use of microarray platforms for the quantitative characterization of gene expression.

The Labyrinth of Product Development and Regulatory Approvals in Liquid Biopsy Diagnostics.

The evolution of chemistries and instrument platforms for next-generation sequencing has led to sequencing of genomic variants in both tumor biopsies as well as in circulating tumor cells (CTCs) and cell-free DNA liquid biopsies. The transition of these analytical platforms into clinical ones has led to challenges in product development as well as regulatory strategies for the approval of diagnostic products with these platforms. Regulatory strategies for liquid biopsy diagnostics depend on a framework that has been developed over the past few years by the US Food and Drug Administration (FDA). This framework includes both guidances that cover enrichment biomarkers and companion diagnostics, as well as regulatory approval precedents, which can be used to design regulatory strategies for new liquid biopsy diagnostic products. However, the regulatory paths for these liquid biopsy diagnostics can also be tortuous, as is the example of CTC-platform liquid biopsies. The ultimate success of regulatory pathways of liquid biopsy diagnostics has been driven by the incremental value of FDA approval for Clinical Laboratory Improvement Amendment (CLIA)-developed tests and by the inherent complexity of these diagnostics, which are practical barriers for the widespread replication of these tests throughout CLIA laboratories. The framework for FDA approval of sequence information from these liquid biopsies has been focused on single-site approvals of diagnostics where sequencing information is considered at different diagnostic risk levels, ranging from novel or follow-on companion diagnostics to variant calls in genomic targets considered independently valuable for therapeutic decision making.

The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies.

BACKGROUND: Reproducibility is a fundamental requirement in scientific experiments. Some recent publications have claimed that microarrays are unreliable because lists of differentially expressed genes (DEGs) are not reproducible in similar experiments. Meanwhile, new statistical methods for identifying DEGs continue to appear in the scientific literature. The resultant variety of existing and emerging methods exacerbates confusion and continuing debate in the microarray community on the appropriate choice of methods for identifying reliable DEG lists. RESULTS: Using the data sets generated by the MicroArray Quality Control (MAQC) project, we investigated the impact on the reproducibility of DEG lists of a few widely used gene selection procedures. We present comprehensive results from inter-site comparisons using the same microarray platform, cross-platform comparisons using multiple microarray platforms, and comparisons between microarray results and those from TaqMan - the widely regarded "standard" gene expression platform. Our results demonstrate that (1) previously reported discordance between DEG lists could simply result from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion with a non-stringent P-value cutoff filtering, the DEG lists become much more reproducible, especially when fewer genes are selected as differentially expressed, as is the case in most microarray studies; and (3) the instability of short DEG lists solely based on P-value ranking is an expected mathematical consequence of the high variability of the t-values; the more stringent the P-value threshold, the less reproducible the DEG list is. These observations are also consistent with results from extensive simulation calculations. CONCLUSION: We recommend the use of FC-ranking plus a non-stringent P cutoff as a straightforward and baseline practice in order to generate more reproducible DEG lists. Specifically, the P-value cutoff should not be stringent (too small) and FC should be as large as possible. Our results provide practical guidance to choose the appropriate FC and P-value cutoffs when selecting a given number of DEGs. The FC criterion enhances reproducibility, whereas the P criterion balances sensitivity and specificity.

Qualification of biomarkers for drug development in organ transplantation.

The drug development process is dependent upon having established end points for measuring drug efficacy and adverse effects. New drug development in organ transplantation suffers from having end points which are either outdated or which do not serve the purpose of addressing the current critical drug therapy problems. Numerous biomarkers have been examined in organ transplantation, but almost all would be classified as exploratory for drug development purposes. Some of the possible pathways out of this dilemma include investigator- or consortium-initiated research that would qualify the biomarkers as either probable or known valid biomarkers, help in identification of new end points in transplantation and their associated biomarkers, co-development of a new biomarker and drug for transplantation and the use of new clinical trial design methods which facilitate enriched or stratified transplant patient populations. With new biomarkers and new study design methodologies for drug development, improvement in the drug development process for transplantation is a real possibility that the transplant clinical and research community can help to bring about.

The first RSBI (ISA-TAB) workshop: "can a simple format work for complex studies?".

This article summarizes the motivation for, and the proceedings of, the first ISA-TAB workshop held December 6-8, 2007, at the EBI, Cambridge, UK. This exploratory workshop, organized by members of the Microarray Gene Expression Data (MGED) Society's Reporting Structure for Biological Investigations (RSBI) working group, brought together a group of developers of a range of collaborative systems to discuss the use of a common format to address the pressing need of reporting and communicating data and metadata from biological, biomedical, and environmental studies employing combinations of genomics, transcriptomics, proteomics, and metabolomics technologies along with more conventional methodologies. The expertise of the participants comprised database development, data management, and hands-on experience in the development of data communication standards. The workshop's outcomes are set to help formalize the proposed Investigation, Study, Assay (ISA)-TAB tab-delimited format for representing and communicating experimental metadata. This article is part of the special issue of OMICS on the activities of the Genomics Standards Consortium (GSC).

Strategic paths for biomarker qualification.

Biomarkers may be qualified using different qualification processes. A passive approach for qualification has been to accept the end of discussions in the scientific literature as an indication that a biomarker has been accepted. An active approach to qualification requires development of a comprehensive process by which a consensus may be reached about the qualification of a biomarker. Active strategies for qualification include those associated with context-independent as well as context-dependent qualifications.

Histopathology of vascular injury in Sprague-Dawley rats treated with phosphodiesterase IV inhibitor SCH 351591 or SCH 534385.

Histopathological and immunohistochemical studies were conducted to characterize vascular injuries in rats treated with phosphodiesterase (PDE) IV inhibitors SCH 351591 or SCH 534385. Sprague-Dawley rats were administered PDE IV inhibitors by gavage at a range of doses and times. The two PDE IV inhibitors induced comparable levels of vascular injury, primarily in the mesentery and to a lesser extent in the pancreas, kidney, liver, small intestine, and stomach. Mesenteric vascular changes occurred as early as one hour, progressively developed over twenty-four to forty-eight hours, peaked at seventy-two hours, and gradually subsided from seven to nine days. The typical morphology of the vascular toxicity consisted of hemorrhage and necrosis of arterioles and arteries, microvascular injury, fibrin deposition, and perivascular inflammation of a variety of blood vessels. The incidence and severity of mesenteric vascular injury increased in a time- and dose-dependent manner in SCH 351591- or SCH 534385-treated rats. Mesenteric vascular injury was frequently associated with activation of mast cells (MC), endothelial cells (EC), and macrophages (MØ). Immunohistochemical studies showed increases in CD63 immunoreactivity of mesenteric MC and in nitrotyrosine immunoreactivity of mesenteric EC and MØ. The present study also provides a morphological and cellular basis for evaluating candidate biomarkers of drug-induced vascular injury.

Biomarkers in peripheral blood associated with vascular injury in Sprague-Dawley rats treated with the phosphodiesterase IV inhibitors SCH 351591 or SCH 534385.

Drug-associated vascular injury can be caused by phosphodiesterase (PDE) IV inhibitors and drugs from several other classes. The pathogenesis is poorly understood, but it appears to include vascular and innate immunological components. This research was undertaken to identify changes in peripheral blood associated with vascular injury caused by PDE IV inhibitors. We evaluated twelve proteins, serum nitrite, and leukocyte populations in peripheral blood of rats treated with experimental PDE IV inhibitors. We found that these compounds produced histological microvascular injury in a dose- and time-dependent manner. Measurement of these serum proteins showed changes in eight of the twelve examined. Changes were seen in the levels of: tissue inhibitor of metalloproteinase-1, alpha1-acid glycoprotein, GRO/CINC-1, vascular endothelial growth factor, C-reactive protein, haptoglobin, thrombomodulin, and interleukin-6. No changes were seen in levels of tumor necrosis factor-alpha, hepatocyte growth factor, nerve growth factor, and granulocyte-monocyte colony stimulating factor. Serum levels of nitrite were also increased. Circulating granulocyte numbers were increased, and lymphocyte numbers were decreased. The changes in these parameters showed both a dose- and time-dependent association with histopathologic changes. These biomarkers could provide an additional tool for the nonclinical and clinical evaluation of investigational compounds.

Regulatory landscapes for biomarkers and diagnostic tests: Qualification, approval, and role in clinical practice.

While the term 'biomarker' is relatively new, the concept is millennia old. However, with the introduction of new technologies to discover potential biomarkers comes the need to assess their utility and veracity for any given use. This is particularly true for the use of biomarkers to support regulatory decisions in medical product development. Hence the US Food and Drug Administration has developed processes for the qualification of biomarkers and other medical product development tools, processes that are underscored by recent legislation (i.e. the 21st Century Cures Act). In addition to these qualification processes, diagnostic tests that measure a biomarker may follow a process for regulatory decision through the processes that evaluate companion diagnostics. This mini-review provides an overview of these processes and their role in pharmaceutical development and clinical use. Impact statement This work summarizes very recent developments in the US FDA's biomarker qualification program. Furthermore, it contrasts biomarker qualification with companion diagnostic evaluation. As such, it will be highly informative for researches considering taking a biomarker discovery farther along the road to validation.

A robust targeted sequencing approach for low input and variable quality DNA from clinical samples.

Next-generation deep sequencing of gene panels is being adopted as a diagnostic test to identify actionable mutations in cancer patient samples. However, clinical samples, such as formalin-fixed, paraffin-embedded specimens, frequently provide low quantities of degraded, poor quality DNA. To overcome these issues, many sequencing assays rely on extensive PCR amplification leading to an accumulation of bias and artifacts. Thus, there is a need for a targeted sequencing assay that performs well with DNA of low quality and quantity without relying on extensive PCR amplification. We evaluate the performance of a targeted sequencing assay based on Oligonucleotide Selective Sequencing, which permits the enrichment of genes and regions of interest and the identification of sequence variants from low amounts of damaged DNA. This assay utilizes a repair process adapted to clinical FFPE samples, followed by adaptor ligation to single stranded DNA and a primer-based capture technique. Our approach generates sequence libraries of high fidelity with reduced reliance on extensive PCR amplification-this facilitates the accurate assessment of copy number alterations in addition to delivering accurate single nucleotide variant and insertion/deletion detection. We apply this method to capture and sequence the exons of a panel of 130 cancer-related genes, from which we obtain high read coverage uniformity across the targeted regions at starting input DNA amounts as low as 10 ng per sample. We demonstrate the performance using a series of reference DNA samples, and by identifying sequence variants in DNA from matched clinical samples originating from different tissue types.

Implementing the U.S. FDA guidance on pharmacogenomic data submissions.

The FDA Guidance for Industry: Pharmacogenomics Data Submissions was issued in 2005. This guidance document covers a broad area associated with how and when to submit genomic data to the FDA. Additional tasks associated with genomic data submissions include the implementation of genomic data submissions; the process for qualification of exploratory biomarkers into valid biomarkers; and technical recommendations for the generation and submission of genomic data to the FDA. These tasks have been addressed throughout the past 2 years by a number of initiatives. These initiatives have included the development of the Interdisciplinary Pharmacogenomics Review Group for review of pharmacogenomic data submissions, the pilot process for qualification of biomarkers, and the concept paper on recommendations for the generation and submission of genomic data. These initiatives have contributed to the effective implementation of the Pharmacogenomics Guidance at the FDA.

Questions and answers about the Pilot Process for Biomarker Qualification at the FDA.

The FDA has developed a Pilot Process for Biomarker Qualification. Initial experience with this process has underscored the care that a long-term approach to biomarker qualification independently of development for specific drugs should have in the assembly of external industry consortia as well as the internal regulatory organization for these qualification efforts. There are complex scientific and clinical issues associated with these qualifications, and it is paramount that the expertise needed for their review be identified so that a comprehensive consensus may be reached at the end of this process. Several frequently asked questions associated with this process are presented in this paper, as well as answers reflecting the Agency's current thinking about this process.:

The Predictive Safety Testing Consortium: A synthesis of the goals, challenges and accomplishments of the Critical Path.

The qualification of biomarkers of drug safety requires data on many compounds and nonclinical and clinical studies. The cost and effort associated with these qualifications cannot be easily covered by a single pharmaceutical company. Intellectual property associated with safety biomarkers is also held by many different companies. Consortia between different pharmaceutical companies can overcome cost and intellectual property hurdles to biomarker qualification. The Predictive Safety Testing Consortium (PSTC) is a collaborative effort between 16 different pharmaceutical companies to generate data supporting biomarker qualification. This Consortium is coordinated through the C-Path Institute, and currently has five biomarker qualification working groups engaged in this collaboration: nephrotoxicity, hepatotoxicity, vascular injury, myopathy, and non-genotoxic carcinogenicity. These working groups are aided by a data management team and a translational strategy team. Qualification studies of promising biomarkers are already progressing in several of the working groups, and results in the nephrotoxicity working group warranted a data submission to the FDA and EMEA for regulatory qualification of new nephrotoxicity biomarkers.:

An integrated bioinformatics infrastructure essential for advancing pharmacogenomics and personalized medicine in the context of the FDA's Critical Path Initiative.

Pharmacogenomics (PGx) is identified in the FDA Critical Path document as a major opportunity for advancing medical product development and personalized medicine. An integrated bioinformatics infrastructure for use in FDA data review is crucial to realize the benefits of PGx for public health. We have developed an integrated bioinformatics tool, called ArrayTrack, for managing, analyzing and interpreting genomic and other biomarker data (e.g. proteomic and metabolomic data). ArrayTrack is a highly flexible and robust software platform, which allows evolving with technological advances and changing user needs. ArrayTrack is used in the routine review of genomic data submitted to the FDA; here, three hypothetical examples of its use in the Voluntary eXploratory Data Submission (VXDS) program are illustrated.:

Process map proposal for the validation of genomic biomarkers.

How can we encourage the application of novel genomic biomarkers in drug development? A major step in this direction would be a consensus on how to interpret results from measurements of these biomarkers in regulatory submissions. A transparent process for genomic biomarker validation would be of value both for the pharmaceutical industry as well as for regulatory agencies associated with it. A discussion on process map proposals for genomic biomarker validation can help with drafting of guidance documents for this process.

Application of pharmacogenomics in clinical pharmacology.

Many factors can affect a patient's response to a drug. These include intrinsic factors such as age, gender, race/ethnicity, genetics, disease states, organ dysfunctions, and other physiological changes, including pregnancy, lactation, and extrinsic factors such as smoking, diet (food, juice, dietary supplements), and concomitant medications (ICH E5, 1998 and 2004). The interplay of genotypes of the enzymes, transporters and receptors, among other factors (such as concomitant medications and disease states), can affect the risk/benefit ratio for individual patients. This commentary discusses when the genomic information should be obtained during drug development and when it is to be assimilated into labeling and standards of care that can be used to "individualize" drug therapy and become one of the pillars of "personalized medicine."

Gaining Confidence on Molecular Classification through Consensus Modeling and Validation.

Current advances in genomics, proteomics, and metabonomics would result in a constellation of benefits in human health. Classification applying supervised learning methods to omics data as one of the molecular classification approaches has enjoyed its growing role in clinical application. However, the utility of a molecular classifier will not be fully appreciated unless its quality is carefully validated. A clinical omics data is usually noisy with the number of independent variables far more than the number of subjects and, possibly, with a skewed subject distribution. Given that, the consensus approach holds an advantage over a single classifier. Thus, the focus of this review is mainly placed on how validating a molecular classifier using Decision Forest (DF), a robust consensus approach. We recommended that a molecular classifier has to be assessed with respect to overall prediction accuracy, prediction confidence and chance correlation, which can be readily achieved in DF. The commonalities and differences between external validation and cross-validation are also discussed for perspective use of these methods to validate a DF classifier. In addition, the advantages of using consensus approaches for identification of potential biomarkers are also rationalized. Although specific DF examples are used in this review, the provided rationales and recommendations should be equally applicable to other consensus methods.