A Zika Reference Panel for Molecular-Based Diagnostic Devices as a US Food and Drug Administration Response Tool to a Public Health Emergency.

In 2015, Zika virus (ZIKV) appeared as an emerging pathogen, generating a global and urgent need for accurate diagnostic devices. During this public health crisis, several nucleic acid testing (NAT)-based Zika assays were submitted to the US Food and Drug Administration (FDA) for Emergency Use Authorization. The FDA's Center for Devices and Radiological Health, in collaboration with the FDA's Center for Biologics Evaluation and Research, responded to this Zika emergency by developing and producing a reference panel (RP) for Zika RNA (Zika FDA-RP) suitable for performance assessment of ZIKV NAT-based in vitro diagnostic devices. Reference panels are a fundamental tool for performance assessment of molecular tests. The panel is composed of five vials: two different heat-inactivated ZIKV strains (PRVABC59 and FSS13025) in concentrated stocks and three blinded concentrations prepared from those strains. The Zika FDA-RP was shared with developers who had devices in the final stages of validation. In vitro diagnostic developers tested the Zika FDA-RP using the FDA-provided protocol. Depending on sample type, 85% (12/14) of the NAT assays had analytical sensitivities between 500 and 5000 RNA NAT-detectable units/mL (NDUs/mL). One device showed better performance (100 NDUs/mL), and another one showed lower performance (10,000 to 30,000 NDUs/mL). Vials of the Zika FDA-RP are available on request to developers who have interacted with the FDA through the review process.

FDA-ARGOS is a database with public quality-controlled reference genomes for diagnostic use and regulatory science.

FDA proactively invests in tools to support innovation of emerging technologies, such as infectious disease next generation sequencing (ID-NGS). Here, we introduce FDA-ARGOS quality-controlled reference genomes as a public database for diagnostic purposes and demonstrate its utility on the example of two use cases. We provide quality control metrics for the FDA-ARGOS genomic database resource and outline the need for genome quality gap filling in the public domain. In the first use case, we show more accurate microbial identification of Enterococcus avium from metagenomic samples with FDA-ARGOS reference genomes compared to non-curated GenBank genomes. In the second use case, we demonstrate the utility of FDA-ARGOS reference genomes for Ebola virus target sequence comparison as part of a composite validation strategy for ID-NGS diagnostic tests. The use of FDA-ARGOS as an in silico target sequence comparator tool combined with representative clinical testing could reduce the burden for completing ID-NGS clinical trials.

Approval of novel biomarkers: FDA's perspective and major requests.

FDA has been regulating diagnostic tests (including biomarkers) since passage of the Medical Device Amendments of 1976. Although always of interest as diagnostic tools, biomarkers (particularly genetic/genomic) have become of increased interest because of their potential impact on the development and personalized use of drugs. Unfortunately, there seem to be uncertainties among translational researchers as to the specific analytical and clinical measurement criteria needed for the approval of these novel biomarkers. This meeting presentation describes the current FDA perspective and major requirements and data for the validation/approval of an in vitro diagnostic device (IVD) based on a biomarker. The approval process for an IVD based on a biomarker used in the identification of a disease or condition (diagnosing, screening, monitoring) is well established, and is essentially identical to the process to generate sufficient analytical and clinical data for the approval of regular diagnostic devices. On the contrary, approvals for IVDs based on biomarker which may be designed to evaluate the efficacy or answer safety questions for new drug entities are less streamlined. The clinical studies are more complex, resulting in higher ethical standards, increased costs and requiring complex statistical evaluation. There is a small but growing literature on new models for co-development of drugs and diagnostics which will be discussed. Regulators like the FDA develop and bring a flexible regulatory toolbox to the table and are committed to assuring that scientific and regulatory thresholds are tempered to assure rapid access to new technologies while protecting public health.

Transcript and protein expression profiles of the NCI-60 cancer cell panel: an integromic microarray study.

To evaluate the utility of transcript profiling for prediction of protein expression levels, we compared profiles across the NCI-60 cancer cell panel, which represents nine tissues of origin. For that analysis, we present here two new NCI-60 transcript profile data sets (A based on Affymetrix HG-U95 and HG-U133A chips; Affymetrix, Santa Clara, CA) and one new protein profile data set (based on reverse-phase protein lysate arrays). The data sets are available online at http://discover.nci.nih.gov in the CellMiner program package. Using the new transcript data in combination with our previously published cDNA array and Affymetrix HU6800 data sets, we first developed a "consensus set" of transcript profiles based on the four different microarray platforms. Using that set, we found that 65% of the genes showed statistically significant transcript-protein correlation, and the correlations were generally higher than those reported previously for panels of mammalian cells. Using the predictive analysis of microarray nearest shrunken centroid algorithm for functional prediction of tissue of origin, we then found that (a) the consensus mRNA set did better than did data from any of the individual mRNA platforms and (b) the protein data seemed to do somewhat better (P = 0.027) on a gene-for-gene basis in this particular study than did the consensus mRNA data, but both did well. Analysis based on the Gene Ontology showed protein levels of structure-related genes to be well predicted by mRNA levels (mean r = 0.71). Because the transcript-based technologies are more mature and are currently able to assess larger numbers of genes at one time, they continue to be useful, even when the ultimate aim is information about proteins.

Detailed DNA methylation profiles of the E-cadherin promoter in the NCI-60 cancer cells.

E-cadherin (E-cad) is a transmembrane adhesion glycoprotein, the expression of which is often reduced in invasive or metastatic tumors. To assess E-cad's distribution among different types of cancer cells, we used bisulfite-sequencing for detailed, base-by-base measurement of CpG methylation in E-cad's promoter region in the NCI-60 cell lines. The mean methylation levels of the cell lines were distributed bimodally, with values pushed toward either the high or low end of the methylation scale. The 38 epithelial cell lines showed substantially lower (28%) mean methylation levels compared with the nonepithelial cell lines (58%). The CpG site at -143 with respect to the transcriptional start was commonly methylated at intermediate levels, even in cell lines with low overall DNA methylation. We also profiled the NCI-60 cell lines using Affymetrix U133 microarrays and found E-cad expression to be correlated with E-cad methylation at highly statistically significant levels. Above a threshold of approximately 20% to 30% mean methylation, the expression of E-cad was effectively silenced. Overall, this study provides a type of detailed analysis of methylation that can also be applied to other cancer-related genes. As has been shown in recent years, DNA methylation status can serve as a biomarker for use in choosing therapy.

Asparagine synthetase as a causal, predictive biomarker for L-asparaginase activity in ovarian cancer cells.

L-Asparaginase (l-ASP), a bacterial enzyme used since the 1970s to treat acute lymphoblastic leukemia, selectively starves cells that cannot synthesize sufficient asparagine for their own needs. Molecular profiling of the NCI-60 cancer cell lines using five different microarray platforms showed strong negative correlations of asparagine synthetase (ASNS) expression and DNA copy number with sensitivity to l-ASP in the leukemia and ovarian cancer cell subsets. To assess whether the ovarian relationship is causal, we used RNA interference to silence ASNS in three ovarian lines and observed 4- to 5-fold potentiation of sensitivity to l-ASP with two of the lines. For OVCAR-8, the line that expresses the least ASNS, the potentiation was >500-fold. Significantly, that potentiation was >700-fold in the multidrug-resistant derivative OVCAR-8/ADR, showing that the causal relationship between ASNS expression and l-ASP activity survives development of classical multidrug resistance. Tissue microarrays confirmed low ASNS expression in a subset of clinical ovarian cancers as well as other tumor types. Overall, this pharmacogenomic/pharmacoproteomic study suggests the use of l-ASP for treatment of a subset of ovarian cancers (and perhaps other tumor types), with ASNS as a biomarker for patient selection.

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.

The External RNA Controls Consortium: a progress report.

Standard controls and best practice guidelines advance acceptance of data from research, preclinical and clinical laboratories by providing a means for evaluating data quality. The External RNA Controls Consortium (ERCC) is developing commonly agreed-upon and tested controls for use in expression assays, a true industry-wide standard control.

Cross-platform comparability of microarray technology: intra-platform consistency and appropriate data analysis procedures are essential.

BACKGROUND: The acceptance of microarray technology in regulatory decision-making is being challenged by the existence of various platforms and data analysis methods. A recent report (E. Marshall, Science, 306, 630-631, 2004), by extensively citing the study of Tan et al. (Nucleic Acids Res., 31, 5676-5684, 2003), portrays a disturbingly negative picture of the cross-platform comparability, and, hence, the reliability of microarray technology. RESULTS: We reanalyzed Tan's dataset and found that the intra-platform consistency was low, indicating a problem in experimental procedures from which the dataset was generated. Furthermore, by using three gene selection methods (i.e., p-value ranking, fold-change ranking, and Significance Analysis of Microarrays (SAM)) on the same dataset we found that p-value ranking (the method emphasized by Tan et al.) results in much lower cross-platform concordance compared to fold-change ranking or SAM. Therefore, the low cross-platform concordance reported in Tan's study appears to be mainly due to a combination of low intra-platform consistency and a poor choice of data analysis procedures, instead of inherent technical differences among different platforms, as suggested by Tan et al. and Marshall. CONCLUSION: Our results illustrate the importance of establishing calibrated RNA samples and reference datasets to objectively assess the performance of different microarray platforms and the proficiency of individual laboratories as well as the merits of various data analysis procedures. Thus, we are progressively coordinating the MAQC project, a community-wide effort for microarray quality control.

Diagnostic markers that distinguish colon and ovarian adenocarcinomas: identification by genomic, proteomic, and tissue array profiling.

Colon and ovarian cancers can be difficult to distinguish in the abdomen, and the distinction is important because it determines which drugs will be used for therapy. To identify molecular markers for that differential diagnosis, we developed a multistep protocol starting with the 60 human cancer cell lines used by the National Cancer Institute to screen for new anticancer agents. The steps included: (a) identification of candidate markers using cDNA microarrays; (b) verification of clone identities by resequencing; (c) corroboration of transcript levels using Affymetrix oligonucleotide chips; (d) quantitation of protein expression by "reverse-phase" protein microarray; and (e) prospective validation of candidate markers on clinical tumor sections in tissue microarrays. The two best candidates identified were villin for colon cancer cells and moesin for ovarian cancer cells. Because moesin stained stromal elements in both types of cancer, it would probably not have been identified as a marker if we had started with mRNA or protein profiling of bulk tumors. Villin appears at least as useful as the currently used colon cancer marker cytokeratin 20, and moesin also appears to have utility. The multistep process introduced here has the potential to produce additional markers for cancer diagnosis, prognosis, and therapy.

Exploration, normalization, and summaries of high density oligonucleotide array probe level data.

In this paper we report exploratory analyses of high-density oligonucleotide array data from the Affymetrix GeneChip system with the objective of improving upon currently used measures of gene expression. Our analyses make use of three data sets: a small experimental study consisting of five MGU74A mouse GeneChip arrays, part of the data from an extensive spike-in study conducted by Gene Logic and Wyeth's Genetics Institute involving 95 HG-U95A human GeneChip arrays; and part of a dilution study conducted by Gene Logic involving 75 HG-U95A GeneChip arrays. We display some familiar features of the perfect match and mismatch probe (PM and MM) values of these data, and examine the variance-mean relationship with probe-level data from probes believed to be defective, and so delivering noise only. We explain why we need to normalize the arrays to one another using probe level intensities. We then examine the behavior of the PM and MM using spike-in data and assess three commonly used summary measures: Affymetrix's (i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii) the Li and Wong multiplicative model-based expression index (MBEI). The exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values. We evaluate the four expression summary measures using the dilution study data, assessing their behavior in terms of bias, variance and (for MBEI and RMA) model fit. Finally, we evaluate the algorithms in terms of their ability to detect known levels of differential expression using the spike-in data. We conclude that there is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities.

Induced dendritic cell differentiation of chronic myeloid leukemia blasts is associated with down-regulation of BCR-ABL.

Although differentiation of leukemic blasts to dendritic cells (DC) has promise in vaccine strategies, the mechanisms underlying this differentiation and the differences between leukemia and normal progenitor-derived DC are largely undescribed. In the case of chronic myeloid leukemia (CML), understanding the relationship between the induction of DC differentiation and the expression of the BCR-ABL oncogene has direct relevance to CML biology as well as the development of new therapeutic approaches. We now report that direct activation of protein kinase C (PKC) by the phorbol ester PMA in the BCR-ABL(+) CML cell line K562 and primary CML blasts induced nonterminal differentiation into cells with typical DC morphology (cytoplasmic dendrites), characteristic surface markers (MHC class I, MHC class II, CD86, CD40), chemokine and transcription factor expression, and ability to stimulate T cell proliferation (equivalent to normal monocyte-derived DC). PKC-induced differentiation was associated with down-regulation of BCR-ABL mRNA expression, protein levels, and kinase activity. This down-regulation appeared to be signaled through the mitogen-activated protein kinase pathway. Therefore, PKC-driven differentiation of CML blasts into DC-like cells suggests a potentially novel strategy to down-regulate BCR-ABL activity, yet raises the possibility that CML-derived DC vaccines will be less effective in presenting leukemia-specific Ags.

Transcriptional regulation of mitotic genes by camptothecin-induced DNA damage: microarray analysis of dose- and time-dependent effects.

cDNA microarray technology can be used to establish associations between characteristic gene expression patterns and molecular responses to drug therapy. In this study, we used cDNA microarrays of 1694 cancer-related genes to monitor the gene expression consequences of the treatment of HCT116 colon cancer cells with the topoisomerase I inhibitor camptothecin (CPT). To obtain a more homogeneous cellular response, we synchronized the cells in S-phase using aphidicolin (APH) before CPT treatment. Brief incubation with 20 and 1000 nM CPT caused reversible and irreversible G(2) arrest, respectively, and the patterns of gene expression change (with reference to untreated controls) were strikingly different at the two concentrations. Thirty-three genes, mainly divided into three groups, showed characteristic changes in the first 20 h as a consequence of treatment. Northern blots performed for five of these genes (each under eight experimental conditions) were quite consistent with the microarray results (average correlation coefficient, 0.86). Several p53-activated stress response genes were up-regulated after treatment with 1000 nM CPT or prolonged exposure to APH, but it seemed that the up-regulation did not directly cause cell cycle arrest because the up-regulation induced by prolonged treatment with APH did not prevent cell cycle progression after removal of APH. In contrast, cell cycle-dependent up-regulation of a group of mitosis-related genes was delayed or blocked after CPT treatments. The interrupted up-regulation of this group of genes was directly associated with G(2) arrest. In addition, we observed down-regulation of gene expression in cells that were recovering from cell cycle delay. The observations reported here suggest a fundamental difference at the gene expression level between the molecular mechanism of reversible G(2) delay that follows mild DNA damage and the mechanism of permanent G(2) arrest that follows more extensive DNA damage.