Some statistical and regulatory issues in the evaluation of genetic and genomic tests

Statistical innovations in the medical device world sparked by the FDA

The world of medical devices while highly diverse is extremely innovative, and this facilitates the adoption of innovative statistical techniques. Statisticians in the Center for Devices and Radiological Health (CDRH) at the Food and Drug Administration (FDA) have provided leadership in implementing statistical innovations. The innovations discussed include: the incorporation of Bayesian methods in clinical trials, adaptive designs, the use and development of propensity score methodology in the design and analysis of non-randomized observational studies, the use of tipping-point analysis for missing data, techniques for diagnostic test evaluation, bridging studies for companion diagnostic tests, quantitative benefit-risk decisions, and patient preference studies.

Clinical utility of microarrays: current status, existing challenges and future outlook

Microarray-based clinical tests have become powerful tools in the diagnosis and treatment of diseases. In contrast to traditional DNA-based tests that largely focus on single genes associated with rare conditions, microarray-based tests are ideal for the study of diseases with underlying complex genetic causes. Several microarray based tests have been translated into clinical practice such as MammaPrint and AmpliChip CYP450. Additional cancer-related microarray-based tests are either in the process of FDA review or under active development, including Tissue of Tumor Origin and AmpliChip p53. All diagnostic microarray testing is ordered by physicians and tested by a Clinical Laboratories Improvement Amendment-certified (CLIA) reference laboratory. Recently, companies offering consumer based microarray testing have emerged. Individuals can order tests online and service providers deliver the results directly to the clients via a password-protected secure website. Navigenics, 23andMe and deCODE Genetics represent pioneering companies in this field. Although the progress of these microarray-based tests is extremely encouraging with the potential to revolutionize the recognition and treatment of common diseases, these tests are still in their infancy and face technical, clinical and marketing challenges. In this article, we review microarray-based tests which are currently approved or under review by the FDA, as well as the consumer-based testing. We also provide a summary of the challenges and strategic solutions in the development and clinical use of the microarray-based tests. Finally, we present a brief outlook for the future of microarray-based clinical applications.

Is there a niche for DNA microarrays in molecular diagnostics?

DNA microarrays, 15 years after their appearance, have achieved presence in a number of medical settings. Several tests have been introduced and have obtained regulatory approval, mostly in the fields of bacterial identification, mutation detection and the global assessment of genome alterations, a particularly successful case being the whole-genome assay of copy-number variations. Gene-expression applications have been less successful because of technical issues (e.g., reproducibility, platform-to-platform consistency and statistical issues in data analysis) and difficulties in demonstrating the clinical utility of expression signatures. In their different applications, DNA arrays have faced competition from PCR-based assays for low and intermediate multiplicity. Now they have a new competitor, new-generation sequencing, that can provide a wealth of direct sequence information, or digital gene-expression data, at a constantly decreasing cost. In this article we evaluate the strengths and weaknesses of the DNA microarray approach to diagnostics, and highlight the fields in which it is most likely to achieve a durable presence.

Challenges in DNA microarray studies from the regulatory perspective

Genomic classifiers using DNA microarrays are becoming powerful tools in the medical community with the potential to revolutionize the diagnosis and treatment of disease. However, despite the tremendous interest in using these classifiers in diagnosis and the management of disease, few genomic classifiers have made it into clinical practice. Some of the major challenges for the development and validation of genomic classifiers will be discussed in this article together with some of their difficulties.

Identifying genetic risk factors for serious adverse drug reactions: current progress and challenges

Serious adverse drug reactions (SADRs) are a major cause of morbidity and mortality worldwide. Some SADRs may be predictable, based upon a drug's pharmacodynamic and pharmacokinetic properties. Many, however, appear to be idiosyncratic. Genetic factors may underlie susceptibility to SADRs and the identification of predisposing genotypes may improve patient management through the prospective selection of appropriate candidates. Here we discuss three specific SADRs with an emphasis on genetic risk factors. These SADRs, selected based on wide-sweeping clinical interest, are drug-induced liver injury, statin-induced myotoxicity and drug-induced long QT and torsades de pointes. Key challenges for the discovery of predictive risk alleles for these SADRs are also considered.

Logistic regression

The Medical Subject Headings (MeSH) thesaurus used by the National Library of Medicine defines logistic regression models as "statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable." Logistic regression models are used to study effects of predictor variables on categorical outcomes and normally the outcome is binary, such as presence or absence of disease (e.g., non-Hodgkin's lymphoma), in which case the model is called a binary logistic model. When there are multiple predictors (e.g., risk factors and treatments) the model is referred to as a multiple or multivariable logistic regression model and is one of the most frequently used statistical model in medical journals. In this chapter, we examine both simple and multiple binary logistic regression models and present related issues, including interaction, categorical predictor variables, continuous predictor variables, and goodness of fit.

Pharmacogenetics of irinotecan: clinical perspectives on the utility of genotyping

Depending upon the UDP glucuronosyltransferase 1A1 (UGT1A1) genotype, patients are more or less susceptible to the risk of severe toxicity of irinotecan. As the US FDA-approved label of irinotecan (CPT-11, Camptosar®) has been recently revised to include UGT1A1 genotype among potential risk factors for toxicity, it is expected that UGT1A1 genotyping will be increasingly used in patients undergoing irinotecan treatment. At present, the label states that *28/*28 (7/7) genotype patients are at higher risk of neutropenia and should be treated at a lower dose of irinotecan. Although effective alternative drugs (i.e., oxaliplatin) exist for metastatic colorectal cancer (the main indication of irinotecan), recent studies have confirmed that irinotecan has an important place in the management of this disease. We feel that now is the time for addressing questions around the UGT1A1*28 testing that many oncologists might have had but remained unanswered. For example, does the test have adequate sensitivity/specificity? Can the test results be effectively utilized to guide therapy of metastatic colorectal cancer patients? Is it possible that the *1/*1 (6/6) patients are underdosed? How can the genetic prediction of irinotecan toxicity be improved? Is the UGT1A1*28 test fully predictive of the UGT1A1 deficiency in patients who are not of Caucasian origin? Clinicians and investigators interested in a discussion of each of these points could find this article a useful source.

The US Food and Drug Administration perspective on cancer biomarker development

Despite the intense interest in biomarker development for cancer management, few biomarker assays for diagnostic uses have been submitted to the US Food and Drug Administration (FDA). What challenges must researchers overcome to bring cancer-detection technologies to the market and, therefore, into clinical use?

Gene mapping strategies for complex disease and drug response

The identification of genetic variants involved in disease susceptibility and response to drugs through the use of statistical and epidemiological approaches is a potentially powerful methodology for uncovering causal relationships in human disease and its treatment. Here we introduce and compare the application of genetics in these two fields of research.: