Identification of polymorphisms associated with hypertriglyceridemia and prolonged survival induced by bexarotene in treating non-small cell lung cancer.

BACKGROUND: Bexarotene was evaluated in treating advanced non small cell lung cancer (NSCLC) in two phase III trials. Although a significant survival benefit was not observed for the overall bexarotene-treated population (617 patients), a third of bexarotene-treated patients who developed high-grade hypertriglyceridemia exhibited significantly longer survival. PATIENTS AND METHODS: In order to identify genomic polymorphisms that could serve as potential predictive biomarkers for response and improved survival in NSCLC patients, DNA samples extracted from plasma archived from 403 patients were genotyped using Affymetrix 500K whole genome SNP arrays and/or Sequenom iPLEX™ assays. RESULTS: Fourteen SNPs were identified on nine loci that showed significant associations with high-grade hypertriglyceridemia induced by bexarotene. Four such single nucleotide polymorphisms (SNPs) reside on the region upstream of solute carrier family 10, member 2 (SLC10A2), and one SNP is located close to lymphocyte cytosolic protein 1 (LCP1), whose expression correlated with the activity of bexarotene in tumor cells. CONCLUSION: We identified novel polymorphisms exhibiting significant association with bexarotene induced hypertriglyceridemia, implicating their potential in predicting bexarotene-improved survival response.

Experimental design and statistical methods for improved hit detection in high-throughput screening.

Identification of active compounds in high-throughput screening (HTS) contexts can be substantially improved by applying classical experimental design and statistical inference principles to all phases of HTS studies. The authors present both experimental and simulated data to illustrate how true-positive rates can be maximized without increasing false-positive rates by the following analytical process. First, the use of robust data preprocessing methods reduces unwanted variation by removing row, column, and plate biases. Second, replicate measurements allow estimation of the magnitude of the remaining random error and the use of formal statistical models to benchmark putative hits relative to what is expected by chance. Receiver Operating Characteristic (ROC) analyses revealed superior power for data preprocessed by a trimmed-mean polish method combined with the RVM t-test, particularly for small- to moderate-sized biological hits.

DNA sequence-based phenotypic association analysis.

The availability of cost-effective, high-throughput genotyping technologies has generated a tremendous amount of interest in genetic association studies. This interest has led to the belief that one could possibly test thousands to millions of representative polymorphic sites on the genome for association with a trait or disease in order to identify the few sites that may be of relevance to the expression of that trait or disease. The choice of which polymorphic sites are "representative" and to be interrogated in such studies is problematic and has involved considerations of the putative functional significance of the sites as well as the linkage disequilibrium relationships between variations at those sites and other neighboring sites. We consider an obvious alternative to genotyping-based strategies and settings for association studies for which decisions about which variations to interrogate are obviated. Essentially, we anticipate a time when cost-effective, high-throughput DNA sequencing technologies are available and researchers will have actual sequence information on the individuals under study rather than information about what variations they possess at a few well-chosen polymorphic genomic sites. We consider Multivariate Distance Matrix Regression analysis to evaluate associations between DNA sequence information and quantitative traits such as blood pressure and cholesterol level. We evaluate the potential of the method in a few (albeit contrived) settings via simulation studies. Ultimately, we show that the procedure has promise and argue that consideration of DNA sequence-based association data should usher in a new era in genetic association study designs and methodologies.

Accommodating linkage disequilibrium in genetic-association analyses via ridge regression.

Large-scale genetic-association studies that take advantage of an extremely dense set of genetic markers have begun to produce very compelling statistical associations between multiple makers exhibiting strong linkage disequilibrium (LD) in a single genomic region and a phenotype of interest. However, the ultimate biological or "functional" significance of these multiple associations has been difficult to discern. In fact, the LD relationships between not only the markers found to be associated with the phenotype but also potential functionally or causally relevant genetic variations that reside near those markers have been exploited in such studies. Unfortunately, LD, especially strong LD, between variations at neighboring loci can make it difficult to distinguish the functionally relevant variations from nonfunctional variations. Although there are (rare) situations in which it is impossible to determine the independent phenotypic effects of variations in LD, there are strategies for accommodating LD between variations at different loci, and they can be used to tease out their independent effects on a phenotype. These strategies make it possible to differentiate potentially causative from noncausative variations. We describe one such approach involving ridge regression. We showcase the method by using both simulated and real data. Our results suggest that ridge regression and related techniques have the potential to distinguish causative from noncausative variations in association studies.

An efficient method for the detection and elimination of systematic error in high-throughput screening.

MOTIVATION: High-throughput screening (HTS) is an early-stage process in drug discovery which allows thousands of chemical compounds to be tested in a single study. We report a method for correcting HTS data prior to the hit selection process (i.e. selection of active compounds). The proposed correction minimizes the impact of systematic errors which may affect the hit selection in HTS. The introduced method, called a well correction, proceeds by correcting the distribution of measurements within wells of a given HTS assay. We use simulated and experimental data to illustrate the advantages of the new method compared to other widely-used methods of data correction and hit selection in HTS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

HTS-Corrector: software for the statistical analysis and correction of experimental high-throughput screening data.

MOTIVATION: High-throughput screening (HTS) plays a central role in modern drug discovery, allowing for testing of >100,000 compounds per screen. The aim of our work was to develop and implement methods for minimizing the impact of systematic error in the analysis of HTS data. To the best of our knowledge, two new data correction methods included in HTS-Corrector are not available in any existing commercial software or freeware. RESULTS: This paper describes HTS-Corrector, a software application for the analysis of HTS data, detection and visualization of systematic error, and corresponding correction of HTS signals. Three new methods for the statistical analysis and correction of raw HTS data are included in HTS-Corrector: background evaluation, well correction and hit-sigma distribution procedures intended to minimize the impact of systematic errors. We discuss the main features of HTS-Corrector and demonstrate the benefits of the algorithms.

Statistical practice in high-throughput screening data analysis.

High-throughput screening is an early critical step in drug discovery. Its aim is to screen a large number of diverse chemical compounds to identify candidate 'hits' rapidly and accurately. Few statistical tools are currently available, however, to detect quality hits with a high degree of confidence. We examine statistical aspects of data preprocessing and hit identification for primary screens. We focus on concerns related to positional effects of wells within plates, choice of hit threshold and the importance of minimizing false-positive and false-negative rates. We argue that replicate measurements are needed to verify assumptions of current methods and to suggest data analysis strategies when assumptions are not met. The integration of replicates with robust statistical methods in primary screens will facilitate the discovery of reliable hits, ultimately improving the sensitivity and specificity of the screening process.