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.

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.

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.

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.

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.

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.

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.