Gene Expression Signature for Prediction of Golimumab Response in a Phase 2a Open-Label Trial of Patients With Ulcerative Colitis.

Golimumab, a tumor necrosis factor antagonist, is an effective treatment for patients with moderate-to-severe ulcerative colitis (UC); however, more than 50% of initial responders lose their response to the drug within the first year of therapy. A gene expression signature identified in colon biopsies collected before treatment was associated with response to infliximab, and was subsequently refined to associate with mucosal healing in response to golimumab. We performed a phase 2a open-label study of 103 golimumab-treated patients with moderate-to-severe UC to test whether the baseline gene expression signature could be used to predict which patients would achieve mucosal healing, clinical response, and clinical remission at weeks 6 and 30 of treatment. The gene expression signature identified patients who went on to achieve mucosal healing at treatment week 6 with an area under the receiver operating characteristic curve (AUCROC) of 0.688 (P = .002) and at week 30 with an AUCROC of 0.671 (P = .006). The signature identified patients with mucosal healing with 87% sensitivity, but only 34% specificity, limiting its clinical utility. The baseline gene expression signature did not identify patients who went on to achieve clinical remission or clinical response with statistical significance. Further studies are needed to identify biomarkers that can be used to predict which patients with UC will respond to treatment with anti-tumor necrosis factor agents. ClinicalTrials.gov no: NCT01988961.

Implications for powering biomarker discovery studies.

This study examined variations in gene expression between FFPE blocks within tumors of individual patients. Microarray data were used to measure tumor heterogeneity within and between patients and disease states. Data were used to determine the number of samples needed to power biomarker discovery studies. Bias and variation in gene expression were assessed at the intrapatient and interpatient levels and between adenocarcinoma and squamous samples. A mixed-model analysis of variance was fitted to gene expression data and model signatures to assess the statistical significance of observed variations within and between samples and disease states. Sample size analysis, adjusted for sample heterogeneity, was used to determine the number of samples required to support biomarker discovery studies. Variation in gene expression was observed between blocks taken from a single patient. However, this variation was considerably less than differences between histological characteristics. This degree of block-to-block variation still permits biomarker discovery using either macrodissected tumors or whole FFPE sections, provided that intratumor heterogeneity is taken into account. Failure to consider intratumor heterogeneity may result in underpowered biomarker studies that may result in either the generation of longer gene signatures or the inability to identify a viable biomarker. Moreover, the results of this study indicate that a single biopsy sample is suitable for applying a biomarker in non-small-cell lung cancer.

Methods comparison for high-resolution transcriptional analysis of archival material on Affymetrix Plus 2.0 and Exon 1.0 microarrays.

Microarray gene expression profiling of formalin-fixed paraffin-embedded (FFPE) tissues is a new and evolving technique. This report compares transcript detection rates on Affymetrix U133 Plus 2.0 and Human Exon 1.0 ST GeneChips across several RNA extraction and target labeling protocols, using routinely collected archival FFPE samples. All RNA extraction protocols tested (Ambion-Optimum, Ambion-RecoverAll, and Qiagen-RNeasy FFPE) provided extracts suitable for microarray hybridization. Compared with Affymetrix One-Cycle labeled extracts, NuGEN system protocols utilizing oligo(dT) and random hexamer primers, and cDNA target preparations instead of cRNA, achieved percent present rates up to 55% on Plus 2.0 arrays. Based on two paired-sample analyses, at 90% specificity this equalled an average 30 percentage-point increase (from 50% to 80%) in FFPE transcript sensitivity relative to fresh frozen tissues, which we have assumed to have 100% sensitivity and specificity. The high content of Exon arrays, with multiple probe sets per exon, improved FFPE sensitivity to 92% at 96% specificity, corresponding to an absolute increase of ~600 genes over Plus 2.0 arrays. While larger series are needed to confirm high correspondence between fresh-frozen and FFPE expression patterns, these data suggest that both Plus 2.0 and Exon arrays are suitable platforms for FFPE microarray expression analyses.

An annotation infrastructure for the analysis and interpretation of Affymetrix exon array data.

Affymetrix exon arrays contain probesets intended to target every known and predicted exon in the entire genome, posing significant challenges for high-throughput genome-wide data analysis. X:MAP http://xmap.picr.man.ac.uk, an annotation database, and exonmap http://www.bioconductor.org/packages/2.0/bioc/html/exonmap.html, a BioConductor/R package, are designed to support fine-grained analysis of exon array data. The system supports the application of standard statistical techniques, prior to the use of genome scale annotation to provide gene-, transcript- and exon-level summaries and visualization tools.