Optimized between-group classification: a new jackknife-based gene selection procedure for genome-wide expression data

High-throughput alternative splicing detection using dually constrained correspondence analysis (DCCA)

Alternative splicing is an important component of tumorigenesis. Recent advent of exon array technology enables the detection of alternative splicing at a genome-wide scale. The analysis of high-throughput alternative splicing is not yet standard and methodological developments are still needed. We propose a novel statistical approach-Dually Constrained Correspondence Analysis-for the detection of splicing changes in exon array data. Using this methodology, we investigated the genome-wide alteration of alternative splicing in patients with non-small cell lung cancer treated by bevacizumab/erlotinib. Splicing candidates reveal a series of genes related to carcinogenesis (SFTPB), cell adhesion (STAB2, PCDH15, HABP2), tumor aggressiveness (ARNTL2), apoptosis, proliferation and differentiation (PDE4D, FLT3, IL1R2), cell invasion (ETV1), as well as tumor growth (OLFM4, FGF14), tumor necrosis (AFF3) or tumor suppression (TUSC3, CSMD1, RHOBTB2, SERPINB5), with indication of known alternative splicing in a majority of genes. DCCA facilitates the identification of putative biologically relevant alternative splicing events in high-throughput exon array data.

Gene Expression Signatures Predictive of Bevacizumab/Erlotinib Therapeutic Benefit in Advanced Nonsquamous Non-Small Cell Lung Cancer Patients (SAKK 19/05 trial)

PURPOSE

We aimed to identify gene expression signatures associated with angiogenesis and hypoxia pathways with predictive value for treatment response to bevacizumab/erlotinib (BE) of nonsquamous advanced non-small cell lung cancer (NSCLC) patients.

EXPERIMENTAL DESIGN

Whole-genome gene expression profiling was performed on 42 biopsy samples (from SAKK 19/05 trial) using Affymetrix exon arrays, and associations with the following endpoints: time-to-progression (TTP) under therapy, tumor-shrinkage (TS), and overall survival (OS) were investigated. Next, we performed gene set enrichment analyses using genes associated with the angiogenic process and hypoxia response to evaluate their predictive value for patients' outcome.

RESULTS

Our analysis revealed that both the angiogenic and hypoxia response signatures were enriched within the genes predictive of BE response, TS, and OS. Higher gene expression levels (GEL) of the 10-gene angiogenesis-associated signature and lower levels of the 10-gene hypoxia response signature predicted improved TTP under BE, 7.1 months versus 2.1 months for low versus high-risk patients (P = 0.005), and median TTP 6.9 months versus 2.9 months (P = 0.016), respectively. The hypoxia response signature associated with higher TS at 12 weeks and improved OS (17.8 months vs. 9.9 months for low vs. high-risk patients, P = 0.001).

CONCLUSIONS

We were able to identify gene expression signatures derived from the angiogenesis and hypoxia response pathways with predictive value for clinical outcome in advanced nonsquamous NSCLC patients. This could lead to the identification of clinically relevant biomarkers, which will allow for selecting the subset of patients who benefit from the treatment and predict drug response.

Gene profiling of clinical routine biopsies and prediction of survival in non-small cell lung cancer

RATIONALE

Global gene expression analysis provides a comprehensive molecular characterization of non-small cell lung cancer (NSCLC).

OBJECTIVES

To evaluate the feasibility of integrating expression profiling into routine clinical work-up by including both surgical and minute bronchoscopic biopsies and to develop a robust prognostic gene expression signature.

METHODS

Tissue samples from 41 chemotherapy-naive patients with NSCLC and 15 control patients with inflammatory lung diseases were obtained during routine clinical work-up and gene expression profiles were gained using an oligonucleotide array platform (NovaChip; 34'207 transcripts). Gene expression signatures were analyzed for correlation with histological and clinical parameters and validated on independent published data sets and immunohistochemistry.

MEASUREMENTS AND MAIN RESULTS

Diagnostic signatures for adenocarcinoma and squamous cell carcinoma reached a sensitivity of 80%/80% and a specificity of 83%/94%, respectively, dependent on the proportion of tumor cells. Sixty-seven of the 100 most discriminating genes were validated with independent observations from the literature. A 13-gene metagene refined on four external data sets was built and validated on an independent data set. The metagene was a strong predictor of survival in our data set (hazard ratio = 7.7, 95% CI [2.8-21.2]) and in the independent data set (hazard ratio = 1.6, 95% CI [1.2-2.2]) and in both cases independent of the International Union against Cancer staging. Vascular endothelial growth factor-beta, one of the key prognostic genes, was further validated by immunohistochemistry on 508 independent tumor samples.

CONCLUSIONS

Integration of functional genomics from small bronchoscopic biopsies allows molecular tumor classification and prediction of survival in NSCLC and might become a powerful adjunct for the daily clinical practice.

Correlation between gene expression and clinical data through linear and nonlinear principal components analyses: muscular dystrophies as case studies

The large dimension of microarray data and the complex dependence structure among genes make data analysis extremely challenging. In the last decade several statistical techniques have been proposed to tackle genome-wide expression data; however, clinical and molecular data associated to pathologies have often been considered as separate dimensions of the same phenomenon, especially when clinical variables lie on a multidimensional space. A better comprehension of the relationships between clinical and molecular data can be obtained if both data types are combined and integrated. In this work we adopt a multidimensional correlation strategy together with linear and nonlinear principal component, to integrate genetic and clinical information obtained from two sets of dystrophic patients. With this approach we decompose different aspects of clinical manifestations and correlate these features with the correspondent patterns of differential gene expression.

Stability of gene contributions and identification of outliers in multivariate analysis of microarray data

BACKGROUND

Multivariate ordination methods are powerful tools for the exploration of complex data structures present in microarray data. These methods have several advantages compared to common gene-by-gene approaches. However, due to their exploratory nature, multivariate ordination methods do not allow direct statistical testing of the stability of genes.

RESULTS

In this study, we developed a computationally efficient algorithm for: i) the assessment of the significance of gene contributions and ii) the identification of sample outliers in multivariate analysis of microarray data. The approach is based on the use of resampling methods including bootstrapping and jackknifing. A statistical package of R functions was developed. This package includes tools for both inferring the statistical significance of gene contributions and identifying outliers among samples.

CONCLUSION

The methodology was successfully applied to three published data sets with varying levels of signal intensities. Its relevance was compared with alternative methods. Overall, it proved to be particularly effective for the evaluation of the stability of microarray data.

Expression profiling in granulomatous lung disease

Granulomatous lung diseases, such as sarcoidosis, hypersensitivity pneumonitis, Wegener's granulomatosis, and chronic beryllium disease, along with granulomatous diseases of known infectious etiologies, such as tuberculosis, are major causes of morbidity and mortality throughout the world. Clinical manifestations of these diseases are highly heterogeneous, and the determinants of disease susceptibility and clinical course (e.g., resolution vs. chronic, progressive fibrosis) are largely unknown. The underlying pathogenic mechanisms of these diseases also remain poorly understood. Within this context, these diseases have been approached using genomic and proteomic technologies to allow us to identify patterns of gene/protein expression that track with clinical disease or to identify new pathways involved in disease pathogenesis. The results from these initial studies highlight the potential for these "-omics" approaches to reveal novel insights into the pathogenesis of granulomatous lung disease and provide new tools to improve diagnosis, clinical classification, course prediction, and response to therapy. Realizing this potential will require collaboration among multidisciplinary groups with expertise in the respective technologies, bioinformatics, and clinical medicine for these complex diseases.