Transcriptomics

A microRNA signature associated with prognosis and progression in chronic lymphocytic leukemia.

Calin, GA et al. 2005. "" N. Engl. J. Med. 353(17):1793-1801.

Abstract:

Background MicroRNA expression profiles can be used to distinguish normal B cells from malignant B cells in patients with chronic lymphocytic leukemia (CLL). We investigated whether microRNA profiles are associated with known prognostic factors in CLL. Methods We evaluated the microRNA expression profiles of 94 samples of CLL cells for which the level of expression of 70-kD zeta-associated protein (ZAP-70), the mutational status of the rearranged immunoglobulin heavy-chain variable-region (IgVH) gene, and the time from diagnosis to initial treatment were known. We also investigated the genomic sequence of 42 microRNA genes to identify abnormalities. Results A unique microRNA expression signature composed of 13 genes (of 190 analyzed) differentiated cases of CLL with low levels of ZAP-70 expression from those with high levels and cases with unmutated IgVH from those with mutated IgVH. The same microRNA signature was also associated with the presence or absence of disease progression. We also identified a germ-line mutation in the miR-16-1-miR-15a primary precursor, which caused low levels of microRNA expression in vitro and in vivo and was associated with deletion of the normal allele. Germ-line or somatic mutations were found in 5 of 42 sequenced microRNAs in 11 of 75 patients with CLL, but no such mutations were found in 160 subjects without cancer (P<0.001). Conclusions A unique microRNA signature is associated with prognostic factors and disease progression in CLL. Mutations in microRNA transcripts are common and may have functional importance.

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Comments

A good illustration of the combined use of patient data, molecular techniques (i.e., PCR and RT-PCR, DNA sequencing...) and the new hot topic of microRNAs in the diagnosis and staging of chronic lymphocytic leukemia.

A Five-Gene Signature and Clinical Outcome in Non-Small-Cell Lung Cancer.

Chen, H-Y et al., 2007. N. Engl. J. Med. 356(1):11-20.

Background Current staging methods are inadequate for predicting the outcome of treatment of non-small-cell lung cancer (NSCLC). We developed a five-gene signature that is closely associated with survival of patients with NSCLC. Methods We used computer-generated random numbers to assign 185 frozen specimens for microarray analysis, real-time reverse-transcriptase polymerase chain reaction (RT-PCR) analysis, or both. We studied gene expression in frozen specimens of lung-cancer tissue from 125 randomly selected patients who had undergone surgical resection of NSCLC and evaluated the association between the level of expression and survival. We used risk scores and decision-tree analysis to develop a gene-expression model for the prediction of the outcome of treatment of NSCLC. For validation, we used randomly assigned specimens from 60 other patients. Results Sixteen genes that correlated with survival among patients with NSCLC were identified by analyzing microarray data and risk scores. We selected five genes (DUSP6, MMD, STAT1, ERBB3, and LCK) for RT-PCR and decision-tree analysis. The five-gene signature was an independent predictor of relapse-free and overall survival. We validated the model with data from an independent cohort of 60 patients with NSCLC and with a set of published microarray data from 86 patients with NSCLC. Conclusions Our five-gene signature is closely associated with relapse-free and overall survival among patients with NSCLC.

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Comments:

This paper illustrates how 'ribotyping' (mRNA profiling) can be designed to not only diagnose but also to stage NSCL cancer relative to the design of an effective therapeutic regimen.  Although quite sophisticated in its experimental goal, the procedure described is well illustrated and documented for the moderately sophisticated student or physician.  In addition, it contains a glossary of critical terms.

Microarray Analysis and Tumor Classification

Quackenbush, J., 2006, N. Engl. J. Med. 354(23):2463-2472

Intro Paragraphs

DNA microarray analysis was first described in the mid-1990s as a means to probe the expression of thousands of genes simultaneously and was quickly adopted by the research community for the study of a wide range of biologic processes. Most of the early studies had a simple and powerful design: to compare two biologic classes in order to identify the differential expression of the genes in them — genes with potential relevance to a wide range of biologic processes, such as the progression of cancer, the causes of asthma, heart disease, and neuropsychiatric disorders and the analysis of factors associated with infertility.

Soon after microarrays were introduced, many researchers realized that the technique could be used to find new subclasses in disease states and identify biologic markers (biomarkers) associated with disease and that even the expression patterns of the genes could be used to distinguish subclasses of disease. This realization resulted in a proliferation of searches for patterns of expression that could be used to classify types of tumors and predict the outcome and response to chemotherapy. An example is the Netherlands breast-cancer study, which sought to distinguish between patients who had the same stage of disease but a different response to treatment and a different overall outcome. The study was motivated by the observation that the best clinical predictors of metastasis, including lymph-node status and histologic grade, did not adequately predict clinical outcome, with the result that many patients receive chemotherapy or hormonal therapy regardless of whether they need this additional treatment. The study searched for gene-expression signatures that would indicate which patients would benefit from adjuvant chemotherapy. By profiling tumors of young patients who had received only surgical treatment and searching for correlations with clinical outcome, a signature of poor prognosis consisting of 70 genes was identified and was predictive of a short interval to distant metastasis in patients with tumors that were lymph-node–negative. The analysis showed that microarray-based signatures could outperform clinically based predictions of outcome in identifying patients who would benefit most from adjuvant therapy. These initial results led to a more extensive study that showed that the 70-gene classification profile was a more powerful predictor of disease outcome in young patients with breast cancer than were standard systems based on clinical and histologic criteria.

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Comments

This is an excellent, well-illustrated review of the use of gene expression microarrays in the diagnosis and staging of cancers.  In addition to figures which give a good technical overview there is a glossary table defining the various types of 'omics' and the differences in their applications to clinical situations.

Gene expression profiling of human lung tissue from smokers with severe emphysema

Spira, A. et al., 2004. Am. J. Respir. Cell Mol. Biol. 31(6):601-610

Abstract

The mechanism by which inhaled smoke causes the anatomic lesions and physiologic impairment of chronic obstructive pulmonary disease remains unknown. We used high-density microarrays to measure gene expression in severely emphysematous lung tissue removed from smokers at lung volume reduction surgery (LVRS) and normal or mildly emphysematous lung tissue from smokers undergoing resection of pulmonary nodules. Class prediction algorithms identified 102 genes that accurately distinguished severe emphysema from non-/mildly emphysematous lung tissue. We also defined a number of genes whose expression levels correlated strongly with lung diffusion capacity for carbon monoxide and/or forced expiratory volume at 1 s. Genes related to oxidative stress, extracellular matrix synthesis, and inflammation were increased in severe emphysema, whereas expression of endothelium-related genes was decreased. To identify candidate genes that might be causally involved in the pathogenesis of emphysema, we linked gene expression profiles to chromosomal regions previously associated with chronic obstructive pulmonary disease in genome-wide linkage analyses. Unsupervised hierarchical clustering of the LVRS samples revealed distinct molecular subclasses of severe emphysema, with body mass index as the only clinical variable that differed between the groups. Class prediction models established a set of genes that predicted functional outcome at 6 mo after LVRS. Our findings suggest that the gene expression profiles from human emphysematous lung tissue may provide insight into pathogenesis, uncover novel molecular subclasses of disease, predict response to LVRS, and identify targets for therapeutic intervention.

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Airway epithelial gene expression in the diagnostic evaluation of smokers with suspect lung cancer

Spira, A. et al., 2007. Nat. Med. 13(3):361-366

Abstract

Lung cancer is the leading cause of death from cancer in the US and the world. The high mortality rate (80-85% within 5 years) results, in part, from a lack of effective tools to diagnose the disease at an early stage. Given that cigarette smoke creates a field of injury throughout the airway, we sought to determine if gene expression in histologically normal large-airway epithelial cells obtained at bronchoscopy from smokers with suspicion of lung cancer could be used as a lung cancer biomarker. Using a training set (n = 77) and gene-expression profiles from Affymetrix HG-U133A microarrays, we identified an 80-gene biomarker that distinguishes smokers with and without lung cancer. We tested the biomarker on an independent test set (n = 52), with an accuracy of 83% (80% sensitive, 84% specific), and on an additional validation set independently obtained from five medical centers (n = 35). Our biomarker had approximately 90% sensitivity for stage 1 cancer across all subjects. Combining cytopathology of lower airway cells obtained at bronchoscopy with the biomarker yielded 95% sensitivity and a 95% negative predictive value. These findings indicate that gene expression in cytologically normal large-airway epithelial cells can serve as a lung cancer biomarker, potentially owing to a cancer-specific airway-wide response to cigarette smoke.

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