Can gene expression profiling predict survival for patients with squamous cell carcinoma of the lung?

BACKGROUND: Lung cancer remains to be the leading cause of cancer death worldwide. Patients with similar lung cancer may experience quite different clinical outcomes. Reliable molecular prognostic markers are needed to characterize the disparity. In order to identify the genes responsible for the aggressiveness of squamous cell carcinoma of the lung, we applied DNA microarray technology to a case control study. Fifteen patients with surgically treated stage I squamous cell lung cancer were selected. Ten were one-to-one matched on tumour size and grade, age, gender, and smoking status; five died of lung cancer recurrence within 24 months (high-aggressive group), and five survived more than 54 months after surgery (low-aggressive group). Five additional tissues were included as test samples. Unsupervised and supervised approaches were used to explore the relationship among samples and identify differentially expressed genes. We also evaluated the gene markers' accuracy in segregating samples to their respective group. Functional gene networks for the significant genes were retrieved, and their association with survival was tested. RESULTS: Unsupervised clustering did not group tumours based on survival experience. At p < 0.05, 294 and 246 differentially expressed genes for matched and unmatched analysis respectively were identified between the low and high aggressive groups. Linear discriminant analysis was performed on all samples using the 27 top unique genes, and the results showed an overall accuracy rate of 80%. Tests on the association of 24 gene networks with study outcome showed that 7 were highly correlated with the survival time of the lung cancer patients. CONCLUSION: The overall gene expression pattern between the high and low aggressive squamous cell carcinomas of the lung did not differ significantly with the control of confounding factors. A small subset of genes or genes in specific pathways may be responsible for the aggressive nature of a tumour and could potentially serve as panels of prognostic markers for stage I squamous cell lung cancer

Optimization of laser capture microdissection and RNA amplification for gene expression profiling of prostate cancer

BACKGROUND: To discover prostate cancer biomarkers, we profiled gene expression in benign and malignant cells laser capture microdissected (LCM) from prostate tissues and metastatic prostatic adenocarcinomas. Here we present methods developed, optimized, and validated to obtain high quality gene expression data. RESULTS: RNase inhibitor was included in solutions used to stain frozen tissue sections for LCM, which improved RNA quality significantly. Quantitative PCR assays, requiring minimal amounts of LCM RNA, were developed to determine RNA quality and concentration. SuperScript II™ reverse transcriptase was replaced with SuperScript III™, and SpeedVac concentration was eliminated to optimize linear amplification. The GeneChip(® )IVT labeling kit was used rather than the Enzo BioArray™ HighYield™ RNA transcript labeling kit since side-by-side comparisons indicated high-end signal saturation with the latter. We obtained 72 μg of labeled complementary RNA on average after linear amplification of about 2 ng of total RNA. CONCLUSION: Unsupervised clustering placed 5/5 normal and 2/2 benign prostatic hyperplasia cases in one group, 5/7 Gleason pattern 3 cases in another group, and the remaining 2/7 pattern 3 cases in a third group with 8/8 Gleason pattern 5 cases and 3/3 metastatic prostatic adenocarcinomas. Differential expression of alpha-methylacyl coenzyme A racemase (AMACR) and hepsin was confirmed using quantitative PCR

Magnetophoretic-based microfluidic device for DNA Concentration

Abstract Nucleic acids serve as biomarkers of disease and it is highly desirable to develop approaches to extract small number of such genomic extracts from human bodily fluids. Magnetic particles-based nucleic acid extraction is widely used for concentration of small amount of samples and is followed by DNA amplification in specific assays. However, approaches to integrate such magnetic particles based capture with micro and nanofluidic based assays are still lacking. In this report, we demonstrate a magnetophoretic-based approach for target-specific DNA extraction and concentration within a microfluidic device. This device features a large chamber for reducing flow velocity and an array of μ-magnets for enhancing magnetic flux density. With this strategy, the device is able to collect up to 95 % of the magnetic particles from the fluidic flow and to concentrate these magnetic particles in a collection region. Then an enzymatic reaction is used to detach the DNA from the magnetic particles within the microfluidic device, making the DNA available for subsequent analysis. Concentrations of over 1000-fold for 90 bp dsDNA molecules is demonstrated. This strategy can bridge the gap between detection of low concentration analytes from clinical samples and a range of micro and nanofluidic sensors and devices including nanopores, nanocantilevers, and nanowires

Discovery of ovarian cancer biomarkers in serum using NanoLC electrospray ionization TOF and FT-ICR mass spectrometry

Abstract. Treatment of cancer patients is greatly facilitated by detection of the cancer prior to metastasis. One of the obstacles to early cancer detection is the lack of availability of biomarkers with sufficient specificity. With modern differential proteomic techniques, the potential exists to identify high specificity cancer biomarkers. We have delineated a set of protocols for the isolation and identification of serum biomarkers for ovarian cancer that exist in the low molecular weight serum fraction. After isolation of the low molecular weight fraction by ultrafiltration, the potential biomarkers are separated by reversed phase nano liquid chromatography. Detection via TOF or FT-ICR yields a data set for each sample. We compared stage III/IV ovarian cancer serum with postmenopausal age-matched controls. Using bioinformatics tools developed at Mayo, we normalized each sample for intensity and chromatographic alignment. Normalized data sets are subsequently compared and potential biomarkers identified. Several candidate biomarkers were found. One of these contains the sequence of fibrinopeptide-A known to be elevated in many types of cancer including ovarian cancer. The protocols utilized will be examined and would be applicable to a wide variety of cancers or disease states

Detection and quantification of methylation in DNA using solid-state nanopores.

Epigenetic modifications in eukaryotic genomes occur primarily in the form of 5-methylcytosine (5 mC). These modifications are heavily involved in transcriptional repression, gene regulation, development and the progression of diseases including cancer. We report a new single-molecule assay for the detection of DNA methylation using solid-state nanopores. Methylation is detected by selectively labeling methylation sites with MBD1 (MBD-1x) proteins, the complex inducing a 3 fold increase in ionic blockage current relative to unmethylated DNA. Furthermore, the discrimination of methylated and unmethylated DNA is demonstrated in the presence of only a single bound protein, thereby giving a resolution of a single methylated CpG dinucleotide. The extent of methylation of a target molecule could also be coarsely quantified using this novel approach. This nanopore-based methylation sensitive assay circumvents the need for bisulfite conversion, fluorescent labeling, and PCR and could therefore prove very useful in studying the role of epigenetics in human disease

Impact of sample acquisition and linear amplification on gene expression profiling of lung adenocarcinoma: laser capture micro-dissection cell-sampling versus bulk tissue-sampling

<p>Abstract</p> <p>Background</p> <p>The methods used for sample selection and processing can have a strong influence on the expression values obtained through microarray profiling. Laser capture microdissection (LCM) provides higher specificity in the selection of target cells compared to traditional bulk tissue selection methods, but at an increased processing cost. The benefit gained from the higher tissue specificity realized through LCM sampling is evaluated in this study through a comparison of microarray expression profiles obtained from same-samples using bulk and LCM processing.</p> <p>Methods</p> <p>Expression data from ten lung adenocarcinoma samples and six adjacent normal samples were acquired using LCM and bulk sampling methods. Expression values were evaluated for correlation between sample processing methods, as well as for bias introduced by the additional linear amplification required for LCM sample profiling.</p> <p>Results</p> <p>The direct comparison of expression values obtained from the bulk and LCM sampled datasets reveals a large number of probesets with significantly varied expression. Many of these variations were shown to be related to bias arising from the process of linear amplification, which is required for LCM sample preparation. A comparison of differentially expressed genes (cancer vs. normal) selected in the bulk and LCM datasets also showed substantial differences. There were more than twice as many down-regulated probesets identified in the LCM data than identified in the bulk data. Controlling for the previously identified amplification bias did not have a substantial impact on the differences identified in the differentially expressed probesets found in the bulk and LCM samples.</p> <p>Conclusion</p> <p>LCM-coupled microarray expression profiling was shown to uniquely identify a large number of differentially expressed probesets not otherwise found using bulk tissue sampling. The information gain realized from the LCM sampling was limited to differential analysis, as the absolute expression values obtained for some probesets using this study's protocol were biased during the second round of amplification. Consequently, LCM may enable investigators to obtain additional information in microarray studies not easily found using bulk tissue samples, but it is of critical importance that potential amplification biases are controlled for.</p