Transcriptome sequencing of malignant pleural mesothelioma tumors.

Cancers arise by the gradual accumulation of mutations in multiple genes. We now use shotgun pyrosequencing to characterize RNA mutations and expression levels unique to malignant pleural mesotheliomas (MPMs) and not present in control tissues. On average, 266 Mb of cDNA were sequenced from each of four MPMs, from a control pulmonary adenocarcinoma (ADCA), and from normal lung tissue. Previously observed differences in MPM RNA expression levels were confirmed. Point mutations were identified by using criteria that require the presence of the mutation in at least four reads and in both cDNA strands and the absence of the mutation from sequence databases, normal adjacent tissues, and other controls. In the four MPMs, 15 nonsynonymous mutations were discovered: 7 were point mutations, 3 were deletions, 4 were exclusively expressed as a consequence of imputed epigenetic silencing, and 1 was putatively expressed as a consequence of RNA editing. Notably, each MPM had a different mutation profile, and no mutated gene was previously implicated in MPM. Of the seven point mutations, three were observed in at least one tumor from 49 other MPM patients. The mutations were in genes that could be causally related to cancer and included XRCC6, PDZK1IP1, ACTR1A, and AVEN.

Differentially expressed alternatively spliced genes in malignant pleural mesothelioma identified using massively parallel transcriptome sequencing.

BACKGROUND: Analyses of Expressed Sequence Tags (ESTs) databases suggest that most human genes have multiple alternative splice variants. The alternative splicing of pre-mRNA is tightly regulated during development and in different tissue types. Changes in splicing patterns have been described in disease states. Recently, we used whole-transcriptome shotgun pryrosequencing to characterize 4 malignant pleural mesothelioma (MPM) tumors, 1 lung adenocarcinoma and 1 normal lung. We hypothesized that alternative splicing profiles might be detected in the sequencing data for the expressed genes in these samples. METHODS: We developed a software pipeline to map the transcriptome read sequences of the 4 MPM samples and 1 normal lung sample onto known exon junction sequences in the comprehensive AceView database of expressed sequences and to count how many reads map to each junction. 13,274,187 transcriptome reads generated by the Roche/454 sequencing platform for 5 samples were compared with 151,486 exon junctions from the AceView database. The exon junction expression index (EJEI) was calculated for each exon junction in each sample to measure the differential expression of alternative splicing events. Top ten exon junctions with the largest EJEI difference between the 4 mesothelioma and the normal lung sample were then examined for differential expression using Quantitative Real Time PCR (qRT-PCR) in the 5 sequenced samples. Two of the differentially expressed exon junctions (ACTG2.aAug05 and CDK4.aAug05) were further examined with qRT-PCR in additional 18 MPM and 18 normal lung specimens. RESULTS: We found 70,953 exon junctions covered by at least one sequence read in at least one of the 5 samples. All 10 identified most differentially expressed exon junctions were validated as present by RT-PCR, and 8 were differentially expressed exactly as predicted by the sequence analysis. The differential expression of the AceView exon junctions for the ACTG2 and CDK4 genes were also observed to be statistically significant in an additional 18 MPM and 18 normal lung samples examined using qRT-PCR. The differential expression of these two junctions was shown to successfully classify these mesothelioma and normal lung specimens with high sensitivity (89% and 78%, respectively). CONCLUSION: Whole-transcriptome shotgun sequencing, combined with a downstream bioinformatics pipeline, provides powerful tools for the identification of differentially expressed exon junctions resulting from alternative splice variants. The alternatively spliced genes discovered in the study could serve as useful diagnostic markers as well as potential therapeutic targets for MPM.

Differential expression patterns of housekeeping genes increase diagnostic and prognostic value in lung cancer.

BACKGROUND: Using DNA microarrays, we previously identified 451 genes expressed in 19 different human tissues. Although ubiquitously expressed, the variable expression patterns of these "housekeeping genes" (HKGs) could separate one normal human tissue type from another. Current focus on identifying "specific disease markers" is problematic as single gene expression in a given sample represents the specific cellular states of the sample at the time of collection. In this study, we examine the diagnostic and prognostic potential of the variable expressions of HKGs in lung cancers. METHODS: Microarray and RNA-seq data for normal lungs, lung adenocarcinomas (AD), squamous cell carcinomas of the lung (SQCLC), and small cell carcinomas of the lung (SCLC) were collected from online databases. Using 374 of 451 HKGs, differentially expressed genes between pairs of sample types were determined via two-sided, homoscedastic t-test. Principal component analysis and hierarchical clustering classified normal lung and lung cancers subtypes according to relative gene expression variations. We used uni- and multi-variate cox-regressions to identify significant predictors of overall survival in AD patients. Classifying genes were selected using a set of training samples and then validated using an independent test set. Gene Ontology was examined by PANTHER. RESULTS: This study showed that the differential expression patterns of 242, 245, and 99 HKGs were able to distinguish normal lung from AD, SCLC, and SQCLC, respectively. From these, 70 HKGs were common across the three lung cancer subtypes. These HKGs have low expression variation compared to current lung cancer markers (e.g., EGFR, KRAS) and were involved in the most common biological processes (e.g., metabolism, stress response). In addition, the expression pattern of 106 HKGs alone was a significant classifier of AD versus SQCLC. We further highlighted that a panel of 13 HKGs was an independent predictor of overall survival and cumulative risk in AD patients. DISCUSSION: Here we report HKG expression patterns may be an effective tool for evaluation of lung cancer states. For example, the differential expression pattern of 70 HKGs alone can separate normal lung tissue from various lung cancers while a panel of 106 HKGs was a capable class predictor of subtypes of non-small cell carcinomas. We also reported that HKGs have significantly lower variance compared to traditional cancer markers across samples, highlighting the robustness of a panel of genes over any one specific biomarker. Using RNA-seq data, we showed that the expression pattern of 13 HKGs is a significant, independent predictor of overall survival for AD patients. This reinforces the predictive power of a HKG panel across different gene expression measurement platforms. Thus, we propose the expression patterns of HKGs alone may be sufficient for the diagnosis and prognosis of individuals with lung cancer.

Cell types differ in global coordination of splicing and proportion of highly expressed genes.

Balance in the transcriptome is regulated by coordinated synthesis and degradation of RNA molecules. Here we investigated whether mammalian cell types intrinsically differ in global coordination of gene splicing and expression levels. We analyzed RNA-seq transcriptome profiles of 8 different purified mouse cell types. We found that different cell types vary in proportion of highly expressed genes and the number of alternatively spliced transcripts expressed per gene, and that the cell types that express more variants of alternatively spliced transcripts per gene are those that have higher proportion of highly expressed genes. Cell types segregated into two clusters based on high or low proportion of highly expressed genes. Biological functions involved in negative regulation of gene expression were enriched in the group of cell types with low proportion of highly expressed genes, and biological functions involved in regulation of transcription and RNA splicing were enriched in the group of cell types with high proportion of highly expressed genes. Our findings show that cell types differ in proportion of highly expressed genes and the number of alternatively spliced transcripts expressed per gene, which represent distinct properties of the transcriptome and may reflect intrinsic differences in global coordination of synthesis, splicing, and degradation of RNA molecules.

Gene expression ratio test distinguishes normal lung from lung tumors in solid tissue and FNA biopsies.

Lung cancer is the leading cause of cancer-related deaths worldwide. Prognosis and survival are dependent on cell type, early detection, and surgical treatment. Hence, optimal screening strategies and new therapies are urgently required. Although surveillance with low-dose computed tomography can reduce lung cancer mortality by 20%, the number of false-positive detections is significant. Tissue diagnosis aids in the identification of benign nodules, reducing the number of false positive detections. To determine whether molecular testing of fine-needle aspirations (FNAs) can reduce false-positive detections, we developed a gene expression-based test that distinguishes normal from cancerous lung tissues. The test first was applied to published microarray data, showing overall sensitivity and specificity values of 95% (95% CI, 90%-98%) and 100% (95% CI, 40%-100%), respectively. Subsequently, it was validated on 30 solid and ex vivo FNA lung cancer tumor samples and matched normal lung specimens using real-time PCR. The validation test was 93% (95% CI, 78%-99%) sensitive and 100% (95% CI, 88%-100%) specific for the detection of tumor versus normal lung on solid samples, whereas FNA specimens yielded a sensitivity of 91% (95% CI, 72%-99%) and a specificity of 94% (95% CI, 70%-100%). This study supports the hypothesis that the gene-ratio approach reliably distinguishes normal lung from cancerous tissues in FNA samples and can be optimized to diagnose benign nodules.

Drop-on-demand single cell isolation and total RNA analysis.

Technologies that rapidly isolate viable single cells from heterogeneous solutions have significantly contributed to the field of medical genomics. Challenges remain both to enable efficient extraction, isolation and patterning of single cells from heterogeneous solutions as well as to keep them alive during the process due to a limited degree of control over single cell manipulation. Here, we present a microdroplet based method to isolate and pattern single cells from heterogeneous cell suspensions (10% target cell mixture), preserve viability of the extracted cells (97.0±0.8%), and obtain genomic information from isolated cells compared to the non-patterned controls. The cell encapsulation process is both experimentally and theoretically analyzed. Using the isolated cells, we identified 11 stem cell markers among 1000 genes and compare to the controls. This automated platform enabling high-throughput cell manipulation for subsequent genomic analysis employs fewer handling steps compared to existing methods.

Fine-needle aspiration biopsies for gene expression ratio-based diagnostic and prognostic tests in malignant pleural mesothelioma.

PURPOSE: Malignant pleural mesothelioma (MPM) is an aggressive disease associated with median survival between 9 and 12 months. The correct diagnosis of MPM is sometimes challenging and usually requires solid tissue biopsies rather than fine-needle aspiration biopsies (FNA). We postulated that the accuracy of FNA-based diagnosis might be improved by the addition of molecular tests using a gene expression ratio-based algorithm and that prognostic tests could be similarly performed. EXPERIMENTAL DESIGN: Two MPM and 2 lung cancer cell lines were used to establish the minimal quantity of RNA required to perform the gene ratio test. On the basis of these results, 276 ex vivo FNA biopsies from 63 MPM patients and 250 ex vivo FNA samples from 92 lung cancer patients were analyzed using previously described diagnostic and prognostic tests based on gene expression ratios. RESULTS: We found that the sensitivity of the diagnostic test for MPM was 100% [95% confidence interval (CI): 95%-100%] and the specificity in primary lung adenocarcinoma was 90% (95% CI: 81%-95%). The FNA-based prognostic classification was concordant among 76% (95% CI: 65%-87%) of patients with the risk assignment in a subset of the matched surgical specimens previously analyzed by the prognostic test. CONCLUSIONS: Sufficient RNA can be extracted from most FNA biopsies to perform gene expression molecular tests. In particular, we show that the gene expression ratio algorithms performed well when applied to diagnosis and prognosis in MPM. This study provides support for the development of additional RNA molecular tests that may enhance the utility of FNA in the management of other solid cancers.

Second generation sequencing of the mesothelioma tumor genome.

The current paradigm for elucidating the molecular etiology of cancers relies on the interrogation of small numbers of genes, which limits the scope of investigation. Emerging second-generation massively parallel DNA sequencing technologies have enabled more precise definition of the cancer genome on a global scale. We examined the genome of a human primary malignant pleural mesothelioma (MPM) tumor and matched normal tissue by using a combination of sequencing-by-synthesis and pyrosequencing methodologies to a 9.6X depth of coverage. Read density analysis uncovered significant aneuploidy and numerous rearrangements. Method-dependent informatics rules, which combined the results of different sequencing platforms, were developed to identify and validate candidate mutations of multiple types. Many more tumor-specific rearrangements than point mutations were uncovered at this depth of sequencing, resulting in novel, large-scale, inter- and intra-chromosomal deletions, inversions, and translocations. Nearly all candidate point mutations appeared to be previously unknown SNPs. Thirty tumor-specific fusions/translocations were independently validated with PCR and Sanger sequencing. Of these, 15 represented disrupted gene-encoding regions, including kinases, transcription factors, and growth factors. One large deletion in DPP10 resulted in altered transcription and expression of DPP10 transcripts in a set of 53 additional MPM tumors correlated with survival. Additionally, three point mutations were observed in the coding regions of NKX6-2, a transcription regulator, and NFRKB, a DNA-binding protein involved in modulating NFKB1. Several regions containing genes such as PCBD2 and DHFR, which are involved in growth factor signaling and nucleotide synthesis, respectively, were selectively amplified in the tumor. Second-generation sequencing uncovered all types of mutations in this MPM tumor, with DNA rearrangements representing the dominant type.

A gene expression ratio-based diagnostic test for bladder cancer.

PURPOSE: Bladder cancer is relatively common but early detection techniques such as cystoscopy and cytology are somewhat limited. We developed a broadly applicable, platform-independent and clinically relevant method based on simple ratios of gene expression to diagnose human cancers. In this study, we sought to determine whether this technique could be applied to the diagnosis of bladder cancer. EXPERIMENTAL DESIGN: We developed a model for the diagnosis of bladder cancer using expression profiling data from 80 normal and tumor bladder tissues to identify statistically significant discriminating genes with reciprocal average expression levels in each tissue type. The expression levels of select genes were used to calculate individual gene pair expression ratios in order to assign diagnosis. The optimal model was examined in two additional published microarray data sets and using quantitative RT-PCR in a cohort of 13 frozen benign bladder urothelium samples and 13 bladder cancer samples from our institution. RESULTS: A five-ratio test utilizing six genes proved to be 100% accurate (26 of 26 samples) for distinguishing benign from malignant bladder tissue samples (P < 10(-6)). CONCLUSIONS: : We have provided a proof of principle study for the use of gene expression ratios in the diagnosis of bladder cancer. This technique may ultimately prove to be a useful adjunct to cytopathology in screening urine specimens for bladder cancer.

Four-gene expression ratio test for survival in patients undergoing surgery for mesothelioma.

BACKGROUND: Malignant pleural mesothelioma has few effective treatments, one being cytoreductive surgery. We previously developed a gene ratio test to predict outcome of malignant pleural mesothelioma patients undergoing surgery. In this study, we investigated the predictive value and technical assay performance of this test in patients with malignant pleural mesothelioma. METHODS: Clinical data were obtained prospectively from 120 consecutive patients with malignant pleural mesothelioma who were scheduled for debulking surgery at one institution. Specimens were obtained at surgery or by pleural biopsy examination. Expression data for four genes were collected from tumor specimens, and three ratios of gene expression (TM4SF1/PKM2, TM4SF1/ARHGDIA, and COBLL1/ARHGDIA) were determined by quantitative reverse transcriptase-polymerase chain reaction. Patients were assigned to good or poor outcome groups by the gene ratio test. Survival was estimated by the Kaplan-Meier method and the log-rank test in univariate analyses. A multivariable Cox proportional hazards model was used to control for prognostic factors. Technical robustness was determined by using up to 30 specimens per patient, two biopsy techniques, and two performance sites. All statistical tests were two-sided. RESULTS: The test predicted overall survival (P < .001) and cancer-specific survival (P = .007) in univariate analysis and overall survival in multivariable analysis (hazard ratio for death = 2.09, 95% confidence interval [CI] = 1.27 to 3.45, P = .004). The test was reproducible within patients and repeatable between two determinations for specimens with widely varying tumor cell contents. Repeatability between two determinations was 88.5% (95% CI = 84.0% to 92.2%) or, when technically unacceptable test values were excluded, 91.9% (95% CI = 87.4% to 95.1%). Reproducibility between two determinations was 96.1% (95% CI = 86.5% to 99.5%). Combining the gene ratio test and other prognostic factors allowed prospective discrimination between patients at high risk (median survival = 6.9 months, 95% CI = 2.6 to 8.9 months; 3-year survival = 0%) and low risk (median survival = 31.9 months, 95% CI = 21.9 to 41.7 months; 3-year survival = 42%). CONCLUSION: The gene ratio test for survival of patients with malignant pleural mesothelioma has robust predictive value and technical assay performance.