Both baseline CTC number and change in CTC number after one cycle of chemotherapy are independent prognostic factors for SCLC. Molecular comparison of CTCs to cells in CTM may provide novel insights into SCLC biology.

CTCs are detectable in patients with stage IV NSCLC and are a novel prognostic factor for this disease. Further validation is warranted before routine clinical application.

Both technology platforms detected NSCLC CTCs. ISET detected higher numbers of CTCs including epithelial marker negative tumor cells. ISET also isolated CTM and permitted molecular characterization. Combined with our previous CellSearch data confirming CTC number as an independent prognostic biomarker for NSCLC, we propose that this complementary dual technology approach to CTC analysis allows more complete exploration of CTCs in patients with NSCLC.

RNA-seq facilitates unbiased genome-wide gene-expression profiling. However, its concordance with the well-established microarray platform must be rigorously assessed for confident uses in clinical and regulatory application. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same set of liver samples of rats under varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOA). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is highly correlated with treatment effect size, gene-expression abundance and the biological complexity of the MOA. RNA-seq outperforms microarray (90% versus 76%) in DEG verification by quantitative PCR and the main gain is its improved accuracy for low expressed genes. Nonetheless, predictive classifiers derived from both platforms performed similarly. Therefore, the endpoint studied and its biological complexity, transcript abundance, and intended application are important factors in transcriptomic research and for decision-making.

BackgroundGene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model.ResultsWe generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models.ConclusionsWe demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0694-1) contains supplementary material, which is available to authorized users.

The rat has been used extensively as a model for evaluating chemical toxicities and for understanding drug mechanisms. However, its transcriptome across multiple organs, or developmental stages, has not yet been reported. Here we show, as part of the SEQC consortium efforts, a comprehensive rat transcriptomic BodyMap created by performing RNA-Seq on 320 samples from 11 organs of both sexes of juvenile, adolescent, adult and aged Fischer 344 rats. We catalogue the expression profiles of 40,064 genes, 65,167 transcripts, 31,909 alternatively spliced transcript variants and 2,367 non-coding genes/non-coding RNAs (ncRNAs) annotated in AceView. We find that organ-enriched, differentially expressed genes reflect the known organ-specific biological activities. A large number of transcripts show organ-specific, age-dependent or sex-specific differential expression patterns. We create a web-based, open-access rat BodyMap database of expression profiles with crosslinks to other widely used databases, anticipating that it will serve as a primary resource for biomedical research using the rat model.

Serological cell death biomarkers and circulating tumor cells (CTCs) have potential uses as tools for pharmacodynamic blood-based assays and their subsequent application to early clinical trials. In this study, we evaluated both the expression and clinical significance of CTCs and serological cell death biomarkers in patients with small cell lung cancer. Blood samples from 88 patients were assayed using enzyme-linked immunosorbent assays for various cytokeratin 18 products (eg, M65, cell death, M30, and apoptosis) as well as nucleosomal DNA. CTCs (per 7.5 ml of blood) were quantified using Veridex CellSearch technology. Before therapeutic treatment, cell death biomarkers were elevated in patients compared with controls. CTCs were detected in 86% of patients; additionally, CD56 was detectable in CTCs, confirming their neoplastic origin. M30 levels correlated with the percentage of apoptotic CTCs. M30 , M65 , lactate dehydrogenase , and CTC number were prognostic for patient survival as determined by univariate analysis. Using multivariate analysis , both lactate dehydrogenase and M65 levels remained significant. CTC number fell following chemotherapy , whereas levels of serological cell death biomarkers peaked at 48 hours and fell by day 22 , mirroring the tumor response. A 48-hour rise in nucleosomal DNA and M30 levels was associated with early response and severe toxicity , respectively. Our results provide a rationale to include the use of serological biomark- Small cell lung cancer (SCLC) is initially chemosensitive but invariably relapses with a chemoresistant phenotype.1 A number of molecularly targeted therapies have been evaluated attempting to improve outcome, but none have succeeded to date.2 Ideally, early clinical trials should incorporate validated pharmacodynamic biomarkers, conducted to good clinical laboratory practice, that demonstrate both proof of mechanism (drug hits target) and proof of concept (tumor responds to drug).3 Although possible, serial biopsies are rare in SCLC, and the tissue obtained often insufficient for extensive molecular profiling. Thus, there is a pressing need for blood-based biomarkers that report therapeutic response.Assays of drug-induced cell death are potential proof of concept biomarkers for multiple therapeutics. 4 The M30 Apoptosense and M65 assays (Peviva, Bromma, Sweden) detect cytokeratin (CK) 18, expressed in epithelial but not hematopoietic cells, and released into the blood following cytoskeletal disassembly and degradation during apoptotic and/or necrotic cell death. 5 The M30 antibody recognizes a caspase-cleaved neoepitope of CK18 that is only revealed during apoptosis, whereas the M65 assay detects full length and cleaved forms of CK18 reporting apoptosis and necrosis.6 Nucleosomal DNA (nDNA) results from cleavage of chromatin by apoptotic endonucleases into membrane bound DNA fragments that are phagocytosed by macrophages and sub-

The discovery of novel drug targets is a significant challenge in drug development. Although the human genome comprises approximately 30,000 genes, proteins encoded by fewer than 400 are used as drug targets in the treatment of diseases. Therefore, novel drug targets are extremely valuable as the source for first in class drugs. On the other hand, many of the currently known drug targets are functionally pleiotropic and involved in multiple pathologies. Several of them are exploited for treating multiple diseases, which highlights the need for methods to reliably reposition drug targets to new indications. Network-based methods have been successfully applied to prioritize novel disease-associated genes. In recent years, several such algorithms have been developed, some focusing on local network properties only, and others taking the complete network topology into account. Common to all approaches is the understanding that novel disease-associated candidates are in close overall proximity to known disease genes. However, the relevance of these methods to the prediction of novel drug targets has not yet been assessed. Here, we present a network-based approach for the prediction of drug targets for a given disease. The method allows both repositioning drug targets known for other diseases to the given disease and the prediction of unexploited drug targets which are not used for treatment of any disease. Our approach takes as input a disease gene expression signature and a high-quality interaction network and outputs a prioritized list of drug targets. We demonstrate the high performance of our method and highlight the usefulness of the predictions in three case studies. We present novel drug targets for scleroderma and different types of cancer with their underlying biological processes. Furthermore, we demonstrate the ability of our method to identify non-suspected repositioning candidates using diabetes type 1 as an example.