Purpose: Circulating tumor cells (CTCs) may have utility as surrogate biomarkers and “virtual” biopsies. We report the clinical significance and molecular characteristics of CTCs and CTC clusters, termed circulating tumor microemboli (CTM), detected in patients with small-cell lung cancer (SCLC) undergoing standard treatment. Patients and Methods: Serial blood samples from 97 patients receiving chemotherapy were analyzed using EpCam-based immunomagnetic detection and a filtration-based technique. Proliferation status (Ki67) and apoptotic morphology were examined. Associations of CTC and CTM number with clinical factors and prognosis were determined. Results: CTCs were present in 85% of patients (77 of 97 patients) and were abundant (mean ± standard deviation = 1,589 ± 5,565). CTM and apoptotic CTCs were correlated with total CTC number and were detected in 32% and 57% of patients, respectively. Pretreatment CTCs, change in CTC number after one cycle of chemotherapy, CTM, and apoptotic CTCs were independent prognostic factors. Overall survival was 5.4 months for patients with ≥ 50 CTCs/7.5 mL of blood and 11.5 months (P < .0001) for patients with less than 50 CTCs/7.5 mL of blood before chemotherapy (hazard ratio = 2.45; 95% CI, 1.39 to 4.30; P = .002). Subpopulations of apoptotic and of proliferating solitary CTCs were detected, whereas neither were observed within cell clusters (CTM), implicating both protection from anoikis and relative resistance to cytotoxic drugs for cells within CTM. Conclusion: 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.

Purpose: Lung cancer is the leading cause of cancer-related death worldwide. Non–small-cell lung cancer (NSCLC) lacks validated biomarkers to predict treatment response. This study investigated whether circulating tumor cells (CTCs) are detectable in patients with NSCLC and what their ability might be to provide prognostic information and/or early indication of patient response to conventional therapy. Patients and Methods: In this single-center prospective study, blood samples for CTC analysis were obtained from 101 patients with previously untreated, stage III or IV NSCLC both before and after administration of one cycle of standard chemotherapy. CTCs were measured using a semiautomated, epithelial cell adhesion molecule–based immunomagnetic technique. Results: The number of CTCs in 7.5 mL of blood was higher in patients with stage IV NSCLC (n = 60; range, 0 to 146) compared with patients with stage IIIB (n = 27; range, 0 to 3) or IIIA disease (n = 14; no CTCs detected). In univariate analysis, progression-free survival was 6.8 v 2.4 months with P < .001, and overall survival (OS) was 8.1 v 4.3 months with P < .001 for patients with fewer than five CTCs compared with five or more CTCs before chemotherapy, respectively. In multivariate analysis, CTC number was the strongest predictor of OS (hazard ratio [HR], 7.92; 95% CI, 2.85 to 22.01; P < .001), and the point estimate of the HR was increased with incorporation of a second CTC sample that was taken after one cycle of chemotherapy (HR, 15.65; 95% CI, 3.63 to 67.53; P < .001). Conclusion: 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.

Epithelial circulating tumor cells (CTCs) are detectable in patients with non-small cell lung cancer (NSCLC). However, epithelial to mesenchymal transition, a widely reported prerequisite for metastasis, may lead to an underestimation of CTC number. We compared directly an epithelial marker-dependent (CellSearch) and a marker-independent (isolation by size of epithelial tumor cells [ISET]) technology platform for the ability to identify CTCs. Molecular characteristics of CTCs were also explored. Paired peripheral blood samples were collected from 40 chemonäive, stages IIIA to IV NSCLC patients. CTCs were enumerated by Epithelial Cell Adhesion Molecule-based immunomagnetic capture (CellSearch, Veridex) and by filtration (ISET, RareCell Diagnostics). CTCs isolated by filtration were assessed by immunohistochemistry for epithelial marker expression (cytokeratins, Epithelial Cell Adhesion Molecule, epidermal growth factor receptor) and for proliferation status (Ki67). CTCs were detected using ISET in 32 of 40 (80%) patients compared with 9 of 40 (23%) patients using CellSearch. A subpopulation of CTCs isolated by ISET did not express epithelial markers. Circulating tumor microemboli (CTM, clusters of ≥ 3 CTCs) were observed in 43% patients using ISET but were undetectable by CellSearch. Up to 62% of single CTCs were positive for the proliferation marker Ki67, whereas cells within CTM were nonproliferative. 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.

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.

Background: Gene 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. Results: We 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. Conclusions: We 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.