High-throughput Gene Expression Profiling Research Articles

Molecular Biomarkers in the Personalized Treatment of Colorectal Cancer.

Colorectal cancer (CRC) is a disease in which pathogenesis is influenced by genetic and epigenetic events that occur with tumor initiation and progression. Large variation exists in individual patient prognosis and response to chemotherapy, caused by molecular heterogeneity. Certain biomarkers have been identified that can predict clinical outcome beyond tumor staging, and inform treatment selection. Molecular testing is routinely performed in clinical practice for the selection of patients for targeted biological agents or immunotherapy, and is advocated for prognostic stratification. Estimating prognosis can avoid undertreatment or overtreatment and also guide the intensity of patient follow-up. Classifiers of CRC have been developed that integrate genetic and/or epigenetic features. The mutational status of KRAS and BRAF(V600E) oncogenes combined with analysis of the DNA mismatch repair system with/without the CpG island methylator phenotype (CIMP) has been shown to identify colon cancer subtypes with distinct clinical features and prognoses. Gene expression profiling has also been used to subtype CRCs and can overcome the limitations of single/limited gene testing. A recent effort identified 4 consensus molecular subtypes of biological relevance that were associated with different patient outcomes. Efforts to validate and refine these subtypes to include additional genomic features are ongoing. The focus of this article is to highlight molecular markers that can inform clinical decision-making in patients with CRC.

Actions of Huangqi decoction against rat liver fibrosis: a gene expression profiling analysis.

BackgroundHuangqi decoction (HQD) is used for liver fibrosis and cirrhosis treatment in Chinese medicine. This study aims to investigate the pharmacological actions of HQD against liver fibrosis in rats by high-throughput gene expression profiling, network analysis and real-time qRT-PCR.MethodsWe analyzed the profiles of differentially expressed genes (DEGs) in dimethylnitrosamine (DMN)-induced liver fibrosis in rat. The liver tissue samples of control group (n = 3), model group (n = 3) and HQD group (n = 3) were examined by microarrays. Pathways were analyzed by KEGG. Pathway-gene and protein–protein interaction (PPI) networks were constructed with Cytoscape software. The expression of candidate genes was verified by qRT-PCR. P values less than 0.05 indicated statistical significance.ResultsCollagen deposition and hydroxyproline (Hyp) content were decreased in the HQD group compared with the model group (P < 0.001), while that of Hyp in the model group were increased compared with the control group (P < 0.001). In comparison with the model group, 1085 DEGs (all P < 0.05, |fold change| >1.5) and 52 pathways in the HQD group were identified. TGF-beta, ECM-receptor interaction, and the cell adhesion molecules pathways were significantly recovered by HQD (P < 0.001). A pathway-gene network was constructed, including 303 DEGs and 52 pathways, and 514 nodes and 2602 edges, among 142 genes with node degrees greater than 10. The expressions of PDGFra, PDGFrb, PDGFb, PDGFd, COL1A1, COL1A2, COL5A2, and THBS1 were significantly down-regulated by HQD (P < 0.001).ConclusionHQD down-regulated the expressions of PDGFra, PDGFrb, PDGFb, PDGFd, COL1A1, COL1A2, COL5A2 and THBS1, and TGF-β and PDGF signaling pathways in the DMN-induced liver fibrosis in rats.Electronic supplementary materialThe online version of this article (doi:10.1186/s13020-015-0066-5) contains supplementary material, which is available to authorized users.

Abstract PR04: High-throughput gene expression profiling as a generalizable assay for determination of mutation impact on gene function

Abstract Recently, the decline in the cost of genome sequencing has led to the rapid identification of thousands of cancer-associated somatic mutations. However, progress in characterization of these genetic events has lagged significantly behind. Understanding mutation function is critical not only for research purposes but also for determining targeted treatment strategies based on individual tumor genetic profiles, yet determination of mutation impact remains a significant bottleneck. Here we describe a high-throughput approach to classify somatic mutations that is robust, scalable, and requires no prior information of gene function. We generated a lentiviral cDNA expression library of ~550 mutated and wild-type alleles of genes mutated in lung adenocarcinoma and introduced these alleles into four human lung cell lines. 96 hours post-infection, gene expression profiles were generated using Luminex-based L1000 profiling. In total, more than 2000 gene expression signatures were generated. We discovered that gain-of-function mutants induce expression signatures with a greater signal strength or different identity than the corresponding wild-type gene signature. In contrast, loss-of-function mutants could be identified by their incapability to induce strong signatures. Based on these features of signature strength and signature identity, we developed a decision-tree approach to classify mutations as either dominant, loss-of-function, or likely inert. An orthogonal functional approach, an EGFR inhibitor resistance screen, was used as validation. The gene expression approach correctly classified known gain-of-function mutations in KRAS (13/13), EGFR (6/7), and ARAF (2/2) and identified dozens of never-characterized gain-of-function and loss-of-function missense mutations. In addition to rare, dominant mutations in clinically-actionable oncogenes such as PIK3CA and AKT1, we identified unexpected dominant mutations in the transcription factor MAX and the phosphatase subunit PPP2R1A, among others. We also observed a substantial enrichment of loss-of-function mutations in tumor suppressor genes such as STK11, KEAP1, FBXW7, and CASP8 as well as in genes not previously connected to lung adenocarcinoma, including GPR137B and MAPK7. Most genes assayed also harbored variants that are likely inert, further underscoring the importance of characterizing individual variant alleles. The method developed here can, in principle, characterize any genetic variant, independent of prior knowledge of gene function, and should significantly advance the pace of functional characterization of mutations identified from genome sequencing. Citation Format: Alice Berger, Angela Brooks, Xiaoyun Wu, Larson Hogstrom, Itay Tirosh, Federica Piccioni, Mukta Bagul, Cong Zhu, Yashaswi Shretha, David Root, Pablo Tamayo, Ryo Sakai, Bang Wong, Aravind Subramanian, Todd Golub, Matthew Meyerson, Jesse Boehm. High-throughput gene expression profiling as a generalizable assay for determination of mutation impact on gene function. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr PR04.

Abstract PR12: High-throughput gene expression profiling as a generalizable assay for determination of mutation impact on gene function

Abstract Recently, the decline in the cost of genome sequencing has led to the rapid identification of thousands of cancer-associated somatic mutations. However, progress in characterization of these genetic events has lagged significantly behind. Understanding mutation function is critical not only for research purposes but also for determining targeted treatment strategies based on individual tumor genetic profiles, yet determination of mutation impact remains a significant bottleneck. Here we describe a high-throughput approach to classify somatic mutations that is robust, scalable, and requires no prior information of gene function. We generated a lentiviral cDNA expression library of ~550 mutated and wild-type alleles of genes mutated in lung adenocarcinoma and introduced these alleles into four human lung cell lines. 96 hours post-infection, gene expression profiles were generated using Luminex-based L1000 profiling. In total, more than 2000 gene expression signatures were generated. We discovered that gain-of-function mutants induce expression signatures with a greater signal strength or different identity than the corresponding wild-type gene signature. In contrast, loss-of-function mutants could be identified by their incapability to induce strong signatures. Based on these features of signature strength and signature identity, we developed a decision-tree approach to classify mutations as either dominant, loss-of-function, or likely inert. An orthogonal functional approach, an EGFR inhibitor resistance screen, was used as validation. The gene expression approach correctly classified known gain-of-function mutations in KRAS (13/13), EGFR (6/7), and ARAF (2/2) and identified dozens of never-characterized gain-of-function and loss-of-function missense mutations. In addition to rare, dominant mutations in clinically-actionable oncogenes such as PIK3CA and AKT1, we identified unexpected dominant mutations in the transcription factor MAX and the phosphatase subunit PPP2R1A, among others. We also observed a substantial enrichment of loss-of-function mutations in tumor suppressor genes such as STK11, KEAP1, FBXW7, and CASP8 as well as in genes not previously connected to lung adenocarcinoma, including GPR137B and MAPK7. Most genes assayed also harbored variants that are likely inert, further underscoring the importance of characterizing individual variant alleles. The method developed here can, in principle, characterize any genetic variant, independent of prior knowledge of gene function, and should significantly advance the pace of functional characterization of mutations identified from genome sequencing. Citation Format: Alice Berger, Angela Brooks, Xiaoyun Wu, Larson Hogstrom, Itay Tirosh, Federica Piccioni, Mukta Bagul, Cong Zhu, Yashaswi Shretha, David Root, Pablo Tamayo, Ryo Sakai, Bang Wong, Aravind Subramanian, Todd Golub, Matthew Meyerson, Jesse Boehm. High-throughput gene expression profiling as a generalizable assay for determination of mutation impact on gene function. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr PR12.

Discovery of Potent 17β-Hydroxywithanolides for Castration-Resistant Prostate Cancer by High-Throughput Screening of a Natural Products Library for Androgen-Induced Gene Expression Inhibitors.

Prostate cancer (PC) is the second most prevalent cancer among men in Western societies, and those who develop metastatic castration-resistant PC (CRPC) invariably succumb to the disease. The need for effective treatments for CRPC is a pressing concern, especially due to limited durable responses with currently employed therapies. Here, we demonstrate the successful application of a high-throughput gene-expression profiling assay directly targeting genes of the androgen receptor pathway to screen a natural products library leading to the identification of 17β-hydroxywithanolides 1-5, of which physachenolide D (5) exhibited potent and selective in vitro activity against two PC cell lines, LNCaP and PC-3. Epoxidation of 5 afforded physachenolide C (6) with higher potency and stability. Structure-activity relationships for withanolides as potential anti-PC agents are presented together with in vivo efficacy studies on compound 6, suggesting that 17β-hydroxywithanolides are promising candidates for further development as CRPC therapeutics.

Evaluating whole transcriptome amplification for gene profiling experiments using RNA-Seq.

BackgroundRNA-Seq has enabled high-throughput gene expression profiling to provide insight into the functional link between genotype and phenotype. Low quantities of starting RNA can be a severe hindrance for studies that aim to utilize RNA-Seq. To mitigate this bottleneck, whole transcriptome amplification (WTA) technologies have been developed to generate sufficient sequencing targets from minute amounts of RNA. Successful WTA requires accurate replication of transcript abundance without the loss or distortion of specific mRNAs. Here, we test the efficacy of NuGEN’s Ovation RNA-Seq V2 system, which uses linear isothermal amplification with a unique chimeric primer for amplification, using white adipose tissue from standard laboratory rats (Rattus norvegicus). Our goal was to investigate potential biological artifacts introduced through WTA approaches by establishing comparisons between matched raw and amplified RNA libraries derived from biological replicates.ResultsWe found that 93% of expressed genes were identical between all unamplified versus matched amplified comparisons, also finding that gene density is similar across all comparisons. Our sequencing experiment and downstream bioinformatic analyses using the Tuxedo analysis pipeline resulted in the assembly of 25,543 high-quality transcripts. Libraries constructed from raw RNA and WTA samples averaged 15,298 and 15,253 expressed genes, respectively. Although significant differentially expressed genes (P < 0.05) were identified in all matched samples, each of these represents less than 0.15% of all shared genes for each comparison.ConclusionsTranscriptome amplification is efficient at maintaining relative transcript frequencies with no significant bias when using this NuGEN linear isothermal amplification kit under ideal laboratory conditions as presented in this study. This methodology has broad applications, from clinical and diagnostic, to field-based studies when sample acquisition, or sample preservation, methods prove challenging.Electronic supplementary materialThe online version of this article (doi:10.1186/s12896-015-0155-7) contains supplementary material, which is available to authorized users.

31. Cryoprotectant toxicity: Biochemical mechanisms and functional genomics

The constraints placed by delicate biological structures set many challenges for the science of cryopreservation. The cryoprotective agents (CPAs) which block ice formation and remove intracellular water are also toxic, to varying degrees. The complexity of this toxicity effect has been a major barrier to clinical application of vitrification in human tissues and organs. In this talk, I will discuss biochemical mechanisms of CPA toxicity. Moreover, we are employing high-throughput gene expression profiling to study CPA toxicity and cryopreservation. Our goal is to identify genes and pathways at work in CPA toxicity; this may give us direct targets for drug discovery. Ultimately we aim to improve cryopreservation protocols to make long-term storage of stem cells, engineered tissues and organs more efficient. Results will be available online ( http://cryopreservation.org.uk/ ) as our project proceeds.

Microfluidic droplet-based PCR instrumentation for high-throughput gene expression profiling and biomarker discovery

PCR is a common and often indispensable technique used in medical and biological research labs for a variety of applications. Real-time quantitative PCR (RT-qPCR) has become a definitive technique for quantitating differences in gene expression levels between samples. Yet, in spite of this importance, reliable methods to quantitate nucleic acid amounts in a higher throughput remain elusive. In the following paper, a unique design to quantify gene expression levels at the nanoscale in a continuous flow system is presented. Fully automated, high-throughput, low volume amplification of deoxynucleotides (DNA) in a droplet based microfluidic system is described. Unlike some conventional qPCR instrumentation that use integrated fluidic circuits or plate arrays, the instrument performs qPCR in a continuous, micro-droplet flowing process with droplet generation, distinctive reagent mixing, thermal cycling and optical detection platforms all combined on one complete instrument. Detailed experimental profiling of reactions of less than 300nl total volume is achieved using the platform demonstrating the dynamic range to be 4 order logs and consistent instrument sensitivity. Furthermore, reduced pipetting steps by as much as 90% and a unique degree of hands-free automation makes the analytical possibilities for this instrumentation far reaching. In conclusion, a discussion of the first demonstrations of this approach to perform novel, continuous high-throughput biological screens is presented. The results generated from the instrument, when compared with commercial instrumentation, demonstrate the instrument reliability and robustness to carry out further studies of clinical significance with added throughput and economic benefits.

Unraveling the association between mRNA expressions and mutant phenotypes in a genome-wide assessment of mice

High-throughput gene expression profiling has revealed substantial leaky and extraneous transcription of eukaryotic genes, challenging the perceptions that transcription is strictly regulated and that changes in transcription have phenotypic consequences. To assess the functional implications of mRNA transcription directly, we analyzed mRNA expression data derived from microarrays, RNA-sequencing, and in situ hybridization, together with phenotype data of mouse mutants as a proxy of gene function at the tissue level. The results indicated that despite the presence of widespread ectopic transcription, mRNA expression and mutant phenotypes of mammalian genes or tissues remain associated. The expression-phenotype association at the gene level was particularly strong for tissue-specific genes, and the association could be underestimated due to data insufficiency and incomprehensive phenotyping of mouse mutants; the strength of expression-phenotype association at the tissue level depended on tissue functions. Mutations on genes expressed at higher levels or expressed at earlier embryonic stages more often result in abnormal phenotypes in the tissues where they are expressed. The mRNA expression profiles that have stronger associations with their phenotype profiles tend to be more evolutionarily conserved, indicating that the evolution of transcriptome and the evolution of phenome are coupled. Therefore, mutations resulting in phenotypic aberrations in expressed tissues are more likely to occur in highly transcribed genes, tissue-specific genes, genes expressed during early embryonic stages, or genes with evolutionarily conserved mRNA expression profiles.

WNT5A promotes stemness characteristics in nasopharyngeal carcinoma cells leading to metastasis and tumorigenesis.

Nasopharyngeal carcinoma (NPC) has the highest metastasis rate among head and neck cancers with unclear mechanism. WNT5A belongs to the WNT family of cysteine-rich secreted glycoproteins. Our previous high-throughput gene expression profiling revealed that WNT5A was up-regulated in highly metastatic cells. In the present study, we first confirmed the elevated expression of WNT5A in metastatic NPC tissues at both the mRNA and protein levels. We then found that WNT5A promoted epithelial-mesenchymal transition (EMT) in NPC cells, induced the accumulation of CD24-CD44+ cells and side population, which are believed to be cancer stem cell characteristics. Moreover, WNT5A promoted the migration and invasion of NPC cells in vitro, while in vivo treatment with recombinant WNT5A promoted lung metastasis. Knocking down WNT5A diminished NPC tumorigenesis in vivo. When elevated expression of WNT5A coincided with the elevated expression of vimentin in the primary NPC, the patients had a poorer prognosis. Among major signaling pathways, protein kinase C (PKC) signaling was activated by WNT5A in NPC cells. A positive feedback loop between WNT5A and phospho-PKC to promote EMT was also revealed. Taken together, these data suggest that WNT5A is an important molecule in promoting stem cell characteristics in NPC, leading to tumorigenesis and metastasis.

Differential Expression Analysis for RNA-Seq: An Overview of Statistical Methods and Computational Software.

Deep sequencing has recently emerged as a powerful alternative to microarrays for the high-throughput profiling of gene expression. In order to account for the discrete nature of RNA sequencing data, new statistical methods and computational tools have been developed for the analysis of differential expression to identify genes that are relevant to a disease such as cancer. In this paper, it is thus timely to provide an overview of these analysis methods and tools. For readers with statistical background, we also review the parameter estimation algorithms and hypothesis testing strategies used in these methods.

Gene expression profiling in non-Hodgkin lymphomas.

Although the current WHO classification (Swerdlow et al. WHO classification of tumours of haematopoietic and lymphoid tissues. International Agency for Research on Cancer, Lyon, 2008 [1]) for hematolymphoid neoplasms has delineated lymphomas based on the combined morphologic, immunophenotypic, and genotypic findings, further refinement is necessary especially in regard to therapeutics and prognostic implications. High-throughput gene expression profiling (GEP) using microarray technology (Schena et al. Science 270:467-470, 1995 [2]; Augenlicht et al. Proc Natl Acad Sci USA 88:3286-3289, 1991 [3]) was developed about 20 years ago, and further refinement of the technology and analytical approaches has enabled us to routinely evaluate practically the entire transcriptome at a time. GEP has helped to improve the classification and prognostication of non-Hodgkin lymphomas (NHL) as well as improved our understanding of their pathophysiology and response to new therapeutics. In this paper, we will briefly review how this revolutionary tool has transformed our understanding of lymphomas and given us insight into targeted therapeutics. We will also discuss the current efforts in adapting the findings to routine clinical practice, the evolution of the research technology and directions in the future.

Quantitative high-throughput gene expression profiling of human striatal development to screen stem cell–derived medium spiny neurons

A systematic characterization of the spatio-temporal gene expression during human neurodevelopment is essential to understand brain function in both physiological and pathological conditions. In recent years, stem cell technology has provided an in vitro tool to recapitulate human development, permitting also the generation of human models for many diseases. The correct differentiation of human pluripotent stem cell (hPSC) into specific cell types should be evaluated by comparison with specific cells/tissue profiles from the equivalent adult in vivo organ. Here, we define by a quantitative high-throughput gene expression analysis the subset of specific genes of the whole ganglionic eminence (WGE) and adult human striatum. Our results demonstrate that not only the number of specific genes is crucial but also their relative expression levels between brain areas. We next used these gene profiles to characterize the differentiation of hPSCs. Our findings demonstrate a temporal progression of gene expression during striatal differentiation of hPSCs from a WGE toward an adult striatum identity. Present results establish a gene expression profile to qualitatively and quantitatively evaluate the telencephalic hPSC-derived progenitors eventually used for transplantation and mature striatal neurons for disease modeling and drug-screening.

Transcriptomics of Hepatocytes Treated with Toxicants for Investigating Molecular Mechanisms Underlying Hepatotoxicity.

Transcriptomics is a powerful tool for high-throughput gene expression profiling. Transcriptome microarray experiments conducted with RNA isolated from hepatocytes after exposure to toxicants enable a deep insight into the molecular mechanisms of hepatotoxicity. This understanding, along with structure-activity relationships underlying hepatotoxicity, will provide a novel strategy to design cost-effective and safer therapeutics. Transcriptomics studies conducted with established hepatotoxic drugs in various in vitro and in vivo hepatotoxicity test systems have contributed to the elucidation of the mechanistic basis of liver insults, which were later on substantiated at the proteomics and metabolomics levels. The present chapter is focused on comprehensive transcriptomics of cultured primary hepatocytes treated with chemicals by applying Affymetrix microarray technology. It also describes the detailed protocol for culturing of hepatocytes, their exposure to toxicants as well as sample collection, including RNA isolation, RNA target preparation and finally the hybridization to gene chips for microarray expression analysis.

High-throughput gene expression profiling of opioid-induced alterations in discrete brain areas.

Whole-genome screening methods are unique approach to search for novel genes and molecular pathways involved in drug action. High-throughput profiling allows the gene expression levels of tens of thousands of transcripts to be measured simultaneously. Here, we describe transcriptional profiling in a specific area of the brain using DNA microarrays and next-generation sequencing.

Anterior tongue cancer with no history of tobacco and alcohol use may be a distinct molecular and clinical entity.

A small, albeit significant, number of head and neck squamous cell carcinoma (HNSCC) patients has no history of tobacco and alcohol use. Such non-habits associated HNSCCs may represent a distinct clinical entity and exhibit increased aggressiveness. The objective of the study was to understand differences in molecular etiology of habits, and non-habits associated tongue carcinomas. High-throughput gene expression profiling of 22 tumor samples was carried out. This was followed by quantitative real-time PCR validation of four of the identified differentially expressed genes. Eighteen genes were identified that correlate strongly with the habits- and non-habits distinction. Among the genes significantly overexpressed in the non-habits group are CCND1, a key cell-cycle regulator, DACT3, a modulator of the Wnt/beta-catenin pathway, and three genes associated with the Notch signaling pathway. CCND1 and DACT3 overexpression in non-habits associated tongue carcinomas were subsequently validated by quantitative real-time PCR in an independent cohort (n = 18) of patient samples. Gene expression data were integrated with publicly available protein interaction data to build a small protein interaction network containing five of 18 differentially expressed genes. This suggested that a functional 'network module' can be implicated in the subgroup distinction. All the tumors analyzed here were human papillomavirus (HPV) negative samples. An association between CCND1 overexpression in oral tumors and poor prognosis has previously been reported. Thus, CCND1 overexpression in non-habits associated anterior tongue carcinomas may contribute to their increased clinical aggressiveness.

Activation of endogenous type I IFN signaling contributes to persistent HCV infection.

HCV infection is a major world health problem, leading to both end-stage liver disease and primary liver cancer. Great efforts have been made in developing new therapies for HCV infection; however, combination therapy with pegylated IFN-α and ribavirin (pegIFN-RBV) remains the first choice of treatment for chronic HCV infection in most countries. The treatment response to pegIFN-RBV remains relatively low. Understanding the molecular mechanisms of persistent HCV infection and pegIFN-RBV resistance will suggest ways of improving the current standard of care and offers new antiviral therapies for both HCV and other viral infections. Recent data suggest that increased expression of hepatic IFN-stimulated genes (ISGs) before treatment is associated with treatment nonresponse in patients chronically infected with HCV. Although ISGs are generally antiviral in nature, in the case of HCV, the virus may exploit some of them to its benefit. This is not unique to HCV: Blockade of type I IFN signaling has been shown to control persistent LCMV infection. Thus, in certain viral infections, preactivation or overactivation of type I IFN signaling may contribute to viral persistence. In this review, we briefly summarize the findings from high-throughput gene expression profiling from patients chronically infected with HCV, then focus on a novel ubiquitin-like signaling pathway (ISG15/USP18) and its potential role in HCV persistence. Finally, the role of activation of endogenous type I IFN signaling in persistent HCV infection will be discussed in the context of recent studies indicating that blocking IFN signaling controls persistent LCMV infection.

Integrative ChIP-seq/Microarray Analysis Identifies a CTNNB1 Target Signature Enriched in Intestinal Stem Cells and Colon Cancer

BackgroundDeregulation of canonical Wnt/CTNNB1 (beta-catenin) pathway is one of the earliest events in the pathogenesis of colon cancer. Mutations in APC or CTNNB1 are highly frequent in colon cancer and cause aberrant stabilization of CTNNB1, which activates the transcription of Wnt target genes by binding to chromatin via the TCF/LEF transcription factors. Here we report an integrative analysis of genome-wide chromatin occupancy of CTNNB1 by chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) and gene expression profiling by microarray analysis upon RNAi-mediated knockdown of CTNNB1 in colon cancer cells.ResultsWe observed 3629 CTNNB1 binding peaks across the genome and a significant correlation between CTNNB1 binding and knockdown-induced gene expression change. Our integrative analysis led to the discovery of a direct Wnt target signature composed of 162 genes. Gene ontology analysis of this signature revealed a significant enrichment of Wnt pathway genes, suggesting multiple feedback regulations of the pathway. We provide evidence that this gene signature partially overlaps with the Lgr5+ intestinal stem cell signature, and is significantly enriched in normal intestinal stem cells as well as in clinical colorectal cancer samples. Interestingly, while the expression of the CTNNB1 target gene set does not correlate with survival, elevated expression of negative feedback regulators within the signature predicts better prognosis.ConclusionOur data provide a genome-wide view of chromatin occupancy and gene regulation of Wnt/CTNNB1 signaling in colon cancer cells.

Activation of ErbB2- ErbB3 signaling pathway supports potential therapeutic activity of ErbB inhibitors in AT/RT

To the Editor, With interest, we read Fouladi and colleagues’ results from a molecular biology and phase II trial of lapatinib in children with refractory CNS malignancies [1]. We share the authors’ interest in finding new therapeutic options for pediatric CNS tumors that are refractory to treatment. Our focus of interest is atypical teratoid rhabdoid tumors (AT/ RTs), which are associated with significantly worse overall survival when compared to other pediatric CNS tumors. Here, we would like to share our results obtained through high throughput gene expression (GE) profiling in a series of frozen tumor samples that underwent surgical excision at our institution. Our results suggest that ErbB inhibitors, including lapatinid, could be beneficial to treat AT/RT patients. Because only one AT/RT patient was enrolled in the phase I trial [2] of the above mentioned study, and no AT/RT patient was included in the phase II trial [1], we put

Gene expression profiling reveals GC and CEACAM1 as new tools in the diagnosis of lung carcinoids

Background:Classification of lung carcinoids into typical and atypical is a diagnostic challenge since no immunohistochemical tools are available to support pathologists in distinguishing between the two subtypes. A differential diagnosis is essential for clinicians to correctly discuss therapy, prognosis and follow-up with patients. Indeed, the distinction between the two typical and atypical subtypes on biopsies/cytological specimens is still unfeasible and sometimes limited also after radical surgeries. By comparing the gene expression profile of typical (TC) and atypical carcinoids (AC), we intended to find genes specifically expressed in one of the two subtypes that could be used as diagnostic markers.Methods:Expression profiling, with Affymetrix arrays, was performed on six typical and seven atypical samples. Data were validated on an independent cohort of 29 tumours, by means of quantitative PCR and immunohistochemistry (IHC).Results:High-throughput gene expression profiling was successfully used to identify a gene signature specific for atypical lung carcinoids. Among the 273 upregulated genes in the atypical vs typical subtype, GC (vitamin D-binding protein) and CEACAM1 (carcinoembryonic antigen family member) emerged as potent diagnostic markers. Quantitative PCR and IHC on a validation set of 17 ACs and 12 TCs confirmed their reproducibility and feasibility.Conclusions:GC and CEACAM1 can distinguish between TC and AC, defining an IHC assay potentially useful for routine cytological and histochemical diagnostic procedures. The high sensitivity and reproducibility of this new diagnostic algorithm strongly support a further validation on a wider sample size.