Cancer Genome Anatomy Project Research Articles

Single-Cell RNA-Sequencing Shift in the Interaction Pattern Between Glioma Stem Cells and Immune Cells During Tumorigenesis.

Glioblastoma is one of the most common neoplasms in the central nervous system characterized by limited immune response and unlimited expansion capability. Cancer stem cells (GSCs), a small fraction of the tumor cells, possess a pivotal regulation capability in the tumor microenvironment with a superior proliferation ability. We aimed to reveal the interaction between glioma stem cells (GSCs) and immune cells during tumorigenesis. Single-cell sequencing data from seven surgical specimens of glioblastoma patients and patient-derived GSCs cocultured with peripheral leukocytes were used for the analysis. Cell grouping and trajectory analysis were performed using Seurat and Monocle 3 packages in R software. The gene set of Cancer Genome Anatomy Project was used to define different cell types. Cells with the ability of proliferation and differentiation in glioblastoma tissue were defined as GSCs, which had a similar expression pattern to that in the GSCs in vitro. Astrocytes in glioblastoma were mainly derived from differentiated GSCs, while oligodendrocytes were most likely to be derived from different precursor cells. No remarkable evolutionary trajectory was observed among the subgroups of T cells in glioblastoma. The immune checkpoint interaction between GSCs and immune cells was changed from stimulatory to inhibitory during tumorigenesis. The patient-derived GSCs system is an ideal model for GSC research. The above research revealed that the interaction pattern between GSC glioma stem cells and immune cells during tumorigenesis provides a theoretical basis for GSC glioma stem cell-targeted immunotherapy.

Abstract CN03-01: Functional genomics approaches to the discovery of paralog dependencies in cancer

Abstract Advances in next-generation sequencing and projects including the Cancer Genome Anatomy project and the International Cancer Genome Consortium have given us a detailed description of the genetic alterations across a broad spectrum of cancer. This however, does not immediately lead to a detailed functional understanding of those genes whose protein products are necessary for the maintenance of cancer viability. In order to more deeply understand the genes and pathways critical to the survival of cancer cells previous and ongoing efforts have provided functional annotation of the Cancer Cell Line Encyclopedia through the use of deep pooled shRNA or sgRNA libraries. For example, Project DRIVE (Deep RNAi-screening for Viablity Effects in cancer) characterized the viability effects of knockdown of 7,837 genes using 20 shRNAs per gene across 398 cancer cell lines. The DepMap project targeted 18,333 genes in 625 cell lines using CRISPR/CAS9. The data from these efforts are highly robust identifying all known oncogenes mutated in the set of interrogated cell lines, along with multiple new cancer dependent features not previously described. Notably, very few synthetic lethal nodes have emerged from these analyses downstream or within the critical oncogenic pathways that dominate the landscape of human cancer. We hypothesize that this is due to functionally redundant paralogous genes that, while potentially targetable by single therapeutics, are not targeted in current knockdown/knockout screens. This is exemplified by discordance between certain therapeutics and shRNA or sgRNA gene inactivation. For example, the cell line sensitivity profiles of MEK inhibitors which target MEK1 and MEK2, or CDK4/6 inhibitors do not robustly correlate with single gene knockout data. Based on prior work using dual S. pyogenes and S. aureus CAS9 systems, we have built a pairwise sgRNA library targeting roughly 3000 genes and the resulting 5,000 paralog pairs. Initial screening data has demonstrated robustness of the methods. More importantly, new paralog dependencies relevant to NRAS mutant cancers have emerged. Citation Format: William Sellers. Functional genomics approaches to the discovery of paralog dependencies in cancer [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr CN03-01. doi:10.1158/1535-7163.TARG-19-CN03-01

Abstract IA01: The discovery of next-generation cancer targets and therapeutics

Abstract Cancer is driven by the accumulation of somatic genetic alterations that alter the activity of tumor suppressor and oncogenes. Together these activities lead to the dysregulation of cell proliferation, cell survival and metabolic pathways, among many others, that culminate in the acquisition of the transformed phenotype characteristic of malignant tumors. Reversing the consequences of oncogenic mutations through therapeutic intervention has been repeatedly demonstrated to bring significant patient benefit across a spectrum of oncogenes including BCR-ABL, BRAF, EGFR, cKIT, HER2, RET, ROS and ALK among others. Exemplary of both this paradigm is the longstanding experience of treating BCR-ABL+ chronic myelogenous leukemia (CML) patients with inhibitors of the Abelson kinase (ABL). Small clinical studies have recently shown that drug withdrawal can be associated with treatment free remission in a subset of patients achieving deep molecular responses. Towards the goal of eradicating CML, we have developed a novel potent and selective allosteric inhibitor of ABL, known as ABL001. ABL001 binds at a regulatory site typically occupied by a myristoyl group in wild-type ABL and inhibits ABL kinase activity through a mechanism distinct from catalytic site inhibitors. As a result, mutagenesis and genetic bar-coding experiments revealed a spectrum of resistance highly distinct from that seen with catalytic site inhibitors suggesting the possibility that co-administration of CML tumor cells with both types of inhibitors might act to block the development of therapeutic resistance. In vivo experiments showed that combination but not the single agent ABL001 and nilotinib could eradicate CML xenografts with sustained responses after treatment cessation. ABL001 is now in phase I clinical testing. Advances in next-generation sequencing and projects including the Cancer Genome Anatomy project and the International Cancer Genome Consortium have given us a detailed description of the genetic alterations across a broad spectrum of cancer. This however, does not immediately lead to a detailed functional understanding of those genes whose protein products are necessary for the maintenance of cancer viability. In order to more deeply understand the genes and pathways critical to the survival of cancer cells we have taken on the functional annotation of the Cancer Cell Line Encyclopedia (Nature 2012;483:603-7) through the use of deep pooled shRNA libraries. The resulting effort termed Project DRIVE (Deep RNAi-screening for Viablity Effects in cancer) has led to the generation of drop-out shRNA data for 7,500 genes targeted by 20 shRNAs per gene across 390 cancer cell lines. The data from this effort are highly robust identifying all known oncogenes mutated in the set of interrogated cell lines, along with multiple new cancer dependent features not previously described. Most recently, this project led to the discovery of PRMT5 as a novel dependency in cancers harboring co-deletion of MTAP and CDKN2A (Science 2016 351:1208-1) a finding that will be further described in the lecture. Citation Format: William Sellers. The discovery of next-generation cancer targets and therapeutics. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Targeting the Vulnerabilities of Cancer; May 16-19, 2016; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(1_Suppl):Abstract nr IA01.

Serial analysis of gene expression reveals promising therapeutic targets for liver fluke-associated cholangiocarcinoma.

Cholangiocarcinoma (CCA) continues to be a serious health problem and is the most common fatal cancer in northeastern Thailand. Comprehensive gene expression analysis was here used to identify possible therapeutic targets for CCA treatment. We assessed liver fluke-associated CCA tissue using serial analysis of gene expression (SAGE) and compared the data to normal liver tissue as a part of the Cancer Genome Anatomy Project (CGAP). The analysis identified 509 differentially expressed genes. Of 142 up-regulated examples, we selected candidates including TMSB10, GAL3, VDR, CYPA and CD147 for further validation in CCA tissues by immunohistochemistry. VDR, CYPA and CD147 were confirmed to be consistently overexpressed in the samples tested. The therapeutic and diagnostic potential of these genes warrants further investigation.

Application of BT and PC-BT in Homo sapiens gene prediction

Digital signal processing is gaining popularity in gene classification and prediction. Spectral content of gene is estimated using Blackman---Tukey (BT) method for identification of Homo sapiens cancer genes. A data reduction method; principal component analysis (PCA) is used prior to Blackman---Tukey method (PC-BT) for better identification of genes. Selection of proper model order is prime important in PCA method for considering the number of principal components (PCs) and the performance of the method depends on optimal selection of PCs. Cumulative percent variance and scree test residual percent variance are used as measurement metric for selection of PCs. Quality factor is used to judge the performance of the estimators. Simulation results show the clarity of spectrum plot in PC-BT method is 200 % higher than BT method. The spectral peaks observed in cancer genes are used to screen out cancer genes from healthy genes. The methods are successfully tested on breast, prostate and colon Homo sapiens. Healthy and cancer Homo sapiens genes are downloaded from Cancer Genome Anatomy Project (CGAP) site and National Centre for Biotechnology Information (NCBI) site.

NPY1R is a novel peripheral blood marker predictive of metastasis and prognosis in breast cancer patients.

The aim of the current study was to evaluate a novel tumor marker, neuropeptide Y receptor Y1 (NPY1R), for the detection of circulating cancer cells and to investigate its clinical significance in breast cancer patients. The Digital Gene Expression Displayer tool of the Cancer Genome Anatomy Project was used to identify the marker gene NPY1R, which is able to detect circulating cancer cells. Nested quantitative polymerase chain reaction was performed to correlate the NPY1R expression levels with the clinicopathological features of 142 breast cancer patients. A follow-up study of 131 of the breast cancer patients was conducted for 38 months. Compared with the 60 normal control individuals, NPY1R was highly expressed in the cancer patients (P<0.01). These high levels of NPY1R expression were positively correlated with the clinical stage and lymph node metastasis status of the disease, as well as with the status of the estrogen and progesterone receptors (P<0.05). Breast cancer patients with circulating cancer cells that expressed NPY1R exhibited shorter tumor-specific survival when compared with those with no NPY1R expression (P<0.01). Additionally, the mortality rate was associated with HER2 expression in the NPY1R positive and negative groups. These results indicate that NPY1R may serve as a useful marker to predict breast cancer metastasis and to evaluate the prognosis of breast cancer patients.

Heparin co-factor II enhances cell motility and promotes metastasis in non-small cell lung cancer.

Using the Serial Analysis of Gene Expression (SAGE) database from the Cancer Genome Anatomy Project, we identified heparin co-factor II (HCII), which is over-expressed in non-small cell lung cancer (NSCLC). Here, we investigated the clinical significance of HCII and provided molecular evidence to support the suggestion that HCII could enhance cancer metastasis in NSCLC. We found that high HCII expression in tumour tissue was associated with increased cancer recurrence and shorter overall survival times in 75 clinically operable NSCLC patients. High pretreatment plasma concentration of HCII was associated with reduced overall survival in 57 consecutive NSCLC patients. We over-expressed and knocked down HCII expression in lung cancer cell lines and confirmed that HCII could promote cell motility, invasion ability and filopodium dynamics in NSCLC cells in vitro and increased metastatic colonization in an in vivo mouse model. Exogenous treatment of HCII promoted cancer cell migration, and this promigratory effect of HCII was independent of thrombin. We further showed that HCII could up-regulate cancer cell migration through the activation of PI3K, which acts upstream of Rac1 and Cdc42, and this effect could be blocked by heparin. We suggest that HCII is a novel metastasis enhancer and may be used as a prognostic predictor for heparin treatment in NSCLC.

Identification of novel human glioblastoma-specific transcripts by serial analysis of gene expression data mining

Glioblastoma multiforme (GBM) remains the most common and aggressive primary brain tumor in adults with a poor median survival, and molecular biomarkers for GBM pathogenesis are in need. The objective of this study is to identify potential novel genes for GBM pathogenesis by gene expression data mining. Available SAGE libraries of GBM, astrocytoma, and normal brain tissues were collected from the Cancer Genome Anatomy Project (CGAP). Significance analysis for microarray (SAM) and CGAP-SAGE-Genie-DGED were used to identify differentially expressed tags, and specific tags that were differentially expressed only in GBM were further selected. Tags to genes association was performed by CGAP-SAGE-Genie-SAV. Immunohistochemistry was used to investigate distribution and validate expression of the interested gene. Three genes were significantly differentially expressed just in brain. up-regulated expression of STAB1 and down-regulated expression of SH3GL2 and DNM3. Immunohistochemistry assay indicated that STAB1 mainly expressed in vascular endothelial cells and over-expressed in GBM samples compared to normal samples. Our study shows that data mining of public sources of gene expression is an effective way to identify novel tumor-associated genes, and this work may contribute to the identification of candidate genes for GBM angiogenesis.

Laser‐Capture Microdissection

Laser‐Capture Microdissection

Relative quantification of protein expression in metastatic renal cell carcinoma using iTRAQ LC-MS/MS analysis reveals galectin-1 as a potential prognostic marker and therapeutic target.

412 Background: Metastatic renal cell carcinoma (RCC) is one of the most treatment-resistant cancers. Identification of proteins involved in tumor progression will help gain a better understanding of the disease and will form the basis for the identification of novel therapeutic targets. Methods: Using six fresh-frozen primary and six unmatched metastatic RCC tumors, we used iTRAQ labeling and LC-MS/MS analysis to identify proteins differentially expressed in metastatic versus primary RCC. We verified protein expression by western blot and immunohistochemical analyses and the measured the effect of dysregulated protein expression on biological processes with RCC cell line models. Results: After analysis, we identified 29 proteins differentially expressed in metastatic versus primary RCC. We verified expressions of profilin-1, 14-3-3 zeta/delta, and galectin-1 (Gal-1) on two independent tissue sets by western blot (10 primary and 10 metastatic RCC tissues) and immunohistochemistry (22 primary and 23 metastatic tissues). Overexpression of Gal-1 in CAKI-1 cells lead to decreased actin, increased vimentin expression, and increased cellular migration. Additionally, when Gal-1 was decreased via siRNA, cells showed decreased cellular migration. Protein array analysis showed expression of cell motility-related proteins HSP27, JNK, and RSK, were altered after siRNA transfection. We also showed that Gal-1 expression was increased in response to HIF-1alpha. Furthermore, we analyzed the expression of Gal-1 mRNA in 404 RCC patients using the Cancer Genome Anatomy Project, and found that patients who had higher Gal-1 expression in the primary RCC had significantly decreased overall survival (41 vs. 78 months; p &lt; 0.01). Conclusions: Gal-1 is increased in metastatic RCC and can effect cell migration by targeting proteins involved in cell motility. This may be a downstream effect of HIF-1α dysregulation. Decreased Gal-1 significantly decreased cellular migration suggesting Gal-1 may serve as a potential therapeutic target. Additionally, we showed that increased Gal-1 expression was associated with decreased overall survival.

Transcriptome-wide expansion of non-coding regulatory switches: evidence from co-occurrence of Alu exonization, antisense and editing.

Non-coding RNAs from transposable elements of human genome are gaining prominence in modulating transcriptome dynamics. Alu elements, as exonized, edited and antisense components within same transcripts could create novel regulatory switches in response to different transcriptional cues. We provide the first evidence for co-occurrences of these events at transcriptome-wide scale through integrative analysis of data sets across diverse experimental platforms and tissues. This involved the following: (i) positional anchoring of Alu exonization events in the UTRs and CDS of 4663 transcript isoforms from RefSeq mRNAs and (ii) mapping on to them A→I editing events inferred from ∼7 million ESTs from dbEST and antisense transcripts identified from virtual serial analysis of gene expression tags represented in Cancer Genome Anatomy Project next-generation sequencing data sets across 20 tissues. We observed significant enrichment of these events in the 3′UTR as well as positional preference within the embedded Alus. More than 300 genes had co-occurrence of all these events at the exon level and were significantly enriched in apoptosis and lysosomal processes. Further, we demonstrate functional evidence of such dynamic interactions between Alu-mediated events in a time series data from Integrated Personal Omics Profiling during recovery from a viral infection. Such ‘single transcript—multiple fate’ opportunity facilitated by Alu elements may modulate transcriptional response, especially during stress.

Highlights from Recent Cancer Literature

Highlights from Recent Cancer Literature

English

Lung cancer is a disease of uncontrolled cell growth in tissues of the lung. This growth may lead to metastasis, which is the invasion of adjacent tissue and infiltration beyond the lungs. The vast majority of primary lung cancers are carcinomas of the lung, derived from epithelial cells. Therefore, the present study is aimed at detecting the highly expressed genes, responsible for lung cancer through Serial Analysis of Genome Expression (SAGE) Genie at Cancer Genome Anatomy Project (CGAP). IGL@ (Immunoglobulin lambda locus) gene is predominantly highly expressed in the lung cancer and is considered to be a new target for the Cancerous diseases. The protein was retrieved from the Swissprot/Uniprot KB with accession number Q6GMX3 and was modelled using MODELLER9v7 for predicting the 3D structure of the IGL @ protein which provides an accurate and efficient module to build loops and side chains, found to be identical in sequence. Modelled structure revealed appreciable measures when subjected to structure verification and evaluation using PROCHECK. Ramachandran plot signified the present work undertaken through conformational parameters ᶲ (phi) and ψ (psi) angles calculated from model with 93.4% residues in most favoured region. Further, PROCHECK results confirmed acceptance of model through main and side)chain values. Root mean square distance of planarity was found below 0.02. Hence the model was revealed to have good stereochemistry. Structure of IGL@ Protein can be important tool for future endeavours towards structure based drug designing techniques to impel the search of new efficient inhibitors. Keywords ) SAGE, Lung Cancer, Highly Expressed Genes, Comparative Modeling, PROCHECK

Errors in CGAP xProfiler and cDNA DGED: the importance of library parsing and gene selection algorithms.

BackgroundThe Cancer Genome Anatomy Project (CGAP) xProfiler and cDNA Digital Gene Expression Displayer (DGED) have been made available to the scientific community over a decade ago and since then were used widely to find genes which are differentially expressed between cancer and normal tissues. The tissue types are usually chosen according to the ontology hierarchy developed by NCBI. The xProfiler uses an internally available flat file database to determine the presence or absence of genes in the chosen libraries, while cDNA DGED uses the publicly available UniGene Expression and Gene relational databases to count the sequences found for each gene in the presented libraries.ResultsWe discovered that the CGAP approach often includes libraries from dependent or irrelevant tissues (one third of libraries were incorrect on average, with some tissue searches no correct libraries being selected at all). We also discovered that the CGAP approach reported genes from outside the selected libraries and may omit genes found within the libraries. Other errors include the incorrect estimation of the significance values and inaccurate settings for the library size cut-off values. We advocated a revised approach to finding libraries associated with tissues. In doing so, libraries from dependent or irrelevant tissues do not get included in the final library pool. We also revised the method for determining the presence or absence of a gene by searching the UniGene relational database, revised calculation of statistical significance and sorted the library cut-off filter.ConclusionOur results justify re-evaluation of all previously reported results where NCBI CGAP expression data and tools were used.

Molecular Genetics and Cytogenetics in Cancer

This special issue of Genetics Research International focuses on molecular genetics and cytogenetics in cancer. The hunt for informative disease-related genetic biomarkers may become so valuable for the differential diagnosis, prognosis, and disease monitoring of cancer patients that numerous researchers are committed to this challenge. Indeed, a colossal effort of current molecular medicine to progress in the understanding of the hallmarks of cancer is focusing on the field of biomarker discovery through genetic and cytogenetic profiling. Ambitious initiatives such as the Cancer Genome Atlas (http://cancergenome.nih.gov/) or the Cancer Genome Anatomy Project (http://cgap.nci.nih.gov/cgap.html) from the US National Institutes of Health (NIH) are devoted to provide collaborative platforms to better comprehend and fight the malignant signature of cancer. The fast development of high-throughput technologies is bringing to biomedical research more affordable and high-resolution ways to explore whole genomes, chromosomes, genes, proteins, RNA, and metabolites. In consequence, numerous genomic datasets accumulate in international repositories, such as GEO from the US National Center for Biotechnology Information (NCBI), and around 500,000 clonal chromosomal abnormalities belonging to more than 60,000 human neoplasms are described in the latest catalogue of chromosome aberrations in cancer. However, translational application of biomarker discovery still has to face its ultimate challenge: incorporation into routine clinical practice. We are under the pressure to combine the multidisciplinary efforts of basic, clinical, and computational research fields to identify meaningful genetic profiles that can be used as powerful tools for diagnosis, prognosis, and treatment. Every patient who suffers from cancer hopes that our research will deliver alternatives. For scientists, the identification of the specific genetic abnormalities responsible for tumor diversity and heterogeneity is crucial to shed light on the molecular mechanisms of cancer and the identification of druggable targets and functional pathways. For clinicians, the appropriate molecular categorization of patients would allow to enroll them in tailored—more adequate and less toxic—treatments, improving healthcare and optimizing medical costs. Ultimately, any effort to (i) frame the important unsolved problems, (ii) to review what is known about the molecular pathways involved in the pathogenesis of distinct types of cancer, (iii) to catalog recurrent genetic alterations that could serve as biomarkers, (iv) to suggest standardization of routine protocols of diagnosis and molecular detection, and (v) to rank available or potential drugs and their targets, is decisive to help molecular medicine in its challenge against cancer. This is the scope of this special issue where the biographies of some of the most aggressive hematological and solid tumors are discussed. The journal Genetics Research International is serving here as a forum for discussion and review of major genetic risks involved in breast cancer metastasis to the brain as well as in the evolution of myxoid soft-tissue sarcomas and parathyroid tumors. The importance of applying genetic protocols in the risk-based stratification of multiple myeloma is also reviewed here, and new evidences supporting genetic aberrations in a rare variant of T-cell lymphomas known as Sezary syndrome are presented. Finally, myeloid malignancies are revealed as a paradigm to discuss the role of dicentric chromosomes as instigators of genomic instability and suggest a model for their implication in the formation of unbalanced translocations in malignancy. Breaking the barriers of cancer is a difficult task that is continuously challenging the research community. We, the editors of this special issue, are hopeful that the ideas presented here can inspire the critical thinking of the specialized readers and contribute to the common efforts to better understand and fight the multiple faces of cancer. Jose Maria Sayagues Sergio Roa Norma C. gutierrez Ilana Zalcberg Renault

DPEP1, expressed in the early stages of colon carcinogenesis, affects cancer cell invasiveness

We investigated changes in the gene expression profile in colon cancer in order to identify gene markers that may be useful in the management of this disease. The Cancer Genome Anatomy Project was used to detect differences in gene expression between normal and cancer tissue. The overexpression of dipeptidase-1 (DPEP1) in cancer tissue was confirmed in a sample of 76 patients by real-time PCR. To identify the function of DPEP1, RNA interference (RNAi) was used to inactivate this gene in the colon cancer cell line. Immunohistochemical analysis was performed to characterize the pattern of DPEP1 expression in colon cancer. DPEP1 expression in cancer was significantly higher than that in normal tissue. However, DPEP1 expression decreased with pathological differentiation, lymph-node and distant metastasis. Patients with tumors with decreased DPEP1 expression showed a poorer prognosis, and this was also true of patients with tumors who are treated with curative intent. RNAi-mediated DPEP1 reduction in the colon cancer cell line did not result in cell proliferation or apoptosis, but was associated with an increased invasive ability. DPEP1 protein was observed on the apical side of the cancer cells, and is expressed in the early stages of carcinogenesis, even in adenomas of both sporadic colorectal cancer and familial adenomatous polyposis patients. DPEP1 expression in normal colonic mucosa is very low, but it is highly expressed in colorectal adenoma and cancer specimens and is negatively correlated with parameters of pathological aggressiveness and poor prognosis. DPEP1 is expressed in the early stages of colon carcinogenesis and affects cancer cell invasiveness.

Identification by a Digital Gene Expression Displayer (DGED) and test by RT-PCR analysis of new mRNA candidate markers for colorectal cancer in peripheral blood

Evidence from the literature widely supports the efficacy of screening for colorectal cancer (CRC) in reducing mortality. A blood-based assay, potentially, represents a more accessible early detection tool for the identification of circulating tumour cells originating from a primary tumour site in the body. The present work aimed at identifying a set of specific mRNAs expressed in colon tissue but not in blood cells. These mRNAs may represent useful markers for early detection of circulating colon cancer cells by a simple, qualitative RT-PCR assay, following RNA extraction from peripheral blood samples. Using a data-mining tool called cDNA digital gene expression displayer (DGED), based on serial analysis of gene expression (SAGE) from the Cancer Genome Anatomy Project (CGAP) database, 4-colon and 14-blood cDNA libraries were analyzed. We selected 7 genes expressed in colon tissue but not in blood and were able to test 6 of them by RT-PCR in peripheral blood of CRC patients and healthy controls. We present a relatively easy and highly reproducible technique for the detection of mRNA expression of genes as candidate markers of malignancy in blood samples of patients with colon cancer. SAGE DGED provided a list of the best candidate mRNAs predicted to detect colon cells in the blood, namely those encoding the following proteins: hypothetical protein LOC644844 (LOC644844, whose cDNA was not amplifiable), fatty acid binding protein 1 (FABP1), carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5), mucin 13 cell surface associated (MUC13), guanylate cyclase activator 2A (GUCA2A), amiloride binding protein 1 (ABP1), galactoside-binding, solute carrier family 26, member 3 (SLC26A3). The mRNA expression of these genes was evaluated in 8 samples from subjects diagnosed with CRC and 9 from healthy controls. We observed the expression of 2 of the 6 investigated genes in the blood samples of the vast majority of patients considered, but also in a subset of the controls. Our data confirm the extreme sensitivity of RT-PCR, making this technique able to detect minimal amounts of mRNA expressed in a non-tissue-specific manner. Moreover, DGED remains a powerful tool to identify candidate epithelial markers in blood, such as colon related mRNAs. However, to date, none of these qualified as tumour markers.

Expression of the Wnt antagonist DKK3 is frequently suppressed in sporadic epithelial ovarian cancer

The Wnt pathway plays an important role in embryonic development, and defects in this pathway have been implicated in the tumorigenesis. The Dickkopf 3 (DKK3) is a putative Wnt signaling inhibitor that is frequently inactivated in human cancers. However, the expression of DKK3 in ovarian cancer remains unknown. We investigated the expression of DKK3 in silico using the Digital Differential Display. DKK3 mRNA expression was also analyzed by real-time RT-PCR in ovarian carcinomas and normal ovarian tissues. DKK3 protein expression was determined by immunohistochemistry in the same ovarian carcinomas and normal ovarian tissues. A significantly reduced expression of DKK3 (P < 0.05) was found after comparison of normal ovary- and tumor-derived libraries in the Cancer Genome Anatomy Project (CGAP). DKK3 mRNA expression was reduced in 63% (35 of 56) of tumors compared with normal ovarian samples (P < 0.02). Analysis of 13 matched pairs of ovarian carcinomas and adjacent normal tissues showed significant transcriptional downregulation of DKK3 (>twofold) in 9 paired carcinomas (69%). Loss or weak membranous expression of DKK3 protein was observed in 66% of ovarian cancers (37 of 56) including all tumors with low transcriptional level of DKK3 gene analyzed by real-time PCR. To our best knowledge, this is the first time to demonstrate altered expression of DKK3 in ovarian cancer. The latter could be a relevant mechanism for the activation of the Wnt pathway in the carcinogenesis of ovarian cancer but additional studies are required to elucidate the function of DKK3 silencing in ovarian carcinogenesis.

The promoter for intestinal cell kinase is head-to-head with F-Box 9 and contains functional sites for TCF7L2 and FOXA factors

BackgroundIntestinal cell kinase (ICK; GeneID 22858) is a conserved MAPK and CDK-like kinase that is widely expressed in human tissues. Data from the Cancer Genome Anatomy Project indicated ICK mRNA is increased in cancer, and that its expression correlated with expression of mRNA for an uncharacterized F-box protein, FBX9 (GeneID: 26268). ICK and FBX9 genes are arranged head-to-head on opposite strands, with start sites for transcription separated by ~3.3 kb. We hypothesized ICK and FBX9 are potentially important genes in cancer controlled by a bidirectional promoter.ResultsWe assessed promoter activity of the intergenic region in both orientations in cancer cell lines derived from breast (AU565, SKBR3), colon (HCT-15, KM12), and stomach (AGS) cancers, as well as in embryonic human kidney (HEK293T) cells. The intergenic segment was active in both orientations in all of these lines, and ICK promoter activity was greater than FBX9 promoter activity. Results from deletions and truncations defined a minimal promoter for ICK, and revealed that repressors and enhancers differentially regulate ICK versus FBX9 promoter activity. The ICK promoter contains consensus motifs for several FOX-family transcription factors that align when mouse and human are compared using EMBOSS. FOXA1 and FOXA2 increase luciferase activity of a minimal promoter 10-20 fold in HEK293T cells. Consensus sites for TCF7L2 (TCF4) (Gene Id: 6934) are also present in both mouse and human. The expression of β-catenin increased activity of the minimal promoter ~10 fold. ICK reference mRNAs (NM_014920.3, NM_016513) are expressed in low copy number and increased in some breast cancers, using a ten base tag 5'-TCAACCTTAT-3' specific for both ICK transcripts.ConclusionICK and FBX9 are divergently transcribed from a bidirectional promoter that is GC-rich and contains a CpG island. A minimal promoter for ICK contains functional sites for β-cateinin/TCF7L2 and FOXA. These data are consistent with functions that have been proposed for ICK in development and in proliferation or survival of some breast and colon cancers.

Making cancer transcriptome sequencing assays practical for the research and clinical scientist

Next generation DNA sequencing (NGS) technologies are increasing in their appeal for studying cancer genomics. High-throughput data and a growing repertoire of applications that quantitatively measure gene expression, splicing, noncoding RNAs, and genomic variation are revealing that cancer is a more complex and heterogeneous disease than previously imagined. Fully characterizing the ~10,000 types and subtypes of cancer that exist to develop biomarkers that can be used to clinically define tumors and target specific treatments requires large studies that examine specific tumors in thousands of patients. This goal will fail without significantly reducing both data production and analysis costs, so that most cancer biologists and clinicians can conduct NGS assays and analyze their data in routine ways. Currently, most cancer biology NGS papers are published either by genome centers or through collaborations with instrument vendors. However, this is going to change rapidly with efforts like the Cancer Genome Anatomy project. In any case, large teams of bioinformaticians are involved in analyzing data through labor-intensive processes. With refinements offered by the Illumina HiSeq 2000, or Life Technologies SOLiD 4, the cost of collecting data for transcriptome analysis and mate-pair genome sequencing is sufficiently inexpensive for small groups and individuals, beyond genome centers, to conduct the required studies. However, current data analysis methods need to be automated with established tools in scalable and adaptable systems that provide standard reports to make results available to enable interactive exploration by biologists and clinicians. In our presentation, we will examine the time and costs required to analyze data that will be collected in future cancer studies. Using data from existing matched tumor and normal transcriptome studies from random oral cancer samples, and samples grouped by drinking and smoking behavior (as a tool to define data analysis requirements), we will compare the costs of conducting large studies using current data analysis approaches with those using integrated software systems to demonstrate how automation reduces costs, while providing comparable results for identifying transcript isoforms, mutations and novel translocations. Geospiza's GeneSifter distributed cloud- based software architecture, including open source tools, like BioHDF, will be described to share insights into high performance computing requirements for scalable data processing.