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Comparison of Nanostring nCounter® Data on FFPE Colon Cancer Samples and Affymetrix Microarray Data on Matched Frozen Tissues.
Chen X, Deane NG, Lewis KB, Li J, Zhu J, Washington MK, Beauchamp RD
(2016) PLoS One 11: e0153784
MeSH Terms: Colonic Neoplasms, Formaldehyde, Frozen Sections, Gene Expression Regulation, Neoplastic, Humans, Oligonucleotide Array Sequence Analysis, Paraffin Embedding, RNA, Neoplasm, Survival Analysis, Tissue Fixation
Show Abstract · Added August 10, 2016
The prognosis of colorectal cancer (CRC) stage II and III patients remains a challenge due to the difficulties of finding robust biomarkers suitable for testing clinical samples. The majority of published gene signatures of CRC have been generated on fresh frozen colorectal tissues. Because collection of frozen tissue is not practical for routine surgical pathology practice, a clinical test that improves prognostic capabilities beyond standard pathological staging of colon cancer will need to be designed for formalin-fixed paraffin-embedded (FFPE) tissues. The NanoString nCounter® platform is a gene expression analysis tool developed for use with FFPE-derived samples. We designed a custom nCounter® codeset based on elements from multiple published fresh frozen tissue microarray-based prognostic gene signatures for colon cancer, and we used this platform to systematically compare gene expression data from FFPE with matched microarray array data from frozen tissues. Our results show moderate correlation of gene expression between two platforms and discovery of a small subset of genes as candidate biomarkers for colon cancer prognosis that are detectable and quantifiable in FFPE tissue sections.
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10 MeSH Terms
Gastric adenocarcinoma microRNA profiles in fixed tissue and in plasma reveal cancer-associated and Epstein-Barr virus-related expression patterns.
Treece AL, Duncan DL, Tang W, Elmore S, Morgan DR, Dominguez RL, Speck O, Meyers MO, Gulley ML
(2016) Lab Invest 96: 661-71
MeSH Terms: Adenocarcinoma, Aged, Aged, 80 and over, Case-Control Studies, Epstein-Barr Virus Infections, Female, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Herpesvirus 4, Human, Humans, Male, MicroRNAs, Middle Aged, Pilot Projects, RNA, Neoplasm, RNA, Viral, Stomach Neoplasms
Show Abstract · Added May 18, 2016
MicroRNA expression in formalin-fixed paraffin-embedded tissue (FFPE) or plasma may add value for cancer management. The GastroGenus miR Panel was developed to measure 55 cancer-specific human microRNAs, Epstein-Barr virus (EBV)-encoded microRNAs, and controls. This Q-rtPCR panel was applied to 100 FFPEs enriched for adenocarcinoma or adjacent non-malignant mucosa, and to plasma of 31 patients. In FFPE, microRNAs upregulated in malignant versus adjacent benign gastric mucosa were hsa-miR-21, -155, -196a, -196b, -185, and -let-7i. Hsa-miR-18a, 34a, 187, -200a, -423-3p, -484, and -744 were downregulated. Plasma of cancer versus non-cancer controls had upregulated hsa-miR-23a, -103, and -221 and downregulated hsa-miR-378, -346, -486-5p, -200b, -196a, -141, and -484. EBV-infected versus uninfected cancers expressed multiple EBV-encoded microRNAs, and concomitant dysregulation of four human microRNAs suggests that viral infection may alter cellular biochemical pathways. Human microRNAs were dysregulated between malignant and benign gastric mucosa and between plasma of cancer patients and non-cancer controls. Strong association of EBV microRNA expression with known EBV status underscores the ability of microRNA technology to reflect disease biology. Expression of viral microRNAs in concert with unique human microRNAs provides novel insights into viral oncogenesis and reinforces the potential for microRNA profiles to aid in classifying gastric cancer subtypes. Pilot studies of plasma suggest the potential for a noninvasive addition to cancer diagnostics.
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17 MeSH Terms
Comprehensive Molecular Characterization of Papillary Renal-Cell Carcinoma.
Cancer Genome Atlas Research Network, Linehan WM, Spellman PT, Ricketts CJ, Creighton CJ, Fei SS, Davis C, Wheeler DA, Murray BA, Schmidt L, Vocke CD, Peto M, Al Mamun AA, Shinbrot E, Sethi A, Brooks S, Rathmell WK, Brooks AN, Hoadley KA, Robertson AG, Brooks D, Bowlby R, Sadeghi S, Shen H, Weisenberger DJ, Bootwalla M, Baylin SB, Laird PW, Cherniack AD, Saksena G, Haake S, Li J, Liang H, Lu Y, Mills GB, Akbani R, Leiserson MD, Raphael BJ, Anur P, Bottaro D, Albiges L, Barnabas N, Choueiri TK, Czerniak B, Godwin AK, Hakimi AA, Ho TH, Hsieh J, Ittmann M, Kim WY, Krishnan B, Merino MJ, Mills Shaw KR, Reuter VE, Reznik E, Shelley CS, Shuch B, Signoretti S, Srinivasan R, Tamboli P, Thomas G, Tickoo S, Burnett K, Crain D, Gardner J, Lau K, Mallery D, Morris S, Paulauskis JD, Penny RJ, Shelton C, Shelton WT, Sherman M, Thompson E, Yena P, Avedon MT, Bowen J, Gastier-Foster JM, Gerken M, Leraas KM, Lichtenberg TM, Ramirez NC, Santos T, Wise L, Zmuda E, Demchok JA, Felau I, Hutter CM, Sheth M, Sofia HJ, Tarnuzzer R, Wang Z, Yang L, Zenklusen JC, Zhang J, Ayala B, Baboud J, Chudamani S, Liu J, Lolla L, Naresh R, Pihl T, Sun Q, Wan Y, Wu Y, Ally A, Balasundaram M, Balu S, Beroukhim R, Bodenheimer T, Buhay C, Butterfield YS, Carlsen R, Carter SL, Chao H, Chuah E, Clarke A, Covington KR, Dahdouli M, Dewal N, Dhalla N, Doddapaneni HV, Drummond JA, Gabriel SB, Gibbs RA, Guin R, Hale W, Hawes A, Hayes DN, Holt RA, Hoyle AP, Jefferys SR, Jones SJ, Jones CD, Kalra D, Kovar C, Lewis L, Li J, Ma Y, Marra MA, Mayo M, Meng S, Meyerson M, Mieczkowski PA, Moore RA, Morton D, Mose LE, Mungall AJ, Muzny D, Parker JS, Perou CM, Roach J, Schein JE, Schumacher SE, Shi Y, Simons JV, Sipahimalani P, Skelly T, Soloway MG, Sougnez C, Tam A, Tan D, Thiessen N, Veluvolu U, Wang M, Wilkerson MD, Wong T, Wu J, Xi L, Zhou J, Bedford J, Chen F, Fu Y, Gerstein M, Haussler D, Kasaian K, Lai P, Ling S, Radenbaugh A, Van Den Berg D, Weinstein JN, Zhu J, Albert M, Alexopoulou I, Andersen JJ, Auman JT, Bartlett J, Bastacky S, Bergsten J, Blute ML, Boice L, Bollag RJ, Boyd J, Castle E, Chen YB, Cheville JC, Curley E, Davies B, DeVolk A, Dhir R, Dike L, Eckman J, Engel J, Harr J, Hrebinko R, Huang M, Huelsenbeck-Dill L, Iacocca M, Jacobs B, Lobis M, Maranchie JK, McMeekin S, Myers J, Nelson J, Parfitt J, Parwani A, Petrelli N, Rabeno B, Roy S, Salner AL, Slaton J, Stanton M, Thompson RH, Thorne L, Tucker K, Weinberger PM, Winemiller C, Zach LA, Zuna R
(2016) N Engl J Med 374: 135-45
MeSH Terms: Carcinoma, Papillary, CpG Islands, DNA Methylation, Humans, Kidney Neoplasms, MicroRNAs, Mutation, NF-E2-Related Factor 2, Phenotype, Proto-Oncogene Proteins c-met, RNA, Messenger, RNA, Neoplasm, Sequence Analysis, RNA, Signal Transduction
Show Abstract · Added August 8, 2016
BACKGROUND - Papillary renal-cell carcinoma, which accounts for 15 to 20% of renal-cell carcinomas, is a heterogeneous disease that consists of various types of renal cancer, including tumors with indolent, multifocal presentation and solitary tumors with an aggressive, highly lethal phenotype. Little is known about the genetic basis of sporadic papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist.
METHODS - We performed comprehensive molecular characterization of 161 primary papillary renal-cell carcinomas, using whole-exome sequencing, copy-number analysis, messenger RNA and microRNA sequencing, DNA-methylation analysis, and proteomic analysis.
RESULTS - Type 1 and type 2 papillary renal-cell carcinomas were shown to be different types of renal cancer characterized by specific genetic alterations, with type 2 further classified into three individual subgroups on the basis of molecular differences associated with patient survival. Type 1 tumors were associated with MET alterations, whereas type 2 tumors were characterized by CDKN2A silencing, SETD2 mutations, TFE3 fusions, and increased expression of the NRF2-antioxidant response element (ARE) pathway. A CpG island methylator phenotype (CIMP) was observed in a distinct subgroup of type 2 papillary renal-cell carcinomas that was characterized by poor survival and mutation of the gene encoding fumarate hydratase (FH).
CONCLUSIONS - Type 1 and type 2 papillary renal-cell carcinomas were shown to be clinically and biologically distinct. Alterations in the MET pathway were associated with type 1, and activation of the NRF2-ARE pathway was associated with type 2; CDKN2A loss and CIMP in type 2 conveyed a poor prognosis. Furthermore, type 2 papillary renal-cell carcinoma consisted of at least three subtypes based on molecular and phenotypic features. (Funded by the National Institutes of Health.).
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14 MeSH Terms
TRIzol and Alu qPCR-based quantification of metastatic seeding within the skeleton.
Preston Campbell J, Mulcrone P, Masood SK, Karolak M, Merkel A, Hebron K, Zijlstra A, Sterling J, Elefteriou F
(2015) Sci Rep 5: 12635
MeSH Terms: Alu Elements, Animals, Biomarkers, Tumor, Bone Neoplasms, Cell Line, Tumor, DNA, Neoplasm, Gene Expression Profiling, Genetic Markers, Guanidines, Mice, Mice, Nude, Phenols, RNA, Neoplasm, Real-Time Polymerase Chain Reaction, Reproducibility of Results, Sensitivity and Specificity
Show Abstract · Added September 14, 2016
Current methods for detecting disseminated tumor cells in the skeleton are limited by expense and technical complexity. We describe a simple and inexpensive method to quantify, with single cell sensitivity, human metastatic cancer in the mouse skeleton, concurrently with host gene expression, using TRIzol-based DNA/RNA extraction and Alu sequence qPCR amplification. This approach enables precise quantification of tumor cells and corresponding host gene expression during metastatic colonization in xenograft models.
1 Communities
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16 MeSH Terms
Mitochondria sequence mapping strategies and practicability of mitochondria variant detection from exome and RNA sequencing data.
Zhang P, Samuels DC, Lehmann B, Stricker T, Pietenpol J, Shyr Y, Guo Y
(2016) Brief Bioinform 17: 224-32
MeSH Terms: Algorithms, Base Sequence, Breast Neoplasms, Cell Line, Tumor, DNA, Mitochondrial, Exome, Genetic Variation, Humans, Molecular Sequence Data, Neoplastic Cells, Circulating, Polymorphism, Single Nucleotide, RNA, Neoplasm, Reproducibility of Results, Sensitivity and Specificity, Sequence Analysis, RNA
Show Abstract · Added February 15, 2016
The rapid progress in high-throughput sequencing has significantly enriched our capacity for studying the mitochondrial DNA (mtDNA). In addition to performing specific mitochondrial targeted sequencing, an increasingly popular alternative approach is using the off-target reads from exome sequencing to infer mtDNA variants, including single nucleotide polymorphisms (SNPs) and heteroplasmy. However, the effectiveness and practicality of this approach have not been tested. Recently, RNAseq data have also been suggested as a good source for alternative data mining, but whether mitochondrial variants can be detected from RNAseq data has not been validated. We designed a study to evaluate the practicability of mtDNA variant detection using exome and RNA sequencing data. Five breast cancer cell lines were sequenced through mitochondrial targeted, exome, and RNA sequencing. Mitochondrial targeted sequencing was used as the gold standard to compute the validation and false discovery rates of SNP and heteroplasmy detection in exome and RNAseq data. We found that exome and RNA sequencing can accurately detect mitochondrial SNPs. However, the lower false discovery rate makes exome sequencing a better choice for heteroplasmy detection than RNAseq. Furthermore, we examined three alignment strategies and found that aligning reads directly to the mitochondrial reference genome or aligning reads to the nuclear and mitochondrial references genomes simultaneously produced the best results, and that aligning to the nuclear genome first and afterwards to the mitochondrial genome performed poorly. In conclusion, our study provides important guidelines for future studies that intend to use either exome sequencing or RNAseq data to infer mitochondrial SNPs and heteroplasmy.
© The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.
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15 MeSH Terms
Comprehensive genomic characterization of head and neck squamous cell carcinomas.
Cancer Genome Atlas Network
(2015) Nature 517: 576-82
MeSH Terms: Carcinoma, Squamous Cell, DNA Copy Number Variations, DNA, Neoplasm, Female, Gene Expression Regulation, Neoplastic, Genome, Human, Genomics, Head and Neck Neoplasms, Humans, Male, Molecular Targeted Therapy, Mutation, Oncogenes, RNA, Neoplasm, Signal Transduction, Squamous Cell Carcinoma of Head and Neck, Transcription Factors
Show Abstract · Added August 8, 2016
The Cancer Genome Atlas profiled 279 head and neck squamous cell carcinomas (HNSCCs) to provide a comprehensive landscape of somatic genomic alterations. Here we show that human-papillomavirus-associated tumours are dominated by helical domain mutations of the oncogene PIK3CA, novel alterations involving loss of TRAF3, and amplification of the cell cycle gene E2F1. Smoking-related HNSCCs demonstrate near universal loss-of-function TP53 mutations and CDKN2A inactivation with frequent copy number alterations including amplification of 3q26/28 and 11q13/22. A subgroup of oral cavity tumours with favourable clinical outcomes displayed infrequent copy number alterations in conjunction with activating mutations of HRAS or PIK3CA, coupled with inactivating mutations of CASP8, NOTCH1 and TP53. Other distinct subgroups contained loss-of-function alterations of the chromatin modifier NSD1, WNT pathway genes AJUBA and FAT1, and activation of oxidative stress factor NFE2L2, mainly in laryngeal tumours. Therapeutic candidate alterations were identified in most HNSCCs.
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17 MeSH Terms
Proteogenomic characterization of human colon and rectal cancer.
Zhang B, Wang J, Wang X, Zhu J, Liu Q, Shi Z, Chambers MC, Zimmerman LJ, Shaddox KF, Kim S, Davies SR, Wang S, Wang P, Kinsinger CR, Rivers RC, Rodriguez H, Townsend RR, Ellis MJ, Carr SA, Tabb DL, Coffey RJ, Slebos RJ, Liebler DC, NCI CPTAC
(2014) Nature 513: 382-7
MeSH Terms: Chromosomes, Human, Pair 20, Colonic Neoplasms, CpG Islands, DNA Copy Number Variations, DNA Methylation, Genomics, Hepatocyte Nuclear Factor 4, Humans, Microsatellite Repeats, Mitochondrial Membrane Transport Proteins, Mutation, Missense, Neoplasm Proteins, Point Mutation, Proteome, Proteomics, Proto-Oncogene Proteins pp60(c-src), RNA, Messenger, RNA, Neoplasm, Rectal Neoplasms, Transcriptome
Show Abstract · Added December 4, 2014
Extensive genomic characterization of human cancers presents the problem of inference from genomic abnormalities to cancer phenotypes. To address this problem, we analysed proteomes of colon and rectal tumours characterized previously by The Cancer Genome Atlas (TCGA) and perform integrated proteogenomic analyses. Somatic variants displayed reduced protein abundance compared to germline variants. Messenger RNA transcript abundance did not reliably predict protein abundance differences between tumours. Proteomics identified five proteomic subtypes in the TCGA cohort, two of which overlapped with the TCGA 'microsatellite instability/CpG island methylation phenotype' transcriptomic subtype, but had distinct mutation, methylation and protein expression patterns associated with different clinical outcomes. Although copy number alterations showed strong cis- and trans-effects on mRNA abundance, relatively few of these extend to the protein level. Thus, proteomics data enabled prioritization of candidate driver genes. The chromosome 20q amplicon was associated with the largest global changes at both mRNA and protein levels; proteomics data highlighted potential 20q candidates, including HNF4A (hepatocyte nuclear factor 4, alpha), TOMM34 (translocase of outer mitochondrial membrane 34) and SRC (SRC proto-oncogene, non-receptor tyrosine kinase). Integrated proteogenomic analysis provides functional context to interpret genomic abnormalities and affords a new paradigm for understanding cancer biology.
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20 MeSH Terms
ClearCode34: A prognostic risk predictor for localized clear cell renal cell carcinoma.
Brooks SA, Brannon AR, Parker JS, Fisher JC, Sen O, Kattan MW, Hakimi AA, Hsieh JJ, Choueiri TK, Tamboli P, Maranchie JK, Hinds P, Miller CR, Nielsen ME, Rathmell WK
(2014) Eur Urol 66: 77-84
MeSH Terms: Adult, Aged, Aged, 80 and over, Animals, Biomarkers, Tumor, Carcinoma, Renal Cell, Disease-Free Survival, Female, Gene Expression, Gene Expression Profiling, Humans, Kidney Neoplasms, Male, Middle Aged, Neoplasm Recurrence, Local, RNA, Neoplasm, Risk Assessment, Sequence Analysis, RNA, Survival Rate
Show Abstract · Added October 17, 2015
BACKGROUND - Gene expression signatures have proven to be useful tools in many cancers to identify distinct subtypes of disease based on molecular features that drive pathogenesis, and to aid in predicting clinical outcomes. However, there are no current signatures for kidney cancer that are applicable in a clinical setting.
OBJECTIVE - To generate a signature biomarker for the clear cell renal cell carcinoma (ccRCC) good risk (ccA) and poor risk (ccB) subtype classification that could be readily applied to clinical samples to develop an integrated model for biologically defined risk stratification.
DESIGN, SETTING, AND PARTICIPANTS - A set of 72 ccRCC sample standards was used to develop a 34-gene classifier (ClearCode34) for assigning ccRCC tumors to subtypes. The classifier was applied to RNA-sequencing data from 380 nonmetastatic ccRCC samples from the Cancer Genome Atlas (TCGA), and to 157 formalin-fixed clinical samples collected at the University of North Carolina.
OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS - Kaplan-Meier analyses were performed on the individual cohorts to calculate recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS). Training and test sets were randomly selected from the combined cohorts to assemble a risk prediction model for disease recurrence.
RESULTS AND LIMITATIONS - The subtypes were significantly associated with RFS (p<0.01), CSS (p<0.01), and OS (p<0.01). Hazard ratios for subtype classification were similar to those of stage and grade in association with recurrence risk, and remained significant in multivariate analyses. An integrated molecular/clinical model for RFS to assign patients to risk groups was able to accurately predict CSS above established, clinical risk-prediction algorithms.
CONCLUSIONS - The ClearCode34-based model provides prognostic stratification that improves upon established algorithms to assess risk for recurrence and death for nonmetastatic ccRCC patients.
PATIENT SUMMARY - We developed a 34-gene subtype predictor to classify clear cell renal cell carcinoma tumors according to ccA or ccB subtypes and built a subtype-inclusive model to analyze patient survival outcomes.
Copyright © 2014 European Association of Urology. Published by Elsevier B.V. All rights reserved.
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19 MeSH Terms
Signaling between transforming growth factor β (TGF-β) and transcription factor SNAI2 represses expression of microRNA miR-203 to promote epithelial-mesenchymal transition and tumor metastasis.
Ding X, Park SI, McCauley LK, Wang CY
(2013) J Biol Chem 288: 10241-53
MeSH Terms: Animals, Breast Neoplasms, Cell Line, Tumor, Dogs, Epithelial-Mesenchymal Transition, Female, Gene Expression Regulation, Neoplastic, Humans, MicroRNAs, Neoplasm Metastasis, Neoplasm Proteins, RNA, Neoplasm, Signal Transduction, Snail Family Transcription Factors, Transcription Factors, Transforming Growth Factor beta
Show Abstract · Added March 5, 2014
TGF-β promotes tumor invasion and metastasis by inducing an epithelial-mesenchymal transition (EMT). Understanding the molecular and epigenetic mechanisms by which TGF-β induces EMT may facilitate the development of new therapeutic strategies for metastasis. Here, we report that TGF-β induced SNAI2 to promote EMT by repressing miR-203. Although miR-203 targeted SNAI2, SNAI2 induced by TGF-β could directly bind to the miR-203 promoter to inhibit its transcription. SNAI2 and miR-203 formed a double negative feedback loop to inhibit each other's expression, thereby controlling EMT. Moreover, we found that miR-203 was significantly down-regulated in highly metastatic breast cancer cells. The restoration of miR-203 in highly metastatic breast cancer cells inhibited tumor cell invasion in vitro and lung metastatic colonization in vivo by repressing SNAI2. Taken together, our results suggest that the SNAI2 and miR-203 regulatory loop plays important roles in EMT and tumor metastasis.
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16 MeSH Terms
SPDEF functions as a colorectal tumor suppressor by inhibiting β-catenin activity.
Noah TK, Lo YH, Price A, Chen G, King E, Washington MK, Aronow BJ, Shroyer NF
(2013) Gastroenterology 144: 1012-1023.e6
MeSH Terms: Adenocarcinoma, Animals, Apoptosis, Cell Proliferation, Colon, Colonic Neoplasms, Disease Progression, Gene Expression Regulation, Neoplastic, Humans, Mice, Mice, Transgenic, Neoplasms, Experimental, Polymerase Chain Reaction, Proto-Oncogene Proteins c-ets, RNA, Neoplasm, Tissue Array Analysis, Tumor Cells, Cultured, beta Catenin
Show Abstract · Added April 12, 2016
BACKGROUND & AIMS - Expression of the SAM pointed domain containing ETS transcription factor (SPDEF or prostate-derived ETS factor) is regulated by Atoh1 and is required for the differentiation of goblet and Paneth cells. SPDEF has been reported to suppress the development of breast, prostate, and colon tumors. We analyzed levels of SPDEF in colorectal tumor samples from patients and its tumor-suppressive functions in mouse models of colorectal cancer (CRC).
METHODS - We analyzed levels of SPDEF messenger RNA and protein in more than 500 human CRC samples and more than 80 nontumor controls. Spdef(-/-)and wild-type mice (controls) were either bred with Apc(Min/+) mice, or given azoxymethane (AOM) and dextran sodium sulfate (DSS), or 1,2-dimethylhydrazine and DSS, to induce colorectal tumors. Expression of Spdef also was induced transiently by administration of tetracycline to Spdef(dox-intestine) mice with established tumors, induced by the combination of AOM and DSS or by breeding with Apc(Min/+) mice. Colon tissues were collected and analyzed for tumor number, size, grade, and for cell proliferation and apoptosis. We also analyzed the effects of SPDEF expression in HCT116 and SW480 human CRC cells.
RESULTS - In colorectal tumors from patients, loss of SPDEF was observed in approximately 85% of tumors and correlated with progression from normal tissue, to adenoma, to adenocarcinoma. Spdef(-/-); Apc(Min/+) mice developed approximately 3-fold more colon tumors than Spdef(+/+); Apc(Min/+) mice. Likewise, Spdef(-/-) mice developed approximately 3-fold more colon tumors than Spdef(+/+) mice after administration of AOM and DSS. After administration of 1,2-dimethylhydrazine and DSS, invasive carcinomas were observed exclusively in Spdef(-/-) mice. Conversely, expression of SPDEF was sufficient to promote cell-cycle exit in cells of established adenomas from Spdef(dox-intestine); Apc(Min/+) mice and in Spdef(dox-intestine) mice after administration of AOM + DSS. SPDEF inhibited the expression of β-catenin-target genes in mouse colon tumors, and interacted with β-catenin to block its transcriptional activity in CRC cell lines, resulting in lower levels of cyclin D1 and c-MYC.
CONCLUSIONS - SPDEF is a colon tumor suppressor and a candidate therapeutic target for colon adenomas and adenocarcinoma.
Copyright © 2013 AGA Institute. Published by Elsevier Inc. All rights reserved.
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18 MeSH Terms