Title Clinical Applications of Molecular Profiling in ColorectalCancer

Purpose Colorectal cancer (CRC) is a clinically diverse disease whose molecular etiology remains poorly understood. The purpose of this study was to identify miRNA expression patterns predictive of CRC tumor status and to investigate associations between microRNA (miRNA) expression and clinicopathological parameters. Methods Expression profiling of 380 miRNAs was performed on 20 paired stage II tumor and normal tissues. Artificial neural network (ANN) analysis was applied to identify miRNAs predictive of tumor status. The validation of specific miRNAs was performed on 102 tissue specimens of varying stages. Results Thirty-three miRNAs were identified as differentially expressed in tumor versus normal tissues. ANN analysis identified three miRNAs (miR-139-5p, miR-31, and miR-1792 cluster) predictive of tumor status in stage II disease. Elevated expression of miR-31 (p=0.004) and miR-139-5p (p<0.001) and reduced expression of miR-143 (p=0.016) were associated with aggressive mucinous phenotype. Increased expression of miR-10b was also associated with mucinous tumors (p=0.004). Furthermore, progressively increasing levels of miR-10b expression were observed from T1 to T4 lesions and from stage I to IV disease. Conclusion Association of specific miRNAs with clinicopathological features indicates their biological relevance and highlights the power of ANN to reliably predict clinically relevant miRNA biomarkers, which it is hoped will better stratify patients to guide adjuvant therapy.

[1]  Zhaohui Huang,et al.  Plasma microRNAs are promising novel biomarkers for early detection of colorectal cancer , 2010, International journal of cancer.

[2]  Michael J Kerin,et al.  Circulating microRNAs as Novel Minimally Invasive Biomarkers for Breast Cancer , 2010, Annals of surgery.

[3]  A. Bardelli,et al.  Molecular mechanisms of resistance to cetuximab and panitumumab in colorectal cancer. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[4]  Takashi Suzuki,et al.  Changes in microRNA expression levels correlate with clinicopathological features and prognoses in endometrial serous adenocarcinomas , 2010, Cancer science.

[5]  J. Vandesompele,et al.  MicroRNA expression profiling to identify and validate reference genes for relative quantification in colorectal cancer , 2010, BMC Cancer.

[6]  M. A. van de Wiel,et al.  MiR-17-92 cluster is associated with 13q gain and c-myc expression during colorectal adenoma to adenocarcinoma progression , 2009, British Journal of Cancer.

[7]  B. Xiao,et al.  Differential expression of microRNA species in human gastric cancer versus non‐tumorous tissues , 2009, Journal of gastroenterology and hepatology.

[8]  X. Chen,et al.  Role of miR-143 targeting KRAS in colorectal tumorigenesis , 2009, Oncogene.

[9]  Zongguang Zhou,et al.  Clinicopathological Significance of microRNA-31, -143 and -145 Expression in Colorectal Cancer , 2009, Disease markers.

[10]  E. Ng,et al.  Differential expression of microRNAs in plasma of patients with colorectal cancer: a potential marker for colorectal cancer screening , 2009, Gut.

[11]  Christophe Lemetre,et al.  MicroRNA signatures predict oestrogen receptor, progesterone receptor and HER2/neu receptor status in breast cancer , 2009, Breast Cancer Research.

[12]  Avner Friedman,et al.  MicroRNA regulation of a cancer network: Consequences of the feedback loops involving miR-17-92, E2F, and Myc , 2008, Proceedings of the National Academy of Sciences.

[13]  M. Fukushima,et al.  Down regulation of c-Myc and induction of an angiogenesis inhibitor, thrombospondin-1, by 5-FU in human colon cancer KM12C cells. , 2008, Cancer letters.

[14]  X. Chen,et al.  Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases , 2008, Cell Research.

[15]  Bernat Gel,et al.  Overlapping expression of microRNAs in human embryonic colon and colorectal cancer , 2008, Cell Research.

[16]  Frank J. Sørensen,et al.  Diagnostic and prognostic microRNAs in stage II colon cancer. , 2008, Cancer research.

[17]  Daniel B. Martin,et al.  Circulating microRNAs as stable blood-based markers for cancer detection , 2008, Proceedings of the National Academy of Sciences.

[18]  S. Mukherjee,et al.  Adjuvant therapy for completely resected stage II colon cancer. , 2008, The Cochrane database of systematic reviews.

[19]  George A Calin,et al.  MicroRNA expression profiles associated with prognosis and therapeutic outcome in colon adenocarcinoma. , 2008, JAMA.

[20]  D. Kerr,et al.  Adjuvant chemotherapy versus observation in patients with colorectal cancer: a randomised study , 2007, The Lancet.

[21]  George A Calin,et al.  mRNA/microRNA gene expression profile in microsatellite unstable colorectal cancer , 2007, Molecular Cancer.

[22]  K. Ghoshal,et al.  MicroRNA-21 regulates expression of the PTEN tumor suppressor gene in human hepatocellular cancer. , 2007, Gastroenterology.

[23]  D. Schadendorf,et al.  Diagnostic biomarkers differentiating metastatic melanoma patients from healthy controls identified by an integrated MALDI‐TOF mass spectrometry/bioinformatic approach , 2007, Proteomics. Clinical applications.

[24]  G. Hutvagner,et al.  Principles and effects of microRNA-mediated post-transcriptional gene regulation , 2006, Oncogene.

[25]  Y. Akao,et al.  MicroRNAs 143 and 145 are possible common onco-microRNAs in human cancers. , 2006, Oncology reports.

[26]  X. Agirre,et al.  Identification by Real-time PCR of 13 mature microRNAs differentially expressed in colorectal cancer and non-tumoral tissues , 2006, Molecular Cancer.

[27]  C. Croce,et al.  A microRNA expression signature of human solid tumors defines cancer gene targets , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Ma Dong,et al.  Bevacizumab plus Irinotecan,Fluorouracil,and Leucovorin for Metastatic Colorectal Cancer , 2006 .

[29]  Z. Hua.  Cetuximab Monotherapy and Cetuximab plus Irinotecan in Irinotecan-Refractory Metastatic Colorectal Cancer , 2006 .

[30]  C. Croce,et al.  miRNAs, Cancer, and Stem Cell Division , 2005, Cell.

[31]  H. Horvitz,et al.  MicroRNA expression profiles classify human cancers , 2005, Nature.

[32]  Graham R. Ball,et al.  Classification of bacterial species from proteomic data using combinatorial approaches incorporating artificial neural networks, cluster analysis and principal components analysis , 2005, Bioinform..

[33]  R. Simon,et al.  Effectiveness of gene expression profiling for response prediction of rectal adenocarcinomas to preoperative chemoradiotherapy. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[34]  C. Burge,et al.  Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that Thousands of Human Genes are MicroRNA Targets , 2005, Cell.

[35]  David C. Atkins,et al.  Gene expression profiles and molecular markers to predict recurrence of Dukes' B colon cancer. , 2004, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[36]  D. Bartel,et al.  MicroRNAs Modulate Hematopoietic Lineage Differentiation , 2004, Science.

[37]  Sam Griffiths-Jones,et al.  The microRNA Registry , 2004, Nucleic Acids Res..

[38]  T. Ørntoft,et al.  Classification of Dukes' B and C colorectal cancers using expression arrays , 2003, Journal of Cancer Research and Clinical Oncology.

[39]  M. Radmacher,et al.  Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. , 2003, Journal of the National Cancer Institute.

[40]  E. Lai.  Micro RNAs are complementary to 3′ UTR sequence motifs that mediate negative post-transcriptional regulation , 2002, Nature Genetics.

[41]  G. Li,et al.  An integrated approach utilizing artificial neural networks and SELDI mass spectrometry for the classification of human tumours and rapid identification of potential biomarkers , 2002, Bioinform..

[42]  Thomas D. Schmittgen,et al.  Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. , 2001, Methods.

[43]  R. Tibshirani,et al.  Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[44]  M. Ringnér,et al.  Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks , 2001, Nature Medicine.

[45]  H. Rockette,et al.  Comparative efficacy of adjuvant chemotherapy in patients with Dukes' B versus Dukes' C colon cancer: results from four National Surgical Adjuvant Breast and Bowel Project adjuvant studies (C-01, C-02, C-03, and C-04) , 1999, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.