Profiles of gene expression & diagnosis/prognosis of cancer Lorena Roa de la Cruz

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1 Genomics Profiles of gene expression & diagnosis/prognosis of cancer Lorena Roa de la Cruz

2 Gene expression profiling Measurement of the activity of thousands of genes at once

3 Techniques used for gene expression profiling Microarrays Measures the relative activity of previously identified target genes cdna microarrays Oligonucleotide microarrays mirna microarrays Deep sequencing Serial analysis of gene expression (SAGE) qpcr

4 Microarrays Most common method of studying global gene expression Helps in clinical/diagnostic testing of cancer and other diseases Whole genome arrays Kawasaki, E. S. (2006). The End of the Microarray Tower of Babel: Will Universal Standards Lead the Way? Journal of Biomolecular Techniques : JBT 17(3), Retrieved from

5 Microarrays in Solid Tumors Used to evaluate genetic markers and changes in gene expression associated with cancer onset and progression for certain types of solid tumors

6 Microarrays in Solid Tumors Ulrich Hengge et al. Human Melanoma Using cdna à Identified two potential independent predictors of malignant behavior: Activator of S phase kinase (ASK/HuDbf4) Tumor potentiation region (Tpr) Primary melanomas Subcutaneous melanoma metastasis Metastatic melanoma cell lines 86% of the melanoma metastases ASK/HuDbf4 and Tpr Compared to other markers à Detection of melanoma progression/ metastasis

7 Microarrays in Solid Tumors Bertrand Rihn et al. Microarray findings: Described cdna array based portraits of normal and cancerous pleura Understanding asbestos-mediated carcinogenesis In three independent studies

8 Microarrays in Hematology cdna microarrays Segregation of morphologically identical tumors Helped to define molecular taxonomy of leukemias & lymphomas Discovery of new hematological disease subclasses characterized by unique molecular profiles Molecular predictive patterns for clinical outcomes Development of diagnostic strategies based on gene expression profiling

9 Microarrays in Hematology Using oligonucleotide microarrays (Affymetrix) High predictive à accuracy 95.1% & specificity 93.8% Microarray Technologies in Clinical Oncology: Potential and Perspectives Worshop

10 Microarrays in Hematology Robin Foà et al. DNA microarrays à Patients with adult ALL Identification of patterns specific for characterized molecular abnormalities: ALL1/AF4, E2A/PBX1, TEL/AML1 and BCR/ABL Association with immunophenotypic characteristics Cellular origin of a leukemia Degree of leukemic cell differentiation Gene profile analysis Provided insights into the pathogenesis of multiple myeloma (MM) Plassifying patients according to their aggressiveness degree New prognostic markers in adult ALL

11 MicroRNA Profiling Most microrna expression analyses of human cancers have arrived at the common conclusions that mirnas are dysregulated in cancer, and that the expression pattern is histotype specific

12 Techniques for MicroRNA expression profiling mirna Arrays Real-Time Transcription-Mediated Amplification Assays Methylation-Specific PCR And Bisulphate Sequencing Spotted Locked Nucleic Acid-Based Oligonucleotide Microarrays LNA-Modified Probes In A Liquid-Phase Bead-Based Array Beadbased, liquid-phase hybridization assay

13 MicroRNA Profiling Lu, J., Getz, G., Miska, E. A., Alvarez-Saavedra, E., Lamb, J., Peck, D., Golub, T. R. (2005). MicroRNA expression profiles classify human cancers. Nature, 435(7043), doi: /nature03702

14 MicroRNA Profiling Comparison between normal and tumour samples Most of the mirnas had lower expression levels in tumours Lu, J., Getz, G., Miska, E. A., Alvarez-Saavedra, E., Lamb, J., Peck, D., Golub, T. R. (2005). MicroRNA expression profiles classify human cancers. Nature, 435(7043), doi: /nature03702

15 MicroRNA Profiling Of Human Cancers Advantages of mirnas Utile of monitoring their expression in human cancer Unlike with mrna expression 200 mirnas might be sufficient to classify human cancers Higer accuracy mirnas remain largely intact in routinely collected, FFP embedded clinical tissues Bead-based mirna detection method Accurate and specific but also easy to implement in a routine clinical setting.

16 MicroRNAs in Cancer Modified from Cho, W. C. S. (2010). MicroRNAs: potential biomarkers for cancer diagnosis, prognosis and targets for therapy. The International Journal of Biochemistry & Cell Biology, 42(8), doi: /j.biocel

17 Serial Analysis Of Gene Expression Unbiased and quantitative approach Identifying differentially expressed transcripts in human neoplasms Facilitating discovery of cancer biomarkers, imaging targets, & therapeutic avenues SAGE does not require a priori knowledge of the queried transcripts Platform for quantification & gene discovery

18 Serial Analysis Of Gene Expression Alvarez et al. Built the first series of SAGE libraries from GBC and nonneoplastic gallbladdermucosa SAGE libraries were generated from three stage-matched GBC patients Hispanic/ Latino NativeAmerican Caucasian Histologically alithiasic gallbladder Alvarez, H., Corvalan, A., Roa, J. C., Argani, P., Murillo, F., Edwards, J., Maitra, A. (2008). Serial analysis of gene expression identifies connective tissue growth factor expression as a prognostic biomarker in gallbladder cancer. Clinical Cancer Research : An Official Journal of the American Association for Cancer Research, 14(9), doi: / ccr

19 SAGE SAGE tags corresponding to connective tissue growth factor (CTGF) transcripts Differentially overexpressed in GBC PROGNOSTIC BIOMARKER Alvarez, H., Corvalan, A., Roa, J. C., Argani, P., Murillo, F., Edwards, J., Maitra, A. (2008). Serial analysis of gene expression identifies connective tissue growth factor expression as a prognostic biomarker in gallbladder cancer. Clinical Cancer Research : An Official Journal of the American Association for Cancer Research, 14(9), doi: / ccr

20 Real Time-PCR

21 Real Time-PCR Diffuse large B-cell lymphoma (DLBCL) NonHodgkin s lymphoma subtype Losses et al. Based on Microarray data 36 genes à Expression may be used to predict survival in DLBCL Measured with qpcr in 66 patients. LMO2 BCL6 FN1 CCND2 SCYA3 BCL2 Strong predictors survival

22 References Alvarez, H., Corvalan, A., Roa, J. C., Argani, P., Murillo, F., Edwards, J., Maitra, A. (2008). Serial analysis of gene expression identifies connective tissue growth factor expression as a prognostic biomarker in gallbladder cancer. Clinical Cancer Research : An Official Journal of the American Association for Cancer Research, 14(9), doi: / CCR Cho, W. C. S. (2010). MicroRNAs: potential biomarkers for cancer diagnosis, prognosis and targets for therapy. The International Journal of Biochemistry & Cell Biology, 42(8), doi: /j.biocel Gabriele, L., Moretti, F., Pierotti, M. A., Marincola, F. M., Foà, R., & Belardelli, F. M. (2006). The use of microarray technologies in clinical oncology. Journal of Translational Medicine, 4, 8. doi: / Jay, C., Nemunaitis, J., Chen, P., Fulgham, P., & Tong, A. W. (2007). mirna profiling for diagnosis and prognosis of human cancer. DNA and Cell Biology, 26(5), doi: /dna Kawasaki, E. S. (2006). The End of the Microarray Tower of Babel: Will Universal Standards Lead the Way? Journal of Biomolecular Techniques : JBT, 17(3), Retrieved from Lu, J., Getz, G., Miska, E. A., Alvarez-Saavedra, E., Lamb, J., Peck, D., Golub, T. R. (2005). MicroRNA expression profiles classify human cancers. Nature, 435(7043), doi: /nature03702 Ludwig, J. A., & Weinstein, J. N. (2005). Biomarkers in Cancer Staging, Prognosis and Treatment Selection. Nature Reviews. Cancer, 5(11), doi: Ståhlberg, A., Zoric, N., Åman, P., & Kubista, M. (2005). Quantitative real-time PCR for cancer detection: the lymphoma case. Expert Review of Molecular Diagnostics, 5(2), doi: /

23 Thank you for your attention! Questions?