Profiles of gene expression & diagnosis/prognosis of cancer Lucia Gómez Pardo. Advanced Genetics. Genomics December 2016

Central Dogma of Biology Transcriptome Gene expression Flow of genetic information

Gene expression profiles Gene expression profiling is the determination of the pattern of genes expressed, at the level of transcription, under specific circumstances or in a specific cell to give a global picture of cellular function. Identify the molecular basis of phenotypic differences

Clustering of expression profiles defines in breast cancer cell line subtypes. Kao, J. et al. Molecular profiling of breast cancer cell lines defines relevant tumor models and provides a resource for cancer gene discovery. PLoS ONE 4, e6146 (2009).

Gene expression profiles in cancer Uncontrolled cell growth and proliferation. The genes regulating cell growth and differentiation must be altered; these mutations are then maintained through subsequent cell divisions. Gene expression profiling has been used to more accurately classify tumours. Impact on predicting the patient`s clinical outcome.

Gene expression profiles in cancer Workflow individualized therapies in cancer Tumor harvest

Aplications Develop new biological concepts Disease classification Improve diagnostic and prognostic accuracy Identify new molecular targets for drugs Help to choose an adequate therapy for every patient

Techniques DNA microarray Real time PCR (qPCR) Serial Analysis of Gene Expression SAGE RNA seq MicroRNA

Most common method of studying gene expression. Since the advent of genome‐wide transcriptome profiling by DNA microarray, gene signatures of poor prognosis and chemoresistance in cancer tissue have been studied to improved prognostic and diagnostic tools in the clinic. The Gene Expression Omnibus (GEO) database.

RT-qPCR Steady-state levels of mRNA are quantitated by reverse transcription of the RNA to cDNA followed by quantitative PCR (qPCR) The amount of each specific target is determined by measuring the increase in fluorescence signal from DNA-binding dyes or probes during successive rounds of enzyme-mediated amplification.

High-throughput gene expression analysis aims to quantify messenger RNA (mRNA) populations in a given tissue of Breast cancer tumor. Marchionni L, Wilson RF, Wolff AC, Marinopoulos S, Parmigiani G, Bass EB, et al. Systematic Review: Gene Expression Profiling Assays in Early-Stage Breast Cancer. Ann Intern Med. 2008;148:358-369. 

Serial Analysis of Gene Expression (SAGE) Serial analysis of gene expression is an approach that allows rapid and detailed analysis of gene expression patterns. SAGE provides quantitative and comprehensive expression profiling in a given cell population.

Serial Analysis of Gene Expression (SAGE) Data output is a specific gene fragment sequence and a count for the frequency with which that fragment appeared in a sample.

RNA-seq RNA-Seq is a recently developed approach to transcriptome profiling that uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample at a given moment in time. While gene expression microarrays are effective for identifying the expression of known genes and transcripts, they cannot detect previously unknow transcripts. In contrast, RNA-Seq offers a gene expression profiling solution, allowing researchers to detect both known and novel in a single assay. Wang, Z. et al., 2009

MicroRNA expression profiling MiRNAs are promising in the diagnosis of cancer, drug target identification and clinical treatment in the future. MicroRNA expression profiling miRNAs are small non- coding regions of 20-22 nt, they play an important role in all biological pathways by safeguarding key biological processes: Cell proliferation Differentiation Apoptosis. Deregulation of the miRNAs has severe consequences in the expression patterns of a lot of mRNAs and this deregulation is involved in the development of many disease. Chen et al., 2012

Find new, more precise molecular markers for leukemia classification. Although the methods by which leukemia is classified have been improved for effective therapies, leukemia patients occasionally exhibit diverse, sometimes unpredictable, responses to treatment. Objective: Find new, more precise molecular markers for leukemia classification. Experiment: Analyzed the gene expression profiles from 65 diagnostic bone marrow specimens of adult patients with: AML ALL CML CLL High-throughput DNA microarrays harboring approximately 8300 unique human genes or expression sequence tags.

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Conclusions and Future Perspectives Complex trait analysis describes an area of biology that is extremely crucial to our understanding of most prevalent human diseases, such as cancer. How is modern biomedical science expected to identify genes modulating complex disease and produce the development of new treatments? Tools for the organization of genomic-level data into networks. Analysis of multiple experimental conditions. Quantitative measurements of gene expression are important for a wide range of research and diagnostic applications. Molecular profiles of tumors hold great promise as biomarkers of clinical outcomes. It is likely that gene expression will increase the basis for diagnostic assays that identify the onset of a disease and monitor its progression and prognosis. Enviromental factor Phenotypic expression of didease Multiple genes

References Marchionni L, Wilson RF, Wolff AC, Marinopoulos S, Parmigiani G, Bass EB, et al. Systematic Review: Gene Expression Profiling Assays in Early-Stage Breast Cancer. Ann Intern Med. 2008;148:358-369. doi: 10.7326/0003-4819-148-5-200803040-00208. Bio-Rad. (2015). What is Gene Expression Analysis? Online at: http://www.bio-rad.com/eses/applications-technologies/what-gene-expression-analysis. Lun, J. et al. (2005). MicroRNA expression profiles classify human cancers. Nature, 435. 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:10.1186/1479-5876-4-8 . Ross M, Mahfouz R, Onciu M, (2004). Gene expression profiling of pediatric acute myelogenous leukemia. 10.1182/blood-2004-03-1154. Ju Song, Hyeoung-joon kim et al. (2006). Identification of gene expression signatures for molecular classification in human leukemia cells. International Journal of Oncology 29: 57-64. Su AI, Welsh JB, Sapinoso LM, et al: Molecular classification of human carcinomas by use of gene expression signatures. Cancer Res 61: 7388-7393, 2001. Ramaswamy S, Tamayo P, Rifkin R, et al: Multiclass cancer diagnosis using tumor gene expression signatures. Proc Natl Acad Sci USA 98: 15149-15154, 2001.