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

2. Central Dogma of Biology
Transcriptome
Gene expression
Flow of genetic information

3. 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

4. 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).

5. 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.

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

7. 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

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

9. 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.

10. 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.

11. 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:  

12. 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.

13. 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.

14. 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

15. 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 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

16. 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.

18. A B

19. 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

20. 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:  doi: /
Bio-Rad. (2015). What is Gene Expression Analysis? Online at:
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: /
Ross M, Mahfouz R, Onciu M, (2004). Gene expression profiling of pediatric acute myelogenous leukemia /blood
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:
Su AI, Welsh JB, Sapinoso LM, et al: Molecular classification of human carcinomas by use of gene expression signatures. Cancer Res 61: , 2001.
Ramaswamy S, Tamayo P, Rifkin R, et al: Multiclass cancer diagnosis using tumor gene expression signatures. Proc Natl Acad Sci USA 98: , 2001.