1. Transcriptomics: A general overview By Todd, Mark, and Tom

2. Intro Transcriptomics => RNA in a cell
Either coding or non-coding (ncRNA).
mRNA vs microRNA, siRNA
Also non-functional RNA (pseudo-genes)

3. Transcriptomics focuses sets on: how where when why Also diagnosing : developmental stages tissue differentials viruses response to stimuli

4. Microarray Used for Biological Assays DNA Microarrays MMChips
Protein Microarrays
Tissue Microarrays
Antibody Microarrays

5. DNA Microarray Can be used to measure Expression levels SNPs
Genotyping
Comparative Genome Hybridization

6. Basic DNA Microarray Experiment

7. Labeling
Lockhart and Winzeler 2000

8. Probes and Targets Probes Target Known sequence bonded to substrate
Sample obtained to wash over chip
See what and how much is hybridized

9. Hybridization and Wash

10. Hybridization and Wash

11. Basic DNA Microarray Experiment

12. Results
Lockhart and Winzeler 2000

13. Tiling Array Genome array consisting of overlapping probes
Finer Resolution
Better at finding RNA in the cell
mRNA
Alternative splicing
Not Polyadenylated
miRNA

14. Tiling Arrays

15. Tiling Array

16. Microarray
Wheelan et al. 2008

17. Gene expression profiling predicts clinical outcome of breast cancer Laura J. van 't Veer1,2, Hongyue Dai2,3, Marc J. van de Vijver1,2, Yudong D. He3, Augustinus A. M. Hart1, Mao Mao3, Hans L. Peterse1, Karin van der Kooy1, Matthew J. Marton3, Anke T. Witteveen1, George J. Schreiber3, Ron M. Kerkhoven1, Chris Roberts3, Peter S. Linsley3, René Bernards1 and Stephen H. Friend3 Divisions of Diagnostic Oncology, Radiotherapy and Molecular Carcinogenesis and Center for Biomedical Genetics, The Netherlands Cancer Institute, 121 Plesmanlaan, 1066 CX Amsterdam, The Netherlands Rosetta Inpharmatics, th Avenue NE, Kirkland, Washington 98034, USA These authors contributed equally to this work Nature, January 2002

18. • Use DNA microarray analysis and applied supervised classification to identify a gene expression signature predictive of metastases and BRCA1 carriers.
• Authors predicted that the expression profile would outperform all currently used clinical parameters in predicting disease outcome.
• Strategy to select patients who would benefit from adjuvant therapy (chemotherapy).

19. Metastases – spread of cancer from one area to another; characteristic of malignant tumor cells. Angiogenesis – process of growing new blood vessels from pre-existing vessels. A normal process in growth and development, however also a fundamental step in the transition of tumors from a dormant state to a malignant state. Estrogen Receptor alpha (ERα) – activated by sex hormone estrogen; DNA binding transcription factor which regulates gene expression; association with cancer known from immunohistochemical data (IHC). BRCA1 – Human gene, Breast Cancer 1; Mutations associated with significant increase in risk of breast cancer.
Belongs to a class of genes known as tumor suppressors (DNA damage repair, transcriptional regulation).
BRCA1 represses ERα-mediated transcription, with a reduction of BRCA1 activity results in elevated ERα-mediated transcription and enhanced cell proliferation.

20. 98 primary breast cancers: 34 from patients who developed metastases within 5 years 44 from patients who continued to be disease-free after 5 years 18 from patients with BRCA1 germline mutations 2 from BRCA2 carriers
Total RNA isolated from patients and used to derive complementary RNA (cRNA)
A reference cRNA pool was made by pooling equal amounts of cRNA from each cancer, for use in quantification of transcript abundance (fluorescence intensity in relation to reference pool).
• Hybridizations carried out on micoarrays (synthesized by inkjet technology) containing ~ 25,000 human genes
• ~ 5,000 genes found to be significantly regulated across the group of samples

21. Two distinct groups of tumours apparent on the basis of the set of ~5,000 significant genes.
• In upper group only 34% of patients were from group developing metastases within 5 years.
• In lower group 70% of patients had progressive disease.
• Clustering detects two subgroups of cancer which differ in ER status and lymphocytic infiltration

22. To identify tumours that could reliably represent either a good or poor prognosis a three-step supervised classification method was applied:
1) The correlation coefficient of the expression of ~ 5,000 significant genes was calculated, with 231 genes determined to be significantly associated with disease outcome. 2) These 231 genes were ranked on basis of magnitude. 3) Number of genes in ‘prognosis classifier’ optimized with the optimal number of marker genes reached at 70 genes.

23. Prognosis signature with prognostic reporter genes identifying two types of disease outcome:
above dashed line good prognosis
below dashed line poor prognosis
Predicted correctly the actual outcome of disease for 65 out of 78 patients (83%).
To validate prognosis classifier additional set analyzed (Fig. 2C).

24. The functional annotation of genes provided insight into the underlying mechanisms leading to rapid metastases with the following genes significantly upregulated in the poor prognosis signiture: • genes involved in cell cycle • invasion and metastasis • angiogenesis • signal transduction

25. A third classification was performed to look at the expression patterns associated with ER-positive and ER-negative tumours. ER clustering has predictive power for prognosis although it does not reach the level of significance of the prognosis classifier.

26. Consensus conference developed guidelines for eligibility of adjuvant chemotherapy based on histological and clinical characteristics.
Prognosis classifier selects as effectively high-risk patients who would benefit from therapy, but reduces number to receive unnecessary treatment.

27. Conclusions:
• Results indicate that breast cancer prognosis can be derived from gene expression profile of primary tumor.
Recogmendations:
• ER signature - can be used to decide on hormonal therapy
• BRCA1 - knowing status of can improve diagnosis of hereditary breast cancer.
• Genes overexpressed in tumors with poor prognosis profile are targets for development of new cancer drugs

28. MicroRNA expression profiles classify human cancers
Jun Lu1,4*, Gad Getz1*, Eric A. Miska2*†, Ezequiel Alvarez-Saavedra2, Justin Lamb1, David Peck1,
Alejandro Sweet-Cordero3,4, Benjamin L. Ebert1,4, Raymond H. Mak1,4, Adolfo A. Ferrando4, James R. Downing5,
Tyler Jacks2,3, H. Robert Horvitz2 & Todd R. Golub1,4,6
Nature, June 2005

29. Short size of microRNAs (miRNAs) and sequence similarity between miRNA family members has resulted in cross-hybridization of related miRNAs on glass-slide microarrays. Development of bead-based flow cytometric expression profiling of miRNAs.

31. miRNA profiles are informative with a general down regulation of miRNA in tumors compared with normal tissue Expression profiles of miRNA are also able to classify poorly differentiated tumors, highlighting the potential for miRNA profiling in cancer diagnosis

33. RNA-Seq
Lockhart and Winzeler 2000
Wang et al. 2009

34. RNA-Seq Whole Transcriptome Shotgun Sequencing
Sequencing cDNA
Using NexGen technology
Revolutionary Tool for Transcriptomics
More precise measurements
Ability to do large scale experiments with little starting material

35. RNA-Seq Experiment
Wang et al. 2009

36. Mapping Place reads onto a known genomic scaffold
Requires known genome and depends on accuracy of the reference

37. Mapping Create unique scaffolds
Harder algorithms with such short reads

38. Comparisons
Wang et al. 2009

39. Comparisons
Wang et al. 2009

40. Biases
Wang et al. 2009

41. Directionality
Wang et al. 2009

42. Coverage Versus Depth
Wang et al. 2009

43. New Insights Mapping Genes and Exon Boundries Transcript Complexity
Single Base Resolution
Transcript Complexity
Exon Skipping
Novel Transcription
More accurate
No cross hybridization

44. Transcription Levels Can measure Transcript levels more accurately
Confirmed with qPCR and RNA spike-in
Can compare measurements with different cellular states and environmental conditions
Without sophistication of normalization of data

45. What does mRNA tell you?
Gene expression not the same as phenotypic expression

46. Why no line? Reasons? Noise and bias of sample Lag time of translation
Post-translational control
RNA/Protein half life ?

47. Where is the genetics? How do you study the transcriptome?
What are the patterns of expression telling you?
Differences between gene expression vs gene function (i. e. protein code vs concentration)?

48. 1) ‘guilt by association’ 2) Change environment, look for patterns; compare known phenotypic mutants (cancer) 3) Add ‘controlled’ knockout (specific locations/ times/ concentrations) 4)Evolution; diversity of expression across intra and inter speices 5) Add entire chromosome

49. Evolution model: neutral vs selection

51. Mouse with Down syndrome What happens? What would Mendel do?

53. Different environments

56. Rhythm