Unit 7: Transcriptomics & Bioinformatics

Background

New technologies and high-tech equipment have become available in recent years to collect various data about mutations, gene activities, and gene functions. miRNAs added another level to complexity to these regulatory networks. Independent evaluation of each of such complex datasets provided already valuable insights into the origins and mechanisms of cancer in general and cancer stem cells in specific. However, superimposed biological relations are often not immediately visible from the isolated data sets, creating a risk of missing important starting points for therapeutical intervention.


Goal

UNIT 7 employs transcriptomics approaches, e.g. by mRNA and miRNA expression profiling as well as by next generation-sequencing, in order to recover new markers and sites of vulnerability for breast cancer stem cells. Further, UNIT 7 intends to connect interdisciplinary data-sets from genomics, proteomics, transcriptomics and systematic functional screens, using bioinformatic methods. This approach allows for an encompassing statistical evaluation, which will help to create novel insights into the complex biology of cancer stem cells and relations in cancer development.


The origin of our data

Functional, genomics, and proteomics data are at the heart of the bioinformatics platform. Functional and proteomics date are generated in @@@UNIT 5 and UNIT 8, respectively. Furthermore, we include genomics and transcriptomics data generated in UNIT 7 itself from clinical samples in the Human Micoarray Center at Odense University Hospital, and from relevant in vitro models.


Statistical methods allow for Quality Control and help to assess significance

In order to gain a deeper understanding of high-throughput data, it is imperative to apply well understood statistical methods.

First of all, data needs to be normalized, which allows to counteract measurement errors. Positive and negative controls need to be incorporated in the experimental design and later on used in statistical tests to validate the data and to ensure its comparability.

Looking at quantitative instead of qualitative data, one needs to determine how much of a measured effect is to be considered as significant. This depends mainly on the accuracy of the measurement.

 

What happens with the results?

Approximately 20,000 genes are sequentially silenced in a genome wide siRNA screen. The list of putative hits affecting cancer cells needs to be compared to effects on corresponding normal cell lines. Further validation will take cancer specific pathways into account. In this way, a list of candidate genes can be obtained, which is then further tested for their potential use in new strategies in cancer therapy in UNIT 6 (Preclinical Validation). A comparison to profiling data sets from primary clinical samples shall serve to estimate the clinical relevance of a given target pathway for prioritization.

 

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