Network Based Integration of Proteomic and Transcriptomic Data: Study of BCR and WNT11 Signaling Pathways in Cancer Cells
Maren Sitte
Doctoral thesis
Date of Examination:
2020-05-08
Date of issue:
2020-05-12
Advisor:
Beißbarth, Tim Prof. Dr.
Referee:
Beißbarth, Tim Prof. Dr.
Referee:
Waack, Stephan Prof. Dr.
Persistent Address: http://hdl.handle.net/21.11130/00-1735-0000-0005-1397-B
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Abstract
English
Bioinformatics applications in cancer research expanded rapidly over several years in the past. Due to the fast development of high throughput technologies, it became feasible to study the presence of hundreds of genes or proteins measured parallel in one experiment. The challenge is to understand how the regulatory network alters under different conditions or in disease. Their expression values can be used to learn more about their interactions. To study their interplay under different conditions network reconstruction methods were utilized. This thesis demonstrates a general workflow for integrating data sets from different data sources into a signaling network analysis for cancer cells. Exemplary, BCR signaling in lymphomas and WNT11 signaling in breast cancer was analyzed utilizing gene, proteinn and patient data to elucidate the changes of BCR signaling and WNT11 signaling after specific cell treatment. The aim of the first study was to investigate proteomic data together with existing gene expression data to predict how lymphomas translate signaling stimuli to expressed phenotypes. BCR-related pathway interplays were reconstructed by analyzing several gene and phospho-protein expression profiles. Therefore, the two network reconstruc- tion techniques NEM and DDEPN were applied to transcriptomic and proteomic measurements, followed by an integrative analysis to identify alterations in BCR signaling after external stimulation. In the second study, the WNT11 pathways were analyzed in relation to their interplay to one of its receptors ROR2 in human breast cancer. It has been shown that WNT11 signaling highly depends on its receptors and ligands who determine downstream signaling. In an integrative analysis pipeline, transcriptomic and proteo-mic data sets were combined to estimate downstream signaling interplay. Subsequently, patient data was included to associate the findings with clinical outcome. In both studies, the analysis identified genes, proteins and pathways considered to be biologically important along with potentially new results that can be used to encourage ongoing research.
Keywords: integrative analysis
