Exploring Cellular Signalling: Perspectives on phosphoproteomic data use and interpretation in a model of drug resistance

Research output: ThesisDoctoral thesis 1 (Research UU / Graduation UU)

Abstract

Every single cell in our body is different from another, caused by their unique set of proteins they behold. This set, or the proteome, is highly dynamic and will change accordingly to the wishes of the cell. This dynamic character makes the study of proteins, or proteomics, an important source of information about our cells, in sickness and in health. But, at the same time it is the biggest hurdle in the field: due to its complexity it is a highly challenging task to study the proteome properly. The most important technique used in proteomics is mass spectrometry (MS). In this thesis I discussed several ways of studying the proteome with higher efficiency by using specific techniques and by re-using published data. Due to their importance in the cell, is it possible to use the study of proteins to study each existing disease. In this thesis I used MS to study drug resistance in cancer. Treatment of cancer cells with drugs will lead to change of shape, number and type of proteins within cells. This can lead to adaptation of cells to the drug, i.e. resistance, after which the tumour is able to re-grow. By comparing the proteome of cells with and without resistance, causes of resistance and weak points of cancer cells can be exposed. With the use of new knowledge of the behaviour of proteins in response to drug treatment and during development of resistance, it might be possible to prevent development of resistance, rather than cure it.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Utrecht University
Supervisors/Advisors
  • Heck, Albert, Supervisor
  • Lemeer, Simone, Co-supervisor
Award date10 Jul 2019
Place of PublicationUtrecht
Publisher
Print ISBNs978-90-393-7151-0
Publication statusPublished - 10 Jul 2019

Keywords

  • phosphoproteomics
  • big data
  • mass spectrometry
  • cancer resistance
  • tyrosine kinase inhibitors
  • EGFR

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