Mathematical modeling of treatment resistance in cancer

Emilia Kozłowska

Research output: ThesisDoctoral ThesisCollection of Articles

Abstract

Cancer is one of the world’s most lethal diseases. Although our understanding of this disease is expanding continuously, treatments for many types of cancers are still ineffective. The main reason for the high mortality of cancer patients is resistant to therapy. Since resistance to therapy is a complex and dynamical process, an interdisciplinary approach is necessary to understand it. The emergence of a new field called integrative mathematical oncology can tackle many urgent clinical problems in the treatment of cancer that are impossible to address using, for example, an in vitro or in vivo approach. The primary goal of this new field is to translate the biological complexity of a tumor into a precise language, such as mathematical formulas, and to perform model simulations. Therefore, integrative mathematical oncology allows for biological experiments to be performed inexpensively and rapidly. This thesis applies the integrative mathematical oncology approach to investigate resistance to treatment in solid tumors at the molecular and cellular levels. A mathematical model of the most commonly dysregulated pathway in cancer (the p53 signaling pathway) underwent a bifurcation analysis to investigate the possibility of restoring its proper dynamics in two types of cancer: osteosarcoma and breast cancer. Next, a stochastic model of resistance to platinum compounds was developed to improve our understanding of chemo-resistance to this group of drugs in advanced high-grade serous ovarian cancer (HGSOC). Finally, virtual clinical trial simulations (VCTS) were performed to identify a novel drug combination in ovarian cancer. The application of integrative mathematical oncology deepened our understanding of radio- and chemo-resistance in solid tumors. Firstly, the results from the bifurcation analysis of the p53 signaling pathway suggested silencing Mdm2 using siRNA to overcome radio-resistance in breast cancer and osteosarcoma. Next, the stochastic model of platinum resistance was utilized to answer two urgent clinical questions about ovarian cancer: i) how many platinum resistance mechanisms are active at diagnosis, and ii) how many drug-resistance mechanisms must be targeted to improve patient outcomes. Finally, the clinical trial simulations suggested a novel drug combination to overcome platinum resistance in advanced high-grade serous ovarian cancer.
Original languageEnglish
Awarding Institution
  • University of Helsinki
Supervisors/Advisors
  • Hautaniemi, Sampsa, Supervisor
Place of PublicationHelsinki
Publisher
Print ISBNs978-951-51-5194-0
Electronic ISBNs978-951-51-5195-7
Publication statusPublished - 2019
MoE publication typeG5 Doctoral dissertation (article)

Bibliographical note

M1 - 52 s. + liitteet

Fields of Science

  • Drug Resistance, Neoplasm
  • Treatment Failure
  • Drug Combinations
  • Platinum Compounds
  • Models, Theoretical
  • RNA, Small Interfering
  • Tumor Suppressor Protein p53
  • Proto-Oncogene Proteins c-mdm2
  • Chemoradiotherapy
  • 3122 Cancers
  • 317 Pharmacy

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