.jpg)
Click here to stream the public defence
Due to copyright issues, an electronic copy of the thesis must be ordered from the faculty. For the faculty to have time to process the order, the order must be received by the faculty at the latest 2 days before the public defence. Orders received later than 2 days before the defence will not be processed. After the public defence, please address any inquiries regarding the thesis to the candidate.
Trial Lecture – time and place
See Trial Lecture.
Adjudication committee
- First opponent: Professor Zohar Yakhini, Reichman University,
- Second opponent: Group Leader Paul Kirk, University of Cambridge,
- Third member and chair of the evaluation committee: Associate professor Helene Knævelsrud, University of Oslo
Chair of the Defence
Professor Vessela Kristensen, University of Oslo
Principal Supervisor
Senior Researcher Alvaro Köhn-Luque, Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital
Summary
Personalized cancer medicine aims to improve the lives of cancer patients by adapting treatment to each patient individually. This thesis approaches personalized cancer medicine by developing statistical models for cell population dynamics in heterogeneous cancers, where treatment-resistant cells can survive treatment and lead to relapse. Two different types of data are used:
- Cell count data from high-throughput drug screening experiments with cell-line and biopsy-derived material.
- Longitudinal tumor volume indicators from a phase III randomized controlled trial in patients with Multiple Myeloma.
This thesis falls within the field of mathematical oncology, which uses mathematical and computational models to improve understanding of cancer initiation, progression, and response to treatment. The work has focused on heterogeneous cancer types, where treatment-resistant subpopulations of cancer cells can survive treatment and lead to relapse. In this thesis, it is shown that:
- High-throughput drug screening data combined with a mechanistic population model can estimate the composition of a tumor in subpopulations with differing sensitivity to a specific treatment. This information can guide optimal treatment strategies in cases where different candidate drugs are available.
- Time series measurements of tumor burden indicators in Multiple myeloma can be used in a hierarchical Bayesian model of subpopulation dynamics to model tumor progression and predict disease relapse. This model can be used to test the benefit of changing treatment ahead of imminent relapse rather than in response to relapse, which is hypothesized to increase the chance of driving the cancer population extinct.
In conclusion, this thesis shows how statistical modeling can provide estimates of intra-tumor heterogeneity and predict relapse caused by treatment-resistant subpopulations. It provides building blocks for personalized cancer therapy and opinions on its future development.
Additional information
Contact the research support staff.