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<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/"><rdf:Description rdf:about="https://repozitorij.uni-lj.si/IzpisGradiva.php?id=120530"><dc:title>Physical models of immunotherapy</dc:title><dc:creator>Valentinuzzi,	Damijan	(Avtor)
	</dc:creator><dc:creator>Jeraj,	Robert	(Mentor)
	</dc:creator><dc:subject>cancer</dc:subject><dc:subject>modelling</dc:subject><dc:subject>immunotherapy</dc:subject><dc:subject>anti-PD-1</dc:subject><dc:subject>iRADIOMICS</dc:subject><dc:subject>non-small-cell lung cancer</dc:subject><dc:subject>MHC class I</dc:subject><dc:description>Immunotherapy with immune-checkpoint inhibitors has changed the paradigm of cancer treatment. For the first time in the history of oncology, it is possible to realistically expect complete remissions of metastatic disease in certain types of cancers such as melanoma and non-small-cell lung cancer (NSCLC), which were previously considered almost incurable. However, patients with such favourable responses are the minority, and the reasons remain mostly unknown.
 
The overall goal of this thesis was to identify tumour characteristics that drive patient response to anti-programmed-death-1 (anti-PD-1) immunotherapy, using computational modelling. To examine this objective, we exploited two opposite modelling approaches: a mechanistic bottom-up approach and a data-driven top-down approach.
 
Our bottom-up modelling was focused on pre-clinical anti-PD-1 experiments to ensure sufficient experimental data, needed for model tuning and validation. Two similar models were developed, which were tuned and validated on murine data from literature and data obtained by on-site experiments, respectively. Both models identified tumour major histocompatibility complex (MHC) class I expression as a biomarker of resistance to anti-PD-1 therapy. Moreover, they suggested that complete responses to anti-PD-1 can occur only in the case of homogeneous (MHC class I positive) tumours.

On the other hand, a data-driven top-down model was developed to examine a clinical study containing sparse data, where metastatic NSCLC patients were treated with pembrolizumab and scanned with [18F]FDG PET/CT. This model was based on radiomics analyses of PET scans of primary tumours (iRADIOMICS) and was found predictive of patient overall survival. Interestingly, a detailed inspection of model components suggested that tumour homogeneity is associated with favourable response to anti-PD-1 immunotherapy, similar to the bottom-up approach findings.

If findings arising from this thesis are confirmed in independent studies, they will have a significant impact on the clinical practice, allowing for better management of cancer patients treated with anti-PD-1 immunotherapy.</dc:description><dc:date>2020</dc:date><dc:date>2020-09-22 08:15:25</dc:date><dc:type>Doktorsko delo/naloga</dc:type><dc:identifier>120530</dc:identifier><dc:language>sl</dc:language></rdf:Description></rdf:RDF>
