A5086366456- Cancer Immunotherapy and Biomarkers
- Mathematical Biology Tumor Growth
- Cancer Genomics and Diagnostics
- Medical Imaging Techniques and Applications
- Immunotherapy and Immune Responses
- Radiopharmaceutical Chemistry and Applications
- Radiomics and Machine Learning in Medical Imaging
- Prostate Cancer Treatment and Research
- Lung Cancer Research Studies
- Colorectal Cancer Surgical Treatments
- Statistical Methods in Clinical Trials
- Monoclonal and Polyclonal Antibodies Research
- Computational Drug Discovery Methods
- Protein purification and stability
- Estrogen and related hormone effects
Jožef Stefan Institute
2014-2021
University of Ljubljana
2017-2020
Abstract Background Immune checkpoint inhibitors have changed the paradigm of cancer treatment; however, non-invasive biomarkers response are still needed to identify candidates for non-responders. We aimed investigate whether immunotherapy [ 18 F]FDG PET radiomics signature (iRADIOMICS) predicts metastatic non-small-cell lung (NSCLC) patients pembrolizumab better than current clinical standards. Patients and methods Thirty receiving were scanned with PET/CT at baseline, month 1 4....
Cancer immunotherapy is a rapidly developing field, with numerous drugs and therapy combinations waiting to be tested in pre-clinical clinical settings. However, the costly time-consuming trial-and-error approach development of new treatment paradigms creates research bottleneck, motivating complementary approaches. Computational modelling compelling candidate for this task, however, difficulties associated validation such models limit their use Here we propose bottom-up deterministic...
Modern cancer immunotherapy has revolutionised oncology and carries the potential to radically change approach treatment. However, numerous questions remain be answered understand response better further improve benefit for future patients. Computational models are promising tools that can contribute accelerated research by providing new clues hypotheses could tested in trials, based on preceding simulations addition empirical rationale. In this topical review, we briefly summarise history...
Metastatic cancer patients invariably develop treatment resistance. Different levels of resistance lead to observed heterogeneity in response. The main goal was evaluate response with a computation model simulating the dynamics drug-sensitive and drug-resistant cells. Model parameters included proliferation, drug-induced death, transition proportion intrinsically resistant benchmarked imaging metrics extracted from 39 metastatic prostate who had 18F-NaF-PET/CT scans performed at baseline...
Purpose: Patient response to anti‐angiogenic therapies with vascular endothelial growth factor receptor – tyrosine kinase inhibitors (VEGFR TKIs) is heterogeneous. This study investigates key biological characteristics that drive differences in patient via Monte Carlo computational modeling capable of simulating tumor therapy VEGFR TKI. Methods: TKIs potently block receptors, responsible for promoting angiogenesis tumors. The model incorporates drug pharmacokinetic and pharmacodynamic...
Abstract Cancer treatment with combination of radiotherapy (RT) and immunotherapy (IT) (immune check-point inhibitors) has gained promising results in preclinical clinical studies. Accumulating evidence suggests that RT is beneficial not only because its direct cytocidal effect but it also acts as an immunogenic hub, turning the irradiated tumor into situ cancer vaccine. In around 25% patients such combined shrinkage distant metastases (abscopal effect). However, little known about optimal...
Abstract Immune checkpoint inhibitors have shown impressive benefits for patients with various types of cancer. However, who respond are still a minority. To improve the response rates, combinations immunotherapies, as well other conventional therapies, been extensively studied. Due to an unmanageably high number all possible and dosing regimens, alternatives costly time-consuming trial-and-error approach utmost importance. Our main goal was develop verifiable computational model that would...
Abstract Introduction: Treatment resistance contributes importantly to clinical response of metastatic prostate cancer (mPC) patients. We hypothesize that patients with more resistant lesions would lead unfavorable treatment response. The goal this study was use a computational model investigate the association between and individual lesion enzalutamide therapy. Materials Methods: A deterministic population used simulate dynamics drug-sensitive drug-resistant cells in lesions. Intrinsic...