Modeling glioblastoma growth patterns and their mechanistic origins

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Rosén, EmilUppsala universitet,Neuroonkologi (författare)

Modeling glioblastoma growth patterns and their mechanistic origins

BokEngelska2021

Förlag, utgivningsår, omfång ...

Uppsala :Acta Universitatis Upsaliensis,2021
58 s.
electronicrdacarrier

Nummerbeteckningar

LIBRIS-ID:oai:DiVA.org:uu-459422
ISBN:9789151313504
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-459422URI

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Språk:engelska
Sammanfattning på:engelska

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Ämneskategori:vet swepub-contenttype
Ämneskategori:dok swepub-publicationtype

Serie

Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine,1651-6206 ;1792

Anmärkningar

Glioblastoma (GBM) is the most common and aggressive primary brain cancer. GBM cells migrate away from the primary lesion and invade healthy brain tissue. The invading cells escape surgical resection, radiotherapy and develop resistance to chemotherapy. Consequently, despite treatment, recurrence is inevitable, and survival is only 14 months. For this purpose, we conducted four studies where we integrated experimental data from extensive patient material with image analysis and mathematical modeling.In study 1, we developed a tool, TargetTranslator, integrating different data modalities to identify new treatments. We implemented an image analysis pipeline to validate our results using a deep artificial neural network to quantify neuroblastoma cell differentiation.In study 2, we integrated the zebrafish and image analysis from study 1 to develop a high-throughput in vivo assay. Zebrafish were orthotopically injected with GBM cells, and each fish's tumor growth and vital status were automatically measured. We characterized the in vivo proliferation rate, survival, and treatment response to the drug marizomib for several patient-derived cell cultures. Light-sheet imaging also revealed two distinct growth types. The first set of cell cultures grew as bulk tumors, whereas the second set invaded vasculature as single cells.In study 3, we used the image analysis from study 1, coupled with an agent-based model to estimate in vitro cell migration and proliferation from single end-point images. The method was validated by a time series data set and applied to a large high-content drug screen of GBM cells. We identified three promising candidates for reducing GBM cell migration. The method can estimate migration on any end-point images of adherent cells without any additional experimental cost.Study 4 characterized the growth and invasive patterns of 45 patient-derived GBM cell cultures in orthogonal mouse xenografts. We found that up to four independent axes of variation could describe the phenotypes and were associated with distinct transcriptomic pathways. The transcriptomic pathways were in part associated with common genomic alterations and subtypes in GBM. We further identified a particularly aggressive GBM phenotype.In conclusion, this thesis was interdisciplinary and aimed to measure survival, invasion, and morphology from extensive patient material. The work had given us new insight into GBM invasion and growth and developed several scalable models suitable for evaluating new therapies.

Ämnesord och genrebeteckningar

MEDICIN OCH HÄLSOVETENSKAP Klinisk medicin Cancer och onkologi hsv//swe
MEDICAL AND HEALTH SCIENCES Clinical Medicine Cancer and Oncology hsv//eng
MEDICIN OCH HÄLSOVETENSKAP Medicinska och farmaceutiska grundvetenskaper Cell- och molekylärbiologi hsv//swe
MEDICAL AND HEALTH SCIENCES Basic Medicine Cell and Molecular Biology hsv//eng
NATURVETENSKAP Data- och informationsvetenskap Bioinformatik hsv//swe
NATURAL SCIENCES Computer and Information Sciences Bioinformatics hsv//eng
glioblastoma
invasion
image analysis
neural networks
Mathematics with specialization in Applied Mathematics
Matematik med inriktning mot tillämpad matematik
Oncology
Onkologi
Molekylär medicin
Molecular Medicine

Biuppslag (personer, institutioner, konferenser, titlar ...)

Nelander, Sven,ProfessorUppsala universitet,Science for Life Laboratory, SciLifeLab,Neuroonkologi(Swepub:uu)svene843 (preses)
Jörnsten, Rebecka,ProfessorChalmers University of Technology (preses)
Gerlee, Philip,ProfessorChalmers University of Technology & University of Gothenburg (preses)
Swanson, Kristin,ProfessorDirector of Mathematical NeuroOncology Laboratory, Mayo Clinic (opponent)
Uppsala universitetNeuroonkologi (creator_code:org_t)

Internetlänk

https://uu-se.zoom.us/j/63002096609Online defence on Zoom
https://uu.diva-portal.org/smash/get/diva2:1613835/FULLTEXT01.pdffulltext
https://uu.diva-portal.org/smash/get/diva2:1613835/PREVIEW01.jpgpreview image
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-459422

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Av författaren/redakt...: Rosén, Emil; Nelander, Sven, ...; Jörnsten, Rebeck ...; Gerlee, Philip, ...; Swanson, Kristin ...

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MEDICIN OCH HÄLSOVETENSKAP: MEDICIN OCH HÄLS ...; och Klinisk medicin; och Cancer och onkol ...

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