Characterizing the anti-angiogenic resistance potential of cross-family PDGF:VEGFR2 interactions in glioblastoma

Glioblastoma is the most common and lethal primary brain tumor in adults. Anti-angiogenic treatment has shown positive results in improving GBM patient survival. Bevacizumab, a drug that sequesters the angiogenic growth factor, VEGF-A, has been approved by the FDA for use in GBM patients and moderately improves patient survival; however, these GBM bevacizumab responders eventually acquire bevacizumab resistance. New approaches must be pursued to understand anti-VEGF resistance in GBM. The involvement of other signaling axes potentially explains anti-VEGF failings. PDGF:VEGFR2 interactions were recently discovered and prior computational modeling predicts that PDGF:VEGFR2 interactions could constitute a large proportion of VEGFR2-ligand complexes in certain physiological and breast cancer conditions. Under PDGF:VEGFR2 cross-family signaling, upregulated PDGFs would directly activate VEGFRs and lead to anti-VEGF therapy resistance. We aimed to use computational tools to assess how strongly PDGFs can affect VEGFR occupancy in GBM, and to assess the potential of PDGF:VEGFR2 interactions as resistance mechanisms for anti-VEGF treatment in GBM. However, the GBM ligand and receptor parameter space has not been established such that a GBM condition can be computationally modelled, and an analysis platform has not yet been developed to assess the ability of cross-family PDGF ligands to control VEGFR-occupancy in relation to canonical VEGF-family ligands in pathology. These challenges have been addressed in two ways: (1) I created and analyze a toolbox of computational models to compare PDGF:VEGFR2 interactions with canonical VEGF:VEGFR interactions across several mechanistic differences and assumptions in angiogenic signaling, and (2) I performed an in-depth meta-analysis of GBM growth factors, including: VEGF-A, Ang-2, PDGF-BB, FGF-2, EGF, PlGF, and IGF in order to better characterize the GBM growth factor landscape, and to consolidate concentration data for the ligands aiding tumor growth and angiogenesis.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Bioengineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Castleberry, Colin

Contributors dc:contributor

• Imoukhuede, Princess I
• Amos, Jennifer
• Jensen, Paul
• Dobrucki, Wawrzyniec
• Chen, Jie

• RTK
• VEGF
• PDGF
• cross-family signaling
• cell signaling
• cancer
• angiogenesis
• computational modeling
• mass action kinetics
• global sensitivity analysis
• structural alignment
• meta-analysis
• glioblastoma
• growth factors

Statement dc:rights

• Copyright by Colin Castleberry 2022. All Rights Reserved.

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/115508

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F115508