Determining biological factors associated with chemotherapy response in glioblastoma patients

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Copyright: Sevim, Hatice
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Abstract
Glioblastomas are characterised by remarkably high resistance to chemotherapy. The majority of patients are unresponsive to standard therapy including concurrent chemo/radiotherapy and adjuvant chemotherapy with TMZ. Investigation of potential contributors of chemoresistance is needed so that patients who do not respond can avoid futile chemotherapy and be better matched to something that might work. The aim of this thesis was to investigate factors associated with chemotherapy resistance. The expression of single biomarkers were examined, namely Topoisomerase II alpha (TopoIIα), Neuro/glial2 (NG2) and MGMT and their association with response to etoposide (TopoIIα-inhibitor), cilengitide (ανβ3 and ανβ5 integrin-inhibitor) and temozolomide, respectively. We found that the response to therapy in each case was much more complex than one drug and one biomarker. Rather, resistance is defined by the interplay of multiple proteins and genes. In response, I took a global proteomic approach to discover novel biomarkers which might be associated with response and survival outcome of the patients. Data collected using chemically-modified ProteinChip surfaces revealed significantly differentially expressed peaks between non-responders vs. responders and short-term survivors (STS) vs. long-term survivors (LTS). Biomarker panels including multiple proteins were generated. A panel of three proteins (combined predictability = 100%) overexpressed in non-responders were further identified as DEFA3, MIF and S100A8. MIF protein was up-regulated both in non-responders and STS. The expression of these biomarkers was also examined in a validation cohort (n = 66) using IHC which we are not yet able to extract entire data. All three proteins serve as components of pro-inflammatory and inflammatory tumour microenvironment and have important roles in pro-inflammatory and pro-tumorigenic processes. These features are not taken into consideration in the majority of laboratory-based models which has limited predictive biomarker discovery. Overall, the findings of this thesis showed the complex nature of the response mechanisms. While it is unlikely for a single biomarker to be highly effective for detecting response and survival outcome for patients, our data demonstrated an alternative and efficient approach to predict response and survival outcome of the glioblastoma patients using a novel combination of multiple biomarkers.
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Author(s)
Sevim, Hatice
Supervisor(s)
McDonald, Kerrie Leanne
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Publication Year
2012
Resource Type
Thesis
Degree Type
PhD Doctorate
UNSW Faculty
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