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University researcher(s)

    High throughput screening of patient specific tumour cells: towards personalised treatment in recurrent glioblastoma multiforme

    Taylor, Jessica Tanya

    [Thesis]. Manchester, UK: The University of Manchester; 2019.

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    Abstract

    Glioblastoma Multiforme is the most common adult primary brain tumour, with median survival rates of around 15 months. The aggressive and heterogeneous nature of this disease means that despite multimodal treatment incorporating maximal surgical resection with radiotherapy and chemotherapy, the tumour inevitably recurs. Second-line therapeutic options are limited and so to address the need for novel options at recurrence, this project aimed to combine phenotypic screening with drug repurposing in patient-derived glioma stem-like cells to rationally select second-line chemotherapeutics based on the sensitivity of the patient’s tumour cells. The first part of this project aimed to validate a proof-of-principle high content screen in vivo to elucidate whether cell culture format had an effect on drug-sensitivity. We determined through the evaluation of a sub-set of drugs that suggested three-dimensional culture of glioma stem-like cells as neurospheres correlated better with our in vivo results when compared with traditional two-dimensional culture. The second part of this project then built on the learnings taken from the high content screen and developed a reproducible, 384-well multicellular spheroid high throughput screen that identified actinomycin-D to be a potent cytotoxic agent in all of our patient-derived lines. We subsequently validated this effect in a patient-derived orthotopic xenograft model of recurrent glioblastoma, demonstrating that this drug may also have the potential to specifically target the stem-cell population of tumours through downregulation of SOX2. Thirdly, based on literature that suggested a class of amphiphilic cationic drugs, also known as Functional Inhibitors of Acid Sphingomyelinase (FIASMA), could specifically target cancer cells we incorporated loratadine, a typical FIASMA, into our high throughput screen. We demonstrate that loratadine induces glioma cell-specific death through lysosomal membrane permeabilisation, as well as validating the in vitro synergistic anti-tumour activity of afatinib and loratadine in our recurrent glioblastoma patient-derived orthotopic xenograft. Overall, this project has established a workflow that streamlines patient tumour processing, giving high quality drug sensitivity data on patient-derived cancer stem cells in a clinically actionable timeframe. The ability to predict synergistic combinations of chemotherapeutics in a short window of time is vital in glioblastoma, as many patients are found to have an increased resistance to therapy at recurrence. The screen developed in this project quickly enables the stratification of patients by phenotypic response. Through future screening of a larger cohort of patients to enrich the data set, we hope to strengthen its prognostic power and optimise this platform into an invaluable tool that can help clinicians as well as improving the outlook for many patients.

    Bibliographic metadata

    Type of resource:
    Content type:
    Form of thesis:
    Type of submission:
    Degree type:
    Doctor of Philosophy
    Degree programme:
    PhD Medicine 3yr (CMB)
    Publication date:
    Location:
    Manchester, UK
    Total pages:
    290
    Abstract:
    Glioblastoma Multiforme is the most common adult primary brain tumour, with median survival rates of around 15 months. The aggressive and heterogeneous nature of this disease means that despite multimodal treatment incorporating maximal surgical resection with radiotherapy and chemotherapy, the tumour inevitably recurs. Second-line therapeutic options are limited and so to address the need for novel options at recurrence, this project aimed to combine phenotypic screening with drug repurposing in patient-derived glioma stem-like cells to rationally select second-line chemotherapeutics based on the sensitivity of the patient’s tumour cells. The first part of this project aimed to validate a proof-of-principle high content screen in vivo to elucidate whether cell culture format had an effect on drug-sensitivity. We determined through the evaluation of a sub-set of drugs that suggested three-dimensional culture of glioma stem-like cells as neurospheres correlated better with our in vivo results when compared with traditional two-dimensional culture. The second part of this project then built on the learnings taken from the high content screen and developed a reproducible, 384-well multicellular spheroid high throughput screen that identified actinomycin-D to be a potent cytotoxic agent in all of our patient-derived lines. We subsequently validated this effect in a patient-derived orthotopic xenograft model of recurrent glioblastoma, demonstrating that this drug may also have the potential to specifically target the stem-cell population of tumours through downregulation of SOX2. Thirdly, based on literature that suggested a class of amphiphilic cationic drugs, also known as Functional Inhibitors of Acid Sphingomyelinase (FIASMA), could specifically target cancer cells we incorporated loratadine, a typical FIASMA, into our high throughput screen. We demonstrate that loratadine induces glioma cell-specific death through lysosomal membrane permeabilisation, as well as validating the in vitro synergistic anti-tumour activity of afatinib and loratadine in our recurrent glioblastoma patient-derived orthotopic xenograft. Overall, this project has established a workflow that streamlines patient tumour processing, giving high quality drug sensitivity data on patient-derived cancer stem cells in a clinically actionable timeframe. The ability to predict synergistic combinations of chemotherapeutics in a short window of time is vital in glioblastoma, as many patients are found to have an increased resistance to therapy at recurrence. The screen developed in this project quickly enables the stratification of patients by phenotypic response. Through future screening of a larger cohort of patients to enrich the data set, we hope to strengthen its prognostic power and optimise this platform into an invaluable tool that can help clinicians as well as improving the outlook for many patients.
    Thesis main supervisor(s):
    Thesis co-supervisor(s):
    Language:
    en

    Institutional metadata

    University researcher(s):
    Academic department(s):

    Record metadata

    Manchester eScholar ID:
    uk-ac-man-scw:319176
    Created by:
    Taylor, Jessica
    Created:
    10th April, 2019, 14:27:35
    Last modified by:
    Taylor, Jessica
    Last modified:
    1st May, 2020, 11:32:38

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