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  1. Article ; Online: Ex-vivo drug screening of surgically resected glioma stem cells to replace murine avatars and provide personalise cancer therapy for glioblastoma patients [version 1; peer review

    Hannah Gagg / Greg Wells / Sarah J. Danson / Spencer J. Collis / Juha Rantala / Ola Rominiyi / Callum Jones / Thomas Helleday / Katie N. Myers / Connor McGarrity-Cottrell / Sophie T. Williams / Samantha Conroy

    F1000Research, Vol

    2 approved]

    2023  Volume 12

    Abstract: With diminishing returns and high clinical failure rates from traditional preclinical and animal-based drug discovery strategies, more emphasis is being placed on alternative drug discovery platforms. Ex vivo approaches represent a departure from both ... ...

    Abstract With diminishing returns and high clinical failure rates from traditional preclinical and animal-based drug discovery strategies, more emphasis is being placed on alternative drug discovery platforms. Ex vivo approaches represent a departure from both more traditional preclinical animal-based models and clinical-based strategies and aim to address intra-tumoural and inter-patient variability at an earlier stage of drug discovery. Additionally, these approaches could also offer precise treatment stratification for patients within a week of tumour resection in order to direct tailored therapy. One tumour group that could significantly benefit from such ex vivo approaches are high-grade gliomas, which exhibit extensive heterogeneity, cellular plasticity and therapy-resistant glioma stem cell (GSC) niches. Historic use of murine-based preclinical models for these tumours has largely failed to generate new therapies, resulting in relatively stagnant and unacceptable survival rates of around 12-15 months post-diagnosis over the last 50 years. The near universal use of DNA damaging chemoradiotherapy after surgical resection within standard-of-care (SoC) therapy regimens provides an opportunity to improve current treatments if we can identify efficient drug combinations in preclinical models that better reflect the complex inter-/intra-tumour heterogeneity, GSC plasticity and inherent DNA damage resistance mechanisms. We have therefore developed and optimised a high-throughput ex vivo drug screening platform; GliExP, which maintains GSC populations using immediately dissociated fresh surgical tissue. As a proof-of-concept for GliExP, we have optimised SoC therapy responses and screened 30+ small molecule therapeutics and preclinical compounds against tumours from 18 different patients, including multi-region spatial heterogeneity sampling from several individual tumours. Our data therefore provides a strong basis to build upon GliExP to incorporate combination-based oncology therapeutics in tandem with SoC therapies as ...
    Keywords Glioblastoma ; ex vivo drug screening ; functional precision medicine ; glioma stem cells ; cancer therapeutics ; GliExP ; eng ; Medicine ; R ; Science ; Q
    Subject code 610
    Language English
    Publishing date 2023-08-01T00:00:00Z
    Publisher F1000 Research Ltd
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer

    Haonan Lu / Mubarik Arshad / Andrew Thornton / Giacomo Avesani / Paula Cunnea / Ed Curry / Fahdi Kanavati / Jack Liang / Katherine Nixon / Sophie T. Williams / Mona Ali Hassan / David D. L. Bowtell / Hani Gabra / Christina Fotopoulou / Andrea Rockall / Eric O. Aboagye

    Nature Communications, Vol 10, Iss 1, Pp 1-

    2019  Volume 11

    Abstract: Radiomics—the quantification of features within tumor images—has shown prognostic potential in cancer. Here, the authors use a machine learning approach to develop a radiomic-based small set of descriptors to predict ovarian cancer patient survival based ...

    Abstract Radiomics—the quantification of features within tumor images—has shown prognostic potential in cancer. Here, the authors use a machine learning approach to develop a radiomic-based small set of descriptors to predict ovarian cancer patient survival based on CT scans acquired pre-operatively in 364 patients.
    Keywords Science ; Q
    Language English
    Publishing date 2019-02-01T00:00:00Z
    Publisher Nature Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer

    Haonan Lu / Mubarik Arshad / Andrew Thornton / Giacomo Avesani / Paula Cunnea / Ed Curry / Fahdi Kanavati / Jack Liang / Katherine Nixon / Sophie T. Williams / Mona Ali Hassan / David D. L. Bowtell / Hani Gabra / Christina Fotopoulou / Andrea Rockall / Eric O. Aboagye

    Nature Communications, Vol 10, Iss 1, Pp 1-

    2019  Volume 11

    Abstract: Radiomics—the quantification of features within tumor images—has shown prognostic potential in cancer. Here, the authors use a machine learning approach to develop a radiomic-based small set of descriptors to predict ovarian cancer patient survival based ...

    Abstract Radiomics—the quantification of features within tumor images—has shown prognostic potential in cancer. Here, the authors use a machine learning approach to develop a radiomic-based small set of descriptors to predict ovarian cancer patient survival based on CT scans acquired pre-operatively in 364 patients.
    Keywords Science ; Q
    Language English
    Publishing date 2019-02-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Imaging timing after glioblastoma surgery (INTERVAL-GB)

    Oliver Burton / Julie Woodfield / Daniel Richardson / Giles Critchley / Angelos Kolias / Thomas Santarius / Lewis Thorne / Peter Whitfield / Ola Rominiyi / Bharti Kewlani / Harsh Bhatt / Ahmed Ahmed / Michael O’Sullivan / Rasheed Zakaria / Michael D Jenkinson / Gerard Thompson / Paul M Brennan / Rory Piper / Seong Hoon Lee /
    Neil Barua / Engelbert Mthunzi / Sara Venturini / Daniel M Fountain / Najma Ahmed / Stephen J Price / Colin Watts / Michael T C Poon / Stuart Smith / Anand Pandit / Ryan K Mathew / Soham Bandyopadhyay / Rosa Sun / Setthasorn Zhi Yang Ooi / Victoria Wykes / William Bolton / Abdullah Egiz / Samantha J Mills / Babar Vaqas / Natalie Simon / Puneet Plaha / Georgios Solomou / Aswin Chari / Grainne McKenna / Melissa Gough / Simon Lammy / Sophie T Williams / Isabelle Williams / Andrew J Martin / Sam Hodgson / Robert Spencer

    BMJ Open, Vol 12, Iss

    protocol for a UK and Ireland, multicentre retrospective cohort study

    2022  Volume 9

    Abstract: Introduction Glioblastoma is the most common malignant primary brain tumour with a median overall survival of 12–15 months (range 6–17 months), even with maximal treatment involving debulking neurosurgery and adjuvant concomitant chemoradiotherapy. The ... ...

    Abstract Introduction Glioblastoma is the most common malignant primary brain tumour with a median overall survival of 12–15 months (range 6–17 months), even with maximal treatment involving debulking neurosurgery and adjuvant concomitant chemoradiotherapy. The use of postoperative imaging to detect progression is of high importance to clinicians and patients, but currently, the optimal follow-up schedule is yet to be defined. It is also unclear how adhering to National Institute for Health and Care Excellence (NICE) guidelines—which are based on general consensus rather than evidence—affects patient outcomes such as progression-free and overall survival. The primary aim of this study is to assess MRI monitoring practice after surgery for glioblastoma, and to evaluate its association with patient outcomes.Methods and analysis ImagiNg Timing aftER surgery for glioblastoma: an eVALuation of practice in Great Britain and Ireland is a retrospective multicentre study that will include 450 patients with an operated glioblastoma, treated with any adjuvant therapy regimen in the UK and Ireland. Adult patients ≥18 years diagnosed with glioblastoma and undergoing surgery between 1 August 2018 and 1 February 2019 will be included. Clinical and radiological scanning data will be collected until the date of death or date of last known follow-up. Anonymised data will be uploaded to an online Castor database. Adherence to NICE guidelines and the effect of being concordant with NICE guidelines will be identified using descriptive statistics and Kaplan-Meier survival analysis.Ethics and dissemination Each participating centre is required to gain local institutional approval for data collection and sharing. Formal ethical approval is not required since this is a service evaluation. Results of the study will be reported through peer-reviewed presentations and articles, and will be disseminated to participating centres, patients and the public.
    Keywords Medicine ; R
    Subject code 616
    Language English
    Publishing date 2022-09-01T00:00:00Z
    Publisher BMJ Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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