About the Project
Attrition is a major problem in anticancer drug development, with up to 95% of drugs tested in Phase I trials not reaching the market. Better pre-clinical models that accurately predict success in clinical trials are urgently needed, as well as the development of predictive and pharmacodynamic biomarkers.
It is being increasingly recognised that there is a need to re-capitulate inter- and intra-tumour heterogeneity in pre-clinical cancer models in order to more robustly assess interventional strategies. Current model systems fail to take into account the diverse histopathological and molecular characteristics of tumours, leading to sub-optimal efficacy readouts. Although Patient-Derived Xenograft (PDX) mouse models allow the clonal architecture of tumours to be preserved for high-throughput drug screens, they are limited by loss of the supporting tumour microenvironment (TME) over time. This is a particular problem for assessing the efficacy of agents that target the TME or tumour cell-TME interactions, particularly newly developed immune-oncology agents.
To overcome these problems, through work funded by a previous MRC Doctoral Training Grant, we have invested in the development of a patient-relevant tumour “explant” platform. In this approach, tumour samples are obtained fresh from surgery and cultured ex vivo as small tumour fragments. We have shown that tumour architecture and the TME are retained intact and drug responses can be assessed in situ. Our work has led to a recent publication that includes our MRC-funded PhD student as first author1. In this study, we derived Non-Small Cell Lung Cancer (NSCLC) explants and undertook a proof-of-principle study with the standard-of-care therapeutic cisplatin, demonstrating that drug responses in the ex vivo explant platform are predictive of patient responses in vivo. We also used the platform to monitor clinically-relevant biomarkers.
The aim of the new studentship project will be to further develop the platform and particularly incorporate the use of digital pathology for evaluating tumour and stroma responses to a range of anticancer agents.
Digital Pathology is a key digital technology that is revolutionising diagnostic medicine2. It is an image-based platform enabled by computer technology that allows for conversion of images on glass to digital slides that can be analysed on a computer. In collaboration with the MRC Toxicology Unit, the Department of Cancer Studies at Leicester has invested in Hamamatsu digital scanning technologies, which have recently been upgraded to include multi-fluorescence capabilities. The student involved in this project will gain expertise in this technology and so will be trained in cutting-edge digital methods and their application to cancer diagnosis, development of new therapies and identification of biomarkers.
Digital image analysis platforms have recently been shown to be superior to manual biomarker scoring. Digital imaging also offers the facility for high-throughput multi-colour fluorescence imaging and analysis, and is particularly advantageous in providing in-depth analyses of immune-oncology-related endpoints. We have worked closely with Hamamatsu to incorporate a digitised readout of drug efficacy into our explant models using their high-throughput brightfield imaging system, and now aim for similarly robust endpoints allowing incorporation of multi-colour fluorescence imaging into the cancer explant model as part of this studentship project.
A collaboration with Cancer Research UK Therapeutic Discovery Labs is already operational with this organization already investing >£150K in explant technology at Leicester by providing postdoctoral research support, equipment and consumables. This is allowing exploitation of our cancer explant platform for evaluating patient-relevant responses to a range of their novel agents including immune-oncology drugs (Figure 1). During the course of this studentship project, we will work with the Discovery Labs, through Dr Tim Hammonds, to further understand how tumour cells as well as different cell types within the TME respond to anticancer drugs using multi-colour fluorescence staining and digital scanning. Key biomarker endpoints relevant to the particular drug under study will also be evaluated.
Drug responses in explants will be correlated to clinical data from patients in order to provide histopathology, recurrence, progression-free survival (PFS) and overall survival (OS) data. Integration of all drug, biomarker, molecular and clinical parameters will allow the student to gain expertise in cutting-edge digital pathology, biostatistics and bioinformatics support.
The “explant lab” is an ideal environment for training the student in a range of technologies. The team comprises two postdoctoral fellows, two technicians, and a tissue collection manager who have expertise in the various aspects associated with explant generation and evaluation and will be able to pass on their skills and knowledge to the PhD student. The team is overseen by both Professors Pritchard and MacFarlane, who have driven explant technology at Leicester and have long-standing expertise in cancer mechanisms and targeted therapies, along with Dr Tim Hammonds, a drug discovery expert. The team also benefits from close interactions with academic pathologist Dr John Le Quesne and bioinformatics/biostatistics experts as well as surgical colleagues within the University Hospitals of Leicester. The explant lab is supported by the infrastructure of the Leicester Experimental Cancer Medicine Centre (ECMC) as well as the Hope Clinical Trials Unit that delivers early-phase clinical studies and can provide advice on clinical trial design incorporating data from explant drug responses.
References
1. Karekla et al. (2017) Ex vivo explant cultures of Non-Small Cell Lung Carcinoma enable evaluation of primary tumour responses to anticancer therapy. Cancer Research 77: 2029-2039.
2. Stålhammar G, Fuentes Martinez N, Lippert M, Tobin NP, Mølholm I, Kis L, Rosin G, Rantalainen M, Pedersen L, Bergh J, Grunkin M, Hartman J (2016) Digital image analysis outperforms manual biomarker assessment in breast cancer. Modern Pathology 29: 318-329.
Please also see press recent release:
https://www2.le.ac.uk/offices/press/press-releases/2017/november/tumour-analysis-following-surgery-could-provide-breakthrough-in-predicting-how-well-cancer-patients-respond-to-drug-treatment