Background: A tremendous amount of cancer ‘-omics’ data have been generated with the intention to stratify patients for personalized diagnosis and treatment. However, almost all studies have been using whole tumour tissues that often contain large amount of tumour stromal cells, which greatly complicates data interpretation. It has now been accepted that tumour stromal cells play critical roles in cancer metastasis and therapy response and therefore is imperative to understand the omics data of cancer stromal cells to fully understand the disease mechanism. It is challenging and expensive to isolate different stromal cell types from patient samples in omics studies. Alternatively, transcriptome signature of tumour associated immune cells have been extrapolated from cancer transcriptome data based on immune cell specific markers using bioinformatics approaches 1. This illustrated the possibility to obtain omics data of tumour-associated stromal cells to fully understand the disease mechanism and achieve personalized cancer therapy. However, current approach has been relying on limited number of markers, which induces bias and is difficult to verify with functional assays.
Clinically relevant in vivo models have led to significant breakthroughs in tumour stroma study. Previous studies of the primary supervisor indicated that macrophages, a type of innate immune cells, play critical roles in promoting cancer metastasis and chemotherapy resistance 2,3. This is supplemented by ongoing preclinical models 4 of treatment resistant metastatic prostate cancer as well as clinical resources of primary and metastatic tumours in Prof. Leung’s laboratory (co-supervisor). While macrophages are the major hematopoietic infiltration of prostate cancer, mesenchymal cells compose the major non-hematopoietic stroma. Transcriptome data has been generated in tumour-associated macrophages and mesenchymal stromal cells derived from a novel preclinical models of metastatic hormone refractory prostate cancer.
Aims: The current project aims to use novel computational approaches developed through collaboration between Prof. Tom Freeman and Dr. Andy Sims (co-supervisor) to integrate the transcriptome data of tumour stromal cells generated from in vivo preclinical models and use them to extrapolate from public database (TCGA and SU2C, already curated in house) the key gene networks associated with prostate cancer metastasis and hormone therapy resistance (Johnson et al. (2018) Cancer Immunology Research – accepted, in press). Key markers will be further validated at protein level in situ within prostate cancer biopsies using immunofluorescent staining in archived prostate cancer patient samples collected in Vancouver Prostate Centre and Glasgow (available in the laboratory of Prof. Leung and Wiklund). Comprehensive multivariate analysis using a novel analysis method ‘survivALL’ (developed by the Sims lab, Pearce et.al (2018) BMC Cancer – minor revisions) will be performed to determine how stromal heterogeneity may predict disease stage and therapy response in comparison with known predictors.
In vitro and in vivo functional assays have been established to test key stromal gene networks in both Dr. Qian and Prof. Leung’s laboratories. These tests will be used to test the function of stromal genes identified in silicon to provide novel insights of the disease mechanism and identify potential therapeutic targets. Together, this study will significantly improve our understanding of stroma heterogeneity of prostate cancer, but also advance diagnostic tools enabling the stratification of patients for personalised treatment to effectively manage the disease.
Training outcomes: Lack of understanding of tumour stroma has become a missing piece in delivering precision medicine. The proposed project will identify key transcriptome and epigenetic signatures of stromal cells associated with prostate cancer metastasis and therapy resistance that are the major challenges in clinic. The supervisor team offers complimentary skills in precision medicine, tumour microenvironment, translational and clinical research, bioinformatics and computational biology. The PhD candidate will obtain robust training to become an expert in combining both quantitative skills and in vivo techniques to maximize the impact of multidisciplinary research and precision medicine.
This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.
All applications should be made via the University of Edinburgh, irrespective of project location: http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919
Please note, you must apply to one of the projects and you should contact the primary supervisor prior to making your application. Additional information on the application process if available from the link above.
For more information about Precision Medicine visit: http://www.ed.ac.uk/usher/precision-medicine
Start: September 2019
Qualifications criteria: Applicants applying for a MRC DTP in Precision Medicine studentship must have obtained, or will soon obtain, a first or upper-second class UK honours degree or equivalent non-UK qualifications, in an appropriate science/technology area.
Residence criteria: The MRC DTP in Precision Medicine grant provides tuition fees and stipend of at least £14,777 (RCUK rate 2018/19) for UK and EU nationals that meet all required eligibility criteria.
Full eligibility details are available: View Website
Enquiries regarding programme: [email protected]
1. Ogino, S., Galon, J., Fuchs, C. S. & Dranoff, G. Cancer immunology—analysis of host and tumor factors for personalized medicine. Nat. Rev. Clin. Oncol. 8, 711–719 (2011).
2. Qian, B.-Z. et al. CCL2 recruits inflammatory monocytes to facilitate breast-tumour metastasis. Nature 475, 222–225 (2011).
3. Hughes, R. et al. Perivascular M2 Macrophages Stimulate Tumor Relapse after Chemotherapy. Cancer Res. 75, 3479–3491 (2015).
4. Patel, R. et al. Sprouty2, PTEN, and PP2A interact to regulate prostate cancer progression. J. Clin. Invest. 123, 1157–75 (2013).