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Precision Medicine DTP - Detection of novel prognostic macrophage-associated genes in cancer

  • Full or part time
    Dr D Oyarzun
    Dr Binzhi Qian
  • Application Deadline
    Wednesday, January 08, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Background

The grand goal of precision medicine is to deliver the right treatment to the right person at the right time. Thanks to tremendous progress in omics technologies we can now access ever more detailed maps of the molecular make-up of patients. Such technologies offer clinicians a new lens to characterize disease and stratify patients across stages of disease progression. This can ultimately pave the way for patient-specific therapeutic strategies that exploit such detailed molecular screens.

In this project, the student will advanced computational modelling and validation experiments to detect prognostic genes in macrophages present in tumours. Several types of human carcinoma display a link between tumour-infiltrating immune cells and prognosis. Therefore a key step to intervene the disease mechanisms is to understand

the interactions between cancer stromal cells. Because immune infiltrates contain several cell types that interact with each other, untangling their individual roles is challenging and experimentally laborious. Single cell transcriptomics is a popular approach to study the composition of these infiltrates, but this technique usually requires stressing cells, which changes their gene expression profiles and can potentially damage or even kill them. A useful alternative is to infer abundances of immune cells from bulk tumour expression data where cell and tissue integrity is better maintained before analysis, using publicly available transcriptomic data from patient samples.

Aims

This general aim of the project is to identify novel immune markers and pathways with prognostic implications in breast and prostate cancer. We will do this through identification of stroma-tumor crosstalk using bioinformatics approaches and efficiently map the interactions between cell types in immune infiltrates. The successful candidate will use patient data to:

1) Implement different computational tumour/stroma purification approaches. 2) Detect macrophage-associated genetic markers. 2) Perform in vitro experiments to validate the findings. 3) Delineate the stroma-tumour crosstalk and compare across different tumour types. 4) Disseminate the results and algorithms developed via an online software tool.

The datasets will be mined from publicly available datasets, including TCGA-PRAD and TCGA-BRCA [1] for prostate and breast cancer, respectively. Patients will be stratified based on survival measures. Correlation analysis using ImSig macrophage genes [2] will be used to delineate macrophage-associated new genes. Tumour cell gene expression will be purified from bulk expression using computational estimation algorithms [3], together with unsupervised machine learning methods applied to a gene-gene network built with from suitable similarity expression scores. Various network clustering techniques will be employed to detect clusters of nodes that undergo changes in expression patterns. Algorithms including CCCExplorer [4] will be used to identify interactions between cell types in bulk tumour.

Training Outcomes

The student will gain extensive expertise on computational methods, biomedical data science, and in vitro techniques. The student will be trained in a multidisciplinary environment with access to both computational and experimental infrastructure and expertise available from both supervisors. In particular, the student will gain skills in:

1) Multivariate statistics, data science and machine learning for biomedical data.

2) Network analysis and processing, scientific computing, algorithm testing and deployment.

4) Cell culture, expression analysis (qRT-PCR, western blotting), proliferation and apoptosis assays.

The project will expose the student to the challenges of noisy, high-dimensional biological datasets and offer a broad range of skills that will enhance their future employment opportunities in both industry and academia.

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. For those applying to a University of Glasgow project, your application along with any supporting documents will be shared with University of Glasgow.

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 must contact the primary supervisor prior to making your application. Additional information on the application process is available from the link above.

For more information about Precision Medicine visit:
http://www.ed.ac.uk/usher/precision-medicine

Funding Notes

Start: September 2020

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 qualification, in an appropriate science/technology area.
Residence criteria: The MRC DTP in Precision Medicine grant provides tuition fees and stipend of at least £15,009 (RCUK rate 2019/20) for UK and EU nationals that meet all required eligibility criteria.

Full eligibility details are available: View Website

Enquiries regarding programme:

References

[1] National Cancer Institute, GDC Data Portal, https://portal.gdc.cancer.gov/

[2] Nirmal et al (2018), Immune Cell Gene Signatures for Profiling the Microenvironment of Solid Tumors, Cancer Immunol Res. 2018 6(11):1388-1400.

[3] Schelker et al (2017), Estimation of immune cell content in tumour tissue using single-cell RNA-seq data. Nature Communications, 8: 2032.

[4] Choi et al (2015), Transcriptome analysis of individual stromal cell populations identifies stroma-tumor crosstalk in mouse lung cancer model. Cell Reports 10(7):1187- 201

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