FREE Virtual Study Fair | 1 - 2 March | REGISTER NOW FREE Virtual Study Fair | 1 - 2 March | REGISTER NOW

Precision Oncology: Spatial transcriptomics of patient prostate tumour microenvironment to establish an atlas of cancer progression.


   Cardiff School of Engineering

This project is no longer listed on FindAPhD.com and may not be available.

Click here to search FindAPhD.com for PhD studentship opportunities
  Prof Aled Clayton, Dr Hywel Williams, Dr Kate Milward, Prof Emiliano Spezi  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

Most tumours are heterogeneous, consisting of distinct cell types, variably implicated in cancer progression. Conventionally, a single assay aiming to determine cellular transcriptome effectively results in a pool of gene expression profiles from multiple cell populations. This makes identifying the driver genes difficult and prevents us from understanding the biological mechanisms driving the cancer. Recently developed technologies have allowed for a spatially distributed characterization of cell gene expressions, which can closely map the evolution of cancer.

We are seeking a candidate with bioinformatics/computational background keen to pursue an interdisciplinary career, to work on the detailed genomic/transcriptomic profiling of cancer microenvironment and the development of novel therapeutic pathways in the emerging area of precision oncology and medicine.

Aims and objectives

Spatial Biology and its use in precision oncology

  1. Establish a workflow for the spatial characterisation of prostate microenvironment
  2. Genomic and phenotypic data integration
  3. Pattern identification in data
  4. Hypothesis target seeking hypoxia new biomarkers, across cell types and tumour infiltration
  5. Protein profiling and next-generation sequencing to evaluate hypotheses

Materials and methods

Spatial Biology is a novel cutting-edge approach which enables spatially resolved, digital characterisation of proteins or mRNA in tissue specimens, in a highly multiplexed assay. Combined with precise spatial information, this technology is leading to fundamental changes in how tissue samples are analysed within their 2D and 3D context. We use these tools to gain fundamental insights into the complex spatial architecture that is a hallmark of all tissues, in steady state, developmental and disease scenarios.

This technology is essential to examine cellular interactions, tissue heterogeneity, pathogenicity, and response to therapy – up to the single cell level.

Spatial patterns will be analysed with artificial intelligence pattern recognition tools and machine learning algorithms to establish quantitative measures of the various stages of cancer progression.

Anticipated results

We expect to understand the extent, location and molecular phenotypes of infiltrating cells within prostate cancer and other cancer tissue specimens, and the nature of the stromal compartment in general. These include interests at both primary and metastatic sites such as bone. A transcriptomic atlas of the tumour microenvironment and its responsiveness to anti-cancer drug treatments has the potential to establish a new range of cancer therapies.

Environment

The student will be performing research of international importance which will attract global exposure and collaborations that will benefit their future careers. Students will have the opportunity to disseminate their findings and ideas through conferences and high impact publications.

The candidate will be able to work across the Schools of Medicine, Engineering and Computer Science, thus benefiting from long established expertise in interdisciplinary bio-medical research and development. The candidate will be part of a cohort of seven PhD students, under the IDTH umbrella, which will establish a peer community environment.

Essential skills required: Good computational skills (particularly Python, Jupyter Notebook, Matlab)

Desirable skills required: Machine learning tools, understanding of artificial intelligence principles and applications 

Student skills development

The candidate will have the opportunity to learn state-of-the-art imaging technology, supported by image analytics. Complex tissue characterization will require the development of bespoke machine learning and AI frameworks. These tools will be tested from lab to the clinical diagnostic/therapeutic level, thus providing a solid understanding of patient-specific treatment pathway.

Bespoke training at post-graduate level will support students throughout their degree.

How to apply:

Complete the online Cardiff University Post-Graduate Application Form at  Medicine - Study - Cardiff University

When completing your personal statement please consider giving examples of your achievements in research related activities and examples of your achievements in non-research related activities. Describe why you have chosen this project and What do you hope to gain from doing a PhD with IPOCH?

Please quote funding reference IDHub-4 in your application and the project title Precision Oncology: Spatial transcriptomics of patient prostate tumour microenvironment to establish an atlas of cancer progression.

Applications should be received no later than Monday 30th January 2023.

Applicants will be selected for interview by Monday 6th February 2023.

Interviews held 13th – 17th February 2023.

Project starting date: April 2023 or June 2023

Eligibility:

Candidates should hold or expect to gain a first-class degree or a good 2.1 (or their equivalent) in Engineering/Computer Science/Bioengineering or a related subject. International students will also need to meet the English Language requirements of the programme. To be eligible for a full award a student must have no restrictions on how long they can stay in the UK.

EPSRC studentships are available to home and international students. International students will not be charged the fee difference between the UK and international rate. Applicants should satisfy the UKRI eligibility requirements. 

Cardiff University is committed to supporting and promoting equality and diversity and to creating an inclusive environment for all. We welcome applications from all members of the global community irrespective of age, disability, sex, gender identity, gender reassignment, marital or civil partnership status, pregnancy or maternity, race, religion or belief and sexual orientation.

We welcome applications for both full and part-time study and from candidates with non-traditional academic backgrounds. For further information about modes of study, please contact us.

Assessment:

Applicants are reminded to submit all relevant documents by the deadline. Incomplete applications will not be considered.

Short-listed applicants will be invited to interview. As part of the interview process, applicants will be asked to give a short presentation, answer a series of panel questions etc – as seen appropriate to the recruitment process.

Interviews are expected to take place remotely via Zoom/Teams with a possible second interview of shortlisted candidates in person.

Additional Information

All applications should be submitted via the online application portal, SIMS – [https://www.cardiff.ac.uk/study/postgraduate/funding/phd-studentships-and-projects].

Further details on the application process can be found in the “how to apply” page [https://www.cardiff.ac.uk/study/postgraduate/applying, with instructions for form completion at https://www.cardiff.ac.uk/study/postgraduate/applying/application-forms.

Online application portal is found at the School of Medicine page https://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/medicine

Along with the online application the candidate is asked to upload a covering letter, a CV, and two academic references. Transcripts of degrees and additional supportive documents can be provided at the interview stage.


Funding Notes

The studentship is for 3.5 years and covers tuition fees, an annual tax-free living stipend of £16,062 (subject to change) and includes access to a Research Training Support Grant (currently £4000).
PhD saved successfully
View saved PhDs