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Investigating the mechanisms underlying cancer cell plasticity in metastasis


Barts and The London School of Medicine and Dentistry

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Dr M Efremova , Dr T Graham No more applications being accepted Funded PhD Project (European/UK Students Only)

About the Project

Cancer cells maintain an intrinsic plasticity that allows them to reversibly change their phenotype in response to microenvironmental signals and switch between cellular states. Single-cell studies have revealed extensive transcriptional heterogeneity along with lineage mixing and plasticity in several cancer types. In metastases, cells co-opt developmental programs and are reset to an even more primitive differentiation state, mimicking organ formation to reinitiate growth in a new location. 

Dr Efremova’s Lab at the Barts Cancer Institute investigates the cellular and molecular mechanisms that promote cancer cell plasticity and adaptation in the metastatic niche. Specifically, we combine computational and experimental approaches including single-cell multi-omics, spatial imaging-based data and computational methods to reconstruct the cellular environment of primary and metastatic cancers and dissect the regulatory and cell-cell communication networks that promote metastasis and therapy resistance, focusing on pancreatic and colorectal cancer.

This project will focus on integration of single-cell multi-omics and spatial imaging-based data from patient samples to interrogate the mechanisms underlying plasticity in metastasis. The position offers an opportunity to apply and develop computational methods to study plasticity and the cellular communication and gene regulatory networks that promote it. The student will be integrated in a multidisciplinary community and work closely with clinicians, biologists and computational scientists. 

Academic Entry Requirements

These studentships are open to graduates with:

  • a 2:1 or 1st degree in a relevant quantitative discipline (e.g. Computational Biology, Bioinformatics, Genetics, Biotechnology, Engineering or Mathematics).

The successful candidate should have strong background in the field of genomics and high-throughput data analysis. Experience with developing statistical and machine learning methods for analysis of genomics data would be a plus.


Funding Notes

The studentship includes the following funding for 3 years:
- A tax-free annual stipend of £21,000
- Tuition fees at the Home/EU rate*
- Project consumables
*If you are considered an overseas student for fee purposes, you are welcome to apply for this studentship, however you will be required to cover the difference in tuition fees.
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