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Precision Medicine DTP - Accurate methods for rare variant calling in low-coverage cancer exome sequencing data

  • Full or part time

    Dr G Sanguinetti
  • 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

High-throughput sequencing technologies enabled the characterization of the genomic landscape of many tumours (1,2). This has led to the discovery of many genes that, when mutated, support tumorigenesis, metastasis and affect outcome; these genes include the well-known tumour suppressor TP53 and the oncogenes KRAS and MYC.

However, during the course of the disease, cancer cells continuously acquire mutations, leading to subpopulations of tumour cells with different molecular phenotypes and resistance to treatment. This is particularly true for blood cancers, such as multiple myeloma, where very small sub-populations resist to treatment and usually cause a relapse; this particular stage of the malignancy is called minimal residual disease (MRD).

There is little knowledge about the mutations acquired by cancer cells in multiple myeloma MRD; this is mostly due to the small number of cells (~500) that persist in the patients, which ultimately are difficult to select and sequence (3). Custom library preparation protocols have been developed to make exome sequencing possible, but they usually introduce artefacts that make detection of rare variants impossible with standard variant calling methods.

Aims

The aim is to develop new variant calling methods for detecting rare variants in blood cancers from low-coverage exome sequencing data, by accurately modelling biases introduced by experimental protocols. In collaboration with the Department of Hemato-oncology at the University of Ostrava, we built a cohort of multiple myeloma MRD patients (60 patients by 2021), and already generated exome sequencing data for 24 of them. We aim at analysing this data to identify targets for treatment.

Training outcomes

The student will develop both generative (hierarchical Bayesian models) and deep learning approaches (CNN, autoencoders). He/she will also learn how to write reproducible sequencing analyses pipelines and research software. We expect the student to build a competitive profile in machine learning and cancer genomics, which ultimately will support a career in academia or industry.

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. Stracquadanio, G., Wang, X., Wallace, M. D., Grawenda, A. M., Zhang, P., Hewitt, J., … Bond, G. L. (2016). The importance of p53 pathway genetics in inherited and somatic cancer genomes. Nature Reviews Cancer, 16(4), 251–265. doi:10.1038/nrc.2016.15

2. Fanfani, V., Citi, L., Harris, A. L., Pezzella, F., & Stracquadanio, G. (2019). Gene-level heritability analysis explains the polygenic architecture of cancer. doi:10.1101/599753

3. Martina Zátopková, Tereza Sevcikova, …, Giovanni Stracquadanio and Roman Hajek (2018). Whole Exome Sequencing of Residual Disease in Multiple Myeloma: Searching for Novel Therapeutic Targets. Blood. https://doi.org/10.1182/blood-2018-99-112611

How good is research at University of Edinburgh in Biological Sciences?

FTE Category A staff submitted: 109.70

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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