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Integrated multi-omic analysis of non-coding (epi)genomic drivers as biomarkers for detection, progression and treatment response of patients with breast cancer


   Cancer Studies

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  Dr Ricky Joshi  No more applications being accepted  Funded PhD Project (UK Students Only)

About the Project

According to the World Health Organisation, Breast Cancer (BC) is the leading cause of cancer deaths in women worldwide. Stage IV BC has the worst overall survival rate with only 25% of patients surviving five years or more, compared to stages I to III (98%, 90% and 70% respectively). This strongly suggests that early detection and treatment provide the best outcome for women, but accurately detecting and diagnosing BC remains a significant challenge. From a molecular perspective, BC is highly heterogeneous comprising multiple subtypes that correspond to abnormal gene expression profiles. These transcriptomic changes lead to differential clinical behaviours and treatment response. The advent of next generation sequencing technologies has enabled an improved perspective of the BC molecular environment, however most studies are single-omic with a limited view of overall tumour complexity. 

The role of CNVs and coding mutations are well documented however, most genetic variants in BC are passenger mutations with unknown function and located in non-coding regions of the genome. DNA methylation is an epigenetic regulator of gene expression and in cancer, hypermethylation at key tumour suppressor promoters result in gene silencing. DNA methylation profiles serve as clinical biomarkers for stage-stratification, prognosis and treatment response. However, few genes have been extensively assayed in BC presenting an interesting opportunity for analysis. 

This computational project aims to examine the combined role of epigenetic and genetic abnormalities at regulatory regions that affect the expression of genes habitually mutated in cancer with malignant consequences. This multi-omics analysis will provide a holistic view of the genome and highlight hidden insights into pathogenic variation and biomarker detection. Integrated clinical and multi-omics data from >1000 BC samples will be evaluated for epigenetic aberrations, absent in controls, and affecting key regulatory gene networks at different stages of neoplastic growth and targets for anti-cancer drugs, such as cellular metabolism (Enasidenib), DNA repair (Olaparib) and chromatin-remodellers (Vorinostat). The same regions will be targeted for genetic analyses to detect pathogenic variations such as eQTLs, microsatellite/repeat expansions and SVs. Genes that affect tumour evolution and patient survival will be determined. Finally, In silico analyses will be carried out to identify biomarkers for early BC detection and sensitivity to standard-of-care drugs and actionable drug targets using knowledge-based computational tools.

The main research aims are:

1. Identification of epimutations at cis-acting regulatory regions that affect clinically relevant genes in patients with breast cancer at defined stages of tumour growth.

2. Integration of genetic mutation profiling at regulatory elements of clinically relevant genes to establish a holistic view of the molecular mechanisms underlying breast cancer evolution.

3. Analysis of the clinical impact of these (epi)genomic aberrations for improved early detection, stage-classification, treatment response and as targets for anti-cancer therapy.

The student will generate a body of data relevant to the development of improved therapeutics for BC patients. The host group encourages active collaborations, and we have future project plans for the development of biomarkers and drug targets. The student will play a key role in advancing this exciting research area and fostering academic and industrial collaborations. Publication and dissemination of results is central to our endeavours and the student will have the opportunity to present results at both national and international research meetings. Previous experience with programming languages such as UNIX, R and Python is essential and prior knowledge of breast cancer is desirable. Thorough training will be provided in areas such as cancer biology, biostatistics and advanced bioinformatics as well as opportunities to enhance the student’s academic portfolio with teaching and mentoring engagements.

Entry requirements

Applicants are required to hold/or expect to obtain a UK Bachelor's Degree 2:1 or better (or overseas equivalent) in a relevant subject (Genetics, Biomedicine, Biotechnology, Bioinformatics).

Master’s degree in Bioinformatics is desired but not essential. 

The University of Leicester English language requirements apply where applicable.

Informal enquiries

Project enquiries: Dr Ricky Joshi ([Email Address Removed])

How to apply

To submit your application, please follow the guidance at: https://le.ac.uk/study/research-degrees/funded-opportunities/cls-lcrc-joshi

Include with your application:

  • CV
  • Personal statement explaining your interest in the project, your experience and why we should consider you
  • Degree Certificates and Transcripts of study already completed and if possible transcript to date of study currently being undertaken
  • Evidence of English language proficiency if applicable
  • In the reference section please enter the contact details of your two academic referees in the boxes provided or upload letters of reference if already available.
  • In the funding section please specify that you wish to be considered for the CLS Joshi studentship 
  • In the research proposal section please provide the name of the project supervisor and the project title (a proposal is not required)

Funding Notes

This 3.5-year PhD Studentship provides:
UK tuition fee waiver
Annual Stipend at standard UKRI rates (£16,062 for 2022/23)

References

1. Ortiz-Barahona et al. Using DNA Methylation Profiling in Translational Oncology. Semin Cancer Biol. 2020 Dec 19;S1044-579X(20)30271-6.
2. Barbosa et al Identification of rare de novo epigenetic variations in congenital disorders. Nat Commun. 2018 May 25;9(1):2064.
3. Gómez-Cebrián et al. Multi-Omic Approaches to Breast Cancer Metabolic Phenotyping: Applications in Diagnosis, Prognosis, and the Development of Novel treatments. Cancers 2021, 13, 4544.
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