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Integrative computational analysis of molecular and clinical data to dissect heterogeneity of therapy response in sarcomas

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  • Full or part time
    Dr P Huang
    Dr M Cheang
    Dr A Sadanandam
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

About This PhD Project

Project Description

The Institute of Cancer Research, London, is one of the world’s most influential cancer research institutes. We are committed to attracting and developing the best minds in the world to join us in our mission—to make the discoveries that defeat cancer.

Integrative computational analysis of molecular and clinical data to dissect heterogeneity of therapy response in sarcomas
Project Description:
Advanced soft tissue sarcomas (STS) comprise a heterogeneous group of rare mesenchymal cancers where patients suffer from poor outcomes despite treatment with a repertoire of systemic agents. It has been shown that a subset of STS patients benefit from targeted agents including tyrosine kinase inhibitors such as pazopanib and cediranib. However, there are currently no established predictive biomarkers for such therapies in this disease. There is thus a clinical need for the development of novel predictive biomarkers to prospectively select STS patients most likely to benefit from targeted therapy while identifying those patients unlikely to respond to such treatments and who would be better served by alternative treatment.

This studentship will apply machine learning and statistical modelling strategies to identify predictive biomarkers and develop predictive algorithms for targeted therapy treatment based on molecular profiling data generated from multi-centre cohorts of STS patients who have been treated with various therapies including pazopanib, and the CASPS trial - a randomised placebo controlled Phase II trial of cediranib in a form of STS known as alveolar soft part sarcoma (ASPS).

The study will initially focus on work involving integrative analysis of RNA-seq and/or Nanostring-based transcriptomic and mass spectrometry-based proteomic data of clinical specimens from cutting-edge international and multi-centre studies. The goal is to develop de novo and/or validate signatures that can be implemented as predictive biomarkers for drug response. Additionally, the successful candidate will evaluate if dynamic alterations in circulating levels of cytokines and cfDNA at multiple time points during the course of drug treatment have utility in monitoring treatment response in STS patients. Discoveries from this project will provide the basis for new biomarker-guided clinical trials that will enable personalisation of sarcoma treatment to individual patients whilst providing important new insights into how this class of drugs work in the context of STS.

The project is composed of 4 aims.
Aim 1: Identification of transcriptional and proteomic signatures for prediction of targeted therapy response,

Aim 2: Evaluate if changes in the levels of circulating biomolecules (cytokines and circulating free DNA) over the course of treatment are useful in monitoring response to targeted therapy in STS.

Aim 3: Analysis of transcriptomic and proteomic data to identify candidate genes/proteins and signalling networks that are associated with intrinsic and acquired targeted therapy resistance

Aim 4: To identify and validate the clinical validity of molecular subtypes of STS using datasets from publicly available such as The Cancer Genome Atlas.

The student will be trained to apply modern computational approaches, including mathematical and statistical modelling, machine learning and network analysis, to address these aims.

Training and development
The PhD student will be integrated into the multi-disciplinary Molecular and Systems Oncology team in the Division of Molecular Pathology. The student will benefit from mentorship and training from other members of the Huang laboratory within a collaborative and supportive environment. You will work as part of a team of inter-disciplinary data scientists who are experts in NGS-data analysis, machine learning and software development; and therefore offering unique learning opportunities. There will be a collaboration with the Sarcoma Unit led by Dr. Robin Jones as well as the Chief Investigator of the CASPS trial Prof Ian Judson. This project will be done in close collaboration with Dr Maggie Cheang, an expert in bioinformatics and biostatistics, who leads the Genomics Analysis – Clinical Trials Team within the ICR-Clinical Trials and Statistics Unit. She is co-inventor the breast cancer intrinsic subtypes PAM50 classifier and experienced in integrating clinical data with genomics for association testing. The student will be exposed to STS biology, clinical trials, biomarker discovery and bioinformatics.

Funding Notes

Students receive an annual stipend, currently £21,000 per annum, as well as having tuition fees (both UK/EU and overseas) and project costs paid for the four-year duration. We are open to applications from any eligible candidates and are committed to attracting and developing the best minds in the world.
See to apply
Applications close 11:55pm UK time on Sunday 17th November 2019

Candidates must have a First or 2:1 Honours degree or a Masters in computational subject and have experience in statistical programming and scripting, and must have a basic knowledge of biology.

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