Personalising cancer treatment with modelling tumour evolution based on genomics biomarkers and PK/PD

Description

Tumour heterogeneity is a major problem limiting the efficacy of targeted oncological therapies. Most advanced tumours eventually become resistant to the treatments, ultimately making the patient succumb to metastatic disease.

This project invites you to develop advanced computational modelling to maximise survival of a patient by combining cancer genomics data with pharmacometrics information for the first time, which might inform personalised cancer treatments.

For successful model development, we need to articulate these 4 main questions with AI-driven and PK/PD modelling methods.

1. Can genomics predict survival?

You will classify patients based on whole-genome sequencing data and molecular interaction networks to establish robust relationship between cancer genotypes and patient survival for different treatments. You will also apply gene signatures to infer personal attributes and pre-treatments for each patient.

2. Can genotypes and other factors predict tumour dynamics?

Using information from step 1 as covariates, you will train and test a tumour dynamics model with longitudinal tumour size data. This model will help infer important hidden parameters such as the fraction of sensitive cells prior to treatment and the rates of growth and death for treated tumours. We aim to test the model for 5 important types of cancer, including breast, colorectal, lung, ovarian and prostate cancer.

3. Can pharmacometrics information refine tumour dynamics prediction?  

You will evaluate whether the parameters of the model from step 2 can be predicted by drug pharmacokinetics and tumour pharmacodynamics.

4. Can this tumour dynamics model predict tumour evolution?

You will gain an impressive set of skills in mathematical modelling for patient selection and efficacy evaluation in drug discovery and development, which is in short supply for the pharmaceutical industry.

This project is in collaboration with GSK. We encourage students from quantitative sciences and pharmacology backgrounds to apply. Good B.Sc minimum. Master’s degree desirable.

 

Availability

Open to students worldwide

Funding information

Self-funded project

The project is open to both European/UK and International students. It is UNFUNDED and applicants are encouraged to contact the Principal Supervisor directly. 

Assistance will be given to those who are applying to international funding schemes. 

The successful applicant will be expected to provide the funding for tuition fees (~£4.6k per year for UK students) and living expenses (~£12k per year). Research costs is minimum. 

A £2000 ISMIB Travel and Training Support Grant may be available to new self-funded applicants who are paying for their own tuition fees

Details of costs can be found on the University website: 

https://www.liverpool.ac.uk/study/postgraduate-research/fees-and-funding/fees-and-costs/

Supervisors

References

Zhang et al. (2018) Classifying Tumors by Supervised Network Propagation. Bioinformatics. 34(13):i484-i493.

Yates & Mistry. (2020) Clone Wars: Quantitatively Understanding Cancer Drug Resistance. JCO Clin Cancer Inform. 4:938-946.