Integrative analysis of wealth of biological and clinical data generated from clinical trials allow us to fully delineate these tumours to prognostic and predictive biomarkers for treatment response. These multi-omics data provide valuable resource for trial methodologists to draw biological data to inform future trial designs. The increased popularity of trials with biomarker-enrichment designs, or marker-adaptive or treatment-adaptive designs call for more efficient study design. Randomised study is not always feasible (or ideal), especially for rare cancer like sarcoma, or rare subgroup within breast cancer (e.g., basal-like tumour within estrogen receptor positive breast cancer). In such case, use of external controls to supplement single-arm data could be an attractive approach that can be explored.
We are developing an artificial intelligence-based integrated genomics clinical system, called COUNTERPOINT. This system is expandable to draw upon new biological findings to accelerate discovery of molecular features for clinical implementation. For example, drug response/clinical outcome could be used to infer clinico-phenotypic associations by incorporating data from co-clinical PDX or organoid models. Biological connectivity could inform innovative trial designs e.g. by incorporating therapeutic-specific biological pathway impact scores/patterns as endpoints in biological-response adaptive designs, or by creation of matched synthetic controls for Bayesian trials, in line with the FDA’s Real-World Evidence Program guidelines.
This PhD project will focus on application of agile software development methodology to produce a roadmap of the creation of matched synthetic controls (based on biology) to design more efficient Bayesian trials based on exemplars from breast cancer (common cancer) and sarcoma (rare cancer).
The student will be based within ICR-Clinical Trials and Statistics Unit (ICR CTSU) and work closely with cancer biologists.
The objectives of this project are to:
(a) Develop knowledge and acquire hands on experience of multi-omics data analyses
(b) Review existing literature on how synthetic controls have been used in oncology trials
(c) Evaluate the feasibility of new and/or existing methodologies in the creation of matched synthetic control based on omics for biologically-rich trials
(d) Develop a roadmap and/or pipeline to influence the methodologies of future such trials.
At least 2 peer reviewed publications are anticipated in methodological/clinical trial journals and 1 peer reviewed publications in translational medicine journals.
The supervisory team includes molecular pathology and integrative genomics analysis in clinical trials expert (Cheang), early phase expert methodologist (Yap) and Phase II/III and dynamic biomarker modelling expert methodologist (Porta).
There will be opportunities for a Secondments of research visit to SOLTI (Spain) and Alliance for Clinical Trials in Oncology Statistics group (US) to work with leading international biomarker driven trialists. The translational project(s) involve patient and public partners for patient centred biomarker discovery and the student will have opportunity to work with the PPI.
HOW TO APPLY
You are applying for a PhD studentship from the MRC TMRP DTP. A list of potential projects and the application form is available online at:
Please complete the form fully. Incomplete forms will not be considered. CVs will not be accepted for this scheme.
Please apply giving details for your first choice project. You can provide details of up to two other TMRP DTP projects you may be interested in at section B of the application form.
Before making an application, applicants should contact the project supervisor to find out more about the project and to discuss their interests in the research before 09 January 2023.
The deadline for applications is 4pm (GMT) 16 January 2023. Late applications will not be considered.
Completed application forms must be returned to: [Email Address Removed]
Informal enquiries may be made to Dr Maggie Cheang - [Email Address Removed]