Predictive and prognostic factors in early breast cancer and long-term side-effects of therapy CRUK DPHIL SCHOLARSHIPS AVAILABLE
Breast cancer has a long natural history with a substantial risk of recurrence continuing for at least 20 years after diagnosis. Chemotherapy and, in oestrogen-receptor-positive early breast cancer, long-term endocrine therapy substantially reduce breast cancer recurrence and mortality but can cause potentially life-threatening side-effects including pulmonary embolus or endometrial cancer with tamoxifen, osteoporotic fracture with aromatase inhibitors, and leukaemia and heart disease with chemotherapy. Tamoxifen may also be associated with several positive effects such as reducing risk of coronary heart disease or preserving bone mineral density.
The Early Breast Cancer Trialists’ Collaborative Group (EBCTCG), coordinated from CTSU, collect and centrally analyse data from randomised clinical trials worldwide on any aspect of the treatment of early breast cancer. The EBCTCG database includes over 500,000 women and has comprehensive data about patient, tumour, and treatment characteristics, along with extensive long-term follow-up of recurrence, second cancers and cause-specific mortality. Mature follow up data are also available from the CTSU-led international ATLAS trial comparing 10 versus 5 years of tamoxifen in over 15 000 women, and from its UK counterpart, aTTom (n=8 000). The National Cancer Institute’s SEER database and the Cancer Genome Atlas database can also be accessed.
This DPhil project will use large-scale data from the EBCTCG and other databases to investigate how patient and tumour characteristics impact on the risks and benefits of various therapies for early breast cancer. This will involve detailed analyses of recurrence risk, side effects and treatment efficacy according to potential predictive and prognostic factors to determine how they should influence individual breast cancer management. Such analyses are complicated by the inter-dependence of variables such as ER status, tumour proliferation rates and tumour grade. Moreover, commonly used methods for multivariable analysis such as Cox analysis may not be valid if proportional hazards assumptions do not hold. In addition, information from individual trials and from different databases varies in methods and completeness of data recording. For all these reasons, the validity of different methods of data analysis needs to be carefully explored to optimise the methodology and thereby generate information that will be trustworthy, and be of practical benefit to women with breast cancer and their medical teams by helping them make informed treatment decisions.
An Advisory Group will be formed to develop and refine the above research proposals and to propose additional research areas. Members of the EBCTCG Secretariat on this group include Richard Gray (Professor of Medical Statistics, University of Oxford and leader of the EBCTCG Secretariat, NDPH), Robert Hills (Professor of Medical Statistics and EBCTCG Secretariat co-Lead), Jeremy Braybrooke (Consultant Medical Oncologist, University of Bristol and, Oxford), Hongchao Pan (Senior Statistician, NDPH). Other members will be co-opted to the Advisory Group as appropriate.
RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING
The student will work within a multidisciplinary team and will gain research experience in systematic literature reviews, meta-analysis of patient-level data, epidemiological and statistical methods, programming, data analysis, scientific writing and presentation of findings at scientific meetings.
FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING
The student will be part of the EBCTCG secretariat and will have all support required for undertaking the work. In addition, the student will have access to the range of training programmes offered by the University for postgraduate students including teaching and personal development.
This project involves statistical analysis of big data to improve breast cancer treatment. It requires previous training/experience or the ability to develop skills in data analysis and statistical programming and an interest in applying these skills to medical research.
Students should have a good degree in Medicine or other mathematical/science subject and, training, ideally with a qualification, in epidemiology, statistics, public health, or bioinformatics.