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background

Breast cancer has a long natural history, and women still have a substantial risk of recurrence 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 also cause long-term side-effects, some of which may be life-threatening. These adverse effects can include 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. As the breast cancer and non-breast cancer related effects of therapy can be seen at different times, reliable evaluation of the balance of benefits and risks can depend on how long a woman is followed up for.

Data from randomised trials allow an investigation of the relative benefits and harms of different therapies, a single trial may be too small to give a reliable answer, especially for rare events. 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 data on over 700,000 women and has comprehensive information on 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 affect the risks and benefits of various therapies for early breast cancer. The work will involve detailed analyses of recurrence risk, side effects and overall treatment efficacy according to potential predictive and prognostic factors to determine how these factors can assist 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. The project will therefore examine the validity of different methods of data analysis to develop optimal methodologies that generate trustworthy information that will be of practical benefit to women with breast cancer and their medical teams by helping them make informed treatment decisions.

In addition to working with the listed supervisors, the proposal will involve working with external members of the EBCTCG collaboration, including international trialists who have contributed data to the project. 

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.

prospective candidate

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.

Supervisors