A population based study of genetic predisposition and gene-environment interactions in breast, ovarian and endometrial cancer (follow up of pre-2019 patients only) ( ODR1920_097 )

The University of Cambridge requires access to NHS England data for the purpose of the following research project:

A population based study of genetic predisposition and gene-environment interactions in breast, ovarian and endometrial cancer (follow up of pre-2019 patients only) (ODR1920_097 )

The following is a summary of the aims of the research project provided by the University of Cambridge:

The role of germline genetic variation in susceptibility to breast, endometrial and ovarian cancer; predisposing to different molecular sub-types of breast, endometrial and ovarian cancer, and their determining of clinical outcomes.

The purpose of the study is to obtain clinical and epidemiological information

and lymphocyte DNA on a population-based series of breast, endometrial and ovarian cancer cases. The broad scientific questions being addressed are:

• The role of germline genetic variation in susceptibility to breast, endometrial and ovarian cancer

• The role of germline genetic variation in predisposing to different molecular subtypes of breast, endometrial and ovarian cancer

• The role of germline variation and molecular subtypes in determining clinical outcomes –including response to treatment, disease progression and survival –after a diagnosis of breast, endometrial or ovarian cancer

The following NHS England Data will be accessed:

• NDRS – Cancer Registration Data, Systemic Anti-Cancer Therapy Data (SACT), Linked HES Outpatient (OP) and National Radiotherapy Dataset (RTDS).

The level of the Data held is:

• Identifiable

The Data will be minimised as follows:

• Limited to a study cohort identified by the University of Cambridge – men and women diagnosed with breast cancer and women diagnosed with endometrial or epithelial ovarian cancer in England, Wales and Scotland. Patients must be aged between 18 and 69 at diagnosis, and have been diagnosed during the last five years.

• Limited to conditions relevant to the study identified by specific ICD-10 codes

The University of Cambridge is the research sponsor and the controller as the organisation responsible for ensuring that the Data will only be processed for the purpose described above.

The lawful basis for processing personal data under the UK GDPR is:

Article 6(1)(e) - processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller

The lawful basis for processing special category data under the UK GDPR is:

Article 9(2)(j) - processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject.

No data will flow to NHS England for the purposes of this Data Sharing Agreement (DSA).

SEARCH is a study that has been running for over 25 years. The data from the study have been used in multiple projects investigating the genetic epidemiology, clinical epidemiology and molecular pathology of cancer. We have published over 500 papers that have used data from the study in peer reviewed journals. Recent examples include:

1. Su YR, Sakoda LC, Jeon J, et al. Validation of a genetic-enhanced risk prediction model for colorectal cancer in a large community-based cohort. Cancer Epidemiol Biomarkers Prev 2023 doi: 10.1158/1055-9965.EPI-22-0817 [published Online First: 20230109]

2. Weir A, Kang EY, Meagher NS, et al. Increased FOXJ1 protein expression is associated with improved overall survival in high-grade serous ovarian carcinoma: an Ovarian Tumor Tissue Analysis Consortium Study. Br J Cancer 2023;128(1):137-47. doi: 10.1038/s41416-022-02014-y [published Online First: 20221102]

3. Wilcox N, Dumont M, Gonzalez-Neira A, et al. Exome sequencing identifies breast cancer susceptibility genes and defines the contribution of coding variants to breast cancer risk. Nat Genet 2023;55(9):1435-39. doi: 10.1038/s41588-023-01466-z [published Online First: 20230817]

Current and future analyses include an investigation into the role of tissue infiltrating lymphocytes in the response to therapy in breast and ovarian cancer, an analysis of the role of germline loss-of-function variants in 50 genes and risk of ovarian cancer, the development of methods to create multi-ancestry polygenic risk scores for cancer risk prediction, and a study of the association of the spatial distribution of the expression of 44 proteins in breast and ovarian cancer with germline risk factors and clinical outcomes. Results will be written up for publications and submitted to peer-reviewed journals. As per standard research practice, results may also be included in presentations. In all instances only aggregate data will be presented in manuscripts and presentations, with small numbers suppressed in line with HES analysis guide. Data will not be used for sales and marketing purposes. As per University of Cambridge publication policies, all publications are open access.

Not stated in the register.

In addition to the substantial contribution to scientific knowledge on factors that influence cancer risk and prognosis following a cancer diagnosis the results from SEARCH have contributed directly to web-based applications used to aid the management of patients.

CanRisk (www.canrisk.org) is a tool to predict future cancer risk based on a combination of lifestyle risk factors and inherited genetic risk factors. It has been endorsed by clinical management guidelines in UK, North America, Europe and Australia. Since it was released in 2020 it has been used for more than 2million risk assessments for breast and ovarian cancer by healthcare professionals.

PREDICT breast (https://breast.predict.nhs.uk/tool ) is a breast cancer prognostication and treatment benefit model which is now widely used by healthcare professionals and patients all over the world. It has been used over 400,000 times in the past 12 months including 30,000 uses in the United Kingdom. There are ongoing analyses to refine and improve the predictive performance of PREDICT that will include the addition of novel molecular prognostic factors into the algorithm.

Teaching and training the next generation of researchers is an important role of our institution. The data from SEARCH has been used over many years for multiple PhD and masters level research projects within the University of Cambridge.