Academic research community (GECIP)

Ovarian cancer, like many other cancers, is not in fact a single disease, but a diverse set of different diseases. Further, examination of individual tumours invariably reveals substantial variation within a single cancer, with different cells within a tumour often having different sets of variants. The two types of variation in ovarian cancer, between different ovarian tumours and within a single tumour, both make cancers difficult to treat. We will explore the differences between tumours by classifying them into subtypes based on their genomic characteristics, and will explore variation within tumours by identifying sub-populations of cells within each single tumour. We will also explore how ovarian cancers change over time, which can lead to treatment resistance and spread to distant organs.

Dysgerminomas are rare tumours that occur in the ovary. Previous study of these tumours has been limited to the protein-coding genes, accounting for just 2% of the tumour DNA. This has revealed some insights into key variants that may drive the cancer but what is lacking an analysis of the entire tumour DNA. Through the 100,000 Genomes Project, we hope to examine the whole genome and identify the mutational process at work in these tumours. We hope to combine this work with the clinical data, in order to identify new treatment options for these patients.

Co-written by Helen White, PPI representative Endometrial, or womb, cancer affects roughly 9,000 women each year in the UK and has become considerably more common during the past two decades. Although more than 80% of affected women are cured by surgery and post-operative treatments, the outcome for certain disease subgroups is considerably poorer. Improving this requires a better understanding of the underpinnings of endometrial cancer at a basic level. To fit DNA inside the nucleus of cells it has to be packed very tightly. This DNA packaging is controlled by genes and we know that alterations to these genes are common in womb cancer. These gene alterations change the way DNA is packaged, a dynamic process which determines whether genes are switched on (active) or off (inactive). This in turn may affect how the cancer grows, its response to treatment, and likely outcome. We intend to study this, with the aim of identifying its causes and consequences to better understand womb cancer and better treat women with this common disease.

Endometrial (womb) cancer is the most common gynaecological malignancy in the Western world, affecting 100,000 women each year in Europe alone. While most women with endometrial cancers are cured by treatment, our inability to identify when surgery alone is sufficient means that many patients receive radiotherapy or chemotherapy that they did not need. For the appreciable fraction of women whose disease has spread at the time of diagnosis, or returns after surgery, the prognosis is poor and treatment options are limited. Predicting spread or recurrence of endometrial cancer following surgery, and decisions to give additional treatments, have traditionally been based on the appearance of the cancer under a microscope. Our previous work has shown that these predictions can be improved by also doing molecular (genetic) testing. This helps us to determine the specific type of endometrial cancer and predict how it will behave; however, such molecular testing is costly and not available everwhere. We can use computers (artificial intelligence) to analyse images of a cancer. We believe this could provide additional information to that which we currently get from molecular testing, and in hospitals where molecular testing is unavailable, serve as an alternative. We therefore aim to combine image analysis with whole genome sequences from participants with womb cancer in the NGRL. We can then apply this technology to a large number of endometrial cancer images generated from clinical trials that we already have access to. This should help us to better predict outcomes for patients depending on their specific endometrial cancer type and tailor treatments accordingly.

The promise of precision oncology is therapy tailored to the patient’s own cancer. The reality is that while we can identify alterations in the DNA of the patient’s tumour most of these variants are not actionable with current drugs. The goal of this project is to combine genomic profiling of the patient tumour, with the response of the patient’s own tumour, grown in the laboratory, to drugs. The power of machine learning is used to analyse this data creating a “virtual” model of the tumour to provide a roadmap to guide the oncologist in selecting appropriate standard of care drugs to best treat the patient. In addition, this model can be used in partnership with pharmaceutical companies to drive the development of new therapies. This project will focus on Ovarian Cancer, as it is often identified at late stage and often recurs, hence treatment selection is key to improving outcome for this cancer.

Removing all the disease at the time of an operation for ovarian cancer substantially increases survival for patients. We wish to investigate any association between errors in the DNA repair pathways of ovarian cancer cells, and the ability to surgically remove a cancer.

Although the number of variants that help a cancer to grow varies among cancer types, there is also striking variation among cancers of the same type. Specifically, whilst a typical cancer has about 5 of these “driver” changes – mostly involving small-scale sequence changes in genes like KRAS, TP53 and PIK3CA – a few have no identifiable driver changes and many have just a single change. About 5-10% of colorectal cancers, for example, carry no identifiable driver small-scale variants other than in the APC gene. We wish to explore why cancers such as these can grow, apparently readily, despite their deficiency of driver genes. An obvious reason is that standard sequencing technology and data analysis miss some variants. Alternatively, the “missing” driver genes might be activated or inactivated by “atypical” changes that have the same effect as SNVs or indels, including copy number or structural changes, epivariants or other forms of epigenetic change, SNVs or indels in non-coding regions, et cetera. We aim to determine whether cancers with an apparently low driver mutation burden actually have equivalent functional derangement, using one or more of additional data analysis methods, methylation and gene expression profiling, and long read sequencing (as samples allow). If we do find cancers with a truly very low driver mutation burdens, we shall determine whether their clinicopathological, molecular and evolutionary differ in any way from typical cancers of the same type. The study has potentially important clinical implications for the use of molecularly targeted therapies.

To what degree cancer is “bad luck” is a controversial question. It’s clear that cancer is ultimately caused by variants to a cell’s DNA, and it’s also clear that some environmental exposures (like smoking for example) cause variants. But whether or not the environmental exposures cause the exact variants that are important for endometrial cancer development is unclear. Here we will assess the relationship between mutational exposures (for example the effect of smoking), that can be read out from the ‘scar’ of variants they leave on the genome, and the risk of finding particular cancer genes mutated in a tumour.

Womb cancer is the most common gynaecological malignancy in the Western world, affecting 100,000 women each year in Europe alone. An increasing amount of research shows that the strength of the immune response against womb cancer provides important information about the likelihood that the cancer will be cured by treatment. However, this knowledge is only partial and is not currently used in clinical practice. We propose to use the unique resource of cases analysed by the 100,000 Genomes Project to identify the determinants and consequences of the immune response against womb cancer, with the intention of improving our ability to predict prognosis and to identify new therapeutic targets.

Ovarian cancer is the fifth most prevalent gynaecological cancer in the western countries. The most common subtype is high grade serous ovarian cancer, with a late diagnosis and a poor prognosis. The susceptibility is shown to be increased in families with history of ovarian cancer, and having a first degree relative increases the risk by three fold. Some genes have already been associated with increased inherited susceptibility, but little is known about the other regions of the genome. Our research aims to discover novel variants in non-coding and regulatory regions, associated with the inheritance of the susceptibility of ovarian cancer.

Low Grade Serous Ovarian Carcinoma (LGSOC) is a rare form of serous epithelial ovarian cancer, that acount for less that 10% of al cases and commonly arises from serous borderline tumours (SBT). It tends to affect younger women and is resistant to platinum based chemotherapy making it dificult to manage clinically.It is relatively well established that two thirds of cases are secondary to variants of the MAPK (mitogen - activated protein kinases) pathway (in particular BRAF and KRAS ) but there is little consensus on the root of oncogenesis in the remaining third. This project seeks to characterise patients with KRAS and BRAF variants - their spectrum of clinical outcomes and mutational landscape.

Using molecular and clinical data in combination with supervised and unsupervised machine learning approaches we aim to subtype and classify endometrial cancers in order to gain novel insights and clinical actionable predictors for endometrial cancers.

Womb cancer is the most common gynaecological malignancy, affecting 100,000 women each year in Europe alone. While most women with endometrial cancer are cured by treatment, our inability to identify when surgery alone is sufficient means that many patients receive radiotherapy or chemotherapy that they did not need. For the appreciable fraction of women whose disease has spread at the time of diagnosis, or returns after surgery, the prognosis is poor and treatment options are limited. Better understanding of the biology of womb cancer has considerable potential to help us personalise risk prediction for patients, and identify new therapeutic targets to improve survival. We aim to do this by analysis of womb cancers recruited in the Genomics England 100,000 Genomes Project

Womb cancer is the most common gynaecological malignancy in the Western world, affecting 100,000 women a year in Europe alone. Identifying rare and common genetic variants which predispose women to this disease will enable doctors to target screening and prevention measures towards the women at the highest risk, whilst avoiding unnecessary interventions in those at lowest risk. It may also be possible to identify patients at risk of toxic side-effects from treatment or who might be suitable for intensive therapies. We will compare the genotypes of women with and without womb cancer in the Genomics England 100,000 Genomes Project in order to find genetic variants which are associated with risk of this disease.

Between one sixth and one third of bowel and womb cancers carry a much higher than average number of errors in their DNA. We know that in some settings, this is associated with better prognosis and benefit from particular therapies, but the explanations for this are only partially understood. We propose to address this by analysis of the substantial number of these cancers in the Genomics England 100,000 Genomes Project.

Infertility has many different causes and a large cohort of couples experience infertility during their reproductive life. It can manifest because of factors influencing males, like poor sperm a parameters, influencing females, like endometriosis or influencing embryo development like the presence of an extra chromosome in Down’s syndrome pregnancies. Couples can experience repeated implantation failure and recurrent miscarriage due to primary or secondary infertility. Because the causes of the condition are so heterogeneous, it is difficult to design and complete studies that involve large populations of participants with similar characteristics. For this reason a targeted approach of smaller, but very highly selected population studies could allow for more detailed examination of the genome of the participants and the different subgroups that exist among them. This study examines and deciphers whole genomes from highly selected patient populations and involves the use of the 100000 genomes project database (Genomics England) as well as genomic data produced by our research group. The aim of the analysis is the identification of specific sets of factors in parental physiology and genetic makeup that can be associated with certain types of infertility and foetal abnormalities. As a more complex picture of a polygenetic risk is arising for human health, the results from these targeted population studies can help in the identification and prediction of genetic risk for heterogeneous conditions and towards a personalised approach to reproductive healthcare.

For our research, we plan to analysis the genomes of ovarian cancer case patients and non-ovarian cancer control patients. We will look for large deletions, duplications, or other rearrangements of genetic material within genes that are known to increase risk for ovarian cancer. These types of changes in the known high and moderate risk ovarian cancer genes are thought to predispose individuals to ovarian cancer like other known variant types. Analyzing the genomes available within Genomics England will allow us to better estimate population frequency of these types of mutations and inform future genetic risk screening decisions for ovarian cancer.