Precision medicine and the mutational landscape of high grade serous ovarian cancer

Lead Research Organisation: University of Edinburgh
Department Name: MRC Human Genetics Unit

Abstract

There are over 600 new cases of ovarian cancer diagnosed annually in Scotland. High grade serous ovarian cancer (HGSOC) is an aggressive subtype of ovarian cancer that makes up 75% of new diagnoses. HGSOC is almost always diagnosed at an advanced stage when the disease has spread beyond the pelvis. As a result, HGSOC has a poor prognosis with the 5-year overall survival rate remaining low at about 40%. High grade serous ovarian cancers generally have mutations, which are small changes in the DNA sequence, in the TP53 gene which codes for a protein known to suppress the development of a tumour. In addition, the DNA in HGSOC is particularly damaged and rearranged with large sections of sequence in different locations from where they would be in non-cancerous cells. However, the pattern of mutations in HGSOC is poorly understood and previous studies have only been able to analyse data from a modest number of patients. A project led by Prof. Gourley, at the Institute of Genetics and Molecular Medicine at the University of Edinburgh, and co-funded by AstraZeneca has sequenced all the DNA in high-grade serous ovarian cancers from 200 Scottish patients as well as sequencing the DNA from non-cancerous cells in these same patients. We also have detailed clinical information about the tumours from these patients as well as about the patients themselves through the Edinburgh Ovarian Cancer Database. Using this resource, we will examine where the patterns of mutation in HGSOC come from and also what the consequences of these patterns of mutation will be for the patient. We will identify the patterns of mutation in the tumours using mathematical techniques and explore the ability of these identified patterns to predict clinical information about the patient including disease progression and response to therapy. We will also use statistical methods to investigate whether inherited genetic information influences the patterns of mutation that develop in HGSOC as well as considering whether inherited genetic information affects patterns in other types of information gleaned from the tumour, for example, the degree to which particular genes provide instructions to produce their corresponding proteins. We will then explore whether we see the same results from the analysis of this dataset as we do from the analysis of another set of previously published HGSOC sequencing data which also has clinical information about the patients. This will reassure us that our results will be applicable to HGSOC generally and not just to our dataset. Large collections of HGSOC tumour cells have been preserved for storage globally using a technique called formalin-fixed paraffin embedding. We will investigate whether tumour samples that have been preserved in this way can be used for this type of analysis without the preservation method having an effect on the results. This could mean that cancer studies may be able to use information on a larger number of patients in the future without huge additional cost. Finally, new methods are being developed to identify the larger changes in the DNA sequence of the tumour in comparison to the DNA in non-cancerous cells. We will explore what the presence of these larger changes or rearrangements tells us about particular subsets of high grade serous ovarian cancer patients. In addition, we will investigate whether including information about these large changes, along with the smaller mutations, in the patterns of mutation in the tumour affects how these patterns relate to the clinical information for the patient.

Technical Summary

High grade serous ovarian cancer (HGSOC) makes up 75% of new diagnoses of ovarian cancer in Scotland and is almost always diagnosed after the disease has spread. As a result, the 5-year overall survival rate remains low at around 40%. HGSOC is characterised by ubiquitous somatic TP53 mutations and elevated rates of structural variants (SVs) which are thought to drive tumourigenesis. However, HGSOC is poorly understood at the genomic level and the number of available samples is modest. We will analyse high quality whole genome sequencing (WGS) data emerging from a large study of HGSOC (led by Prof. Gourley and co-funded by AstraZeneca) in 200 Scottish patients. Detailed histopathological and clinical data are available for these patients through the Edinburgh Ovarian Cancer Database (Gourley). Using this resource, we will examine the origins and consequences of the somatic mutational landscape of HGSOC. We will calculate mutational spectra in HGSOC WGS samples, as has been done for breast cancer (Nik-Zainal et al, Cell, 2012), and explore their utility in predicting clinical variables, including disease progression and therapeutic responses. We will investigate the role of germline genetic variation in determining mutational spectra and other tumour signatures, such as gene expression signatures, using association testing. The generalisability of our observations will be established by integrating our data with previously published HGSOC WGS data (Patch et al, Nature, 2015) and associated clinical data. We will explore the clinical utility of mutational spectra extracted from the larger cohort (N=450) of FFPE samples. Developing analyses for large collections of FFPE samples may have broader translational impact across oncology globally. We will investigate the utility of newly identified complex SVs in HGSOC in patient stratification as well as investigating the predictive power gained from including these variants in mutational spectra.
 
Description Erasmus+ Higher Education Staff Mobility grant
Amount £1,104 (GBP)
Organisation erasmus + 
Sector Public
Country United Kingdom
Start 09/2018 
End 09/2018
 
Description AstraZeneca HGSOC project 
Organisation AstraZeneca
Country United Kingdom 
Sector Private 
PI Contribution Computational analyses of tumour sequencing data (WGS, RNA-seq).
Collaborator Contribution Provision of high grade serous ovarian cancer samples and raw sequencing data
Impact This is a multi-disciplinary collaboration between bioinformaticists, experimental biologists and clinicians
Start Year 2016
 
Description AstraZeneca HGSOC project 
Organisation Queen Elizabeth University Hospital
Department Stratified Medicine Scotland Innovation Centre
Country United Kingdom 
Sector Hospitals 
PI Contribution Computational analyses of tumour sequencing data (WGS, RNA-seq).
Collaborator Contribution Provision of high grade serous ovarian cancer samples and raw sequencing data
Impact This is a multi-disciplinary collaboration between bioinformaticists, experimental biologists and clinicians
Start Year 2016
 
Description AstraZeneca HGSOC project 
Organisation Scottish Genome Partnership
Country United Kingdom 
Sector Learned Society 
PI Contribution Computational analyses of tumour sequencing data (WGS, RNA-seq).
Collaborator Contribution Provision of high grade serous ovarian cancer samples and raw sequencing data
Impact This is a multi-disciplinary collaboration between bioinformaticists, experimental biologists and clinicians
Start Year 2016
 
Description Liver Cancer Evolution Consortium 
Organisation German Cancer Research Center
Country Germany 
Sector Academic/University 
PI Contribution Analysis of RNA-seq, WGS data on samples of mouse liver throughout cancer evolution.
Collaborator Contribution Provision of RNAseq, WGS data on samples from liver from experimental mouse model of mutagenesis.
Impact This is a multi-disciplinary collaboration between research labs with expertise in bioinformatics, experimental biology, genetic evolution and cancer.
Start Year 2017
 
Description Liver Cancer Evolution Consortium 
Organisation Institute for Research in Biomedicine (IRB)
Country Spain 
Sector Academic/University 
PI Contribution Analysis of RNA-seq, WGS data on samples of mouse liver throughout cancer evolution.
Collaborator Contribution Provision of RNAseq, WGS data on samples from liver from experimental mouse model of mutagenesis.
Impact This is a multi-disciplinary collaboration between research labs with expertise in bioinformatics, experimental biology, genetic evolution and cancer.
Start Year 2017
 
Description Cross-disciplinary Challenge Day 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach Regional
Primary Audience Undergraduate students
Results and Impact Students from mathematics, physics, chemistry, informatics and engineering are invited to work with IGMM scientists on challenging problems in biology.

Myself and a colleague prepared a series of activities to make up a day long challenge investigating the problems of analysing ancient DNA and how cross-disciplinary approaches can be applied to help solve this current biological problem.

The students learned about working with modern DNA and ancient DNA and about how their skills could be applied in this setting. The aim was to encourage more cross-disciplinary research/recruitment in the future.
Year(s) Of Engagement Activity 2018
URL https://www.ed.ac.uk/igmm/news-and-events/events/latest-events/xd-challenge-2018
 
Description Participation in a promotional video advertising the new cross-disciplinary fellowship programme at the institute 
Form Of Engagement Activity A broadcast e.g. TV/radio/film/podcast (other than news/press)
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Postgraduate students
Results and Impact I was interviewed as part of a promotional video which was uploaded to youtube (75 views, checked on 06/02/2019) to promote the new Cross-disciplinary Fellowship programme at the IGMM.
Year(s) Of Engagement Activity 2018
URL https://www.youtube.com/watch?v=AazsnsHemsY
 
Description Science Insights 2018 
Form Of Engagement Activity Participation in an open day or visit at my research institution
Part Of Official Scheme? No
Geographic Reach Regional
Primary Audience Schools
Results and Impact Science Insights provides an opportunity for 40 high school pupils to spend a week of their summer holidays following a varied programme of activities on four different University of Edinburgh campuses, gaining a real insight into research and work in biological sciences, medicine and veterinary medicine.

Pairs of 16-17 year old pupils shadowed researchers at the institute, including myself, to find out more about research in biological sciences.

Pupils surprised by the extensive use of mathematics and statistics in biomedical research.
Year(s) Of Engagement Activity 2018
URL https://www.ed.ac.uk/medicine-vet-medicine/outreach/science-insights
 
Description Speed networking event for 16-17 year old female pupils interested in science careers. 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach Regional
Primary Audience Schools
Results and Impact Speed networking event for 16-17 year old female pupils interested in science careers. Held at Dunfermline High School and in my role as a STEM ambassador I spoke to pupils in pairs where they had the opportunity to ask about my career in science. This sparked interest and awareness of the application of maths/statistics to scientific/clinical research.
Year(s) Of Engagement Activity 2019