Uncovering the drivers of ovarian cancer
The ovarian cancer research group forms part of our department's Cancer theme and is led by Professor Ahmed Ahmed.
Interested in this research?
Ovarian cancer project
Why this project is important?
Ovarian cancer is the sixth most common cancer in women in worldwide. Early detection and diagnosis are important for improving the chances of successful treatment, but ovarian cancer is often diagnosed at a later stage.
Treatment can also become less effective when cancer cells develop resistance to chemotherapy. By improving our understanding of how ovarian cancer begins, evolves and develops treatment resistance, researchers can gain important insights into the biology of the disease.
How the ovarian cancer project can help
Advances in single-cell RNA sequencing and DNA sequencing are allowing researchers to study ovarian cancer biology in unprecedented detail.
The Ovarian Cancer research group is developing sequencing strategies and analysis methods to investigate the mechanisms that drive ovarian cancer initiation, evolution and chemotherapy resistance.
Using these technologies alongside samples from patients taking part in clinical research studies, the group aims to identify the cells where ovarian cancer originates, characterise tumour-initiating cells that remain after treatment, and understand T-cell immune responses as ovarian cancer develops and evolves.
This research is funded by Ovarian Cancer Action and The Oxford Biomedical Research Centre, National Institute of Health Research.
Video: Scientists closer to finding the cell of origin for ovarian cancer
This video explores research by members of the Ovarian Cancer Research Group using single-cell RNA sequencing to better understand the origins of ovarian cancer. By studying cells from women with and without cancer, researchers identified new subtypes of normal fallopian tube cells and a group of patients with poorer survival who may not benefit from current treatments.
Research highlights
A new sequencing approach to uncover active mutational processes
The challenge
Genetic variability of cells in tumours is one of the biggest challenges in cancer therapy. As cells in a tumour become more heterogeneous, new mutations can result in resistance to treatments.
Whole-genome sequencing is often used to find past mutations present in the majority of tumour cells but recent mutations often remain undetectable. Methods to uncover rarer mutations have been difficult, expensive, and prone to errors that can be mistaken for cancer mutations.
In a paper published in eLife, Professor Ahmed Ahmed and colleagues, along with collaborators from the United Kingdom and Germany, developed a new sequencing technique to overcome these limitations and detect rare mutations from small numbers of cells.
Finding clone-specific mutations
As cells divide, they can acquire new mutations that are passed on to their daughter cells, creating groups of genetically related cells known as clones. These clone-specific mutations can reveal mutational processes that are currently active or have occurred recently, making them potentially important targets for cancer research.
However, detecting these mutations is challenging. Standard next-generation sequencing is effective at identifying older mutations shared by many cells, but less effective at detecting recent, clone-specific changes. The sequencing process can also introduce errors that resemble genuine mutations, leading to false positives and an overestimation of the number of mutations present.
The method
The method developed by Professor Ahmed and colleagues (called DigiPico/MutLX: Digital whole-genome sequencing of picogram quantities of DNA (DigiPico) uses a DNA preparation technique that enables the separation of initial DNA material to near single molecules, by distributing the picogram amount of DNA to 384 individual compartments. The researchers then made many copies of DNA from the original DNA molecules in each compartment. They then bar-coded each compartment, before sequencing the DNA from all compartments together.
The advantage of this method is that a mutation that is present in all daughter molecules from the template original molecule is highly likely to be ‘true’. A mutation that is present in some but not all daughter molecules is highly likely to be false since artefacts due to DNA damage propagate randomly during the amplification process.
Elimination of false mutations
This process helps to eliminate a large proportion of false mutations, but not every single one. So, the researchers then implemented an artificial neural network-based algorithm, which they call Mutation Learning (MutLX). MutLX takes as input parameters all the quality measures of the resulting sequencing information (reads) and their distribution pattern across the original compartments. By learning the patterns of true mutations and those of false mutations, MutLX gives a probability for a mutation being a true one and also estimates how uncertain it is about the probability. The mutations that have high probability and low uncertainty are the true positives.
The researchers found that this reduced the number of false positives from tens of thousands to less than ten mutations while keeping more than 70% of the true ones.
The team then applied the method to an individual group of cancer cells from a recurrent tumour of an ovarian cancer patient and found that this clone had an active mutational process that was not detectable by standard sequencing.
Detecting rare mutations in tumour cells
(a) Cancer usually begins with a mutation (dark blue shape in the top cell) in a single tumour cell, passed on to daughter cells that may also gain new mutations (in pink), which then divide and can acquire still new mutations (various colours). Over time this leads to a population of cells that are genetically distinct from each other: the initial mutation is present in all the cells, whereas mutations that occurred later are present in a smaller number of cells (bottom row).
(b) Researchers extracted genetic material from a single cell, diluted it down to single DNA molecules, and plated these onto individual wells (top panel), then amplified each well is amplified to create individual libraries, which are then combined and sequenced (bottom panel). The MutLX algorithm then determines which of the genetic variants (in dark red) are mutations that appear later during tumour evolution (in dark blue) and which are artefacts generated by the amplification process.
Source of figure and caption – eLife article by Nadine Bley.
Future outlook
The research team now hope to apply this method to discover active mutational processes in cancer and pre-cancer conditions. For example, this method could be used to study mutational processes in the disease ‘left over’ after chemotherapy, to understand why these cancer cells are resistant to chemotherapy.
The techniques could also be used for genomic characterisation of circulating tumour cells and of a small number of cells from needle biopsies.
Useful links
Cancer theme
Nuffield Department of Women's and Reproductive Health manages over 30 research groups that fall within either Global Health, Cancer, Maternal & Fetal Health; Big data; and Reproductive Medicine & Genetics.
NIHR Biomedical Research Centre: Oxford
The NIHR Biomedical Research Centre: Oxford is a collaboration between the University of Oxford and Oxford University Hospitals NHS Foundation Trust to fund medical research.
Ovarian Cancer Action
Ovarian Cancer Action are the UK's leading ovarian cancer research charity working on early detection, prevention and treatments so that no woman dies of ovarian cancer.
The research team
-
Ahmed Ahmed
Professor of Gynaecological Oncology
-
Mara Artibani
Postdoctoral Research Assistant in Cancer Stem Cells
-
Lena Rai
Postdoctoral Research Assistant in Ovarian Cancer
-
May Sallam
Postdoctoral Research Assistant in Stem Cell Biology
-
Luyao Wang
Postdoctoral Research Assistant in Translational Cancer Immunology
-
Nancy Zaarour
Research Fellow
-
Aneesh Aggarwal
MSc by Research Student
-
Jingyi Sang
PA to Prof Ahmed Ahmed
How can you help?
You can support the ongoing work of the Ovarian Cancer Research Project through donations, collaborations and research support. The department also offers Postdoctoral Research Fellow & Postgraduate opportunities.
If you wish to support our work or enquire about current opportunities, please contact us or email Professor Ahmed using the button at the top of the page.