This is the website of the LICSP Pre-Cancer Genomics Group, incorporating the Yorkshire Cancer Research Centre for Pre-Cancer Genomics.
Based at Leeds Institute of Cancer Studies and Pathology (formerly part of Leeds Institute of Molecular Medicine), part of the University of Leeds, we are a multidisciplinary team dedicated to finding the earliest driver mutations in cancer.
Working primarily on lung and head and neck cancer, we aim to observe the genomic changes that occur as normal tissue passes through a pre-cancerous stage on the way to becoming a tumour, and to understand why some lesions become cancerous and some do not. We have two approaches to this: firstly, we follow patients at high risk of cancer development, collecting material in a longitudinal series for later investigation: for lung cancer, this work is part of a clinical trial. Secondly, we collect synchronously presenting pre-cancerous and cancerous lesions, mainly but not exclusively from the oral cavity.
We use a number of molecular biology techniques, with particular emphasis on development of methodology for very small lesions, including those that are formalin fixed: currently we are investigating the value of next-generation sequencing technology for these types of samples.
We collaborate closely with NHS colleagues on site at St James’s University Hospital and the surrounding region in order to provide excellent clinical material. There is always at least one clinical fellow in our group undertaking a higher degree. Working as a pipeline, we have dedicated clinical and archival staff to provide the best possible specimens, molecular biologists to carry out genomic and transcriptomic experiments and a team of bioinformaticians to analyse data.
Sample Collection and Curation
We are able to take advantage of our location on site at St James’s University Hospital to foster excellent links with clinical collaborators. We have access to both fresh frozen surgical material and to the wealth of fixed material from the hospital archives. We also maintain a number of cell lines.
Nucleic acids from our samples are stored with the aid of a database, which enables links between clinical features and sequence data to be anonymously maintained.
Pamela Rabbitts is a co-investigator of the CR-UK funded lungSEARCH, a seven year, multicentre, randomised controlled trial of surveillance for the early detection of lung cancer in a group of patients at high risk of the disease. She is joint principle investigator in a parallel study, “reSEARCH,” a tissue bank based at the Leeds Institute of Molecular Medicine, storing samples collected in the LungSEARCH Clinical Trial.
Most lung cancers are diagnosed when curative treatment is not possible. The LungSEARCH Trial is using sensitive diagnostic techniques on a group of high risk individuals in the hope that lung cancer can be detected at an early, curative stage. Ten centres across the UK have recruited 1570 mild to moderate COPD sufferers with a smoking history. They were randomised into either the control arm – usual clinical care, or the surveillance arm – annual sputum cytology/cytometry, with CT scans and auto-fluorescence bronchoscopies for those patients who produce an abnormal sputum sample. Both groups will be followed up for 5 years.
Patients randomised to the surveillance arm were also invited to donate a range of biological specimens to the reSEARCH Specimen Store. Annual sputum, annual blood, bronchial biopsies and brushings are been collected alongside any archived FFPE pathology samples. This will provide an invaluable series of sequential samples, in the hope that predictive biomarkers may be found, not only in pre- cancerous and cancerous lesions, but also in blood and sputum, taking us closer to a non-invasive diagnostic tool for the early detection of lung cancer.
Also see here for further details.
Professor Pamela Rabbitts,
Tissue Bank Manager
Ms Deborah Clarke,
LungSEARCH Trial Nurse for St James’ University Hospital, Leeds.
Cell and Molecular Biology
To complement the next-generation sequencing data which forms the bulk of the output of our group, we utilise a number of cell and molecular biology techniques.
Examples include, FISH, qPCR, digital PCR, Sanger Sequencing.
To reduce the costs involved in whole genome sequencing, we are involved in sequence capture, whereby only a small but potentially more interesting subset of the genome is sequenced.
We are particularly interested in studying the distal portion of chromosome 3q, and its role in tumour development.
We have been making extensive use of CNV-seq, a method to study copy number using low coverage whole genome sequencing data. We have developed techniques to use in house Illumina sequencing technology to create copy number karyograms. Although not the first to describe this concept, we have published methods to work with nanogram amounts of DNA from FFPE samples and introduced multiplexing to allow for adjustable resolution dependent on requirements and resources. We use this technique as a quality control method, to check the amount of genomic damage in a sample, and as a research technique in its own right.
We have recently published methods which allow for the detection of viral DNA sequences from tumour samples using the same raw data used for CNV-seq. This shows concordance with established methods of HPV detection, but has broader scope, being able to distinguish between viral subtypes and provide an estimate of viral load. Any viral species which is present as DNA in the host tissue can be detected, and the data can be simultaneously used to proved genomic copy number karyograms.
Whole Genome Sequencing
For a small subset of samples we have undertaken whole genome sequencing to fully examine the genomic changes which occur from normal to dysplasia to tumour.
Most of this work has been outsourced so as not to overwhelm local capacity. Data analysis and validation are being performed in house.
Next-generation sequencing techniques produce vast quantities of data. Our bioinformatics unit has two functions: to handle and store the sheer volume of data produced, and to develop
novel algorithms to best analyse this data and link back to the basic biology.