To investigate the molecular pathogenesis of aUPD in Follicular Lymphoma.
Lead Research Organisation:
Queen Mary University of London
Department Name: UNLISTED
Abstract
Cancer can be viewed as a genetic disease resulting from the accumulation of chromosomal damage within a cell. A tumour‘s subsequent clinical behaviour (for example the age of onset, aggressiveness, responsiveness to treatment and overall outcome or length of survival) will reflect the pattern of these genetic abnormalities. The clinical course and long median survival of Follicular Lymphoma provides a unique opportunity to profile these genetic events in sequential biopsy material with a view to follow the changing molecular and clinical face of this disease over time. By correlating these events with clinical behaviour we have the possibility to predict for poor prognostic patients and treat these patients more aggressively at an earlier stage of their disease. These genetic events also represent therapeutic targets so that patient treatment can be individualised and directed at a patient specific abnormality.
Technical Summary
The clinical behaviour of cancer is a direct result of the secondary genetic events that arise prior to the onset or that accumulate during disease progression. The use of Single Nucleotide Polymorphism (SNP) array profiling has lately uncovered extensive regions of acquired uniparental disomy (aUPD) in the cancer genome that go undetected using cytogenetics or array-CGH platforms. These sites usually arise by mitotic recombination and can render a cell homozygous for a pre-existing mutation.
In lymphoma these sites are non-random and include recurring sites of aUPD (>20%) on several chromosomes including 6p, 9p, 12q and 17p. The study sets out to determine the prognostic relevance of aUPD in the indolent B cell malignancy Follicular Lymphoma, using the 100K SNP chip diagnostic and sequentially acquired biopsies. These profiles will allow determination of the temporal relationship of aUPD with the clonal and clinical evolution of the tumour (diagnosis, progression and transformation to aggressive disease). In order to characterise the mutational events locating to these regions (i) a systematic mutation analysis of known candidate genes locating to these sites of aUPD for example CDKN2A on 9p and TP53 on 17p will be completed;
(ii) the detection of small regions of homozygous deletion will inform positional studies to a small number of novel candidate genes for analysis and (iii) allied gene expression data of the corresponding tumour sample will be used to search for aberrant gene expression arising in response to allelic imbalance (TNF polymorphisms on 6p) or methylation (CDKN2A) of candidate genes.
By combining these data with allied clinical, gene expression and tissue micro-array data we should be able to tailor treatment according to predicted outcome and select specific genes for the development of novel treatment based on the underlying genetic abnormality.
In lymphoma these sites are non-random and include recurring sites of aUPD (>20%) on several chromosomes including 6p, 9p, 12q and 17p. The study sets out to determine the prognostic relevance of aUPD in the indolent B cell malignancy Follicular Lymphoma, using the 100K SNP chip diagnostic and sequentially acquired biopsies. These profiles will allow determination of the temporal relationship of aUPD with the clonal and clinical evolution of the tumour (diagnosis, progression and transformation to aggressive disease). In order to characterise the mutational events locating to these regions (i) a systematic mutation analysis of known candidate genes locating to these sites of aUPD for example CDKN2A on 9p and TP53 on 17p will be completed;
(ii) the detection of small regions of homozygous deletion will inform positional studies to a small number of novel candidate genes for analysis and (iii) allied gene expression data of the corresponding tumour sample will be used to search for aberrant gene expression arising in response to allelic imbalance (TNF polymorphisms on 6p) or methylation (CDKN2A) of candidate genes.
By combining these data with allied clinical, gene expression and tissue micro-array data we should be able to tailor treatment according to predicted outcome and select specific genes for the development of novel treatment based on the underlying genetic abnormality.