Hosted by David Skillicorn
DANIEL CATCHPOOLE
HEAD, TUMOUR BANK
THE CHILDREN'S HOSPITAL AT WESTMEAD
SYDNEY, AUSTRALIA
TUESDAY, APRIL 5, 2005
DUP215
2:30-3:30
cDNA microarrays and childhood acute lymphoblastic leukaemia:
developing a simplified molecular diagnosis for a complex illness
The optimal treatment of patients with childhood acute lymphoblastic leukaemia (ALL) depends on establishing accurate diagnosis. Recently, researchers have attempted to assess global transcription using microarray technology to identify gene expression 'signatures' that correlate with known ALL subtypes based on clinical presentation, immunophenotype and chromosomal rearrangement. Our investigations seek to strategically develop the application of microarray gene expression profiling to identify ALL patients with clinically homogenous presentations but which may respond differently to established treatment regimens. We have determined the gene expression profiles of ALL bone marrow (BM) samples taken from patients at diagnosis. Data analysis has focussed on the use of a novel and innovative statistical technology, Gene_RaVE. This series of patent protected algorithms builds a multinomial regression model using Bayesian variable selection. Gene_RaVE leads to the generation of a parsimonious and simple diagnostic gene expression signature, but which provides increased predictive ability over current analysis approaches. We describe our analysis of both Affymetrix (HU133A) and cDNA (10.5K) microarray gene expression profiles generated from diagnostic BM from paediatric ALL patients covering the broad leukaemia subtypes including T and B lineage as well as T cell lymphoma leukaemia as compared to pooled normal BM specimens. Gene expression profiles from a cohort of 39 ALL patients, identified as 'standard risk' at diagnosis, were compared on the basis of clinical outcome: relapse within 2 yrs vs non_relapse. Gene_RaVE analysis identified a small subset of genes whose expression was distinct between the two outcome groups. The Gene_RaVE algorithm also provides a generic framework for survival analysis and indicated that this small numbers of genes, which included Nedd4BP3 and Ribosomal Protein L38, were able to build a survival index using expression profiles from diagnosis BM, which correlated with the time to a relapse event. Our results are suggestive of a way forward in the development of an informative, yet efficient diagnostic tool for this childhood malignancy using microarrays.
REFRESHMENTS WILL BE SERVED IN GOODWIN 620 FOLLOWING THE SEMINAR
top |
Government
