Biomedical subjects

Tony Bellotti

Publications and source records attributed to Tony Bellotti.

3 recordsLinked to original sources

PubMed2006

Complex genomic alterations and gene expression in acute lymphoblastic leukemia with intrachromosomal amplification of chromosome 21.

We have previously identified a unique subtype of acute lymphoblastic leukemia (ALL) associated with a poor outcome and characterized by intrachromosomal amplification of chromosome 21 including the RUNX1 gene (iAMP21). In this study, array-based comparative genomic hybridization (aCGH) (n = 10) detected a common region of amplification (CRA) between 33.192 and 39.796 Mb and a common region of deletion (CRD) between 43.7 and 47 Mb in 100% and 70% of iAMP21 patients, respectively. High-resolution genotypic analysis (n = 3) identified allelic imbalances in the CRA. Supervised gene expression analysis showed a distinct signature for eight patients with iAMP21, with 10% of overexpressed genes located within the CRA. The mean expression of these genes was significantly higher in iAMP21 when compared to other ALL samples (n = 45). Although genomic copy number correlated with overall gene expression levels within areas of loss or gain, there was considerable individual variation. A unique subset of differentially expressed genes, outside the CRA and CRD, were identified when gene expression signatures of iAMP21 were compared to ALL samples with ETV6-RUNX1 fusion (n = 21) or high hyperdiploidy with additional chromosomes 21 (n = 23). From this analysis, LGMN was shown to be overexpressed in patients with iAMP21 (P = 0.0012). Genomic and expression data has further characterized this ALL subtype, demonstrating high levels of 21q instability in these patients leading to proposals for mechanisms underlying this clinical phenotype and plausible alternative treatments.

Alleles↗

PubMed2005

Prospective gene expression analysis accurately subtypes acute leukaemia in children and establishes a commonality between hyperdiploidy and t(12;21) in acute lymphoblastic leukaemia.

We have prospectively analysed and correlated the gene expression profiles of children presenting with acute leukaemia to the Royal London and Great Ormond Street Hospitals with morphological diagnosis, immunophenotype and karyotype. Total RNA extracted from freshly sorted blast cells was obtained from 84 lymphoblastic [acute lymphoblastic leukaemia (ALL)], 20 myeloid [acute myeloid leukaemia (AML)] and three unclassified acute leukaemias and hybridised to the high density Affymetrix U133A oligonucleotide array. Analysis of variance and significance analysis of microarrays was used to identify discriminatory genes. A novel 50-gene set accurately identified all patients with ALL and AML and predicted for a diagnosis of AML in three patients with unclassified acute leukaemia. A unique gene set was derived for each of eight subtypes of acute leukaemia within our data set. A common profile for children with ALL with an ETV6-RUNX1 fusion, amplification or deletion of ETV6, amplification of RUNX1 or hyperdiploidy with an additional chromosome 21 was identified. This suggests that these rearrangements share a commonality in biological pathways that maintains the leukaemic state. The gene TERF2 was most highly expressed in this group of patients. Our analyses demonstrate that not only is microarray analysis the single most effective tool for the diagnosis of acute leukaemias of childhood but it has the ability to identify unique biological pathways. To further evaluate its prognostic value it needs to be incorporated into the routine diagnostic analysis for large-scale clinical trials in childhood acute leukaemias.

Acute Disease↗

PubMed2005

Qualified predictions for microarray and proteomics pattern diagnostics with confidence machines.

We focus on the problem of prediction with confidence and describe a recently developed learning algorithm called transductive confidence machine for making qualified region predictions. Its main advantage, in comparison with other classifiers, is that it is well-calibrated, with number of prediction errors strictly controlled by a given predefined confidence level. We apply the transductive confidence machine to the problems of acute leukaemia and ovarian cancer prediction using microarray and proteomics pattern diagnostics, respectively. We demonstrate that the algorithm performs well, yielding well-calibrated and informative predictions whilst maintaining a high level of accuracy.

Algorithms↗