Predictive response-relevant clustering of expression data provides insights into disease processes

This article describes and illustrates a novel method of microarray data analysis that couples model-based clustering and binary classification to form clusters of ;response-relevant' genes; that is, genes that are informative when discriminating between the different values of the response. Predictions are subsequently made using an appropriate statistical summary of each gene cluster, which we call the ;meta-covariate' representation of the cluster, in a probit regression model. We first illustrate this method by analysing a leukaemia expression dataset, before focusing closely on the meta-covariate analysis of a renal gene expression dataset in a rat model of salt-sensitive hypertension. We explore the biological insights provided by our analysis of these data. In particular, we identify a highly influential cluster of 13 genes-including three transcription factors (Arntl, Bhlhe41 and Npas2)-that is implicated as being protective against hypertension in response to increased dietary sodium. Functional and canonical pathway analysis of this cluster using Ingenuity Pathway Analysis implicated transcriptional activation and circadian rhythm signalling, respectively. Although we illustrate our method using only expression data, the method is applicable to any high-dimensional datasets

Cyclooxygenase-2 (COX-2) expression is more in plasma cells from patients with clonal plasmacytosis

Molecular events in AML (e.g. Flt3-ITD) may be exploited as prognostic indicators or therapeutic targets. The cancer- and stem/ progenitor cell-related enzyme telomerase is central to maintaining the immortal phenotype. We investigated whether levels of telomerase expression are of prognostic value in AML. Following validation of the assay in a range of telomerase positive and negative cell lines and tissues, 300 samples of archived peripheral blood MNC RNA (MRC AML12 study) were analysed blindly by Q-PCR (Light- Cycler) for hTERT (the catalytic component of telomerase). Internal QA utilised Q-PCR for the control gene PBGD. Only samples generating 4103 PBGD transcripts were analysed. Results were expressed as hTERT/PBGD transcripts (%) and as either hTERT positive or negative, or high, intermediate or low (43.2%, 40.7o 3.2, o0.7, respectively, according to levels detected in normal CD341 PBSC and PBL MNC, median of 3.2% and 0.7%, respectively) to facilitate statistical analysis. 1169 samples were analysed, with hTERT transcripts undetectable in 75 (hTERT range 0–255.9%, median 0.17%, mean 4.3%). When analysed on an hTERT-positive or negative basis, there was no correlation between hTERT levels and cytogenetic risk group, FAB subgroups, white cell count or age. Using multivariate regression analysis, adjusted for the known prognostic factors (as previous, plus performance status), there was no evidence of differences in CR rate (OR 1.14, 95%CI 0.43–3.02, p50.8), but borderline evidence of worse overall survival (OS) and disease-free survival (DFS) among hTERT positive patients (5 year OS: 39% vs 53% HR 1.54 (95%CI 0.97–2.45), p50.07; 5 year DFS: 42% vs 54% HR 1.62 (95%CI 0.96–2.74), p50.07). Classifying hTERT as high, intermediate or low, 5 year OS was 34%, 50% and 47% (p50.5 for trend). hTERT expression in AML is heterogeneous and may be of prognostic significance. Larger studies are required to investigate further these findings

Transcriptional analysis of quiescent and proliferating CD34+human hemopoietic cells from normal and chronic myeloid leukemia sources

Quiescent and dividing hemopoietic stem cells (HSC) display marked differences in their ability to move between the peripheral circulation and the bone marrow. Specifically, long-term engraftment potential predominantly resides in the quiescent HSC subfraction, and G-CSF mobilization results in the preferential accumulation of quiescent HSC in the periphery. In contrast, stem cells from chronic myeloid leukemia (CML) patients display a constitutive presence in the circulation. To understand the molecular basis for this, we have used microarray technology to analyze the transcriptional differences between dividing and quiescent, normal, and CML-derived CD34+ cells. Our data show a remarkable transcriptional similarity between normal and CML dividing cells, suggesting that the effects of BCR-ABL on the CD34+ cell transcriptome are more limited than previously thought. In addition, we show that quiescent CML cells are more similar to their dividing counterparts than quiescent normal cells are to theirs. We also show these transcriptional differences to be reflected in the altered proliferative activity of normal and CML CD34+ cells. Of the most interest is that the major class of genes that is more abundant in the quiescent cells compared with the dividing cells encodes members of the chemokine family. We propose a role for chemokines expressed by quiescent HSC in the orchestration of CD34+ cell mobilization