Molecular High-Grade B-Cell Lymphoma: Defining a Poor-Risk Group That Requires Different Approaches to Therapy.

PURPOSE: Biologic heterogeneity is a feature of diffuse large B-cell lymphoma (DLBCL), and the existence of a subgroup with poor prognosis and phenotypic proximity to Burkitt lymphoma is well known. Conventional cytogenetics identifies some patients with rearrangements of MYC and BCL2 and/or BCL6 (double-hit lymphomas) who are increasingly treated with more intensive chemotherapy, but a more biologically coherent and clinically useful definition of this group is required. PATIENTS AND METHODS: We defined a molecular high-grade (MHG) group by applying a gene expression-based classifier to 928 patients with DLBCL from a clinical trial that investigated the addition of bortezomib to standard rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) therapy. The prognostic significance of MHG was compared with existing biomarkers. We performed targeted sequencing of 70 genes in 400 patients and explored molecular pathology using gene expression signature databases. Findings were validated in an independent data set. RESULTS: The MHG group comprised 83 patients (9%), with 75 in the cell-of-origin germinal center B-cell-like group. MYC rearranged and double-hit groups were strongly over-represented in MHG but comprised only one half of the total. Gene expression analysis revealed a proliferative phenotype with a relationship to centroblasts. Progression-free survival rate at 36 months after R-CHOP in the MHG group was 37% (95% CI, 24% to 55%) compared with 72% (95% CI, 68% to 77%) for others, and an analysis of treatment effects suggested a possible positive effect of bortezomib. Double-hit lymphomas lacking the MHG signature showed no evidence of worse outcome than other germinal center B-cell-like cases. CONCLUSION: MHG defines a biologically coherent high-grade B-cell lymphoma group with distinct molecular features and clinical outcomes that effectively doubles the size of the poor-prognosis, double-hit group. Patients with MHG may benefit from intensified chemotherapy or novel targeted therapies.Supported by Bloodwise grant number 15002: Precision Medicine for Aggressive Lymphoma. The Randomized Evaluation of Molecular-Guided Therapy for DLBCL With Bortezomib (REMoDL-B) trial was endorsed by Cancer Research UK, reference number CRUKE/10/024, and Janssen-Cillag provided funding. A.S. is partly funded by the National Institute for Health Research Oxford Biomedical Research Centre. D.R.W. acknowledges UK Medical Research Council grant MR/L01629X/1 for infrastructure support

Indentification of early genetic events in therapy-related acute myeloid leukaemia : investigating the role of Mut S Homolog 2

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A Microarray Platform-Independent Classification Tool for Cell of Origin Class Allows Comparative Analysis of Gene Expression in Diffuse Large B-cell Lymphoma

<div><p>Cell of origin classification of diffuse large B-cell lymphoma (DLBCL) identifies subsets with biological and clinical significance. Despite the established nature of the classification existing studies display variability in classifier implementation, and a comparative analysis across multiple data sets is lacking. Here we describe the validation of a cell of origin classifier for DLBCL, based on balanced voting between 4 machine-learning tools: the DLBCL automatic classifier (DAC). This shows superior survival separation for assigned Activated B-cell (ABC) and Germinal Center B-cell (GCB) DLBCL classes relative to a range of other classifiers. DAC is effective on data derived from multiple microarray platforms and formalin fixed paraffin embedded samples and is parsimonious, using 20 classifier genes. We use DAC to perform a comparative analysis of gene expression in 10 data sets (2030 cases). We generate ranked meta-profiles of genes showing consistent class-association using ≥6 data sets as a cut-off: ABC (414 genes) and GCB (415 genes). The transcription factor <em>ZBTB32</em> emerges as the most consistent and differentially expressed gene in ABC-DLBCL while other transcription factors such as <em>ARID3A</em>, <em>BATF,</em> and <em>TCF4</em> are also amongst the 24 genes associated with this class in all datasets. Analysis of enrichment of 12323 gene signatures against meta-profiles and all data sets individually confirms consistent associations with signatures of molecular pathways, chromosomal cytobands, and transcription factor binding sites. We provide DAC as an open access Windows application, and the accompanying meta-analyses as a resource.</p> </div

ABC and GCB DLBCL meta-profiles.

<p>Genes shown are differentially expressed and up-regulated in the indicated class in all data sets (shown for > = 6) that have a corresponding probe (ABC (<b>left)</b> and GCB (<b>right</b>)). <b>ClassifierGene</b>: genes used in classifier; <b>Median NFC</b>: median normalised fold change (0–1 for differentially expressed genes); <b>NumFiles</b>: number of files in which gene is differentially expressed and upregulated in the indicated class. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055895#pone.0055895.s008" target="_blank">Table S7</a> for complete lists.</p

Overview of classifier generation, testing and downstream analysis.

<p>Colored boxes (gray/green) depict different training data sets. Step 1- assessment of individual machine-learning tools vs LPS; Step 2– assessment of machine-learning tool combinations; Step 3–assessment of classifier gene sets, training on GSE10846_R-CHOP, and testing on previously seen and unseen data sets: Step 4- further assessment on unseen data sets; Step 5– classification of additional data sets, evaluation of differential gene expression in all-by-all comparison, downstream analysis with meta-profiles and enrichment of molecular signatures.</p

Selected gene signatures significantly enriched or depleted in the ABC meta-profile.

<p>Gene signature enrichments in the meta-profiles were assessed with a hypergeometric test. Shown are selected signatures discussed in the text, including those related to the reciprocal class, a comprehensive list is provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055895#pone.0055895.s009" target="_blank">Table S8</a>. <b><i>Gene Signature</i></b>: name of signature, <b><i>Overlapping</i></b><i>:</i> how many of the ABC meta-profile genes overlap with signature, <b><i>GeneSigSize</i></b>: number of genes in gene signature (after classifier genes are removed), <b><i>randAvg/randSD</i></b>: average/standard-deviation overlap from distribution containing 1 million random samplings, <b><i>%Overlap</i></b>: percentage of gene signature that overlaps with meta-profile, <b><i>Zscore</i></b>: standard score of observed normalised against random distribution, <b><i>FDR</i></b>: Benjamini-Hochberg false discovery rate, <b><i>Source</i></b>: gene signature origin. Shown is a selection of gene signatures (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055895#pone.0055895.s009" target="_blank">Table S8</a> for complete list). <b>**</b> FDR <0.01, <b>*</b> FDR <0.0.</p

Effect of training data set and classifier gene number on survival separation.

<p>The results obtained with classifiers trained on the Wright et al. data using 20 classifier genes were compared against those obtained with classifiers trained on the GSE10846 R-CHOP component using either the same 20 classifier genes, or a range of different classifier gene selections. Shown are the results for classifying the GSE10846 CHOP data (left) and GSE32918 (right). In each table the survival separation observed with the published GEO classes (top) was compared to the meta-classifier (middle) and the LPS (bottom). The Classifier identity, Hazard Ratio (GCB vs ABC as baseline), 95% confidence interval of the Hazard Ratio, and the resulting p-value for survival separation are shown. In the meta-classifier and LPS portions of the tables the results are shown for training on the Wright et al. data set (20 classifier genes) followed by the results for classifiers trained on the GSE10846 R-CHOP data set with different sets of classifier genes (Wright20-Wright5, Dave185-Dave10, All185-All10).</p

Assessment of individual machine-learning tool classifiers.

<p>Results obtained with individual machine-learning tools, trained on the Wright et al. data set and using 20 classifier genes are shown. Survival separation between ABC and GCB classes for the data sets GSE32918, and GSE10846 divided into CHOP and R-CHOP components, was used for assessment. Hazard Ratios were generated for GCB relative to ABC as baseline. The classifiers were ordered by their average rank across the data sets; with rank determined by the p-value of the ABC/GCB separation. The LPS classifier was used for comparison with either a 0.8 or 0.9 p-value cut-off, with either MaxAvgMerge or MedianMerge methods of combining probes (see Materials and Methods). The Classifier Identity, Hazard Ratio (GCB vs ABC as baseline), 95% confidence interval of the Hazard Ratio, and the resulting p-value for survival separation are shown.</p