Loss of synergistic transcriptional feedback loops drives diverse B-cell cancers.

BACKGROUND: The most common B-cell cancers, chronic lymphocytic leukemia/lymphoma (CLL), follicular and diffuse large B-cell (FL, DLBCL) lymphomas, have distinct clinical courses, yet overlapping "cell-of-origin". Dynamic changes to the epigenome are essential regulators of B-cell differentiation. Therefore, we reasoned that these distinct cancers may be driven by shared mechanisms of disruption in transcriptional circuitry. METHODS: We compared purified malignant B-cells from 52 patients with normal B-cell subsets (germinal center centrocytes and centroblasts, naïve and memory B-cells) from 36 donor tonsils using >325 high-resolution molecular profiling assays for histone modifications, open chromatin (ChIP-, FAIRE-seq), transcriptome (RNA-seq), transcription factor (TF) binding, and genome copy number (microarrays). FINDINGS: From the resulting data, we identified gains in active chromatin in enhancers/super-enhancers that likely promote unchecked B-cell receptor signaling, including one we validated near the immunoglobulin superfamily receptors FCMR and PIGR. More striking and pervasive was the profound loss of key B-cell identity TFs, tumor suppressors and their super-enhancers, including EBF1, OCT2(POU2F2), and RUNX3. Using a novel approach to identify transcriptional feedback, we showed that these core transcriptional circuitries are self-regulating. Their selective gain and loss form a complex, iterative, and interactive process that likely curbs B-cell maturation and spurs proliferation. INTERPRETATION: Our study is the first to map the transcriptional circuitry of the most common blood cancers. We demonstrate that a critical subset of B-cell TFs and their cognate enhancers form self-regulatory transcriptional feedback loops whose disruption is a shared mechanism underlying these diverse subtypes of B-cell lymphoma. FUNDING: National Institute of Health, Siteman Cancer Center, Barnes-Jewish Hospital Foundation, Doris Duke Foundation.

Transcriptional Response to a Dauer-Inducing Ascaroside Cocktail in Late L1 in C. elegans.

The dauer diapause stage in C. elegans is a non-feeding alternative to the L3 larval stage that is highly resistant to harsh environmental conditions. The decision to enter dauer is a two-step process. First, L1 larvae encounter adverse conditions such as lack of food or overcrowding and decide to enter the L2d rather than the L2 stage. Second, L2d worms that continue to experience disadvantageous conditions decide to enter dauer instead of L3. Here, we have used RNA-seq to characterize the transcriptional response to a cocktail of dauer-inducing ascaroside pheromones at the late L1 stage as worms enter the L2d phase. We find that, in response to ascarosides, C. elegans L1 larvae preparing to enter the L2d stage begin upregulating genes involved in stress response and downregulating genes associated with growth and metabolism.

Targeted polymerase chain reaction-based enrichment and next generation sequencing for diagnostic testing of congenital disorders of glycosylation.

PURPOSE: Congenital disorders of glycosylation are a heterogeneous group of disorders caused by deficient glycosylation, primarily affecting the N-linked pathway. It is estimated that more than 40% of congenital disorders of glycosylation patients lack a confirmatory molecular diagnosis. The purpose of this study was to improve molecular diagnosis for congenital disorders of glycosylation by developing and validating a next generation sequencing panel for comprehensive mutation detection in 24 genes known to cause congenital disorders of glycosylation. METHODS: Next generation sequencing validation was performed on 12 positive control congenital disorders of glycosylation patients. These samples were blinded as to the disease-causing mutations. Both RainDance and Fluidigm platforms were used for sequence enrichment and targeted amplification. The SOLiD platform was used for sequencing the amplified products. Bioinformatic analysis was performed using NextGENe® software. RESULTS: The disease-causing mutations were identified by next generation sequencing for all 12 positive controls. Additional variants were also detected in three controls that are known or predicted to impair gene function and may contribute to the clinical phenotype. CONCLUSIONS: We conclude that development of next generation sequencing panels in the diagnostic laboratory where multiple genes are implicated in a disorder is more cost-effective and will result in improved and faster patient diagnosis compared with a gene-by-gene approach. Recommendations are also provided for data analysis from the next generation sequencing-derived data in the clinical laboratory, which will be important for the widespread use of this technology.