The long noncoding RNA, treRNA, decreases DNA damage and is associated with poor response to chemotherapy in chronic lymphocytic leukemia.

The study of long noncoding RNAs (lncRNAs) is an emerging area of cancer research, in part due to their ability to serve as disease biomarkers. However, few studies have investigated lncRNAs in chronic lymphocytic leukemia (CLL). We have identified one particular lncRNA, treRNA, which is overexpressed in CLL B-cells. We measured transcript expression in 144 CLL patient samples and separated samples into high or low expression of treRNA relative to the overall median. We found that high expression of treRNA is significantly associated with shorter time to treatment. High treRNA also correlates with poor prognostic indicators such as unmutated IGHV and high ZAP70 protein expression. We validated these initial findings in samples collected in a clinical trial comparing the nucleoside analog fludarabine alone or in combination with the alkylating agent cyclophosphamide in untreated CLL samples collected prior to starting therapy (E2997). High expression of treRNA was independently prognostic for shorter progression free survival in patients receiving fludarabine plus cyclophosphamide. Given these results, in order to study the role of treRNA in DNA damage response we generated a model cell line system where treRNA was over-expressed in the human B-CLL cell line OSU-CLL. Relative to the vector control line, there was less cell death in OSU-CLL over-expressing treRNA after exposure to fludarabine and mafosfamide, due in part to a reduction in DNA damage. Therefore, we suggest that treRNA is a novel biomarker in CLL associated with aggressive disease and poor response to chemotherapy through enhanced protection against cytotoxic mediated DNA damage

Dysregulation of PRMT5 in chronic lymphocytic leukemia promotes progression with high risk of Richter's transformation

: Richter's Transformation (RT) is a poorly understood and fatal progression of chronic lymphocytic leukemia (CLL) manifesting histologically as diffuse large B-cell lymphoma. Protein arginine methyltransferase 5 (PRMT5) is implicated in lymphomagenesis, but its role in CLL or RT progression is unknown. We demonstrate herein that tumors uniformly overexpress PRMT5 in patients with progression to RT. Furthermore, mice with B-specific overexpression of hPRMT5 develop a B-lymphoid expansion with increased risk of death, and Eµ-PRMT5/TCL1 double transgenic mice develop a highly aggressive disease with transformation that histologically resembles RT; where large-scale transcriptional profiling identifies oncogenic pathways mediating PRMT5-driven disease progression. Lastly, we report the development of a SAM-competitive PRMT5 inhibitor, PRT382, with exclusive selectivity and optimal in vitro and in vivo activity compared to available PRMT5 inhibitors. Taken together, the discovery that PRMT5 drives oncogenic pathways promoting RT provides a compelling rationale for clinical investigation of PRMT5 inhibitors such as PRT382 in aggressive CLL/RT cases

Par-eXpress: A tool for analysis of sequencing experiments with ambiguous assignment of fragments in parallel

With new high-throughput and low-cost sequencing technologies, an increasing amount of genetic data is becoming available to researchers. While the analysis of this vast amount of data has great potential for future scientific advances, it becomes imperative to exploit parallelism in order to process this data efficiently. In this paper, we address probabilistic assignment of ambiguously mapped fragments. This is a very significant, but time consuming, process for downstream analysis of genomic data. We develop a distributed-memory parallel version of a popular probabilistic fragment assignment tool, eXpress. In our experiments, we show that our approach achieves significant speedups over eXpress without decreasing its accuracy. The speedup we achieve increases as the number of iterations and/or data size increases.Scopu

Cotargeting of XPO1 Enhances the Antileukemic Activity of Midostaurin and Gilteritinib in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a hematopoietic stem-cell-derived leukemia with often successive derived driver mutations. Late onset acquisition of internal tandem duplication in FLT3 (FLT3-ITD) at a high variant allele frequency often contributes to full transformation to a highly proliferative, rapidly progressive disease with poor outcome. The FLT3-ITD mutation is targetable with approved FLT3 small molecule inhibitors, including midostaurin and gilteritinib. However, outside of patients receiving allogeneic transplant, most patients fail to respond or relapse, suggesting alternative approaches of therapy will be required. We employed genome-wide pooled CRISPR knockout screening as a method for large-scale identification of targets whose knockout produces a phenotypic effect that enhances the antitumor properties of FLT3 inhibitors. Among the candidate targets we identified the effect of XPO1 knockout to be synergistic with midostaurin treatment. Next, we validated the genetic finding with pharmacologic combination of the slowly reversible XPO1 inhibitor selinexor with midostaurin and gilteritinib in FLT3-ITD AML cell lines and primary patient samples. Lastly, we demonstrated improved survival with either combination therapy compared to its monotherapy components in an aggressive AML murine model, supporting further evaluation and rapid clinical translation of this combination strategy

DNA methylation epitypes highlight underlying developmental and disease pathways in acute myeloid leukemia

Acute myeloid leukemia (AML) is a molecularly complex disease characterized by heterogeneous tumor genetic profiles and involving numerous pathogenic mechanisms and pathways. Integration of molecular data types across multiple patient cohorts may advance current genetic approaches for improved sub-classification and understanding of the biology of the disease. Here we analyzed genome-wide DNA methylation in 649 AML patients using Illumina arrays and identified a configuration of 13 subtypes (termed 'epitypes') using unbiased clustering. Integration of genetic data revealed that most epitypes were associated with a certain recurrent mutation (or combination) in a majority of patients, yet other epitypes were largely independent. Epitypes demonstrated developmental blockage at discrete stages of myeloid differentiation, revealing epitypes that retain arrested hematopoietic stem cell-like phenotypes. Detailed analyses of DNA methylation patterns identified unique patterns of aberrant hyper- and hypomethylation among epitypes, with variable involvement of transcription factors influencing promoter, enhancer, and repressed regions. Patients in epitypes with stem cell-like methylation features showed inferior overall survival along with upregulated stem cell gene expression signatures. We further identified a DNA methylation signature involving STAT motifs associated with FLT3-ITD mutations. Finally, DNA methylation signatures were stable at relapse for the large majority of patients, and rare epitype switching accompanied loss of the dominant epitype mutations and reversion to stem cell-like methylation patterns. These results demonstrate that DNA methylation-based classification integrates important molecular features of AML to reveal the diverse pathogenic and biological aspects of the disease