A comprehensive inventory of TLX1 controlled long non-coding RNAs in T-cell acute lymphoblastic leukemia through polyA+ and total RNA sequencing

Graft-versus-host disease (GvHD) assessment has been shown to be a challenge for healthcare professionals, leading to the development of the eGVHD App (www.uzleuven.be/egvhd). In this study, we formally evaluated the accuracy of using the App compared to traditional assessment methods to assess GvHD. Our national multicenter randomized controlled trial involved seven Belgian transplantation centers and 78 healthcare professionals selected using a 2-stage convenience sampling approach between January and April 2017. Using a 1:1 randomization stratified by profession, healthcare professionals were assigned to use either the App ("APP") or their usual GvHD assessment aids ("No APP") to assess the diagnosis and severity score of 10 expert-validated clinical vignettes. Our main outcome measure was the difference in accuracy for GvHD severity scoring between both groups. The odds of being correct were 6.14 (95% CI: 2.83-13.34) and 6.29 (95% CI: 4.32-9.15) times higher in favor of the "APP" group for diagnosis and scoring, respectively (P<0.001). Appassisted GvHD severity scoring was significantly superior for both acute and chronic GvHD, with an Odds Ratio of 17.89 and 4.34 respectively (P<0.001) and showed a significantly increased inter-observer agreement compared to standard practice. Despite a mean increase of 24 minutes (95% CI: 20.45-26.97) in the time needed to score the whole GvHD test package in the "APP" group (P<0.001), usability feedback was positive. The eGVHD App shows superior GvHD assessment accuracy compared to standard practice and has the potential to improve the quality of outcome data registration in allogeneic stem cell transplantation

14q32 rearrangements deregulating BCL11B mark a distinct subgroup of T-lymphoid and myeloid immature acute leukemia

Acute leukemias (ALs) of ambiguous lineage are a heterogeneous group of high-risk leukemias characterized by coexpression of myeloid and lymphoid markers. In this study, we identified a distinct subgroup of immature acute leukemias characterized by a broadly variable phenotype, covering acute myeloid leukemia (AML, M0 or M1), T/myeloid mixed-phenotype acute leukemia (T/M MPAL), and early T-cell precursor acute lymphoblastic leukemia (ETP-ALL). Rearrangements at 14q32/BCL11B are the cytogenetic hallmark of this entity. In our screening of 915 hematological malignancies, there were 202 AML and 333 T-cell acute lymphoblastic leukemias (T-ALL: 58, ETP; 178, non-ETP; 8, T/M MPAL; 89, not otherwise specified). We identified 20 cases of immature leukemias (4% of AML and 3.6% of T-ALL), harboring 4 types of 14q32/BCL11B translocations: t(2,14)(q22.3;q32) (n = 7), t(6;14)(q25.3;q32) (n = 9), t(7;14)(q21.2;q32) (n = 2), and t(8;14)(q24.2;q32) (n = 2). The t(2;14) produced a ZEB2-BCL11B fusion transcript, whereas the other 3 rearrangements displaced transcriptionally active enhancer sequences close to BCL11B without producing fusion genes. All translocations resulted in the activation of BCL11B, a regulator of T-cell differentiation associated with transcriptional corepressor complexes in mammalian cells. The expression of BCL11B behaved as a disease biomarker that was present at diagnosis, but not in remission. Deregulation of BCL11B co-occurred with variants at FLT3 and at epigenetic modulators, most frequently the DNMT3A, TET2, and/or WT1 genes. Transcriptome analysis identified a specific expression signature, with significant downregulation of BCL11B targets, and clearly separating BCL11B AL from AML, T-ALL, and ETP-ALL. Remarkably, an ex vivo drug-sensitivity profile identified a panel of compounds with effective antileukemic activity

High Accuracy Mutation Detection in Leukemia on a Selected Panel of Cancer Genes

<div><p>With the advent of whole-genome and whole-exome sequencing, high-quality catalogs of recurrently mutated cancer genes are becoming available for many cancer types. Increasing access to sequencing technology, including bench-top sequencers, provide the opportunity to re-sequence a limited set of cancer genes across a patient cohort with limited processing time. Here, we re-sequenced a set of cancer genes in T-cell acute lymphoblastic leukemia (T-ALL) using Nimblegen sequence capture coupled with Roche/454 technology. First, we investigated how a maximal sensitivity and specificity of mutation detection can be achieved through a benchmark study. We tested nine combinations of different mapping and variant-calling methods, varied the variant calling parameters, and compared the predicted mutations with a large independent validation set obtained by capillary re-sequencing. We found that the combination of two mapping algorithms, namely <em>BWA-SW</em> and <em>SSAHA2</em>, coupled with the variant calling algorithm <em>Atlas-SNP2</em> yields the highest sensitivity (95%) and the highest specificity (93%). Next, we applied this analysis pipeline to identify mutations in a set of 58 cancer genes, in a panel of 18 T-ALL cell lines and 15 T-ALL patient samples. We confirmed mutations in known T-ALL drivers, including PHF6, NF1, FBXW7, NOTCH1, KRAS, NRAS, PIK3CA, and PTEN. Interestingly, we also found mutations in several cancer genes that had not been linked to T-ALL before, including JAK3. Finally, we re-sequenced a small set of 39 candidate genes and identified recurrent mutations in TET1, SPRY3 and SPRY4. In conclusion, we established an optimized analysis pipeline for Roche/454 data that can be applied to accurately detect gene mutations in cancer, which led to the identification of several new candidate T-ALL driver mutations.</p> </div

Characterization of genomic and transcriptomic landscapes of T-cell acute lymphoblastic leukemia with the next generation sequencing

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Decoding transcriptional states in cancer

Gene regulatory networks determine cellular identity. In cancer, aberrations of gene networks are caused by driver mutations that often affect transcription factors and chromatin modifiers. Nevertheless, gene transcription in cancer follows the same cis-regulatory rules as normal cells, and cancer cells have served as convenient model systems to study transcriptional regulation. Tumours often show regulatory heterogeneity, with subpopulations of cells in different transcriptional states, which has important therapeutic implications. Here, we review recent experimental and computational techniques to reverse engineer cancer gene networks using transcriptome and epigenome data. New algorithms, data integration strategies, and increasing amounts of single cell genomics data provide exciting opportunities to model dynamic regulatory states at unprecedented resolution.publisher: Elsevier articletitle: Decoding transcriptional states in cancer journaltitle: Current Opinion in Genetics & Development articlelink: http://dx.doi.org/10.1016/j.gde.2017.01.003 content_type: article copyright: © 2017 Published by Elsevier Ltd.status: publishe

Identification of High-Impact cis-Regulatory Mutations Using Transcription Factor Specific Random Forest Models.

Cancer genomes contain vast amounts of somatic mutations, many of which are passenger mutations not involved in oncogenesis. Whereas driver mutations in protein-coding genes can be distinguished from passenger mutations based on their recurrence, non-coding mutations are usually not recurrent at the same position. Therefore, it is still unclear how to identify cis-regulatory driver mutations, particularly when chromatin data from the same patient is not available, thus relying only on sequence and expression information. Here we use machine-learning methods to predict functional regulatory regions using sequence information alone, and compare the predicted activity of the mutated region with the reference sequence. This way we define the Predicted Regulatory Impact of a Mutation in an Enhancer (PRIME). We find that the recently identified driver mutation in the TAL1 enhancer has a high PRIME score, representing a "gain-of-target" for MYB, whereas the highly recurrent TERT promoter mutation has a surprisingly low PRIME score. We trained Random Forest models for 45 cancer-related transcription factors, and used these to score variations in the HeLa genome and somatic mutations across more than five hundred cancer genomes. Each model predicts only a small fraction of non-coding mutations with a potential impact on the function of the encompassing regulatory region. Nevertheless, as these few candidate driver mutations are often linked to gains in chromatin activity and gene expression, they may contribute to the oncogenic program by altering the expression levels of specific oncogenes and tumor suppressor genes

Identification of High-Impact cis-Regulatory Mutations Using Transcription Factor Specific Random Forest Models

Cancer genomes contain vast amounts of somatic mutations, many of which are passenger mutations not involved in oncogenesis. Whereas driver mutations in protein-coding genes can be distinguished from passenger mutations based on their recurrence, non-coding mutations are usually not recurrent at the same position. Therefore, it is still unclear how to identify cis-regulatory driver mutations, particularly when chromatin data from the same patient is not available, thus relying only on sequence and expression information. Here we use machine-learning methods to predict functional regulatory regions using sequence information alone, and compare the predicted activity of the mutated region with the reference sequence. This way we define the Predicted Regulatory Impact of a Mutation in an Enhancer (PRIME). We find that the recently identified driver mutation in the TAL1 enhancer has a high PRIME score, representing a "gain-of-target" for MYB, whereas the highly recurrent TERT promoter mutation has a surprisingly low PRIME score. We trained Random Forest models for 45 cancer-related transcription factors, and used these to score variations in the HeLa genome and somatic mutations across more than five hundred cancer genomes. Each model predicts only a small fraction of non-coding mutations with a potential impact on the function of the encompassing regulatory region. Nevertheless, as these few candidate driver mutations are often linked to gains in chromatin activity and gene expression, they may contribute to the oncogenic program by altering the expression levels of specific oncogenes and tumor suppressor genes.status: publishe

Identification of cis-regulatory mutations generating de novo edges in personalized cancer gene regulatory networks

Abstract The identification of functional non-coding mutations is a key challenge in the field of genomics. Here we introduce μ-cisTarget to filter, annotate and prioritize cis-regulatory mutations based on their putative effect on the underlying “personal” gene regulatory network. We validated μ-cisTarget by re-analyzing the TAL1 and LMO1 enhancer mutations in T-ALL, and the TERT promoter mutation in melanoma. Next, we re-sequenced the full genomes of ten cancer cell lines and used matched transcriptome data and motif discovery to identify master regulators with de novo binding sites that result in the up-regulation of nearby oncogenic drivers. μ-cisTarget is available from http://mucistarget.aertslab.org

Mutation analysis of the tyrosine phosphatase PTPN2 in Hodgkin lymphoma and T-cell non-Hodgkin lymphoma

We recently reported deletion of the protein tyrosine phosphatase gene PTPN2 in T-cell acute lymphoblastic leukemia. Functional analyses confirmed that PTPN2 acts as classical tumor suppressor repressing the proliferation of T-cells, in part through inhibition of JAK/STAT signaling. We investigated the expression of PTPN2 in leukemia as well as lymphoma cell lines and identified bi-allelic inactivation of PTPN2 in the Hodgkin lymphoma cell line SUP-HD1, which was associated with activation of the JAK/STAT pathway. Subsequent sequence analysis of Hodgkin lymphoma and T-cell non-Hodgkin lymphoma identified bi-allelic inactivation of PTPN2 in 2 out of 39 cases of peripheral T-cell lymphoma, not otherwise specified, but not in Hodgkin lymphoma. These data together with our data on T-cell acute lymphoblastic leukemia demonstrate that PTPN2 is a tumor suppressor gene in T-cell malignancies.status: publishe