Aberrant microRNA (miRNA) expression contributes to tumorigenesis and cancer progression. Although the number of reported deregulated miRNAs in various cancer types is growing fast, the underlying ...mechanisms of aberrant miRNA regulation are still poorly studied. Epigenetic alterations including aberrant DNA methylation deregulate miRNA expression, which was first shown by reexpression of miRNAs upon pharmacologic DNA demethylation. However, studying the influence of DNA methylation on miRNA transcription on a genome-wide level was hampered by poor miRNA promoter annotation. Putative miRNA promoters were identified on a genome-wide level by using common promoter surrogate markers (e.g., histone modifications) and were later validated as such in different tumor entities. Integrating promoter datasets and global DNA methylation analysis revealed an extensive influence of DNA hyper- as well as hypomethylation on miRNA regulation. In this review, we summarize the current knowledge of the field and discuss recent efforts to map miRNA promoter sequences and to determine the contribution of epigenetic mechanisms to the regulation of miRNA expression. We discuss examples of tumor suppressive and oncogenic miRNAs such as the miR-34 and miR-21 family, respectively, which highlight the complexity and consequences of epigenetic miRNA deregulation.
Myeloid neoplasms are characterized by frequent mutations in at least seven components of the spliceosome that have distinct roles in the process of pre-mRNA splicing. Hotspot mutations in SF3B1, ...SRSF2, U2AF1 and loss of function mutations in ZRSR2 have revealed widely different aberrant splicing signatures with little overlap. However, previous studies lacked the power necessary to identify commonly mis-spliced transcripts in heterogeneous patient cohorts. By performing RNA-Seq on bone marrow samples from 1258 myeloid neoplasm patients and 63 healthy bone marrow donors, we identified transcripts frequently mis-spliced by mutated splicing factors (SF), rare SF mutations with common alternative splicing (AS) signatures, and SF-dependent neojunctions. We characterized 17,300 dysregulated AS events using a pipeline designed to predict the impact of mis-splicing on protein function. Meta-splicing analysis revealed a pattern of reduced levels of retained introns among disease samples that was exacerbated in patients with splicing factor mutations. These introns share characteristics with "detained introns," a class of introns that have been shown to promote differentiation by detaining pro-proliferative transcripts in the nucleus. In this study, we have functionally characterized 17,300 targets of mis-splicing by the SF mutations, identifying a common pathway by which AS may promote maintenance of a proliferative state.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The microenvironment provides essential growth and survival signals to chronic lymphocytic leukemia (CLL) cells and contributes to their resistance to cytotoxic agents. Pharmacologic inhibition of ...spleen tyrosine kinase (SYK), a key mediator of B-cell receptor (BCR) signaling, induces apoptosis in primary CLL cells and prevents stroma contact-mediated cell survival. This report demonstrates a role of SYK in molecularly defined pathways that mediate the CLL-microenvironmental crosstalk independent from the BCR. Chemokine and integrin stimulation induced SYK phosphorylation, SYK-dependent Akt phosphorylation, and F-actin formation in primary CLL cells. Inhibition of SYK by 2 pharmacologic inhibitors and siRNA-knockdown abrogated downstream SYK signaling and morphologic changes induced by these stimuli. CLL cell migration toward CXCL12, the major homing attractor, and CLL cell adhesion to VCAM-1, a major integrin ligand expressed on stromal cells, were markedly reduced by SYK inhibition. In combination with fludarabine, the SYK inhibitor R406 abrogated stroma-mediated drug resistance by preventing up-regulation of the antiapoptotic factor Mcl-1 in CLL cells. SYK blockade in CLL is a promising therapeutic principle not only for its inhibition of the BCR signaling pathway, but also by inhibiting protective stroma signals in a manner entirely independent of BCR signaling.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Chronic myeloid leukemia cells acquire resistance to tyrosine kinase inhibitors through mutations in the ABL1 kinase domain. The T315I mutation mediates resistance to imatinib, dasatinib, nilotinib ...and bosutinib, whereas sensitivity to ponatinib remains. Mutation detection by conventional Sanger sequencing requires 10%-20% expansion of the mutated subclone. We studied the T315I mutation development by ultra-deep sequencing on the 454 XL+ platform (Roche) in comparison to Sanger sequencing. By ultra-deep sequencing, mutations were detected at loads of 1%-2%. We selected 40 patients who had failed first-line to third-line treatment (imatinib, dasatinib, nilotinib) and had high loads of the T315I mutation detected by Sanger sequencing. We confirmed T315I mutations by ultra-deep sequencing and investigated the mutation dynamics by backtracking earlier samples. In 20 of 40 patients, we identified the T315I three months (median) before Sanger sequencing detection limits were reached. To exclude sporadic low percentage mutation development without subsequent mutation outgrowth, we selected 42 patients without resistance mutations detected by Sanger sequencing but loss of major molecular response. Here, no mutation was detected by ultradeep sequencing. Additional non-T315I resistance mutations were found in 20 of 40 patients. Only 15% had two mutations per cell; the other cases showed multiple independently mutated clones and the T315I clone demonstrated a rapid outgrowth. In conclusion, T315I mutations could be detected earlier by ultra-deep sequencing compared to Sanger sequencing in a selected group of cases. Earlier mutation detection by ultra-deep sequencing might allow treatment to be changed before clonal increase of cells with the T315I mutation.
The confluence of deep sequencing and powerful machine learning is providing an unprecedented peek at the darkest of the dark genomic matter, the non-coding genomic regions lacking any functional ...annotation. While deep sequencing uncovers rare tumor variants, the heterogeneity of the disease confounds the best of machine learning (ML) algorithms. Here we set out to answer if the dark-matter of the genome encompass signals that can distinguish the fine subtypes of disease that are otherwise genomically indistinguishable. We introduce a novel stochastic regularization, ReVeaL, that empowers ML to discriminate subtle cancer subtypes even from the same 'cell of origin'. Analogous to heritability, implicitly defined on whole genome, we use predictability (F1 score) definable on portions of the genome. In an effort to distinguish cancer subtypes using dark-matter DNA, we applied ReVeaL to a new WGS dataset from 727 patient samples with seven forms of hematological cancers and assessed the predictivity over several genomic regions including genic, non-dark, non-coding, non-genic, and dark. ReVeaL enabled improved discrimination of cancer subtypes for all segments of the genome. The non-genic, non-coding and dark-matter had the highest F1 scores, with dark-matter having the highest level of predictability. Based on ReVeaL's predictability of different genomic regions, dark-matter contains enough signal to significantly discriminate fine subtypes of disease. Hence, the agglomeration of rare variants, even in the hitherto unannotated and ill-understood regions of the genome, may play a substantial role in the disease etiology and deserve much more attention.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
CG Mullighan and T Haferlach: are co-senior authors
Introduction: Recent genomic sequencing studies have advanced our understanding of the pathogenesis of myeloid malignancies, including acute ...myeloid leukemia (AML) and myelodysplastic syndrome (MDS), and improved classification of specific subgroups. Unfortunately, these studies have mostly analyzed specific subtypes and/or used targeted DNA-sequencing, thus limiting discovery of novel mutational patterns and gene expression clusters. Here, we performed an integrated genome-wide mutational/transcriptomic analysis of a large cohort of adult AML and MDS samples to accurately define subtypes of diagnostic, prognostic and therapeutic relevance.
Methods: We performed unbiased whole genome (WGS) and transcriptome sequencing (RNA-seq) of 1,304 adult individuals (598 AML and 706 MDS; Fig. 1A), incorporating analysis of somatic and presumed germline sequence mutations, chimeric fusions and structural complex variations. Transcriptomic gene expression data were processed by a rigorous bootstrap procedure to define gene expression subgroups in an unsupervised manner. Associations between genetic variants, gene expression groups and outcome were examined.
Results: Genomic/transcriptome sequencing confirmed diagnosis according to WHO 2016 of AML with recurrent genetic abnormalities in 10.9% of cases. These cases had a distinct gene expression profile (Fig. 1A), good prognosis (Fig. 1B) and a combination of mutations in the following genes: KIT, ZBTB7A, ASXL2, RAD21, CSF3R and DNM2 in RUNX1-RUNXT1 leukemia; FLT3, DDX54, WT1 and CALR in PML-RARA promyelocytic leukemia; KIT and BCORL1 in CBFB-rearranged leukemia. In addition, 9% of cases showed rearrangements of KMT2A, with known (e.g. MLLT3) and non-canonical partners (e.g. ACACA, and NCBP1) and poor outcome. Although common targets of mutations have been previously described for myeloid malignancies, the heterogeneity and complexity of mutational patterns, their expression signature and outcome here described are novel. Gene expression analysis identified groups of AML and/or MDS lacking recurrent cytogenetic abnormalities (87%). The spectrum of the most frequently mutated genes (>10 cases) and associated gene expression subtypes is summarized in Figure 1A. TET2 (more frequent in MDS than AML, p=0.0011) and DNMT3A (more frequent in AML than MDS, p<0.0001) were the most frequently mutated genes. Interestingly, mutations in these genes promoting clonal hematopoiesis were significantly enriched in the subgroup with NPM1 mutations. Overall, NPM1 mutations occurred in 27.4% of AML and 1% of MDS and were characterized by four expression signatures with different combination of cooperating mutations in cohesin and signaling genes and outcome (Fig. 1C, gene expression, GE, groups 2, 3, 7 and 8). Co-occurring NPM1 and FLT3 mutations conferred poorer outcome compared to only NPM1, in contrast co-occurring mutations with cohesin genes had better outcome (Fig. 1D). Additional mutations that significantly co-occurred with NPM1 were in PTPN11, IDH1/2, RAD21 and SMC1A. Three gene expression clusters accounted for additional 9% of cases with mutual exclusive mutations in RUNX1,TP53 and CEBPA and co-occurring with a combination of mutations in DNA methylation, splicing and signaling genes (Fig. 1E, GE groups 4, 5 and 6). Interestingly, RUNX1 mutations were significantly associated with SRSF2 mutations but not with SF3B1, showed high expression of MN1 and poor outcome (Fig. 1F). In contrast to the distinct, mutation-associated patterns of gene expression in AML samples, the gene expression profile of MDS was less variable despite diversity in patterns of mutation. MDS was enriched in mutations of SF3B1 (27.2%), mutually exclusive with SFRS2 (14.4%) and U2AF1 (5.5%); TP53 (13.7%) and RUNX1 (10.5%) and a combination of mutations in epigenetic regulators with outcome dependent on mutational pattern (Fig. 1A, G-H). Moreover, structural variations and/or missense mutations of MECOM accounted for 2% of cases.
Conclusions: the integration of mutational and expression data from a large cohort of adult pan myeloid leukemia cases enabled the definition of subtypes and constellations of mutations and have prognostic significance that transcends prior gene panel-based classification schema.
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Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Nadarajah:MLL Munich Leukemia Laboratory: Employment. Baer:MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Mullighan:Illumina: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: sponsored travel; Pfizer: Honoraria, Other: speaker, sponsored travel, Research Funding; AbbVie: Research Funding; Loxo Oncology: Research Funding; Amgen: Honoraria, Other: speaker, sponsored travel. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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