Pediatric T-ALL type-1 and type-2 relapses develop along distinct pathways of clonal evolution

The mechanisms underlying T-ALL relapse remain essentially unknown. Multilevel-omics in 38 matched pairs of initial and relapsed T-ALL revealed 18 (47%) type-1 (defined by being derived from the major ancestral clone) and 20 (53%) type-2 relapses (derived from a minor ancestral clone). In both types of relapse, we observed known and novel drivers of multidrug resistance including MDR1 and MVP, NT5C2 and JAK-STAT activators. Patients with type-1 relapses were specifically characterized by IL7R upregulation. In remarkable contrast, type-2 relapses demonstrated (1) enrichment of constitutional cancer predisposition gene mutations, (2) divergent genetic and epigenetic remodeling, and (3) enrichment of somatic hypermutator phenotypes, related to BLM, BUB1B/PMS2 and TP53 mutations. T-ALLs that later progressed to type-2 relapses exhibited a complex subclonal architecture, unexpectedly, already at the time of initial diagnosis. Deconvolution analysis of ATAC-Seq profiles showed that T-ALLs later developing into type-1 relapses resembled a predominant immature thymic T-cell population, whereas T-ALLs developing into type-2 relapses resembled a mixture of normal T-cell precursors. In sum, our analyses revealed fundamentally different mechanisms driving either type-1 or type-2 T-ALL relapse and indicate that differential capacities of disease evolution are already inherent to the molecular setup of the initial leukemia

A prognostic DNA methylation signature for stage I non-small-cell lung cancer

Purpose Non-small-cell lung cancer (NSCLC) is a tumor in which only small improvements in clinical outcome have been achieved. The issue is critical for stage I patients for whom there are no available biomarkers that indicate which high-risk patients should receive adjuvant chemotherapy. We aimed to find DNA methylation markers that could be helpful in this regard. Patients and Methods A DNA methylation microarray that analyzes 450,000 CpG sites was used to study tumoral DNA obtained from 444 patients with NSCLC that included 237 stage I tumors. The prognostic DNA methylation markers were validated by a single-methylation pyrosequencing assay in an independent cohort of 143 patients with stage I NSCLC. Results Unsupervised clustering of the 10,000 most variable DNA methylation sites in the discovery cohort identified patients with high-risk stage I NSCLC who had shorter relapse-free survival (RFS; hazard ratio [HR], 2.35; 95% CI, 1.29 to 4.28; P = .004). The study in the validation cohort of the significant methylated sites from the discovery cohort found that hypermethylation of five genes was significantly associated with shorter RFS in stage I NSCLC: HIST1H4F, PCDHGB6, NPBWR1, ALX1, and HOXA9. A signature based on the number of hypermethylated events distinguished patients with high-and low-risk stage I NSCLC (HR, 3.24; 95% CI, 1.61 to 6.54; P = .001). Conclusion The DNA methylation signature of NSCLC affects the outcome of stage I patients, and it can be practically determined by user-friendly polymerase chain reaction assays. The analysis of the best DNA methylation biomarkers improved prognostic accuracy beyond standard staging. (C) 2013 by American Society of Clinical Oncology

RAS-pathway mutation patterns define epigenetic subclasses in juvenile myelomonocytic leukemia

Juvenile myelomonocytic leukemia (JMML) is an aggressive myeloproliferative disorder of early childhood characterized by mutations activating RAS signaling. Established clinical and genetic markers fail to fully recapitulate the clinical and biological heterogeneity of this disease. Here we report DNA methylome analysis and mutation profiling of 167 JMML samples. We identify three JMML subgroups with unique molecular and clinical characteristics. The high methylation group (HM) is characterized by somatic PTPN11 mutations and poor clinical outcome. The low methylation group is enriched for somatic NRAS and CBL mutations, as well as for Noonan patients, and has a good prognosis. The intermediate methylation group (IM) shows enrichment for monosomy 7 and somatic KRAS mutations. Hypermethylation is associated with repressed chromatin, genes regulated by RAS signaling, frequent co-occurrence of RAS pathway mutations and upregulation of DNMT1 and DNMT3B, suggesting a link between activation of the DNA methylation machinery and mutational patterns in JMML

A promoter DNA demethylation landscape of human hematopoietic differentiation

Global mechanisms defining the gene expression programs specific for hematopoiesis are still not fully understood. Here, we show that promoter DNA demethylation is associated with the activation of hematopoietic-specific genes. Using genome-wide promoter methylation arrays, we identified 694 hematopoietic-specific genes repressed by promoter DNA methylation in human embryonic stem cells and whose loss of methylation in hematopoietic can be associated with gene expression. The association between promoter methylation and gene expression was studied for many hematopoietic-specific genes including CD45, CD34, CD28, CD19, the T cell receptor (TCR), the MHC class II gene HLA-DR, perforin 1 and the phosphoinositide 3-kinase (PI3K) and results indicated that DNA demethylation was not always sufficient for gene activation. Promoter demethylation occurred either early during embryonic development or later on during hematopoietic differentiation. Analysis of the genome-wide promoter methylation status of induced pluripotent stem cells (iPSCs) generated from somatic CD34+ HSPCs and differentiated derivatives from CD34+ HSPCs confirmed the role of DNA methylation in regulating the expression of genes of the hemato-immune system, and indicated that promoter methylation of these genes may be associated to stemness. Together, these data suggest that promoter DNA demethylation might play a role in the tissue/cell-specific genome-wide gene regulation within the hematopoietic compartment

DNA methylation at an enhancer of the three prime repair exonuclease 2 gene (TREX2) is linked to gene expression and survival in laryngeal cancer

Background: Genetic aberrations in DNA repair genes are linked to cancer, but less is reported about epigenetic regulation of DNA repair and functional consequences. We investigated the intragenic methylation loss at the three prime repair exonuclease 2 (TREX2) locus in laryngeal (n = 256) and colorectal cancer cases (n = 95) and in pan-cancer data from The Cancer Genome Atlas (TCGA). Results: Significant methylation loss at an intragenic site of TREX2 was a frequent trait in both patient cohorts (p = 0.016 and < 0.001, respectively) and in 15 out of 22 TCGA studies. Methylation loss correlated with immunohistochemically staining for TREX2 (p < 0.0001) in laryngeal tumors and improved overall survival of laryngeal cancer patients (p = 0.045). Chromatin immunoprecipitation, demethylation experiments, and reporter gene assays revealed that the region of methylation loss can function as a CCAAT/enhancer binding protein alpha (CEBPA)-responsive enhancer element regulating TREX2 expression. Conclusions: The data highlight a regulatory role of TREX2 DNA methylation for gene expression which might affect incidence and survival of laryngeal cancer. Altered TREX2 protein levels in tumors may affect drug-induced DNA damage repair and provide new tailored therapies

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Identifizierung und Priorisierung von genomischen Loci mit krankheitsspezifischen Methylierungsmustern

Epigenetic systems are an indispensable mechanism in development, they respond to environmental stimuli and are dysregulated in cancer and other diseases. DNA methylation is the best characterized and extensively studied epigenetic mark to date. In the past years, a number of assays have been designed to measure DNA methylation levels genome-wide. This thesis introduces computational techniques for handling DNA methylation data from microarray- and enrichment-based methods. It focuses on disease-oriented studies and addresses the questions of quality control and normalization, inter- and intra-group variability, identification of differentially methylated loci, prioritization of biomarker candidates and prediction of cancer type and other phenotypes. The presented statistical approaches and heuristics facilitated important discoveries with clinical application. We showed that neurological and autoimmune disorders can be characterized by their distinct methylation profiles. We observed a strong tissue-specific signal in the methylation profiles of healthy and cancer samples. We were able to accurately predict tumor type of origin of metastatic samples. We showed that neither adenocarcinoma, nor squamous cell carcinoma can be separated into two distinct subtypes with a characteristic global methylation profile. In colon cancer, we identified differentially methylated regions with a potential to be used as biomarkers for predicting microsatellite instability.Epigenetische Systeme sind ein unverzichtbarer Regulationsmechanismus in der Entwicklung von Lebewesen. Sie werden im Rahmen von Krebs und anderen Krankheiten fehlreguliert. DNA-Methylierung ist eine umfassend untersuchte und die am besten charakterisierte epigenetische Markierung. In den vergangenen Jahren wurde eine Reihe von Assays entwickelt, um DNA-Methylierungslevel genomweit zu messen. Diese Arbeit stellt Rechenverfahren für den Umgang mit DNA-Methylierungsdaten von Microarray- und Anreicherungs-basierten Methoden vor, mit dem Fokus auf krankheitsorientierte Studien. Sie befasst sich mit den Fragen der Qualitätskontrolle und Normalisierung, inter- und intra-Gruppen Variabilität, der Identifizierung von differentiell methylierten Regionen, Priorisierung von Biomarker-Kandidaten, sowie der Prognose von Krebstyp und anderen Phänotypen

Identifizierung und Priorisierung von genomischen Loci mit krankheitsspezifischen Methylierungsmustern

Epigenetic systems are an indispensable mechanism in development, they respond to environmental stimuli and are dysregulated in cancer and other diseases. DNA methylation is the best characterized and extensively studied epigenetic mark to date. In the past years, a number of assays have been designed to measure DNA methylation levels genome-wide. This thesis introduces computational techniques for handling DNA methylation data from microarray- and enrichment-based methods. It focuses on disease-oriented studies and addresses the questions of quality control and normalization, inter- and intra-group variability, identification of differentially methylated loci, prioritization of biomarker candidates and prediction of cancer type and other phenotypes. The presented statistical approaches and heuristics facilitated important discoveries with clinical application. We showed that neurological and autoimmune disorders can be characterized by their distinct methylation profiles. We observed a strong tissue-specific signal in the methylation profiles of healthy and cancer samples. We were able to accurately predict tumor type of origin of metastatic samples. We showed that neither adenocarcinoma, nor squamous cell carcinoma can be separated into two distinct subtypes with a characteristic global methylation profile. In colon cancer, we identified differentially methylated regions with a potential to be used as biomarkers for predicting microsatellite instability.Epigenetische Systeme sind ein unverzichtbarer Regulationsmechanismus in der Entwicklung von Lebewesen. Sie werden im Rahmen von Krebs und anderen Krankheiten fehlreguliert. DNA-Methylierung ist eine umfassend untersuchte und die am besten charakterisierte epigenetische Markierung. In den vergangenen Jahren wurde eine Reihe von Assays entwickelt, um DNA-Methylierungslevel genomweit zu messen. Diese Arbeit stellt Rechenverfahren für den Umgang mit DNA-Methylierungsdaten von Microarray- und Anreicherungs-basierten Methoden vor, mit dem Fokus auf krankheitsorientierte Studien. Sie befasst sich mit den Fragen der Qualitätskontrolle und Normalisierung, inter- und intra-Gruppen Variabilität, der Identifizierung von differentiell methylierten Regionen, Priorisierung von Biomarker-Kandidaten, sowie der Prognose von Krebstyp und anderen Phänotypen

Differential Analysis of Genetic, Epigenetic, and Cytogenetic Abnormalities in AML

Acute myeloid leukemia (AML) is a haematological malignancy characterized by the excessive proliferation of immature myeloid cells coupled with impaired differentiation. Many AML cases have been reported without any known cytogenetic abnormalities and carry no mutation in known AML-associated driver genes. In this study, 200 AML cases were selected from a publicly available cohort and differentially analyzed for genetic, epigenetic, and cytogenetic abnormalities. Three genes (FLT3, DNMT3A, and NPMc) are found to be predominantly mutated. We identified several aberrations to be associated with genome-wide methylation changes. These include Del (5q), T (15; 17), and NPMc mutations. Four aberrations—Del (5q), T (15; 17), T (9; 22), and T (9; 11)—are significantly associated with patient survival. Del (5q)-positive patients have an average survival of less than 1 year, whereas T (15; 17)-positive patients have a significantly better prognosis. Combining the methylation and mutation data reveals three distinct patient groups and four clusters of genes. We speculate that combined signatures have the better potential to be used for subclassification of AML, complementing cytogenetic signatures. A larger sample cohort and further investigation of the effects observed in this study are required to enable the clinical application of our patient classification aided by DNA methylation