Deconvolute brain tumor genomic alterations based on DNA methylation

Molecular classification based on mutations, expression subtypes, and copy number variants has improved diagnosis and treatment decision-making for patients with brain tumors, particularly malignant gliomas. However, the association between epigenetic signature and genetic alterations is poorly understood. For example, mutation of isocitrate dehydrogenase (IDH) is associated with genome-wide hypermethylation of CpG islands in gliomas. But other subtype-associated alterations, including telomerase reverse transcriptase (TERT) promoter mutation, alpha thalassemia/mental retardation syndrome X-linked (ATRX) mutation, chromosome 1p19q co-deletion (chr1p19q codel), and gene expression subtypes, have yet to be associated with any epigenetic signature. Therefore, we hypothesized that DNA methylation signatures can classify gliomas based on these alterations and give insight into subgroup characteristics. Machine learning models, including elastic net and random forest, were used to predict somatic mutations of IDH, TERTp, and ATRX, chr1p19q codel, and gene expression subtype of gliomas. Data from the NOA-04 randomized phase III trial were used for external validation. In total, 926 cases from The Cancer Genome Atlas were included in this study. Prediction accuracies for IDH, TERTp, and ATRX mutations, and chr1p19q codel were 100%, 98.3%, 90.48%, and 99.21%, respectively in test set. Accuracy for gene expression subtype prediction was 72.2%. The methylation-based prediction models for both ATRX and chr1p19q codel statuses proved superior to conventional assays for these biomarkers. Similarly, characteristic alterations associated with gene expression subtypes were better discriminated using methylation compared to transcriptome-based classification. DNA methylation signatures accurately predicted somatic alterations and improved over existing classifiers. The established Unified Diagnostic Pipeline (UniD) is a rapid and cost-effective diagnostic platform of genomic alterations and gene expression subtypes at initial clinical diagnosis and improves over individual assays currently in clinical use. The significant relationship between genetic alterations and epigenetic signatures indicates the broad applicability of our approach to other malignancies

DNA 5-hydroxymethylcytosine in pediatric central nervous system tumors may impact tumor classification and is a positive prognostic marker

Background: Nucleotide-specific 5-hydroxymethylcytosine (5hmC) remains understudied in pediatric central nervous system (CNS) tumors. 5hmC is abundant in the brain, and alterations to 5hmC in adult CNS tumors have been reported. However, traditional approaches to measure DNA methylation do not distinguish between 5-methylcytosine (5mC) and its oxidized counterpart 5hmC, including those used to build CNS tumor DNA methylation classification systems. We measured 5hmC and 5mC epigenome-wide at nucleotide resolution in glioma, ependymoma, and embryonal tumors from children, as well as control pediatric brain tissues using tandem bisulfite and oxidative bisulfite treatments followed by hybridization to the Illumina Methylation EPIC Array that interrogates over 860,000 CpG loci. Results: Linear mixed effects models adjusted for age and sex tested the CpG-specific differences in 5hmC between tumor and non-tumor samples, as well as between tumor subtypes. Results from model-based clustering of tumors was used to test the relation of cluster membership with patient survival through multivariable Cox proportional hazards regression. We also assessed the robustness of multiple epigenetic CNS tumor classification methods to 5mC-specific data in both pediatric and adult CNS tumors. Compared to non-tumor samples, tumors were hypohydroxymethylated across the epigenome and tumor 5hmC localized to regulatory elements crucial to cell identity, including transcription factor binding sites and super-enhancers. Differentially hydroxymethylated loci among tumor subtypes tended to be hypermethylated and disproportionally found in CTCF binding sites and genes related to posttranscriptional RNA regulation, such as DICER1. Model-based clustering results indicated that patients with low 5hmC patterns have poorer overall survival and increased risk of recurrence. Our results suggest 5mC-specific data from OxBS-treated samples impacts methylation-based tumor classification systems giving new opportunities for further refinement of classifiers for both pediatric and adult tumors. Conclusions: We identified that 5hmC localizes to super-enhancers, and genes commonly implicated in pediatric CNS tumors were differentially hypohydroxymethylated. We demonstrated that distinguishing methylation and hydroxymethylation is critical in identifying tumor-related epigenetic changes. These results have implications for patient prognostication, considerations of epigenetic therapy in CNS tumors, and for emerging molecular neuropathology classification approaches

A systematic assessment of cell type deconvolution algorithms for DNA methylation data

We performed systematic assessment of computational deconvolution methods that play an important role in the estimation of cell type proportions from bulk methylation data. The proposed framework methylDeConv (available as an R package) integrates several deconvolution methods for methylation profiles (Illumina HumanMethylation450 and MethylationEPIC arrays) and offers different cell-type-specific CpG selection to construct the extended reference library which incorporates the main immune cell subsets, epithelial cells and cell-free DNAs. We compared the performance of different deconvolution algorithms via simulations and benchmark datasets and further investigated the associations of the estimated cell type proportions to cancer therapy in breast cancer and subtypes in melanoma methylation case studies. Our results indicated that the deconvolution based on the extended reference library is critical to obtain accurate estimates of cell proportions in non-blood tissues.U01 OH011478/OH/NIOSH CDC HHS/United StatesU01 OH012257/OH/NIOSH CDC HHS/United StatesU01OH011478/ACL HHS/United StatesU01 OH011478/OH/NIOSH CDC HHS/United State

The epigenetic evolution of glioma is determined by the IDH1 mutation status and treatment regimen

Tumor adaptation or selection is thought to underlie therapy resistance in glioma. To investigate longitudinal epigenetic evolution of gliomas in response to therapeutic pressure, we performed an epigenomic analysis of 132 matched initial and recurrent tumors from patients with IDH-wildtype (IDHwt) and IDH-mutant (IDHmut) glioma. IDHwt gliomas showed a stable epigenome over time with relatively low levels of global methylation. The epigenome of IDHmut gliomas showed initial high levels of genome-wide DNA methylation that was progressively reduced to levels similar to those of IDHwt tumors. Integration of epigenomics, gene expression, and functional genomics identified HOXD13 as a master regulator of IDHmut astrocytoma evolution. Furthermore, relapse of IDHmut tumors was accompanied by histological progression that was associated with survival, as validated in an independent cohort. Finally, the initial cell composition of the tumor microenvironment varied between IDHwt and IDHmut tumors and changed differentially following treatment, suggesting increased neo-angiogenesis and T-cell infiltration upon treatment of IDHmut gliomas. This study provides one of the largest cohorts of paired longitudinal glioma samples with epigenomic, transcriptomic, and genomic profiling and suggests that treatment of IDHmut glioma is associated with epigenomic evolution towards an IDHwt-like phenotype

Identification of a Robust Methylation Classifier for Cutaneous Melanoma Diagnosis

Early diagnosis improves melanoma survival, yet the histopathological diagnosis of cutaneous primary melanoma can be challenging, even for expert dermatopathologists. Analysis of epigenetic alterations, such as DNA methylation, that occur in melanoma can aid in its early diagnosis. Using a genome-wide methylation screening, we assessed CpG methylation in a diverse set of 89 primary invasive melanomas, 73 nevi, and 41 melanocytic proliferations of uncertain malignant potential, classified based on interobserver review by dermatopathologists. Melanomas and nevi were split into training and validation sets. Predictive modeling in the training set using ElasticNet identified a 40-CpG classifier distinguishing 60 melanomas from 48 nevi. High diagnostic accuracy (area under the receiver operator characteristic curve = 0.996, sensitivity = 96.6%, and specificity = 100.0%)was independently confirmed in the validation set (29 melanomas, 25 nevi)and other published sample sets. The 40-CpG melanoma classifier included homeobox transcription factors and genes with roles in stem cell pluripotency or the nervous system. Application of the 40-CpG melanoma classifier to the diagnostically uncertain samples assigned melanoma or nevus status, potentially offering a diagnostic tool to assist dermatopathologists. In summary, the robust, accurate 40-CpG melanoma classifier offers a promising assay for improving primary melanoma diagnosis

The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma

Renal cell carcinoma(RCC) is not a single disease, but several histologically defined cancers with different genetic drivers, clinical courses, and therapeutic responses. The current study evaluated 843 RCC from the three major histologic subtypes, including 488 clear cell RCC, 274 papillary RCC, and 81 chromophobe RCC. Comprehensive genomic and phenotypic analysis of the RCC subtypes reveals distinctive features of each subtype that provide the foundation for the development of subtype-specific therapeutic and management strategies for patients affected with these cancers. Somatic alteration of BAP1, PBRM1, and PTEN and altered metabolic pathways correlated with subtype-specific decreased survival, while CDKN2A alteration, increased DNA hypermethylation, and increases in the immune-related Th2 gene expression signature correlated with decreased survival within all major histologic subtypes. CIMP-RCC demonstrated an increased immune signature, and a uniform and distinct metabolic expression pattern identified a subset of metabolically divergent (MD) ChRCC that associated with extremely poor survival

Characterization of cell type-specific molecular heterogeneity in cancer using multi-omic approaches

Tumors are composed of heterogeneous cell types each with its own unique molecular profiles. Recent advances in single cell genomics technologies have begun to increase our understanding of the molecular heterogeneity that exists in tumors with particular focus on gene expression and chromatin accessibility profiles. However, due to limitations in methods for certain sample types and high cost for single cell genomics, bulk tumor molecular profiling has been and remains widely used. In addition, other facets of single cell epigenomic profiling, particularly methylation and hydroxymethylation, remains underexplored. Thus, investigations to understand the cell type specific epigenetic heterogeneity and the cooperation among various molecular layers to regulate tumorigenesis are needed. In this thesis, I utilize a multi-omic approach integrating DNA methylation, hydroxymethylation, chromatin accessibility, and gene expression profiles to investigate unique single cell type-specific features in 1) epithelial-to-mesenchymal transition and in 2) pediatric central nervous system tumors. First, I demonstrate the shared and distinct epigenetic profiles that are associated with single cells undergoing epithelial-to-mesenchymal transition. With a multi-omic approach, I identify increased hydroxymethylation in binding motifs of transcription factors critical in regulating epithelial-to-mesenchymal transition. Then, I shift my focus to characterize the cellular heterogeneity in pediatric central nervous system tumors and transcriptomic alterations associated with these tumors, while accounting for cell type composition, with single nuclei gene expression data. I detect novel pediatric central nervous system tumor associated genes that are differentially expressed. Finally, I illustrate the cytosine modification alterations that occur predominantly in the progenitorlike cell types of pediatric central nervous system tumors with a multi-omic approach. I determine associations between cell type-specific hydroxymethylation alterations with cell type-specific gene expression changes. Together, these findings emphasize the need for consideration of cellular identity to determine molecular heterogeneity that exist in various cancer contexts. Moreover, these works collectively suggest the utility of multiomic approaches to uncover novel insights in underlying tumor biology

The cancer genome atlas comprehensive molecular characterization of renal cell carcinoma

Renal cell carcinoma (RCC) is not a single disease, but several histologically defined cancers with different genetic drivers, clinical courses, and therapeutic responses. The current study evaluated 843 RCC from the three major histologic subtypes, including 488 clear cell RCC, 274 papillary RCC, and 81 chromophobe RCC. Comprehensive genomic and phenotypic analysis of the RCC subtypes reveals distinctive features of each subtype that provide the foundation for the development of subtype-specific therapeutic and management strategies for patients affected with these cancers. Somatic alteration of BAP1, PBRM1, and PTEN and altered metabolic pathways correlated with subtype-specific decreased survival, while CDKN2A alteration, increased DNA hypermethylation, and increases in the immune-related Th2 gene expression signature correlated with decreased survival within all major histologic subtypes. CIMP-RCC demonstrated an increased immune signature, and a uniform and distinct metabolic expression pattern identified a subset of metabolically divergent (MD) ChRCC that associated with extremely poor survival

Pan-cancer deconvolution of cellular composition identifies molecular correlates of antitumour immunity and checkpoint blockade response

The nature and extent of immune cell infiltration into solid tumours are key determinants of therapeutic response. Here, using a novel DNA methylation-based approach to tumour cell fraction deconvolution, we report the integrated analysis of tumour composition and genomics across a wide spectrum of solid cancers. Initially studying head and neck squamous cell carcinoma, we identify two distinct tumour subgroups: ‘immune hot’ and ‘immune cold’, which display differing prognosis, mutation burden, cytokine signalling, cytolytic activity, and oncogenic driver events. We demonstrate the existence of such tumour subgroups pan-cancer, link clonal-neoantigen burden to hot tumours, and show that transcriptional signatures of hot tumours are selectively engaged in immunotherapy responders. We also find that treatment-naive hot tumours are markedly enriched for known immune-resistance genomic alterations and define a catalogue of novel and known mediators of active antitumour immunity, deriving biomarkers and potential targets for precision immunotherapy