We show that DNA methyltransferase inhibitors (DNMTis) upregulate immune signaling in cancer through the viral defense pathway. In ovarian cancer (OC), DNMTis trigger cytosolic sensing of ...double-stranded RNA (dsRNA) causing a type I interferon response and apoptosis. Knocking down dsRNA sensors TLR3 and MAVS reduces this response 2-fold and blocking interferon beta or its receptor abrogates it. Upregulation of hypermethylated endogenous retrovirus (ERV) genes accompanies the response and ERV overexpression activates the response. Basal levels of ERV and viral defense gene expression significantly correlate in primary OC and the latter signature separates primary samples for multiple tumor types from The Cancer Genome Atlas into low versus high expression groups. In melanoma patients treated with an immune checkpoint therapy, high viral defense signature expression in tumors significantly associates with durable clinical response and DNMTi treatment sensitizes to anti-CTLA4 therapy in a pre-clinical melanoma model.
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•DNMTis induce an interferon response in cancer cells by activating dsRNA sensors•DNMTis induce ERV demethylation and expression helping trigger the dsRNA response•DNMTi viral defense genes in melanoma track with patient response to immune therapy•DNMTi treatment sensitizes to anti-CTLA-4 immunotherapy in a melanoma mouse model
DNA methyltransferase inhibitors upregulate endogenous retroviruses in tumor cells to induce an growth-inhibiting immune response. High expression of the genes associated with the anti-viral response seems to potentiate a response to immune checkpoint therapy.
We have profiled promoter DNA methylation alterations in 272 glioblastoma tumors in the context of The Cancer Genome Atlas (TCGA). We found that a distinct subset of samples displays concerted ...hypermethylation at a large number of loci, indicating the existence of a glioma-CpG island methylator phenotype (G-CIMP). We validated G-CIMP in a set of non-TCGA glioblastomas and low-grade gliomas. G-CIMP tumors belong to the proneural subgroup, are more prevalent among lower-grade gliomas, display distinct copy-number alterations, and are tightly associated with
IDH1 somatic mutations. Patients with G-CIMP tumors are younger at the time of diagnosis and experience significantly improved outcome. These findings identify G-CIMP as a distinct subset of human gliomas on molecular and clinical grounds.
► Identification of a CpG island methylator phenotype (G-CIMP) in gliomas ► G-CIMP is tightly associated with
IDH1 mutation ► G-CIMP patients are younger at diagnosis and display improved survival ► G-CIMP is more prevalent among low- and intermediate-grade gliomas
We have created a statistically grounded tool for determining the correlation of genomewide data with other datasets or known biological features, intended to guide biological exploration of ...high-dimensional datasets, rather than providing immediate answers. The software enables several biologically motivated approaches to these data and here we describe the rationale and implementation for each approach. Our models and statistics are implemented in an R package that efficiently calculates the spatial correlation between two sets of genomic intervals (data and/or annotated features), for use as a metric of functional interaction. The software handles any type of pointwise or interval data and instead of running analyses with predefined metrics, it computes the significance and direction of several types of spatial association; this is intended to suggest potentially relevant relationships between the datasets.
The package, GenometriCorr, can be freely downloaded at http://genometricorr.sourceforge.net/. Installation guidelines and examples are available from the sourceforge repository. The package is pending submission to Bioconductor.
Many high-grade serous carcinomas (HGSCs) of the pelvis are thought to originate in the distal portion of the fallopian tube. Serous tubal intra-epithelial carcinoma (STIC) lesions are the putative ...precursor to HGSC and identifiable in ~ 50% of advanced stage cases. To better understand the molecular etiology of HGSCs, we report a multi-center integrated genomic analysis of advanced stage tumors with and without STIC lesions and normal tissues. The most significant focal DNA SCNAs were shared between cases with and without STIC lesions. The RNA sequence and the miRNA data did not identify any clear separation between cases with and without STIC lesions. HGSCs had molecular profiles more similar to normal fallopian tube epithelium than ovarian surface epithelium or peritoneum. The data suggest that the molecular features of HGSCs with and without associated STIC lesions are mostly shared, indicating a common biologic origin, likely to be the distal fallopian tube among all cases.High-grade serous carcinomas (HGSCs) are associated with precursor lesions (STICs) in the fallopian epithelium in only half of the cases. Here the authors report the molecular analysis of HGSCs with and without associated STICs and show similar profiles supporting a common origin for all HGSCs.
Purpose
Although age is a recognized independent prognostic risk factor, its relative importance among molecular subtypes of Breast cancer (BCA) is not well documented. The aim of this study was to ...evaluate the prognostic role of age at diagnosis among different immunohistochemical subtypes of BCA.
Methods
We conducted a retrospective study of women with invasive BCA undergoing surgery at the Johns Hopkins Hospital, excluding patients presenting with stage IV breast cancer. Patients were stratified into three age groups: ≤ 40, 41–60, and > 60 years, and multivariable analysis was performed using Cox regression. We also identified differentially expressed genes (DEG) between age groups among BCA subtypes in the public TCGA dataset. Finally, we identified key driver genes within the DEGs using a weighted gene co-expression network analysis.
Results
Luminal A breast cancer patients had significantly lower 5 year disease-free survival (DFS) and distant metastasis-free survival (DMFS) in the ≤ 40 year age group compared to the 41–60 year age group, while the other molecular subtypes showed no significant association of DFS or DMFS with age. Age was a stronger outcome predictor than tumor grade or proliferative index in Luminal A BCA patients, but not other subtypes. BCA TCGA gene expression data were divided into two groups (≤ 40 years, > 40 years). We identified 374 DEGs in the Luminal A BCA subset, which were enriched in seven pathways and two modules of co-expressed genes. No age group-specific DEGs were identified in non-Luminal A subtypes.
Conclusions
Age at diagnosis may be an important prognostic factor in Luminal A BCA.
High-grade serous ovarian carcinoma (HGSOC) typically remains undiagnosed until advanced stages when peritoneal dissemination has already occurred. Here, we sought to identify HGSOC-specific ...alterations in DNA methylation and assess their potential to provide sensitive and specific detection of HGSOC at its earliest stages.
MethylationEPIC genome-wide methylation analysis was performed on a discovery cohort comprising 23 HGSOC, 37 non-HGSOC malignant, and 36 histologically unremarkable gynecologic tissue samples. The resulting data were processed using selective bioinformatic criteria to identify regions of high-confidence HGSOC-specific differential methylation. Quantitative methylation-specific real-time PCR (qMSP) assays were then developed for 8 of the top-performing regions and analytically validated in a cohort of 90 tissue samples. Lastly, qMSP assays were used to assess and compare methylation in 30 laser-capture microdissected (LCM) fallopian tube epithelia samples obtained from cancer-free and serous tubal intraepithelial carcinoma (STIC) positive women.
Bioinformatic selection identified 91 regions of robust, HGSOC-specific hypermethylation, 23 of which exhibited an area under the receiver-operator curve (AUC) value ≥ 0.9 in the discovery cohort. Seven of 8 top-performing regions demonstrated AUC values between 0.838 and 0.968 when analytically validated by qMSP in a 90-patient cohort. A panel of the 3 top-performing genes (
and
) was able to perfectly discriminate HGSOC (AUC 1.0). Hypermethylation within these loci was found exclusively in LCM fallopian tube epithelia from women with STIC lesions, but not in cancer-free fallopian tubes.
A panel of methylation biomarkers can be used to accurately identify HGSOC, even at precursor stages of the disease.
To better understand the biology of hormone receptor-positive and-negative breast cancer and to identify methylated gene markers of disease progression, we carried out a genome-wide methylation array ...analysis on 103 primary invasive breast cancers and 21 normal breast samples, using the Illumina Infinium HumanMethylation27 array that queried 27,578 CpG loci. Estrogen and/or progesterone receptor-positive tumors displayed more hypermethylated loci than estrogen receptor (ER)-negative tumors. However, the hypermethylated loci in ER-negative tumors were clustered closer to the transcriptional start site compared with ER-positive tumors. An ER-classifier set of CpG loci was identified, which independently partitioned primary tumors into ER subtypes. A total of 40 (32 novel and 8 previously known) CpG loci showed differential methylation specific to either ER-positive or ER-negative tumors. Each of the 40 ER subtype-specific loci was validated in silico, using an independent, publicly available methylome dataset from the Cancer Genome Atlas. In addition, we identified 100 methylated CpG loci that were significantly associated with disease progression; the majority of these loci were informative particularly in ER-negative breast cancer. Overall, the set was highly enriched in homeobox containing genes. This pilot study shows the robustness of the breast cancer methylome and illustrates its potential to stratify and reveal biological differences between ER subtypes of breast cancer. Furthermore, it defines candidate ER-specific markers and identifies potential markers predictive of outcome within ER subgroups.
In this study, our goal was to determine probe-specific thresholds for identifying aberrant, or outlying, DNA methylation and to provide guidance on the relative merits of using continuous or outlier ...methylation data. To construct a reference database, we downloaded Illumina Human 450K array data for more than 2,000 normal samples, characterized the distribution of DNA methylation and derived probe-specific thresholds for identifying aberrations. We made the decision to restrict our reference database to solid normal tissue and morphologically normal tissue found adjacent to solid tumours, excluding blood which has very distinctive patterns of DNA methylation. Next, we explored the utility of our outlier thresholds in several analyses that are commonly performed on DNA methylation data. Outliers are as effective as the full continuous dataset for simple tasks, like distinguishing tumour tissue from normal, but becomes less useful as the complexity of the problem increases. We developed an R package called OutlierMeth containing our thresholds, as well as functions for applying them to data.
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