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 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.
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
High mobility group A1 (HMGA1) chromatin regulators are upregulated in diverse tumors where they portend adverse outcomes, although how they function in cancer remains unclear. Pancreatic ductal ...adenocarcinomas (PDACs) are highly lethal tumors characterized by dense desmoplastic stroma composed predominantly of cancer-associated fibroblasts and fibrotic tissue. Here, we uncover an epigenetic program whereby HMGA1 upregulates FGF19 during tumor progression and stroma formation. HMGA1 deficiency disrupts oncogenic properties in vitro while impairing tumor inception and progression in KPC mice and subcutaneous or orthotopic models of PDAC. RNA sequencing revealed HMGA1 transcriptional networks governing proliferation and tumor-stroma interactions, including the FGF19 gene. HMGA1 directly induces FGF19 expression and increases its protein secretion by recruiting active histone marks (H3K4me3, H3K27Ac). Surprisingly, disrupting FGF19 via gene silencing or the FGFR4 inhibitor BLU9931 recapitulates most phenotypes observed with HMGA1 deficiency, decreasing tumor growth and formation of a desmoplastic stroma in mouse models of PDAC. In human PDAC, overexpression of HMGA1 and FGF19 defines a subset of tumors with extremely poor outcomes. Our results reveal what we believe is a new paradigm whereby HMGA1 and FGF19 drive tumor progression and stroma formation, thus illuminating FGF19 as a rational therapeutic target for a molecularly defined PDAC subtype.
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.
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 density oligonucleotide array technology is widely used in many areas of biomedical research for quantitative and highly parallel measurements of gene expression. Affymetrix GeneChip arrays are ...the most popular. In this technology each gene is typically represented by a set of 11–20 pairs of probes. In order to obtain expression measures it is necessary to summarize the probe level data. Using two extensive spike‐in studies and a dilution study, we developed a set of tools for assessing the effectiveness of expression measures. We found that the performance of the current version of the default expression measure provided by Affymetrix Microarray Suite can be significantly improved by the use of probe level summaries derived from empirically motivated statistical models. In particular, improvements in the ability to detect differentially expressed genes are demonstrated.