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Background/objective: Triple-negative breast cancer (TNBC) is a heterogeneous collection of breast tumors with numerous differences including morphological characteristics, genetic makeup, immune-cell infiltration, and response to systemic therapy. DNA methylation profiling is a robust tool to accurately identify disease-specific subtypes. We aimed to generate an epigenetic subclassification of TNBC tumors (epitypes) with utility for clinical decision-making., Methods: Genome-wide DNA methylation profiles from TNBC patients generated in the Cancer Genome Atlas project were used to build machine learning-based epigenetic classifiers. Clinical and demographic variables, as well as gene expression and gene mutation data from the same cohort, were integrated to further refine the TNBC epitypes., Results: This analysis indicated the existence of four TNBC epitypes, named as Epi-CL-A, Epi-CL-B, Epi-CL-C, and Epi-CL-D. Patients with Epi-CL-B tumors showed significantly shorter disease-free survival and overall survival [log rank; P = 0.01; hazard ratio (HR) 3.89, 95% confidence interval (CI) 1.3-11.63 and P = 0.003; HR 5.29, 95% CI 1.55-18.18, respectively]. Significant gene expression and mutation differences among the TNBC epitypes suggested alternative pathway activation that could be used as ancillary therapeutic targets. These epigenetic subtypes showed complementarity with the recently described TNBC transcriptomic subtypes., Conclusions: TNBC epigenetic subtypes exhibit significant clinical and molecular differences. The links between genetic make-up, gene expression programs, and epigenetic subtypes open new avenues in the development of laboratory tests to more efficiently stratify TNBC patients, helping optimize tailored treatment approaches.

Brain metastases (BM) are one the most lethal and poorly managed clinical complications in cancer patients. These secondary tumors represent the most common intracranial neoplasm in adults, most frequently originating from lung cancer, breast cancer, and cutaneous melanoma. In primary brain tumors, such as gliomas, recent advances in DNA methylation profiling have allowed for a comprehensive molecular classification. Such data provide prognostic information, in addition to helping predict patient response to specific systemic therapies. However, epigenetic alterations of metastatic brain tumors with specific biological and translational relevance still require much further exploration. Using the widely employed Illumina Infinium HumanMethylation 450K platform, we have generated a cohort of genome-wide DNA methylomes from ninety-six needle-dissected BM specimens from patients with lung cancer, breast cancer, and cutaneous melanoma with clinical, pathological, and demographic annotations. This resource offers an unprecedented and unique opportunity to identify novel DNA methylation features influencing the behavior of brain metastasis, and thus accelerate the discovery of BM-specific theranostic epigenetic alterations.

Optimal treatment of brain metastases is often hindered by limitations in diagnostic capabilities. To meet this challenge, here we profile DNA methylomes of the three most frequent types of brain metastases: melanoma, breast, and lung cancers (n = 96). Using supervised machine learning and integration of DNA methylomes from normal, primary, and metastatic tumor specimens (n = 1860), we unravel epigenetic signatures specific to each type of metastatic brain tumor and constructed a three-step DNA methylation-based classifier (BrainMETH) that categorizes brain metastases according to the tissue of origin and therapeutically relevant subtypes. BrainMETH predictions are supported by routine histopathologic evaluation. We further characterize and validate the most predictive genomic regions in a large cohort of brain tumors (n = 165) using quantitative-methylation-specific PCR. Our study highlights the importance of brain tumor-defining epigenetic alterations, which can be utilized to further develop DNA methylation profiling as a critical tool in the histomolecular stratification of patients with brain metastases.

Background: Molecular alterations impact tumor prognosis and response to treatment. This study was designed to identify transcriptomic and epigenomic signatures of breast cancer (BC) tumors from patients with any prior malignancy., Methods: RNA-sequencing and genome-wide DNA methylation profiles from BCs were generated in the Cancer Genome Atlas project. Patients with secondary breast cancer (SBC) were separated by histological subtype and matched to primary breast cancer controls to create two independent cohorts of invasive ductal (IDC, n = 36) and invasive lobular (ILC, n = 40) carcinoma. Differentially expressed genes, as well as differentially methylated genomic regions, were integrated to identify epigenetically regulated abnormal gene pathways in SBCs., Results: Differentially expressed genes were identified in IDC SBCs (n = 727) and in ILC SBCs (n = 261; Wilcoxon's test; P < 0.05). In IDC SBCs, 105 genes were upregulated and hypomethylated, including an estrogen receptor gene, and 73 genes were downregulated and hypermethylated, including genes involved in antigen presentation and interferon response pathways (HLA-E, IRF8, and RELA). In ILC SBCs, however, only 17 genes were synchronously hypomethylated and upregulated, whereas 46 genes hypermethylated and downregulated. Interestingly, the SBC gene expression signatures closely corresponded with each histological subtype with only 1.51% of genes overlapping between the two histological subtypes., Conclusions: Differential gene expression and DNA methylation signatures are seen in both IDC and ILC SBCs, including genes that are relevant to tumor growth and proliferation. Differences in gene expression signatures corresponding with each histological subtype emphasize the importance of disease subtype-specific evaluations of molecular alterations.

Purpose: Pituitary adenomas are one of the most common benign neoplasms of the central nervous system. Although emerging evidence suggests roles for both genetic and epigenetic factors in tumorigenesis, the degree to which these factors contribute to disease remains poorly understood. Experimental Design: A multiplatform analysis was performed to identify the genomic and epigenomic underpinnings of disease among the three major subtypes of surgically resected pituitary adenomas in 48 patients: growth hormone (GH)-secreting ( n = 17), adrenocorticotropic hormone (ACTH)-secreting ( n = 13, including 3 silent-ACTH adenomas), and endocrine-inactive ( n = 18). Whole-exome sequencing was used to profile the somatic mutational landscape, whole-transcriptome sequencing was used to identify disease-specific patterns of gene expression, and array-based DNA methylation profiling was used to examine genome-wide patterns of DNA methylation. Results: Recurrent single-nucleotide and small indel somatic mutations were infrequent among the three adenoma subtypes. However, somatic copy-number alterations (SCNA) were identified in all three pituitary adenoma subtypes. Methylation analysis revealed adenoma subtype-specific DNA methylation profiles, with GH-secreting adenomas being dominated by hypomethylated sites. Likewise, gene-expression patterns revealed adenoma subtype-specific profiles. Integrating DNA methylation and gene-expression data revealed that hypomethylation of promoter regions are related with increased expression of GH1 and SSTR5 genes in GH-secreting adenomas and POMC gene in ACTH-secreting adenomas. Finally, multispectral IHC staining of immune-related proteins showed abundant expression of PD-L1 among all three adenoma subtypes. Conclusions: Taken together, these data stress the contribution of epigenomic alterations to disease-specific etiology among adenoma subtypes and highlight potential targets for future immunotherapy-based treatments. This article reveals novel insights into the epigenomics underlying pituitary adenomas and highlights how differences in epigenomic states are related to important transcriptome alterations that define adenoma subtypes. Clin Cancer Res; 24(17); 4126-36. ©2018 AACR ., (©2018 American Association for Cancer Research.)