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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.

Background: Pathological response to neoadjuvant chemotherapy (NAC) is critical in prognosis and selection of systemic treatments for patients with triple-negative breast cancer (TNBC). The aim of this study is to identify gene expression-based markers to predict response to NAC., Patients and Methods: A survey of 43 publicly available gene expression datasets was performed. We identified a cohort of TNBC patients treated with NAC (n = 708). Gene expression data from different studies were renormalized, and the differences between pretreatment (pre-NAC), on-treatment (post-C1), and surgical (Sx) specimens were evaluated. Euclidean statistical distances were calculated to estimate changes in gene expression patterns induced by NAC. Hierarchical clustering and pathway enrichment analyses were used to characterize relationships between differentially expressed genes and affected gene pathways. Machine learning was employed to refine a gene expression signature with the potential to predict response to NAC., Results: Forty nine genes consistently affected by NAC were involved in enhanced regulation of wound response, chemokine release, cell division, and decreased programmed cell death in residual invasive disease. The statistical distances between pre-NAC and post-C1 significantly predicted pathological complete response [area under the curve (AUC) = 0.75; p = 0.003; 95% confidence interval (CI) 0.58-0.92]. Finally, the expression of CCND1, a cyclin that forms complexes with CDK4/6 to promote the cell cycle, was the most informative feature in pre-NAC biopsies to predict response to NAC., Conclusions: The results of this study reveal significant transcriptomic changes induced by NAC and suggest that chemotherapy-induced gene expression changes observed early in therapy may be good predictors of response to NAC.

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.

Introduction: Esophageal squamous cell carcinoma (ESCC) is a cancer of variable outcomes with limited effective treatments resulting in poor overall survival (OS). Epigenetic alterations contributing to this deadly cancer type that can be used as novel therapeutic or diagnostic targets are still poorly understood., Methods: We explored genome-wide DNA methylation data from The Cancer Genome Atlas project and identified a panel of tumor-related genes hypermethylated in ESCC. The methylation statuses of RASSF1, RARB, CDKN2A (p16INK4a, p14ARF), APC, and RUNX3 genes and long interspersed nucleotide element-1 (LINE-1) were validated in a large cohort (n = 140) of clinically well-annotated ESCC specimens and esophageal normal mucosa (n = 28) using a quantitative methylation-specific polymerase chain reaction., Results: Hypermethylation of RARB, p16INK4a, RASSF1, APC, RUNX3, and p14ARF were observed in 55%, 24%, 20%, 19%, 14%, and 8% of specimens, respectively. Hypermethylation of APC was significantly associated with tumor depth (p = 0.02) and American Joint Committee on Cancer stage (p = 0.03). Global DNA methylation level, assessed by LINE-1, was significantly lower in ESCC than in normal mucosa (p < 0.0001), and lower in greater than or equal to T2 (n = 69) than T1 tumors (n = 45; p = 0.03). There was a significant inverse correlation between LINE-1 and RARB methylation (p = 0.008). Importantly, hypermethylation of RASSF1 and APC genes was significantly associated with overall survival (OS; p = 0.006 and p = 0.007, respectively). In addition, patients with tumors containing a higher number of methylated genes (greater than two genes) presented worse OS (p = 0.003)., Conclusions: This study demonstrates that epigenetic alterations of a panel of tumor-related genes and the noncoding region LINE-1 can be used as prognostic indicators and help in clinical management of ESCC patients.

Qualitative and quantitative analysis of circulating cell-free DNA (cfDNA) is an emerging non-invasive blood biomarker utilized to assess tumor progression and to evaluate prognosis, diagnosis and response to treatment. There is a need to develop cfDNA biomarkers to avoid complex risk-prone biopsy procedures for primary or metastatic tumors. Given the challenges associated with inter- and intra-tumor heterogeneity, the implementation of genome-wide cfDNA analysis will become an important avenue to understand tumor progression and therapeutic settings, not only for predominant, but also for under-represented tumor subclones with specific genomic aberrations. We summarize the latest publications in cfDNA analysis, including a metric analysis of clinical trials and new high-throughput technology applied to cfDNA analysis in clinical oncology.