Triple-negative breast cancer (TNBC) is a highly heterogeneous disease defined by the absence of estrogen receptor (ER) and progesterone receptor (PR) expression, and human epidermal growth factor receptor 2 (HER2) overexpression that lacks targeted treatments, leading to dismal clinical outcomes. Thus, better stratification systems that reflect intrinsic and clinically useful differences between TNBC tumors will sharpen the treatment approaches and improve clinical outcomes. The lack of a rational classification system for TNBC also impacts current and emerging therapeutic alternatives. In the past years, several new methodologies to stratify TNBC have arisen thanks to the implementation of microarray technology, high-throughput sequencing, and bioinformatic methods, exponentially increasing the amount of genomic, epigenomic, transcriptomic, and proteomic information available. Thus, new TNBC subtypes are being characterized with the promise to advance the treatment of this challenging disease. However, the diverse nature of the molecular data, the poor integration between the various methods, and the lack of cost-effective methods for systematic classification have hampered the widespread implementation of these promising developments. However, the advent of artificial intelligence applied to translational oncology promises to bring light into definitive TNBC subtypes. This review provides a comprehensive summary of the available classification strategies. It includes evaluating the overlap between the molecular, immunohistochemical, and clinical characteristics between these approaches and a perspective about the increasing applications of artificial intelligence to identify definitive and clinically relevant TNBC subtypes.

Melanoma has the highest propensity to metastasize to the brain compared to other cancers, as brain metastases are found frequently high in patients who have prolonged survival with visceral metastasis. Once disseminated in the brain, melanoma cells communicate with brain resident cells that include astrocytes and microglia. Microglia cells are the resident macrophages of the brain and are the main immunological cells in the CNS involved in neuroinflammation. Data on the interactions between brain metastatic melanoma cells and microglia and on the role of microglia-mediated neuroinflammation in facilitating melanoma brain metastasis are lacking. To elucidate the role of microglia in melanoma brain metastasis progression, we examined the bidirectional interactions between microglia and melanoma cells in the tumor microenvironment. We identified the molecular and functional modifications occurring in brain-metastasizing melanoma cells and microglia cells after the treatment of each cell type with supernatants of the counter cell type. Both cells induced alteration in gene expression programs, cell signaling, and cytokine secretion in the counter cell type. Moreover, melanoma cells exerted significant morphological changes on microglia cells, enhanced proliferation, induced matrix metalloproteinase-2 (MMP-2) activation, and cell migration. Microglia cells induced phenotypic changes in melanoma cells increasing their malignant phenotype: increased melanoma proliferation, MMP-2 activity, cell migration, brain endothelial penetration, and tumor cells ability to grow as spheroids in 3D cultures. Our work provides a novel insight into the bidirectional interactions between melanoma and micoglia cells, suggesting the contribution of microglia to melanoma brain metastasis formation.

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.

Breast cancer is a group of clinically, histopathologically and molecularly heterogeneous diseases, with different outcomes and responses to treatment. Triple-negative (TN) breast cancers are defined as tumors that lack the expression of estrogen receptor, progesterone receptor and epidermal growth factor receptor 2. This subgroup accounts for 15% of all types of breast cancer and its prevalence is higher among young African, African-American and Latino women. The hypermethylation of CpG islands (CpGI) is a common epigenetic alteration for suppressing gene expression in breast cancer and has been shown to be a key factor in breast carcinogenesis. In this study we analyzed the hypermethylation of 110 CpGI within 69 cancer-related genes in TN tumors. For the methylation analysis, we used the methyl-specific multiplex-ligation probe amplification assay. We found that the number of methylated CpGI is similar between TN and non-TN tumors, but the methylated genes between the groups are different. The methylation profile of TN tumors is defined by the methylation of five genes (that is, CDKN2B, CD44, MGMT, RB and p73) plus the non-methylation of 11 genes (that is, GSTP1, PMS2, MSH2, MLH1, MSH3, MSH6, DLC1, CACNA1A, CACNA1G, TWIST1 and ID4). We conclude that TN tumors have a specific methylation profile. Our findings give new information for better understanding tumor etiology and encourage future studies on potential drug targets for triple-negative breast tumors, which now lack a specific treatment.

Breast carcinogenesis is a multistep process that involves both genetic and epigenetic alterations. Identification of aberrantly methylated genes in breast tumors and their relation to clinical parameters can contribute to improved diagnostic, prognostic, and therapeutic decision making. Our objective in the present study was to identify the methylation status of 34 cancer-involved genes in invasive ductal carcinomas (IDC). Each of the 70 IDC cases analyzed had a unique methylation profile. The highest methylation frequency was detected in the WT1 (95.7%) and RASSF1 (71.4%) genes. Hierarchical cluster analysis revealed three clusters with different distribution of the prognostic factors tumor grade, lymph node metastasis, and proliferation rate. Methylation of TP73 was associated with high histological grade and high proliferation rate; methylation of RARB was associated with lymph node metastasis. Concurrent methylation of TP73 and RARB was associated with high histological grade, high proliferation rate, increased tumor size, and lymph node metastasis. Patients with more than six methylated genes had higher rates of relapse events and cancer deaths. In multivariate analysis, TP73 methylation and the methylation index were associated with disease outcome. Our results indicate that methylation index and methylation of TP73 and/or RARB are related to unfavorable prognostic factors in patients with IDC. These epigenetic markers should be validated in further studies to improve breast cancer management.