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.

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.

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.

Melanoma brain metastasis (MBM) represents a frequent complication of cutaneous melanoma. Despite aggressive multi-modality therapy, patients with MBM often have a survival rate of <1 year. Alteration in DNA methylation is a major hallmark of tumor progression and metastasis; however, it remains largely unexplored in MBM. In this study, we generated a comprehensive DNA methylation landscape through the use of genome-wide copy number, DNA methylation and gene expression data integrative analysis of melanoma progression to MBM. A progressive genome-wide demethylation in low CpG density and an increase in methylation level of CpG islands according to melanoma progression were observed. MBM-specific partially methylated domains (PMDs) affecting key brain developmental processes were identified. Differentially methylated CpG sites between MBM and lymph node metastasis (LNM) from patients with good prognosis were identified. Among the most significantly affected genes were the HOX family members. DNA methylation of HOXD9 gene promoter affected transcript and protein expression and was significantly higher in MBM than that in early stages. A MBM-specific PMD was identified in this region. Low methylation level of this region was associated with active HOXD9 expression, open chromatin and histone modifications associated with active transcription. Demethylating agent induced HOXD9 expression in melanoma cell lines. The clinical relevance of this finding was verified in an independent large cohort of melanomas (n = 145). Patients with HOXD9 hypermethylation in LNM had poorer disease-free and overall survival. This epigenome-wide study identified novel methylated genes with functional and clinical implications for MBM patients.

Patients with triple-negative breast cancer (TNBC) have a high incidence of early relapse and metastasis; however, the molecular basis for recurrence in these individuals remains poorly understood. Here, we demonstrate that RASAL2, which encodes a RAS-GTPase–activating protein (RAS-GAP), is a functional target of anti-invasive microRNA-203 and is overexpressed in a subset of triple-negative or estrogen receptor–negative (ER-negative) breast tumors. As opposed to luminal B ER-positive breast cancers, in which RASAL2 has been shown to act as a RAS-GAP tumor suppressor, we found that RASAL2 is oncogenic in TNBC and drives mesenchymal invasion and metastasis. Moreover, high RASAL2 expression was predictive of poor disease outcomes in patients with TNBC. RASAL2 acted independently of its RAS-GAP catalytic activity in TNBC; however, RASAL2 promoted small GTPase RAC1 signaling, which promotes mesenchymal invasion, through binding and antagonizing the RAC1-GAP protein ARHGAP24. Together, these results indicate that activation of a RASAL2/ARHGAP24/RAC1 module contributes to TNBC tumorigenesis and identify a context-dependent role of RASAL2 in breast cancer.

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