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Background Triple-negative breast cancer (TNBC) is an aggressive subtype that exhibits a high incidence of distant metastases and lacks targeted therapeutic options. Here we explored how the epigenome contributes to matrix metalloprotease (MMP) dysregulation impacting tumor invasion, which is the first step of the metastatic process. Methods We combined RNA expression and chromatin interaction data to identify insulator elements potentially associated with MMP gene expression and invasion. We employed CRISPR/Cas9 to disrupt the CCCTC-Binding Factor (CTCF) binding site on an insulator element downstream of the MMP8 gene (IE8) in two TNBC cellular models. We characterized these models by combining Hi-C, ATAC-seq, and RNA-seq with functional experiments to determine invasive ability. The potential of our findings to predict the progression of ductal carcinoma in situ (DCIS), was tested in data from clinical specimens. Results We explored the clinical relevance of an insulator element located within the Chr11q22.2 locus, downstream of the MMP8 gene (IE8). This regulatory element resulted in a topologically associating domain (TAD) boundary that isolated nine MMP genes into two anti-correlated expression clusters. This expression pattern was associated with worse relapse-free (HR = 1.57 [1.06 − 2.33]; p = 0.023) and overall (HR = 2.65 [1.31 − 5.37], p = 0.005) survival of TNBC patients. After CRISPR/Cas9-mediated disruption of IE8, cancer cells showed a switch in the MMP expression signature, specifically downregulating the pro-invasive MMP1 gene and upregulating the antitumorigenic MMP8 gene, resulting in reduced invasive ability and collagen degradation. We observed that the MMP expression pattern predicts DCIS that eventually progresses into invasive ductal carcinomas (AUC = 0.77, p < 0.01). Conclusion Our study demonstrates how the activation of an IE near the MMP8 gene determines the regional transcriptional regulation of MMP genes with opposing functional activity, ultimately influencing the invasive properties of aggressive forms of breast cancer.

Background Glioblastoma (GBM) is the most aggressive and prevalent primary brain tumor, with a median survival of 15 months. Advancements in multi-omics profiling combined with computational algorithms have unraveled the existence of three GBM molecular subtypes (Classical, Mesenchymal, and Proneural) with clinical relevance. However, due to the costs of high-throughput profiling techniques, GBM molecular subtyping is not currently employed in clinical settings. Methods Using Random Forest and Nearest Shrunken Centroid algorithms, we constructed transcriptomic, epigenomic, and integrative GBM subtype-specific classifiers. We included gene expression and DNA methylation (DNAm) profiles from 304 GBM patients profiled in the Cancer Genome Atlas (TCGA), the Human Glioblastoma Cell Culture resource (HGCC), and other publicly available databases. Results The i ntegrative Glio blastoma Sub type (iGlioSub) classifier shows better performance (mean AUC = 95.9%) stratifying patients than gene expression (mean AUC = 91.9%) and DNAm-based classifiers (AUC = 93.6%). Also, to expand the understanding of the molecular differences between the GBM subtypes, this study shows that each subtype presents unique DNAm patterns and gene pathway activation. Conclusions The iGlioSub classifier provides the basis to design cost-effective strategies to stratify GBM patients in routine pathology laboratories for clinical trials, which will significantly accelerate the discovery of more efficient GBM subtype-specific treatment approaches.

Identifying molecular drivers in triple-negative breast cancer (TNBC) is crucial. While HER2-low expression predicts response to novel antibody-drug conjugates, its biological influence on TNBC biology is unknown. We performed a comprehensive multi-omics analysis, integrating genomic, epigenomic, transcriptomic, and proteomic profiling to characterize HER2-low TNBC. We generated genome-wide DNA methylation profiles from a multi-institutional cohort and integrated our data with three independent cohorts (TCGA, SCAN-B, I-SPY2). TNBC cases were categorized as HER2-zero (IHC 0) or HER2-low TNBC (IHC 1+/2+, ISH non-amplified). Among 506 patients (HER2-low, n  = 288; HER2-zero, n  = 218), HER2-low TNBC exhibited significantly lower tumor mutational burden ( P  = 0.02). Epigenetic analysis identified 5287 differentially methylated sites, with consistent hypermethylation of HLA genes in HER2-low tumors. Transcriptomic analyses revealed significant downregulation of genes enriched in immune response pathways (e.g., leukocyte activation, T-cell signaling) in HER2-low TNBC (adjusted P  < 0.001). Immune cell deconvolution showed reduced immune cell infiltration in the HER2-low tumor microenvironment ( P  = 0.002). Higher expression of five immune-related genes, downregulated in HER2-low, correlated with improved relapse-free (HR = 0.52; P  < 0.001) and overall survival (HR = 0.36; P  < 0.001). HER2-low TNBC tumors display distinct molecular features compared to HER2-zero, imparting an immune-evasive phenotype. These findings provide critical insights into the unique biology of HER2-low TNBC, warranting further clinical investigation.

Hereditary breast cancer (BC) remains unexplained in a substantial proportion of families who test negative for BRCA1/2 and other known susceptibility genes. To contribute to the genomic characterization of these unresolved cases, we generated a whole-genome sequencing (WGS) dataset from six women belonging to two unrelated high-risk families, each comprising three sisters diagnosed with BC. All participants had previously received negative results in conventional multigene panel testing. WGS was performed on peripheral blood DNA using the Illumina NovaSeq platform, followed by variant calling against GRCh38 and the comprehensive annotation of single-nucleotide variants, indels, and structural variants. For each family, we identified shared ClinVar-annotated variants, rare exonic or splice-site alterations, and intronic variants located within a curated set of 286 cancer-related genes. The dataset includes per-patient VCF files, copy number variation annotations, and family-level variant summaries. Raw and processed data are publicly available through the Sequence Read Archive and Zenodo. This resource supports variant reinterpretation, exploration of regulatory and intronic regions, and methodological benchmarking in the study of familial BC beyond established susceptibility genes.

Background Immune checkpoint inhibitors (ICI) improve clinical outcomes in triple-negative breast cancer (TNBC) patients. However, a subset of patients does not respond to treatment. Biomarkers that show ICI predictive potential in other solid tumors, such as levels of PD-L1 and the tumor mutational burden, among others, show a modest predictive performance in patients with TNBC. Methods We built machine learning models based on pre-ICI treatment gene expression profiles to construct gene expression classifiers to identify primary TNBC ICI-responder patients. This study involved 188 ICI-naïve and 721 specimens treated with ICI plus chemotherapy, including TNBC tumors, HR+/HER2− breast tumors, and other solid non-breast tumors. Results The 37-gene TNBC ICI predictive (TNBC-ICI) classifier performs well in predicting pathological complete response (pCR) to ICI plus chemotherapy on an independent TNBC validation cohort (AUC = 0.86). The TNBC-ICI classifier shows better performance than other molecular signatures, including PD-1 ( PDCD1 ) and PD-L1 ( CD274 ) gene expression (AUC = 0.67). Integrating TNBC-ICI with molecular signatures does not improve the efficiency of the classifier (AUC = 0.75). TNBC-ICI displays a modest accuracy in predicting ICI response in two different cohorts of patients with HR + /HER2- breast cancer (AUC = 0.72 to pembrolizumab and AUC = 0.75 to durvalumab). Evaluation of six cohorts of patients with non-breast solid tumors treated with ICI plus chemotherapy shows overall poor performance (median AUC = 0.67). Conclusion TNBC-ICI predicts pCR to ICI plus chemotherapy in patients with primary TNBC. The study provides a guide to implementing the TNBC-ICI classifier in clinical studies. Further validations will consolidate a novel predictive panel to improve the treatment decision-making for patients with TNBC. Plain language summary Triple-Negative Breast Cancer (TNBC) is an aggressive type of breast cancer, responsible for a substantial burden of breast cancer-related deaths. In recent years, immunotherapy, a therapy that triggers the patient’s immune system to attack the tumor, has arisen as a promising treatment in various cancers, including TNBC. However, a subset of patients with TNBC does not respond to this treatment. Here, we employed advanced computational techniques to predict response to immunotherapy plus chemotherapy in patients with primary TNBC. Our method is more accurate than using other existing markers, such as PD-L1, but is not very accurate in patients with non-TNBC breast cancers or non-breast cancers. This method could potentially be used to better select patients for immunotherapy, upfront, avoiding the side effects and costs of treating patients in which immunotherapy might not work. Ensenyat–Mendez et al. construct a gene expression-based machine learning classifier to predict the response of triple-negative breast cancer to immune checkpoint inhibition combined with chemotherapy. Predictive performance of the 37-gene classifier is better than that of PD-1 or PD-L1.

Objectives Triple-negative breast cancer (TNBC) is a highly aggressive breast cancer subtype with limited treatment options. Unlike other breast cancer subtypes, the scarcity of specific therapies and greater frequencies of distant metastases contribute to its aggressiveness. We aimed to find epigenetic changes that aid in the understanding of the dissemination process of these cancers. Data description Using CRISPR/Cas9, our experimental approach led us to identify and disrupt an insulator element, IE8, whose activity seemed relevant for cell invasion. The experiments were performed in two well-established TNBC cellular models, the MDA-MB-231 and the MDA-MB-436. To gain insights into the underlying molecular mechanisms of TNBC invasion ability, we generated and characterized high-resolution chromatin interaction (Hi-C) and chromatin accessibility (ATAC-seq) maps in both cell models and complemented these datasets with gene expression profiling (RNA-seq) in MDA-MB-231, the cell line that showed more significant changes in chromatin accessibility. Altogether, our data provide a comprehensive resource for understanding the spatial organization of the genome in TNBC cells, which may contribute to accelerating the discovery of TNBC-specific alterations triggering advances for this devastating disease.

Discoveries in the field of genomics have revealed that non-coding genomic regions are not merely \"junk DNA\", but rather comprise critical elements involved in gene expression. These gene regulatory elements (GREs) include enhancers, insulators, silencers, and gene promoters. Notably, new evidence shows how mutations within these regions substantially influence gene expression programs, especially in the context of cancer. Advances in high-throughput sequencing technologies have accelerated the identification of somatic and germline single nucleotide mutations in non-coding genomic regions. This review provides an overview of somatic and germline non-coding single nucleotide alterations affecting transcription factor binding sites in GREs, specifically involved in cancer biology. It also summarizes the technologies available for exploring GREs and the challenges associated with studying and characterizing non-coding single nucleotide mutations. Understanding the role of GRE alterations in cancer is essential for improving diagnostic and prognostic capabilities in the precision medicine era, leading to enhanced patient-centered clinical outcomes.

5-aminolevulinic acid (5-ALA) fluorescence used in glioma surgery has different intensities within tumors and among different patients, some molecular and external factors have been implicated, but there is no clear evidence analyzing the difference of fluorescence according to glioma molecular characteristics. This study aimed to compare molecular factors of glioma samples with fluorescence intensity to identify potential cofounders and associations with clinically relevant tumor features. Tumor samples of high-grade glioma patients operated using 5-ALA for guided resection were included for comparative analysis of fluorescence intensity and molecular features. All the samples were processed under the same conditions. The power for fluorescent stimulation and acquisition time was the same between samples. An inverted fluorescence microscope compared the mean fluorescence for each molecular variation. p53, ATRX and Ki67 expression and IDH1 mutation were assessed by immunohistochemistry. Follow-up of the patients for progression-free survival and overall survival was made. We found that the fluorescence intensity for each specific tumor was independent of the methylation of the methylguanine-DNA-methyltransferase (MGMT) promoter region assessed by pyrosequencing, there was no association of fluorescence with p53, ATRX, IDH1 mutation as assessed by immunochemistry. Also, fluorescence intensity has no relation with time of tumor recurrence or overall survival. With the results, we argue that many factors are involved in fluorescence intensity that may be related to the specific metabolic status of the glioma cells analyzed, which is more likely to be responsible for the variation of fluorescence.