Clinicopathological Features of Triple-Negative Breast Cancer Epigenetic Subtypes

DNA Methylation Identifies Epigenetic Subtypes of Triple-Negative Breast Cancers With Distinct Clinicopathologic and Molecular Features

Triple-negative breast cancers (TNBC) include diverse carcinomas with heterogeneous clinical behavior. DNA methylation is a useful tool in classifying a variety of cancers. In this study, we analyzed TNBC using DNA methylation profiling and compared the results to those of mutational analysis. DNA methylation profiling (Infinium MethylationEPIC array, Illumina) and 50-gene panel-targeted DNA sequencing were performed in 44 treatment-naïve TNBC. We identified 3 distinct DNA methylation clusters with specific clinicopathologic and molecular features. Cluster 1 (phosphoinositide 3-kinase/protein kinase B-enriched cluster; n = 9) patients were significantly older (mean age, 71 years; P = .008) with tumors that were more likely to exhibit apocrine differentiation (78%; P < .001), a lower grade (44% were grade 2), a lower proliferation index (median Ki-67, 15%; P = .002), and lower tumor-infiltrating lymphocyte fractions (median, 15%; P = .0142). Tumors carried recurrent PIK3CA and AKT1 mutations and a higher percentage of low HER-2 expression (89%; P = .033). Cluster 3 (chromosomal instability cluster; n = 28) patients were significantly younger (median age, 57 years). Tumors were of higher grade (grade 3, 93%), had a higher proliferation index (median Ki-67, 75%), and were with a high fraction of tumor-infiltrating lymphocytes (median, 30%). Ninety-one percent of the germline BRCA1/2 mutation carriers were in cluster 3, and these tumors showed the highest level of copy number alterations. Cluster 2 represented cases with intermediate clinicopathologic characteristics and no specific molecular profile (no specific molecular profile cluster; n = 7). There were no differences in relation to stage, recurrence, and survival. In conclusion, DNA methylation profiling is a promising tool to classify patients with TNBC into biologically relevant groups, which may result in better disease characterization and reveal potential targets for emerging therapies.

Epigenetic Profiles of Triple-Negative Breast Cancers of African American and White Females

Importance

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype and appears to have disproportionately higher incidence and worse outcomes among younger African American females.

Objective

To investigate whether epigenetic differences exist in TNBCs of younger African American females that may explain clinical disparities seen in this patient group.

Design, Setting, and Participants

This cross-sectional study used clinical, demographic, DNA methylation (HumanMethylation450; Illumina), and gene expression (RNA sequencing) data for US patient populations from publicly available data repositories (The Cancer Genome Atlas [TCGA], 2006-2012, and Gene Expression Omnibus [GEO], 2004-2013) accessed on April 13, 2021. White and African American females with TNBC identified in TCGA (69 patients) and a validation cohort of 210 African American patients from GEO (GSE142102) were included. Patients without available race or age data were excluded. Data were analyzed from September 2022 through April 2023.

Main Outcomes and Measures

DNA methylation and gene expression profiles of TNBC tumors by race (self-reported) and age were assessed. Age was considered a dichotomous variable using age 50 years as the cutoff (younger [<50 years] vs older [≥50 years]).

Results

A total of 69 female patients (34 African American [49.3%] and 35 White [50.7%]; mean [SD; range] age, 55.7 [11.6; 29-82] years) with TNBC were included in the DNA methylation analysis; these patients and 210 patients in the validation cohort were included in the gene expression analysis (279 patients). There were 1115 differentially methylated sites among younger African American females. The DNA methylation landscape on TNBC tumors in this population had increased odds of enrichment of hormone (odds ratio [OR], 1.82; 95% CI, 1.21 to 2.67; P = .003), muscle (OR, 1.85; 95% CI, 1.44 to 2.36; P < .001), and proliferation (OR, 3.14; 95% CI, 2.71 to 3.64; P < .001) pathways vs other groups (older African American females and all White females). Alterations in regulators of these molecular features in TNBCs of younger African American females were identified involving hormone modulation (downregulation of androgen receptor: fold change [FC] = -2.93; 95% CI, -4.76 to -2.11; P < .001) and upregulation of estrogen-related receptor α (FC = 0.86; 95% CI, 0.34 to 1.38; P = .002), muscle metabolism (upregulation of FOXC1: FC = 1.33; 95% CI, 0.62 to 2.03; P < .001), and proliferation mediators (upregulation of NOTCH1: FC = 0.71; 95% CI, 0.23 to 1.19; P = .004 and MYC (FC = 0.81; 95% CI, 0.18 to 1.45; P = .01).

Conclusions and Relevance

These findings suggest that TNBC of younger African American females may represent a distinct epigenetic entity and offer novel insight into molecular alterations associated with TNBCs of this population. Understanding these epigenetic differences may lead to the development of more effective therapies for younger African American females, who have the highest incidence and worst outcomes from TNBC of any patient group.

PD-1/PD-L1 Inhibitors Response in Triple-Negative Breast Cancer: Can Long Noncoding RNAs Be Associated?

As immune checkpoint inhibitors (ICI) emerge as a paradigm-shifting treatment option for patients with advanced or metastatic cancer, there is a growing demand for biomarkers that can distinguish which patients are likely to benefit. In the case of triple-negative breast cancer (TNBC), characterized by a lack of therapeutic targets, pembrolizumab approval for high-risk early-stage disease occurred regardless of PD-L1 status, which keeps the condition in a biomarker limbus. In this review, we highlight the participation of long non-coding RNAs (lncRNAs) in the regulation of the PD-1/PD-L1 pathway, as well as in the definition of prognostic immune-related signatures in many types of tumors, aiming to shed light on molecules that deserve further investigation for a potential role as biomarkers. We also conducted a bioinformatic analysis to investigate lncRNAs already investigated in PD-1/PDL-1 pathways in other cancer types, considering the TNBC molecular context. In this sense, from the generated data, we evidence here two lncRNAs, UCA1 and HCP5, which have not yet been identified in the context of the tumoral immune response in breast cancer. These candidates can be further explored to verify their use as biomarkers for ICI response. In this article, we present an updated review regarding the use of lncRNA as biomarkers of response to ICI, highlighting the versatility of using these molecules.

Spatial Profile of Tumor Microenvironment in PD-L1-Negative and PD-L1-Positive Triple-Negative Breast Cancer

The problem of finding more precise stratification criteria for identifying the cohort of patients who would obtain the maximum benefit from immunotherapy is acute in modern times. In our study were enrolled 18 triple-negative breast cancer patients. The Ventana SP142 test was used for PD-L1 detection. Spatial transcriptomic analysis by 10x Genomics was used to compare PD-L1-positive and PD-L1-negative tumors. The seven-color multiplex immunofluorescence (by Akoya) was used for the detection of the type of cells that carried the PD1 receptor and the PD-L1 ligand. Using pathway analysis, we showed that PD-L1-positive tumors demonstrate signatures of a cell response to cytokines, among others, and PD-L1-negative tumors demonstrate signatures of antigen presentation. PD-L1-positive and PD-L1-negative tumors have different tumor microenvironment (TME) compositions according to CIBERSORT analysis. Multiplex immunohistochemistry (IHC) confirmed the prevalence of PD1-negative M2 macrophages and PD1-negative T lymphocytes in PD-L1-positive tumors. PD-L1-positive tumors are not characterized by direct contact between cells carrying the PD1 receptor and the PD-L1 ligand. So, the absence of specific immune reactions against the tumor, predominance of pro-tumor microenvironment, and rare contact between PDL1 and PD1-positive cells may be the potential reasons for the lack of an immune checkpoint inhibitor (ICI) effect in triple-negative breast cancer patients.

Machine Learning-Based Epigenetic Classifiers for Axillary Staging of Patients with ER-Positive Early-Stage Breast Cancer

BACKGROUND

In the era of molecular stratification and effective multimodality therapies, surgical staging of the axilla is becoming less relevant for patients with estrogen receptor (ER)-positive early-stage breast cancer (EBC). Therefore, a nonsurgical method for accurately predicting lymph node disease is the next step in the de-escalation of axillary surgery. This study sought to identify epigenetic signatures in the primary tumor that accurately predict lymph node status.

PATIENTS AND METHODS

We selected a cohort of patients in The Cancer Genome Atlas (TCGA) with ER-positive, HER2-negative invasive ductal carcinomas, and clinically-negative axillae (n = 127). Clinicopathological nomograms from the Memorial Sloan Kettering Cancer Center (MSKCC) and the MD Anderson Cancer Center (MDACC) were calculated. DNA methylation (DNAm) patterns from primary tumor specimens were compared between patients with pN0 and those with > pN0. The cohort was divided into training (n = 85) and validation (n = 42) sets. Random forest was employed to obtain the combinations of DNAm features with the highest accuracy for stratifying patients with > pN0. The most efficient combinations were selected according to the area under the curve (AUC).

RESULTS

Clinicopathological models displayed a modest predictive potential for identifying > pN0 disease (MSKCC AUC 0.76, MDACC AUC 0.69, p = 0.15). Differentially methylated sites (DMS) between patients with pN0 and those with > pN0 were identified (n = 1656). DMS showed a similar performance to the MSKCC model (AUC = 0.76, p = 0.83). Machine learning approaches generated five epigenetic classifiers, which showed higher discriminative potential than the clinicopathological variables tested (AUC > 0.88, p < 0.05).

CONCLUSIONS

Epigenetic classifiers based on primary tumor characteristics can efficiently stratify patients with no lymph node involvement from those with axillary lymph node disease, thereby providing an accurate method of staging the axilla.

The Generation of Dual-Targeting Fusion Protein PD-L1/CD47 for the Inhibition of Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is a highly aggressive subset of breast cancer with limited therapeutic options. However, its immune evasion mechanisms, characterized by the over-expression of the immune checkpoint molecules PD-L1 and CD47, can be targeted in order to facilitate cancer elimination by cells of innate and adaptive immunity. In this paper, we describe the design, preparation, and evaluation of three novel dual-targeting fusion proteins that were based on the structure frame of prototype IAB (innate and adaptive dependent bispecific fusion protein) and the “Orcutt-type IgG-scFv” molecular model. Three molecules with different spatial conformations were designed to improve antigen–antibody affinity by the addition of Ag–Ab binding sites from the variable region sequences of the anti-PD-L1 monoclonal antibody (mAb) atezolizumab and CV1, a high-affinity receptor of CD47. The results showed that the best-performing among the three proteins designed in this study was protein Pro3; its CV1 N-terminus and Fc domain C-terminus were not sterically hindered. Pro3 was better at boosting T cell proliferation and the engulfment of macrophages than the IAB prototype and, at the same time, retained a level of ADCC activity similar to that of IAB. Through improved design, the novel constructed dual-targeting immunomodulatory protein Pro3 was superior at activating the anti-tumor immune response and has thus shown potential for use in clinical applications.

A DNA Methylation-Based Gene Signature Can Predict Triple-Negative Breast Cancer Diagnosis

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer (BC) subtype and lacks targeted treatment. It is diagnosed by the absence of immunohistochemical expression of several biomarkers, but this method still displays some interlaboratory variability. DNA methylome aberrations are common in BC, thereby methylation profiling could provide the identification of accurate TNBC diagnosis biomarkers. Here, we generated a signature of differentially methylated probes with class prediction ability between 5 non-neoplastic breast and 7 TNBC tissues (error rate = 0.083). The robustness of this signature was corroborated in larger cohorts of additional 58 non-neoplastic breast, 93 TNBC, and 150 BC samples from the Gene Expression Omnibus repository, where it yielded an error rate of 0.006. Furthermore, we validated by pyrosequencing the hypomethylation of three out of 34 selected probes (FLJ43663, PBX Homeobox 1 (PBX1), and RAS P21 protein activator 3 (RASA3) in 51 TNBC, even at early stages of the disease. Finally, we found significantly lower methylation levels of FLJ43663 in cell free-DNA from the plasma of six TNBC patients than in 15 healthy donors. In conclusion, we report a novel DNA methylation signature with potential predictive value for TNBC diagnosis.

iGlioSub: an integrative transcriptomic and epigenomic classifier for glioblastoma molecular subtypes

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 integrative Glioblastoma Subtype (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.

Multifaceted roles of long non-coding RNAs in triple-negative breast cancer: biology and clinical applications

Triple-negative breast cancer (TNBC) is a heterogeneous breast cancer subtype that lacks targeted therapy due to the absence of estrogen, progesterone, and HER2 receptors. Moreover, TNBC was shown to have a poor prognosis, since it involves aggressive phenotypes that confer significant hindrance to therapeutic treatments. Recent state-of-the-art sequencing technologies have shed light on several long non-coding RNAs (lncRNAs), previously thought to have no biological function and were considered as genomic junk. LncRNAs are involved in various physiological as well as pathological conditions, and play a key role in drug resistance, gene expression, and epigenetic regulation. This review mainly focuses on exploring the multifunctional roles of candidate lncRNAs, and their strong association with TNBC development. We also summarise various emerging research findings that establish novel paradigms of lncRNAs function as oncogenes and/or tumor suppressors in TNBC development, suggesting their role as prospective therapeutic targets.

Tumor Microenvironment: Key Players in Triple Negative Breast Cancer Immunomodulation

Simple Summary

The tumor microenvironment (TME) is a complicated network composed of various cells, signaling molecules, and extra cellular matrix. TME plays a crucial role in triple negative breast cancer (TNBC) immunomodulation and tumor progression, paradoxically, acting as an immunosuppressive as well as immunoreactive factor. Research regarding tumor immune microenvironment has contributed to a better understanding of TNBC subtype classification. Shall we treat patients precisely according to specific subtype classification? Moving beyond traditional chemotherapy, multiple clinical trials have recently implied the potential benefits of immunotherapy combined with chemotherapy. In this review, we aimed to elucidate the paradoxical role of TME in TNBC immunomodulation, summarize the subtype classification methods for TNBC, and explore the synergistic mechanism of chemotherapy plus immunotherapy. Our study may provide a new direction for the development of combined treatment strategies for TNBC.

Abstract

Triple negative breast cancer (TNBC) is a heterogeneous disease and is highly related to immunomodulation. As we know, the most effective approach to treat TNBC so far is still chemotherapy. Chemotherapy can induce immunogenic cell death, release of damage-associated molecular patterns (DAMPs), and tumor microenvironment (TME) remodeling; therefore, it will be interesting to investigate the relationship between chemotherapy-induced TME changes and TNBC immunomodulation. In this review, we focus on the immunosuppressive and immunoreactive role of TME in TNBC immunomodulation and the contribution of TME constituents to TNBC subtype classification. Further, we also discuss the role of chemotherapy-induced TME remodeling in modulating TNBC immune response and tumor progression with emphasis on DAMPs-associated molecules including high mobility group box1 (HMGB1), exosomes, and sphingosine-1-phosphate receptor 1 (S1PR1), which may provide us with new clues to explore effective combined treatment options for TNBC.

Current Triple-Negative Breast Cancer Subtypes: Dissecting the Most Aggressive Form of Breast Cancer

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.

ADAM12 is A Potential Therapeutic Target Regulated by Hypomethylation in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype and currently lacks any effective targeted therapy. Since epigenetic alterations are a common event in TNBC, DNA methylation profiling can be useful for identifying potential biomarkers and therapeutic targets. Here, genome-wide DNA methylation from eight TNBC and six non-neoplastic tissues was analysed using Illumina Human Methylation 450K BeadChip. Results were validated by pyrosequencing in an independent cohort of 50 TNBC and 24 non-neoplastic samples, where protein expression was also assessed by immunohistochemistry. The functional role of disintegrin and metalloproteinase domain-containing protein 12(ADAM12) in TNBC cell proliferation, migration and drug response was analysed by gene expression silencing with short hairpin RNA. Three genes (Von Willenbrand factor C and Epidermal Growth Factor domain-containing protein (VWCE), tetraspanin-9 (TSPAN9) and ADAM12) were found to be exclusively hypomethylated in TNBC. Furthermore, ADAM12 hypomethylation was associated with a worse outcome in TNBC tissues and was also found in adjacent-to-tumour tissue and, preliminarily, in plasma from TNBC patients. In addition, ADAM12 silencing decreased TNBC cell proliferation and migration and improved doxorubicin sensitivity in TNBC cells. Our results indicate that ADAM12 is a potential therapeutic target and its hypomethylation could be a poor outcome biomarker in TNBC.