Genome-wide chromatin accessibility, DNA methylation and gene expression analysis of histone deacetylase inhibition in triple-negative breast cancer.

Triple-negative breast cancer (TNBC), especially the subset with a basal phenotype, represents the most aggressive subtype of breast cancer. Unlike other solid tumors, TNBCs harbor a low number of driver mutations. Conversely, we and others have demonstrated a significant impact of epigenetic alterations, including DNA methylation and histone post-translational modifications, affecting TNBCs. Due to the promising results in pre-clinical studies, histone deacetylase inhibitors (HDACi) are currently being tested in several clinical trials for breast cancer and other solid tumors. However, the genome-wide epigenetic and transcriptomic implications of HDAC inhibition are still poorly understood. Here, we provide detailed information about the design of a multi-platform dataset that describes the epigenomic and transcriptomic effects of HDACi. This dataset includes genome-wide chromatin accessibility (assessed by ATAC-Sequencing), DNA methylation (assessed by Illumina HM450K BeadChip) and gene expression (assessed by RNA-Sequencing) analyses before and after HDACi treatment of HCC1806 and MDA-MB-231, two human TNBC cell lines with basal-like phenotype

Disparities in Risk Reduction Therapy Recommendations for Young Women With Lobular Carcinoma In-Situ.

BACKGROUND: Endocrine therapy (ET) significantly reduces the risk of breast cancer development in high-risk patients diagnosed with lobular carcinoma in situ (LCIS). However, the variables impacting recommendation and use of ET in young adults (YAs) is not well-studied. We examined the role of provider recommendation and patient acceptance for ET for YAs with LCIS. MATERIALS AND METHODS: The National Cancer Database was queried for women aged \u3c 40 years with primary LCIS between 2000 and 2012. Socioeconomic, demographic, and treatment variables were examined to determine their impact on ET provider recommendation and initial patient acceptance of risk-reducing therapy. RESULTS: Among 1650 YA patients with LCIS, only 749 (45.4%) were recommended ET. On multivariable analysis, women \u3e 30 years of age were more likely recommended ET than women \u3c 30 years (odds ratio [OR], 1.64; 95% confidence interval [CI], 1.10-2.47), African Americans more than other ethnicities (OR, 1.48; 95% CI, 1.1-2.0), and YAs treated in New England were more likely than those in the rest of the country (OR, 3.26; 95% CI, 2.0-5.2). Among YA women recommended ET, only 20.2% had a documented refusal. Only geography appeared to independently impact the likelihood of refusal, with YAs in the Southeastern-Central United States being most likely to refuse ET (OR, 5.4; 95% CI, 1.2-24.0). CONCLUSION: ET is underutilized for risk-reduction in YAs with LCIS. This underuse appears dependent on disparities in provider recommendation practices rather than non-acceptance of therapy. This may reflect regional practice patterns, community standards of care, or provider bias regarding the significance of LCIS as a risk factor for development of invasive cancer

ASO Visual Abstract: Epigenetic Signatures Predict Pathologic Nodal Stage in Breast Cancer Patients with Estrogen-Receptor-Positive, Clinically Node-Positive Disease.

BACKGROUND: Breast cancer patients with clinically positive nodes who undergo upfront surgery are often recommended for axillary lymph node dissection (ALND), yet more than half are found to have limited nodal disease (≤ 3 positive nodes, pN1) at surgery. In this study, we examined the efficiency of molecular classifiers in stratifying patients with clinically positive nodes to pN1 versus \u3e pN1 disease. METHODS: We evaluated the clinical and epigenetic data of patients in The Cancer Genome Atlas with estrogen receptor-positive, human epidermal growth factor receptor 2-negative invasive ductal carcinoma who underwent ALND for node-positive disease. Patients were divided into control (pN1, ≤ 3 positive nodes) and case (\u3e pN1, \u3e 3 positive nodes) groups. Machine learning algorithms were trained on 50% of the cohort and validated on the remaining 50% to identify DNA methylation signatures that predict \u3e pN1 disease. Clinical variables and epigenetic signatures were compared. RESULTS: Controls (n = 34) and case (n = 24) cohorts showed similar mean age (56.4 ± 12.2 vs. 57.6 ± 16.7 years; p = 0.77), number of nodes removed (16.1 ± 7.3 vs. 17.5 ± 6.2; p = 0.45), tumor grade (p = 0.76), presence of lymphovascular invasion (p = 0.18), extranodal extension (p = 0.17), tumor laterality (p = 0.89), and tumor location (p = 0.42). The mean number of positive nodes was significantly different (1.76 ± 0.82, pN1; 8.83 ± 5.36, \u3e pN1; p \u3c 0.001). Three epigenetic signatures (EpiSig14, EpiSig13, EpiSig10) based on DNA methylation patterns of the primary tumors demonstrated high accuracy in predicting \u3e pN1 disease (area under the curve 0.98). CONCLUSIONS: Epigenetic signatures have an excellent diagnostic accuracy for stratifying nodal disease in patients with clinically positive nodes. Validation of this tool is warranted and may provide an accurate and cost-effective method of identifying patients with predicted low nodal burden who could be spared the morbidity of ALND

ASO Visual Abstract: 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 \u3e 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 \u3e 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 \u3e pN0 disease (MSKCC AUC 0.76, MDACC AUC 0.69, p = 0.15). Differentially methylated sites (DMS) between patients with pN0 and those with \u3e 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 \u3e 0.88, p \u3c 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

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

Construction and validation of a gene expression classifier to predict immunotherapy response in primary triple-negative breast cancer

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. 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. 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). 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. 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

Microbial Dysbiosis Is Associated with Human Breast Cancer

<div><p>Breast cancer affects one in eight women in their lifetime. Though diet, age and genetic predisposition are established risk factors, the majority of breast cancers have unknown etiology. The human microbiota refers to the collection of microbes inhabiting the human body. Imbalance in microbial communities, or microbial dysbiosis, has been implicated in various human diseases including obesity, diabetes, and colon cancer. Therefore, we investigated the potential role of microbiota in breast cancer by next-generation sequencing using breast tumor tissue and paired normal adjacent tissue from the same patient. In a qualitative survey of the breast microbiota DNA, we found that the bacterium <i>Methylobacterium radiotolerans</i> is relatively enriched in tumor tissue, while the bacterium <i>Sphingomonas yanoikuyae</i> is relatively enriched in paired normal tissue. The relative abundances of these two bacterial species were inversely correlated in paired normal breast tissue but not in tumor tissue, indicating that dysbiosis is associated with breast cancer. Furthermore, the total bacterial DNA load was reduced in tumor versus paired normal and healthy breast tissue as determined by quantitative PCR. Interestingly, bacterial DNA load correlated inversely with advanced disease, a finding that could have broad implications in diagnosis and staging of breast cancer. Lastly, we observed lower basal levels of antibacterial response gene expression in tumor versus healthy breast tissue. Taken together, these data indicate that microbial DNA is present in the breast and that bacteria or their components may influence the local immune microenvironment. Our findings suggest a previously unrecognized link between dysbiosis and breast cancer which has potential diagnostic and therapeutic implications.</p></div