Methylome sequencing in triple-negative breast cancer reveals distinct methylation clusters with prognostic value

Universal penalized regression (Elastic-net) model with differentially methylated promoters for oral cancer prediction

BACKGROUND

DNA methylation showed notable potential to act as a diagnostic marker in many cancers. Many studies proposed DNA methylation biomarker in OSCC detection, while most of these studies are limited to specific cohorts or geographical location. However, the generalizability of DNA methylation as a diagnostic marker in oral cancer across different geographical locations is yet to be investigated.

METHODS

We used genome-wide methylation data from 384 oral cavity cancer and normal tissues from TCGA HNSCC and eastern India. The common differentially methylated CpGs in these two cohorts were used to develop an Elastic-net model that can be used for the diagnosis of OSCC. The model was validated using 812 HNSCC and normal samples from different anatomical sites of oral cavity from seven countries. Droplet Digital PCR of methyl-sensitive restriction enzyme digested DNA (ddMSRE) was used for quantification of methylation and validation of the model with 22 OSCC and 22 contralateral normal samples. Additionally, pyrosequencing was used to validate the model using 46 OSCC and 25 adjacent normal and 21 contralateral normal tissue samples.

RESULTS

With ddMSRE, our model showed 91% sensitivity, 100% specificity, and 95% accuracy in classifying OSCC from the contralateral normal tissues. Validation of the model with pyrosequencing also showed 96% sensitivity, 91% specificity, and 93% accuracy for classifying the OSCC from contralateral normal samples, while in case of adjacent normal samples we found similar sensitivity but with 20% specificity, suggesting the presence of early disease methylation signature at the adjacent normal samples. Methylation array data of HNSCC and normal tissues from different geographical locations and different anatomical sites showed comparable sensitivity, specificity, and accuracy in detecting oral cavity cancer with across. Similar results were also observed for different stages of oral cavity cancer.

CONCLUSIONS

Our model identified crucial genomic regions affected by DNA methylation in OSCC and showed similar accuracy in detecting oral cancer across different geographical locations. The high specificity of this model in classifying contralateral normal samples from the oral cancer compared to the adjacent normal samples suggested applicability of the model in early detection.

Mesenchymal-like immune-altered is the fourth robust triple-negative breast cancer molecular subtype

BACKGROUND

Robust molecular subtyping of triple-negative breast cancer (TNBC) is a prerequisite for the success of precision medicine. Today, there is a clear consensus on three TNBC molecular subtypes: luminal androgen receptor (LAR), basal-like immune-activated (BLIA), and basal-like immune-suppressed (BLIS). However, the debate about the robustness of other subtypes is still open.

METHODS

An unprecedented number (n = 1942) of TNBC patient data was collected. Microarray- and RNAseq-based cohorts were independently investigated. Unsupervised analyses were conducted using k-means consensus clustering. Clusters of patients were then functionally annotated using different approaches. Prediction of response to chemotherapy and targeted therapies, immune checkpoint blockade, and radiotherapy were also screened for each TNBC subtype.

RESULTS

Four TNBC subtypes were identified in the cohort: LAR (19.36%); mesenchymal stem-like (MSL/MES) (17.35%); BLIA (31.06%); and BLIS (32.23%). Regarding the MSL/MES subtype, we suggest renaming it to mesenchymal-like immune-altered (MLIA) to emphasize its specific histological background and nature of immune response. Treatment response prediction results show, among other things, that despite immune activation, immune checkpoint blockade is probably less or completely ineffective in MLIA, possibly caused by mesenchymal background and/or an enrichment in dysfunctional cytotoxic T lymphocytes. TNBC subtyping results were included in the bc-GenExMiner v5.0 webtool ( http://bcgenex.ico.unicancer.fr ).

CONCLUSION

The mesenchymal TNBC subtype is characterized by an exhausted and altered immune response, and resistance to immune checkpoint inhibitors. Consensus for molecular classification of TNBC subtyping and prediction of cancer treatment responses helps usher in the era of precision medicine for TNBC patients.

Management of triple-negative breast cancer by natural compounds through different mechanistic pathways

Triple-negative breast cancer (TNBC) is the most severe form of breast cancer, characterized by the loss of estrogen, progesterone, and human epidermal growth factor receptors. It is caused by various genetic and epigenetic factors, resulting in poor prognosis. Epigenetic changes, such as DNA methylation and histone modification, are the leading mechanisms responsible for TNBC progression and metastasis. This review comprehensively covers the various subtypes of TNBC and their epigenetic causes. In addition, the genetic association of TNBC with all significant genes and signaling pathways linked to the progression of this form of cancer has been enlisted. Furthermore, the possible uses of natural compounds through different mechanistic pathways have also been discussed in detail for the successful management of TNBC.

Epigenetic modulations in triple-negative breast cancer: Therapeutic implications for tumor microenvironment

Triple-negative breast cancer (TNBC) is an aggressive subtype lacking estrogen receptors, progesterone receptors and lacks HER2 overexpression. This absence of critical molecular targets poses significant challenges for conventional therapies. Immunotherapy, remarkably immune checkpoint blockade, offers promise for TNBC treatment, but its efficacy remains limited. Epigenetic dysregulation, including altered DNA methylation, histone modifications, and imbalances in regulators such as BET proteins, plays a crucial role in TNBC development and resistance to treatment. Hypermethylation of tumor suppressor gene promoters and the imbalance of histone methyltransferases such as EZH2 and histone deacetylases (HDACs) profoundly influence tumor cell proliferation, survival, and metastasis. In addition, epigenetic alterations critically shape the tumor microenvironment (TME), including immune cell composition, cytokine signaling, and immune checkpoint expression, ultimately contributing to immune evasion. Targeting these epigenetic mechanisms with specific inhibitors such as EZH2 and HDAC inhibitors in combination with immunotherapy represents a compelling strategy to remodel the TME, potentially overcoming immune evasion and enhancing therapeutic outcomes in TNBC. This review aims to comprehensively elucidate the current understanding of epigenetic modulation in TNBC, its influence on the TME, and the potential of combining epigenetic therapies with immunotherapy to overcome the challenges posed by this aggressive breast cancer subtype.

Hypomethylation of ATP1A1 Is Associated with Poor Prognosis and Cancer Progression in Triple-Negative Breast Cancer

Dysregulated DNA methylation in cancer is critical in the transcription machinery associated with cancer progression. Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype, but no treatment targeting TNBC biomarkers has yet been developed. To identify specific DNA methylation patterns in TNBC, methyl-binding domain protein 2 (MBD) sequencing data were compared in TNBC and the three other major breast cancer subtypes. Integrated analysis of DNA methylation and gene expression identified a gene set showing a correlation between DNA methylation and gene expression. ATPase Na+/K+-transporting subunit alpha 1 (ATP1A1) was found to be specifically hypomethylated in the coding sequence (CDS) region and to show increased expression in TNBC. The Cancer Genome Atlas (TCGA) database also showed that hypomethylation and high expression of ATP1A1 were strongly associated with poor survival in patients with TNBC. Furthermore, ATP1A1 knockdown significantly reduced the viability and tumor-sphere formation of TNBC cells. These results suggest that the hypomethylation and overexpression of ATP1A1 could be a prognostic marker in TNBC and that the manipulation of ATP1A1 expression could be a therapeutic target in this disease.

Detailed DNA methylation characterisation of phyllodes tumours identifies a signature of malignancy and distinguishes phyllodes from metaplastic breast carcinoma

Phyllodes tumours (PTs) are rare fibroepithelial lesions of the breast that are classified as benign, borderline, or malignant. As little is known about the molecular underpinnings of PTs, current diagnosis relies on histological examination. However, accurate classification is often difficult, particularly for distinguishing borderline from malignant PTs. Furthermore, PTs can be misdiagnosed as other tumour types with shared histological features, such as fibroadenoma and metaplastic breast cancers. As DNA methylation is a recognised hallmark of many cancers, we hypothesised that DNA methylation could provide novel biomarkers for diagnosis and tumour stratification in PTs, whilst also allowing insight into the molecular aetiology of this otherwise understudied tumour. We generated whole-genome methylation data using the Illumina EPIC microarray in a novel PT cohort (n = 33) and curated methylation microarray data from published datasets including PTs and other potentially histopathologically similar tumours (total n = 817 samples). Analyses revealed that PTs have a unique methylome compared to normal breast tissue and to potentially histopathologically similar tumours (metaplastic breast cancer, fibroadenoma and sarcomas), with PT-specific methylation changes enriched in gene sets involved in KRAS signalling and epithelial-mesenchymal transition. Next, we identified 53 differentially methylated regions (DMRs) (false discovery rate < 0.05) that specifically delineated malignant from non-malignant PTs. The top DMR in both discovery and validation cohorts was hypermethylation at the HSD17B8 CpG island promoter. Matched PT single-cell expression data showed that HSD17B8 had minimal expression in fibroblast (putative tumour) cells. Finally, we created a methylation classifier to distinguish PTs from metaplastic breast cancer samples, where we revealed a likely misdiagnosis for two TCGA metaplastic breast cancer samples. In conclusion, DNA methylation alterations are associated with PT histopathology and hold the potential to improve our understanding of PT molecular aetiology, diagnostics, and risk stratification. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

A journey from omics to clinicomics in solid cancers: Success stories and challenges

The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.

The protein phosphatase EYA4 promotes homologous recombination (HR) through dephosphorylation of tyrosine 315 on RAD51

Efficient DNA repair and limitation of genome rearrangements rely on crosstalk between different DNA double-strand break (DSB) repair pathways, and their synchronization with the cell cycle. The selection, timing and efficacy of DSB repair pathways are influenced by post-translational modifications of histones and DNA damage repair (DDR) proteins, such as phosphorylation. While the importance of kinases and serine/threonine phosphatases in DDR have been extensively studied, the role of tyrosine phosphatases in DNA repair remains poorly understood. In this study, we have identified EYA4 as the protein phosphatase that dephosphorylates RAD51 on residue Tyr315. Through its Tyr phosphatase activity, EYA4 regulates RAD51 localization, presynaptic filament formation, foci formation, and activity. Thus, it is essential for homologous recombination (HR) at DSBs. DNA binding stimulates EYA4 phosphatase activity. Depletion of EYA4 decreases single-stranded DNA accumulation following DNA damage and impairs HR, while overexpression of EYA4 in cells promotes dephosphorylation and stabilization of RAD51, and thereby nucleoprotein filament formation. Our data have implications for a pathological version of RAD51 in EYA4-overexpressing cancers.

A prognostic prediction model for ovarian cancer using a cross-modal view correlation discovery network

Ovarian cancer is a tumor with different clinicopathological and molecular features, and the vast majority of patients have local or extensive spread at the time of diagnosis. Early diagnosis and prognostic prediction of patients can contribute to the understanding of the underlying pathogenesis of ovarian cancer and the improvement of therapeutic outcomes. The occurrence of ovarian cancer is influenced by multiple complex mechanisms, including the genome, transcriptome and proteome. Different types of omics analysis help predict the survival rate of ovarian cancer patients. Multi-omics data of ovarian cancer exhibit high-dimensional heterogeneity, and existing methods for integrating multi-omics data have not taken into account the variability and inter-correlation between different omics data. In this paper, we propose a deep learning model, MDCADON, which utilizes multi-omics data and cross-modal view correlation discovery network. We introduce random forest into LASSO regression for feature selection on mRNA expression, DNA methylation, miRNA expression and copy number variation (CNV), aiming to select important features highly correlated with ovarian cancer prognosis. A multi-modal deep neural network is used to comprehensively learn feature representations of each omics data and clinical data, and cross-modal view correlation discovery network is employed to construct the multi-omics discovery tensor, exploring the inter-relationships between different omics data. The experimental results demonstrate that MDCADON is superior to the existing methods in predicting ovarian cancer prognosis, which enables survival analysis for patients and facilitates the determination of follow-up treatment plans. Finally, we perform Gene Ontology (GO) term analysis and biological pathway analysis on the genes identified by MDCADON, revealing the underlying mechanisms of ovarian cancer and providing certain support for guiding ovarian cancer treatments.

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.

EYA4 promotes breast cancer progression and metastasis through its role in replication stress avoidance

The Eyes Absent (EYA) family of proteins is an atypical group of four dual-functioning protein phosphatases (PP), which have been linked to many vital cellular processes and organogenesis pathways. The four family members of this PP family possess transcriptional activation and phosphatase functions, with serine/threonine and tyrosine phosphatase domains. EYA4 has been associated with several human cancers, with tumor-suppressing and tumor-promoting roles. However, EYA4 is the least well-characterized member of this unique family of PP, with its biological functions and molecular mechanisms in cancer progression, particularly in breast cancer, still largely unknown. In the present study, we found that the over-expression of EYA4 in breast tissue leads to an aggressive and invasive breast cancer phenotype, while the inhibition of EYA4 reduced tumorigenic properties of breast cancer cells in vitro and in vivo. Cellular changes downstream of EYA4, including cell proliferation and migration, may explain the increased metastatic power of breast cancer cells over-expressing EYA4. Mechanistically, EYA4 prevents genome instability by inhibiting the accumulation of replication-associated DNA damage. Its depletion results in polyploidy as a consequence of endoreplication, a phenomenon that can occur in response to stress. The absence of EYA4 leads to spontaneous replication stress characterized by the activation of the ATR pathway, sensitivity to hydroxyurea, and accumulation of endogenous DNA damage as indicated by increased γH2AX levels. In addition, we show that EYA4, specifically its serine/threonine phosphatase domain, plays an important and so far, unexpected role in replication fork progression. This phosphatase activity is essential for breast cancer progression and metastasis. Taken together, our data indicate that EYA4 is a novel potential breast cancer oncogene that supports primary tumor growth and metastasis. Developing therapeutics aimed at the serine/threonine phosphatase activity of EYA4 represents a robust strategy for killing breast cancer cells, to limit metastasis and overcome chemotherapy resistance caused by endoreplication and genomic rearrangements.

AFEI: adaptive optimized vertical federated learning for heterogeneous multi-omics data integration

Vertical federated learning has gained popularity as a means of enabling collaboration and information sharing between different entities while maintaining data privacy and security. This approach has potential applications in disease healthcare, cancer prognosis prediction, and other industries where data privacy is a major concern. Although using multi-omics data for cancer prognosis prediction provides more information for treatment selection, collecting different types of omics data can be challenging due to their production in various medical institutions. Data owners must comply with strict data protection regulations such as European Union (EU) General Data Protection Regulation. To share patient data across multiple institutions, privacy and security issues must be addressed. Therefore, we propose an adaptive optimized vertical federated-learning-based framework adaptive optimized vertical federated learning for heterogeneous multi-omics data integration (AFEI) to integrate multi-omics data collected from multiple institutions for cancer prognosis prediction. AFEI enables participating parties to build an accurate joint evaluation model for learning more information related to cancer patients from different perspectives, based on the distributed and encrypted multi-omics features shared by multiple institutions. The experimental results demonstrate that AFEI achieves higher prediction accuracy (6.5% on average) than using single omics data by utilizing the encrypted multi-omics data from different institutions, and it performs almost as well as prognosis prediction by directly integrating multi-omics data. Overall, AFEI can be seen as an efficient solution for breaking down barriers to multi-institutional collaboration and promoting the development of cancer prognosis prediction.

DNA methylation differences in noncoding regions in ER negative breast tumors between Black and White women

Introduction

Incidence of estrogen receptor (ER)-negative breast cancer, an aggressive tumor subtype associated with worse prognosis, is higher among African American/Black women than other US racial and ethnic groups. The reasons for this disparity remain poorly understood but may be partially explained by differences in the epigenetic landscape.

Methods

We previously conducted genome-wide DNA methylation profiling of ER- breast tumors from Black and White women and identified a large number of differentially methylated loci (DML) by race. Our initial analysis focused on DML mapping to protein-coding genes. In this study, motivated by increasing appreciation for the biological importance of the non-protein coding genome, we focused on 96 DMLs mapping to intergenic and noncoding RNA regions, using paired Illumina Infinium Human Methylation 450K array and RNA-seq data to assess the relationship between CpG methylation and RNA expression of genes located up to 1Mb away from the CpG site.

Results

Twenty-three (23) DMLs were significantly correlated with the expression of 36 genes (FDR<0.05), with some DMLs associated with the expression of single gene and others associated with more than one gene. One DML (cg20401567), hypermethylated in ER- tumors from Black versus White women, mapped to a putative enhancer/super-enhancer element located 1.3 Kb downstream of HOXB2. Increased methylation at this CpG correlated with decreased expression of HOXB2 (Rho=-0.74, FDR<0.001) and other HOXB/HOXB-AS genes. Analysis of an independent set of 207 ER- breast cancers from TCGA similarly confirmed hypermethylation at cg20401567 and reduced HOXB2 expression in tumors from Black versus White women (Rho=-0.75, FDR<0.001).

Discussion

Our findings indicate that epigenetic differences in ER- tumors between Black and White women are linked to altered gene expression and may hold functional significance in breast cancer pathogenesis.

EYA4 drives breast cancer progression and metastasis through its novel role in replication stress avoidance

The Eyes Absent (EYA) family of proteins is an atypical group of four dual-functioning protein phosphatases, which have been linked to many vital cellular processes and organogenesis pathways. Like the other isoforms, EYA4 possesses transcriptional activation and phosphatase functions, with serine/threonine and tyrosine phosphatase domains. EYA4 has been associated with several human cancers, with tumor-suppressing and tumor-promoting roles. However, EYA4 is the least well-characterized member of this unique family of phosphatases, with its biological functions and molecular mechanisms in cancer progression, particularly in breast cancer, still largely unknown. In the present study, we found that the over-expression of EYA4 in breast tissue leads to an aggressive and invasive breast cancer phenotype, while the inhibition of EYA4 reduced tumorigenic properties of breast cancer cells in vitro and in vivo . Cellular changes downstream of EYA4, including cell proliferation and migration, may explain the increased metastatic power of breast cancer cells over-expressing EYA4. Mechanistically, EYA4 prevents genome instability by inhibiting the accumulation of replication-associated DNA damage. Its depletion results in polyploidy as a consequence of endoreplication, a phenomenon that can occur in response to stress. The absence of EYA4 leads to spontaneous replication stress characterized by the activation of the ATR pathway, sensitivity to hydroxyurea, and accumulation of endogenous DNA damage as indicated by increased γH2AX levels. In addition, we show that EYA4, specifically its serine/threonine phosphatase domain, plays an important and so far, unexpected role in replication fork progression. This phosphatase activity is essential for breast cancer progression and metastasis. Taken together, our data indicate that EYA4 is a novel breast cancer oncogene that supports primary tumor growth and metastasis. Developing therapeutics aimed at the serine/threonine phosphatase activity of EYA4 represents a robust strategy for killing breast cancer cells, to limit metastasis and overcome chemotherapy resistance caused by endoreplication and genomic rearrangements.

Mitochondria-Targeted Nanosystem Enhances Radio-Radiodynamic-Chemodynamic Therapy on Triple Negative Breast Cancer

Radiodynamic therapy (RDT), which produces ¹O₂ and other reactive oxygen species (ROS) in response to X-rays, can be used in conjunction with radiation therapy (RT) to drastically lower X-ray dosage and reduce radio resistance associated with conventional radiation treatment. However, radiation-radiodynamic therapy (RT-RDT) is still impotent in a hypoxic environment in solid tumors due to its oxygen-dependent nature. Chemodynamic therapy (CDT) can generate reactive oxygen species and O₂ by decomposing H₂O₂ in hypoxic cells and thus potentiate RT-RDT to achieve synergy. Herein, we developed a multifunctional nanosystem, AuCu-Ce6-TPP (ACCT), for RT-RDT-CDT. Ce6 photosensitizers were conjugated to AuCu nanoparticles via Au-S bonds to realize radiodynamic sensitization. Cu can be oxidized by H₂O₂ and catalyze the degradation of H₂O₂ to generate ^•OH through the Fenton-like reaction to realize CDT. Meanwhile, the degradation byproduct oxygen can alleviate hypoxia while Au can consume glutathione to increase the oxidative stress. We then attached mercaptoethyl-triphenylphosphonium (TPP-SH) to the nanosystem, targeting ACCT to mitochondria (colocalization Pearson coefficient 0.98) to directly disrupt mitochondrial membranes and more efficiently induce apoptosis. We confirmed that ACCT efficiently generates ¹O₂ and ^•OH upon X-ray irradiation, resulting in strong anticancer efficacy in both normoxic and hypoxic 4T1 cells. The down-regulation of hypoxia-inducible factor 1α expression and reduction of intracellular H₂O₂ concentrations suggested that ACCT could significantly alleviate hypoxia in 4T1 cells. ACCT-enhanced RT-RDT-CDT can successfully shrink or remove tumors in radioresistant 4T1 tumor-bearing mice upon 4 Gy of X-ray irradiation. Our work thus presents a new strategy to treat radioresistant hypoxic tumors.

DNA methylation biomarkers for noninvasive detection of triple-negative breast cancer using liquid biopsy

Noninvasive detection of aberrant DNA methylation could provide invaluable biomarkers for earlier detection of triple-negative breast cancer (TNBC) which could help clinicians with easier and more efficient treatment options. We evaluated genome-wide DNA methylation data derived from TNBC and normal breast tissues, peripheral blood of TNBC cases and controls and reference samples of sorted blood and mammary cells. Differentially methylated regions (DMRs) between TNBC and normal breast tissues were stringently selected, verified and externally validated. A machine-learning algorithm was applied to select the top DMRs, which then were evaluated on plasma-derived circulating cell-free DNA (cfDNA) samples of TNBC patients and healthy controls. We identified 23 DMRs accounting for the methylation profile of blood cells and reference mammary cells and then selected six top DMRs for cfDNA analysis. We quantified un-/methylated copies of these DMRs by droplet digital PCR analysis in a plasma test set from TNBC patients and healthy controls and confirmed our findings obtained on tissues. Differential cfDNA methylation was confirmed in an independent validation set of plasma samples. A methylation score combining signatures of the top three DMRs overlapping with the SPAG6, LINC10606 and TBCD/ZNF750 genes had the best capability to discriminate TNBC patients from controls (AUC = 0.78 in the test set and AUC = 0.74 in validation set). Our findings demonstrate the usefulness of cfDNA-based methylation signatures as noninvasive liquid biopsy markers for the diagnosis of TNBC.

Racial and Ethnic Disparities in Early-Onset Colorectal Cancer Survival

BACKGROUND

Young adults diagnosed with colorectal cancer (CRC) comprise a growing, yet understudied, patient population. We estimated 5-year relative survival of early-onset CRC and examined disparities in survival by race-ethnicity in a population-based sample.

METHODS

We used the National Cancer Institute's Surveillance, Epidemiology, and End Results program of cancer registries to identify patients diagnosed with early-onset CRC (20-49 years of age) between January 1, 1992, and December 31, 2013. For each racial-ethnic group, we estimated 5-year relative survival, overall and by sex, tumor site, and stage at diagnosis. To illustrate temporal trends, we compared 5-year relative survival in 1992-2002 vs 2003-2013. We also used Cox proportional hazards regression models to examine the association of race-ethnicity and all-cause mortality, adjusting for age at diagnosis, sex, county type (urban vs rural), county-level median household income, tumor site, and stage at diagnosis.

RESULTS

We identified 33,777 patients diagnosed with early-onset CRC (58.5% White, 14.0% Black, 13.0% Asian, 14.5% Hispanic). Five-year relative survival ranged from 57.6% (Black patients) to 69.1% (White patients). Relative survival improved from 1992-2002 to 2003-2013 for White patients only; there was no improvement for Black, Asian, or Hispanic patients. This pattern was similar by sex, tumor site, and stage at diagnosis. In adjusted analysis, Black (adjusted hazard ratio [aHR], 1.42; 95% confidence interval [CI], 1.36-1.49), Asian (aHR, 1.06; 95% CI, 1.01-1.12), and Hispanic (aHR, 1.16; 95% CI, 1.10-1.21) race-ethnicity were associated with all-cause mortality.

CONCLUSION

Our study adds to the well-documented disparities in CRC in older adults by demonstrating persistent racial-ethnic disparities in relative survival and all-cause mortality in patients with early-onset CRC.

Searching for DNA methylation in patients triple-negative breast cancer: a liquid biopsy approach

INTRODUCTION

Liquid biopsies are proving to have diagnostic and prognostic value in many different cancers, and in breast cancer they have the potential to improve outcomes by providing valuable information throughout a patient's cancer journey. However, patients with triple negative breast cancer (TNBC) have received little benefit from such liquid biopsies due to underlying limitations in the discovery and utility of robust biomarkers. Here, we examine the development of DNA methylation-based liquid biopsy assays for breast cancer and how they pertain to TNBC.

AREAS COVERED

We conducted a systematic review of liquid biopsy assays for breast cancer and analyzed their relevance in TNBC. We show that the utility of DNA mutation-based assays is poor for TNBC due to the low mutational frequencies across the genome in this subtype. We offer a detailed review of mDETECT - a liquid biopsy specifically designed for assessing tumor burden in TNBC patients.

EXPERT OPINION

DNA methylation are foundational and robust events that occur in cancer evolution and may differentiate almost all forms of cancer, including TNBC. Longitudinal patient monitoring using DNA methylation-based liquid biopsies offers great potential for improving the detection and management of TNBC.

Epigenetic liquid biopsies for minimal residual disease, what's around the corner?

Liquid biopsy assays for minimal residual disease (MRD) are used to monitor and inform oncological treatment and predict the risk of relapse in cancer patients. To-date, most MRD assay development has focused on targeting somatic mutations. However, epigenetic changes are more frequent and universal than genetic alterations in cancer and circulating tumor DNA (ctDNA) retains much of these changes. Here, we review the epigenetic signals that can be used to detect MRD, including DNA methylation alterations and fragmentation patterns that differentiate ctDNA from noncancerous circulating cell-free DNA (ccfDNA). We then summarize the current state of MRD monitoring; highlight the advantages of epigenetics over genetics-based approaches; and discuss the emerging paradigm of assaying both genetic and epigenetic targets to monitor treatment response, detect disease recurrence, and inform adjuvant therapy.

Analysis of Intrinsic Breast Cancer Subtypes: The Clinical Utility of Epigenetic Biomarkers and TP53 Mutation Status in Triple-Negative Cases

This study aimed at analyzing the DNA methylation pattern and TP53 mutation status of intrinsic breast cancer (BC) subtypes for improved characterization and survival prediction. DNA methylation of 17 genes was tested by methylation-specific PCR in 116 non-familial BRCA mutation-negative BC and 29 control noncancerous cases. At least one gene methylation was detected in all BC specimens and a 10-gene panel statistically significantly separated tumors from noncancerous breast tissues. Methylation of FILIP1L and MT1E was predominant in triple-negative (TN) BC, while other BC subtypes were characterized by RASSF1, PRKCB, MT1G, APC, and RUNX3 hypermethylation. TP53 mutation (TP53-mut) was found in 38% of sequenced samples and mainly affected TN BC cases (87%). Cox analysis revealed that TN status, age at diagnosis, and RUNX3 methylation are independent prognostic factors for overall survival (OS) in BC. The combinations of methylated biomarkers, RUNX3 with MT1E or FILIP1L, were also predictive for shorter OS, whereas methylated FILIP1L was predictive of a poor outcome in the TP53-mut subgroup. Therefore, DNA methylation patterns of specific genes significantly separate BC from noncancerous breast tissues and distinguishes TN cases from non-TN BC, whereas the combination of two-to-three epigenetic biomarkers can be an informative tool for BC outcome predictions.

Genome-wide differential DNA methylation analysis of MDA-MB-231 breast cancer cells treated with curcumin derivatives, ST08 and ST09

ST08 and ST09 are potent curcumin derivatives with antiproliferative, apoptotic, and migrastatic properties. Both ST08 and ST09 exhibit in vitro and in vivo anticancer properties. As reported earlier, these derivatives were highly cytotoxic towards MDA-MB-231 triple-negative breast cancer cells with IC50 values in the nanomolar (40-80nM) range.In this study,we performed whole-genome bisulfite sequencing(WGBS) of untreated (control), ST08 and ST09 (treated) triple-negative breast cancer cell line MDA-MB-231 to unravel epigenetic changes induced by the drug. We identified differentially methylated sites (DMSs) enriched in promoter regions across the genome. Analysis of the CpG island promoter methylation identified 12 genes common to both drugs, and 50% of them are known to be methylated in patient samples that were hypomethylated by drugs belonging to the homeobox family transcription factors.Methylation analysis of the gene body revealed 910 and 952 genes to be hypermethylatedin ST08 and ST09 treated MDA-MB-231 cells respectively. Correlation of the gene body hypermethylation with expression revealed CACNAH1 to be upregulated in ST08 treatment and CDH23 upregulation in ST09.Further, integrated analysis of the WGBS with RNA-seq identified uniquely altered pathways - ST08 altered ECM pathway, and ST09 cell cycle, indicating drug-specific signatures.

DNA Methylation Analysis Reveals Distinct Patterns in Satellite Cell-Derived Myogenic Progenitor Cells of Subjects with Spastic Cerebral Palsy

Spastic type cerebral palsy (CP) is a complex neuromuscular disorder that involves altered skeletal muscle microanatomy and growth, but little is known about the mechanisms contributing to muscle pathophysiology and dysfunction. Traditional genomic approaches have provided limited insight regarding disease onset and severity, but recent epigenomic studies indicate that DNA methylation patterns can be altered in CP. Here, we examined whether a diagnosis of spastic CP is associated with intrinsic DNA methylation differences in myoblasts and myotubes derived from muscle resident stem cell populations (satellite cells; SCs). Twelve subjects were enrolled (6 CP; 6 control) with informed consent/assent. Skeletal muscle biopsies were obtained during orthopedic surgeries, and SCs were isolated and cultured to establish patient-specific myoblast cell lines capable of proliferation and differentiation in culture. DNA methylation analyses indicated significant differences at 525 individual CpG sites in proliferating SC-derived myoblasts (MB) and 1774 CpG sites in differentiating SC-derived myotubes (MT). Of these, 79 CpG sites were common in both culture types. The distribution of differentially methylated 1 Mbp chromosomal segments indicated distinct regional hypo- and hyper-methylation patterns, and significant enrichment of differentially methylated sites on chromosomes 12, 13, 14, 15, 18, and 20. Average methylation load across 2000 bp regions flanking transcriptional start sites was significantly different in 3 genes in MBs, and 10 genes in MTs. SC derived MBs isolated from study participants with spastic CP exhibited fundamental differences in DNA methylation compared to controls at multiple levels of organization that may reveal new targets for studies of mechanisms contributing to muscle dysregulation in spastic CP.

Classification of Subgroups with Immune Characteristics Based on DNA Methylation in Luminal Breast Cancer

Luminal breast cancer (BC) accounts for a large proportion of patients in BC, with high heterogeneity. Determining the precise subtype and optimal selection of treatment options for luminal BC is a challenge. In this study, we proposed an MSBR framework that integrate DNA methylation profiles and transcriptomes to identify immune subgroups of luminal BC. MSBR was implemented both on a key module scoring algorithm and “Boruta” feature selection method by DNA methylation. Luminal A was divided into two subgroups and luminal B was divided into three subgroups using the MSBR. Furthermore, these subgroups were defined as different immune subgroups in luminal A and B respectively. The subgroups showed significant differences in DNA methylation levels, immune microenvironment (immune cell infiltration, immune checkpoint PD1/PD-L1 expression, immune cell cracking activity (CYT)) and pathology features (texture, eccentricity, intensity and tumor-infiltrating lymphocytes (TILs)). The results also showed that there is a subgroup in both luminal A and B that has the benefit from immunotherapy. This study proposed a classification of luminal BC from the perspective of epigenetics and immune characteristics, which provided individualized treatment decisions.

A Deep Neural Network for Gastric Cancer Prognosis Prediction Based on Biological Information Pathways

Background

Gastric cancer (GC) is one of the deadliest cancers in the world, with a 5-year overall survival rate of lower than 20% for patients with advanced GC. Genomic information is now frequently employed for precision cancer treatment due to the rapid advancements of high-throughput sequencing technologies. As a result, integrating multiomics data to construct predictive models for the GC patient prognosis is critical for tailored medical care.

Results

In this study, we integrated multiomics data to design a biological pathway-based gastric cancer sparse deep neural network (GCS-Net) by modifying the P-NET model for long-term survival prediction of GC. The GCS-Net showed higher accuracy (accuracy = 0.844), area under the curve (AUC = 0.807), and F1 score (F1 = 0.913) than traditional machine learning models. Furthermore, the GCS-Net not only enables accurate patient survival prognosis but also provides model interpretability capabilities lacking in most traditional deep neural networks to describe the complex biological process of prognosis. The GCS-Net suggested the importance of genes (UBE2C, JAK2, RAD21, CEP250, NUP210, PTPN1, CDC27, NINL, NUP188, and PLK4) and biological pathways (Mitotic Anaphase, Resolution of Sister Chromatid Cohesion, and SUMO E3 ligases) to GC, which is consistent with the results revealed in biological- and medical-related studies of GC.

Conclusion

The GCS-Net is an interpretable deep neural network built using biological pathway information whose structure represents a nonlinear hierarchical representation of genes and biological pathways. It can not only accurately predict the prognosis of GC patients but also suggest the importance of genes and biological pathways. The GCS-Net opens up new avenues for biological research and could be adapted for other cancer prediction and discovery activities as well.

Molecular basis of epigenetic regulation in cancer diagnosis and treatment

The global cancer cases and mortality rates are increasing and demand efficient biomarkers for accurate screening, detection, diagnosis, and prognosis. Recent studies have demonstrated that variations in epigenetic mechanisms like aberrant promoter methylation, altered histone modification and mutations in ATP-dependent chromatin remodelling complexes play an important role in the development of carcinogenic events. However, the influence of other epigenetic alterations in various cancers was confirmed with evolving research and the emergence of high throughput technologies. Therefore, alterations in epigenetic marks may have clinical utility as potential biomarkers for early cancer detection and diagnosis. In this review, an outline of the key epigenetic mechanism(s), and their deregulation in cancer etiology have been discussed to decipher the future prospects in cancer therapeutics including precision medicine. Also, this review attempts to highlight the gaps in epigenetic drug development with emphasis on integrative analysis of epigenetic biomarkers to establish minimally non-invasive biomarkers with clinical applications.

Incremental Value of Radiomics in 5-Year Overall Survival Prediction for Stage II-III Rectal Cancer

Although rectal cancer comprises up to one-third of colorectal cancer cases and several prognosis nomograms have been established for colon cancer, statistical tools for predicting long-term survival in rectal cancer are lacking. In addition, previous prognostic studies did not include much imaging findings, qualitatively or quantitatively. Therefore, we include multiparametric MRI information from both radiologists' readings and quantitative radiomics signatures to construct a prognostic model that allows 5-year overall survival (OS) prediction for advance-staged rectal cancer patients. The result suggested that the model combined with quantitative imaging findings might outperform that of conventional TNM staging or other clinical prognostic factors. It was noteworthy that the identified radiomics signature consisted of three from dynamic contrast-enhanced (DCE)-MRI, four from anatomical MRI, and one from functional diffusion-weighted imaging (DWI). This highlighted the importance of multiparametric MRI to address the issue of long-term survival estimation in rectal cancer. Additionally, the constructed radiomics signature demonstrated value to the conventional prognostic factors in predicting 5-year OS for stage II-III rectal cancer. The presented nomogram also provides a practical example of individualized prognosis estimation and may potentially impact treatment strategies.

Epigenome erosion and SOX10 drive neural crest phenotypic mimicry in triple-negative breast cancer

Intratumoral heterogeneity is caused by genomic instability and phenotypic plasticity, but how these features co-evolve remains unclear. SOX10 is a neural crest stem cell (NCSC) specifier and candidate mediator of phenotypic plasticity in cancer. We investigated its relevance in breast cancer by immunophenotyping 21 normal breast and 1860 tumour samples. Nuclear SOX10 was detected in normal mammary luminal progenitor cells, the histogenic origin of most TNBCs. In tumours, nuclear SOX10 was almost exclusive to TNBC, and predicted poorer outcome amongst cross-sectional (p = 0.0015, hazard ratio 2.02, n = 224) and metaplastic (p = 0.04, n = 66) cases. To understand SOX10’s influence over the transcriptome during the transition from normal to malignant states, we performed a systems-level analysis of co-expression data, de-noising the networks with an eigen-decomposition method. This identified a core module in SOX10’s normal mammary epithelial network that becomes rewired to NCSC genes in TNBC. Crucially, this reprogramming was proportional to genome-wide promoter methylation loss, particularly at lineage-specifying CpG-island shores. We propose that the progressive, genome-wide methylation loss in TNBC simulates more primitive epigenome architecture, making cells vulnerable to SOX10-driven reprogramming. This study demonstrates potential utility for SOX10 as a prognostic biomarker in TNBC and provides new insights about developmental phenotypic mimicry—a major contributor to intratumoral heterogeneity.

Effective Inhibition of Mitochondrial Metabolism by Cryptotanshinone in MDA-MB231 cells: A Proteomic Analysis

Background :

Triple-negative breast cancer (TNBC) is a subtype of invasive cancer in breast with the symptoms of unfavourable prognosis and limited targeted treatment options. Evidence of changes in the metabolic status of TNBC, characterised by increased glycolysis, mitochondrial oxidative phosphorylation, as well as production and utilization of tricarboxylic acid cycle intermediates.

Objective:

Investigate the proteins altered in cryptotanshinone treated MDA-MB-231 cells and explore the key pathways and specific molecular markers involved in cryptotanshinone treatment.

Method:

We use unlabeled quantitative proteomics to gain insight into the anticancer mechanism of cryptotanshinone on MDA-MB231 triple negative breast cancer cells. And flow cytometry was used to detect apoptosis and changes in cell mitochondrial membrane potential.

Results:

We show that inhibiting the expression of electron transport chain complex proteins, also inhibits mitochondrial oxidative phosphorylation. Additionally, down-regulation of the ribosime biogenesis pathway was found to inhibit cell metabolism.

Conclusion:

In summary, results show that cryptotanshinone can trigger rapid and irreversible apoptosis in MDA-MB-231 cells through effectively inhibiting cell metabolism.

Epigenetic Therapies and Biomarkers in Breast Cancer

Epigenetic therapies remain a promising, but still not widely used, approach in the management of patients with cancer. To date, the efficacy and use of epigenetic therapies has been demonstrated primarily in the management of haematological malignancies, with limited supportive data in solid malignancies. The most studied epigenetic therapies in breast cancer are those that target DNA methylation and histone modification; however, none have been approved for routine clinical use. The majority of pre-clinical and clinical studies have focused on triple negative breast cancer (TNBC) and hormone-receptor positive breast cancer. Even though the use of epigenetic therapies alone in the treatment of breast cancer has not shown significant clinical benefit, these therapies show most promise in use in combinations with other treatments. With improving technologies available to study the epigenetic landscape in cancer, novel epigenetic alterations are increasingly being identified as potential biomarkers of response to conventional and epigenetic therapies. In this review, we describe epigenetic targets and potential epigenetic biomarkers in breast cancer, with a focus on clinical trials of epigenetic therapies. We describe alterations to the epigenetic landscape in breast cancer and in treatment resistance, highlighting mechanisms and potential targets for epigenetic therapies. We provide an updated review on epigenetic therapies in the pre-clinical and clinical setting in breast cancer, with a focus on potential real-world applications. Finally, we report on the potential value of epigenetic biomarkers in diagnosis, prognosis and prediction of response to therapy, to guide and inform the clinical management of breast cancer patients.

Multiplex PCR Design for Scalable Resequencing

While conventional PCR applications typically focus on a single PCR assay per reaction, multiplex PCR applications are a convenient and scalable solution becoming more routine. Multiplex methods can be applied to virtually any DNA template source (e.g., plant or human DNA, FFPE DNA isolated from clinical samples, bisulfite-converted DNA for DNA methylation analysis), and offers a cheap, convenient, and scalable solution for experiments that require characterization and analysis of multiple genomic regions.This method will detail the procedures to successfully design, screen, and prepare multiplex amplicon libraries; as well as supporting instructions on how to prepare these libraries for sequencing on Illumina, Ion Torrent, and Oxford Nanopore platforms. The flexibility of assay design allows means that custom multiplex panels can range in size from two assays up to a few hundred amplicons or more. Notably, the method described here is also amenable to whatever PCR buffer system the user prefers to use, making the system globally adaptable to the needs and preferences of the end user.

Identification of DNA methylation biomarkers with potential to predict response to neoadjuvant chemotherapy in triple-negative breast cancer

Neoadjuvant chemotherapy (NAC) is used to treat triple-negative breast cancer (TNBC) prior to resection. Biomarkers that accurately predict a patient's response to NAC are needed to individualise therapy and avoid chemotoxicity from unnecessary chemotherapy. We performed whole-genome DNA methylation profiling on diagnostic TNBC biopsy samples from the Sequential Evaluation of Tumours Undergoing Preoperative (SETUP) NAC study. We found 9 significantly differentially methylated regions (DMRs) at diagnosis which were associated with response to NAC. We show that 4 of these DMRs are associated with TNBC overall survival (P < 0.05). Our results highlight the potential of DNA methylation biomarkers for predicting NAC response in TNBC.

Screening of Breast Cancer Methylation Biomarkers Based on the TCGA Database

Objective

Breast cancer has become a fatal disease for women world-wide. Its incidence in China has been increasing yearly, and the identification of early-stage biomarkers is urgently required.

Methods

ANOVA was carried out in the case of a primary tumor, adjacent normal tissue, and tumor metastasis of breast cancer, and on pan-cancer samples using the genome-wide methylation data of 31 solid tumor Illumina Methylation 450K chips downloaded from The Cancer Genome Atlas (TCGA) website in September 2018. Methylation sites showing a significant difference (P ≤ 0.05) were screened and compared with the whole-genome methylation data of 31 other solid tumor species in the TCGA database using t-tests in order to screen the methylation sites of breast cancer-specific expression. The expression of the screened methylation sites was confirmed through pyrosequencing in 45 cases of breast cancer, lung cancer, gastric cancer, and colorectal cancer.

Results

A total of 10 specific breast cancer methylation sites (cg13683194, cg07996594, cg21646032, cg07671949, cg21185686, cg03625109, cg16429070, cg23601468, cg24818566, and cg01240931) were analyzed; nine genes (C9orf125, RARB, ESR1, RUNX3, PCDHGB7, DBC1, PDGFRB, TIMP3, and APC) were involved. The overall effect was excellent; a total of 4 methylation sites (2 in the DBC1 gene [cg03625109 and cg24818566], 1 in the C9orf125 gene [cg13683194], and 1 in the PDGFRB gene [cg16429070]) could effectively distinguish breast cancer from 31 other cancer species. The pyrosequencing results revealed that 7 screened methylation sites could significantly distinguish between breast cancer, lung cancer, gastric cancer, and colorectal cancer samples; these sites could also specifically distinguish between luminal A, luminal B, HER2, and Basal-like types of breast cancer.

Conclusion

The 10 breast cancer methylation sites screened in the present study can effectively distinguish breast cancer from 31 other solid tumors, and they are expected to be used as biomarkers for early screening of breast cancer.

A Novel Seven Gene Signature-Based Prognostic Model to Predict Distant Metastasis of Lymph Node-Negative Triple-Negative Breast Cancer

Background

The prognosis of lymph node-negative triple-negative breast cancer (TNBC) is still worse than that of other subtypes despite adjuvant chemotherapy. Reliable prognostic biomarkers are required to identify lymph node-negative TNBC patients at a high risk of distant metastasis and optimize individual treatment.

Methods

We analyzed the RNA sequencing data of primary tumor tissue and the clinicopathological data of 202 lymph node-negative TNBC patients. The cohort was randomly divided into training and validation sets. Least absolute shrinkage and selection operator Cox regression and multivariate Cox regression were used to construct the prognostic model.

Results

A clinical prognostic model, seven-gene signature, and combined model were constructed using the training set and validated using the validation set. The seven-gene signature was established based on the genomic variables associated with distant metastasis after shrinkage correction. The difference in the risk of distant metastasis between the low- and high-risk groups was statistically significant using the seven-gene signature (training set: P < 0.001; validation set: P = 0.039). The combined model showed significance in the training set (P < 0.001) and trended toward significance in the validation set (P = 0.071). The seven-gene signature showed improved prognostic accuracy relative to the clinical signature in the training data (AUC value of 4-year ROC, 0.879 vs. 0.699, P = 0.046). Moreover, the composite clinical and gene signature also showed improved prognostic accuracy relative to the clinical signature (AUC value of 4-year ROC: 0.888 vs. 0.699, P = 0.029; AUC value of 5-year ROC: 0.882 vs. 0.693, P = 0.038). A nomogram model was constructed with the seven-gene signature, patient age, and tumor size.

Conclusions

The proposed signature may improve the risk stratification of lymph node-negative TNBC patients. High-risk lymph node-negative TNBC patients may benefit from treatment escalation.

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.

Determination of Genetic and Epigenetic Modifications-Related Prognostic Biomarkers of Breast Cancer: Genome High-Throughput Data Analysis

The high heterogeneity of breast cancer (BRCA) makes it more challenging to interpret the genetic variation mechanisms involved in BRCA pathogenesis and prognosis. Areas with high DNA methylation (such as CpG islands) were accompanied by copy number variation (CNV), and these genomic variations affected the level of DNA methylation. In this study, we characterized intertumor heterogeneity and analyzed the effects of CNV on DNA methylation and gene expression. In addition, we performed a Genetic Set Enrichment Analysis (GSEA) to identify key pathways for changes between patients with low and high expression of genes. Our analysis found two key genes, namely, HPDL and SOX17. The protein expressed by HPDL is 4-hydroxyphenylpyruvate dioxygenase-like protein, which has dioxygenase activity. SOX17 is a transcription factor that can inhibit Wnt signaling, promote the degradation of activated CTNNB1, and participate in cell proliferation. Our analysis found that the CNV of HPDL and SOX17 is not only related to the patient's prognosis, but also related to gene methylation and expression levels affecting the patient's survival time. Among them, the high-methylation, low-expression HPDL and SOX17 showed poor prognosis. And the addition of two copies of SOX17 is associated with a lower survival rate, while a decrease in the copy number of HPDL also suggests a poor prognosis. This study provided an effective bioinformatics basis for further exploration of molecular mechanisms related to BRCA and assessment of patient prognosis, but the development of biomarkers for diagnosis and treatment still requires further clinical data validation.

Strategic Developments & Future Perspective on Gene Therapy for Breast Cancer: Role of mTOR and Brk/ PTK6 as Molecular Targets

Breast cancer is a serious health issue and a major concern in biomedical research. Alteration in major signaling (viz. PI3K-AKT-mTOR, Ras-Raf-MEK-Erk, NF-kB, cyclin D1, JAK-STAT, Wnt, Notch, Hedgehog signaling and apoptotic pathway) contributes to the development of major subtypes of mammary carcinoma such as HER2 positive, TNBC, luminal A and B and normal-like breast cancer. Further, mutation and expression parameters of different genes involved in the growth and development of cells play an important role in the progress of different types of carcinoma, making gene therapy an emerging new therapeutic approach for the management of life-threatening diseases like cancer. The genetic targets (oncogenes and tumor suppressor genes) play a major role in the formation of a tumor. Brk/PTK6 and mTOR are two central molecules that are involved in the regulation of numerous signaling related to cell growth, proliferation, angiogenesis, survival, invasion, metastasis, apoptosis, and autophagy. Since these two proteins are highly upregulated in mammary carcinogenesis, this can be used as targeted genes for the treatment of breast cancer. However, not much work has been done on them. This review highlights the therapeutic significance of Brk and mTOR and their associated signaling in mammary carcinogenesis, which may provide a strategy to develop gene therapy for breast cancer management.

Epigenetic Regulation of Breast Cancer Stem Cells Contributing to Carcinogenesis and Therapeutic Implications

Globally, breast cancer has remained the most commonly diagnosed cancer and the leading cause of cancer death among women. Breast cancer is a highly heterogeneous and phenotypically diverse group of diseases, which require different selection of treatments. Breast cancer stem cells (BCSCs), a small subset of cancer cells with stem cell-like properties, play essential roles in breast cancer progression, recurrence, metastasis, chemoresistance and treatments. Epigenetics is defined as inheritable changes in gene expression without alteration in DNA sequence. Epigenetic regulation includes DNA methylation and demethylation, as well as histone modifications. Aberrant epigenetic regulation results in carcinogenesis. In this review, the mechanism of epigenetic regulation involved in carcinogenesis, therapeutic resistance and metastasis of BCSCs will be discussed, and finally, the therapies targeting these biomarkers will be presented.

The Roles of DNA Demethylases in Triple-Negative Breast Cancer

Triple-negative breast cancers (TNBCs) are very heterogenous, molecularly diverse, and are characterized by a high propensity to relapse or metastasize. Clinically, TNBC remains a diagnosis of exclusion by the lack of hormone receptors (Estrogen Receptor (ER) and Progesterone Receptor (PR)) as well as the absence of overexpression and/or amplification of HER2. DNA methylation plays an important role in breast cancer carcinogenesis and TNBCs have a distinct DNA methylation profile characterized by marked hypomethylation and lower gains of methylations compared to all other subtypes. DNA methylation is regulated by the balance of DNA methylases (DNMTs) and DNA demethylases (TETs). Here, we review the roles of TETs as context-dependent tumor-suppressor genes and/or oncogenes in solid tumors, and we discuss the current understandings of the oncogenic role of TET1 and its therapeutic implications in TNBCs.

The epigenetics of breast cancer - Opportunities for diagnostics, risk stratification and therapy

The stage and molecular pathology-dependent prognosis of breast cancer, the limited treatment options for triple-negative carcinomas, as well as the development of resistance to therapies illustrate the need for improved early diagnosis and the development of new therapeutic approaches. Increasing data suggests that some answers to these challenges could be found in the area of epigenetics. In this study, we focus on the current research of the epigenetics of breast cancer, especially on the potential of epigenetics for clinical application in diagnostics, risk stratification and therapy. The differential DNA methylation status of specific gene regions has been used in the past to differentiate breast cancer cells from normal tissue. New technologies as detection of circulating nucleic acids including microRNAs to early detect breast cancer are emerging. Pattern of DNA methylation and expression of histone-modifying enzymes have been successfully used for risk stratification. However, all these epigenetic biomarkers should be validated in larger clinical studies. Recent preclinical and clinical studies show a therapeutic benefit of epigenetically active drugs for breast cancer entities that are still difficult to treat (triple negative, UICC stage IV). Remarkably, epigenetic therapies combined with chemotherapies or hormone-based therapies represent the most promising strategy. At the current stage, the integration of epigenetic substances into established breast cancer therapy protocols seems to hold the greatest potential for a clinical application of epigenetic research.

Regulation of cancer stem cells in triple negative breast cancer

Triple Negative Breast Cancer (TNBC) is the most lethal subtype of breast cancer. Despite the successes of emerging targeted therapies, relapse, recurrence, and therapy failure rates in TNBC significantly outpace other subtypes of breast cancer. Mounting evidence suggests accumulation of therapy resistant Cancer Stem Cell (CSC) populations within TNBCs contributes to poor clinical outcomes. These CSCs are enriched in TNBC compared to non-TNBC breast cancers. The mechanisms underlying CSC accumulation have been well-characterized and discussed in other reviews. In this review, we focus on TNBC-specific mechanisms that allow the expansion and activity of self-renewing CSCs. We highlight cellular signaling pathways and transcription factors, specifically enriched in TNBC over non-TNBC breast cancer, contributing to stemness. We also analyze publicly available single-cell RNA-seq data from basal breast cancer tumors to highlight the potential of emerging bioinformatic approaches in identifying novel drivers of stemness in TNBC and other cancers.

A DNA methylation-based liquid biopsy for triple-negative breast cancer

Here, we present a next-generation sequencing (NGS) methylation-based blood test called methylation DETEction of Circulating Tumour DNA (mDETECT) designed for the optimal detection and monitoring of metastatic triple-negative breast cancer (TNBC). Based on a highly multiplexed targeted sequencing approach, this assay incorporates features that offer superior performance and included 53 amplicons from 47 regions. Analysis of a previously characterised cohort of women with metastatic TNBC with limited quantities of plasma (<2 ml) produced an AUC of 0.92 for detection of a tumour with a sensitivity of 76% for a specificity of 100%. mDETECT_TNBC was quantitative and showed superior performance to an NGS TP53 mutation-based test carried out on the same patients and to the conventional CA15-3 biomarker. mDETECT also functioned well in serum samples from metastatic TNBC patients where it produced an AUC of 0.97 for detection of a tumour with a sensitivity of 93% for a specificity of 100%. An assay for BRCA1 promoter methylation was also incorporated into the mDETECT assay and functioned well but its clinical significance is currently unclear. Clonal Hematopoiesis of Indeterminate Potential was investigated as a source of background in control subjects but was not seen to be significant, though a link to adiposity may be relevant. The mDETECT_TNBC assay is a liquid biopsy able to quantitatively detect all TNBC cancers and has the potential to improve the management of patients with this disease.

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.

Integrating multi-omics data through deep learning for accurate cancer prognosis prediction

BACKGROUND

Genomic information is nowadays widely used for precise cancer treatments. Since the individual type of omics data only represents a single view that suffers from data noise and bias, multiple types of omics data are required for accurate cancer prognosis prediction. However, it is challenging to effectively integrate multi-omics data due to the large number of redundant variables but relatively small sample size. With the recent progress in deep learning techniques, Autoencoder was used to integrate multi-omics data for extracting representative features. Nevertheless, the generated model is fragile from data noises. Additionally, previous studies usually focused on individual cancer types without making comprehensive tests on pan-cancer. Here, we employed the denoising Autoencoder to get a robust representation of the multi-omics data, and then used the learned representative features to estimate patients' risks.

RESULTS

By applying to 15 cancers from The Cancer Genome Atlas (TCGA), our method was shown to improve the C-index values over previous methods by 6.5% on average. Considering the difficulty to obtain multi-omics data in practice, we further used only mRNA data to fit the estimated risks by training XGboost models, and found the models could achieve an average C-index value of 0.627. As a case study, the breast cancer prognosis prediction model was independently tested on three datasets from the Gene Expression Omnibus (GEO), and shown able to significantly separate high-risk patients from low-risk ones (C-index>0.6, p-values<0.05). Based on the risk subgroups divided by our method, we identified nine prognostic markers highly associated with breast cancer, among which seven genes have been proved by literature review.

CONCLUSION

Our comprehensive tests indicated that we have constructed an accurate and robust framework to integrate multi-omics data for cancer prognosis prediction. Moreover, it is an effective way to discover cancer prognosis-related genes.

Neural EGFL like 2 expressed in myoepithelial cells and suppressed breast cancer cell migration

Breast tissue has a branching structure that contains double-layered cells, consisting primarily of luminal epithelial cells inside and myoepithelial cells outside. Ductal carcinoma in situ (DCIS) still has myoepithelial cells surrounding the cancer cells. However, myoepithelial cells disappear in invasive ductal carcinoma. In this study, we detected expression of neural EGFL like (NELL) 2 and one of its receptors, roundabout guidance receptor (ROBO) 3, in myoepithelial and luminal epithelial cells (respectively) in normal breast tissue. NELL2 also was expressed in myoepithelial cells surrounding the non-cancerous intraductal proliferative lesions and DCIS. However, the expression level and proportion of NELL2-positive cells in DCIS were lower than those in normal and non-cancerous intraductal proliferative lesions. ROBO3 expression was decreased in invasive ductal carcinoma compared to that in normal and non-cancerous intraductal proliferative lesions. An evaluation of NELL2's function in breast cancer cell lines demonstrated that full-length NELL2 suppressed cell adhesion and migration in vitro. In contrast, the N-terminal domain of NELL2 increased cell adhesion in the early phase and migration in vitro in some breast cancer cells. These results suggested that full-length NELL2 protein, when expressed in myoepithelial cells, might serve as an inhibitor of breast cancer cell migration.

Epigenetic Alterations in Triple-Negative Breast Cancer-The Critical Role of Extracellular Matrix

Triple-negative breast cancer (TNBC) is an aggressive subgroup of breast cancer characterized by genomic complexity and therapeutic options limited to only standard chemotherapy. Although it has been suggested that stratifying TNBC patients by pathway-specific molecular alterations may predict benefit from specific therapeutic agents, application in routine clinical practice has not yet been established. There is a growing body of the literature supporting that epigenetic modifications comprised by DNA methylation, chromatin remodeling and non-coding RNAs play a fundamental role in TNBC pathogenesis. Extracellular matrix (ECM) is a highly dynamic 3D network of macromolecules with structural and cellular regulatory roles. Alterations in the expression of ECM components result in uncontrolled matrix remodeling, thus affecting its ability to regulate vital functions of cancer cells, including proliferation, migration, adhesion, invasion and epithelial-to-mesenchymal transition (EMT). Recent molecular data highlight the major role of tumor microenvironment and ECM alterations in TNBC and approaches for targeting tumor microenvironment have recently been recognized as potential therapeutic strategies. Notably, many of the ECM/EMT modifications in cancer are largely driven by epigenetic events, highlighting the pleiotropic effects of the epigenetic network in TNBC. This article presents and critically discusses the current knowledge on the epigenetic alterations correlated with TNBC pathogenesis, with emphasis on those associated with ECM/EMT modifications, their prognostic and predictive value and their use as therapeutic targets.

Neighborhood Social Environmental Factors and Breast Cancer Subtypes among Black Women

BACKGROUND

The disproportionate burden of more aggressive breast cancer subtypes among African American/Black women may stem from multilevel determinants. However, data are limited regarding the impacts of neighborhood social environmental characteristics among Black women.

METHODS

We evaluated the association between neighborhood-level socioeconomic status (nSES) and breast cancer subtypes in the Women's Circle of Health and Women's Circle of Health Follow-up Study, which included 1,220 Black women diagnosed from 2005 to 2017 with invasive breast cancer. nSES at diagnosis was measured using NCI's census tract-level SES index. We used multilevel multinomial logistic regression models to estimate the association of nSES with breast cancer subtypes [triple-negative breast cancer (TNBC), HER2-positive vs. luminal A], adjusting for individual-level SES, body mass index, and reproductive factors. We tested for interactions by neighborhood racial composition.

RESULTS

Compared with census tracts characterized as high nSES, the relative risk ratios (RRR) for TNBC were 1.81 [95% confidence interval (CI): 1.20-2.71] and 1.95 (95% CI: 1.27-2.99) for women residing in areas with intermediate and low nSES, respectively (P trend = 0.002). Neighborhood racial composition modified the association between nSES and TNBC; the highest relative risk of TNBC was among women residing in low nSES areas with low proportions of Black residents.

CONCLUSIONS

Black women residing in socioeconomically disadvantaged neighborhoods may have an increased risk of TNBC, particularly in areas with lower proportions of Black residents.

IMPACT

Places people live may influence breast tumor biology. A deeper understanding of multilevel pathways contributing to tumor biology is needed.

Association of variably methylated tumour DNA regions with overall survival for invasive lobular breast cancer

Background

Tumour DNA methylation profiling has shown potential to refine disease subtyping and improve the diagnosis and prognosis prediction of breast cancer. However, limited data exist regarding invasive lobular breast cancer (ILBC). Here, we investigated the genome-wide variability of DNA methylation levels across ILBC tumours and assessed the association between methylation levels at the variably methylated regions and overall survival in women with ILBC.

Methods

Tumour-enriched DNA was prepared by macrodissecting formalin-fixed paraffin embedded (FFPE) tumour tissue from 130 ILBCs diagnosed in the participants of the Melbourne Collaborative Cohort Study (MCCS). Genome-wide tumour DNA methylation was measured using the HumanMethylation 450K (HM450K) BeadChip array. Variably methylated regions (VMRs) were identified using the DMRcate package in R. Cox proportional hazards regression models were used to assess the association between methylation levels at the ten most significant VMRs and overall survival. Gene set enrichment analyses were undertaken using the web-based tool Metaspace. Replication of the VMR and survival analysis findings was examined using data retrieved from The Cancer Genome Atlas (TCGA) for 168 ILBC cases. We also examined the correlation between methylation and gene expression for the ten VMRs of interest using TCGA data.

Results

We identified 2771 VMRs (P < 10⁻⁸) in ILBC tumours. The ten most variably methylated clusters were predominantly located in the promoter region of the genes: ISM1, APC, TMEM101, ASCL2, NKX6, HIST3H2A/HIST3H2BB, HCG4P3, HES5, CELF2 and EFCAB4B. Higher methylation level at several of these VMRs showed an association with reduced overall survival in the MCCS. In TCGA, all associations were in the same direction, however stronger than in the MCCS. The pooled analysis of the MCCS and TCGA data showed that methylation at four of the ten genes was associated with reduced overall survival, independently of age and tumour stage; APC: Hazard Ratio (95% Confidence interval) per one-unit M-value increase: 1.18 (1.02–1.36), TMEM101: 1.23 (1.02–1.48), HCG4P3: 1.37 (1.05–1.79) and CELF2: 1.21 (1.02–1.43). A negative correlation was observed between methylation and gene expression for CELF2 (R = − 0.25, P = 0.001), but not for TMEM101 and APC.

Conclusions

Our study identified regions showing greatest variability across the ILBC tumour genome and found methylation at several genes to potentially serve as a biomarker of survival for women with ILBC.

A Risk Score Model Based on Nine Differentially Methylated mRNAs for Predicting Prognosis of Patients with Clear Cell Renal Cell Carcinoma

Purpose

DNA methylation alterations play important roles in initiation and progression of clear cell renal cell carcinoma (ccRCC). In this study, we attempted to identify differentially methylated mRNA signatures with prognostic value for ccRCC.

Methods

The mRNA methylation and expression profiling data of 306 ccRCC tumors were downloaded from The Cancer Genome Atlas (TCGA) to screen differentially methylated lncRNAs and mRNAs (DMLs and DMMs) between bad and good prognosis patients. Uni- and multivariable Cox regression analyses and LASSO Cox-PH regression analysis were used to select prognostic lncRNAs and mRNAs. Corresponding risk scores were calculated and compared for predictive performance in the training set using Kaplan-Meier OS and ROC curve analyses. The optimal risk score was then identified and validated in the validation set. Function enrichment analysis was conducted.

Results

This study screened 461 DMMs and 63 DMLs between good prognosis and bad prognosis patients, and furthermore, nine mRNAs and six lncRNAs were identified as potential prognostic molecules. Compared to nine-mRNA status risk score model, six-lncRNA methylation risk score model, and six-lncRNA status risk score model, the nine-mRNA methylation risk score model showed superiority for prognosis stratification of ccRCC patients in the training set. The prognostic ability of the nine-mRNA methylation risk score model was validated in the validation set. The nine prognostic mRNAs were functionally associated with neuroactive ligand receptor interaction and inflammation-related pathways.

Conclusion

The nine-mRNA methylation signature (DMRTA2, DRGX, FAM167A, FGGY, FOXI2, KRTAP2-1, TCTEX1D1, TTBK1, and UBE2QL1) may be a useful prognostic biomarker and tool for ccRCC patients. The present results would be helpful to elucidate the possible pathogenesis of ccRCC.

Cancer Biology, Epidemiology, and Treatment in the 21st Century: Current Status and Future Challenges From a Biomedical Perspective

Since the second half of the 20th century, our knowledge about the biology of cancer has made extraordinary progress. Today, we understand cancer at the genomic and epigenomic levels, and we have identified the cell that starts neoplastic transformation and characterized the mechanisms for the invasion of other tissues. This knowledge has allowed novel drugs to be designed that act on specific molecular targets, the immune system to be trained and manipulated to increase its efficiency, and ever more effective therapeutic strategies to be developed. Nevertheless, we are still far from winning the war against cancer, and thus biomedical research in oncology must continue to be a global priority. Likewise, there is a need to reduce unequal access to medical services and improve prevention programs, especially in countries with a low human development index.