Basal-like Breast Cancer Subtype Research Articles

Non-basal subtype defined by FOXC1 expression as an independent predictor of capecitabine efficacy in the triple negative breast cancer GEICAM/2003-11_CIBOMA/2004-01 trial.

516 Background: In a prespecified correlative analysis of the GEICAM/2003-11_CIBOMA/2004-01 phase III clinical trial (CIBOMA trial), PAM50 non-basal status identified triple-negative breast cancer (TNBC) patients (pts) most likely to benefit from adjuvant capecitabine (cap). FOXC1 cell plasticity/metastasis transcriptional driver assessment by standardized Veresca FOXC1 Immunohistochemistry (IHC) BC test (Veresca) has shown rigorous capacity to identify basal-like breast cancer (BLBC) subtype throughout TNBC cohorts. We sought to explore the concordance of BLBC identification by FOXC1 with PAM50 molecular subtyping and its prognostic/predictive value in the CIBOMA trial (NCT00130533). Methods: We analyzed FOXC1 expression (Veresca, Onconostic Technologies) and the Ki-67 Antigen MIB-1 (Dako) by IHC on tumors from 705 TNBC pts randomized to maintenance with cap (357, 51%) vs observation (obs) (348, 49%) after standard (neo)adjuvant chemotherapy (CT). Veresca score (VFOXC1) was defined as % of tumor cells with positive nuclear staining (Proportion Score 0-5) plus staining intensity (Intensity Score 0-3). BLBC subtyping by Veresca (cutoff ≥4) and PAM50 signature (Nanostring) were compared using ROC analysis and Kappa index (KI). Ki67 ≥20% cutoff was applied. Cox regression models were assessed to predict DRFS (primary endpoint), DFS and OS (secondary endpoints). Results: VFOXC1≥4 identified 460 (65%) BLBC pts in this CIBOMA trial cohort (229 (49.8%) treated with cap, and 231 (50.2%) in obs). VFOXC1 detection was not associated with any clinicopathological characteristic. VFOXC1 expression significantly correlated with PAM50 BLBC subtyping (ROC analysis AUC 0.874, KI 0.43) but moderately with IHC subtyping (AUC 0.548). VFOXC1 BLBC subtype was not associated with DRFS (HR=0.94; 95%CI 0.68-1.30; p=0.713) in the entire cohort. However, VFOXC1 non-BLBC subtype was a strong independent predictor of cap benefit for DRFS (univariate analysis, interaction p-value p=0.117; HR=0.53; 95%CI 0.31-0.90; p=0.019; multivariate analysis, HR=0.44; 95%CI 0.25–0.76; p=0.003). This predictive effect of VFOXC1 non-BLBC on cap benefit was also confirmed at DFS (HR=0.47; 95%CI 0.28–0.78; p=0.003) and OS (HR=0.48; 95%CI 0.24–0.96; p=0.038), and was independent of Ki67 proliferation status (multivariate DRFS; Ki67<20%, HR=0.41; 95%CI 0.20-0.82; p=0.012; Ki67≥20%, HR=0.31; 95%CI 0.10-0.97; p=0.045). VFOXC1 non-BLBC subtype had no association with clinical outcome in obs arm. Conclusions: In the CIBOMA TNBC trial, the non-BLBC definition by a single biomarker (FOXC1 Veresca) provided prognostic and predictive value of cap benefit after (neo)adjuvant CT, corroborating our previous findings by PAM50 and IHC non-BLBC subtyping. This is a pragmatic option to effectively apply findings from this trial in the real-world setting. Clinical trial information: NCT00130533 .

Frequent Amplification and Overexpression of PSMA in Basallike Breast Cancer from Analysis of The Cancer Genome Atlas.

Prostate-specific membrane antigen (PSMA) is frequently overexpressed in nonprostate malignancies. This preclinical study investigated the molecular basis of the application of PMSA-targeting radiopharmaceuticals in breast cancer subtypes. Methods: The somatic copy number status and the transcriptomic and protein expressions of FOLH1 (gene name of PSMA) were analyzed across breast cancer subtypes in 998 patients from The Cancer Genome Atlas dataset. Results: FOLH1 was frequently amplified in basallike breast cancer (BLBC) (32%) compared with luminal and human epidermal growth factor receptor 2-positive subtypes (16% and 17%, respectively; P < 0.01). FOLH1 expression was higher in BLBC (P < 0.001) and was negatively correlated with estrogen-receptor and progesterone-receptor expressions. Consistently, the PSMA protein level was higher in BLBC (P < 0.05). Interestingly, FOLH1 expression was associated with relapse-free and distant metastasis-free survival in patients with BLBC. Conclusion: The BLBC subtype exhibited frequent amplification and overexpression of PSMA, supporting the exploration of PSMA-targeting radiopharmaceuticals in this aggressive breast cancer subtype.

Unveiling the relationship between WWOX and BRCA1 in mammary tumorigenicity and in DNA repair pathway selection

Breast cancer is the leading cause of cancer-related deaths in women worldwide, with the basal-like or triple-negative breast cancer (TNBC) subtype being particularly aggressive and challenging to treat. Understanding the molecular mechanisms driving the development and progression of TNBC is essential. We previously showed that WW domain-containing oxidoreductase (WWOX) is commonly inactivated in TNBC and is implicated in the DNA damage response (DDR) through ATM and ATR activation. In this study, we investigated the interplay between WWOX and BRCA1, both frequently inactivated in TNBC, on mammary tumor development and on DNA double-strand break (DSB) repair choice. We generated and characterized a transgenic mouse model (K14-Cre;Brca1fl/fl;Wwoxfl/fl) and observed that mice lacking both WWOX and BRCA1 developed basal-like mammary tumors and exhibited a decrease in 53BP1 foci and an increase in RAD51 foci, suggesting impaired DSB repair. We examined human TNBC cell lines harboring wild-type and mutant BRCA1 and found that WWOX expression promoted NHEJ repair in cells with wild-type BRCA1. Our findings suggest that WWOX and BRCA1 play an important role in DSB repair pathway choice in mammary epithelial cells, underscoring their functional interaction and significance in breast carcinogenesis.

ID4-dependent secretion of VEGFA enhances the invasion capability of breast cancer cells and activates YAP/TAZ via integrin β3-VEGFR2 interaction

Understanding the mechanisms of breast cancer cell communication underlying cell spreading and metastasis formation is fundamental for developing new therapies. ID4 is a proto-oncogene overexpressed in the basal-like subtype of triple-negative breast cancer (TNBC), where it promotes angiogenesis, cancer stem cells, and BRACA1 misfunction. Here, we show that ID4 expression in BC cells correlates with the activation of motility pathways and promotes the production of VEGFA, which stimulates the interaction of VEGFR2 and integrin β3 in a paracrine fashion. This interaction induces the downstream focal adhesion pathway favoring migration, invasion, and stress fiber formation. Furthermore, ID4/ VEGFA/ VEGFR2/ integrin β3 signaling stimulates the nuclear translocation and activation of the Hippo pathway member’s YAP and TAZ, two critical executors for cancer initiation and progression. Our study provides new insights into the oncogenic roles of ID4 in tumor cell migration and YAP/TAZ pathway activation, suggesting VEGFA/ VEGFR2/ integrin β3 axis as a potential target for BC treatment.

Bioinformatics analysis of the potentially functional circRNA-miRNA-mRNA network in breast cancer.

Breast cancer (BC) is the most common cancer among women with high morbidity and mortality. Therefore, new research is still needed for biomarker detection. GSE101124 and GSE182471 datasets were obtained from the Gene Expression Omnibus (GEO) database to evaluate differentially expressed circular RNAs (circRNAs). The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases were used to identify the significantly dysregulated microRNAs (miRNAs) and genes considering the Prediction Analysis of Microarray classification (PAM50). The circRNA-miRNA-mRNA relationship was investigated using the Cancer-Specific CircRNA, miRDB, miRTarBase, and miRWalk databases. The circRNA-miRNA-mRNA regulatory network was annotated using Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database. The protein-protein interaction network was constructed by the STRING database and visualized by the Cytoscape tool. Then, raw miRNA data and genes were filtered using some selection criteria according to a specific expression level in PAM50 subgroups. A bottleneck method was utilized to obtain highly interacted hub genes using cytoHubba Cytoscape plugin. The Disease-Free Survival and Overall Survival analysis were performed for these hub genes, which are detected within the miRNA and circRNA axis in our study. We identified three circRNAs, three miRNAs, and eighteen candidate target genes that may play an important role in BC. In addition, it has been determined that these molecules can be useful in the classification of BC, especially in determining the basal-like breast cancer (BLBC) subtype. We conclude that hsa_circ_0000515/miR-486-5p/SDC1 axis may be an important biomarker candidate in distinguishing patients in the BLBC subgroup of BC.

A ten long noncoding RNA-based prognostic risk model construction and mechanism study in the basal-like immune-suppressed subtype of triple-negative breast cancer.

According to the Fudan University Shanghai Cancer Center (FUSCC) system, triple-negative breast cancer (TNBC) is divided into four stable subtypes: (I) luminal androgen receptor, (II) immunomodulatory, (III) basal-like immune-suppressed (BLIS), and (IV) mesenchymal-like. However, the treatment outcomes of the corresponding targeted therapies are unsatisfactory, especially for the BLIS subtype. Therefore, we aimed to identify the key long noncoding RNAs (lncRNAs) to construct a prognostic model for BLIS subtype and discover potential targets to explore potential therapeutic strategies in this study. The FUSCC cohort was used to establish a prognostic risk model via least absolute shrinkage and selection operator (LASSO) and Cox regression analysis. The Cancer Genome Atlas (TCGA) cohort was then used to evaluate and verify the model. To understand the functional aspects of the model, functional, immune landscape, mutation, and drug sensitivity analyses were performed between high- and low-risk groups. Ten prognostic-related lncRNAs identified, including C5ORF66-AS2, DIO3OS, FZD10-DT, LINC00393, LNC-ERI1-32, LNC-FOXO1-2, LNC-SPARCL1-1, HCG23, LNC-MMD-4 and LNC-TMEM106C-6, were selected for risk score system construction. The results showed that the model constructed could divide the patients with BLIS subtype into two groups of high and low risk, and patients with higher risk scores had shorter recurrence-free survival. In addition, drug sensitivity analysis identified 3 compounds, including BMS-754807, cytochalasin b, and linifanib, that could have a potential therapeutic effect on patients with the BLIS subtype. The risk prognosis model showed good prognostic value for the BLIS subtype patients, and the ten lncRNAs may be potential therapeutic targets.

MicroRNAs as Molecular Biomarkers for the Characterization of Basal-like Breast Tumor Subtype.

Breast cancer is a heterogeneous disease highlighted by the presence of multiple tumor variants and the basal-like breast cancer (BLBC) is considered to be the most aggressive variant with limited therapeutics and a poor prognosis. Though the absence of detectable protein and hormonal receptors as biomarkers hinders early detection, the integration of genomic and transcriptomic profiling led to the identification of additional variants in BLBC. The high-throughput analysis of tissue-specific micro-ribonucleic acids (microRNAs/miRNAs) that are deemed to have a significant role in the development of breast cancer also displayed distinct expression profiles in each subtype of breast cancer and thus emerged to be a robust approach for the precise characterization of the BLBC subtypes. The classification schematic of breast cancer is still a fluid entity that continues to evolve alongside technological advancement, and the transcriptomic profiling of tissue-specific microRNAs is projected to aid in the substratification and diagnosis of the BLBC tumor subtype. In this review, we summarize the current knowledge on breast tumor classification, aim to collect comprehensive evidence based on the microRNA expression profiles, and explore their potential as prospective biomarkers of BLBC.

A workflow to study mechanistic indicators for driver gene prediction with Moonlight.

Prediction of driver genes (tumor suppressors and oncogenes) is an essential step in understanding cancer development and discovering potential novel treatments. We recently proposed Moonlight as a bioinformatics framework to predict driver genes and analyze them in a system-biology-oriented manner based on -omics integration. Moonlight uses gene expression as a primary data source and combines it with patterns related to cancer hallmarks and regulatory networks to identify oncogenic mediators. Once the oncogenic mediators are identified, it is important to include extra levels of evidence, called mechanistic indicators, to identify driver genes and to link the observed gene expression changes to the underlying alteration that promotes them. Such a mechanistic indicator could be for example a mutation in the regulatory regions for the candidate gene. Here, we developed new functionalities and released Moonlight2 to provide the user with a mutation-based mechanistic indicator as a second layer of evidence. These functionalities analyze mutations in a cancer cohort to classify them into driver and passenger mutations. Those oncogenic mediators with at least one driver mutation are retained as the final set of driver genes. We applied Moonlight2 to the basal-like breast cancer subtype, lung adenocarcinoma and thyroid carcinoma using data from The Cancer Genome Atlas. For example, in basal-like breast cancer, we found four oncogenes (COPZ2, SF3B4, KRTCAP2 and POLR2J) and nine tumor suppressor genes (KIR2DL4, KIF26B, ARL15, ARHGAP25, EMCN, GMFG, TPK1, NR5A2 and TEK) containing a driver mutation in their promoter region, possibly explaining their deregulation. Moonlight2R is available at https://github.com/ELELAB/Moonlight2R.

Nicotinamide N-methyltransferase sustains a core epigenetic program that promotes metastatic colonization in breast cancer.

Metastatic colonization of distant organs accounts for over 90% of deaths related to solid cancers, yet the molecular determinants of metastasis remain poorly understood. Here, we unveil a mechanism of colonization in the aggressive basal-like subtype of breast cancer that is driven by the NAD+ metabolic enzyme nicotinamide N-methyltransferase (NNMT). We demonstrate that NNMT imprints a basal genetic program into cancer cells, enhancing their plasticity. In line, NNMT expression is associated with poor clinical outcomes in patients with breast cancer. Accordingly, ablation of NNMT dramatically suppresses metastasis formation in pre-clinical mouse models. Mechanistically, NNMT depletion results in a methyl overflow that increases histone H3K9 trimethylation (H3K9me3) and DNA methylation at the promoters of PR/SET Domain-5 (PRDM5) and extracellular matrix-related genes. PRDM5 emerged in this study as a pro-metastatic gene acting via induction of cancer-cell intrinsic transcription of collagens. Depletion of PRDM5 in tumor cells decreases COL1A1 deposition and impairs metastatic colonization of the lungs. These findings reveal a critical activity of the NNMT-PRDM5-COL1A1 axis for cancer cell plasticity and metastasis in basal-like breast cancer.

Abstract 6544: Identification of epigenetic regulatory networks associated with the basal-like breast cancer subtype

Abstract Purpose: Basal-like breast cancer (BLBC) is a biologically aggressive and molecular heterogeneous breast cancer subtype that confers to the patient´s poor prognosis. The crosstalk of epigenetic regulators, such as methylation events and microRNA (miRNA) deregulation, drives breast cancer progression and modulates aggressive tumor phenotypes. However, few studies have specifically characterized the epigenetic regulatory networks of genome-wide DNA methylation-based modifications, miRNAs and transcription factors (TF) expression in BLBC. Therefore, the main objective of this study was to identify and correlate the DNA methylation profiles and the transcriptional expression of miRNAs and TFs of the BLBC subtype. Methods: To achieve our goal, a comprehensive computational pipeline using the R language was used. First, the transcriptional expression profiles and DNA methylation data of BLBC and non-tumor samples were downloaded from The Cancer Genome Atlas (TCGA). Then, the ELMER package was used to determine the differentially methylated distal probes between these two groups, identifying gene-probes pairs that can be regulated by un/methylated regions that affect the function of TFs. The different expressed (DE) miRNAs were identified using the DESeq2 package. To determine the regulatory pairs (gene, miRNA and TF interactions) the multiMiR, miRWalk, ENCODE, and hTFtarget were used and the regulatory networks were identified by the FANMOD algorithm. Finally, pathway enrichment analysis (PEA) was conducted using the Reactome database and microT-CDS (Diana Tools mirPath v.3). Results: A total of 147 and 199 pairs of gene-probes were identified in hypermethylation and hypomethylation analyses, respectively. Fifty genes and 59 regulatory TFs whose expression was associated with DNA methylation were selected, including members of the E2F (E2Fs) and Krüppel-like factor (KLFs) family. Eleven DE miRNAs, out of the 51 identified, were selected based on their interaction with genes and TFs. These pairs of miRNAs, TF, and genes generated three types of feed-forward loops (FFLs): coherent FFLs, mediated by miRNAs; incoherent FFLs, mediated by TFs; and compost FFLs, mediated by TF and miRNAs. Finally, the PEA analysis showed 51 pathways in which miRNAs, genes, or TFs may interact, including pathways related to DNA repair, cell cycle, pathways in cancer, as well as the defective binding of RB1 mutants to E2F1, and the ErbB signaling pathway. Conclusion: Our findings demonstrated the epigenetic regulation of methylation patterns and miRNA expression and their impact on the expression of TFs that may exert critical roles in the BLBC. The functional analysis of these regulatory networks in well-established BLBC models, targeting the signaling pathways identified, can determine whether they modulate the BLBC phenotype and can be used as potential clinical targets. Citation Format: Larissa Miyuki Okano, Alexandre Luiz Azevedo, Tamyres Mingorance Carvalho, Fernanda Brandão Berti, Luciane Regina Cavalli. Identification of epigenetic regulatory networks associated with the basal-like breast cancer subtype. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6544.

Abstract P4-09-02: A molecular classification system for basal-like breast cancer based on the tumor microenvironment is prognostic for survival

Abstract Recent advancements in molecular profiling have revealed distinct breast cancer subtypes, but many clinical NGS assays rely on gene panels, such as PAM50, limiting their clinical utility. Basal-like breast cancer (BLBC), one of the most aggressive subtypes, has highly variable molecular and clinical characteristics. Now, the tumor microenvironment (TME) is recognized as a vital participant in tumor progression and therapeutic response. The development of more refined classifications based on the TME, capable of accounting for tissue heterogeneity, may improve NGS clinical utility for BLBC. Here, we apply our transcriptomic-based approach, recently described by Bagaev et. al., to classify the BLBC TME into discrete immune portraits, to potentially improve clinical outcomes and facilitate therapeutic decisions. We collected a cohort of 1,708 BLBC samples based on the expression levels of 50 genes (PAM50) from 10 publicly available datasets, with clinical outcomes available (n = 819). Using methodology described by Bagaev et. al., 31 functional gene expression signatures (Fges) were selected, and unsupervised dense Louvain clustering was performed to identify TME subtypes. A novel RNA-seq deconvolution algorithm was used to determine the cell types within the TME. Validation of the histological features, including stroma, tumor infiltrating lymphocytes (TILs), and tertiary lymphoid structures (TLS), relative to gene expression patterns in the TME subtypes was performed by automated and manual annotation of BLBC H&amp;E slides (n = 146) from an independent TCGA cohort. Overall survival (OS) analysis was performed using Kaplan-Meier and Cox regression methods. We revealed 5 BLBC subtypes with distinct expression patterns: immune-enriched, non-fibrotic (IE, 19%), B-cell–enriched, TLS-like (TLS, 25.5%), granulocyte-enriched (G, 12.8%), fibrotic (F, 28%), and immune desert (D, 17.7%) (Table). IE tumors featured an active immune TME, with high immune checkpoint expression and T cell activity. The TLS subtype also had an immune-rich TME, presenting high levels of B cells, T helper cells, and TLS (p &amp;lt; 0.001). The TLS subtype exhibited the highest number of stromal TILs on TCGA H&amp;E slides. The G subtype was characterized by high expression of granulocytes and granulocyte traffic molecules. In accordance with our findings, deconvolution predicted the highest percent of neutrophils in the G subtype (p &amp;lt; 0.001). The F subtype demonstrated the highest levels of angiogenesis, stromal Fges, and VEGFR1-3, FGFR1, and EGFR expression. By histological evaluation, 84% of F subtype samples demonstrated a medium or high level of fibrosis. The D subtype showed a high proliferation rate and low stromal and immune Fges. Indicative of proliferation rate, CCNB1 and cyclin B1 were highest in G, D, and IE subtypes. OS analysis revealed a significant association between TME subtypes (TLS = baseline; log HR G = 0.87, p &amp;lt; 0.05; IE = 0.39, p = 0.18; F = 0.99, p &amp;lt; 0.05; D = 1.21, p &amp;lt; 0.05), and survival outcomes. The immune-enriched subtypes, IE and TLS, demonstrated good prognosis and higher expression of immune checkpoint genes, while immune desert D and granulocyte-enriched G subtypes exhibited the worst OS. Using our transcriptomic-based approach, BLBC was classified into 5 distinct subtypes, each with unique therapeutic vulnerabilities. Further investigation of these TME subtypes may lead to potential clinical utility as a prognostic tool to improve clinical decision making. Table. Characteristics of Basal-like breast cancer (BLBC) tumor microenvironment (TME) subtypes. TLS - Tertiary lymphoid structures; CAF - cancer-associated fibroblasts Citation Format: Svetlana Khorkova, Diana Shamsutdinova, Vladimir Kushnarev, Lev Popyvanov, Daniil Dymov, Anastasia Zotova, Ivan Valiev, Zoya Antysheva, Anna Love, Jessica H. Brown, Alexander Bagaev, Nikita Kotlov, Nathan Fowler. A molecular classification system for basal-like breast cancer based on the tumor microenvironment is prognostic for survival [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-09-02.

Transcription factor E2F8 is a therapeutic target in the basal-like subtype of breast cancer.

Tumorigenesis in breast cancers usually accompanied by the dysregulation of transcription factors (TFs). Abnormal amplification of TFs leads aberrant expression of its downstream target genes. However, breast cancers are heterogeneous disease with different subtypes that have distinguished clinical behaviours, and the identification of prognostic TFs may enable to provide diagnosis and treatment of breast cancer based on subtypes, especially in Basal-like breast cancer. The RNA-sequencing was performed to screen differential TFs in breast cancer subtypes. The GEPIA dataset analysis was used to analyze the genes expression in invasive breast carcinoma. The expression of MYBL2, HOXC13, and E2F8 was verified by qRT-PCR assay in breast cancers. The depiction analysis of co-expressed proteins was revealed using the STRING datasets. The cellular infiltration level analysis by the TISIDB and TIMER databases. The transwell assay was performed to analyze cellular migration and invasion. CCK-8 assay was used to evaluate cellular drug susceptibility for docetaxel treatment. Predicted targeted drugs in breast cancers by GSCA Lite database online. Kaplan-Meier plotter suggested that high expression of both E2F8 and MYBL2 in Basal-like subtype had a poor relapse-free survival. Functional enrichment results identified that apoptosis, cell cycle, and hormone ER pathway were represented the crucial regulation pathways by both E2F8 and MYBL2. In the meantime, database analysis indicated that high expression of E2F8 responded to chemotherapy, while those patients of high expression of MYBL2 responded to endocrinotherapy, and a positive correlation between the expression of E2F8 and PD-L1/CTLA4. Our cell line experiments confirmed the importance of E2F8 and MYBL2 in proliferation and chemotherapy sensitivity, possibly, the relationship with PD-L1. Additionally, we also observed that the up-regulation of E2F8 was accompanied with higher enrichments of CD4+ T cells and CD8+ T cells in breast cancers. Taken together, our findings elucidated a prospective target in Basal-like breast cancer, providing underlying molecular biomarkers for the development of breast cancer treatment.

Tumor-infiltrating lymphocytes and levels of PD-L1 and BRCA protein expression may identify patients with breast cancer with a higher rate of BRCA1 mutations

Introduction. Breast cancer (BC) is a heterogeneous disease, treated as per the predictive role of immunohistoche­mistry (IHC) identifiers as estrogen/progesterone and HER2 receptor proteins. Deeper molecular classification (MC) identifies molecular subtypes according to the gene-expression profiles, with different molecular genetic alterations and biological features, present in the different subtype. An overlap between IHC and MC exists, even if somewhat incomplete. We aimed to identify the overlap between IHC and MC, and identify patients with basal-like subtype of BC. We hypothesized that the rates of tumor expression of breast cancer-related protein 1 (BRCA1), the type of tumor-infiltrating lymphocytes, and the expression of programmed death ligand 1 (PD-L1) by immune cells vary among different subtypes of BC. Material and methods. Parafin-embedded samples from 100 patients with primary invasive BC were analyzed and expression levels of estrogen and progesterone receptors, HER2 status, and Ki-67 were assessed via IHC, defining four groups – luminal A-like, luminal B-like (LumA, LumB), HER2-positive non-luminal, and triple negative (TN). The pri­mary endpoint of our study was to identify via IHC with CK5/6 and 17 basal-like subtypes of BC amongst others, and to describe specific clinicopathological features together with protein expression of BRCA1 and PD-L1 and tumor-infiltrating lymphocytes, using CD20, CD3, CD4, CD8, and FoxP3. Results. Basal-like BC were predominantly characterized as triple negative by IHC (p &lt; 0.05) and were more frequently seen among special BC subtypes as compared to no special type (NST), with p = 0.036. Their immune response was represented mostly by high concentration of intratumoral cytotoxic CD8 (+) T-lymphocytes (p &lt; 0.05) and stromal PD-L1-positive immune cells (p = 0.008). In these tumors, absence of expression of BRCA1 protein was more frequent (p &lt; 0.001). Basal-like subtype of BC with absent expression of BRCA1 is associated with poorer &lt;5-year survival (p = 0.001 and p = 0.017, respectively). Conclusions. The use of IHC can establish basal-like BC, the type of its immune response and possible dysfunction in the BRCA -gene, reflected in the lack of expression in the BRCA-related protein.

A novel approach for a joint analysis of isomiR and mRNA expression data reveals features of isomiR targeting in breast cancer.

A widely used procedure for selecting significant miRNA-mRNA or isomiR-mRNA pairs out of predicted interactions involves calculating the correlation between expression levels of miRNAs/isomiRs and mRNAs in a series of samples. In this manuscript, we aimed to assess the validity of this procedure by comparing isomiR-mRNA correlation profiles in sets of sequence-based predicted target mRNAs and non-target mRNAs (negative controls). Target prediction was carried out using RNA22 and TargetScan algorithms. Spearman's correlation analysis was conducted using miRNA and mRNA sequencing data of The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) project. Luminal A, luminal B, basal-like breast cancer subtypes, and adjacent normal tissue samples were analyzed separately. Using the sets of putative targets and non-targets, we introduced adjusted isomiR targeting activity (ITA)-the number of negatively correlated potential isomiR targets adjusted by the background (estimated using non-target mRNAs). We found that for most isomiRs a significant negative correlation between isomiR-mRNA expression levels appeared more often in a set of predicted targets compared to the non-targets. This trend was detected for both classical seed region binding types (8mer, 7mer-m8, 7mer-A1, 6mer) predicted by TargetScan and the non-classical ones (G:U wobbles and up to one mismatch or unpaired nucleotide within seed sequence) predicted by RNA22. Adjusted ITA distributions were similar for target sites located in 3'-UTRs and coding mRNA sequences, while 5'-UTRs had much lower scores. Finally, we observed strong cancer subtype-specific patterns of isomiR activity, highlighting the differences between breast cancer molecular subtypes and normal tissues. Surprisingly, our target prediction- and correlation-based estimates of isomiR activities were practically non-correlated with the average isomiR expression levels neither in cancerous nor in normal samples.

Leveraging Deep Learning Techniques and Integrated Omics Data for Tailored Treatment of Breast Cancer.

Multiomics data of cancer patients and cell lines, in synergy with deep learning techniques, have aided in unravelling predictive problems related to cancer research and treatment. However, there is still room for improvement in the performance of the existing models based on the aforementioned combination. In this work, we propose two models that complement the treatment of breast cancer patients. First, we discuss our deep learning-based model for breast cancer subtype classification. Second, we propose DCNN-DR, a deep convolute.ion neural network-drug response method for predicting the effectiveness of drugs on in vitro and in vivo breast cancer datasets. Finally, we applied DCNN-DR for predicting effective drugs for the basal-like breast cancer subtype and validated the results with the information available in the literature. The models proposed use late integration methods and have fairly better predictive performance compared to the existing methods. We use the Pearson correlation coefficient and accuracy as the performance measures for the regression and classification models, respectively.

Characterization of Mitochondrial Proteome and Function in Luminal A and Basal-like Breast Cancer Subtypes Reveals Alteration in Mitochondrial Dynamics and Bioenergetics Relevant to Their Diagnosis.

Breast cancer (BC) is the most prevalent cancer and the one with the highest mortality among women worldwide. Although the molecular classification of BC has been a helpful tool for diagnosing and predicting the treatment of BC, developments are still being made to improve the diagnosis and find new therapeutic targets. Mitochondrial dysfunction is a crucial feature of cancer, which can be associated with cancer aggressiveness. Although the importance of mitochondrial dynamics in cancer is well recognized, its involvement in the mitochondrial function and bioenergetics context in BC molecular subtypes has been scantly explored. In this study, we combined mitochondrial function and bioenergetics experiments in MCF7 and MDA-MB-231 cell lines with statistical and bioinformatics analyses of the mitochondrial proteome of luminal A and basal-like tumors. We demonstrate that basal-like tumors exhibit a vicious cycle between mitochondrial fusion and fission; impaired but not completely inactive mitochondrial function; and the Warburg effect, associated with decreased oxidative phosphorylation (OXPHOS) complexes I and III. Together with the results obtained in the cell lines and the mitochondrial proteome analysis, two mitochondrial signatures were proposed: one signature reflecting alterations in mitochondrial functions and a second signature exclusively of OXPHOS, which allow us to distinguish between luminal A and basal-like tumors.

Evaluation of forkhead-box C1 expression in breast cancer: Prognostic value and histopathological correlations in Egyptian women

Background Forkhead box C1 (FOXC1) is considered an important member of forkhead-box transcription factors. Previous studies have shown that FOXC1 is associated with tumor progression and represents a poor prognostic indicator in triple-negative basal-like breast cancer. Method This is a retrospective study conducted on 187 cases of invasive breast carcinoma obtained from Histopathology Laboratory in Oncology Center, Faculty of Medicine, Mansoura University, starting from 2012. Aim and objectives In this study, we aimed to study FOXC1 protein expression in molecular subtypes of breast cancer and to correlate its expression with other prognostic parameters using immunohistochemistry and tissue microarrays. Results FOXC1-positive expression was seen in 26.7% of cases. Positive FOXC1 expression was most common in triple-negative basal-like breast cancer and HER2-enriched subtypes. Positive FOXC1 expression was higher in ER-negative cases than ER-positive cases (39 vs. 21%) with statistical significance (P = 0.01). Similarly, positive FOXC1 was higher in PR-negative cases than PR-positive cases (37.5 vs. 20%), this was statistically significant (P = 0.009). In our series, FOXC1 expression was not associated with survival. Conclusion FOXC1 expression is associated with negative hormonal-receptor status in breast cancer. FOCX1 is not a prognostic indicator of disease-free survival in our patient series.

Tumor suppressor PLK2 may serve as a biomarker in triple-negative breast cancer for improved response to PLK1 therapeutics.

Polo-like kinase (PLK) family members play important roles in cell cycle regulation. The founding member PLK1 is oncogenic and preclinically validated as a cancer therapeutic target. Paradoxically, frequent loss of chromosome 5q11-35 which includes PLK2 is observed in basal-like breast cancer. In this study, we found that PLK2 was tumor suppressive in breast cancer, preferentially in basal-like and triple-negative breast cancer (TNBC) subtypes. Knockdown of PLK1 rescued phenotypes induced by PLK2-loss both in vitro and in vivo. We also demonstrated that PLK2 directly interacted with PLK1 at prometaphase through the kinase but not the polo-box domains of PLK2, suggesting PLK2 functioned at least partially through the interaction with PLK1. Furthermore, an improved treatment response was seen in both Plk2-deleted/low mouse preclinical and PDX TNBC models using the PLK1 inhibitor volasertib alone or in combination with carboplatin. Re-expression of PLK2 in an inducible PLK2-null mouse model reduced the therapeutic efficacy of volasertib. In summary, this study delineates the effects of chromosome 5q loss in TNBC that includes PLK2, the relationship between PLK2 and PLK1, and how this may render PLK2-deleted/low tumors more sensitive to PLK1 inhibition in combination with chemotherapy.

SRY-box transcription factor 10 is a highly specific biomarker of basal-like breast cancer.

Basal-like breast cancer is an aggressive molecular subtype associated with younger age and early relapse. Most cases lack expression of oestrogen receptor (ER), progesterone receptor, and human epidermal growth factor receptor 2, limiting targeted therapeutic options. Basal-like breast cancer is defined by the expression of genes in the outer/basally located epithelial layer of mammary glands, including those encoding cytokeratin (CK) 5 and CK14, and epidermal growth factor receptor (EGFR). SRY-box transcription factor 10 (SOX10), for which there is a readily available immunohistochemical stain, is expressed in a subset of breast cancers, particularly triple-negative carcinomas. In this study, we sought to: (i) assess the association between SOX10 expression and intrinsic molecular subtypes as defined by Prediction Analysis of Microarray 50 (PAM50) gene expression; and (ii) compare the performance of SOX10 with that of other surrogate markers of the basal-like subtype, including CK5, EGFR, nestin, and inositol polyphosphate 4-phosphatase type II (INPP4B). SOX10 immunostaining was performed on tissue microarrays constructed from a contemporary series enriched for ER-negative and weakly ER-positive cancers that had also undergone PAM50 gene profiling. A total of 211 cases were informative for both SOX10 immunohistochemistry and PAM50 subtype, including 103 basal-like cancers. Staining for SOX10 was positive in 73 of 103 basal-like cancers and in only two of 108 cancers of other subtypes (P < 0.001), resulting in a sensitivity of 70.9% and a specificity of 98.1%. SOX10 was more specific than the other tested basal markers, and the results were independent of ER status. SOX10 is a moderately sensitive, but highly specific, immunohistochemical biomarker for the basal-like intrinsic subtype of breast cancer, which, unlike other commonly used immunohistochemical biomarkers, is independent of hormone receptor status.

Decreased PRC2 activity supports the survival of basal-like breast cancer cells to cytotoxic treatments

Breast cancer (BC) is the most common cancer occurring in women but also rarely develops in men. Recent advances in early diagnosis and development of targeted therapies have greatly improved the survival rate of BC patients. However, the basal-like BC subtype (BLBC), largely overlapping with the triple-negative BC subtype (TNBC), lacks such drug targets and conventional cytotoxic chemotherapies often remain the only treatment option. Thus, the development of resistance to cytotoxic therapies has fatal consequences. To assess the involvement of epigenetic mechanisms and their therapeutic potential increasing cytotoxic drug efficiency, we combined high-throughput RNA- and ChIP-sequencing analyses in BLBC cells. Tumor cells surviving chemotherapy upregulated transcriptional programs of epithelial-to-mesenchymal transition (EMT) and stemness. To our surprise, the same cells showed a pronounced reduction of polycomb repressive complex 2 (PRC2) activity via downregulation of its subunits Ezh2, Suz12, Rbbp7 and Mtf2. Mechanistically, loss of PRC2 activity leads to the de-repression of a set of genes through an epigenetic switch from repressive H3K27me3 to activating H3K27ac mark at regulatory regions. We identified Nfatc1 as an upregulated gene upon loss of PRC2 activity and directly implicated in the transcriptional changes happening upon survival to chemotherapy. Blocking NFATc1 activation reduced epithelial-to-mesenchymal transition, aggressiveness, and therapy resistance of BLBC cells. Our data demonstrate a previously unknown function of PRC2 maintaining low Nfatc1 expression levels and thereby repressing aggressiveness and therapy resistance in BLBC.