Integrative investigation on breast cancer in ER, PR and HER2-defined subgroups using mRNA and miRNA expression profiling

Correlation of PTEN signaling pathway and miRNA in breast cancer

Breast cancer (BC) is one of the most common cancers among women and can be fatal if not diagnosed and treated on time. Various genetic and environmental factors play a significant role in the development and progression of BC. Within the body, different signaling pathways have been identified that contribute to cancer progression, or conversely, cancer prevention. Phosphatase and tensin homolog (PTEN) is one of the proteins that prevent cancer by inhibiting the oncogenic PI3K/Akt/mTOR signaling pathway. MicroRNAs (miRNAs) are molecules with about 18 to 28 base pairs, which regulate about 30% of human genes after transcription. miRNAs play a key role in the progression or prevention of cancer through different signaling pathway and mechanisms, e.g., apoptosis, angiogenesis, and proliferation. miRNAs, which are upstream mediators of PTEN, can reinforce or suppress the effect of PTEN signaling on BC cells, and suppressing the PTEN signaling, linked to weakness of the cancer cells to chemotherapeutic drugs. However, the precise mechanism and function of miRNAs on PTEN in BC are not yet fully understood. Therefore, in the present study, has been focused on miRNAs regulating PTEN function in BC.

Novel Strategies Using Sagacious Targeting for Site-Specific Drug Delivery in Breast Cancer Treatment: Clinical Potential and Applications

For more than a decade, researchers have been working to achieve new strategies and smart targeting drug delivery techniques and technologies to treat breast cancer (BC). Nanotechnology presents a hopeful strategy for targeted drug delivery into the building of new therapeutics using the properties of nanomaterials. Nanoparticles are of high regard in the field of diagnosis and the treatment of cancer. The use of these nanoparticles as an encouraging approach in the treatment of various cancers has drawn the interest of researchers in recent years. In order to achieve the maximum therapeutic effectiveness in the treatment of BC, combination therapy has also been adopted, leading to minimal side effects and thus an enhancement in the quality of life for patients. This review article compares, discusses and criticizes the approaches to treat BC using novel design strategies and smart targeting of site-specific drug delivery systems.

Impact of three miRNA signature as potential diagnostic marker for triple negative breast cancer patients

Breast cancer is a highly aggressive type of cancer and has several subtypes, including triple-negative breast cancer (TNBC), which accounts for 25% of morbidity related to breast cancer. miRNAs are small non-coding RNA molecules that regulate 60% of human genes. Dysregulated expression of miRNA in liquid biopsy of TNBC patients has the potential as a minimally invasive diagnostic biomarker. The Association of miRNA with TNBC was evaluated using in-silico analysis. Highly enriched miRNAs were selected for functional analysis to evaluate the role of miRNA in the progression of TNBC. The qRT-PCR-based expression analysis of miRNA was performed in 190 serum samples (139 TNBC and 51 healthy). Revealed the elevated expression of miRNA-155 and miRNA-21 in TNBC compared to control samples (P < 0.0001), while miRNA-205 was significantly downregulated in TNBC (P < 0.0001). The combined diagnostic value of the miRNA-205, miRNA-155 and miRNA-21 in cohort-I, cohort-II, and cohort-III was AUC of 96.1% (P < 0.0001), 94.9% (P < 0.0001), and 97.1% (P < 0.0001), respectively. Our study revealed that dysregulated expression of miRNA could be used as an independent indicator for discriminating TNBC from healthy patients. In addition, the combined predictive value of miRNA-205 + miRNA - 155 + miRNA-21 has higher AUC, sensitivity, and specificity in the diagnosis of TNBC in all three cohorts.

Immunohistochemistry versus PCR Technology for Molecular Subtyping of Breast Cancer: Multicentered Expereinces from Addis Ababa, Ethiopia

The application of immunohistochemistry (IHC) for molecular characterization of breast cancer (BC) is of paramount importance; however, it is not universally standardized, subject to observer variability and quantifying is a challenge. An alternative molecular technology, such as endpoint reverse transcription (RT)-PCR gene expression analysis, may improve observer variability and diagnostic accuracy. This study was intended to compare IHC with the RT-PCR based technique and assess the potential of RT-PCR for molecular subtyping of BC. In this comparative cross-sectional study, 54 BC tissues were collected from three public hospitals in Addis Ababa and shipped to Gynaecology department at Martin-Luther University (Germany) for laboratory analysis. Only 41 samples were qualified for IHC and RT-PCR investigation of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki-67 protein expression analysis. Kappa statistics was used to assess the concordance between the two techniques. The overall percent agreement between RT-PCR and IHC was 68.3% for ER (positive percent agreement [PPA] 71.1%; negative percent agreement [NPA] 33.3%), 39.0% for PR (PPA 14.3%; NPA 92.3%), and 82.9% for HER2 (PPA 62.5%; NPA 87.9%). Cohen's κ-values of 0.018 (< 0.20), 0.045 (< 0.200), and 0.481 (0.41-0.60) were generated for ER, PR, and HER2, respectively. Concordance for molecular subtypes was only 56.1% (23/41) and 0.20 kappa value. IHC and endpoint RT-PCR techniques have shown to be discordant for 43% samples. Molecular subtyping using endpoint RT-PCR was fairly concordant with IHC. Thus, endpoint RT-PCR may give an objective result, and can be applied for BC subtyping.

Deep neural networks with knockoff features identify nonlinear causal relations and estimate effect sizes in complex biological systems

BACKGROUND

Learning the causal structure helps identify risk factors, disease mechanisms, and candidate therapeutics for complex diseases. However, although complex biological systems are characterized by nonlinear associations, existing bioinformatic methods of causal inference cannot identify the nonlinear relationships and estimate their effect size.

RESULTS

To overcome these limitations, we developed the first computational method that explicitly learns nonlinear causal relations and estimates the effect size using a deep neural network approach coupled with the knockoff framework, named causal directed acyclic graphs using deep learning variable selection (DAG-deepVASE). Using simulation data of diverse scenarios and identifying known and novel causal relations in molecular and clinical data of various diseases, we demonstrated that DAG-deepVASE consistently outperforms existing methods in identifying true and known causal relations. In the analyses, we also illustrate how identifying nonlinear causal relations and estimating their effect size help understand the complex disease pathobiology, which is not possible using other methods.

CONCLUSIONS

With these advantages, the application of DAG-deepVASE can help identify driver genes and therapeutic agents in biomedical studies and clinical trials.

Increasing importance of breast cancer in Nepal

Recently, breast cancer cases in Nepal are on the rise, accounting for approximately 16% of all cancer cases, making it the second most common malignancy. Given the dependence of the Nepalese on agriculture, the rampant use of pesticides as well as the presence of arsenic in water supplies might be contributing to this huge rise in cancer cases. Herein, we provide a brief overview of the status of breast cancer, its burden, risk factors, screening and modes of treatment in Nepal.

Effectiveness of Organized Mammography Screening for Different Breast Cancer Molecular Subtypes

Simple Summary

We evaluated the short-term effectiveness of a mammography screening program in all women who participated in the screening program and were diagnosed with screen-detected or interval breast cancer (BC) in Flanders (2008–2018). The evaluation was performed for the major molecular subtypes of invasive BC separately and considering the regularity of participation. We found that screen-detected BC was more likely to be diagnosed at early stages than interval BC of luminal, luminal-HER2-positive, and triple-negative BC (TNBC) type, but not for the human epidermal growth factor receptor 2-positive (HER2 positive) subtype. In addition, regular participation was related to a higher likelihood of screening detection than irregular participation for luminal, luminal-HER2-positive, and TNBC, but not for the HER2 positive subtype, either. Our results indicate that regular screening as compared to irregular screening is effective for all breast cancers except for the HER2 subtype.

Abstract

Background: Screening program effectiveness is generally evaluated for breast cancer (BC) as one disease and without considering the regularity of participation, while this might have an impact on detection rate. Objectives: To evaluate the short-term effectiveness of a mammography screening program for the major molecular subtypes of invasive BC. Methods: All women who participated in the screening program and were diagnosed with screen-detected or interval BC in Flanders were included in the study (2008–2018). Molecular subtypes considered were luminal and luminal-HER2-positive, human epidermal growth factor receptor 2-positive, and triple-negative BC (TNBC). The relationship between the BC stage at diagnosis (early (I–II) versus advanced (III–IV)) and the method of detection (screen-detected or interval) and the relationship between the method of detection and participation regularity (regular versus irregular) were evaluated by multi-variable logistic regression models. All models were performed for each molecular subtype and adjusted for age. Results: Among the 12,318 included women, BC of luminal and luminal-HER2-positive subtypes accounted for 70.9% and 11.3%, respectively. Screen-detected BC was more likely to be diagnosed at early stages than interval BC with varied effect sizes for luminal, luminal-HER2-positive, and TNBC with OR:2.82 (95% CI: 2.45–3.25), OR:2.39 (95% CI: 1.77–3.24), and OR:2.29 (95% CI: 1.34–4.05), respectively. Regular participation was related to a higher likelihood of screening detection than irregular participation for luminal, luminal-HER2-positive, and TNBC with OR:1.21 (95% CI: 1.09–1.34), OR: 1.79 (95% CI: 1.38–2.33), and OR: 1.62 (95% CI: 1.10–2.41), respectively. Conclusions: Regular screening as compared to irregular screening is effective for all breast cancers except for the HER2 subtype.

miRNAs as therapeutic predictors and prognostic biomarkers of neoadjuvant chemotherapy in breast cancer: a systematic review and meta-analysis

PURPOSE

Accumulating evidence has shown that microRNAs (miRNAs) are promising biomarkers of neoadjuvant chemotherapy (NAC) response in breast cancer (BC). However, their predictive roles remain controversial. Thus, this systematic review and meta-analysis aimed to describe the role of miRNA expression in NAC response and prognosis in BC to increase statistical power and improve translation.

METHODS

A systematic review of electronic databases for relevant studies was conducted following PRISMA guidelines. Data were extracted, collated, and combined by odds ratio (OR) and hazard ratio (HR) with 95% confidence intervals (CIs) to estimate the strength of the associations.

RESULTS

Of the 560 articles screened, 59 studies were included in our systematic review, and 5 studies were included in the subsequent meta-analysis. Sixty of 123 miRNAs were found to be related with NAC response and an elevated baseline miR-7 level in tissues was associated with a higher pathological complete response rate (OR 5.63; 95% CI 2.15-14.79; P = 0.0004). The prognostic value of 39 miRNAs was also studied. Of them, 26 miRNAs were found to be associated with survival. Pooled HRs indicated that patients with increased levels of serum miR-21 from baseline to the end of the second NAC cycle and from baseline to the end of NAC had a worse disease-free survival than those with decreased levels.

CONCLUSION

Our results highlight that a large number of miRNAs have possible associations with NAC response and prognosis in BC patients. Further well-designed studies are needed to elucidate the molecular mechanisms underlying these associations.

The AKT1E17K Allele Promotes Breast Cancer in Mice

Simple Summary

The main finding reported in this manuscript is that the gain-of-function mutation AKT1E17K is a bona fide oncogene for mammary epithelium, being able to efficiently initiate breast cancer in mice. On the basis of high-molecular-weight cytokeratins expressed by AKT1E17K-derived tumors supported by additional integrative gene expression analysis these tumors resulted similar to human basal-like cancer, phenotypically and molecularly. These results indicate that the AKTE17K strain may represent an appropriate model of human basal-like breast cancer for the identification of novel therapies specific for this type of tumor.

Abstract

The gain-of-function mutation in the pleckstrin homology domain of AKT1 (AKT1E17K) occurs in lung and breast cancer. Through the use of human cellular models and of a AKT1E17K transgenic Cre-inducible murine strain (R26-AKT1E17K mice), we have demonstrated that AKT1E17K is a bona fide oncogene for lung epithelial cells. However, the role of AKT1E17K in breast cancer remains to be determined. Here, we report the generation and the characterization of a MMTV-CRE; R26-AKT1E17K mouse strain that expresses the mutant AKT1E17K allele in the mammary epithelium. We observed that AKT1E17K stimulates the development of mammary tumors classified as ductal adenocarcinoma of medium–high grade and presented a variety of proliferative alterations classified as adenosis with low-to-high grade dysplasia in the mammary epithelium. A subsequent immunohistochemical characterization suggested they were PR⁻/HER2⁻/ER⁺, basal-like and CK8⁻/CK10⁻/CK5⁺/CK14⁺. We also observed that, in parallel with an increased proliferation rate, tumors expressing mutant AKT1E17K presented an activation of the GSK3/cyclin D1 pathway in the mammary epithelium and cluster significantly with the human basal-like tumors. In conclusion, we demonstrate AKT1E17K is a bona fide oncogene that can initiate tumors at high efficiency in murine mammary epithelium in vivo.

The Role of Circulating Tumor Cells in the Prognosis of Metastatic Triple-Negative Breast Cancers: A Systematic Review of the Literature

Breast cancer is one of the leading causes of death in women worldwide. One subtype of breast cancer is the triple-negative, which accounts for 15% of total breast cancer cases and is known for its poor prognosis. The main cause of death is due to metastasis. Circulating tumor cells (CTCs) play a key role in the metastatic process. CTCs arise either by detaching from the primary tumor or from cancer stem cells undergoing an epithelial-to-mesenchymal transition (EMT). This review aims to present up-to-date data concerning the role of CTC numbers in relation to the prognostic and treatment response in metastatic triple-negative breast cancer (mTNBC) patients, and also to discuss the methods used for CTCs’ identification. A search in the MEDLINE database was performed. A total of 234 articles were identified. The results of the 24 eligible studies showed that positive CTC status is associated with shorter overall survival (OS) and progression-free survival (PFS) in mTNBC patients. Furthermore, a decrease in number of CTCs during therapy seems to be a favorable prognostic factor, making CTCs’ detection an important prognostic tool before and during therapy in mTNBC patients. The methods used for CTC detection are still developing and need further improvement.

Circulating microRNAs as indicators in the prediction of neoadjuvant chemotherapy response in luminal B breast cancer

PURPOSE

Circulating microRNAs (miRNAs) have been indicated as predictive biomarker for the response to neoadjuvant chemotherapy (NAC) and the prognosis of breast cancer (BC); however, to date the conclusions have been controversial. The biological characteristics of BC were affected by molecular subtypes. Hence, we aimed to investigate the predictive effect of miRNAs on NAC response in luminal B BC patients.

METHODS

Thirty-seven luminal B BC patient under NAC were prospectively enrolled in this study. Based on their clinical, pathological, and comprehensive response, the patients were defined as responder or non-responders, respectively. Circulating miRNAs were isolated from blood samples before and at the middle of NAC, and candidate miRNAs (miR-34a-5p, miR-125b-5p, miR-210, miR-222, miR-375, miR-718, miR-4516, and let-7g) were analyzed by quantitative real-time polymerase chain reaction (PCR). In addition, the association between miRNAs and disease-free survival (DFS) was examined.

RESULTS

miR-718, miR-4516, miR-210, and miR-125b-5p were found to be specific miRNAs associated with chemo-sensitivity of luminal B HER2 negative patients (n = 24). In the luminal B HER2 positive cohort (n = 13), dynamics of miR-222 and let-7g correlated with pathological response. Treatment-induced increase in miR-34a-5p in the responders except who reached pathologic complete response (pCR) was consistently identified across all luminal B patients and its two subgroups. Finally, after adjustments for Neo-Bioscore, patients with increased levels of miR-125b-5p during NAC had a worse DFS than those with decreased levels (HR = 5.86, 95% CI = 1.39-24.62, p = 0.016).

CONCLUSION

Specific circulating miRNAs were identified as predictive markers for NAC response and prognosis in luminal B BC. The underlying mechanism needs further studies.

Maximizing the reusability of gene expression data by predicting missing metadata

Reusability is part of the FAIR data principle, which aims to make data Findable, Accessible, Interoperable, and Reusable. One of the current efforts to increase the reusability of public genomics data has been to focus on the inclusion of quality metadata associated with the data. When necessary metadata are missing, most researchers will consider the data useless. In this study, we developed a framework to predict the missing metadata of gene expression datasets to maximize their reusability. We found that when using predicted data to conduct other analyses, it is not optimal to use all the predicted data. Instead, one should only use the subset of data, which can be predicted accurately. We proposed a new metric called Proportion of Cases Accurately Predicted (PCAP), which is optimized in our specifically-designed machine learning pipeline. The new approach performed better than pipelines using commonly used metrics such as F1-score in terms of maximizing the reusability of data with missing values. We also found that different variables might need to be predicted using different machine learning methods and/or different data processing protocols. Using differential gene expression analysis as an example, we showed that when missing variables are accurately predicted, the corresponding gene expression data can be reliably used in downstream analyses.

Modulatory Role of microRNAs in Triple Negative Breast Cancer with Basal-Like Phenotype

Development of new research, classification, and therapeutic options are urgently required due to the fact that TNBC is a heterogeneous malignancy. The expression of high molecular weight cytokeratins identifies a biologically and clinically distinct subgroup of TNBCs with a basal-like phenotype, representing about 75% of TNBCs, while the remaining 25% includes all other intrinsic subtypes. The triple negative phenotype in basal-like breast cancer (BLBC) makes it unresponsive to endocrine therapy, i.e., tamoxifen, aromatase inhibitors, and/or anti-HER2-targeted therapies; for this reason, only chemotherapy can be considered an approach available for systemic treatment even if it shows poor prognosis. Therefore, treatment for these subgroups of patients is a strong challenge for oncologists due to disease heterogeneity and the absence of unambiguous molecular targets. Dysregulation of the cellular miRNAome has been related to huge cellular process deregulations underlying human malignancy. Consequently, epigenetics is a field of great promise in cancer research. Increasing evidence suggests that specific miRNA clusters/signatures might be of clinical utility in TNBCs with basal-like phenotype. The epigenetic mechanisms behind tumorigenesis enable progress in the treatment, diagnosis, and prevention of cancer. This review intends to summarize the epigenetic findings related to miRNAome in TNBCs with basal-like phenotype.

RNA-Seq-Based Breast Cancer Subtypes Classification Using Machine Learning Approaches

Background

Breast invasive carcinoma (BRCA) is not a single disease as each subtype has a distinct morphology structure. Although several computational methods have been proposed to conduct breast cancer subtype identification, the specific interaction mechanisms of genes involved in the subtypes are still incomplete. To identify and explore the corresponding interaction mechanisms of genes for each subtype of breast cancer can impose an important impact on the personalized treatment for different patients.

Methods

We integrate the biological importance of genes from the gene regulatory networks to the differential expression analysis and then obtain the weighted differentially expressed genes (weighted DEGs). A gene with a high weight means it regulates more target genes and thus holds more biological importance. Besides, we constructed gene coexpression networks for control and experiment groups, and the significantly differentially interacting structures encouraged us to design the corresponding Gene Ontology (GO) enrichment based on gene coexpression networks (GOEGCN). The GOEGCN considers the two-side distinction analysis between gene coexpression networks for control and experiment groups. The method allows us to study how the modulated coexpressed gene couples impact biological functions at a GO level.

Results

We modeled the binary classification with weighted DEGs for each subtype. The binary classifier could make a good prediction for an unseen sample, and the experimental results validated the effectiveness of our proposed approaches. The novel enriched GO terms based on GOEGCN for control and experiment groups of each subtype explain the specific biological function changes according to the two-side distinction of coexpression network structures to some extent.

Conclusion

The weighted DEGs contain biological importance derived from the gene regulatory network. Based on the weighted DEGs, five binary classifiers were learned and showed good performance concerning the “Sensitivity,” “Specificity,” “Accuracy,” “F1,” and “AUC” metrics. The GOEGCN with weighted DEGs for control and experiment groups presented a novel GO enrichment analysis results and the novel enriched GO terms would further unveil the changes of specific biological functions among all the BRCA subtypes to some extent. The R code in this research is available at https://github.com/yxchspring/GOEGCN_BRCA_Subtypes.

Deep Learning to Estimate Human Epidermal Growth Factor Receptor 2 Status from Hematoxylin and Eosin-Stained Breast Tissue Images

Context:

Several therapeutically important mutations in cancers are economically detected using immunohistochemistry (IHC), which highlights the overexpression of specific antigens associated with the mutation. However, IHC panels can be imprecise and relatively expensive in low-income settings. On the other hand, although hematoxylin and eosin (H&E) staining used to visualize the general tissue morphology is a routine and low cost, it does not highlight any specific antigen or mutation.

Aims:

Using the human epidermal growth factor receptor 2 (HER2) mutation in breast cancer as an example, we strengthen the case for cost-effective detection and screening of overexpression of HER2 protein in H&E-stained tissue.

Settings and Design:

We use computational methods that reliably detect subtle morphological changes associated with the over-expression of mutation-specific proteins directly from H&E images.

Subjects and Methods:

We trained a classification pipeline to determine HER2 overexpression status of H&E stained whole slide images. Our training dataset was derived from a single hospital containing 26 (11 HER2+ and 15 HER2–) cases. We tested the classification pipeline on 26 (8 HER2+ and 18 HER2–) held-out cases from the same hospital and 45 independent cases (23 HER2+ and 22 HER2–) from the TCGA-BRCA cohort. The pipeline was composed of a stain separation module and three deep neural network modules in tandem for robustness and interpretability.

Statistical Analysis Used:

We evaluate our trained model through area under the curve (AUC)-receiver operating characteristic.

Results:

Our pipeline achieved an AUC of 0.82 (confidence interval [CI]: 0.65–0.98) on held-out cases and an AUC of 0.76 (CI: 0.61–0.89) on the independent dataset from TCGA. We also demonstrate the region-level correspondence of HER2 overexpression between a patient's IHC and H&E serial sections.

Conclusions:

Our work strengthens the case for automatically quantifying the overexpression of mutation-specific proteins in H&E-stained digital pathology, and it highlights the importance of multi-stage machine learning pipelines for added robustness and interpretability.

Global View of Candidate Therapeutic Target Genes in Hormone-Responsive Breast Cancer

Breast cancer (BC) is a heterogeneous disease characterized by different biopathological features, differential response to therapy and substantial variability in long-term-survival. BC heterogeneity recapitulates genetic and epigenetic alterations affecting transformed cell behavior. The estrogen receptor alpha positive (ERα+) is the most common BC subtype, generally associated with a better prognosis and improved long-term survival, when compared to ERα-tumors. This is mainly due to the efficacy of endocrine therapy, that interfering with estrogen biosynthesis and actions blocks ER-mediated cell proliferation and tumor spread. Acquired resistance to endocrine therapy, however, represents a great challenge in the clinical management of ERα+ BC, causing tumor growth and recurrence irrespective of estrogen blockade. Improving overall survival in such cases requires new and effective anticancer drugs, allowing adjuvant treatments able to overcome resistance to first-line endocrine therapy. To date, several studies focus on the application of loss-of-function genome-wide screenings to identify key (hub) “fitness” genes essential for BC progression and representing candidate drug targets to overcome lack of response, or acquired resistance, to current therapies. Here, we review the biological significance of essential genes and relative functional pathways affected in ERα+ BC, most of which are strictly interconnected with each other and represent potential effective targets for novel molecular therapies.

Integrated copy number and miRNA expression analysis in triple negative breast cancer of Latin American patients

Triple negative breast cancer (TNBC), a clinically aggressive breast cancer subtype, affects 15–35% of women from Latin America. Using an approach of direct integration of copy number and global miRNA profiling data, performed simultaneously in the same tumor specimens, we identified a panel of 17 miRNAs specifically associated with TNBC of ancestrally characterized patients from Latin America, Brazil. This panel was differentially expressed between the TNBC and non-TNBC subtypes studied (p ≤ 0.05, FDR ≤ 0.25), with their expression levels concordant with the patterns of copy number alterations (CNAs), present mostly frequent at 8q21.3-q24.3, 3q24-29, 6p25.3-p12.2, 1q21.1-q44, 5q11.1-q22.1, 11p13-p11.2, 13q12.11-q14.3, 17q24.2-q25.3 and Xp22.33-p11.21. The combined 17 miRNAs presented a high power (AUC = 0.953 (0.78–0.99);95% CI) in discriminating between the TNBC and non-TNBC subtypes of the patients studied. In addition, the expression of 14 and 15 of the 17miRNAs was significantly associated with tumor subtype when adjusted for tumor stage and grade, respectively. In conclusion, the panel of miRNAs identified demonstrated the impact of CNAs in miRNA expression levels and identified miRNA target genes potentially affected by both CNAs and miRNA deregulation. These targets, involved in critical signaling pathways and biological functions associated specifically with the TNBC transcriptome of Latina patients, can provide biological insights into the observed differences in the TNBC clinical outcome among racial/ethnic groups, taking into consideration their genetic ancestry.

An integrative view on breast cancer signature panels

Introduction: Breast cancer is heterogeneous with distinct clinical outcomes. Diverse types of markers are available on the market for breast cancer prognosis, diagnosis, and therapeutics, with distinct assay approaches. These, though they enlarge our selection pool for characterizing breast cancer patients and help improve the precision on the therapeutics, they can complicate our understanding and choice of marker panels. Areas covered: This review aims at classifying the commonly used marker panels according to their functionalities and detection approaches, comparing their advantages and disadvantages, and identifying their shared features to gain a comprehensive understanding of the diversified molecular profiles that drive breast cancer heterogeneity. Expert opinion: Our effort will contribute as a guidebook for clinicians on the use of breast cancer signature panels for disease management, and for researchers on the establishment of novel marker panels with improved precision and reduced complexity. We propose that collectively analyzing all available marker panels is equally important as investigating on entirely novel marker panels. Advances in technologies capturing signals from multiple levels are of practical importance in breaking through limitations on translating markers into clinical use.

HNRNPA2/B1 is upregulated in endocrine-resistant LCC9 breast cancer cells and alters the miRNA transcriptome when overexpressed in MCF-7 cells

MicroRNAs are dysregulated in breast cancer. Heterogeneous Nuclear Ribonucleoprotein A2/B1 (HNRNPA2/B1) is a reader of the N(6)-methyladenosine (m6A) mark in primary-miRNAs (pri-miRNAs) and promotes DROSHA processing to precursor-miRNAs (pre-miRNAs). We examined the expression of writers, readers, and erasers of m6A and report that HNRNPA2/B1 expression is higher in tamoxifen-resistant LCC9 breast cancer cells as compared to parental, tamoxifen-sensitive MCF-7 cells. To examine how increased expression of HNRNPA2/B1 affects miRNA expression, HNRNPA2/B1 was transiently overexpressed (~5.4-fold) in MCF-7 cells for whole genome miRNA profiling (miRNA-seq). 148 and 88 miRNAs were up- and down-regulated, respectively, 48 h after transfection and 177 and 172 up- and down-regulated, respectively, 72 h after transfection. MetaCore Enrichment analysis identified progesterone receptor action and transforming growth factor β (TGFβ) signaling via miRNA in breast cancer as pathways downstream of the upregulated miRNAs and TGFβ signaling via SMADs and Notch signaling as pathways of the downregulated miRNAs. GO biological processes for mRNA targets of HNRNPA2/B1-regulated miRNAs included response to estradiol and cell-substrate adhesion. qPCR confirmed HNRNPA2B1 downregulation of miR-29a-3p, miR-29b-3p, and miR-222 and upregulation of miR-1266-5p, miR-1268a, miR-671-3p. Transient overexpression of HNRNPA2/B1 reduced MCF-7 sensitivity to 4-hydroxytamoxifen and fulvestrant, suggesting a role for HNRNPA2/B1 in endocrine-resistance.

Gene Expression and miRNAs Profiling: Function and Regulation in Human Epidermal Growth Factor Receptor 2 (HER2)-Positive Breast Cancer

Breast cancer is the second most common cause of cancer-related deaths among women worldwide. It is a heterogeneous disease with four major molecular subtypes. One of the subtypes, human epidermal growth factor receptor 2 (HER2)-enriched (HER2-positive) is characterized by the absence of estrogen and progesterone receptors and overexpression of HER2 receptor, and accounts for 15–20% of all breast cancers. Despite the anti-HER2 and cytotoxic chemotherapy, HER2 subtype is an aggressive disease with significant mortality. Recent advances in molecular biology techniques, including gene expression profiling, proteomics, and microRNA analysis, have been extensively used to explore the underlying mechanisms behind human breast carcinogenesis and metastasis including HER2-positive breast cancer, paving the way for developing new targeted therapies. This review focuses on recent advances on gene expression and miRNA status in HER2-positive breast cancer.

FOXA1 is Prognostic of Triple Negative Breast Cancers by Transcriptionally Suppressing SOD2 and IL6

Having markers feasible for breast cancer subtyping, especially for triple negative breast cancer identification is crucial for improving the treatment outcome of such cancers. Here we explore the role of FOXA1 in characterizing triple negative breast cancers and the driving mechanisms. Through in vitro examination of the expression pattern at both transcriptional and translational levels, patient relapse-free survival analysis, immunohistochemistry staining and prediction power assessment using clinical samples, as well as functional studies, we systematically compared the role of FOXA1 in identifying triple negative and luminal type of breast cancers and explored the mechanisms driving such functionalities. We report that FOXA1 under-expression can lead to increased malignancy and cancer stemness, and is a subtyping marker identifying triple negative breast cancers rather than the luminal subtype by transcriptionally suppressing the expression of SOD2 and IL6. We are the first to systematically address the significance of FOXA1 in triple negative breast cancer identification as a biomarker and elucidate the mechanism at the molecular level, through a sequential bioinformatics analysis and experimental validations both in vitro and in clinics. Our discoveries compliment the current biomarker modalities once verified using larger clinical cohorts and improve the precision on characterizing breast cancer heterogeneity.

Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data

It is very significant to explore the intrinsic differences in breast cancer subtypes. These intrinsic differences are closely related to clinical diagnosis and designation of treatment plans. With the accumulation of biological and medicine datasets, there are many different omics data that can be viewed in different aspects. Combining these multiple omics data can improve the accuracy of prediction. Meanwhile; there are also many different databases available for us to download different types of omics data. In this article, we use estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) to define breast cancer subtypes and classify any two breast cancer subtypes using SMO-MKL algorithm. We collected mRNA data, methylation data and copy number variation (CNV) data from TCGA to classify breast cancer subtypes. Multiple Kernel Learning (MKL) is employed to use these omics data distinctly. The result of using three omics data with multiple kernels is better than that of using single omics data with multiple kernels. Furthermore; these significant genes and pathways discovered in the feature selection process are also analyzed. In experiments; the proposed method outperforms other state-of-the-art methods and has abundant biological interpretations.

Roles of Small GTPases in Acquired Tamoxifen Resistance in MCF-7 Cells Revealed by Targeted, Quantitative Proteomic Analysis

Development of tamoxifen resistance remains a tremendous challenge for the treatment of estrogen-receptor (ER)-positive breast cancer. Small GTPases of the Ras superfamily play crucial roles in intracellular trafficking and cell signaling, and aberrant small-GTPase signaling is implicated in many types of cancer. In this study, we employed a targeted, quantitative proteomic approach that relies on stable-isotope labeling by amino acids in cell culture (SILAC), gel fractionation, and scheduled multiple-reaction-monitoring (MRM) analysis, to assess the differential expression of small GTPases in MCF-7 and the paired tamoxifen-resistant breast cancer cells. The method displayed superior sensitivity and reproducibility over the shotgun-proteomic approach, and it facilitated the quantification of 96 small GTPases. Among them, 13 and 10 proteins were significantly down- and up-regulated (with >1.5-fold change), respectively, in the tamoxifen-resistant line relative to in the parental line. In particular, we observed a significant down-regulation of RAB31 in tamoxifen-resistant cells, which, in combination with bioinformatic analysis and downstream validation experiments, supported a role for RAB31 in tamoxifen resistance in ER-positive breast-cancer cells. Together, our results demonstrated that the targeted proteomic method constituted a powerful approach for revealing the role of small GTPases in therapeutic resistance.

Intravoxel incoherent motion magnetic resonance imaging for breast cancer: A comparison with benign lesions and evaluation of heterogeneity in different tumor regions with prognostic factors and molecular classification

The objective of the present study was to compare the differentiation between breast cancer and benign breast lesions and study regional distribution characteristics in various subtypes of breast cancer using intravoxel incoherent motion (IVIM) parameters. This retrospective study involved 119 patients with breast cancer and 22 patients with benign breast lesions, who underwent 3.0T breast magnetic resonance imaging examinations. The apparent diffusion coefficient (ADC) and IVIM parameters (slow ADC, fast ADC and fraction of fast ADC) were obtained from patients with breast cancer and benign lesions using diffusion-weighted imaging (DWI) with b-values of 0, 50, 100, 150, 200, 400, 500, 1,000 and 1,500 sec/mm². Compared with patients with benign breast lesions, patients with breast cancer exhibited decreased ADC (P<0.001), slow ADC (P<0.001) and fast ADC (P<0.001) values, and higher fraction of fast ADC (P<0.001) values. Tumors with metastatic axillary lymph nodes demonstrated increased fraction of fast ADC values (P<0.001) and decreased slow ADC values (P<0.001) compared with tumors without metastatic axillary lymph nodes. The Fast ADC values of tumor tissues in estrogen receptor (ER) and progesterone receptor (PR) negative groups were higher than in positive groups (P<0.001), and the slow ADC values of tumor tissues were lower in ER and PR negative groups than positive groups (P<0.001). Luminal B (HER2- negative) tumor (P<0.001) and peritumor (P<0.001) tissues exhibited decreased fraction of fast ADC values, in comparison with other subtypes. Triple-negative breast cancer (TNBC) tumor tissue exhibited increased fast ADC (P<0.001) and fraction of fast ADC values (P<0.001), and decreased slow ADC values (P<0.001), when compared with other subtypes. The TNBC tumor edge tissues had increased fraction of fast ADC values compared with other subtypes (P<0.01) and TNBC tumor tissues (P<0.05). Therefore, the IVIM parameters of tumor, tumor edge and peritumor tissues in various subtypes of breast cancer may be useful for differentiation of breast cancer subtypes and to assess the invasive extent of the tumors.

Probing the Anticancer Action of Novel Ferrocene Analogues of MNK Inhibitors

Two novel ferrocene-containing compounds based upon a known MNK1/2 kinase (MAPK-interacting kinase) inhibitor have been synthesized. The compounds were designed to use the unique shape of ferrocene to exploit a large hydrophobic pocket in MNK1/2 that is only partially occupied by the original compound. Screening of the ferrocene analogues showed that both exhibited potent anticancer effects in several breast cancer and AML (acute myeloid leukemia) cell lines, despite a loss of MNK potency. The most potent ferrocene-based compound 5 was further analysed in vitro in MDA-MB-231 (triple negative breast cancer cells). Dose–response curves of compound 5 for 2D assay and 3D assay generated IC₅₀ values (half maximal inhibitory concentration) of 0.55 µM and 1.25 µM, respectively.

Intravoxel incoherent motion magnetic resonance imaging for breast cancer: A comparison with benign lesions and evaluation of heterogeneity in different tumor regions with prognostic factors and molecular classification

The objective of the present study was to compare the differentiation between breast cancer and benign breast lesions and study regional distribution characteristics in various subtypes of breast cancer using intravoxel incoherent motion (IVIM) parameters. This retrospective study involved 119 patients with breast cancer and 22 patients with benign breast lesions, who underwent 3.0T breast magnetic resonance imaging examinations. The apparent diffusion coefficient (ADC) and IVIM parameters (slow ADC, fast ADC and fraction of fast ADC) were obtained from patients with breast cancer and benign lesions using diffusion-weighted imaging (DWI) with b-values of 0, 50, 100, 150, 200, 400, 500, 1,000 and 1,500 sec/mm². Compared with patients with benign breast lesions, patients with breast cancer exhibited decreased ADC (P<0.001), slow ADC (P<0.001) and fast ADC (P<0.001) values, and higher fraction of fast ADC (P<0.001) values. Tumors with metastatic axillary lymph nodes demonstrated increased fraction of fast ADC values (P<0.001) and decreased slow ADC values (P<0.001) compared with tumors without metastatic axillary lymph nodes. The Fast ADC values of tumor tissues in estrogen receptor (ER) and progesterone receptor (PR) negative groups were higher than in positive groups (P<0.001), and the slow ADC values of tumor tissues were lower in ER and PR negative groups than positive groups (P<0.001). Luminal B (HER2- negative) tumor (P<0.001) and peritumor (P<0.001) tissues exhibited decreased fraction of fast ADC values, in comparison with other subtypes. Triple-negative breast cancer (TNBC) tumor tissue exhibited increased fast ADC (P<0.001) and fraction of fast ADC values (P<0.001), and decreased slow ADC values (P<0.001), when compared with other subtypes. The TNBC tumor edge tissues had increased fraction of fast ADC values compared with other subtypes (P<0.01) and TNBC tumor tissues (P<0.05). Therefore, the IVIM parameters of tumor, tumor edge and peritumor tissues in various subtypes of breast cancer may be useful for differentiation of breast cancer subtypes and to assess the invasive extent of the tumors.

The Roles of p53 in Mitochondrial Dynamics and Cancer Metabolism: The Pendulum between Survival and Death in Breast Cancer?

Cancer research has been heavily geared towards genomic events in the development and progression of cancer. In contrast, metabolic regulation, such as aberrant metabolism in cancer, is poorly understood. Alteration in cellular metabolism was once regarded simply as a consequence of cancer rather than as playing a primary role in cancer promotion and maintenance. Resurgence of cancer metabolism research has identified critical metabolic reprogramming events within biosynthetic and bioenergetic pathways needed to fulfill the requirements of cancer cell growth and maintenance. The tumor suppressor protein p53 is emerging as a key regulator of metabolic processes and metabolic reprogramming in cancer cells&mdash;balancing the pendulum between cell death and survival. This review provides an overview of the classical and emerging non-classical tumor suppressor roles of p53 in regulating mitochondrial dynamics: mitochondrial engagement in cell death processes in the prevention of cancer. On the other hand, we discuss p53 as a key metabolic switch in cellular function and survival. The focus is then on the conceivable roles of p53 in breast cancer metabolism. Understanding the metabolic functions of p53 within breast cancer metabolism will, in due course, reveal critical metabolic hotspots that cancers advantageously re-engineer for sustenance. Illustration of these events will pave the way for finding novel therapeutics that target cancer metabolism and serve to overcome the breast cancer burden.

Ginkgolic acids inhibit migration in breast cancer cells by inhibition of NEMO sumoylation and NF-κB activity

Ginkgolic acids (GA), a group of alkyl phenols found in crude extracts of Ginkgo biloba leaves, are known to have anticancer activity, but their mode of action is not well understood. Our aim in this study was to investigate the anti-migratory activity of seven GA against breast cancer cells and to determine the molecular mechanism behind this activity. All seven GA and their mixture inhibited wound healing in MCF-7 and MDA-MB 231 breast cancer cells. None of the compounds nor the mixture showed cytotoxicity towards the two cell lines, if tested by the resazurin assay. C13:0 inhibited NF-κB activity in the HEK Blue Null 1 reporter cell line. Furthermore, C13:0 inhibited degradation of nuclear factor of κ-light polypeptide gene enhancer in B-cells inhibitor α (IκBα). Sumoylation assay revealed that GA inhibited sumoylation of NF-κB essential modulator (NEMO). Molecular docking on SUMO-activating enzyme E1 showed that the seven GA bound to the active adenylation site with high calculated affinities ranging from -10.28 to -12.27 kcal/mol. Quantitative RT-PCR using C15:0, C13:0 and the mixture showed a significant down-regulation of urokinase plasminogen activator (uPA), plasminogen activator inhibitor-1 (PAI-1), C-X-C chemokine receptor type 4 (CXCR4) and matrix metalloproteinase 9 (MMP-9). We conclude that GA revealed considerable anti-migratory activity at non-cytotoxic concentrations, indicating anti-metastatic activity with low toxicity. This effect can be explained by the inhibition of NEMO sumoylation leading to inhibition of IκBα degradation and consequently a reduction of NF-κB activity, leading to the down-regulation of metastasis related genes including uPA, PAI-1, CXCR4, and MMP-9.

Beyond molecular tumor heterogeneity: protein synthesis takes control

One of the daunting challenges facing modern medicine lies in the understanding and treatment of tumor heterogeneity. Most tumors show intra-tumor heterogeneity at both genomic and proteomic levels, with marked impacts on the responses of therapeutic targets. Therapeutic target-related gene expression pathways are affected by hypoxia and cellular stress. However, the finding that targets such as eukaryotic initiation factor (eIF) 4E (and its phosphorylated form, p-eIF4E) are generally homogenously expressed throughout tumors, regardless of the presence of hypoxia or other cellular stress conditions, opens the exciting possibility that malignancies could be treated with therapies that combine targeting of eIF4E phosphorylation with immune checkpoint inhibitors or chemotherapy.

Chemoprevention of intestinal tumorigenesis by the natural dietary flavonoid myricetin in APCMin/+ mice

Myricetin is a natural dietary flavonoid compound. We evaluated the efficacy of myricetin against intestinal tumorigenesis in adenomatous polyposis coli multiple intestinal neoplasia (APCMin/+) mice. Myricetin was given orally once a day for 12 consecutive weeks. APCMin/+ mice fed with myricetin developed fewer and smaller polyps without any adverse effects. Histopathological analysis showed a decreased number of dysplastic cells and degree of dysplasia in each polyp. Immunohistochemical and western blot analysis revealed that myricetin selectively inhibits cell proliferation and induces apoptosis in adenomatous polyps. The effects of myricetin were associated with a modulation the GSK-3β and Wnt/β-catenin pathways. ELISA analysis showed a reduced concentration of pro-inflammatory cytokines IL-6 and PGE2 in blood, which were elevated in APCMin/+ mice. The effect of myricetin treatment was more prominent in the adenomatous polyps originating in the colon. Further studies showed that myricetin downregulates the phosphorylated p38 MAPK/Akt/mTOR signaling pathways, which may be the mechanisms for the inhibition of adenomatous polyps by myricetin. Taken together, our data show that myricetin inhibits intestinal tumorigenesis through a collection of biological activities. Given these results, we suggest that myricetin could be used preventatively to reduce the risk of developing colon cancers.

Increased expression of miR-155 and miR-222 is associated with lymph node positive status

Identification of prognostic molecular markers of breast cancer is extremely important. The spreading out of the primary breast tumour cells to the lymphatic system is at the forefront of symbolising the first signs of distant organ metastasis. Deregulated genes in breast cancer tissues that spread to lymph nodes may show early predictive molecular markers. In the present study, we selected five microRNAs, which play a key function in the invasion-metastasis cascade. We investigated the levels of microRNAs in 80 paired samples of BC and matched adjoining tissues, and we examined the potential relationships between microRNA levels and positive lymph node status. Our results attest that three microRNAs (miR-21, miR-155, miR-222) were significantly up-regulated, whilst miR-205 was substantially down-regulated in BC tissues in relation to normal adjoining tissues in a heterogeneous patient cohort. The high levels of two microRNAs, miR-155 and miR-222, showed a statistical relation with the positive lymph node status, especially in patients that had triple negative BC. Conversely, miR-155 was substantially down-regulated in tumour tissues of patients who received preoperative neoadjuvant chemotherapy (NAC) compared with tumour tissues of patients without NAC in cohorts sub-classified to lymph node positive status. Our findings show evidence that the miR-155 and the miR-222 can be defined as molecular markers in regards to cancer patients to prognosticate spread to the lymph node. They also showed that the miR-155 could have crucial significances in BC treatment.

Strategies to Target Matrix Metalloproteinases as Therapeutic Approach in Cancer

Matrix metalloproteinases (MMPs) are a family of zinc-dependent endopeptidases that are capable of degrading numerous extracellular matrix (ECM) components thus participating in physiological and pathological processes. Apart from the remodeling of ECM, they affect cell-cell and cell-matrix interactions and are implicated in the development and progression of various diseases such as cancer. Numerous studies have demonstrated that MMPs evoke epithelial to mesenchymal transition (EMT) of cancer cells and affect their signaling, adhesion, migration and invasion to promote cancer cell aggressiveness. Various studies have suggested MMPs as suitable targets for treatment of malignancies, and several MMP inhibitors (MMPIs) have been developed. Although initial trials have failed to establish MMPIs as anticancer agents due to lack of specificity and side effects, new MMPIs have been developed with improved action that are currently being investigated. Furthermore, novel strategies that target MMPs for improving drug delivery and regulating their activity in tumors are presented. This review summarizes the implication of MMPs in cancer progression and discusses the advancements in their targeting.

Prognostic impact of AnxA1 and AnxA2 gene expression in triple-negative breast cancer

Objective

Previous studies have shown Annexin A1 (AnxA1) and Annexin A2 (AnxA2) association with the aggressive behavior of Triple Negative Breast Cancer (TNBC). Our aim was to determine the correlation of AnxA1 and AnxA2 with poor prognosis of TNBC patients.

Methods

We analyzed the gene expression of the human annexin family from microarray datasets and correlated with clinical outcomes to determine their ability to predict prognosis.

Results

Within a mean follow-up time of 57.2 months in our TNBC cohort, high AnxA1 expression was an independent indicator of poor overall survival (OS) [hazard ratio (HR), 2.14; 95% confidence interval (CI), 1.22-3.78] and relapse-free survival (RFS) prognosis [HR, 1.66; 95% CI, 1.28-2.17]. Additionally, high AnxA2 expression was an independent indicator of poor OS [HR, 2.66; 95% CI, 1.14-6.25], RFS [HR, 1.45; 95% CI, 1.12-1.89], RFS [HR, 1.45; 95% CI, 1.12-1.89), and distant metastasis free survival (DMFS) prognosis [HR, 1.5; 95% CI, 1.16-1.95]. Analyses of TNBC patients with both high AnxA1 and AnxA2, demonstrates a significant decrease in OS (P=0.0017) and RFS (P=0.0002) when compared to the expression of genes independently. Furthermore, AnxA1 prognostic impact relies on high AnxA2 expression and both are preferential for TNBC when compared to other breast cancer subtypes.

Conclusion

Together these findings indicate that AnxA1 and AnxA2 are preferential dual prognostic predictors among TNBC patients.

Cancer as an ecomolecular disease and a neoplastic consortium

Current anticancer paradigms largely target driver mutations considered integral for cancer cell survival and tumor progression. Although initially successful, many of these strategies are unable to overcome the tremendous heterogeneity that characterizes advanced tumors, resulting in the emergence of resistant disease. Cancer is a rapidly evolving, multifactorial disease that accumulates numerous genetic and epigenetic alterations. This results in wide phenotypic and molecular heterogeneity within the tumor, the complexity of which is further amplified through specific interactions between cancer cells and the tumor microenvironment. In this context, cancer may be perceived as an "ecomolecular" disease that involves cooperation between several neoplastic clones and their interactions with immune cells, stromal fibroblasts, and other cell types present in the microenvironment. This collaboration is mediated by a variety of secreted factors. Cancer is therefore analogous to complex ecosystems such as microbial consortia. In the present article, we comment on the current paradigms and perspectives guiding the development of cancer diagnostics and therapeutics and the potential application of systems biology to untangle the complexity of neoplasia. In our opinion, conceptualization of neoplasia as an ecomolecular disease is warranted. Advances in knowledge pertinent to the complexity and dynamics of interactions within the cancer ecosystem are likely to improve understanding of tumor etiology, pathogenesis, and progression. This knowledge is anticipated to facilitate the design of new and more effective therapeutic approaches that target the tumor ecosystem in its entirety.

Prognostic value of microRNA-9 and microRNA-155 expression in triple-negative breast cancer

MicroRNAs (miRNAs) are involved in regulation of epithelial-mesenchymal transition (EMT) during breast cancer progression. The purpose of this study was to analyze the clinicopathologic significance of expression of EMT-related miRNAs, miR-9 and miR-155, in triple-negative breast cancers (TNBCs). We analyzed relative expression levels of miR-9 and miR-155 in 190 surgically resected TNBC specimens using quantitative real-time polymerase chain reaction. Then we analyzed the relationship between these miRNA expression levels and EMT marker expression (vimentin, smooth muscle actin [SMA], osteonectin, N-cadherin, E-cadherin, CD146, and ZEB1) assessed by immunohistochemistry. We also evaluated the prognostic significance of these miRNA expression levels. While miR-9 expression level showed a positive correlation with pT category, miR-155 expression level did not correlate with any clinicopathologic features of TNBCs. In relation to EMT phenotype, miR-9 expression was not associated with EMT marker expression except for SMA. However, miR-155 expression level correlated inversely with the expression of several EMT markers including SMA, osteonectin, and CD146. We observed that both miR-9 and miR-155 could be prognostic markers in TNBC in opposite ways; high level of miR-9 expression showed significant association with poor disease-free survival and distant metastasis-free survival (DMFS) in TNBC, while high level of miR-155 expression was associated with better DMFS. Our study suggests that expression levels of both miR-9 and miR-155 can serve as candidates for prognostic biomarkers in TNBCs.

Integrated diagnostic network construction reveals a 4-gene panel and 5 cancer hallmarks driving breast cancer heterogeneity

Breast cancer encompasses a group of heterogeneous diseases, each associated with distinct clinical implications. Dozens of molecular biomarkers capable of categorizing tumors into clinically relevant subgroups have been proposed which, though considerably contribute in precision medicine, complicate our understandings toward breast cancer subtyping and its clinical translation. To decipher the networking of markers with diagnostic roles on breast carcinomas, we constructed the diagnostic networks by incorporating 6 publically available gene expression datasets with protein interaction data retrieved from BioGRID on previously identified 1015 genes with breast cancer subtyping roles. The Greedy algorithm and mutual information were used to construct the integrated diagnostic network, resulting in 37 genes enclosing 43 interactions. Four genes, FAM134B, KIF2C, ALCAM, KIF1A, were identified having comparable subtyping efficacies with the initial 1015 genes evaluated by hierarchical clustering and cross validations that deploy support vector machine and k nearest neighbor algorithms. Pathway, Gene Ontology, and proliferation marker enrichment analyses collectively suggest 5 primary cancer hallmarks driving breast cancer differentiation, with those contributing to uncontrolled proliferation being the most prominent. Our results propose a 37-gene integrated diagnostic network implicating 5 cancer hallmarks that drives breast cancer heterogeneity and, in particular, a 4-gene panel with clinical diagnostic translation potential.

A new molecular prognostic score for predicting the risk of distant metastasis in patients with HR+/HER2- early breast cancer

To make an optimal treatment decision for early stage breast cancer, it is important to identify risk of recurrence. Here, we developed and validated a new prognostic model for predicting the risk of distant metastasis in patients with pN0-N1, hormone receptor-positive, HER2-negative (HR+/HER2−) breast cancer treated with hormone therapy alone. RNA was extracted from formalin-fixed, paraffin-embedded tumor tissues and gene expression was measured by quantitative real-time reverse transcription-PCR. The relative expression of six novel prognostic genes was combined with two clinical variables (nodal status and tumor size) to calculate a risk score (BCT score). In the validation cohort treated with hormone therapy alone, the 10 year rate of distant metastasis in the high-risk group (26.3%) according to BCT score was significantly higher than that in the low-risk group (3.8%) (P < 0.001). Multivariate analysis adjusted for clinical variables revealed that BCT score is an independent predictor of distant metastasis. Moreover, the C-index estimate revealed that BCT score has a prognostic power superior to that of prognostic models based on clinicopathological parameters. The BCT score outperforms prognostic models based on traditional clinicopathological factors and predicts the risk of distant metastasis in patients with HR+/HER2− early breast cancer.

Integrated MicroRNA-mRNA Profiling Identifies Oncostatin M as a Marker of Mesenchymal-Like ER-Negative/HER2-Negative Breast Cancer

MicroRNAs (miRNAs) simultaneously modulate different oncogenic networks, establishing a dynamic system of gene expression and pathway regulation. In this study, we analyzed global miRNA and messenger RNA (mRNA) expression profiles of 17 cell lines representing different molecular breast cancer subtypes. Spearman's rank correlation test was used to evaluate the correlation between miRNA and mRNA expression. Hierarchical clustering and pathway analysis were also performed. Publicly available gene expression profiles (n = 699) and tumor tissues (n = 80) were analyzed to assess the relevance of key miRNA-regulated pathways in human breast cancer. We identified 39 significantly deregulated miRNAs, and the integration between miRNA and mRNA data revealed the importance of immune-related pathways, particularly the Oncostatin M (OSM) signaling, associated with mesenchymal-like breast cancer cells. OSM levels correlated with genes involved in the inflammatory response, epithelial-to-mesenchymal transition (EMT), and epidermal growth factor (EGF) signaling in human estrogen receptor (ER)-negative/human epidermal growth factor receptor 2 (HER2)-negative breast cancer. Our results suggest that the deregulation of specific miRNAs may cooperatively impair immune and EMT pathways. The identification of the OSM inflammatory pathway as an important mediator of EMT in triple-negative breast cancer (TNBC) may provide a novel potential opportunity to improve therapeutic strategies.

Usefulness of miRNA profiles for predicting pathological responses to neoadjuvant chemotherapy in patients with human epidermal growth factor receptor 2-positive breast cancer

Pathological complete response (pCR) is considered to be a useful prognostic marker for neoadjuvant chemotherapy to improve the survival rate of patients with operable breast cancer. In the present study, we identified differentially expressed microRNAs (miRNAs) between pCR and non-pCR groups of patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer who received neoadjuvant chemotherapy with trastuzumab. Expression profiles were examined by miRNA microarrays using total RNA extracted from formalin-fixed, paraffin-embedded tissues from pretreatment biopsy specimens. Significant differences were observed in miRNAs associated with pCR between the luminal B-like (HER2-positive) and HER2-positive (nonluminal) subtypes, which were further classified according to their estrogen receptor (ER) status. Prediction models constructed with differentially expressed miRNAs performed well. In conclusion, the combination of miRNA profiles and ER status may improve the accuracy of pCR prediction in patients with HER2-positive breast cancer and enable the development of personalized treatment regimens.

Grouping miRNAs of similar functions via weighted information content of gene ontology

Background

Regulation mechanisms between miRNAs and genes are complicated. To accomplish a biological function, a miRNA may regulate multiple target genes, and similarly a target gene may be regulated by multiple miRNAs. Wet-lab knowledge of co-regulating miRNAs is limited. This work introduces a computational method to group miRNAs of similar functions to identify co-regulating miRNAsfrom a similarity matrix of miRNAs.

Results

We define a novel information content of gene ontology (GO) to measure similarity between two sets of GO graphs corresponding to the two sets of target genes of two miRNAs. This between-graph similarity is then transferred as a functional similarity between the two miRNAs. Our definition of the information content is based on the size of a GO term’s descendants, but adjusted by a weight derived from its depth level and the GO relationships at its path to the root node or to the most informative common ancestor (MICA). Further, a self-tuning technique and the eigenvalues of the normalized Laplacian matrix are applied to determine the optimal parameters for the spectral clustering of the similarity matrix of the miRNAs.

Conclusions

Experimental results demonstrate that our method has better clustering performance than the existing edge-based, node-based or hybrid methods. Our method has also demonstrated a novel usefulness for the function annotation of new miRNAs, as reported in the detailed case studies.

'Omics Approaches in Breast Cancer Research and Clinical Practice

Our understanding of the natural history of breast cancer has evolved alongside technologies to study its genomic, transcriptomic, proteomic, and metabolomics landscapes. These technologies have helped decipher multiple molecular pathways dysregulated in breast cancer. First-generation 'omics analyses considered each of these dimensions individually, but it is becoming increasingly clear that more holistic, integrative approaches are required to fully understand complex biological systems. The 'omics represent an exciting era of discovery in breast cancer research, although important issues need to be addressed to realize the clinical utility of these data through precision cancer care. How can the data be applied to predict response to molecular-targeted therapies? When should treatment decisions be based on tumor genetics rather than histology? And with the sudden explosion of "big data" from large 'omics consortia and new precision clinical trials, how do we now negotiate evidence-based pathways to clinical translation through this apparent sea of opportunity? The aim of this review is to provide a broad overview of 'omics technologies used in breast cancer research today, the current state-of-play in terms of applying this new knowledge in the clinic, and the practical and ethical issues that will be central to the public discussion on the future of precision cancer care.

Exploring the intrinsic differences among breast tumor subtypes defined using immunohistochemistry markers based on the decision tree

Exploring the intrinsic differences among breast cancer subtypes is of crucial importance for precise diagnosis and therapeutic decision-making in diseases of high heterogeneity. The subtypes defined with several layers of information are related but not consistent, especially using immunohistochemistry markers and gene expression profiling. Here, we explored the intrinsic differences among the subtypes defined by the estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 based on the decision tree. We identified 30 mRNAs and 7 miRNAs differentially expressed along the tree's branches. The final signature panel contained 30 mRNAs, whose performance was validated using two public datasets based on 3 well-known classifiers. The network and pathway analysis were explored for feature genes, from which key molecules including FOXQ1 and SFRP1 were revealed to be densely connected with other molecules and participate in the validated metabolic pathways. Our study uncovered the differences among the four IHC-defined breast tumor subtypes at the mRNA and miRNA levels, presented a novel signature for breast tumor subtyping, and identified several key molecules potentially driving the heterogeneity of such tumors. The results help us further understand breast tumor heterogeneity, which could be availed in clinics.

Involvement of microRNAs in HER2 signaling and trastuzumab treatment

The prognostic value of HER2 has been demonstrated in many human cancer types such us breast cancer, gastric cancer and ovarian cancer. Trastuzumab is the first anti-HER2 monoclonal antibody that has remarkably improved outcomes of patients with HER2-positive breast cancer. For HER2-positive metastatic gastric cancers, the addition of trastuzumab to traditional chemotherapy also significantly prolonged overall survival. However, intrinsic and acquired resistance to trastuzumab is common and results in disease progression. HER2 signaling network and mechanisms underlying the resistance have been broadly investigated in order to develop strategy to overcome the dilemma. Increasing evidence indicates that microRNAs (miRNA), a group of small non-coding RNAs, are involved in HER2 signaling and trastuzumab treatment. This review summarizes all the miRNAs that target HER2 and describes their activity on biological processes. Moreover, miRNAs that regulate trastuzumab resistance and relevant molecular mechanisms are highlighted. MiRNA signatures associated with HER2, miRNAs that mediate trastuzumab activity, and potential miRNA biomarkers of trastuzumab sensitivity are also discussed.

The complexity of microRNAs in human cancer

MicroRNAs (miRNAs) are small non-coding RNA molecules that have key regulatory roles in cancer, acting as both oncogenes and tumor suppressors. Due to the potential roles of miRNAs in improving cancer prognostic, predictive, diagnostic and therapeutic approaches, they have become an area of intense research focus in recent years. MiRNAs harbor attractive features allowing for translation to the clinical world, such as relatively simple extraction methods, resistance to molecular degradation, and ability to be quantified. Numerous prognostic, predictive and diagnostic miRNA signatures have been developed. To date however, miRNA analysis has not been adopted for routine clinical use. The objectives of this article are to provide an overview of miRNA research and review a selection of miRNA studies in breast cancer, cervical cancer, sarcoma, and nasopharyngeal carcinoma to highlight advances and challenges in miRNA cancer research.

MicroRNA expression and gene regulation drive breast cancer progression and metastasis in PyMT mice

Background

MicroRNAs (miRNAs) are small non-coding RNA molecules of about 22 nucleotides which function to silence the expression of their target genes. Numerous studies have shown that miRNAs are not only key regulators in important cellular processes but are also drivers in the development of many diseases, especially cancer. Estrogen receptor positive luminal B is the second most common but the least studied subtype of breast cancer. Only a few studies have examined the expression profiles of miRNAs in luminal B breast cancer, and their regulatory roles in cancer progression have yet to be investigated.

Methods

In this study, using polyoma middle T antigen (PyMT) mice, a widely used luminal B breast cancer model, we profiled microRNA (miRNA) expression at four time points that represent different key developmental stages of cancer progression. We considered the expression of both miRNAs and messenger RNAs (mRNAs) at these time points to improve the identification of regulatory targets of miRNAs. By combining gene functional and pathway annotation with miRNA-mRNA interactions, we created a PyMT-specific tripartite miRNA-mRNA-pathway network and identified novel functional regulatory programs (FRPs).

Results

We identified 151 differentially expressed miRNAs with a strict dual nature of either upregulation or downregulation during the whole course of disease progression. Among 82 newly discovered breast-cancer-related miRNAs, 35 can potentially regulate 271 protein-coding genes based on their sequence complementarity and expression profiles. We also identified miRNA-mRNA regulatory modules driving specific cancer-related biological processes.

Conclusions

In this study we profiled the expression of miRNAs during breast cancer progression in the PyMT mouse model. By integrating miRNA and mRNA expression profiles, we identified differentially expressed miRNAs and their target genes involved in several hallmarks of cancer. We applied a novel clustering method to an annotated miRNA-mRNA regulatory network and identified network modules involved in specific cancer-related biological processes.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-016-0735-z) contains supplementary material, which is available to authorized users.

Cancer Hallmarks, Biomarkers and Breast Cancer Molecular Subtypes

Breast cancer is a complex disease encompassing multiple tumor entities, each characterized by distinct morphology, behavior and clinical implications. Besides estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2, novel biomarkers have shown their prognostic and predictive values, complicating our understanding towards to the heterogeneity of such cancers. Ten cancer hallmarks have been proposed by Weinberg to characterize cancer and its carcinogenesis. By reviewing biomarkers and breast cancer molecular subtypes, we propose that the divergent outcome observed from patients stratified by hormone status are driven by different cancer hallmarks. 'Sustaining proliferative signaling' further differentiates cancers with positive hormone receptors. 'Activating invasion and metastasis' and 'evading immune destruction' drive the differentiation of triple negative breast cancers. 'Resisting cell death', 'genome instability and mutation' and 'deregulating cellular energetics' refine breast cancer classification with their predictive values. 'Evading growth suppressors', 'enabling replicative immortality', 'inducing angiogenesis' and 'tumor-promoting inflammation' have not been involved in breast cancer classification which need more focus in the future biomarker-related research. This review novels in its global view on breast cancer heterogeneity, which clarifies many confusions in this field and contributes to precision medicine.

A microscopic landscape of the invasive breast cancer genome

Histologic grade is one of the most important microscopic features used to predict the prognosis of invasive breast cancer and may serve as a marker for studying cancer driving genomic abnormalities in vivo. We analyzed whole genome sequencing data from 680 cases of TCGA invasive ductal carcinomas of the breast and correlated them to corresponding pathology information. Ten genetic abnormalities were found to be statistically associated with histologic grade, including three most prevalent cancer driver events, TP53 and PIK3CA mutations and MYC amplification. A distinct genetic interaction among these genomic abnormalities was revealed as measured by the histologic grading score. While TP53 mutation and MYC amplification were synergistic in promoting tumor progression, PIK3CA mutation was found to have alleviated the oncogenic effect of either the TP53 mutation or MYC amplification, and was associated with a significant reduction in mitotic activity in TP53 mutated and/or MYC amplified breast cancer. Furthermore, we discovered that different types of genetic abnormalities (mutation versus amplification) within the same cancer driver gene (PIK3CA or GATA3) were associated with opposite histologic changes in invasive breast cancer. In conclusion, our study suggests that histologic grade may serve as a biomarker to define cancer driving genetic events in vivo.

Pan-cancer subtyping in a 2D-map shows substructures that are driven by specific combinations of molecular characteristics

The use of genome-wide data in cancer research, for the identification of groups of patients with similar molecular characteristics, has become a standard approach for applications in therapy-response, prognosis-prediction, and drug-development. To progress in these applications, the trend is to move from single genome-wide measurements in a single cancer-type towards measuring several different molecular characteristics across multiple cancer-types. Although current approaches shed light on molecular characteristics of various cancer-types, detailed relationships between patients within cancer clusters are unclear. We propose a novel multi-omic integration approach that exploits the joint behavior of the different molecular characteristics, supports visual exploration of the data by a two-dimensional landscape, and inspection of the contribution of the different genome-wide data-types. We integrated 4,434 samples across 19 cancer-types, derived from TCGA, containing gene expression, DNA-methylation, copy-number variation and microRNA expression data. Cluster analysis revealed 18 clusters, where three clusters showed a complex collection of cancer-types, squamous-cell-carcinoma, colorectal cancers, and a novel grouping of kidney-cancers. Sixty-four samples were identified outside their tissue-of-origin cluster. Known and novel patient subgroups were detected for Acute Myeloid Leukemia’s, and breast cancers. Quantification of the contributions of the different molecular types showed that substructures are driven by specific (combinations of) molecular characteristics.