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BackgroundBreast cancer (BC) is the most common cancer in females and despite advances in treatment, it represents the leading cause of cancer mortality in women worldwide. Conventional therapeutic modalities have significantly improved the management of BC patients, but subtype heterogeneity, drug resistance, and tumor relapse remain the major factors to hamper the effectiveness of therapy for BC. In this scenario, miRNA(miR)-based therapeutics offer a very attractive area of study. However, the use of miR-based therapeutics for BC treatment still represents an underdeveloped topic. Therefore, this systematic review aims at summarizing current knowledge on promising miR-based therapeutics for BC exploring original articles focusing on in vivo experiments.MethodsThe current systematic review was performed according to PRISMA guidelines. PubMed and EMBASE databases were comprehensively explored to perform the article search.ResultsTwenty-one eligible studies were included and analyzed: twelve focused on antitumor miR-based therapeutics and nine on metastatic miR-based therapeutics. We found 18 different miRs tested as potential therapeutic molecules in animal model experiments. About 90% of the selected studies evaluate the efficiency and the safety of miRs as therapeutic agents in triple-negative (TN)-BC mouse models. Among all founded miR-based therapeutics, miR-21 emerged to be the most investigated and proposed as a potential antitumoral molecule for TNBC treatment. Besides, miR-34a and miR-205a appeared to be successful antitumoral and antimetastatic molecules.ConclusionsOur analysis provides a snapshot of the current scenario regarding the miRs as therapeutic molecules in BC. Nevertheless, despite many efforts, none of the selected studies goes beyond preclinical studies, and their translatability in the clinical practice seems quite premature.

Breast, ovarian, and endometrial cancers have a major impact on mortality in women. These tumors share hormone-dependent mechanisms involved in female-specific cancers which support tumor growth in a different manner. Integrated computational approaches may allow us to better detect genomic similarities between these different female-specific cancers, helping us to deliver more sophisticated diagnosis and precise treatments. Recently, several initiatives of The Cancer Genome Atlas (TCGA) have encouraged integrated analyses of multiple cancers rather than individual tumors. These studies revealed common genetic alterations (driver genes) even in clinically distinct entities such as breast, ovarian, and endometrial cancers. In this study, we aimed to identify expression similarity signatures by extracting common genes among TCGA breast (BRCA), ovarian (OV), and uterine corpus endometrial carcinoma (UCEC) cohorts and infer co-regulatory protein–protein interaction networks that might have a relationship with the estrogen signaling pathway. Thus, we carried out an unsupervised principal component analysis (PCA)-based computational approach, using RNA sequencing data of 2,015 female cancer and 148 normal samples, in order to simultaneously capture the data heterogeneity of intertumors. Firstly, we identified tumor-associated genes from gene expression profiles. Secondly, we investigated the signaling pathways and co-regulatory protein–protein interaction networks underlying these three cancers by leveraging the Ingenuity Pathway Analysis software. In detail, we discovered 1,643 expression similarity signatures (638 downregulated and 1,005 upregulated genes, with respect to normal phenotype), denoted as tumor-associated genes. Through functional genomic analyses, we assessed that these genes were involved in the regulation of cell-cycle-dependent mechanisms, including metaphase kinetochore formation and estrogen-dependent S-phase entry. Furthermore, we generated putative co-regulatory protein–protein interaction networks, based on upstream regulators such as the ERBB2/HER2 gene. Moreover, we provided an ad-hoc bioinformatics workflow with a manageable list of intertumor expression similarity signatures for the three female-specific cancers. The expression similarity signatures identified in this study might uncover potential estrogen-dependent molecular mechanisms promoting carcinogenesis.

Radiomics is rapidly advancing in precision diagnostics and cancer treatment. However, there are several challenges that need to be addressed before translation to clinical use. This study presents an ad-hoc weighted statistical framework to explore radiomic biomarkers for a better characterization of the radiogenomic phenotypes in breast cancer. Thirty-six female patients with breast cancer were enrolled in this study. Radiomic features were extracted from MRI and PET imaging techniques for malignant and healthy lesions in each patient. To reduce within-subject bias, the ratio of radiomic features extracted from both lesions was calculated for each patient. Radiomic features were further normalized, comparing the z-score, quantile, and whitening normalization methods to reduce between-subjects bias. After feature reduction by Spearman’s correlation, a methodological approach based on a principal component analysis (PCA) was applied. The results were compared and validated on twenty-seven patients to investigate the tumor grade, Ki-67 index, and molecular cancer subtypes using classification methods (LogitBoost, random forest, and linear discriminant analysis). The classification techniques achieved high area-under-the-curve values with one PC that was calculated by normalizing the radiomic features via the quantile method. This pilot study helped us to establish a robust framework of analysis to generate a combined radiomic signature, which may lead to more precise breast cancer prognosis.

Background: Triple-negative breast cancers (TNBCs) display poor prognosis, have a high risk of tumour recurrence, and exhibit high resistance to drug treatments. Based on their gene expression profiles, the majority of TNBCs are classified as basal-like breast cancers. Currently, there are not available widely-accepted prognostic markers to predict outcomes in basal-like subtype, so the selection of new prognostic indicators for this BC phenotype represents an unmet clinical challenge. Results: Here, we attempted to address this challenging issue by exploiting a bioinformatics pipeline able to integrate transcriptomic, genomic, epigenomic, and clinical data freely accessible from public repositories. This pipeline starts from the application of the well-established network-based SWIM methodology on the transcriptomic data to unveil important (switch) genes in relation with a complex disease of interest. Then, survival and linear regression analyses are performed to associate the gene expression profiles of the switch genes with both the patients' clinical outcome and the disease aggressiveness. This allows us to identify a prognostic gene signature that in turn is fed to the last step of the pipeline consisting of an analysis at DNA level, to investigate whether variations in the expression of identified prognostic switch genes could be related to genetic (copy number variations) or epigenetic (DNA methylation differences) alterations in their gene loci, or to the activities of transcription factors binding to their promoter regions. Finally, changes in the protein expression levels corresponding to the so far identified prognostic switch genes are evaluated by immunohistochemical staining results taking advantage of the Human Protein Atlas. Conclusion: The application of the proposed pipeline on the dataset of The Cancer Genome Atlas (TCGA)-Breast Invasive Carcinoma (BRCA) patients affected by basal-like subtype led to an in silico recognition of a basal-like specific gene signature composed of 11 potential prognostic biomarkers to be further investigated. [ABSTRACT FROM AUTHOR]

Background: Breast cancer (BC) is the most common cancer in females and despite advances in treatment, it represents the leading cause of cancer mortality in women worldwide. Conventional therapeutic modalities have significantly improved the management of BC patients, but subtype heterogeneity, drug resistance, and tumor relapse remain the major factors to hamper the effectiveness of therapy for BC. In this scenario, miRNA(miR)-based therapeutics offer a very attractive area of study. However, the use of miR-based therapeutics for BC treatment still represents an underdeveloped topic. Therefore, this systematic review aims at summarizing current knowledge on promising miR-based therapeutics for BC exploring original articles focusing on in vivo experiments. Methods: The current systematic review was performed according to PRISMA guidelines. PubMed and EMBASE databases were comprehensively explored to perform the article search. Results: Twenty-one eligible studies were included and analyzed: twelve focused on antitumor miR-based therapeutics and nine on metastatic miR-based therapeutics. We found 18 different miRs tested as potential therapeutic molecules in animal model experiments. About 90% of the selected studies evaluate the efficiency and the safety of miRs as therapeutic agents in triple-negative (TN)-BC mouse models. Among all founded miR-based therapeutics, miR-21 emerged to be the most investigated and proposed as a potential antitumoral molecule for TNBC treatment. Besides, miR-34a and miR-205a appeared to be successful antitumoral and antimetastatic molecules. Conclusions: Our analysis provides a snapshot of the current scenario regarding the miRs as therapeutic molecules in BC. Nevertheless, despite many efforts, none of the selected studies goes beyond preclinical studies, and their translatability in the clinical practice seems quite premature. [ABSTRACT FROM AUTHOR]

Breast, ovarian, and endometrial cancers have a major impact on mortality in women. These tumors share hormone-dependent mechanisms involved in female-specific cancers which support tumor growth in a different manner. Integrated computational approaches may allow us to better detect genomic similarities between these different female-specific cancers, helping us to deliver more sophisticated diagnosis and precise treatments. Recently, several initiatives of The Cancer Genome Atlas (TCGA) have encouraged integrated analyses of multiple cancers rather than individual tumors. These studies revealed common genetic alterations (driver genes) even in clinically distinct entities such as breast, ovarian, and endometrial cancers. In this study, we aimed to identify expression similarity signatures by extracting common genes among TCGA breast (BRCA), ovarian (OV), and uterine corpus endometrial carcinoma (UCEC) cohorts and infer co-regulatory protein–protein interaction networks that might have a relationship with the estrogen signaling pathway. Thus, we carried out an unsupervised principal component analysis (PCA)-based computational approach, using RNA sequencing data of 2,015 female cancer and 148 normal samples, in order to simultaneously capture the data heterogeneity of intertumors. Firstly, we identified tumor-associated genes from gene expression profiles. Secondly, we investigated the signaling pathways and co-regulatory protein–protein interaction networks underlying these three cancers by leveraging the Ingenuity Pathway Analysis software. In detail, we discovered 1,643 expression similarity signatures (638 downregulated and 1,005 upregulated genes, with respect to normal phenotype), denoted as tumor-associated genes. Through functional genomic analyses, we assessed that these genes were involved in the regulation of cell-cycle-dependent mechanisms, including metaphase kinetochore formation and estrogen-dependent S-phase entry. Furthermore, we generated putative co-regulatory protein–protein interaction networks, based on upstream regulators such as the ERBB2/HER2 gene. Moreover, we provided an ad-hoc bioinformatics workflow with a manageable list of intertumor expression similarity signatures for the three female-specific cancers. The expression similarity signatures identified in this study might uncover potential estrogen-dependent molecular mechanisms promoting carcinogenesis. [ABSTRACT FROM AUTHOR]

Background The aim of this study was to evaluate the value of CA15-3 for the diagnostic integration of molecular imaging findings performed with hybrid positron emission tomography and computed tomography (PETCT) technology. Methods We retrospectively selected 45 patients with a median age of 60 years (range 39-85 years) and a previous history of breast cancer (BC) who had already been treated with surgery and other treatments. Three measurements of CA15-3 were collected within 1 year before PETCT examination, at 6-9 months 3-6 months and 0-3 months before PETCT. The prolonged clinical outcome or imaging follow-up was used to define disease relapse. An increase in tumor marker value was compared with PETCT findings and disease relapse. Sensitivity and specificity for both tests were calculated with respect to clinical outcome. Results Disease relapse was detected in 16 out of 45 BC patients. CA15-3 and PETCT showed 75% sensitivity with a specificity percentage of 76% for CA15-3 and 79% for PETCT. Serum CA15-3 expression levels were significantly higher in BC patients with multiple metastatic sites with hepatic involvement. Analysis of serial CA15-3 serum levels showed an increase in CA15-3 3-6 months before PETCT could identify BC patients at risk for relapse (AUC = 0.81). Moreover, patients receiving anti-hormonal or chemotherapy medications with negative PETCT and positive CA15-3 relapsed after a median time of 158 days compared to patients who were negative for both tests and who were free from disease for at least 1 year. Conclusions Our results showed that serial increases in CA15-3 can be used to predict positive PETCT results in BC patients during follow-up. Increased levels of CA15-3 may be considered an early warning sign in patients needing accurate molecular imaging investigations, as they are at higher risk of recurrence. In cases of elevated levels, multiple lesions or liver involvement may exist. Also, patients receiving chemotherapeutic or anti-hormonal treatment who have negative PETCT scans and increased CA15-3 serum levels should be considered at risk for relapse, because the CA15-3-linked biochemical signal of the presence of a tumor can predict positive metabolic imaging. [ABSTRACT FROM AUTHOR]

Hormonal therapy is the preferred systemic treatment for recurrent or metastatic, post-menopausal hormone-receptor-positive breast cancer. Previous studies have shown that there is no cross-resistance between exemestane and reversible aromatase inhibitors. Exposure to hormonal therapy does not hamper later response to chemotherapy. Patients with locally advanced or metastatic, hormonal receptor positive or unknown, breast cancer were treated with oral anastrozole, until disease progression, followed by oral exemestane until new evidence of disease progression. The primary end point of the study was clinical benefit, defined as the sum of complete responses (CR), partial responses (PR) and > 24 weeks stable disease (SD). In all, 100 patients were enrolled in the study. Anastrozole produced eight CR and 19 PR for an overall response rate of 27% (95% CI: 18.6-36.8%). An additional 46 patients had long-term (> 24 weeks) SD for an overall clinical benefit of 73% (95% CI: 63.2-81.4). Median time to progression (TTP) was 11 months (95% CI: 10-12). A total of 50 patients were evaluated for the second-line treatment: exemestane produced one CR and three PR; 25 patients had SD which lasted > or = 6 months in 18 patients. Median TTP was 5 months. Toxicity of treatment was low. Our study confirms that treatment with sequential hormonal agents can extend the period of time during which endocrine therapy can be used, thereby deferring the decision to use chemotherapy. [ABSTRACT FROM AUTHOR]

Radiomics is rapidly advancing in precision diagnostics and cancer treatment. However, there are several challenges that need to be addressed before translation to clinical use. This study presents an ad-hoc weighted statistical framework to explore radiomic biomarkers for a better characterization of the radiogenomic phenotypes in breast cancer. Thirty-six female patients with breast cancer were enrolled in this study. Radiomic features were extracted from MRI and PET imaging techniques for malignant and healthy lesions in each patient. To reduce within-subject bias, the ratio of radiomic features extracted from both lesions was calculated for each patient. Radiomic features were further normalized, comparing the z-score, quantile, and whitening normalization methods to reduce between-subjects bias. After feature reduction by Spearman's correlation, a methodological approach based on a principal component analysis (PCA) was applied. The results were compared and validated on twenty-seven patients to investigate the tumor grade, Ki-67 index, and molecular cancer subtypes using classification methods (LogitBoost, random forest, and linear discriminant analysis). The classification techniques achieved high area-under-the-curve values with one PC that was calculated by normalizing the radiomic features via the quantile method. This pilot study helped us to establish a robust framework of analysis to generate a combined radiomic signature, which may lead to more precise breast cancer prognosis. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is a heterogeneous and complex disease as witnessed by the existence of different subtypes and clinical characteristics that poses significant challenges in disease management. The complexity of this tumor may rely on the highly interconnected nature of the various biological processes as stated by the new paradigm of Network Medicine. We explored The Cancer Genome Atlas (TCGA)-BRCA data set, by applying the network-based algorithm named SWItch Miner, and mapping the findings on the human interactome to capture the molecular interconnections associated with the disease modules. To characterize BC phenotypes, we constructed protein–protein interaction modules based on "hub genes", called switch genes, both common and specific to the four tumor subtypes. Transcriptomic profiles of patients were stratified according to both clinical (immunohistochemistry) and genetic (PAM50) classifications. 266 and 372 switch genes were identified from immunohistochemistry and PAM50 classifications, respectively. Moreover, the identified switch genes were functionally characterized to select an interconnected pathway of disease genes. By intersecting the common switch genes of the two classifications, we selected a unique signature of 28 disease genes that were BC subtype-independent and classification subtype-independent. Data were validated both in vitro (10 BC cell lines) and ex vivo (66 BC tissues) experiments. Results showed that four of these hub proteins (AURKA, CDC45, ESPL1, and RAD54L) were over-expressed in all tumor subtypes. Moreover, the inhibition of one of the identified switch genes (AURKA) similarly affected all BC subtypes. In conclusion, using a network-based approach, we identified a common BC disease module which might reflect its pathological signature, suggesting a new vision to face with the disease heterogeneity. [ABSTRACT FROM AUTHOR]

There is an unmet need for novel non-invasive prognostic molecular tumour markers for breast cancer (BC). Accumulating evidence shows that miR-155 plays a pivotal role in tumorigenesis. Generally, miR-155 is considered an oncogenic miRNA promoting tumour growth, angiogenesis and aggressiveness of BC. Therefore, many researchers have focused on its use as a prognostic biomarker and therapeutic target. However, its prognostic value for BC patients remains controversial. To address this issue, the present systematic review aims to summarize the available evidence and give a picture of a prognostic significance of miR-155 in BC pathology. All eligible studies were searched on PubMed and EMBASE databases through various search strategies. Starting from 289 potential eligible records, data were examined from 28 studies, comparing tissue and circulating miR-155 expression levels with clinicopathological features and survival rates in BC patients. We discuss the pitfalls and challenges that need to be assessed to understand the power of miR-155 to respond to real clinical needs, highlighting the consistency, robustness or lack of results obtained to sate in translating this molecule to clinical practice. Our paper suggests that the prognostic role of miR-155 in the management of BC needs to be further verified. [ABSTRACT FROM AUTHOR]

The effective management of patients with breast cancer (BC) depends on the early diagnosis of the disease. Currently, BC diagnosis is based on diagnostic imaging and biopsy, while the use of non-invasive circulating biomarkers for diagnosis remains an unmet need. Among the plethora of proposed non-invasive biomarkers, circulating microRNAs (miRNAs) have been considered promising diagnostic molecules because they are very stable in biological fluids and easily detectable. Although the discovery of miRNAs has opened a new avenue for their clinical application, the clinical translatability of these molecules remains unclear. This review analyses the role of circulating miRNAs as BC diagnostic biomarkers and focuses on two essential requirements to evaluate their clinical validity: i) Specificity and ii) consistent expression between the blood and tissue. These two issues were analyzed in depth using the Human miRNA Disease Database (HMDD v3.0) and the free search engine PubMed. One hundred and sixty three BC-associated miRNAs were selected and analyzed for their specificity among all human pathologies that shared deregulation (291) and consistent expression in the bloodstream and the tissue. In addition, we provide an overview of the current clinical trials examining miRNAs in BC. In conclusion, we highlight pitfalls in the translatability of circulating miRNAs into clinical practice due to the lack of specificity and a consistent expression pattern between the tissue and blood. [ABSTRACT FROM AUTHOR]

The aim of this study was to identify new disease-related circulating miRNAs with high diagnostic accuracy for breast cancer (BC) and to correlate their deregulation with the morpho-functional characteristics of the tumour, as assessed in vivo by positron emission tomography/magnetic resonance (PET/MR) imaging. A total of 77 untreated female BC patients underwent same-day PET/MR and blood collection, and 78 healthy donors were recruited as negative controls. The expression profile of 84 human miRNAs was screened by using miRNA PCR arrays and validated by real-time PCR. The validated miRNAs were correlated with the quantitative imaging parameters extracted from the primary BC samples. Circulating miR-125b-5p and miR-143-3p were upregulated in BC plasma and able to discriminate BC patients from healthy subjects (miR-125-5p area under the receiver operating characteristic ROC curve (AUC) = 0.85 and miR-143-3p AUC = 0.80). Circulating CA15-3, a soluble form of the transmembrane glycoprotein Mucin 1 (MUC-1) that is upregulated in epithelial cancer cells of different origins, was combined with miR-125b-5p and improved the diagnostic accuracy from 70% (CA15-3 alone) to 89% (CA15-3 plus miR-125b-5p). MiR-143-3p showed a strong and significant correlation with the stage of the disease, apparent diffusion coefficient (ADCmean), reverse efflux volume transfer constant (Kepmean) and maximum standardized uptake value (SUVmax), and it might represent a biomarker of tumour aggressiveness. Similarly, miR-125b-5p was correlated with stage and grade 2 but inversely correlated with the forward volume transfer constant (Ktransmean) and proliferation index (Ki67), suggesting a potential role as a biomarker of a relatively more favourable prognosis. In situ hybridization (ISH) experiments revealed that miR-143-3p was expressed in endothelial tumour cells, miR-125-5p in cancer-associated fibroblasts, and neither in epithelial tumour cells. Our results suggested that miR-125-5p and miR-143-3p are potential biomarkers for the risk stratification of BC, and Kaplan-Maier plots confirmed this hypothesis. In addition, the combined use of miR-125-b-5p and CA15-3 enhanced the diagnostic accuracy up to 89%. This is the first study that correlates circulating miRNAs with in vivo quantified tumour biology through PET/MR biomarkers. This integration elucidates the link between the plasmatic increase in these two potential circulating biomarkers and the biology of untreated BC. In conclusion, while miR-143-3b and miR-125b-5p provide valuable information for prognosis, a combination of miR-125b-5p with the tumour marker CA15-3 improves sensitivity for BC detection, which warrants consideration by further validation studies. [ABSTRACT FROM AUTHOR]

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