Predictors of breast cancer cell types and their prognostic power in breast cancer patients

Ferroptosis contributes to the progression of female-specific neoplasms, from breast cancer to gynecological malignancies in a manner regulated by non-coding RNAs: Mechanistic implications

Ferroptosis, a recently identified type of non-apoptotic cell death, triggers the elimination of cells in the presence of lipid peroxidation and in an iron-dependent manner. Indeed, ferroptosis-stimulating factors have the ability of suppressing antioxidant capacity, leading to the accumulation of reactive oxygen species (ROS) and the subsequent oxidative death of the cells. Ferroptosis is involved in the pathophysiological basis of different maladies, such as multiple cancers, among which female-oriented malignancies have attracted much attention in recent years. In this context, it has also been unveiled that non-coding RNA transcripts, including microRNAs, long non-coding RNAs, and circular RNAs have regulatory interconnections with the ferroptotic flux, which controls the pathogenic development of diseases. Furthermore, the potential of employing these RNA transcripts as therapeutic targets during the onset of female-specific neoplasms to modulate ferroptosis has become a research hotspot; however, the molecular mechanisms and functional alterations of ferroptosis still require further investigation. The current review comprehensively highlights ferroptosis and its association with non-coding RNAs with a focus on how this crosstalk affects the pathogenesis of female-oriented malignancies, from breast cancer to ovarian, cervical, and endometrial neoplasms, suggesting novel therapeutic targets to decelerate and even block the expansion and development of these tumors.

Exploiting Long Non-Coding RNAs and Circular RNAs as Pharmacological Targets in Triple-Negative Breast Cancer Treatment

Breast cancer is one of the most frequent causes of cancer death among women worldwide. In particular, triple-negative breast cancer (TNBC) represents the most aggressive breast cancer subtype because it is characterized by the absence of molecular targets, thus making it an orphan type of malignancy. The discovery of new molecular druggable targets is mandatory to improve treatment success. In that context, non-coding RNAs represent an opportunity for modulation of cancer. They are RNA molecules with apparently no protein coding potential, which have been already demonstrated to play pivotal roles within cells, being involved in different processes, such as proliferation, cell cycle regulation, apoptosis, migration, and diseases, including cancer. Accordingly, they could be used as targets for future TNBC personalized therapy. Moreover, the peculiar characteristics of non-coding RNAs make them reliable biomarkers to monitor cancer treatment, thus, to monitor recurrence or chemoresistance, which are the most challenging aspects in TNBC. In the present review, we focused on the oncogenic or oncosuppressor role of long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) mostly involved in TNBC, highlighting their mode of action and depicting their potential role as a biomarker and/or as targets of new non-coding RNA-based therapeutics.

Development and Validation of Ferroptosis-Related lncRNAs as Prognosis and Diagnosis Biomarkers for Breast Cancer

Breast cancer (BC) is one of the most common malignancies affecting women. Ferroptosis is a novel cancer treatment option. The present study is aimed to identify suitable ferroptosis-related lncRNAs to predict and diagnose BC. Differential expression and Cox regression analyses were used to screen suitable prognostic biomarkers and construct a suitable risk model. We identified four ferroptosis-related differentially expressed lncRNAs (FR-DELs) (LINC01152, AC004585.1, MAPT-IT1, and AC026401.3), which were independently correlated with the overall survival of BC patients. The area under the curve value of the prognostic model using those four biomarkers was over 0.60 in all three groups. The sensitivity and specificity of the diagnostic model using those four biomarkers were 86.89% and 86.73%, respectively. Our present study indicated that these four FR-DELs (LINC01152, AC004585.1, MAPT-IT1, and AC026401.3) could be prognostic biomarkers for BC, although clinical validation studies are required.

Deep Learning Mechanism for Predicting the Axillary Lymph Node Metastasis in Patients with Primary Breast Cancer

The second largest cause of mortality worldwide is breast cancer, and it mostly occurs in women. Early diagnosis has improved further treatments and reduced the level of mortality. A unique deep learning algorithm is presented for predicting breast cancer in its early stages. This method utilizes numerous layers to retrieve significantly greater amounts of information from the source inputs. It could perform automatic quantitative evaluation of complicated image properties in the medical field and give greater precision and reliability during the diagnosis. The dataset of axillary lymph nodes from the breast cancer patients was collected from Erasmus Medical Center. A total of 1050 images were studied from the 850 patients during the years 2018 to 2021. For the independent test, data samples were collected for 100 images from 95 patients at national cancer institute. The existence of axillary lymph nodes was confirmed by pathologic examination. The feed forward, radial basis function, and Kohonen self-organizing are the artificial neural networks (ANNs) which are used to train 84% of the Erasmus Medical Center dataset and test the remaining 16% of the independent dataset. The proposed model performance was determined in terms of accuracy (Ac), sensitivity (Sn), specificity (Sf), and the outcome of the receiver operating curve (Roc), which was compared to the other four radiologists' mechanism. The result of the study shows that the proposed mechanism achieves 95% sensitivity, 96% specificity, and 98% accuracy, which is higher than the radiologists' models (90% sensitivity, 92% specificity, and 94% accuracy). Deep learning algorithms could accurately predict the clinical negativity of axillary lymph node metastases by utilizing images of initial breast cancer patients. This method provides an earlier diagnostic technique for axillary lymph node metastases in patients with medically negative changes in axillary lymph nodes.

Recent advances of transcriptomics and proteomics in triple-negative breast cancer prognosis assessment

Triple-negative breast cancer (TNBC), a heterogeneous tumour that lacks the expression of oestrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2), is often characterized by aggressiveness and tends to recur or metastasize. TNBC lacks therapeutic targets compared with other subtypes and is not sensitive to endocrine therapy or targeted therapy except chemotherapy. Therefore, identifying the prognostic characteristics and valid therapeutic targets of TNBC could facilitate early personalized treatment. Due to the rapid development of various technologies, researchers are increasingly focusing on integrating 'big data' and biological systems, which is referred to as 'omics', as a means of resolving it. Transcriptomics and proteomics analyses play an essential role in exploring prospective biomarkers and potential therapeutic targets for triple-negative breast cancers, which provides a powerful engine for TNBC's therapeutic discovery when combined with complementary information. Here, we review the recent progress of TNBC research in transcriptomics and proteomics to identify possible therapeutic goals and improve the survival of patients with triple-negative breast cancer. Also, researchers may benefit from this article to catalyse further analysis and investigation to decipher the global picture of TNBC cancer.

The Other Side of the Coin: May Androgens Have a Role in Breast Cancer Risk?

Breast cancer prevention is a major challenge worldwide. During the last few years, efforts have been made to identify molecular breast tissue factors that could be linked to an increased risk of developing the disease in healthy women. In this concern, steroid hormones and their receptors are key players since they are deeply involved in the growth, development and lifetime changes of the mammary gland and play a crucial role in breast cancer development and progression. In particular, androgens, by binding their own receptor, seem to exert a dichotomous effect, as they reduce cell proliferation in estrogen receptor α positive (ERα+) breast cancers while promoting tumour growth in the ERα negative ones. Despite this intricate role in cancer, very little is known about the impact of androgen receptor (AR)-mediated signalling on normal breast tissue and its correlation to breast cancer risk factors. Through an accurate collection of experimental and epidemiological studies, this review aims to elucidate whether androgens might influence the susceptibility for breast cancer. Moreover, the possibility to exploit the AR as a useful marker to predict the disease will be also evaluated.

DeCompress: tissue compartment deconvolution of targeted mRNA expression panels using compressed sensing

Targeted mRNA expression panels, measuring up to 800 genes, are used in academic and clinical settings due to low cost and high sensitivity for archived samples. Most samples assayed on targeted panels originate from bulk tissue comprised of many cell types, and cell-type heterogeneity confounds biological signals. Reference-free methods are used when cell-type-specific expression references are unavailable, but limited feature spaces render implementation challenging in targeted panels. Here, we present DeCompress, a semi-reference-free deconvolution method for targeted panels. DeCompress leverages a reference RNA-seq or microarray dataset from similar tissue to expand the feature space of targeted panels using compressed sensing. Ensemble reference-free deconvolution is performed on this artificially expanded dataset to estimate cell-type proportions and gene signatures. In simulated mixtures, four public cell line mixtures, and a targeted panel (1199 samples; 406 genes) from the Carolina Breast Cancer Study, DeCompress recapitulates cell-type proportions with less error than reference-free methods and finds biologically relevant compartments. We integrate compartment estimates into cis-eQTL mapping in breast cancer, identifying a tumor-specific cis-eQTL for CCR3 (C-C Motif Chemokine Receptor 3) at a risk locus. DeCompress improves upon reference-free methods without requiring expression profiles from pure cell populations, with applications in genomic analyses and clinical settings.

Intratumor heterogeneity index of breast carcinomas based on DNA methylation profiles

Background

Cancer cells evolve and constitute heterogeneous populations that fluctuate in space and time and are subjected to selection generating intratumor heterogeneity. This phenomenon is determined by the acquisition of genetic/epigenetic alterations and their selection over time which has clinical implications on drug resistance.

Methods

DNA extracted from different tumor cell populations (breast carcinomas, cancer cell lines and cellular clones) were analyzed by MS-MLPA. Methylation profiles were used to generate a heterogeneity index to quantify the magnitude of epigenetic heterogeneity in these populations. Cellular clones were obtained from single cells derived of MDA-MB 231 cancer cell lines applying serial limiting dilution method and morphology was analyzed by optical microscopy and flow cytometry. Clones characteristics were examined through cellular proliferation, migration capacity and apoptosis. Heterogeneity index was also calculated from beta values derived from methylation profiles of TCGA tumors.

Results

The study of methylation profiles of 23 fresh breast carcinomas revealed heterogeneous allele populations in these tumor pieces. With the purpose to measure the magnitude of epigenetic heterogeneity, we developed an heterogeneity index based on methylation information and observed that all tumors present their own heterogeneity level. Applying the index calculation in pure cancer cell populations such as cancer cell lines (MDA-MB 231, MCF-7, T47D, HeLa and K-562), we also observed epigenetic heterogeneity. In addition, we detected that clones obtained from the MDA-MB 231 cancer cell line generated their own new heterogeneity over time. Using TCGA tumors, we determined that the heterogeneity index correlated with prognostic and predictive factors like tumor size (p = 0.0088), number of affected axillary nodes (p = 0.007), estrogen receptor expression (p < 0.0001) and HER2 positivity (p = 0.0007). When we analyzed molecular subtypes we found that they presented different heterogeneity levels. Interestingly, we also observed that all mentioned tumor cell populations shared a similar Heterogeneity index (HI) mean.

Conclusions

Our results show that each tumor presents a unique epigenetic heterogeneity level, which is associated with prognostic and predictive factors. We also observe that breast tumor subtypes differ in terms of epigenetic heterogeneity, which could serve as a new contribution to understand the different prognosis of these groups.

(-)-Oleocanthal Combined with Lapatinib Treatment Synergized against HER-2 Positive Breast Cancer In Vitro and In Vivo

Dysregulation of epidermal growth factor receptor (EGFR)/human epidermal growth factor-2 (HER2) family is a hallmark of aggressive breast cancer. Small-molecule tyrosine kinase inhibitors are among the most effective cancer targeted treatments. (−)-Oleocanthal (OC) is a naturally occurring phenolic secoiridoid lead from extra-virgin olive oil with documented anti-cancer activities via targeting mesenchymal epithelial transition factor (c-Met). Dysregulation of c-Met promotes aggressiveness to breast cancer-targeted therapies. Lapatinib (LP) is an FDA-approved dual EGFR/HER2 inhibitor for HER2-amplified breast cancer. HER2-Positive tumor cells can escape targeted therapies like LP effects by overexpressing c-Met. Combined OC-LP treatment is hypothesized to be mechanistically synergistic against HER2-overexpressing breast cancer. Combined sub-effective treatments of OC-LP resulted in synergistic anti-proliferative effects against the HER2-positive BT-474 and SK-BR-3 breast cancer cell lines, compared to OC or LP monotherapy. Antibody array and Western blot analysis showed that combined OC-LP treatment significantly inhibited EGFR, HER2, and c-Met receptor activation, as well as multiple downstream signaling proteins, compared to individual OC or LP treatment. OC-LP Combination significantly inhibited invasion and migration of breast cancer cells through reduced activation of focal adhesion kinase (FAK) and paxillin. Combined treatment of OC-10 mg/kg with LP-12.5 mg/kg suppressed more than 90% of BT-474 tumor cells growth in a nude mouse xenograft model, compared to individual OC or LP treatment. Activated c-Met, EGFR, HER2, and protein kinase B (AKT) were significantly suppressed in combination-treated mice tumors, compared to OC or LP monotherapy. This study reveals the OC future potential as combination therapy to sensitize HER2-overexpressing breast cancers and significantly reduce required doses of targeted HER family therapeutics.

Cholinergic Receptor Nicotinic Alpha 5 (CHRNA5) RNAi is associated with cell cycle inhibition, apoptosis, DNA damage response and drug sensitivity in breast cancer

Cholinergic Receptor Nicotinic Alpha 5 (CHRNA5) is an important susceptibility locus for nicotine addiction and lung cancer. Depletion of CHRNA5 has been associated with reduced cell viability, increased apoptosis and alterations in cellular motility in different cancers yet not in breast cancer. Herein we first showed the expression of CHRNA5 was variable and positively correlated with the fraction of total genomic alterations in breast cancer cell lines and tumors indicating its potential role in DNA damage response (DDR). Next, we demonstrated that silencing of CHRNA5 expression in MCF7 breast cancer cell line by RNAi affected expression of genes involved in cytoskeleton, TP53 signaling, DNA synthesis and repair, cell cycle, and apoptosis. The transcription profile of CHRNA5 depleted MCF7 cells showed a significant positive correlation with that of A549 lung cancer cell line while exhibiting a negative association with the CHRNA5 co-expression profile obtained from Cancer Cell Line Encylopedia (CCLE). Moreover, it exhibited high similarities with published MCF7 expression profiles obtained from exposure to TP53 inducer nutlin-3a and topoisomerase inhibitors. We then demonstrated that CHRNA5 siRNA treatment reduced cell viability and DNA synthesis indicating G1 arrest while it significantly increased apoptotic sub-G1 cell population. Accordingly, we observed lower levels of phosphorylated RB (Ser807/811) and an increased BAX/BCL2 ratio in RNAi treated MCF7 cells. We also showed that CHRNA5 RNAi transcriptome correlated negatively with DDR relevant gene expression profile in breast cancer gene expression datasets while the coexposure to topoisomerase inhibitors in the presence of CHRNA5 RNAi enhanced chemosensitivity potentially due to reduced DDR. CHRNA5 RNAi consistently lowered total CHEK1 mRNA and protein levels as well as phosphorylated CHEK1 (Ser345) in MCF7 cells. We also detected a significant positive correlation between the expression levels of CHRNA5 and CHEK1 in CCLE, TCGA and METABRIC breast cancer datasets. Our study suggests CHRNA5 RNAi is associated with cell cycle inhibition, apoptosis as well as reduced DDR and increased drug sensitivity in breast cancer yet future studies are warranted since dose- and cell line-specific differences exist in response to CHRNA5 depletion. Gene expression microarray data can be accessed from GEO database under the accession number GSE89333.

Long non-coding RNAs: implications in targeted diagnoses, prognosis, and improved therapeutic strategies in human non- and triple-negative breast cancer

Triple-negative breast cancer (TNBC) has been clinically difficult to manage because of tumor aggressiveness, cellular and histological heterogeneity, and molecular mechanisms’ complexity. All this in turn leads us to evaluate that tumor biological behavior is not yet fully understood. Additionally, the heterogeneity of tumor cells represents a great biomedicine challenge in terms of the complex molecular—genetical-transcriptional and epigenetical—mechanisms, which have not been fully elucidated on human solid tumors.

Recently, human breast cancer, but specifically TNBC is under basic and clinical-oncology research in the discovery of new molecular biomarkers and/or therapeutic targets to improve treatment responses, as well as for seeking algorithms for patient stratification, seeking a positive impact in clinical-oncology outcomes and life quality on breast cancer patients.

In this sense, important knowledge is emerging regarding several cancer molecular aberrations, including higher genetic mutational rates, LOH, CNV, chromosomal, and epigenetic alterations, as well as transcriptome aberrations in terms of the total gene-coding ribonucleic acids (RNAs), known as mRNAs, as well as non-coding RNA (ncRNA) sequences. In this regard, novel investigation fields have included microRNAs (miRNAs), as well as long ncRNAs (lncRNAs), which have been importantly related and are likely involved in the induction, promotion, progression, and/or clinical therapeutic response trackers of TNBC. Based on this, in general terms according with the five functional archetype classification, the lncRNAs may be involved in the regulation of several molecular mechanisms which include genetic expression, epigenetic, transcriptional, and/or post-transcriptional mechanisms, which are nowadays not totally understood.

Here, we have reviewed the main dis-regulated and functionally non- and well-characterized lncRNAs and their likely involvement, from a molecular enrichment and mechanistic point of view, as tumor biomarkers for breast cancer and its specific histological subtype, TNBC. In reference to the abovementioned, it has been described that some lncRNA expression profiles correspond or are associated with the TNBC histological subtype, potentially granting their use for TNBC malignant progression, diagnosis, tumor clinical stage, and likely therapy. Based on this, lncRNAs have been proposed as potential biomarkers which might represent potential predictive tools in the differentiated breast carcinomas versus TNBC malignant disease. Finally, elucidation of the specific or multi-functional archetypal of lncRNAs in breast cancer and TNBC could be fundamental, as these molecular intermediary-regulator “lncRNAs” are widely involved in the genome expression, epigenome regulation, and transcriptional and post-transcriptional tumor biology, which in turn will probably represent a new prospect in clinical and/or therapeutic molecular targets for the oncological management of breast carcinomas in general and also for TNBC patients.