Therapeutic potential of PLK1 inhibition in triple-negative breast cancer

Centrosomes and associated proteins in pathogenesis and treatment of breast cancer

Breast cancer is the most prevalent malignancy among women worldwide. Despite significant advances in treatment, it remains one of the leading causes of female mortality. The inability to effectively treat advanced and/or treatment-resistant breast cancer demonstrates the need to develop novel treatment strategies and targeted therapies. Centrosomes and their associated proteins have been shown to play key roles in the pathogenesis of breast cancer and thus represent promising targets for drug and biomarker development. Centrosomes are fundamental cellular structures in the mammalian cell that are responsible for error-free execution of cell division. Centrosome amplification and aberrant expression of its associated proteins such as Polo-like kinases (PLKs), Aurora kinases (AURKs) and Cyclin-dependent kinases (CDKs) have been observed in various cancers, including breast cancer. These aberrations in breast cancer are thought to cause improper chromosomal segregation during mitosis, leading to chromosomal instability and uncontrolled cell division, allowing cancer cells to acquire new genetic changes that result in evasion of cell death and the promotion of tumor formation. Various chemical compounds developed against PLKs and AURKs have shown meaningful antitumorigenic effects in breast cancer cells in vitro and in vivo. The mechanism of action of these inhibitors is likely related to exacerbation of numerical genomic instability, such as aneuploidy or polyploidy. Furthermore, growing evidence demonstrates enhanced antitumorigenic effects when inhibitors specific to centrosome-associated proteins are used in combination with either radiation or chemotherapy drugs in breast cancer. This review focuses on the current knowledge regarding the roles of centrosome and centrosome-associated proteins in breast cancer pathogenesis and their utility as novel targets for breast cancer treatment.

Exploring Prognostic Gene Factors in Breast Cancer via Machine Learning

Breast cancer remains the most prevalent cancer in women. To date, its underlying molecular mechanisms have not been fully uncovered. The determination of gene factors is important to improve our understanding on breast cancer, which can correlate the specific gene expression and tumor staging. However, the knowledge in this regard is still far from complete. Thus, this study aimed to explore these knowledge gaps by analyzing existing gene expression profile data from 3149 breast cancer samples, where each sample was represented by the expression of 19,644 genes and classified into Nottingham histological grade (NHG) classes (Grade 1, 2, and 3). To this end, a machine learning-based framework was designed. First, the profile data were analyzed by using seven feature ranking algorithms to evaluate the importance of features (genes). Seven feature lists were generated, each of which sorted features in accordance with feature importance evaluated from a special aspect. Then, the incremental feature selection method was applied to each list to determine essential features for classification and building efficient classifiers. Consequently, overlapping genes, such as AURKA, CBX2, and MYBL2, were deemed as potentially related to breast cancer malignancy and prognosis, indicating that such genes were identified to be important by multiple feature ranking algorithms. In addition, the study formulated classification rules to reflect special gene expression patterns for three NHG classes. Some genes and rules were analyzed and supported by recent literature, providing new references for studying breast cancer.

PLK1 and PARP positively correlate in Middle Eastern breast cancer and their combined inhibition overcomes PARP inhibitor resistance in triple negative breast cancer

Background

Despite advancements in treatment approaches, patients diagnosed with aggressive breast cancer (BC) subtypes typically face an unfavorable prognosis. Globally, these cancers continue to pose a significant threat to women's health, leading to substantial morbidity and mortality. Consequently, there has been a significant struggle to identify viable molecular targets for therapeutic intervention in these patients. Polo-like Kinase-1 (PLK1) represents one of these molecular targets currently undergoing rigorous scrutiny for the treatment of such tumors. Yet, its role in the pathogenesis of BC in Middle Eastern ethnicity remains unexplored.

Methods

We investigated the expression of PLK1 protein in a cohort of more than 1500 Middle Eastern ethnicity BC cases by immunohistochemistry. Association with clinicopathological parameters and prognosis were performed. In vitro studies were conducted using the PLK1 inhibitor volasertib and the PARP inhibitor olaparib, either alone or in combination, in PTC cell lines.

Results

Overexpression of PLK1 was detected in 27.4% of all BC cases, and this was notably correlated with aggressive clinicopathological markers. PLK1 was enriched in the triple-negative breast cancer (TNBC) subtype and exhibited poor overall survival (p = 0.0347). Notably, there was a positive correlation between PLK1 and PARP overexpression, with co-expression of PLK1 and PARP observed in 15.7% of cases and was associated with significantly poorer overall survival (OS) compared to the overexpression of either protein alone (p = 0.0050). In vitro, we studied the effect of PLK1 and PARP inhibitors either single or combined treatments in two BRCA mutated, and one BRCA proficient TNBC cell lines. We showed that combined inhibition significantly reduced cell survival and persuaded apoptosis in TNBC cell lines. Moreover, our findings indicate that inhibition of PLK1 can reinstate sensitivity in PARP inhibitor (PARPi) resistant TNBC cell lines.

Conclusion

Our results shed light on the role of PLK1 in the pathogenesis and prognosis of Middle Eastern BC and support the potential clinical development of combined inhibition of PLK1 and PARP, a strategy that could potentially broaden the use of PLK1 and PARP inhibitors beyond BC cases lacking BRCA.

Chronic chromosome instability induced by Plk1 results in immune suppression in breast cancer

Chromosome instability (CIN) contributes to resistance to therapies and tumor evolution. Although natural killer (NK) cells can eliminate cells with complex karyotypes, high-CIN human tumors have an immunosuppressive phenotype. To understand which CIN-associated molecular features alter immune recognition during tumor evolution, we overexpress Polo-like kinase 1 (Plk1) in a Her2+ breast cancer model. These high-CIN tumors activate a senescence-associated secretory phenotype (SASP), upregulate PD-L1 and CD206, and induce non-cell-autonomous nuclear factor κB (NF-κβ) signaling, facilitating immune evasion. Single-cell RNA sequencing from pre-neoplastic mammary glands unveiled the presence of Arg1+ macrophages, NK cells with reduced effector functions, and increased resting regulatory T cell infiltration. We further show that high PLK1-expressing human breast tumors display gene expression patterns associated with SASP, NF-κβ signaling, and immune suppression. These findings underscore the need to understand the immune landscape in CIN tumors to identify more effective therapies, potentially combining immune checkpoint or NF-κβ inhibitors with current treatments.

Recent Advances in Drug Discovery for Triple-Negative Breast Cancer Treatment

Triple-negative breast cancer (TNBC) is one of the most heterogeneous and aggressive breast cancer subtypes with a high risk of death on recurrence. To date, TNBC is very difficult to treat due to the lack of an effective targeted therapy. However, recent advances in the molecular characterization of TNBC are encouraging the development of novel drugs and therapeutic combinations for its therapeutic management. In the present review, we will provide an overview of the currently available standard therapies and new emerging therapeutic strategies against TNBC, highlighting the promises that newly developed small molecules, repositioned drugs, and combination therapies have of improving treatment efficacy against these tumors.

Characteristics and prognostic significance of polo-like kinase-1 (PLK1) expression in breast cancer

AIM

Polo-like kinase-1 (PLK1) plays a crucial role in cell cycle progression, and it is considered a potential therapeutic target in many cancers. Although the role of PLK1 is well established in triple-negative breast cancer (TNBC) as an oncogene, its role in luminal BC is still controversial. In this study, we aimed to evaluate the prognostic and predictive role of PLK1 in BC and its molecular subtypes.

METHODS

A large BC cohort (n = 1208) were immunohistochemically stained for PLK1. The association with clinicopathological, molecular subtypes, and survival data was analysed. PLK1 mRNA was evaluated in the publicly available datasets (n = 6774), including The Cancer Genome Atlas and the Kaplan-Meier Plotter tool.

RESULTS

20% of the study cohort showed high cytoplasmic PLK1 expression. High PLK1 expression was significantly associated with a better outcome in the whole cohort, luminal BC. In contrast, high PLK1 expression was associated with a poor outcome in TNBC. Multivariate analyses indicated that high PLK1 expression is independently associated with longer survival in luminal BC, and in poorer prognosis in TNBC. At the mRNA levels, PLK1 expression was associated with short survival in TNBC consistent with the protein expression. However, in luminal BC, its prognostic value significantly varies between cohorts.

CONCLUSION

The prognostic role of PLK1 in BC is molecular subtype-dependent. As PLK1 inhibitors are introduced to clinical trials for several cancer types, our study supports evaluation of the pharmacological inhibition of PLK1 as an attractive therapeutic target in TNBC. However, in luminal BC, PLK1 prognostic role remains controversial.

Bioinformatics analysis to identify breast cancer-related potential targets and candidate small molecule drugs

OBJECTIVE

The purpose of this study is to identify potential targets associated with breast cancer and screen potential small molecule drugs using bioinformatics analysis.

METHODS

DEGs analysis of breast cancer tissues and normal breast tissues was performed using R language limma analysis on the GSE42568 and GSE205185 datasets. Functional enrichment analysis was conducted on the intersecting DEGs. The STRING analysis platform was used to construct a PPI network, and the top 10 core nodes were identified using Cytoscape software. QuartataWeb was utilized to build a target-drug interaction network and identify potential drugs. Cell survival and proliferation were assessed using CCK8 and colony formation assays. Cell cycle analysis was performed using flow cytometry. Western blot analysis was conducted to assess protein levels of PLK1, MELK, AURKA, and NEK2.

RESULTS

A total of 54 genes were consistently upregulated in both datasets, which were functionally enriched in mitotic cell cycle and cell cycle-related pathways. The 226 downregulated genes were functionally enriched in pathways related to hormone level regulation and negative regulation of cell population proliferation. Ten key genes, namely CDK1, CCNB2, ASPM, AURKA, TPX2, TOP2A, BUB1B, MELK, RRM2, and NEK2 were identified. The potential drug Fostamatinib was predicted to target AURKA, MELK, CDK1, and NEK2. In vitro experiments demonstrated that Fostamatinib inhibited the proliferation of breast cancer cells, induced cell arrest in the G2/M phase, and down-regulated MELK, AURKA, and NEK2 proteins.

CONCLUSION

In conclusion, Fostamatinib shows promise as a potential drug for the treatment of breast cancer by regulating the cell cycle and inhibiting the proliferation of breast cancer cells.

Z-Guggulsterone Induces Cell Cycle Arrest and Apoptosis by Targeting the p53/CCNB1/PLK1 Pathway in Triple-Negative Breast Cancer

Myrrh is the dried resin of Commiphora Myrrh Engl., which exerts anticancer properties. However, its effects and molecular mechanisms in triple-negative breast cancer (TNBC) remain unclear. In this study, we used network pharmacology to screen Z-Guggulsterone (Z-GS) as a characteristic active component of myrrh. Cell Counting Kit-8 proliferation assays showed that Z-GS inhibited proliferation of the TNBC cell lines MDA-MB-468 and BT-549. Transwell assays also showed that Z-GS inhibited TNBC migration and invasion phenotypes. Our network pharmacology combined with RNA-sequencing analyses showed that Z-GS affected cell cycle and apoptosis processes in TNBC cells, mainly via p53 signaling, to regulate key CCNB1 (cyclin B1), PLK1 (polo-like kinase 1), and p53 targets. Flow cytometry revealed that Z-GS arrested the cell cycle at the G2/M phase and increased apoptosis in TNBC cells. Western blotting and quantitative real-time polymerase chain reaction studies confirmed that Z-GS functioned via the p53-mediated downregulation of CCNB1 and PLK1 expression. In vivo studies showed that Z-GS effectively inhibited TNBC progression. Collectively, Z-GS exhibited potential anti-TNBC activity and may functions via the p53/CCNB1/PLK1 pathway.

Inhibitors of the PLK1 polo-box domain: drug design strategies and therapeutic opportunities in cancer

INTRODUCTION

Polo Like Kinase 1 (PLK1) is a key regulator of mitosis and its overexpression is frequently observed in a wide variety of human cancers, while often being associated with poor survival rates. Therefore, it is considered a potential and attractive target for cancer therapeutic development. The Polo like kinase family is characterized by the presence of a unique C terminal polobox domain (PBD) involved in regulating kinase activity and subcellular localization. Among the two functionally essential, druggable sites with distinct properties that PLK1 offers, targeting the PBD presents an alternative approach for therapeutic development.

AREAS COVERED

Significant progress has been made in progressing from the peptidic PBD inhibitors first identified, to peptidomimetic and recently drug-like small molecules. In this review, the rationale for targeting the PBD over the ATP binding site is discussed, along with recent progress, challenges, and outlook.

EXPERT OPINION

The PBD has emerged as a viable alternative target for the inhibition of PLK1, and progress has been made in using compounds to elucidate mechanistic aspects of activity regulation and in determining roles of the PBD. Studies have resulted in proof of concept of in vivo efficacy suggesting promise for PBD binders in clinical development.

HN1L/AP-2γ/PLK1 signaling drives tumor progression and chemotherapy resistance in esophageal squamous cell carcinoma

Hematological and neurological expressed 1 like (HN1L) is a newly identified oncogene in lung cancer and hepatocellular carcinoma recently identified by our team, but its roles in the development and treatment of esophageal squamous cell carcinoma (ESCC) remain incompletely cataloged. Here, using ESCC tissue array and public database analysis, we demonstrated that HN1L was highly expressed in ESCC tissues, which was associated with tumor tissue invasion, poor clinical stage and short survival for ESCC patients. Loss- and gain-of-function studies in ESCC cells revealed that HN1L enhances ESCC cell metastasis and proliferation in vitro and in mice models. Moreover, high level of HN1L reduces the sensibility of ESCC cells to chemotherapeutic drugs, such as Docetaxel. Mechanism studies revealed that HN1L activated the transcription of polo-like kinase 1 (PLK1) by interacting with transcription factor AP-2γ, which increased the expression of malignancy related proteins Cyclin D1 and Slug in ESCC cells. Blocking PLK1 with inhibitor BI-2356 abrogated the oncogenic function of HN1L and significantly suppressed ESCC progression by combining with chemotherapy. Therefore, this study demonstrates the vital pro-tumor role of HN1L/AP-2γ/PLK1 signaling axis in ESCC, offering a potential therapeutic strategy for ESCC patients with high HN1L by blocking PLK1.

WINNER: A network biology tool for biomolecular characterization and prioritization

Background and contribution

In network biology, molecular functions can be characterized by network-based inference, or “guilt-by-associations.” PageRank-like tools have been applied in the study of biomolecular interaction networks to obtain further the relative significance of all molecules in the network. However, there is a great deal of inherent noise in widely accessible data sets for gene-to-gene associations or protein-protein interactions. How to develop robust tests to expand, filter, and rank molecular entities in disease-specific networks remains an ad hoc data analysis process.

Results

We describe a new biomolecular characterization and prioritization tool called Weighted In-Network Node Expansion and Ranking (WINNER). It takes the input of any molecular interaction network data and generates an optionally expanded network with all the nodes ranked according to their relevance to one another in the network. To help users assess the robustness of results, WINNER provides two different types of statistics. The first type is a node-expansion p-value, which helps evaluate the statistical significance of adding “non-seed” molecules to the original biomolecular interaction network consisting of “seed” molecules and molecular interactions. The second type is a node-ranking p-value, which helps evaluate the relative statistical significance of the contribution of each node to the overall network architecture. We validated the robustness of WINNER in ranking top molecules by spiking noises in several network permutation experiments. We have found that node degree–preservation randomization of the gene network produced normally distributed ranking scores, which outperform those made with other gene network randomization techniques. Furthermore, we validated that a more significant proportion of the WINNER-ranked genes was associated with disease biology than existing methods such as PageRank. We demonstrated the performance of WINNER with a few case studies, including Alzheimer's disease, breast cancer, myocardial infarctions, and Triple negative breast cancer (TNBC). In all these case studies, the expanded and top-ranked genes identified by WINNER reveal disease biology more significantly than those identified by other gene prioritizing software tools, including Ingenuity Pathway Analysis (IPA) and DiAMOND.

Conclusion

WINNER ranking strongly correlates to other ranking methods when the network covers sufficient node and edge information, indicating a high network quality. WINNER users can use this new tool to robustly evaluate a list of candidate genes, proteins, or metabolites produced from high-throughput biology experiments, as long as there is available gene/protein/metabolic network information.

Improved delivery of Mcl-1 and survivin siRNA combination in breast cancer cells with additive siRNA complexes

This study aimed at investigating the influence of commercial transfection reagents (Prime-Fect, Leu-Fect A, and Leu-Fect C) complexed with different siRNAs (CDC20, HSP90, Mcl-1 and Survivin) in MDA-MB-436 breast cancer cells and the impact of incorporating an anionic additive, Trans-Booster, into siRNA formulations for improving in vitro gene silencing and delivery efficiency. Gene silencing was quantitatively analyzed by real-time RT-PCR while cell proliferation and siRNA uptake were evaluated by the MTT assay and flow cytometry, respectively. Amongst the investigated siRNAs and transfection reagents, Mcl-1/Prime-Fect complexes showed the highest inhibition of cell viability and the most effective siRNA delivery. The effect of various formulations on transfection efficiency showed that the additive with 1:1 ratio with siRNA was optimal achieving the lowest cell viability compared to untreated cells and negative control siRNA treatment (p < 0.05). Furthermore, the combination of Mcl-1 and survivin siRNA suppressed the growth of MDA-MB-436 cells more effectively than treatment with the single siRNAs and resulted in cell viability as low as ~ 20% (vs. non-treated cells). This aligned well with the induction of apoptosis as analyzed by flow cytometry, which revealed higher apoptotic cells with the combination treatment group. We conclude that commercial transfection reagents formulated with Mcl-1/Survivin siRNA combination could serve as a potent anti-proliferation agent in the treatment of breast cancers.

Investigating the molecular mechanisms of Tamoxifen on the EMT pathway among patients with breast cancer

Tamoxifen is one of the most used drugs for breast cancer. This study aimed to investigate the effect of the Tamoxifen mechanism on the epithelial-mesenchymal transition (EMT) pathway among breast cancer patients due to its resistance to breast cancer cells. We selected the appropriate datasets from the GEO database using continuous and integrated bioinformatics analysis. We examined the signaling pathways, gene ontology, and protein association of genes after classifying the gene expression profile. Finally, we confirmed the candidate genes using the GEPIA database. Two groups were defined for gene expression profiles. The first group in which the expression profile of genes increased after Tamoxifen was evaluated using the expression profile of genes that decreased in the EMT pathway. The second group was the opposite of the first group. 253 genes in the first group and 302 genes in the second group were shared. The genes in the first group were involved in various pathways of cell death, focal adhesion, and cellular aging. The second group was more involved in different phases of the cell cycle. Finally, MYLK, SOCS3, and STAT5B proteins from the first group and BIRC5, PLK1, and RAPGAP1 proteins from the second group were selected as candidate proteins in connection with the effect of Tamoxifen on the EMT pathway. We evaluated Tamoxifen's effect on the EMT pathway more accurately. However, for a closer look at Tamoxifen, more studies need to be done on target genes and proteins to clarify their role.

A review of Plks: Thinking outside the (polo) box

The polo-like kinase (Plk) family is comprised of five different members (Plk1-5), each with their own distinct functions. Plk family members participate in pivotal cell division processes as well as in non-mitotic roles. Importantly, Plk expression has been correlated with various disease states, including cancer. Multiples therapies, which primarily target Plk1, are currently being investigated alone or in combination with other agents for clinical use in different cancers. As the role of Plks in disease progression becomes more prominent, it is important to outline their functions as cell cycle regulators and more. This review summarizes the structure and both mitotic and non-mitotic functions of each of the five Plk family members, sequentially. Additionally, the proposed mechanisms for how Plks contribute to tumorigenesis and the therapeutics currently under investigation are outlined.

Combination of Abemaciclib following Eribulin Overcomes Palbociclib-Resistant Breast Cancer by Inhibiting the G2/M Cell Cycle Phase

Breast cancer remains a leading cancer burden among women worldwide. Acquired resistance of cyclin-dependent kinase (CDK) 4/6 inhibitors occurs in almost all hormone receptor (HR)-positive subtype cases, comprising 70% of breast cancers, although CDK4/6 inhibitors combined with endocrine therapy are highly effective. CDK4/6 inhibitors are not expected to cooperate with cytotoxic chemotherapy based on the basic cytotoxic chemotherapy mode of action that inhibits rapidly proliferating cells. The palbociclib-resistant preclinical model developed in the current study investigated whether the combination of abemaciclib, CDK4/6 inhibitor with eribulin, an antimitotic chemotherapy could be a strategy to overcome palbociclib-resistant HR-positive breast cancer. The current study demonstrated that sequential abemaciclib treatment following eribulin synergistically suppressed CDK4/6 inhibitor-resistant cells by inhibiting the G2/M cell cycle phase more effectively. The current study showed the significant association of the pole-like kinase 1 (PLK1) level and palbociclib resistance. Moreover, the cumulative PLK1 inhibition in the G2/M phase by each eribulin or abemaciclib proved to be a mechanism of the synergistic effect. The synergistic antitumor effect was also supported by in vivo study. The sequential combination of abemaciclib following eribulin merits further clinical trials to overcome resistance to CDK4/6 inhibitors in HR-positive breast cancer.

Smell Detection Agent Optimisation Framework and Systems Biology Approach to Detect Dys-Regulated Subnetwork in Cancer Data

Network biology has become a key tool in unravelling the mechanisms of complex diseases. Detecting dys-regulated subnetworks from molecular networks is a task that needs efficient computational methods. In this work, we constructed an integrated network using gene interaction data as well as protein–protein interaction data of differentially expressed genes derived from the microarray gene expression data. We considered the level of differential expression as well as the topological weight of proteins in interaction network to quantify dys-regulation. Then, a nature-inspired Smell Detection Agent (SDA) optimisation algorithm is designed with multiple agents traversing through various paths in the network. Finally, the algorithm provides a maximum weighted module as the optimum dys-regulated subnetwork. The analysis is performed for samples of triple-negative breast cancer as well as colorectal cancer. Biological significance analysis of module genes is also done to validate the results. The breast cancer subnetwork is found to contain (i) valid biomarkers including PIK3CA, PTEN, BRCA1, AR and EGFR; (ii) validated drug targets TOP2A, CDK4, HDAC1, IL6, BRCA1, HSP90AA1 and AR; (iii) synergistic drug targets EGFR and BIRC5. Moreover, based on the weight values assigned to nodes in the subnetwork, PLK1, CTNNB1, IGF1, AURKA, PCNA, HSPA4 and GAPDH are proposed as drug targets for further studies. For colorectal cancer module, the analysis revealed the occurrence of approved drug targets TYMS, TOP1, BRAF and EGFR. Considering the higher weight values, HSP90AA1, CCNB1, AKT1 and CXCL8 are proposed as drug targets for experimentation. The derived subnetworks possess cancer-related pathways as well. The SDA-derived breast cancer subnetwork is compared with that of tools such as MCODE and Minimum Spanning Tree, and observed a higher enrichment (75%) of significant elements. Thus, the proposed nature-inspired algorithm is a novel approach to derive the optimum dys-regulated subnetwork from huge molecular network.

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

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

Bayesian Gene Selection Based on Pathway Information and Network-Constrained Regularization

High-throughput data make it possible to study expression levels of thousands of genes simultaneously under a particular condition. However, only few of the genes are discriminatively expressed. How to identify these biomarkers precisely is significant for disease diagnosis, prognosis, and therapy. Many studies utilized pathway information to identify the biomarkers. However, most of these studies only incorporate the group information while the pathway structural information is ignored. In this paper, we proposed a Bayesian gene selection with a network-constrained regularization method, which can incorporate the pathway structural information as priors to perform gene selection. All the priors are conjugated; thus, the parameters can be estimated effectively through Gibbs sampling. We present the application of our method on 6 microarray datasets, comparing with Bayesian Lasso, Bayesian Elastic Net, and Bayesian Fused Lasso. The results show that our method performs better than other Bayesian methods and pathway structural information can improve the result.

TBC1D9: An Important Modulator of Tumorigenesis in Breast Cancer

Triple-negative breast cancer (TNBC) is a major concern among the different subtypes of breast cancer (BC) due to the lack of effective treatment. In a previous study by our group aimed at understanding the difference between TNBC and non-TNBC tumors, we identified the gene TBC1 domain family member 9 (TBC1D9), the expression of which was lower in TNBC as compared to non-TNBC tumors. In the present study, analysis of TBC1D9 expression in TNBC (n = 58) and non-TNBC (n = 25) patient tumor samples validated that TBC1D9 expression can differentiate TNBC (low) from non-TNBC (high) samples and that expression of TBC1D9 was inversely correlated with grade and proliferative index. Moreover, we found that downregulation of the TBC1D9 gene decreases the proliferation marginally in non-TNBC and was associated with increased migratory and tumorigenic potential in both TNBC and luminal BC cell lines. This increase was mediated by the upregulation of ARL8A, ARL8B, PLK1, HIF1α, STAT3, and SPP1 expression in TBC1D9 knockdown cells. Our results suggest that TBC1D9 expression might limit tumor aggressiveness and that it has a differential expression in TNBC vs. non-TNBC tumors.

Second-Generation Antimitotics in Cancer Clinical Trials

Mitosis represents a promising target to block cancer cell proliferation. Classical antimitotics, mainly microtubule-targeting agents (MTAs), such as taxanes and vinca alkaloids, are amongst the most successful anticancer drugs. By disrupting microtubules, they activate the spindle assembly checkpoint (SAC), which induces a prolonged delay in mitosis, expected to induce cell death. However, resistance, toxicity, and slippage limit the MTA's effectiveness. With the desire to overcome some of the MTA's limitations, mitotic and SAC components have attracted great interest as promising microtubule-independent targets, leading to the so-called second-generation antimitotics (SGAs). The identification of inhibitors against most of these targets, and the promising outcomes achieved in preclinical assays, has sparked the interest of academia and industry. Many of these inhibitors have entered clinical trials; however, they exhibited limited efficacy as monotherapy, and failed to go beyond phase II trials. Combination therapies are emerging as promising strategies to give a second chance to these SGAs. Here, an updated view of the SGAs that reached clinical trials is here provided, together with future research directions, focusing on inhibitors that target the SAC components.

Tumor Microenvironment Subtypes and Immune-Related Signatures for the Prognosis of Breast Cancer

Objective

To better understand the immune-related heterogeneity of tumor microenvironment (TME) and establish a prognostic model for breast cancer in clinical practice.

Methods

For the 2620 breast cancer cases obtained from The Cancer Genome Atlas and the Molecular Taxonomy of Breast Cancer International Consortium, the CIBERSORT algorithm was performed to identify the immunological pattern, which underwent consensus clustering to curate TME subtypes, and biological profiles were explored by enrichment analysis. Random forest analysis, least absolute shrinkage, and selection operator analysis, in addition to uni- and multivariate COX regression analyses, were successively employed to precisely select the significant genes with prediction values for the introduction of the prognostic model.

Results

Three TME subtypes with distinct molecular and clinical features were identified by an unsupervised clustering approach, of which the molecular heterogeneity could be the result of cell cycle dysfunction and the variation of cytotoxic T lymphocyte activity. A total of 15 significant genes were proposed to construct the prognostic immune-related score system, and a predictive model was established in combination with clinicopathological characteristics for the survival of breast cancer patients. For immunological signatures, proactivity of CD8 T lymphocytes and hyperangiogenesis could be attributed to heterogeneous survival profiles.

Conclusions

We developed and validated a prognostic model based on immune-related signatures for breast cancer. This promising model is justified for validation and optimized in future clinical practice.

Identification of a DNA Methylation-Based Prognostic Signature for Patients with Triple-Negative Breast Cancer

BACKGROUND Aberrant DNA methylation is an important biological regulatory mechanism in malignant tumors. However, it remains underutilized for establishing prognostic models for triple-negative breast cancer (TNBC). MATERIAL AND METHODS Methylation data and expression data downloaded from The Cancer Genome Atlas (TCGA) were used to identify differentially methylated sites (DMSs). The prognosis-related DMSs were selected by univariate Cox regression analysis. Functional enrichment was analyzed using DAVID. A protein-protein interaction (PPI) network was constructed using STRING. Finally, a methylation-based prognostic signature was constructed using LASSO method and further validated in 2 validation cohorts. RESULTS Firstly, we identified 743 DMSs corresponding to 332 genes, including 357 hypermethylated sites and 386 hypomethylated sites. Furthermore, we selected 103 prognosis-related DMSs by univariate Cox regression. Using a LASSO algorithm, we established a 5-DMSs prognostic signature in TCGA-TNBC cohort, which could classify TNBC patients with significant survival difference (log-rank p=4.97E-03). Patients in the high-risk group had shorter overall survival than patients in the low-risk group. The excellent performance was validated in GSE78754 (HR=2.42, 95%CI: 1.27-4.59, log-rank P=0.0055). Moreover, for disease-free survival, the prognostic performance was verified in GSE141441 (HR=2.09, 95%CI: 1.28-3.44, log-rank P=0.0027). Multivariate Cox regression analysis indicated that the 5-DMSs signature could serve as an independent risk factor. CONCLUSIONS We constructed a 5-DMSs signature with excellent performance for the prediction of disease-free survival and overall survival, providing a guide for clinicians in directing personalized therapeutic regimen selection of TNBC patients.

Breast Cancer and the Other Non-Coding RNAs

Breast cancer is very heterogenous and the most common gynaecological cancer, with various factors affecting its development. While its impact on human lives and national health budgets is still rising in almost all global areas, many molecular mechanisms affecting its onset and development remain unclear. Conventional treatments still prove inadequate in some aspects, and appropriate molecular therapeutic targets are required for improved outcomes. Recent scientific interest has therefore focused on the non-coding RNAs roles in tumour development and their potential as therapeutic targets. These RNAs comprise the majority of the human transcript and their broad action mechanisms range from gene silencing to chromatin remodelling. Many non-coding RNAs also have altered expression in breast cancer cell lines and tissues, and this is often connected with increased proliferation, a degraded extracellular environment, and higher endothelial to mesenchymal transition. Herein, we summarise the known abnormalities in the function and expression of long non-coding RNAs, Piwi interacting RNAs, small nucleolar RNAs and small nuclear RNAs in breast cancer, and how these abnormalities affect the development of this deadly disease. Finally, the use of RNA interference to suppress breast cancer growth is summarised.

PLK1 Induces Chromosomal Instability and Overrides Cell-Cycle Checkpoints to Drive Tumorigenesis

Polo-like kinase 1 (PLK1) is an essential cell-cycle regulator that is frequently overexpressed in various human cancers. To determine whether Plk1 overexpression drives tumorigenesis, we established transgenic mouse lines that ubiquitously express increased levels of Plk1. High Plk1 levels were a driving force for different types of spontaneous tumors. Increased Plk1 levels resulted in multiple defects in mitosis and cytokinesis, supernumerary centrosomes, and compromised cell-cycle checkpoints, allowing accumulation of chromosomal instability (CIN), which resulted in aneuploidy and tumor formation. Clinically, higher expression of PLK1 positively associated with an increase in genome-wide copy-number alterations in multiple human cancers. This study provides in vivo evidence that aberrant expression of PLK1 triggers CIN and tumorigenesis and highlights potential therapeutic opportunities for CIN-positive cancers. SIGNIFICANCE: These findings establish roles for PLK1 as a potent proto-oncogene and a CIN gene and provide insights for the development of effective treatment regimens across PLK1-overexpressing and CIN-positive cancers.

METTL3-mediated m6A modification regulates cell cycle progression of dental pulp stem cells

Background

Dental pulp stem cells (DPSCs) are a promising cell source in endodontic regeneration and tissue engineering with limited self-renewal and pluripotency capacity. N⁶-methyladenosine (m⁶A) is the most prevalent, reversible internal modification in RNAs associated with stem cell fate determination. In this study, we aim to explore the biological effect of m⁶A methylation in DPSCs.

Methods

m⁶A immunoprecipitation with deep sequencing (m⁶A RIP-seq) demonstrated the features of m⁶A modifications in DPSC transcriptome. Lentiviral vectors were constructed to knockdown or overexpress methyltransferase like 3 (METTL3). Cell morphology, viability, senescence, and apoptosis were analyzed by β-galactosidase, TUNEL staining, and flow cytometry. Bioinformatic analysis combing m⁶A RIP and shMETTL3 RNA-seq functionally enriched overlapped genes and screened target of METTL3. Cell cycle distributions were assayed by flow cytometry, and m⁶A RIP-qPCR was used to confirm METTL3-mediated m⁶A methylation.

Results

Here, m⁶A peak distribution, binding area, and motif in DPSCs were first revealed by m⁶A RIP-seq. We also found a relatively high expression level of METTL3 in immature DPSCs with superior regenerative potential and METTL3 knockdown induced cell apoptosis and senescence. A conjoint analysis of m⁶A RIP and RNA sequencing showed METTL3 depletion associated with cell cycle, mitosis, and alteration of METTL3 resulted in cell cycle arrest. Furthermore, the protein interaction network of differentially expressed genes identified Polo-like kinase 1 (PLK1), a critical cycle modulator, as the target of METTL3-mediated m⁶A methylation in DPSCs.

Conclusions

These results revealed m⁶A methylated hallmarks in DPSCs and a regulatory role of METTL3 in cell cycle control. Our study shed light on therapeutic approaches in vital pulp therapy and served new insight into stem cell-based tissue engineering.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13287-021-02223-x.

Facilitating Drug Discovery in Breast Cancer by Virtually Screening Patients Using In Vitro Drug Response Modeling

(1) Background: Drug imputation methods often aim to translate in vitro drug response to in vivo drug efficacy predictions. While commonly used in retrospective analyses, our aim is to investigate the use of drug prediction methods for the generation of novel drug discovery hypotheses. Triple-negative breast cancer (TNBC) is a severe clinical challenge in need of new therapies. (2) Methods: We used an established machine learning approach to build models of drug response based on cell line transcriptome data, which we then applied to patient tumor data to obtain predicted sensitivity scores for hundreds of drugs in over 1000 breast cancer patients. We then examined the relationships between predicted drug response and patient clinical features. (3) Results: Our analysis recapitulated several suspected vulnerabilities in TNBC and identified a number of compounds-of-interest. AZD-1775, a Wee1 inhibitor, was predicted to have preferential activity in TNBC (p < 2.2 × 10-16) and its efficacy was highly associated with TP53 mutations (p = 1.2 × 10-46). We validated these findings using independent cell line screening data and pathway analysis. Additionally, co-administration of AZD-1775 with standard-of-care paclitaxel was able to inhibit tumor growth (p < 0.05) and increase survival (p < 0.01) in a xenograft mouse model of TNBC. (4) Conclusions: Overall, this study provides a framework to turn any cancer transcriptomic dataset into a dataset for drug discovery. Using this framework, one can quickly generate meaningful drug discovery hypotheses for a cancer population of interest.

Screening and Identification of Key Common and Specific Genes and Their Prognostic Roles in Different Molecular Subtypes of Breast Cancer

Breast cancer is one of the most common cancers. Although the present molecular classification improves the treatment effect and prognosis of breast cancer, the heterogeneity of the molecular subtype remains very complex, and the applicability and effectiveness of treatment methods are still limited leading to poorer patient prognosis than expected. Further identification of more refined molecular typing based on gene expression profile will yield better understanding of the heterogeneity, improving treatment effects and prolonging prognosis of patients. Here, we downloaded the mRNA expression profiles and corresponding clinical data of patients with breast cancer from public databases and performed typical molecular typing using PAM50 (Prediction Analysis of Microarray 50) method. Comparative analyses were performed to screen the common and specific differentially expressed genes (DEGs) between cancer and corresponding para-cancerous tissues in each breast cancer subtype. The GO and KEGG analyses of the DEGs were performed to enrich the common and specific functional progress and signaling pathway involved in breast cancer subtypes. A total of 38 key common and specific DEGs were identified and selected based on the validated results, GO/KEGG enrichments, and the priority of expression, including four common DEGs and 34 specific DEGs in different subtypes. The prognostic value of these key common and specific DEGs was further analyzed to obtain useful potential markers in clinic. Finally, the potential roles and the specific prognostic values of the common and specific DEGs were speculated and summarized in total breast cancer and different subtype breast cancer based on the results of these analyses. The findings of our study provide the basis of more refined molecular typing of breast cancer, potential new therapeutic targets and prognostic markers for different breast cancer subtypes

Small-molecule inhibitors of breast cancer-related targets: Potential therapeutic agents for breast cancer

Despite dramatic advances in cancer research and therapy, breast cancer remains a tricky health problem and represents a top biomedical research priority. Nowadays, breast cancer is still the leading cause of malignancy-related deaths in women, and incidence and mortality rates of it are expected to increase significantly the next years. Currently more and more researchers are interested in the study of breast cancer by its arising in young women. The common treatment options of breast cancer are chemotherapy, immunotherapy, hormone therapy, surgery, and radiotherapy. Most of them require chemical agents, such as PARP inhibitors, CDK4/6 inhibitors, and HER2 inhibitors. Recent studies suggest that some targets or pathways, including BRD4, PLK1, PD-L1, HDAC, and PI3K/AKT/mTOR, are tightly related to the occurrence and development of breast cancer. This article reviews the interplay between these targets and breast cancer and summarizes the progress of current research on small molecule inhibitors of these anti-breast cancer targets. The review aims to provide structural and theoretical basis for designing novel anti-breast cancer agents.

Bioinformatics analysis of key genes in triple negative breast cancer and validation of oncogene PLK1

Background

Breast cancer is the most common malignancy in women. Triple-negative breast cancer (TNBC) refers to a special subtype that is deficient in the expression of estrogen (ER), progesterone (PR), and human epidermal growth factor receptor 2 (HER-2). In this study, a variety of bioinformatics analysis tools were used to screen Hub genes related to the occurrence and development of triple negative breast cancer, and their biological functions were analyzed.

Methods

Gene Expression Omnibus (GEO) breast cancer microarray data GSE62931 was selected as the research object. The differentially expressed genes (DEGs) were screened and the protein-protein interaction (PPI) network of DEGs was constructed using bioinformatics tools. The Hub genes were also screened. The Gene Ontology (GO) knowledgebase and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for biological enrichment analysis. The Gene Expression Profiling Interactive Analysis (GEPIA) online tool was used to verify the expression of the screened genes and patient survival. The effects of polo-like kinase 1 (PLK1) on the proliferation, invasion, migration, and dryness of breast cancer cells were verified using cell counting kit 8 (CCK-8), transwell migration assays, scratch tests, and clone formation tests. An animal model of subcutaneous xenotransplantation of breast cancer was established to evaluate the effect of PLK1 on the proliferation of breast cancer.

Results

A total of 824 DEGs were screened by GSE62931 microarray data; 405 of which were up-regulated and 419 of which were down-regulated. Functional enrichment analysis showed that these DEGs were mainly enriched in cancer-related pathways and were primarily involved in biological processes (BP) such as cell and mitotic division. From the Hub gene screening, PLK1 was further identified as the Hub gene associated with TNBC. Cell and animal experiments indicated that PLK1 promotes the proliferation, invasion, migration, and clone formation of breast cancer cells.

Conclusions

Gene chip combined with bioinformatics methods can effectively analyze the DEGs related to the occurrence and development of breast cancer, and the screening of PLK1 can provide theoretical guidance for further research on the molecular mechanism of breast cancer and the screening of molecular markers.

Targeting DNA damage response kinases in cancer therapy

Cancer cells die when their decimated DNA damage response (DDR) unsuccessfully handles DNA damage. This notion has been successfully exploited when targeting PARP (poly ADP-ribose polymerase) in homologous recombination-deficient cells. With the greater understanding of DDR achieved in the last decade, new cancer therapy targets within the DDR network have been identified. Intriguingly, many of the molecules that have advanced into clinical trials are inhibitors of DDR kinases. This special issue is devoted to discussing the mechanism of cell killing and the level of success that such inhibitors have reached in pre-clinical and clinical settings.

Screening and Identification of Key Biomarkers in Inflammatory Breast Cancer Through Integrated Bioinformatic Analyses

Background: Inflammatory breast cancer (IBC) is a rare type of breast cancer with poor prognoses, moreover its pathogenesis is not entirely clear. The aim of this study was to identify key genes of IBC, which might serve as diagnostic biomarkers and/or therapeutic targets. Methods: Two microarray datasets, GSE23720 and GSE45581, were obtained from the Gene Expression Omnibus database, and a differential expression analysis was performed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to understand the potential biological functions of the differentially expressed genes (DEGs). Next, a protein-protein interaction (PPI) network was constructed and visualized by Cytoscape. Functional modules and hub genes were screened using MCODE and cytohubba plug-ins, and the Cancer Genome Atlas survival analysis along with quantitative reverse transcriptional polymerase chain reactions of clinical samples was used to validate the effect that the hub genes have on IBC. Results: A total of 215 DEGs were identified, consisting of 105 upregulated and 110 downregulated genes. GO and KEGG analyses showed that the enriched terms and pathways were mainly associated with cell cycle, proliferation, drug metabolism, and oncogenesis. From the PPI network, we identified six hub genes, including Cell Division Cycle 45 (CDC45), Polo Like Kinase 1 (PLK1), BUB1 Mitotic Checkpoint Serine/Threonine Kinase B (BUB1B), Cell Division Cycle 20 (CDC20), Aurora Kinase A (AURKA), and Mitotic Arrest Deficient 2 Like 1 (MAD2L1). The survival analyses and expression validation studies verified the robustness of these hub genes. Conclusion: This study provides new insights into the understanding of the molecular mechanisms of IBC; in addition, the identified hub genes may serve as potential targets for diagnosis and treatment.

The Analysis of the Anti-Tumor Mechanism of Ursolic Acid Using Connectively Map Approach in Breast Cancer Cells Line MCF-7

BACKGROUND

Ursolic acid (UA), a primary bioactive triterpenoid, was reported as an anti-cancer agent. However, the current knowledge of UA and its potential anti-cancer mechanisms and targets in breast cancer cells are limited. In this study, we aimed to illustrate the potential mechanisms and targets of UA in breast cancer cells MCF-7.

METHODS

The effect of UA on cell growth was determined in MCF-7 cells by MTT assay. The anti-tumor mechanism of UA was evaluated by microarray, CAMP, and Western blot. Moreover, the molecular docking between UA and potential receptors were predicted by iGEMDOCK software.

RESULTS

The result of MTT assay demonstrated that UA could inhibit MCF-7 cell growth with IC50 values of 20 μM. Microarray and CMAP analysis, validated by Western blot, indicated that UA significantly modulated IKK/NF-κB, RAF/ERK pathways, and down-regulated the phosphorylation level of PLK1 in MCF-7 cells.

CONCLUSION

Our data indicated that the anti-tumor effects of UA are due to the inhibited RAF/ERK pathway and IKK/NF-κB pathway. It could also be explained by the reduced phosphorylation of PLK1 in MCF-7 cells. This study provides a new insight for deep understanding of the new anti-cancer mechanisms of UA in MCF-7 breast cancer cells.

Therapeutic opportunities for PLK1 inhibitors: Spotlight on BRCA1-deficiency and triple negative breast cancers

Polo-Like Kinases (PLKs) are central players of mitotic progression in Eukaryotes. Given the intimate relationship between cell cycle progression and cancer development, PLKs in general and PLK1 in particular have been thoroughly studied as biomarkers and potential therapeutic targets in oncology. The oncogenic properties of PLK1 overexpression across different types of human cancers are attributed to its roles in promoting mitotic entry, centrosome maturation, spindle assembly and cytokinesis. While several academic labs and pharmaceutical companies were able to develop potent and selective inhibitors of PLK1 (PLK1i) for preclinical research, such compounds have reached only limited success in clinical trials despite their great pharmacokinetics. Even though this could be attributed to multiple causes, the housekeeping roles of PLK1 in both normal and cancer cells are most likely the main reason for clinical trials failure and withdraw due to toxicities issues. Therefore, great efforts are being invested to position PLK1i in the treatment of specific types of cancers with revised dosages schemes. In this mini review we focus on two potential niches for PLK1i that are supported by recent evidence: triple negative breast cancers (TNBCs) and BRCA1-deficient cancers. On the one hand, we recollect several lines of strong evidence indicating that TNBCs are among the cancers with highest PLK1 expression and sensitivity to PLK1i. These findings are encouraging because of the limited therapeutics options available for TNBC patients, which rely mainly on classic chemotherapy. On the other hand, we discuss recent evidence that unveils synthetic lethality induction by PLK1 inhibition in BRCA1-deficient cancers cells. This previously unforeseen therapeutic link between PLK1 and BRCA1 is promising because it defines novel therapeutic opportunities for PLK1i not only for breast cancer (i.e. TNBCs with BRCA1 deficiencies), but also for other types of cancers with BRCA1-deficiencies, such as pancreatic and prostate cancers.

Suppression of multiple processes relevant to cancer progression by benzyl isothiocyanate may result from the inhibition of Aurora A kinase activity

The anti-cancer properties of BITC may result from the inhibition of Aurora A kinase activity.

Radiosensitization of Non-Small Cell Lung Cancer Cells by the Plk1 Inhibitor Volasertib Is Dependent on the p53 Status

Polo-like kinase 1 (Plk1), a master regulator of mitotic cell division, is highly expressed in non-small cell lung cancer (NSCLC) making it an interesting drug target. We examined the in vitro therapeutic effects of volasertib, a Plk1 inhibitor, in combination with irradiation in a panel of NSCLC cell lines with different p53 backgrounds. Pretreatment with volasertib efficiently sensitized p53 wild type cells to irradiation. Flow cytometric analysis revealed that significantly more cells were arrested in the G₂/M phase of the cell cycle after the combination therapy compared to either treatment alone (p < 0.005). No significant synergistic induction of apoptotic cell death was observed, but, importantly, significantly more senescent cells were detected when cells were pretreated with volasertib before irradiation compared to both monotherapies alone (p < 0.001), especially in cells with functional p53. Consequently, while most cells with functional p53 showed permanent growth arrest, more p53 knockdown/mutant cells could re-enter the cell cycle, resulting in colony formation and cell survival. Our findings assign functional p53 as a determining factor for the observed radiosensitizing effect of volasertib in combination with radiotherapy for the treatment of NSCLC.

A long non-coding RNA signature to improve prognostic prediction in clear cell renal cell carcinoma

BACKGROUND

Accumulating research reports have indicated that long non-coding RNAs (lncRNAs) are abnormally expressed in many types of cancers. However, few lncRNA signatures for predicting cancer prognosis have been established. Our goal is to establish a lncRNA signature for predicting the prognosis of clear cell renal cell carcinoma (ccRCC).

METHODS

We downloaded KIRC lncRNA FPKM (Fragments Per Kilobase of transcript per Million Fragments) standardized expression data from The Cancer Genome Atlas (TCGA) by using the TANRIC tool. We established an 11-lncRNA signature that was clearly linked to the overall survival (OS) rates in the training and test sets.

RESULTS

The training set was divided into the high-risk and low-risk subgroups, between which the OS was disparate (HR = 1.51, 95%CI = 1.39-1.64, P < 0.0001). The accuracy of the 11-lncRNA signature for predicting prognosis was confirmed in the test set. Further analysis revealed that the prognostic value of this signature was independent of the neoplasm grade and TNM stage. Gene set enrichment analysis (GSEA) was performed, and a summary of 4 gene sets related to canonical pathway, biological process, molecular function and cellular component was obtained. We demonstrated the biological function of these lncRNAs in ccRCC cell lines and found that LINC00488 and HOTTIP promoted tumour proliferation and inhibited apoptosis. However, LINC-PINT had the opposite effect.

CONCLUSIONS

The establishment of the 11-lncRNA signature indicated the underlying biochemical functional roles of the selected lncRNAs in ccRCC. Our results may provide a reliable theoretical basis for clinical evaluation of ccRCC prognosis.