Aurora kinase A as a possible marker for endocrine resistance in early estrogen receptor positive breast cancer

Elevated expression of Aurora-A/AURKA in breast cancer associates with younger age and aggressive features

BACKGROUND AND OBJECTIVE

Aurora kinase A (AURKA) is reported to be overexpressed in breast cancer. In addition to its role in regulating cell cycle and mitosis, studies have reported AURKA involvements in oncogenic signaling in suppressing BRCA1 and BRCA2. We aimed to characterize AURKA protein and mRNA expression in a breast cancer cohort of the young, investigating its relation to clinico-pathologic features and survival, and exploring age-related AURKA-associated biological processes.

METHODS

Aurora kinase A immunohistochemical staining was performed on tissue microarrays of primary tumors from an in-house breast cancer cohort (n = 355) with information on clinico-pathologic data, molecular markers, and long and complete follow-up. A subset of the in-house cohort (n = 127) was studied by the NanoString Breast Cancer 360 expression panel for exploration of mRNA expression. METABRIC cohorts < 50 years at breast cancer diagnosis (n = 368) were investigated for differentially expressed genes and enriched gene sets in AURKA mRNA high tumors stratified by age. Differentially expressed genes and gene sets were investigated using network analyses and g:Profiler.

RESULTS

High Aurora kinase A protein expression associated with aggressive clinico-pathologic features, a basal-like subtype, and high risk of recurrence score. These patterns were confirmed using mRNA data. High AURKA gene expression demonstrated independent prognostic value when adjusted for traditional clinico-pathologic features and molecular subtypes. Notably, high AURKA expression significantly associated with reduced disease-specific survival within patients below 50 years, also within the luminal A subtype. Tumors of high AURKA expression showed gene expression patterns reflecting increased DNA damage activation and higher BRCAness score.

CONCLUSIONS

Our findings indicate higher AURKA expression in young breast cancer, and associations between high Aurora-A/AURKA and aggressive tumor features, including higher tumor cell proliferation, and shorter survival, in the young. Our findings point to AURKA as a marker for increased DNA damage and DNA repair deficiency and suggest AURKA as a biomarker of clinical relevance in young breast cancer.

Assessment of AURKA expression and prognosis prediction in lung adenocarcinoma using machine learning-based pathomics signature

Objective

This study aimed to develop quantitative feature-based models from histopathological images to assess aurora kinase A (AURKA) expression and predict the prognosis of patients with lung adenocarcinoma (LUAD).

Methods

A dataset of patients with LUAD was derived from the cancer genome atlas (TCGA) with information on clinical characteristics, RNA sequencing and histopathological images. The TCGA-LUAD cohort was randomly divided into training (n = 229) and testing (n = 98) sets. We extracted quantitative image features from histopathological slides of patients with LUAD using computational approaches, constructed a predictive model for AURKA expression in the training set, and estimated their predictive performance in the test set. A Cox proportional hazards model was used to assess whether the pathomic scores (PS) generated by the model independently predicted LUAD survival.

Results

High AURKA expression was an independent risk factor for overall survival (OS) in patients with LUAD (hazard ratio = 1.816, 95 % confidence intervals = 1.257-2.623, P = 0.001). The model based on histopathological image features had significant predictive value for AURKA expression: the area under the curve of the receiver operating characteristic curve in the training set and validation set was 0.809 and 0.739, respectively. Decision curve analysis showed that the model had clinical utility. Patients with high PS and low PS had different survival rates (P = 0.019). Multivariate analysis suggested that PS was an independent prognostic factor for LUAD (hazard ratio = 1.615, 95 % confidence intervals = 1.071-2.438, P = 0.022).

Conclusion

Pathomics models based on machine learning can accurately predict AURKA expression and the PS generated by the model can predict LUAD prognosis.

Aurora-A inhibitor synergistically enhances the inhibitory effect of anlotinib on hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a highly malignant tumor with a global prevalence. In addition to the existing clinical guidelines, the effectiveness of anlotinib and Aurora-A inhibitors in treating HCC has also been demonstrated. However, Anlotinib, as an anti-angiogenesis therapy, has shown significant benefits in clinical trials but is limited by its single-agent treatment and the development of drug resistance. Aurora-A inhibitors are currently being tested in clinical trials but have limited efficacy. Combination therapy may offer clear advantages over monotherapy in this context.

METHODS

In this study, we used HCC cell lines to investigate whether the combination of the two drugs could enhance their individual strengths and mitigate their weaknesses, thereby providing greater clinical benefits both in vitro and in vivo.

RESULTS

Our findings confirmed that the Aurora-A inhibitor alisertib and anlotinib exhibited a time-dose-dependent inhibitory effect on HCC cells. In vitro cytological experiments demonstrated that the combination of the two drugs synergistically inhibited cell proliferation, invasion, and metastasis, while promoting cell apoptosis. Furthermore, we identified the underlying molecular mechanism by which the combination of the Aurora-A inhibitor alisertib and anlotinib inhibited HCC through the inhibition of the NF-ĸB signaling pathway.

CONCLUSIONS

In summary, we have demonstrated the effectiveness of combining anlotinib with an Aurora-A inhibitor, which expands the potential applications of anlotinib in the clinical treatment of HCC in the future.

Expression of Soluble Form of Aurora A as a Predictive Factor for Neoadjuvant Therapy in Breast Cancer Patients: A Single-Center Pilot Study

PURPOSE

To search for new predictive breast cancer biomarkers. We analyzed the serum concentrations of biomarkers involved in carcinogenesis, which can also be targeted by therapy.

METHODS

In a single-center prospective study, the serum levels of Aurora A, thymidine kinase 1, and human epidermal growth factor receptor type 3 (HER3) were determined in 119 women with BC before neoadjuvant treatment using ELISA kits.

RESULTS

The following clinical data were analyzed: age; TNM; the expression of ER, PGR, HER2, and Ki67; histological grade (G); and the response to neoadjuvant treatment (NAT) in the residual tumor burden classification (RCB). A complete pathological response (pCR) was achieved after NAT in 41 patients (34%). The highest proportion of the patients with a confirmed pCR was found for triple negative breast cancer (TNBC) (62.5%); non-luminal HER2-positive (52.6%) cancer subtypes (p = 0.0003); and in the G3 group (50%; p = 0.0078). The patients with higher levels of Aurora A were more likely to achieve pCR (p = 0.039). In the multivariate analysis, the serum Aurora A levels ≥ 4.75 ng/mL correlated with a higher rate of pCR (OR: 3.5; 95% CI: 1.2-10.1; p = 0.023).

CONCLUSIONS

We showed that in a biologically heterogeneous group of BC patients, the pretreatment serum Aurora A levels were of significant value in predicting the response to NAT.

A new prognostic model including immune biomarkers, genomic proliferation tumor markers (AURKA and MYBL2) and clinical-pathological features optimizes prognosis in neoadjuvant breast cancer patients

Background

Up to 30% of breast cancer (BC) patients treated with neoadjuvant chemotherapy (NCT) will relapse. Our objective was to analyze the predictive capacity of several markers associated with immune response and cell proliferation combined with clinical parameters.

Methods

This was a single-center, retrospective cohort study of BC patients treated with NCT (2001-2010), in whom pretreatment biomarkers were analyzed: neutrophil-to-lymphocyte ratio (NLR) in peripheral blood, CD3+ tumor-infiltrating lymphocytes (TILs), and gene expression of AURKA, MYBL2 and MKI67 using qRT-PCR.

Results

A total of 121 patients were included. Median followup was 12 years. In a univariate analysis, NLR, TILs, AURKA, and MYBL2 showed prognostic value for overall survival. In multivariate analyses, including hormone receptor, HER2 status, and response to NCT, NLR (HR 1.23, 95% CI 1.01-1.75), TILs (HR 0.84, 95% CI 0.73-0.93), AURKA (HR 1.05, 95% CI 1.00-1.11) and MYBL2 (HR 1.19, 95% CI 1.05-1.35) remained as independent predictor variables.

Conclusion

Consecutive addition of these biomarkers to a regression model progressively increased its discriminatory capacity for survival. Should independent cohort studies validate these findings, management of early BC patients may well be changed.

The cell cycle-related genes RHAMM, AURKA, TPX2, PLK1, and PLK4 are associated with the poor prognosis of breast cancer patients

Breast cancer is the third most common type of cancer diagnosed. Cell cycle is a complex but highly organized and controlled process, in which normal cells sense mitogenic growth signals that instruct them to enter and progress through their cell cycle. This process culminates in cell division generating two daughter cells with identical amounts of genetic material. Uncontrolled proliferation is one of the hallmarks of cancer. In this study, we analyzed the expression of the cell cycle-related genes receptor for hyaluronan (HA)-mediated motility (RHAMM), AURKA, TPX2, PLK1, and PLK4 and correlated them with the prognosis in a collective of 3952 breast cancer patients. A high messenger RNA expression of all studied genes correlated with a poor prognosis. Stratifying the patients according to the expression of hormonal receptors, we found that in patients with estrogen and progesterone receptor-positive and human epithelial growth factor receptor 2-negative tumors, and Luminal A and Luminal B tumors, the expression of the five analyzed genes correlates with worse survival. qPCR analysis of a panel of breast cancer cell lines representative of major molecular subtypes indicated a predominant expression in the luminal subtype. In vitro experiments showed that radiation influences the expression of the five analyzed genes both in luminal and triple-negative model cell lines. Functional analysis of MDA-MB-231 cells showed that small interfering RNA knockdown of PLK4 and TPX2 and pharmacological inhibition of PLK1 had an impact on the cell cycle and colony formation. Looking for a potential upstream regulation by microRNAs, we observed a differential expression of RHAMM, AURKA, TPX2, PLK1, and PLK4 after transfecting the MDA-MB-231 cells with three different microRNAs. Survival analysis of miR-34c-5p, miR-375, and miR-142-3p showed a different impact on the prognosis of breast cancer patients. Our study suggests that RHAMM, AURKA, TPX2, PLK1, and PLK4 can be used as potential targets for treatment or as a prognostic value in breast cancer patients.

Nuclear Aurora kinase A triggers programmed death-ligand 1-mediated immune suppression by activating MYC transcription in triple-negative breast cancer

Background

Increasing studies have reported that oncogenes regulate components of the immune system, suggesting that this is a mechanism for tumorigenesis. Aurora kinase A (AURKA), a serine/threonine kinase, is involved in cell mitosis and is essential for tumor cell proliferation, metastasis, and drug resistance. However, the mechanism by which AURKA is involved in immune response regulation is unclear. Therefore, this study aimed to investigate the role of AURKA in immune regulation in triple‐negative breast cancer (TNBC).

Methods

Peripheral blood mononuclear cells (PBMCs) were co‐cultured with TNBC cells. The xCELLigence Real‐Time Cell Analyzer‐MP system was used to detect the killing efficiency of immune cells on TNBC cells. The expression of immune effector molecules was tested by quantitative real‐time polymerase chain reaction (qRT‐PCR) to evaluate immune function. Furthermore, to validate AURKA‐regulated immune response in vivo, 4T1 murine breast cancer cell line with AURKA overexpression or downregulation was engrafted into BALB/c mice. The distribution and proportion of immune cells in tumors were further evaluated by immunohistochemistry and flow cytometry.

Results

Downregulation of AURKA in TNBC cells increased immune response by activating CD8⁺ T cell proliferation and activity. Nuclear rather than cytoplasmic AURKA‐derived programmed death‐ligand 1 (PD‐L1) expression was independent of its kinase activity. Mechanistic investigations showed that nuclear AURKA increased PD‐L1 expression via an MYC‐dependent pathway. PD‐L1 overexpression mostly reversed AURKA silencing‐induced expression of immune effector molecules, including interleukin‐ (IL‐2), interferon‐γ (IFN‐γ), and perforin. Moreover, AURKA expression was negatively correlated with the enrichment and activity of tumor‐infiltrating CD8⁺ T cells in 4T1 engrafted BALB/c mouse model.

Conclusions

Nuclear AURKA elevated PD‐L1 expression via an MYC‐dependent pathway and contributed to immune evasion in TNBC. Therapies targeting nuclear AURKA may restore immune responses against tumors.

Apocrine Variant of Pleomorphic Lobular Carcinoma In Situ: Further Clinical, Histopathologic, Immunohistochemical, and Molecular Characterization of an Emerging Entity

To date, the apocrine variant of lobular carcinoma in situ (AP-LCIS) has been cursorily described as a subtype of lobular carcinoma in situ (LCIS). We retrospectively reviewed 34 cases of AP-LCIS (including 23 associated with invasive lobular carcinoma) to fully characterize it. AP-LCIS typically presented with screen-detected calcifications in older women (mean age: 65 y) and was characterized by distended terminal duct lobular units with relatively large "pleomorphic" cells, central necrosis, and calcifications. AP-LCIS cells exhibited abundant eosinophilic occasionally granular cytoplasm, hyperchromatic nuclei, and prominent nucleoli. Synchronous classic and/or florid LCIS was identified in 24/34 (70%) AP-LCIS, and in 9/11 (82%) pure AP-LCIS. Most (68%) cases of AP-LCIS were estrogen receptor-positive (50% strongly), 35% were progesterone receptor-positive, 26% were human epidermal growth factor 2-positive, 18% demonstrated high-proliferation rate (Ki67: >15%), and 90% were androgen receptor-positive. Aurora kinase A, immunoreactive in 38% of AP-LCIS cases, was not significantly associated with recurrence, development of invasion, or nodal positivity (P>0.05). Compared with conventional (nonapocrine) pleomorphic lobular carcinoma in situ (P-LCIS), aurora kinase A was expressed in a significantly greater proportion of P-LCIS (100%). AP-LCIS and P-LCIS did not otherwise differ in clinicopathologic features. Next-generation sequencing utilizing the Oncomine Comprehensive Panel v2, performed on 27 AP-LCIS cases, showed no specific molecular findings. In a mean follow-up of 57 months, 2 (of 11, 18%) pure AP-LCIS cases recurred (2 both in situ and invasive) and none metastasized or proved fatal. AP-LCIS should be regarded as another high-grade LCIS similar to P-LCIS in many respects, and pending additional studies should be managed similarly.

Identification of Potential Hub Genes and Therapeutic Drugs in Malignant Pleural Mesothelioma by Integrated Bioinformatics Analysis

INTRODUCTION

Malignant pleural mesothelioma (MPM) is closely linked to asbestos exposure and is an extremely aggressive tumor with poor prognosis.

OBJECTIVE

Our study aimed to elucidate hub genes and potential drugs in MPM by integrated bioinformatics analysis.

METHODS

GSE42977 was download from the Gene Expression Omnibus (GEO) database; the differentially expressed genes (DEGs) with adj.p value <0.05 and |logFC| ≥2 were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed by DAVID database. The STRING database was used to construct a protein-protein interaction network, and modules analysis and hub genes acquisition were performed by Cytoscape. The Gene Expression Profiling Interactive Analysis (GEPIA) database was used to assess the impact of hub genes on the prognosis of MPM patients. The Drug-Gene Interaction database (DGIdb) was used to select the related drugs.

RESULTS

A total of 169 upregulated and 70 downregulated DEGs were identified. These DEGs are enriched in the pathway of extracellular matrix-receptor interaction, focal adhesion, PI3K-Akt signaling pathway, and PPAR signaling pathway. Finally, 10 hub genes (CDC20, CDK1, UBE2C, TOP2A, CCNB2, NUSAP1, KIF20A, AURKA, CEP55, and ASPM) were identified, which are considered to be closely related to the poor prognosis of MPM. In addition, 119 related drugs that may have a therapeutic effect on MPM were filtered out.

CONCLUSION

These discovered genes and small-molecule drugs provide some new ideas for further research on MPM.

Impact of Aurora Kinase A Polymorphism and Epithelial Growth Factor Receptor Mutations on the Clinicopathological Characteristics of Lung Adenocarcinoma

Lung adenocarcinoma (LADC) is the most common subtype of lung cancer worldwide and the epidermal growth factor receptor (EGFR) has a great influence on its clinical course, mainly due to the influence of different phenotypes. The Aurora kinase A (AURKA) would influence the progression of several solid malignancies. However, whether the interaction between EGFR phenotypes and AURKA would influence the clinical characteristics of LADC remains unknown. Herein, this study aimed to explore the effects of single-nucleotide polymorphisms (SNPs) of AURKA and EGFR phenotypes on the clinicopathological characteristics of LADC. Four loci of AURKA SNPs (rs1047972, rs2273535, rs6024836, and rs2064863) were genotyped using TaqMan allelic discrimination in 105 wild-type EGFR individuals and 167 LADC patients with EGFR mutations. After the statistical analysis, patients with LADC who had CT heterozygotes of AURKA rs1047972 had a lower risk of EGFR mutations than patients with wild-type homozygotes. Moreover, female and nonsmoking patients who carried the CT genotype of AURKA rs1047972 had a lower risk of EGFR mutation (p = 0.008 and p = 0.004, respectively). Moreover, in patients with EGFR mutations, AURKA SNP rs6024836 G allele (AG + GG) carriers had a lower risk of developing advanced-stage LADC (stage III or IV; odds ratio = 0.423, 95% confidence interval: 0.203–0.879, p = 0.019) than patients with AA homozygotes. Our results suggested that AURKA rs1047972 variants are significantly associated with EGFR mutations among patients with LADC, particularly in female and nonsmoking patients. AURKA variants may contribute to the pathological development of LADC.

Identification of Aurora Kinase A as a Biomarker for Prognosis in Obesity Patients with Early Breast Cancer

BACKGROUND

Obesity is associated both with a higher risk of developing breast cancer, particularly in postmenopausal women, and with worse disease outcome for women of all ages. Previous investigation suggested Aurora A kinase was able to partially restore the functionalities of obese adipose-derived mesenchymal stem cells by stabilizing their primary cilia and reestablishing a balance of multiple stemness-associated genes. The association between Aurora A and obesity breast cancer is still unclear. We hypothesized that overexpression of Aurora A was associated with poor survival in obesity breast cancer and the related axis mechanism was involved.

METHODS

A total of 517 primary breast cancer specimens were collected from the First Affiliated Hospital of China Medical University between January 2011 and November 2016. Our independent variable was BMI at baseline, categorized as overweight (BMI ≥25 kg/m2, as obesity cohort), and normal (18.5 ≤ BMI <25 kg/m2, as non-obesity cohort). The immunohistochemical (IHC) staining was performed with Aurora A, Survivin, MMP11, Cyclin B1, and Cathepsin L. Kaplan-Meier curve was used to analyze overall survival in our cohorts and TCGA-BRCA data (GSE3494). Log rank test was used to calculate P values. Protein-protein interaction (PPI) network analysis and MCODE model were used to analyze the Aurora-altered signal pathway from GSE78958.

RESULTS

Among 517 breast patients, Aurora A-positive (staining scores ≥4) was significantly higher in obesity breast carcinoma compared with non-obesity cancer carcinoma (χ 2=9.79, P=0.002), with more frequency in hormone receptor-negative (68.4% vs 77.9%, P=0.015) and HER2-positive patients (28.7% vs 17.9%, P=0.003). High Aurora A expression was remarkably and significantly associated with overall survival (OS) (8-year OS ratio: 69.5% vs 81.1%, OR=1.76, 95% CI: 1.03~3.02, P=0.041) in obesity cohort. Interestingly, higher expression of Aurora A was not associated with a shorter overall survival time among the non-obesity breast cancer (8-year OS ratio: 81.4% vs 85.8%, OR=1.40, 95% CI: 0.79~2.45, P=0.229). As for RFS, the expression levels of Aurora A expression genes have no significance with RFS statistically in non-obesity and obesity patients. Aurora A and lymph node metastases were significantly poor prognostic factors for OS, and borderline significance was noted for high BMI. Kaplan-Meier survival analysis from TCGA database confirmed that the high Aurora A expression group had worse prognosis (HR=1.47, 95% CI: 1.14-1.90, P=0.003). The KEGG pathway enrichment results were consistent with GO biological process term analysis, in which CCNB1 was enriched for upregulated Aurora A. In our samples, Aurora A level on tumor cytoplasm had broad connections with Cyclin B1 by IHC correlation analysis (correlation coefficient = 0.227, P=0.001).

CONCLUSION

Our finding demonstrates here for the first time that high expression of Aurora A was notably correlated with early recurrence and poor overall survival in obesity patients with early breast cancer. The Aurora A-Cyclin B1 axis could be a potential promising therapeutic target for cancer intervention and therapy.

Identification and validation of key genes with prognostic value in non-small-cell lung cancer via integrated bioinformatics analysis

Background

Lung cancer is the most common cause of cancer‐related death among all human cancers and the five‐year survival rates are only 23%. The precise molecular mechanisms of non‐small cell lung cancer (NSCLC) are still unknown. The aim of this study was to identify and validate the key genes with prognostic value in lung tumorigenesis.

Methods

Four GEO datasets were obtained from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (DEGs) were selected for Kyoto Encyclopedia of Genes and Genomes pathway analysis and Gene Ontology enrichment analysis. Protein‐protein interaction (PPI) networks were constructed using the STRING database and visualized by Cytoscape software and Molecular Complex Detection (MCODE) were utilized to PPI network to pick out meaningful DEGs. Hub genes, filtered from the CytoHubba, were validated using the Gene Expression Profiling Interactive Analysis database. The expressions and prognostic values of hub genes were carried out through Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan‐Meier plotter. Finally, quantitative PCR and the Oncomine database were used to verify the differences in the expression of hub genes in lung cancer cells and tissues.

Results

A total of 121 DEGs (49 upregulated and 72 downregulated) were identified from four datasets. The PPI network was established with 121 nodes and 588 protein pairs. Finally, AURKA, KIAA0101, CDC20, MKI67, CHEK1, HJURP, and OIP5 were selected by Cytohubba, and they all correlated with worse overall survival (OS) in NSCLC.

Conclusion

The results showed that AURKA, KIAA0101, CDC20, MKI67, CHEK1, HJURP, and OIP5 may be critical genes in the development and prognosis of NSCLC.

Key points

Our results indicated that AURKA, KIAA0101, CDC20, MKI67, CHEK1, HJURP, and OIP5 may be critical genes in the development and prognosis of NSCLC. Our methods showed a new way to explore the key genes in cancer development.

Aurora kinase A stabilizes FOXM1 to enhance paclitaxel resistance in triple-negative breast cancer

Triple-negative breast cancer (TNBC) has a relatively poor outcome. Acquired chemoresistance is a major clinical challenge for TNBC patients. Previously, we reported that kinase-dead Aurora kinase A (Aurora-A) could effectively transactivate the FOXM1 promoter. Here, we demonstrate an additional pathway through which Aurora-A stabilizes FOXM1 by attenuating its ubiquitin in TNBC. Specifically, Aurora-A stabilizes FOXM1 in late M phase and early G1 phase of the cell cycle, which promotes proliferation of TNBC cells. Knock-down of Aurora-A significantly suppresses cell proliferation in TNBC cell lines and can be rescued by FOXM1 overexpression. We observe that paclitaxel-resistant TNBC cells exhibit high expression of Aurora-A and FOXM1. Overexpression of Aurora-A offers TNBC cells an additional growth advantage and protection against paclitaxel. Moreover, Aurora-A and FOXM1 could be simultaneously targeted by thiostrepton. Combination of thiostrepton and paclitaxel treatment reverses paclitaxel resistance and significantly inhibits cell proliferation. In conclusion, our study reveals additional mechanism through which Aurora-A regulates FOXM1 and provides a new therapeutic strategy to treat paclitaxel-resistant triple-negative breast cancer.