A gene expression signature that can predict the recurrence of tamoxifen-treated primary breast cancer

An analysis of the prognostic role of reactive oxygen species-associated genes in breast cancer

BACKGROUND

This study aimed to type breast cancer in relation to reactive oxygen species (ROS), clinical indicators, single nucleotide variant (SNV) mutations, functional differences, immune infiltration, and predictive responses to immunotherapy or chemotherapy, and constructing a prognostic model.

METHODS

We used uniCox analysis, ConsensusClusterPlus, and the proportion of ambiguous clustering (PAC) to analyze The Cancer Genome Atlas (TCGA) data to determine optimal groupings and obtain differentially expressed ROS-related genes. Clinical indicators were then combined with the classification results and the Chi-square test was used to assess differences. We further examined SNV mutations, and functional differences using gene set enrichment analysis (GSEA) analysis, the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, immune cell infiltration, and response to immunotherapy and chemotherapy. A prognostic model for breast cancer was constructed using these differentially expressed genes, immunotherapy or chemotherapy responses, and survival curves. RT-qPCR was used to detect the differences in the expression of LCE3D, CA1, PIRT and SMR3A in breast cancer cell lines and normal breast epithelial cell line.

RESULTS

We identified two distinct tumor types with significant differences in ROS-related gene expression, clinical indicators, SNV mutations, functional pathways, and immune infiltration. The response to specific chemotherapy drugs and immunotherapy treatments also documented significant differences. The prognostic model constructed with 16 genes linked to survival could efficiently divide patients into high- and low-risk groups. The high-risk group showed a poorer prognosis, higher tumor purity, distinct immune microenvironment, and lower immunotherapy response. RT-qPCR results showed that LCE3D, CA1, PIRT and SMR3A are highly expressed in breast cancer.

CONCLUSION

Our methodical examination presented an enhanced insight into the molecular and immunological heterogeneity of breast cancer. It can contribute to the understanding of prognosis and offer valuable insights for personalized treatment strategies. Further, the prognostic model can potentially serve as a powerful tool for risk stratification and therapeutic decision-making in clinical settings.

Txnrd1 as a prognosticator for recurrence, metastasis and response to neoadjuvant chemotherapy and radiotherapy in breast cancer patients

Thioredoxin reductase 1 (Txnrd1) is known to have prognostic significance in a subset of breast cancer patients. Despite the pivotal role of Txnrd1 in regulating several cellular and physiological processes in cancer progression and metastasis, its clinical significance is largely unrecognized. Here, we undertook a retrospective comprehensive meta-analysis of 13,322 breast cancer patients from 43 independent cohorts to assess prognostic and predictive roles of Txnrd1. We observed that Txnrd1 has a positive correlation with tumor grade and size and it is over-expressed in higher-grade and larger tumors. Further, hormone receptor-negative and HER2-positive tumors exhibit elevated Txnrd1 gene expression. Patients with elevated Txnrd1 expression exhibit significant hazards for shorter disease-specific and overall survival. While Txnrd1 has a positive correlation with tumor recurrence and metastasis, it has a negative correlation with time to recurrence and metastasis. Txnrd1High patients exhibit 2.5 years early recurrence and 1.3 years early metastasis as compared to Txnrd1Low cohort. Interestingly, patients with high Txnrd1 gene expression exhibit a pathologic complete response (pCR) to neoadjuvant chemotherapy, but they experience early recurrence after radiotherapy. Txnrd1High MDA-MB-231 cells exhibit significant ROS generation and reduced viability after doxorubicin treatment compared to Txnrd1Low MCF7 cells. Corroborating with findings from meta-analysis, Txnrd1 depletion leads to decreased survival, enhanced sensitivity to radiation induced killing, poor scratch-wound healing, and reduced invasion potential in MDA-MB-231 cells. Thus, Txnrd1 appears to be a potential predictor of recurrence, metastasis and therapy response in breast cancer patients.

Hi-C profiling in tissues reveals 3D chromatin-regulated breast tumor heterogeneity and tumor-specific looping-mediated biological pathways

Current knowledge in three-dimensional (3D) chromatin regulation in normal and disease states was mostly accumulated through Hi-C profiling in in vitro cell culture system. The limitations include failing to recapitulate disease-specific physiological properties and often lacking clinically relevant disease microenvironment. In this study, we conduct tissue-specific Hi-C profiling in a pilot cohort of 12 breast tissues comprising of two normal tissues (NTs) and ten ER+ breast tumor tissues (TTs) including five primary tumors (PTs), and five tamoxifen-treated recurrent tumors (RTs). We find largely preserved compartments, highly heterogeneous topological associated domains (TADs) and intensively variable chromatin loops among breast tumors, demonstrating 3D chromatin-regulated breast tumor heterogeneity. Further cross-examination identifies RT-specific looping-mediated biological pathways and suggests CA2, an enhancer-promoter looping (EPL)-mediated target gene within the bicarbonate transport metabolism pathway, might play a role in driving the tamoxifen resistance. Remarkably, the inhibition of CA2 not only impedes tumor growth both in vitro and in vivo , but also reverses chromatin looping. Our study thus yields significant mechanistic insights into the role and clinical relevance of 3D chromatin architecture in breast cancer endocrine resistance.

MCM2 is involved in subtyping and tamoxifen resistance of ERα-positive breast cancer by acting as the downstream factor of ERα

Tamoxifen (TAM) resistance is finally developed in over 40% of patients with estrogen receptor α-positive breast cancer (ERα+ -BC), documenting that discovering new molecular subtype is needed to confer perception to the heterogeneity of ERα+ -BC. We obtained representative gene sets subtyping ERα+ -BC using gene set variation analysis (GSVA), non-negative matrix factorization (NMF), and COX regression methods on the basis of METABRIC, TCGA, and GEO databases. Furthermore, the risk score of ERα+ -BC subtyping was established using least absolute shrinkage and selection operator (LASSO) regression on the basis of genes in the representative gene sets, thereby generating the two subtypes of ERα+ -BC. We further found that minichromosome maintenance complex component 2 (MCM2) functioned as the hub gene subtyping ERα+ -BC using GO, KEGG, and MCODE. MCM2 expression was capable for specifically predicting 1-year overall survival (OS) of ERα+ -BC and correlated with T stage, AJCC stage, and tamoxifen (TAM) sensitivity of ERα+ -BC. The downregulation of MCM2 expression inhibited proliferation, migration, and invasion of TAM-resistant cells and promoted G0/G1 arrest. Altogether, tamoxifen resistance entails that MCM2 is a hub gene subtyping ERα+ -BC, providing a novel dimension for discovering a potential target of TAM-resistant BC.

MIRS: An AI scoring system for predicting the prognosis and therapy of breast cancer

Tumor-infiltrating immune cells (TIICs) and metastasis are crucial characteristics for tumorigenesis. However, the potential role of their combination in breast cancer (BRCA) remains elusive. Herein, on the basis of quantifying TIICs and tumor metastasis together, we established a precise prognostic scoring system named metastatic and immunogenomic risk score (MIRS) using a neural network model. MIRS showed better performance when compared with other published signatures. MIRS stratifies patients into a high risk subtype (MIRShigh) and a low risk subtype (MIRSlow). The MIRShigh patients exhibit significantly lower survival rate compared with MIRSlow patients (P < 0.0001 ), higher response to chemotherapy, but lower response to immunotherapy. Conversely, higher infiltration level of TIICs and significantly prolonged survival (P = 0.029 ) are observed in MIRSlow patients, indicating sensitive response in immunotherapy. This work presents a promising indicator to guide treatment options of the BRCA population and provides a predicted webtool that is almost universally applicable to BRCA patients.

Re-evaluation of publicly available gene-expression databases using machine-learning yields a maximum prognostic power in breast cancer

Gene expression signatures refer to patterns of gene activities and are used to classify different types of cancer, determine prognosis, and guide treatment decisions. Advancements in high-throughput technology and machine learning have led to improvements to predict a patient's prognosis for different cancer phenotypes. However, computational methods for analyzing signatures have not been used to evaluate their prognostic power. Contention remains on the utility of gene expression signatures for prognosis. The prevalent approaches include random signatures, expert knowledge, and machine learning to construct an improved signature. We unify these approaches to evaluate their prognostic power. Re-evaluation of publicly available gene-expression data from 8 databases with 9 machine-learning models revealed previously unreported results. Gene-expression signatures are confirmed to be useful in predicting a patient's prognosis. Convergent evidence from [Formula: see text] 10,000 signatures implicates a maximum prognostic power. By calculating the concordance index, which measures how well patients with different prognoses can be discriminated, we show that a signature can correctly discriminate patients' prognoses no more than 80% of the time. Additionally, we show that more than 50% of the potentially available information is still missing at this value. We surmise that an accurate prognosis must incorporate molecular, clinical, histological, and other complementary factors.

The benefits and pitfalls of machine learning for biomarker discovery

Prospects for the discovery of robust and reproducible biomarkers have improved considerably with the development of sensitive omics platforms that can enable measurement of biological molecules at an unprecedented scale. With technical barriers to success lowering, the challenge is now moving into the analytical domain. Genome-wide discovery presents a problem of scale and multiple testing as standard statistical methods struggle to distinguish signal from noise in increasingly complex biological systems. Machine learning and AI methods are good at finding answers in large datasets, but they have a tendency to overfit solutions. It may be possible to find a local answer or mechanism in a specific patient sample or small group of samples, but this may not generalise to wider patient populations due to the high likelihood of false discovery. The rise of explainable AI offers to improve the opportunity for true discovery by providing explanations for predictions that can be explored mechanistically before proceeding to costly and time-consuming validation studies. This review aims to introduce some of the basic concepts of machine learning and AI for biomarker discovery with a focus on post hoc explanation of predictions. To illustrate this, we consider how explainable AI has already been used successfully, and we explore a case study that applies AI to biomarker discovery in rheumatoid arthritis, demonstrating the accessibility of tools for AI and machine learning. We use this to illustrate and discuss some of the potential challenges and solutions that may enable AI to critically interrogate disease and response mechanisms.

ALDH1L2 regulation of formate, formyl-methionine, and ROS controls cancer cell migration and metastasis

Mitochondrial 10-formyltetrahydrofolate (10-formyl-THF) is utilized by three mitochondrial enzymes to produce formate for nucleotide synthesis, NADPH for antioxidant defense, and formyl-methionine (fMet) to initiate mitochondrial mRNA translation. One of these enzymes-aldehyde dehydrogenase 1 family member 2 (ALDH1L2)-produces NADPH by catabolizing 10-formyl-THF into CO2 and THF. Using breast cancer cell lines, we show that reduction of ALDH1L2 expression increases ROS levels and the production of both formate and fMet. Both depletion of ALDH1L2 and direct exposure to formate result in enhanced cancer cell migration that is dependent on the expression of the formyl-peptide receptor (FPR). In various tumor models, increased ALDH1L2 expression lowers formate and fMet accumulation and limits metastatic capacity, while human breast cancer samples show a consistent reduction of ALDH1L2 expression in metastases. Together, our data suggest that loss of ALDH1L2 can support metastatic progression by promoting formate and fMet production, resulting in enhanced FPR-dependent signaling.

Glutamine synthetase regulates the immune microenvironment and cancer development through the inflammatory pathway

Although adjuvant tamoxifen therapy is beneficial to estrogen receptor-positive (ER⁺) breast cancer patients, a significant number of patients still develop metastasis or undergo recurrence. Therefore, identifying novel diagnostic and prognostic biomarkers for these patients is urgently needed. Predictive markers and therapeutic strategies for tamoxifen-resistant ER⁺ breast cancer are not clear, and micro (mi)RNAs have recently become a focal research point in cancer studies owing to their regulation of gene expressions, metabolism, and many other physiological processes. Therefore, systematic investigation is required to understand the modulation of gene expression in tamoxifen-resistant patients. High-throughput technology uses a holistic approach to observe differences among expression profiles of thousands of genes, which provides a comprehensive level to extensively investigate functional genomics and biological processes. Through a bioinformatics analysis, we revealed that glutamine synthetase/glutamate-ammonia ligase (GLUL) might play essential roles in the recurrence of tamoxifen-resistant ER⁺ patients. GLUL increases intracellular glutamine usage via glutaminolysis, and further active metabolism-related downstream molecules in cancer cell. However, how GLUL regulates the tumor microenvironment for tamoxifen-resistant ER⁺ breast cancer remains unexplored. Analysis of MetaCore pathway database demonstrated that GLUL is involved in the cell cycle, immune response, interleukin (IL)-4-induced regulators of cell growth, differentiation, and metabolism-related pathways. Experimental data also confirmed that the knockdown of GLUL in breast cancer cell lines decreased cell proliferation and influenced expressions of specific downstream molecules. Through a Connectivity Map (CMap) analysis, we revealed that certain drugs/molecules, including omeprazole, methacholine chloride, ioversol, fulvestrant, difenidol, cycloserine, and MK-801, may serve as potential treatments for tamoxifen-resistant breast cancer patients. These drugs may be tested in combination with current therapies in tamoxifen-resistant breast cancer patients. Collectively, our study demonstrated the crucial roles of GLUL, which provide new targets for the treatment of tamoxifen-resistant breast cancer patients.

Early myeloid-derived suppressor cells accelerate epithelial-mesenchymal transition by downregulating ARID1A in luminal A breast cancer

Early myeloid-derived suppressor cells (eMDSCs) are a newly characterized subclass of MDSCs, which exhibit more potent immunosuppressive capacity than classical MDSCs. Previously, we found high eMDSCs infiltration was correlated with poor prognosis of breast cancer, though the regulatory mechanisms have not been fully understood. Here, we constructed a 21-gene signature to evaluate the status of eMDSCs infiltration within breast cancer tissues and found that highly infiltrated eMDSCs affected the prognosis of breast cancer patients, especially in luminal A subtype. We also found that eMDSCs promoted epithelial-mesenchymal transition (EMT) and accelerated cell migration and invasion in vitro. Meanwhile, eMDSCs significantly downregulated ARID1A expression in luminal A breast cancer, which was closely associated with EMT and was an important prognostic factor in breast cancer patients. Moreover, significant changes of EMT-related genes were detected in luminal A breast cancer cells after co-cultured with eMDSCs or ARID1A knock-down and overexpression of ARID1A significantly reversed this procedure. These results implied that eMDSCs might suppress the ARID1A expression to promote EMT in luminal A breast cancer cells, which might provide a new light on developing novel treatment regimens for relapsed luminal A breast cancer after conventional therapies.

Biomarker identification by reversing the learning mechanism of an autoencoder and recursive feature elimination

RNA-Seq has made significant contributions to various fields, particularly in cancer research. Recent studies on differential gene expression analysis and the discovery of novel cancer biomarkers have extensively used RNA-Seq data. New biomarker identification is essential for moving cancer research forward, and early cancer diagnosis improves patients' chances of recovery and increases life expectancy. There is an urgency and scope of improvement in both sections. In this paper, we developed an autoencoder-based biomarker identification method by reversing the learning mechanism of the trained encoders. We devised an explainable post hoc methodology for identifying influential genes with a high likelihood of becoming biomarkers. We applied recursive feature elimination to shorten the list further and presented a list of 17 potential biomarkers that are 99.93% accurate in identifying cancer types using support vector machine for the UCI gene expression cancer RNA-Seq dataset consisting of five cancerous tumor types. Our methodology outperforms all of the state-of-the-art methods, confirming the potential of the newly identified biomarkers as well as the efficacy of the biomarker identification procedure. Moreover, we have evaluated the performance of our methodology using six independent RNA-Seq gene expression datasets for several tasks, i.e., classification of tumors from non-tumors, detecting the origin of circulating tumor cells (CTCs), and predicting if metastasis occurs or not. Our methodology achieved stimulating results for these tasks as well. The source code of this project is available at https://github.com/fuad021/biomarker-identification.

Low expression of cytosolic NOTCH1 predicts poor prognosis of breast cancer patients

The incidence of breast cancer is increasing, and is one of the leading causes of cancer death worldwide. Dysregulation of NOTCH1 signaling is reported in breast cancer. In present study, bioinformatics was utilized to study the expression of NOTCH1 gene in breast cancer from public databases, including the Kaplan-Meier Plotter, PrognoScan, Human Protein Atlas, and cBioPortal. The relationship between NOTCH1 mRNA expression and survival of patients was inconsistent in public databases. In addition, we performed immunohistochemistry (IHC) staining of 135 specimens from our hospital. Lower cytoplasmic staining of NOTCH1 protein was correlated with cancer recurrence, bone metastasis, and a worse disease-free survival of patients, especially those with estrogen receptor-positive and human epidermal growth factor receptor 2-positive (HER2⁺) cancers. In TCGA breast cancer dataset, lower expression of NOTCH1 in breast cancer specimens was correlated with higher level of CCND1 (protein: cyclin D1). Decreased expression of NOTCH1 was correlated with lower level of CCNA1 (protein: cyclin A1), CCND2 (protein: cyclin D2), CCNE1 (protein: cyclin E1), CDK6 (protein: CDK6), and CDKN2C (protein: p18). In conclusion, NOTCH1 mRNA expression is not consistently correlated with clinical outcomes of breast cancer patients. Low cytoplasmic expression of NOTCH1 in IHC study is correlated with poor prognosis of breast cancer patients. Cytoplasmic localization of NOTCH1 protein failed to initial oncogenic signaling in present study. Expression of NOTCH1 mRNA was discordant with cell cycle-related genes. Regulation of NOTCH1 in breast cancer involves gene expression, protein localization and downstream signaling.

Targeting CDK7 reverses tamoxifen resistance through regulating stemness in ER+ breast cancer

BACKGROUND

Although tamoxifen is the mainstay endocrine therapy for estrogen receptor-positive (ER+) breast cancer patients, the emergence of tamoxifen resistance is still the major challenge that results in treatment failure. Tamoxifen is very effective in halting breast cancer cell proliferation; nonetheless, the ability of tamoxifen to target cancer stem and progenitor cell populations (CSCs), a major key player for the emergence of tamoxifen resistance, has not been adequately investigated yet. Thus, we explored whether targeting CDK7 modulates CSCs subpopulation and tamoxifen resistance in ER+ breast cancer cells.

METHODS

Mammosphere-formation assay, stem cell biomarkers and tamoxifen sensitivity were analyzed in MCF7 tamoxifen-sensitive cell line and its resistant counterpart, LCC2, following CDK7 targeting by THZ1 or siRNA.

RESULTS

Analysis of clinically relevant data indicated that expression of stemness factor, SOX2, was positively correlated with CDK7 expression in tamoxifen-treated patients. Moreover, overexpression of the stemness gene, SOX2, was associated with shorter overall survival in those patients. Importantly, the number of CSC populations and the expression of CDK7, P-Ser118-ER-α and c-MYC were significantly higher in LCC2 cells compared with parental MCF-7 cells. Moreover, targeting CDK7 inhibited mammosphere formation, CSC-regulating genes, and CSC biomarkers expression in MCF-7 and LCC2 cells.

CONCLUSION

Our data indicate, for the first time, that CDK7-targeted therapy in ER+ breast cancer ameliorates tamoxifen resistance, at least in part, by inhibiting cancer stemness. Thus, targeting CDK7 might represent a potential approach for relieving tamoxifen resistance in ER+ breast cancer.

JMJD6 orchestrates a transcriptional program in favor of endocrine resistance in ER+ breast cancer cells

High expression of Jumonji domain containing protein 6 (JMJD6) is strongly associated with poor prognosis in estrogen receptor positive (ER+) breast cancer. We overexpressed JMJD6 in MCF7 cells (JOE cells) and performed RNA-seq analysis. 76% of differentially expressed genes (DEGs) overlapped with ER target genes. Pathway analysis revealed that JMJD6 upregulated a larger subset of genes related to cell proliferation as compared to ER. Interestingly, JOE cells showed a decrease in ER target gene expression prompting us to check ER levels. Indeed, JOE cells showed a significant decrease in both ESR1 and ER levels and JMJD6 siRNA transfection increased the expression of both. Additionally, JOE cells showed increased RET and ERK1 expression, events associated with resistance to endocrine therapy. Accordingly, JOE cells displayed lower sensitivity and survived better at higher doses of 4-hydroxy tamoxifen (Tam) as compared to parental MCF-7 cells. Conversely, LTED-I and TAM R that resist Tam induced death, showed high expression of JMJD6. Further, JMJD6 siRNA treatment decreased growth and improved Tam sensitivity in TAM R. Comparison of JOE DEGs with known Tam signature genes showed a substantial overlap. Overall, these data suggest that blocking ER alone in patients may not eradicate proliferation of JMJD6 expressing ER+ cells and JMJD6 may predispose and sustain endocrine therapy resistance. We propose that immunostaining for JMJD6 could be developed as a potential marker for predicting endocrine therapy resistance. Further, antagonizing JMJD6 action in women expressing higher amounts of this protein, may offer a greater clinical benefit than endocrine therapy.

Association Between RSK2 and Clinical Indexes of Primary Breast Cancer: A Meta-Analysis Based on mRNA Microarray Data

Background: Although ribosomal protein S6 kinases, 90 kDa, polypeptide 3 (RSK2, RPS6KA3) has been reported to play an important role in cancer cell proliferation, invasion, and migration, including breast cancer, its clinical implication in primary breast cancer patients is not well understood, and there were not many studies to explore the relationship between RSK2 and breast cancer on a clinical level.

Methods: A systematic series matrix file search uploaded from January 1, 2008 to November 31, 2017 was undertaken using ArrayExpress and Gene Expression Omnibus (GEO) databases. Search filters were breast cancer, RNA assay, and array assay. Files eligible for inclusion met the following criteria: a) sample capacity is over 100, b) tumor sample comes from unselected patient’s primary breast tumor tissue, and c) expression of RSK2 and any clinical parameters of patients were available from the files. We use median as the cutoff value to assess the association between the expression of RSK2 and the clinical indexes of breast cancer patients.

Finding: The meta-analysis identified 13 series matrix files from GEO database involving 3,122 samples that come from patients’ primary breast cancer tissue or normal tissue. The expression of RSK2 in tumor tissues is lower than that in normal tissues [odds ratio (OR), 0.54; 95% credible interval (CI), 0.44–0.67; Cochran’s Q test p = 0.14; I² = 41.7%]. Patients with a high expression of RSK2 showed more favorable overall survival [hazard ratio (HR), 0.71; 95% CI, 0.49–0.94; Cochran’s Q test p = 0.95; I² = 0.0%] and less potential of distant metastasis (OR, 0.59; 95% CI, 0.41–0.87; Cochran’s Q test p = 0.88; I² = 0.0%) and lymph node infiltration (OR, 0.81; 95% CI, 0.65–0.998; Cochran’s Q test p = 0.09; I² = 42.8%). Besides, the expression of RSK2 in luminal breast cancer is lower than Cochran’s Q test p = 0.06; I² = 63.5%). RSK2 overexpression corresponded with higher histological grade (OR, 1.329; 95% CI, 1.03–1.721; Cochran’s Q test p = 0.69; I² = 0.0%). RSK2 expression is also associated with estrogen receptor (ER) and age.

Conclusion: The meta-analysis provides evidence that RSK2 is a potential biomarker in breast cancer patients. The expression of RSK2 is distinctive in different intrinsic subtypes of breast cancer, indicating that it may play an important role in specific breast cancer. Further study is needed to uncover the mechanism of RSK2 in breast cancer.

Systematic Review Registration: (website), identifier (registration number).

The Oncogenic Role and Immune Infiltration for CARM1 Identified by Pancancer Analysis

Chromatin-modifying enzymes, especially protein arginine methyltransferases (PRMTs), have been identified as candidate targets for cancer. Cellular or animal-based evidence has suggested an association between coactivator-linked arginine methyltransferase 1 (CARM1) and cancer progression. However, the relationship between CARM1 and patient prognosis and immune infiltration in pancancer patients is unknown. On the basis of the GEO and TCGA databases, we first investigated the possible oncogenic functions of CARM1 in thirty-three tumor types. CARM1 expression was elevated in many types of tumors. In addition, there was a significant association between CARM1 expression and the survival rate of tumor patients. Uterine corpus endometrial carcinoma (UCES) samples had the highest CARM1 mutation frequency of all cancer types. In head and neck squamous cell carcinoma (HNSC) and lung squamous cell carcinoma (LUSC), CARM1 expression was associated with the level of CD8+ T cell infiltration, and cancer-associated fibroblast infiltration was also observed in other tumors including kidney renal papillary cell carcinoma (KIRC) and prostate adenocarcinoma (PRAD). CARM1 was involved in immune modulation and played an important role in the tumor microenvironment (TME). Furthermore, activities associated with RNA transport and its metabolism were included in the possible mechanisms of CARM1. Herein, our first pancancer research explores the oncogenic role of CARM1 in various tumors. CARM1 is associated with immune infiltrates and can be employed as a predictive biomarker in pancancer.

Unsupervised Learning Framework With Multidimensional Scaling in Predicting Epithelial-Mesenchymal Transitions

Clustering tumor metastasis samples from gene expression data at the whole genome level remains an arduous challenge, in particular, when the number of experimental samples is small and the number of genes is huge. We focus on the prediction of the epithelial-mesenchymal transition (EMT), which is an underlying mechanism of tumor metastasis, here, rather than tumor metastasis itself, to avoid confounding effects of uncertainties derived from various factors. In this paper, we propose a novel model in predicting EMT based on multidimensional scaling (MDS) strategies and integrating entropy and random matrix detection strategies to determine the optimal reduced number of dimension in low dimensional space. We verified our proposed model with the gene expression data for EMT samples of breast cancer and the experimental results demonstrated the superiority over state-of-the-art clustering methods. Furthermore, we developed a novel feature extraction method for selecting the significant genes and predicting the tumor metastasis. The source code is available at "https://github.com/yushanqiu/yushan.qiu-szu.edu.cn".

Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression-Morphology Analysis in Breast Cancer

Molecular profiling is central in cancer precision medicine but remains costly and is based on tumor average profiles. Morphologic patterns observable in histopathology sections from tumors are determined by the underlying molecular phenotype and therefore have the potential to be exploited for prediction of molecular phenotypes. We report here the first transcriptome-wide expression-morphology (EMO) analysis in breast cancer, where individual deep convolutional neural networks were optimized and validated for prediction of mRNA expression in 17,695 genes from hematoxylin and eosin-stained whole slide images. Predicted expressions in 9,334 (52.75%) genes were significantly associated with RNA sequencing estimates. We also demonstrated successful prediction of an mRNA-based proliferation score with established clinical value. The results were validated in independent internal and external test datasets. Predicted spatial intratumor variabilities in expression were validated through spatial transcriptomics profiling. These results suggest that EMO provides a cost-efficient and scalable approach to predict both tumor average and intratumor spatial expression from histopathology images. SIGNIFICANCE: Transcriptome-wide expression morphology deep learning analysis enables prediction of mRNA expression and proliferation markers from routine histopathology whole slide images in breast cancer.

A pan-cancer analysis of the prognostic and immunological role of β-actin (ACTB) in human cancers

Beta-actin (ACTB), a highly conserved cytoskeleton structural protein, has been regarded as a common housekeep gene and used as a reference gene for years. However, accumulating evidence indicates that ACTB is abnormally expressed in multiple cancers and hence changes the cytoskeleton to affect the invasiveness and metastasis of tumors. This study aimed to investigate the function and clinical significance of ACTB in pan-cancer. The role of ACTB for prognosis and immune regulation across 33 tumors was explored based on the datasets of gene expression omnibus and the cancer genome atlas. Differential expression of ACTB was found between cancer and adjacent normal tissues, and significant associations was found between ACTB expression and prognosis of tumor patients. In most cancers, ACTB expression was associated with immune cells infiltration, immune checkpoints and other immune modulators. Relevance between ACTB and metastasis and invasion was identified in various types of cancers by CancerSEA. Moreover, focal adhesion and actin regulation-associated pathways were included in the functional mechanisms of ACTB. The expression of ACTB was verified by quantitative real-time polymerase chain reaction. Knockdown of ACTB inhibited head and neck squamous carcinoma cell migration and invasion by NF-κB and Wnt/β-catenin pathways. Our first pan-cancer study of ACTB offers insight into the prognostic and immunological roles of ACTB across different tumors, indicating ACTB may be a potential biomarker for poor prognosis and immune infiltration in cancers, and the role of ACTB as a reference gene in cancers was challenged.

A review of the endocrine resistance in hormone-positive breast cancer

Hormone-positive breast cancer (BC) is a unique heterogeneous disease with a favorable prognosis compared to other types of breast cancer. As tumor biology influences the prognosis and clinical treatment, a deep understanding of how the molecular mechanisms regulate hormone sensitivity or resistance is critical in improving the efficacy and overcoming the endocrine resistance. This article comprehensively reviews the endocrine resistance in hormone-positive BC from a molecular and genetic perspective, encompassing the updated treatment and developing direction. This review includes the mechanisms of hormone resistance, which vary from epigenetic changes, crosstalk between signaling networks, cell cycle aberrance, and even change in the tumor microenvironment (TME) or stem cell. These mechanisms may contribute to treatment resistance. Current targeted therapy for hormone-resistant tumors includes PI3K/AKT/mTOR and cdk4/6 inhibitors. Several relevant pathways, biomarkers, and predictor genes have also been identified. Immunotherapy so far has a relatively less crucial role in hormone-positive than in triple-negative BC. Furthermore, the methodology to identify the PDL1 is not standardized. In a molecule and gene study, next-generation sequencing with circulating tumor DNA (ctDNA) has recently appeared as a sensitive and minimally invasive tool worth investigating.

Identification of genes and pathways related to breast cancer metastasis in an integrated cohort

BACKGROUND

Breast cancer is the most common malignant disease in women. Metastasis is the most common cause of death from this cancer. Screening genes related to breast cancer metastasis may help elucidate the mechanisms governing metastasis and identify molecular targets for antimetastatic therapy. The development of advanced algorithms enables us to perform cross-study analysis to improve the robustness of the results.

MATERIALS AND METHODS

Ten data sets meeting our criteria for differential expression analyses were obtained from the Gene Expression Omnibus (GEO) database. Among these data sets, five based on the same platform were formed into a large cohort using the XPN algorithm. Differentially expressed genes (DEGs) associated with breast cancer metastasis were identified using the differential expression via distance synthesis (DEDS) algorithm. A cross-platform method was employed to verify these DEGs in all ten selected data sets. The top 50 validated DEGs are represented with heat maps. Based on the validated DEGs, Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Protein interaction (PPI) networks were constructed to further illustrate the direct and indirect associations among the DEGs. Survival analysis was performed to explore whether these genes can affect breast cancer patient prognosis.

RESULTS

A total of 817 DEGs were identified using the DEDS algorithm. Of these DEGs, 450 genes were validated by the second algorithm. Enriched KEGG pathway terms demonstrated that these 450 DEGs may be involved in the cell cycle and oocyte meiosis in addition to their functions in ECM-receptor interaction and protein digestion and absorption. PPI network analysis for the proteins encoded by the DEGs indicated that these genes may be primarily involved in the cell cycle and extracellular matrix. In particular, several genes played roles in multiple signalling pathways and were related to patient survival. These genes were also observed to be targetable in the CTD2 database.

CONCLUSIONS

Our study analysed multiple cross-platform data sets using two different algorithms, helping elucidate the molecular mechanisms and identify several potential therapeutic targets of metastatic breast cancer. In addition, several genes exhibited promise for applications in targeted therapy against metastasis in future research.

Targeting monoamine oxidase A-regulated tumor-associated macrophage polarization for cancer immunotherapy

Targeting tumor-associated macrophages (TAMs) is a promising strategy to modify the immunosuppressive tumor microenvironment and improve cancer immunotherapy. Monoamine oxidase A (MAO-A) is an enzyme best known for its function in the brain; small molecule MAO inhibitors (MAOIs) are clinically used for treating neurological disorders. Here we observe MAO-A induction in mouse and human TAMs. MAO-A-deficient mice exhibit decreased TAM immunosuppressive functions corresponding with enhanced antitumor immunity. MAOI treatment induces TAM reprogramming and suppresses tumor growth in preclinical mouse syngeneic and human xenograft tumor models. Combining MAOI and anti-PD-1 treatments results in synergistic tumor suppression. Clinical data correlation studies associate high intratumoral MAOA expression with poor patient survival in a broad range of cancers. We further demonstrate that MAO-A promotes TAM immunosuppressive polarization via upregulating oxidative stress. Together, these data identify MAO-A as a critical regulator of TAMs and support repurposing MAOIs for TAM reprogramming to improve cancer immunotherapy.

Cellular dormancy in minimal residual disease following targeted therapy

BACKGROUND

Breast cancer mortality is principally due to tumor recurrence, which can occur following extended periods of clinical remission that may last decades. While clinical latency has been postulated to reflect the ability of residual tumor cells to persist in a dormant state, this hypothesis remains unproven since little is known about the biology of these cells. Consequently, defining the properties of residual tumor cells is an essential goal with important clinical implications for preventing recurrence and improving cancer outcomes.

METHODS

To identify conserved features of residual tumor cells, we modeled minimal residual disease using inducible transgenic mouse models for HER2/neu and Wnt1-driven tumorigenesis that recapitulate cardinal features of human breast cancer progression, as well as human breast cancer cell xenografts subjected to targeted therapy. Fluorescence-activated cell sorting was used to isolate tumor cells from primary tumors, residual lesions following oncogene blockade, and recurrent tumors to analyze gene expression signatures and evaluate tumor-initiating cell properties.

RESULTS

We demonstrate that residual tumor cells surviving oncogenic pathway inhibition at both local and distant sites exist in a state of cellular dormancy, despite adequate vascularization and the absence of adaptive immunity, and retain the ability to re-enter the cell cycle and give rise to recurrent tumors after extended latency periods. Compared to primary or recurrent tumor cells, dormant residual tumor cells possess unique features that are conserved across mouse models for human breast cancer driven by different oncogenes, and express a gene signature that is strongly associated with recurrence-free survival in breast cancer patients and similar to that of tumor cells in which dormancy is induced by the microenvironment. Although residual tumor cells in both the HER2/neu and Wnt1 models are enriched for phenotypic features associated with tumor-initiating cells, limiting dilution experiments revealed that residual tumor cells are not enriched for cells capable of giving rise to primary tumors, but are enriched for cells capable of giving rise to recurrent tumors, suggesting that tumor-initiating populations underlying primary tumorigenesis may be distinct from those that give rise to recurrence following therapy.

CONCLUSIONS

Residual cancer cells surviving targeted therapy reside in a well-vascularized, desmoplastic microenvironment at both local and distant sites. These cells exist in a state of cellular dormancy that bears little resemblance to primary or recurrent tumor cells, but shares similarities with cells in which dormancy is induced by microenvironmental cues. Our observations suggest that dormancy may be a conserved response to targeted therapy independent of the oncogenic pathway inhibited or properties of the primary tumor, that the mechanisms underlying dormancy at local and distant sites may be related, and that the dormant state represents a potential therapeutic target for preventing cancer recurrence.

Development of a Gene-Based Prediction Model for Recurrence of Colorectal Cancer Using an Ensemble Learning Algorithm

It is difficult to determine which patients with stage I and II colorectal cancer are at high risk of recurrence, qualifying them to undergo adjuvant chemotherapy. In this study, we aimed to determine a gene signature using gene expression data that could successfully identify high risk of recurrence among stage I and II colorectal cancer patients. First, a synthetic minority oversampling technique was used to address the problem of imbalanced data due to rare recurrence events. We then applied a sequential workflow of three methods (significance analysis of microarrays, logistic regression, and recursive feature elimination) to identify genes differentially expressed between patients with and without recurrence. To stabilize the prediction algorithm, we repeated the above processes on 10 subsets by bagging the training data set and then used support vector machine methods to construct the prediction models. The final predictions were determined by majority voting. The 10 models, using 51 differentially expressed genes, successfully predicted a high risk of recurrence within 3 years in the training data set, with a sensitivity of 91.18%. For the validation data sets, the sensitivity of the prediction with samples from two other countries was 80.00% and 91.67%. These prediction models can potentially function as a tool to decide if adjuvant chemotherapy should be administered after surgery for patients with stage I and II colorectal cancer.

CC Chemokine Receptors in Lung Adenocarcinoma: The Inflammation-Related Prognostic Biomarkers and Immunotherapeutic Targets

Background

Lung adenocarcinoma (LUAD) is the most common type of lung cancer with a high incidence and increased mortality. CC chemokine receptors were participating in the modulation of the tumor microenvironment and involved in carcinogenesis and tumor development. However, the potential mechanistic values of CC chemokine receptors as clinical biomarkers and therapeutic targets in LUAD have not been fully clarified.

Methodology

ONCOMINE, UALCAN, GEPIA, Kaplan-Meier Plotter, SurvExpress, MethSurv, SurvivalMeth, cBioPortal, String, GeneMANIA, DAVID, Metascape, TRRUST, LinkedOmics, and Timer were applied in this work.

Results

The transcriptional levels of CCR1/10 in LUAD tissues were significantly reduced while the transcriptional levels of CCR3/6/7/8 were significantly elevated, and the expression of CCR1 was the highest in LUAD among these CC chemokine receptors. A significant correlation was found between the expression of CCR2/4/6/7 and the pathological stage of LUAD patients. There were significant associations between CCR2/3/4/5/6/10 expression levels and OS in LUAD, and LUAD patients with high transcriptional levels of CCR3/4 had inferior first-progression survival. In addition, the prognostic values of CC chemokine receptors signature in LUAD were explored in three independent cohorts, the high-risk group displayed unfavorable OS compared with the low-risk group, and the LUAD cases in the high-risk group also suffered inferior RFS than that in the low-risk group. And for the prognostic value of the DNA methylation of CC chemokine receptors, we found 1 CpG of CCR2, 2 CpGs of CCR3, 1 CpG of CCR4, 3 CpGs of CCR6, 3 CpGs of CCR7, 1 CpG of CCR8, and 3 CpGs of CCR9 were significantly associated with prognosis in LUAD patients. However, the DNA methylation signature analysis showed there was no statistically significant association between the high- and low-risk group. For potential mechanism, the neighbor gene networks, interaction analyses, functional enrichment analyses of CC chemokine receptors in LUAD were performed, the transcription factor targets, kinase targets, and miRNA targets of CC chemokine receptors were also identified in LUAD. We also found significant correlations among CC chemokine receptors expression and the infiltration of immune cells, the tumor infiltration levels among LUAD with different somatic copy number alterations of these chemokine receptors were also assessed. Moreover, the Cox proportional hazard model showed that CCR1/2/10, B_cell, CD4_Tcell were significantly related to the clinical outcome of LUAD patients.

Conclusion

CC chemokine receptors might serve as immunotherapeutic targets and prognostic biomarkers in LUAD.

LINC00483 Has a Potential Tumor-Suppressor Role in Colorectal Cancer Through Multiple Molecular Axes

Long non-coding RNAs (lncRNAs) are the most heterogeneous class of non-protein-coding RNAs involved in a broad spectrum of molecular mechanisms controlling genome function, including the generation of complex networks of RNA-RNA competitive interactions. Accordingly, their dysregulation contributes to the onset of many tumors, including colorectal cancer (CRC). Through a combination of in silico approaches (statistical screening of expression datasets) and in vitro analyses (enforced expression, artificial inhibition, or activation of pathways), we identified LINC00483 as a potential tumor suppressor lncRNA in CRC. LINC00483 was downregulated in CRC biopsies and metastases and its decreased levels were associated with severe clinical features. Inhibition of the MAPK pathway and cell cycle arrest by starvation induced an upregulation of LINC00483, while the epithelial to mesenchymal transition activation by TGFβ-1 and IL-6 caused its down-modulation. Moreover, enforced expression of LINC00483 provoked a slowing down of cell migration rate without affecting cell proliferation. Since LINC00483 was predominantly cytoplasmic, we hypothesized a “miRNA sponge” role for it. Accordingly, we computationally reconstructed the LINC00483/miRNA/mRNA axes and evaluated the expression of mRNAs in different experimental conditions inducing LINC00483 alteration. By this approach, we identified a set of mRNAs sharing the miRNA response elements with LINC00483 and modulated in accordance with it. Moreover, we found that LINC00483 is potentially under negative control of transcription factor HNF4α. In conclusion, we propose that LINC00483 is a tumor suppressor in CRC that, through an RNA-RNA network, may control cell migration and participate in proliferation signaling.

FUT8 Alpha-(1,6)-Fucosyltransferase in Cancer

Aberrant glycosylation is a universal feature of cancer cells that can impact all steps in tumour progression from malignant transformation to metastasis and immune evasion. One key change in tumour glycosylation is altered core fucosylation. Core fucosylation is driven by fucosyltransferase 8 (FUT8), which catalyses the addition of α1,6-fucose to the innermost GlcNAc residue of N-glycans. FUT8 is frequently upregulated in cancer, and plays a critical role in immune evasion, antibody-dependent cellular cytotoxicity (ADCC), and the regulation of TGF-β, EGF, α3β1 integrin and E-Cadherin. Here, we summarise the role of FUT8 in various cancers (including lung, liver, colorectal, ovarian, prostate, breast, melanoma, thyroid, and pancreatic), discuss the potential mechanisms involved, and outline opportunities to exploit FUT8 as a critical factor in cancer therapeutics in the future.

An immune-related risk gene signature predicts the prognosis of breast cancer

BACKGROUND

Accurate prediction of the outcome of breast cancer remains as a challenge due to its heterogeneous nature. We aimed to construct an immune-related risk signature to predict the overall outcome of breast cancer using bioinformatic approaches.

METHODS

In this study, transcriptome and survival data obtained from The Cancer Genome Atlas database and the Gene Expression Omnibus database were used to identify differentially expressed genes between breast cancer and normal samples. A regulatory network was constructed based on the immune-related prognostic genes and transcription factors screened from the differently expressed genes. The immune-related risk gene signature was obtained using the least absolute shrinkage and selection operator (LASSO) method and Cox regression model. The immune-related prognostic scores of breast cancer (IPSBC) calculated from the risk signature were used to group breast cancer patients by risk levels. The accuracy of IPSBC was evaluated by survival analysis and receiver operating characteristic curve analysis. The independency and the relationship of IPSBC with clinicopathological characteristics and abundance of tumor-infiltrated immune cells were also investigated.

RESULTS

A total of 4296 differentially expressed genes between breast cancer and normal samples were identified, and a total of 13 prognostic immune-related genes were eventually selected as the risk gene signature, which was an independent prognostic factor of the overall survival of breast cancer. The IPSBC stratified breast cancer patients into low- and high-risk groups. Breast cancer patients in the high-risk group were associated with worse overall outcomes, more advanced stage and less abundance of tumor-infiltrated immune cells, including B cells, CD4⁺ T cells, CD8⁺ T cells, neutrophils, macrophages, and dendritic cells compared to low-risk group.

CONCLUSION

In this study, an immune-related gene signature of breast cancer was identified, which could be used as potential prognostic and therapeutic targets of breast cancer.

LINC00467, Driven by Copy Number Amplification and DNA Demethylation, Is Associated with Oxidative Lipid Metabolism and Immune Infiltration in Breast Cancer

Breast cancer (BRCA) is a malignant tumor with a high incidence and poor prognosis in females. However, its pathogenesis remains unclear. In this study, based on bioinformatic analysis, we found that LINC00467 was highly expressed in BRCA and was associated with tumor metastasis and poor prognosis. The genomic and epigenetic analysis showed that LINC00467 may also be regulated by copy number amplification (CNA), chromatin openness, and DNA methylation. In vitro experiments showed that it could promote the proliferation, migration, and invasion of BRCA cells. Competitive endogenous RNA (ceRNA) regulatory network analysis and weighted gene coexpression network analysis (WGCNA) suggested that LINC00467 may play a role in signaling pathways of peroxisomal lipid metabolism, immunity, and others through microRNAs (miRNAs) targeting transforming growth factor beta 2 (TGFB2). In addition, copy number amplification and high expression of LINC00467 were associated with the low infiltration of CD8+ and CD4+ T cells. In conclusion, we found that LINC00467, driven by copy number amplification and DNA demethylation, may be a potential biomarker for the diagnosis and prognosis of BRCA and a tumor promoter acting as a potential therapeutic target for BRCA as well.

Prognostic and predictive value of Pleckstrin homology-like domain, family A family members in breast cancer

Aim: The PHLDA (pleckstrin homology like domain, family A) gene family encodes proteins capable of inhibiting AKT (serine/threonine kinase) signaling through phosphoinositol binding competition. Results & methodology: Using in silico analysis, we found that Luminal A and B patients' short relapse-free survival was associated with low PHLDA1 or PHLDA3 and high PHLDA2 expression. In a cohort of 393 patients with luminal breast cancer evaluated by immunohistochemistry on tissue microarrays, we found a direct association of PHLDA3 expression with hormonal therapy response (p = 0.013). Conclusion: Our findings provide new information on the role played by the PHLDA family members as prognostic markers in breast cancer, and more importantly, we provide evidence that they might also predict a response to endocrine therapy.

TRIM28 is a distinct prognostic biomarker that worsens the tumor immune microenvironment in lung adenocarcinoma

The tumor immune microenvironment (TIME) is an important determinant of cancer prognosis and treatment efficacy. To identify immune-related prognostic biomarkers of lung adenocarcinoma, we used the ESTIMATE algorithm to calculate the immune and stromal scores of 517 lung adenocarcinoma patients from The Cancer Genome Atlas (TCGA). We detected 985 differentially expressed genes (DEGs) between patients with high and low immune and stromal scores, and we analyzed their functions and protein-protein interactions. TRIM28 was upregulated in lung adenocarcinoma patients with low immune and stromal scores, and was associated with a poor prognosis. The TISIDB and TIMER databases indicated that TRIM28 expression correlated negatively with immune infiltration. We then explored genes that were co-expressed with TRIM28 in TCGA, and investigated DEGs based on TRIM28 expression in GSE43580 and GSE7670. The 429 common DEGs from these analyses were functionally analyzed. We also performed a Gene Set Enrichment Analysis using TCGA data, and predicted substrates of TRIM28 using UbiBrowser. The results indicated that TRIM28 may negatively regulate the TIME by increasing the SUMOylation of IRF5 and IRF8. Correlation analyses and validations in two lung adenocarcinoma cell lines (PC9 and H1299) confirmed these findings. Thus, TRIM28 may worsen the TIME and prognosis of lung adenocarcinoma.

Characterization of BRCA1-deficient premalignant tissues and cancers identifies Plekha5 as a tumor metastasis suppressor

Single-cell whole-exome sequencing (scWES) is a powerful approach for deciphering intratumor heterogeneity and identifying cancer drivers. So far, however, simultaneous analysis of single nucleotide variants (SNVs) and copy number variations (CNVs) of a single cell has been challenging. By analyzing SNVs and CNVs simultaneously in bulk and single cells of premalignant tissues and tumors from mouse and human BRCA1-associated breast cancers, we discover an evolution process through which the tumors initiate from cells with SNVs affecting driver genes in the premalignant stage and malignantly progress later via CNVs acquired in chromosome regions with cancer driver genes. These events occur randomly and hit many putative cancer drivers besides p53 to generate unique genetic and pathological features for each tumor. Upon this, we finally identify a tumor metastasis suppressor Plekha5, whose deficiency promotes cancer metastasis to the liver and/or lung.

Prognostic biomarkers related to breast cancer recurrence identified based on Logit model analysis

BACKGROUND

This study intended to determine important genes related to the prognosis and recurrence of breast cancer.

METHODS

Gene expression data of breast cancer patients were downloaded from TCGA database. Breast cancer samples with recurrence and death were defined as poor disease-free survival (DFS) group, while samples without recurrence and survival beyond 5 years were defined as better DFS group. Another gene expression profile dataset (GSE45725) of breast cancer was downloaded as the validation data. Differentially expressed genes (DEGs) were screened between better and poor DFS groups, which were then performed function enrichment analysis. The DEGs that were enriched in the GO function and KEGG signaling pathway were selected for cox regression analysis and Logit regression (LR) model analysis. Finally, correlation analysis between LR model classification and survival prognosis was analyzed.

RESULTS

Based on the breast cancer gene expression profile data in TCGA, 540 DEGs were screened between better DFS and poor DFS groups, including 177 downregulated and 363 upregulated DEGs. A total of 283 DEGs were involved in all GO functions and KEGG signaling pathways. Through LR model screening, 10 important feature DEGs were identified and validated, among which, ABCA3, CCL22, FOXJ1, IL1RN, KCNIP3, MAP2K6, and MRPL13, were significantly expressed in both groups in the two data sets. ABCA3, CCL22, FOXJ1, IL1RN, and MAP2K6 were good prognostic factors, while KCNIP3 and MRPL13 were poor prognostic factors.

CONCLUSION

ABCA3, CCL22, FOXJ1, IL1RN, and MAP2K6 may serve as good prognostic factors, while KCNIP3 and MRPL13 may be poor prognostic factors for the prognosis of breast cancer.

SUMOylation of E2F1 Regulates Expression of EZH2

Elevated expression of EZH2, the enzymatic subunit of polycomb repressive complex 2 (PRC2), often occurs in cancer. EZH2 expression results in the silencing of genes that suppress tumor formation and metastasis through trimethylation of histone H3 at lysine 27 (H3K27me3) at their promoters. However, inhibitors of EZH2 enzymatic activity have not shown the expected efficacy against cancer in clinical trials, suggesting a need for other strategies to address EZH2 overexpression. Here, we show that SUMOylation, a posttranslational modification characterized by covalent attachment of small ubiquitin-like modifier (SUMO) proteins to a lysine (Lys) residue on target proteins, enhances EZH2 transcription. Either knockdown of the SUMO-activating enzyme SAE2 or pharmacologic inhibition of SUMOylation resulted in decreased levels of EZH2 mRNA and protein as well as reduced H3K27me3 levels. SUMOylation regulated EZH2 expression by enhancing binding of the E2F1 transcriptional activator to the EZH2 promoter. Inhibition of SUMOylation not only resulted in reduced EZH2 mRNA and protein levels but also increased expression of genes silenced by EZH2, such as E-cadherin, which suppresses epithelial-mesenchymal transition and metastasis. In more than 6,500 patient tumor samples across different cancer types, expression of UBA2 and EZH2 was positively correlated. Taken together, our findings suggest that inhibition of SUMOylation may serve as a potential strategy to address EZH2 overexpression and improve current cancer therapeutic approaches. SIGNIFICANCE: These findings provide important biological insights into the mechanism of EZH2 overexpression in cancers and suggest that inhibiting SUMOylation may improve current cancer therapeutic approaches.

SRC Promotes Tamoxifen Resistance in Breast Cancer via Up-Regulating SIRT1

Background

Endocrine therapy plays a key role in estrogen receptor-positive breast cancer patients; but, tamoxifen resistance could be a real difficulty for these patients. Several attempts have been made to explore the mechanism and new therapies for these patients. We intend to clarify the expression change of SRC and SIRT1 in tamoxifen-resistant breast cancer cells and explore their functions on tamoxifen resistance.

Methods

SRC and SIRT1 expressions were analyzed by RNA sequencing, qPCR and Western blotting. Loss and gain of function of SRC and SIRT1 were utilized to indicate their oncogenic roles in tamoxifen resistance in vitro and in vivo. Kaplan–Meier analysis and receiver operating characteristic curve were used to evaluate the survival and the predicted effects of SRC and SIRT1 on patients’ prognosis.

Results

High expressions of SRC and/or SIRT1 were found in tamoxifen-resistant cells and related to poor overall survival (p<0.05 for SRC, p<0.001 for SIRT1, p<0.001 for SRC and SIRT1) and cancer-specific survival (p<0.05 for SRC, p<0.01 for SIRT1, p<0.01 for SRC and SIRT1) of tamoxifen-treated breast cancer patients. Down-regulation of SRC (p<0.01) or SIRT1 (p<0.05) separately reversed the resistance to tamoxifen and the minimal concentration of SRC inhibitor KX-01 (p<0.05) or SIRT1 inhibitor EX527 (p<0.001) could also suppress cell proliferation. The expression level of SIRT1 was positively correlated with that of SRC. Overexpression of SRC significantly promotes the cell resistance to tamoxifen inhibited by SIRT1 (p<0.01). In vivo experiments confirmed the effects of SRC on tumor growth by over- or down-regulating SRC expression (p<0.001 and p<0.001, respectively).

Conclusion

SRC and SIRT1 are both up-regulated in tamoxifen-resistant breast cancer cells and related to a poor prognosis in tamoxifen-treated breast cancer. Moreover, SRC could promote tamoxifen resistance by up-regulating SIRT1. SRC and SIRT1 might be novel therapeutic targets in tamoxifen-resistant breast cancer and the interaction between SRC and SIRT1 needs to be further explored.

Blockade of CDK7 Reverses Endocrine Therapy Resistance in Breast Cancer

Cyclin-dependent kinase (CDK)-7 inhibitors are emerging as promising drugs for the treatment of different types of cancer that show chemotherapy resistance. Evaluation of the effects of CDK7 inhibitor, THZ1, alone and combined with tamoxifen is of paramount importance. Thus, in the current work, we assessed the effects of THZ1 and/or tamoxifen in two estrogen receptor-positive (ER+) breast cancer cell lines (MCF7) and its tamoxifen resistant counterpart (LCC2) in vitro and in xenograft mouse models of breast cancer. Furthermore, we evaluated the expression of CDK7 in clinical samples from breast cancer patients. Cell viability, apoptosis, and genes involved in cell cycle regulation and tamoxifen resistance were determined. Tumor volume and weight, proliferation marker (Ki67), angiogenic marker (CD31), and apoptotic markers were assayed. Bioinformatic data indicated CDK7 expression was associated with negative prognosis, enhanced pro-oncogenic pathways, and decreased response to tamoxifen. Treatment with THZ1 enhanced tamoxifen-induced cytotoxicity, while it inhibited genes involved in tumor progression in MCF-7 and LCC2 cells. In vivo, THZ1 boosted the effect of tamoxifen on tumor weight and tumor volume, reduced Ki67 and CD31 expression, and increased apoptotic cell death. Our findings identify CDK7 as a possible therapeutic target for breast cancer whether it is sensitive or resistant to tamoxifen therapy.

Histone methyltransferases regulate the transcriptional expression of ERα and the proliferation of tamoxifen-resistant breast cancer cells

Purpose

Although tamoxifen remains the frontline treatment for ERα-positive breast cancers, resistance to this drug limits its clinical efficacy. Most tamoxifen-resistant patients retain ERα expression which may support growth and progression of breast cancers. Therefore, we investigated epigenetic regulation of ERα that may provide a rationale for targeting ERα in these patients.

Methods

Expression levels of the mixed-lineage leukemia (MLL) family of proteins in tamoxifen-resistant breast cancer cells and publicly available breast cancer patient data sets were analyzed. Histone methylation levels in ERα promoter regions were assessed using chromatin immunoprecipitation. Expression levels of ERα and its target gene were analyzed using western blotting and real-time qPCR. Cell-cycle was analyzed by flow cytometry.

Results

The expression of MLL3 and SET-domain-containing 1A (SET1A) were increased in tamoxifen-resistant breast cancers. An MLL3 chromatin immunoprecipitation-sequencing data analysis and chromatin immunoprecipitation experiments for MLL3 and SET1A suggested that these proteins bound to enhancer or intron regions of the ESR1 gene and regulated histone H3K4 methylation status. Depletion of MLL3 or SET1A downregulated the expression level of ERα and inhibited the growth of tamoxifen-resistant breast cancer cells. Additional treatment with fulvestrant resulted in a synergistic reduction of ERα levels and the growth of the cells.

Conclusions

The enhanced expression of MLL3 and SET1A in tamoxifen-resistant breast cancer cells supported the ERα-dependent growth of these cells by increasing ERα expression. Our results suggest that targeting these histone methyltransferases might provide an attractive strategy to overcome endocrine resistance.

Electronic supplementary material

The online version of this article (10.1007/s10549-019-05517-0) contains supplementary material, which is available to authorized users.

OSbrca: A Web Server for Breast Cancer Prognostic Biomarker Investigation With Massive Data From Tens of Cohorts

Potential prognostic mRNA biomarkers are exploited to assist in the clinical management and treatment of breast cancer, which is the first life-threatening tumor in women worldwide. However, it is technically challenging for untrained researchers to process high dimensional profiling data to screen and validate the potential prognostic values of genes of interests in multiple cohorts. Our aim is to develop an easy-to-use web server to facilitate the screening, developing, and evaluating of prognostic biomarkers in breast cancers. Herein, we collected more than 7,400 cases of breast cancer with gene expression profiles and clinical follow-up information from The Cancer Genome Atlas and Gene Expression Omnibus data, and built an Online consensus Survival analysis web server for Breast Cancers, abbreviated OSbrca, to generate the Kaplan–Meier survival plot with a hazard ratio and log rank P-value for given genes in an interactive way. To examine the performance of OSbrca, the prognostic potency of 128 previously published biomarkers of breast cancer was reassessed in OSbrca. In conclusion, it is highly valuable for biologists and clinicians to perform the preliminary assessment and validation of novel or putative prognostic biomarkers for breast cancers. OSbrca could be accessed at http://bioinfo.henu.edu.cn/BRCA/BRCAList.jsp.

Cathepsin L interacts with CDK2-AP1 as a potential predictor of prognosis in patients with breast cancer

Cathepsin L (CTSL) is a lysosomal acid cysteine protease that has been implicated in tumorigenesis and malignant progression. In the present study, the role of CTSL in tumorigenesis and prognosis of breast cancer was evaluated. The prognostic value of CTSL was analyzed using immunohistochemistry in patients with breast cancer, as well as online microarray datasets. CTSL expression was knocked down in the breast cancer cell line T-47D using RNA interference. MTT and colony formation assays were performed to assess the role of CTSL in the proliferation of breast cancer cells. Cell cycle progression and apoptosis were measured using flow cytometry. A physical interaction of CTSL and cyclin dependent kinase 2 associated protein 1 (CDK2-AP1) was determined using a glutathione S-transferase pull-down assay. Endogenous CTSL expression was high in breast cancer cells and exhibited an inverse association with CDK2-AP1 expression; aberrant expression of CTSL in breast cancer tissues predicted an improved clinical outcome and prognosis. In addition, CTSL knockdown decelerated the progression of breast cancer cells by arresting cell cycle progression and increasing apoptosis. Thus, CTSL may be a potential therapeutic target for treating patients with breast cancer.

Label propagation defines signaling networks associated with recurrently mutated cancer genes

Human tumors have distinct profiles of genomic alterations, and each of these alterations has the potential to cause unique changes to cellular homeostasis. Detailed analyses of these changes could reveal downstream effects of genomic alterations, contributing to our understanding of their roles in tumor development and progression. Across a range of tumor types, including bladder, lung, and endometrial carcinoma, we determined genes that are frequently altered in The Cancer Genome Atlas patient populations, then examined the effects of these alterations on signaling and regulatory pathways. To achieve this, we used a label propagation-based methodology to generate networks from gene expression signatures associated with defined mutations. Individual networks offered a large-scale view of signaling changes represented by gene signatures, which in turn reflected the scope of molecular events that are perturbed in the presence of a given genomic alteration. Comparing different networks to one another revealed common biological pathways impacted by distinct genomic alterations, highlighting the concept that tumors can dysregulate key pathways through multiple, seemingly unrelated mechanisms. Finally, altered genes inducing common changes to the signaling network were used to search for genomic markers of drug response, connecting shared perturbations to differential drug sensitivity.

Amplification of the CD24 Gene Is an Independent Predictor for Poor Prognosis of Breast Cancer

CD24 is a glycosyl-phosphatidyl-inositol linked glycoprotein expressed in a broad range of cell types including cancer cells. Although it is overexpressed in nearly 70% of human cancers, copy number variation of the CD24 locus has not been reported for any cancer. Here, we analyzed the genomics, transcriptomics, and clinical data of 1082 breast cancer (BRCA) samples and other cancer samples from the clinically annotated genomic database, The Cancer Genome Atlas (TCGA). The GISTIC2 method was applied to stratify the CD24 copy number, and Cox regression was performed to compare hazard ratio (HR) of CD24 overexpression, amplification and other traditional prognosis features for overall survival (OS). Our data demonstrated that CD24 amplification strongly correlated with its mRNA overexpression as well as TP53 mutant, cancer proliferation and metastasis features. In particular, CD24 amplification was enriched in basal-like subtype samples and associated with poor clinical outcome. Surprisingly, based on the univariate Cox regression analysis, CD24 overexpression (HR = 1.62, P = 0.010) and copy number amplification (HR = 1.79, P = 0.022) was more relevant to OS than TP53 mutant, mutation counts, diagnosis age, and BRCA subtypes. And based on multivariate survival analysis, CD24 amplification remained the most significant and independent predictor for worse OS (HR = 1.88, P = 0.015).

Parity predisposes breasts to the oncogenic action of PAPP-A and activation of the collagen receptor DDR2

Background

Women who had children at a young age (less than 25) show a reduced overall risk of breast cancer. However, epidemiological studies showed that for all other women, pregnancy increases the risk of breast cancer and the risk remains higher for decades. Further, even in women who had children at a young age, there is a transient increase risk that peaks 6 years after pregnancy. Women diagnosed with breast cancer following pregnancy show a higher rate of metastasis. Yet, the factors that increase the predisposition of post-partum breasts to more aggressive cancers remain unknown. Pregnancy-associated plasma protein A (PAPP-A) is a secreted protease that is overexpressed in more than 70% of breast cancers. However, PAPP-A is a collagen-dependent oncogene. We initiated this study to test the effect of PAPP-A on the predisposition of post-partum breasts.

Methods

We used PAPP-A mouse models for the analysis of its effect on virgin, involuting, or post-partum mammary glands. We performed second-harmonic generation microscopy for the analysis of collagen, defined tumor-associated collagen signature (TACS), the rate of mammary tumors, and the status of the collagen-DDR2-Snail axis of metastasis. We knockdown DDR2 by CRISPR and performed invasion assays. A transcriptomic approach was used to define a PAPP-A and parity-dependent genetic signature and assess its correlation with breast cancer recurrence in humans.

Results

We confirmed that post-partum mammary glands have a higher level of collagen than virgin glands and that this collagen is characterized by an anti-proliferative architecture. However, PAPP-A converts the anti-proliferative post-partum collagen into pro-tumorigenic collagen. We show that PAPP-A activates the collagen receptor DDR2 and metastasis. Further, deletion of DDR2 by CRISPR abolished the effect of PAPP-A on invasion. We defined a PAPP-A-driven genetic signature that identifies patients at higher risk of metastasis.

Conclusions

These results support the notion that information about pregnancy may be critical in the prognosis of breast cancer as passage through a single pregnancy predisposes to the oncogenic action of PAPP-A. Our data indicate that history of pregnancy combined with the expression of PAPP-A-driven genetic signature may be useful to identify patients at higher risk of metastatic disease.

Electronic supplementary material

The online version of this article (10.1186/s13058-019-1142-z) contains supplementary material, which is available to authorized users.

Transcriptional positive cofactor 4 promotes breast cancer proliferation and metastasis through c-Myc mediated Warburg effect

Background

The human positive cofactor 4 (PC4) is initially identified as a transcriptional cofactor and has an important role in embryonic development and malignant transformation. However, the clinical significance and the molecular mechanisms of PC4 in breast cancer development and progression are still unknown.

Methods

We investigated PC4 expression in 114 cases of primary breast cancer and matched normal breast tissue specimens, and studied the impact of PC4 expression as well as the molecular mechanisms of this altered expression on breast cancer growth and metastasis both in vitro and in vivo.

Results

PC4 was significantly upregulated in breast cancer and high PC4 expression was positively correlated with metastasis and poor prognosis of patients. Gene set enrichment analysis (GSEA) demonstrated that the gene sets of cell proliferation and Epithelial-Mesenchymal Transition (EMT) were positively correlated with elevated PC4 expression. Consistently, loss of PC4 markedly inhibited the growth and metastasis of breast cancer both in vitro and in vivo. Mechanistically, PC4 exerted its oncogenic functions by directly binding to c-Myc promoters and inducing Warburg effect.

Conclusions

Our study reveals for the first time that PC4 promotes breast cancer progression by directly regulating c-Myc transcription to promote Warburg effect, implying a novel therapeutic target for breast cancer.

Electronic supplementary material

The online version of this article (10.1186/s12964-019-0348-0) contains supplementary material, which is available to authorized users.

Cancer Target Gene Screening: a web application for breast cancer target gene screening using multi-omics data analysis

Breast cancer comprises several molecular subtypes with distinct clinical features and treatment responses, and a substantial portion of each subtype remains incurable. A comprehensive analysis of multi-omics data and clinical profiles is required in order to better understand the biological complexity of this cancer type and to identify new prognostic and therapeutic markers. Thus, there arises a need for useful analytical tools to assist in the investigation and clinical management of the disease. We developed Cancer Target Gene Screening (CTGS), a web application that provides rapid and user-friendly analysis of multi-omics data sets from a large number of primary breast tumors. It allows the investigation of genomic and epigenomic aberrations, evaluation of transcriptomic profiles and performance of survival analyses and of bivariate correlations between layers of omics data. Notably, the genome-wide screening function of CTGS prioritizes candidate genes of clinical and biological significance among genes with copy number alteration, DNA methylation and dysregulated expression by the integrative analysis of different types of omics data in customized subgroups of breast cancer patients. These features may help in the identification of druggable cancer driver genes in a specific subtype or the clinical condition of human breast cancer. CTGS is available at http://ctgs.biohackers.net.

Robust estimation in accelerated failure time models

The accelerated failure time model is widely used for analyzing censored survival times often observed in clinical studies. It is well-known that the ordinary maximum likelihood estimators of the parameters in the accelerated failure time model are generally sensitive to potential outliers or small deviations from the underlying distributional assumptions. In this paper, we propose and explore a robust method for fitting the accelerated failure time model to survival data by bounding the influence of outliers in both the outcome variable and associated covariates. We also develop a sandwich-type variance-covariance function for approximating the variances of the proposed robust estimators. The finite-sample properties of the estimators are investigated based on empirical results from an extensive simulation study. An application is provided using actual data from a clinical study of primary breast cancer patients.

Antineoplastic effects of selective CDK9 inhibition with atuveciclib on cancer stem-like cells in triple-negative breast cancer

Treatment options for triple-negative breast cancer (TNBC) are limited due to the lack of efficient targeted therapies, frequently resulting in recurrence and metastatic disease. Accumulating evidence suggests that a small population of cancer stem-like cells (CSLCs) is responsible for tumor recurrence and therapy resistance. Here we investigated the role of cyclin-dependent kinase 9 (CDK9) in TNBC. Using The Cancer Genome Atlas (TCGA) data we found high-CDK9 expression correlates with worse overall survival in TNBC patients. Pharmacologic inhibition of CDK9 with atuveciclib in high-CDK9 expressing TNBC cell lines reduced expression of CDK9 targets MYC and MCL1 and decreased cell proliferation and survival. Importantly, atuveciclib inhibited the growth of mammospheres and reduced the percentage of CD24^low/CD44^high cells, indicating disruption of breast CSLCs (BCSLCs). Furthermore, atuveciclib impaired 3D invasion of tumorspheres suggesting inhibition of both invasion and metastatic potential. Finally, atuveciclib enhanced the antineoplastic effects of Cisplatin and promoted inhibitory effects on BCSLCs grown as mammospheres. Together, these findings suggest CDK9 as a potential therapeutic target in aggressive forms of CDK9-high TNBC.

KDM5 Histone Demethylase Activity Links Cellular Transcriptomic Heterogeneity to Therapeutic Resistance

Members of the KDM5 histone H3 lysine 4 demethylase family are associated with therapeutic resistance, including endocrine resistance in breast cancer, but the underlying mechanism is poorly defined. Here we show that genetic deletion of KDM5A/B or inhibition of KDM5 activity increases sensitivity to anti-estrogens by modulating estrogen receptor (ER) signaling and by decreasing cellular transcriptomic heterogeneity. Higher KDM5B expression levels are associated with higher transcriptomic heterogeneity and poor prognosis in ER⁺ breast tumors. Single-cell RNA sequencing, cellular barcoding, and mathematical modeling demonstrate that endocrine resistance is due to selection for pre-existing genetically distinct cells, while KDM5 inhibitor resistance is acquired. Our findings highlight the importance of cellular phenotypic heterogeneity in therapeutic resistance and identify KDM5A/B as key regulators of this process.

The Expression of Hypoxia-Induced Gene 1 (Higd1a) in the Central Nervous System of Male and Female Rats Differs According to Age

HIGD1A (hypoxia-induced gene domain protein-1a), a mitochondrial inner membrane protein present in various cell types, has been mainly associated with anti-apoptotic processes in response to stressors. Our previous findings have shown that Higd1a mRNA is widely expressed across the central nervous system (CNS), exhibiting an increasing expression in the spinal cord from postnatal day 1 (P1) to 15 (P15) and changes in the distribution pattern from P1 to P90. During the first weeks of postnatal life, the great plasticity of the CNS is accompanied by cell death/survival decisions. So we first describe HIGD1A expression throughout the brain during early postnatal life in female and male pups. Secondly, based on the fact that in some areas this process is influenced by the sex of individuals, we explore HIGD1A expression in the sexual dimorphic nucleus (SDN) of the medial preoptic area, a region that is several folds larger in male than in female rats, partly due to sex differences in the process of apoptosis during this period. Immunohistochemical analysis revealed that HIGD1A is widely but unevenly expressed throughout the brain. Quantitative Western blot analysis of the parietal cortex, diencephalon, and spinal cord from both sexes at P1, P5, P8, and P15 showed that the expression of this protein is predominantly high and changes with age but not sex. Similarly, in the sexual dimorphic nucleus, the expression of HIGD1A varied according to age, but we were not able to detect significant differences in its expression according to sex. Altogether, these results suggest that HIGD1A protein is expressed in several areas of the central nervous system following a pattern that quantitatively changes with age but does not seem to change according to sex.