Low E2F1 transcript levels are a strong determinant of favorable breast cancer outcome

The functional role of circular RNAs in the pathogenesis of retinoblastoma: a new potential biomarker and therapeutic target?

Retinoblastoma (RB) is a common cancer in infants and children. It is a curable disease; however, a delayed diagnosis or treatment makes the treatment difficult. Genetic mutations have a central role in the pathogenesis of RB. Genetic materials such as RNAs (coding and non-coding RNAs) are also involved in the progression of the tumor. Circular RNA (circRNA) is the most recently identified RNA and is involved in regulating gene expression mainly through "microRNA sponges". The dysregulation of circRNAs has been observed in several diseases and tumors. Also, various studies have shown that circRNAs expression is changed in RB tissues. Due to their role in the pathogenesis of the disease, circRNAs might be helpful as a diagnostic or prognostic biomarker in patients with RB. In addition, circRNAs could be a suitable therapeutic target to treat RB in a targeted therapy approach.

Epigenetic alterations impede epithelial-mesenchymal transition by modulating centrosome amplification and Myc/RAS axis in triple negative breast cancer cells

Alterations in centrosome proteins may result in centrosome abnormalities such as disorganized spindles and centrosome amplification, leading to aneuploidy and genomic instability. Centrosomes exhibit unique epigenetic properties in which structural or positional information is propagated through somatic lineage by non-genetic pathways. Excessive centrosome amplification in breast cancer is accompanied by efficient clustering and loss of E-cadherin, indicating an important adaptive mechanism of cancer. This study sought to elucidate the effect of epigenetic alterations on centrosome amplification, epithelial-mesenchymal transition (EMT) and apoptosis in triple negative human breast adenocarcinoma derived MDA-MB-231 cell line. The results obtained here show that siRNA mediated silencing of DNMT1 and specific inhibition of HDAC1 & HDAC2 by Tricostatin A (TSA) synergistically inhibit cell proliferation through modulation of centrosome proteins γ-tubulin, TUBGCP2 and pericentrin. In addition, induction of apoptosis was observed by downregulation of Bcl2, upregulation of Bax and activation of PARP cleavage. Inhibition of EMT was confirmed through upregulation of E-cadherin and downregulation of N-cadherin and vimentin. Similarly, downregulation of Myc, RAS and CDK2, which plays important roles in proliferation and survival, was observed. Nuclear protein analysis revealed downregulation in the nuclear translocation of E2F1, which regulates centrosome amplification and metastasis in breast cancer. In conclusion, this study confirmed the role of epigenetic regulators in centrosome amplification and suggests that inhibition of DNA methylation and histone deacetylation-mediated chromatin remodelling synergistically disrupt EMT through modulation of centrosome amplification and Myc/RAS axis to potentiate apoptosis and attenuate cell proliferation in triple negative breast cancer cells.

An Insight into the Role of E2F1 in Breast Cancer Progression, Drug Resistance, and Metastasis

AIMS

This study aimed to investigate the role of E2F1 in breast cancer biology.

BACKGROUND

Expression of E2F1, a transcription factor of many oncogenes and tumor suppressor genes, is lowered in several malignancies, including breast carcinoma.

OBJECTIVES

In the present study, we analyzed the status of E2F1 expression in association with diverse attributes of breast malignancy and its impact on cancer progression.

METHODS

For this purpose, we used various freely available online applications for gene enrichment, expression, and methylation analysis to extract mutation-based E2F1 map, to measure E2F1 drug sensitivity, and to determine E2F1 association with DNA damage response proteins.

RESULTS

Results revealed tissue-specific regulatory behavior of E2F1. Moreover, the key role of E2F1 in the promotion of metastasis, stem cell-mediated carcinogenesis, estrogen-mediated cell proliferation, and cellular defense system, has therefore highlighted it as a metaplastic marker and hot member of key resistome pathways.

CONCLUSION

The information thus generated can be employed for future implications in devising rational therapeutic strategies. Moreover, this study has provided a more detailed insight into the diagnostic and prognostic potential of E2F1.

KAT2A/E2F1 Promotes Cell Proliferation and Migration via Upregulating the Expression of UBE2C in Pan-Cancer

Various studies have shown that lysine acetyltransferase 2A (KAT2A), E2F transcription factor 1 (E2F1), and ubiquitin conjugating enzyme E2 C (UBE2C) genes regulated the proliferation and migration of tumor cells through regulating the cell cycle. However, there is a lack of in-depth and systematic research on their mechanisms of action. This study analyzed The Cancer Genome Atlas (TCGA) to screen potential candidate genes and the regulation network of KAT2A and E2F1 complex in pan-cancer. Quantitative real-time PCR (qRT-PCR) and Western blotting (WB), cell phenotype detection, immunofluorescence co-localization, chromatin immunoprecipitation assay (ChIP), and RNA-Seq techniques were used to explore the functional of a candidate gene, UBE2C. We found that the expression of these three genes was significantly higher in more than 10 tumor types compared to normal tissue. Moreover, UBE2C was mainly expressed in tumor cells, which highlighted the impacts of UBE2C as a specific therapeutic strategy. Moreover, KAT2A and E2F1 could promote cell proliferation and the migration of cancer cells by enhancing the expression of UBE2C. Mechanically, KAT2A was found to cooperate with E2F1 and be recruited by E2F1 to the UBE2C promoter for elevating the expression of UBE2C by increasing the acetylation level of H3K9.

Eugenol modulates genomic methylation and inactivates breast cancer-associated fibroblasts through E2F1-dependent downregulation of DNMT1/DNMT3A

Active cancer-associated fibroblasts (CAFs) are major components of the tumor microenvironment, which promote carcinogenesis and modulate response to therapy. Therefore, targeting these cells or reducing their paracrine pro-carcinogenic effects could be of great therapeutic value. To this end, we sought to investigate the effect of eugenol, a natural phenolic molecule, on active breast CAFs. We have shown that decitabine (5-Aza-2'-deoxycytidine, DAC) and eugenol inhibit the expression of the DNA methyltransferase genes DNMT1 and DNMT3A at both the protein and mRNA levels in breast CAF cells. While the effect of eugenol was persistent, DAC had only a transient inhibitory effect on the mRNA level of both DNMT genes. Furthermore, eugenol and DAC suppressed the invasive/migratory and proliferative potential of CAF cells as well as their paracrine pro-carcinogenic effects both in vitro and in humanized orthotopic tumor xenografts. Interestingly, these inhibitory effects of decitabine and eugenol were mediated through E2F1 downregulation. Indeed, ectopic expression of E2F1 upregulated both genes and attenuated the effects of eugenol. Additionally, we provide clear evidence that eugenol, like DAC, strongly modulates the methylation pattern in active CAF cells, through methylating several oncogenes and demethylating various important tumor suppressor genes, which affected their mRNA expression levels. Importantly, the E2F1 promoter was also hypermethylated and the gene downregulated in response to eugenol. Together, these findings show that the active features of breast CAF cells can be normalized through eugenol-dependent targeting of DNMT1/DNMT3A and the consequent modulation in gene methylation.

Boron phenyl alanine targeted ionic liquid decorated chitosan nanoparticles for mitoxantrone delivery to glioma cell line

Nanotechnology has revolutionized drug delivery in cancer treatment. In this study, novel efficient pH-responsive boron phenylalanine (BPA) targeted nanoparticles (NPs) based on ionic liquid modified chitosan have been introduced for selective mitoxantrone (MTO) delivery to the U87MG glioma cells. Urocanic acid (UA) and imidazolium (Im) based ionic liquids were used for structural modification simultaneously. The NPs were prepared by ionic gelation and fully characterized; the pH-responding and swelling index of NPs were studied carefully. The drug release was studied at a pH of 5.5 in comparison to the neutral state. Also, the cytotoxicity of loaded NPs was evaluated on U87MG glial cells, and cellular uptake was studied. The NPs were smaller than 250 nm, with a spherical pattern and acceptable uniformity with a zeta potential around +20 mV. The loading efficacy was about 85%, and most of the loaded MTO released at a pH of 5.5 after 48 h with a swelling-controlled mechanism. The NPs showed a relatively lower IC₅₀ than the free MTO, and the BPA-targeted NPs have lower IC₅₀ and better cellular uptake than non-targeted NPs in U87MG cells. More studies on this promising formula are on the way, and the results will be published soon.

Gypenoside LI arrests the cell cycle of breast cancer in G0/G1 phase by down-regulating E2F1

ETHNOPHARMACOLOGICAL RELEVANCE

Gynostemma pentaphyllum (Thunb.) Makino, a traditional medicine in China, has been widely used for the treatment of various diseases. Gypenoside LI (Gyp LI) is a major constituent from steamed G. pentaphyllum. Previous studies have shown that gypnenoside LI possess inhibitory effect on the growth of many cancer cells. However, its pharmacological effect in breast cancer and the mechanism have not been reported yet.

AIM OF THE STUDY

To investigate the anti-breast cancer activity of gypenoside LI and underlying mechanisms of gypenoside LI in MDA-MB-231 and MCF-7 cells.

MATERIAL/METHODS

The cytotoxicity of gypenoside LI was determined by MTT, colony-formation and three-dimensional spheroid assay. The migration, cell apoptosis and the cell cycle were investigated through cell morphology observation, flow cytometry analysis and key proteins detection. The anticancer mechanisms of gypenoside LI were detected by RNA sequencing (RNA-seq) and Gene Set Enrichment Analysis (GSEA) transcriptome analysis.

RESULTS

Gypenoside LI inhibited cell proliferation, migration, induced cell apoptosis and cell cycle arrest. Gypenoside LI arrested cell cycle at G0/G1 phase by regulating E2F1. It also inhibited tumor proliferation by regulating the expression of ERCC6L. Interestingly, we found that E2F1 siRNA also down-regulated the expression of ERCC6L. Gypenoside LI showed potential anti-breast cancer cells activity, especially on triple-negative breast cancer cells.

CONCLUSIONS

These data indicate that gypenoside LI could inhibit human breast cancer cells through inhibiting proliferation and migration, inducing apoptosis, arresting cell cycle at G0/G1 phase by regulating E2F1. It could be used as potential multi-target chemopreventive agents for cancer.

Quantum processor-inspired machine learning in the biomedical sciences

Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex molecular underpinnings of human disease from a genome-wide perspective. While the deluge of genomic information is expected to increase, a bottleneck in conventional high-performance computing is rapidly approaching. Inspired by recent advances in physical quantum processors, we evaluated several unconventional machine-learning (ML) strategies on actual human tumor data, namely “Ising-type” methods, whose objective function is formulated identical to simulated annealing and quantum annealing. We show the efficacy of multiple Ising-type ML algorithms for classification of multi-omics human cancer data from The Cancer Genome Atlas, comparing these classifiers to a variety of standard ML methods. Our results indicate that Ising-type ML offers superior classification performance with smaller training datasets, thus providing compelling empirical evidence for the potential future application of unconventional computing approaches in the biomedical sciences.

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Ising-type algorithms perform better with small datasets

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There is less overfitting with Ising-type algorithms

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Ising-type algorithms are amenable for drug discovery

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Ising-type algorithms are useful for assessment of drug efficacy

Advances in sequencing technology, leading to an ever-increasing volume and variety of data, present an opportunity to probe the molecular underpinnings of disease. Inspired in part by recent developments in physical quantum processors, we evaluated several Ising-type algorithms, which are relatively unused in the biomedical sciences, on actual human tumor data from The Cancer Genome Atlas. Our results show performance competitive with conventional machine-learning algorithms in classifying human cancer types and associated molecular subtypes when training with all available data; but perhaps more strikingly, the Ising-type algorithms demonstrate superior performance with smaller training datasets. This gain in performance suggests a tantalizing application for rare diseases or other clinical applications where the number of training samples may be quite small. In addition, the features extracted from the Ising-type algorithms have biological relevance.

miRGTF-net: Integrative miRNA-gene-TF network analysis reveals key drivers of breast cancer recurrence

Analysis of regulatory networks is a powerful framework for identification and quantification of intracellular interactions. We introduce miRGTF-net, a novel tool for construction of miRNA-gene-TF networks. We consider multiple transcriptional and post-transcriptional interaction types, including regulation of gene and miRNA expression by transcription factors, gene silencing by miRNAs, and co-expression of host genes with their intronic miRNAs. The underlying algorithm uses information on experimentally validated interactions as well as integrative miRNA/mRNA expression profiles in a given set of samples. The latter ensures simultaneous tissue-specificity and biological validity of interactions. We applied miRGTF-net to paired miRNA/mRNA-sequencing data of breast cancer samples from The Cancer Genome Atlas (TCGA). Together with topological analysis of the constructed network we showed that considered players can form reliable prognostic gene signatures for ER-positive breast cancer. A number of signatures demonstrated remarkably high accuracy on transcriptomic data obtained by both microarrays and RNA sequencing from several independent patient cohorts. Furthermore, an essential part of prognostic genes were identified as direct targets of transcription factor E2F1. The putative interplay between estrogen receptor alpha and E2F1 was suggested as a potential recurrence factor in patients treated with tamoxifen. Source codes of miRGTF-net are available at GitHub (https://github.com/s-a-nersisyan/miRGTF-net).

Regulation of breast cancer metastasis signaling by miRNAs

Despite the decline in death rate from breast cancer and recent advances in targeted therapies and combinations for the treatment of metastatic disease, metastatic breast cancer remains the second leading cause of cancer-associated death in U.S. women. The invasion-metastasis cascade involves a number of steps and multitudes of proteins and signaling molecules. The pathways include invasion, intravasation, circulation, extravasation, infiltration into a distant site to form a metastatic niche, and micrometastasis formation in a new environment. Each of these processes is regulated by changes in gene expression. Noncoding RNAs including microRNAs (miRNAs) are involved in breast cancer tumorigenesis, progression, and metastasis by post-transcriptional regulation of target gene expression. miRNAs can stimulate oncogenesis (oncomiRs), inhibit tumor growth (tumor suppressors or miRsupps), and regulate gene targets in metastasis (metastamiRs). The goal of this review is to summarize some of the key miRNAs that regulate genes and pathways involved in metastatic breast cancer with an emphasis on estrogen receptor α (ERα+) breast cancer. We reviewed the identity, regulation, human breast tumor expression, and reported prognostic significance of miRNAs that have been documented to directly target key genes in pathways, including epithelial-to-mesenchymal transition (EMT) contributing to the metastatic cascade. We critically evaluated the evidence for metastamiRs and their targets and miRNA regulation of metastasis suppressor genes in breast cancer progression and metastasis. It is clear that our understanding of miRNA regulation of targets in metastasis is incomplete.

LINC00511 Promotes Osteosarcoma Tumorigenesis and Invasiveness through the miR-185-3p/E2F1 Axis

Osteosarcoma is a malignant tumor that seriously threatens human health. Numerous studies have pointed out the potential of long noncoding RNAs (lncRNAs) as new therapeutic targets for various human cancers. Therefore, we mainly investigate whether there is a new type of lncRNA pathway involved in regulating the development of osteosarcoma. The present study shows the higher expression levels of LINC00511 correlates to a shorter overall survival and disease-free survival time in patients with sarcoma. It is significantly higher in the clinical samples of osteosarcoma patients than in normal adjacent cancer tissues. We used U373 and SW1353 osteosarcoma cells to determine the effect of lncRNA on osteosarcoma proliferation and invasion by knocking down LINC00511 compared with controls. The results showed that the LINC00511 knockdown significantly suppressed osteosarcoma cell growth and metastasis. To explore the mechanisms of LINC00511 in osteosarcoma, we tested whether LINC00511 could competitively stimulate miR-185-3p and regulate E2F1 as a ceRNA. The results showed that LINC00511 knockdown induced the increased level of miR-185-3p levels; however, miR-185-3p overexpression suppressed LINC00511 levels. In addition, the results also demonstrated that LINC00511 knockdown or miR-185-3p overexpression could reduce E2F1 levels in osteosarcoma cells. The dual-luciferase reporter assay verified the direct interaction between miR-185-3p and LINC00511 or E2F1. These results may offer an explanation of how the lncRNA affects the progression of osteosarcoma, and our study shows that LINC00511 can be a novel biomarker in osteosarcoma.

The E2F Pathway Score as a Predictive Biomarker of Response to Neoadjuvant Therapy in ER+/HER2- Breast Cancer

E2F transcription factors play critical roles in the cell cycle. Therefore, their activity is expected to reflect tumor aggressiveness and responsiveness to therapy. We scored 3905 tumors of nine breast cancer cohorts for this activity based on their gene expression for the Hallmark E2F targets gene set. As expected, tumors with a high score had an increased expression of cell proliferation-related genes. A high score was significantly associated with shorter patient survival, greater MKI67 expression, histological grade, stage, and genomic aberrations. Furthermore, metastatic tumors had higher E2F scores than the primary tumors from which they arose. Although tumors with a high score had greater infiltration by both pro- and anti-cancerous immune cells, they had an increased expression of immune checkpoint genes. Estrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative cancer with a high E2F score achieved a significantly higher pathological complete response (pCR) rate to neoadjuvant chemotherapy. The E2F score was significantly associated with the expression of cyclin-dependent kinase (CDK)-related genes and strongly correlated with sensitivity to CDK inhibition in cell lines. In conclusion, the E2F score is a marker of breast cancer aggressiveness and predicts the responsiveness of ER-positive/HER2-negative patients to neoadjuvant chemotherapy and possibly to CDK and immune checkpoint inhibitors.

Landscape perspectives of tumor, EMT, and development

A tumor is rarely fatal until becoming metastatic. Recent discoveries suggest that epithelial mesenchymal transition(EMT) is an important process which contributes to not only cancer metastasis but also increased stemness. Cancer cells with stem cell characteristics are called cancer stem cells (CSCs). We review recent efforts to quantify and delineate the relationship among EMT, CSC and tumor development. When the gene regulatory network is tightly regulated through the effectively fast regulatory binding, Cancer, Premalignant, Normal, CSC, stem cell (SC), Lesion and Hyperplasia states emerged. The corresponding landscape topography for all of these states can be quantified to a global way for uncovering the relationship among the tumor, metastasis, and development. On the other hand, phenotypic and functional heterogeneity is regarded as one of the greatest challenge in cancer treatment. Cancer and CSCs are heterogeneous and give rise to tumorigenic and non-tumorigenic cells during self-renewal, differentiation and epigenetic diversification. Further, if the gene regulatory network is weakly regulated through the effective slow regulatory binding (by DNA methylation or histone modification etc), multiple meta-stable states can emerge. This model can provide an epigenetic and physical rather than genetic and fixed origin of heterogeneity. Elucidating the origin of and dynamic nature of tumor cells will likely help better understand the cellular basis of therapeutic response, resistance, and relapse.

Drug Repositioning Inferred from E2F1-Coregulator Interactions Studies for the Prevention and Treatment of Metastatic Cancers

Metastasis management remains a long-standing challenge. High abundance of E2F1 triggers tumor progression by developing protein-protein interactions (PPI) with coregulators that enhance its potential to activate a network of prometastatic transcriptional targets.

Methods: To identify E2F1-coregulators, we integrated high-throughput Co-immunoprecipitation (IP)/mass spectometry, GST-pull-down assays, and structure modeling. Potential inhibitors of PPI discovered were found by bioinformatics-based pharmacophore modeling, and transcriptome profiling was conducted to screen for coregulated downstream targets. Expression and target gene regulation was validated using qRT-PCR, immunoblotting, chromatin IP, and luciferase assays. Finally, the impact of the E2F1-coregulator complex and its inhibiting drug on metastasis was investigated in vitro in different cancer entities and two mouse metastasis models.

Results: We unveiled that E2F1 forms coactivator complexes with metastasis-associated protein 1 (MTA1) which, in turn, is directly upregulated by E2F1. The E2F1:MTA1 complex potentiates hyaluronan synthase 2 (HAS2) expression, increases hyaluronan production and promotes cell motility. Disruption of this prometastatic E2F1:MTA1 interaction reduces hyaluronan synthesis and infiltration of tumor-associated macrophages in the tumor microenvironment, thereby suppressing metastasis. We further demonstrate that E2F1:MTA1 assembly is abrogated by small-molecule, FDA-approved drugs. Treatment of E2F1/MTA1-positive, highly aggressive, circulating melanoma cells and orthotopic pancreatic tumors with argatroban prevents metastasis and cancer relapses in vivo through perturbation of the E2F1:MTA1/HAS2 axis.

Conclusion: Our results propose argatroban as an innovative, E2F-coregulator-based, antimetastatic drug. Cancer patients with the infaust E2F1/MTA1/HAS2 signature will likely benefit from drug repositioning.

Expressions and prognostic values of the E2F transcription factors in human breast carcinoma

E2F transcription factors (E2Fs) are a family of transcription factors involved in cell proliferation, differentiation, and apoptosis. Their important roles in the development and metastasis of breast carcinoma (BC) have been discovered by previous in vitro and in vivo studies. Yet, expressions and distinct prognostic values of these eight E2Fs in human BC remain unclear in many respects. In this study, we aimed to reveal their roles in BC through analyzing the transcription and survival data of the E2Fs in BC patients from four online databases including ONCOMINE, Breast Cancer Gene-Expression Miner v4.1, cBioPortal for Cancer Genomics, and Kaplan–Meier Plotter. We found the overexpression of E2Fs in BC tissues compared with normal breast tissues, except for E2F4. Higher expression levels of E2Fs, except for E2F4 and E2F6, were associated with higher levels of Scarff–Bloom–Richardson grade of BC. Alterations of E2Fs were found to be significantly correlated with poorer overall survival of BC patients. Through plotting the survival curve in the Kaplan–Meier Plotter, it was found that higher mRNA levels of E2F1, E2F3, E2F7, and E2F8 were associated with poorer relapse-free survival in all BC patients, indicating that they are potential targets for individualized treatments of BC patients. Conversely, higher mRNA expression level of E2F4 predicted better RFS in BC patients, suggesting E2F4 as a new biomarker for BC prognosis. Considering currently available limited evidence, further studies need to be performed to investigate the roles of E2Fs in BC.

NEDD4-induced degradative ubiquitination of phosphatidylinositol 4-phosphate 5-kinase α and its implication in breast cancer cell proliferation

Phosphatidylinositol 4‐phosphate 5‐kinase (PIP5K) family members generate phosphatidylinositol 4,5‐bisphosphate (PIP2), a critical lipid regulator of diverse physiological processes. The PIP5K‐dependent PIP2 generation can also act upstream of the oncogenic phosphatidylinositol 3‐kinase (PI3K)/Akt pathway. Many studies have demonstrated various mechanisms of spatiotemporal regulation of PIP5K catalytic activity. However, there are few studies on regulation of PIP5K protein stability. Here, we examined potential regulation of PIP5Kα, a PIP5K isoform, via ubiquitin‐proteasome system, and its implication for breast cancer. Our results showed that the ubiquitin ligase NEDD4 (neural precursor cell expressed, developmentally down‐regulated gene 4) mediated ubiquitination and proteasomal degradation of PIP5Kα, consequently reducing plasma membrane PIP2 level. NEDD4 interacted with the C‐terminal region and ubiquitinated the N‐terminal lysine 88 in PIP5Kα. In addition, PIP5Kα gene disruption inhibited epidermal growth factor (EGF)‐induced Akt activation and caused significant proliferation defect in breast cancer cells. Notably, PIP5Kα K88R mutant that was resistant to NEDD4‐mediated ubiquitination and degradation showed more potentiating effects on Akt activation by EGF and cell proliferation than wild‐type PIP5Kα. Collectively, these results suggest that PIP5Kα is a novel degradative substrate of NEDD4 and that the PIP5Kα‐dependent PIP2 pool contributing to breast cancer cell proliferation through PI3K/Akt activation is negatively controlled by NEDD4.

ANP32E induces tumorigenesis of triple-negative breast cancer cells by upregulating E2F1

Triple‐negative breast cancer (TNBC) lacks expression of estrogen receptor (ER), progesterone receptor, and the HER2 receptor; it is highly proliferative and becomes the deadliest forms of breast cancer. Effective prognostic methods and therapeutic targets for TNBC are required to improve patient outcomes. Here, we report that acidic nuclear phosphoprotein 32 family member E (ANP32E), which promotes cell proliferation in mammalian development, is highly expressed in TNBC cells compared to other types of breast cancer. High expression of ANP32E correlates significantly with worse overall survival (OS; P < 0.001) and higher risks of disease recurrence (P < 0.001) in patients with TNBC. Univariate and multivariate Cox‐regression models show that ANP32E is an independent prognostic factor in TNBC. Furthermore, we discovered that ANP32E promotes tumor proliferation in vitro by inducing G1/S transition, and ANP32E inhibition suppresses tumor formation in vivo. By examining the expression of E2F1, cyclin E1, and cyclin E2, we discovered that ANP32E promotes the G1/S transition by transcriptionally inducing E2F1. Taken together, our study shows that ANP32E is an efficient prognostic marker, and it promotes the G1/S transition and induces tumorigenesis of TNBC cells by transcriptionally inducing E2F1.

Expression patterns of E2F transcription factors and their potential prognostic roles in breast cancer

E2Fs, as a family of pivotal transcription factors, have been implicated in multiple biological functions in human cancer; however, the expression and prognostic significance of E2Fs in breast cancer remains unknown. In the present study, the mRNA expression patterns of E2Fs in breast cancer were investigated with Oncomine and The Cancer Genome Atlas data. Prognostic values of E2Fs for patients with breast cancer were determined using the Kaplan-Meier plotter database. The results strongly indicated that E2F1, E2F2, E2F3, E2F5, E2F7 and E2F8 were overexpressed in patients with breast cancer, whereas E2F4 and E2F6 exhibited no expression difference between patients with cancer and healthy controls. In survival analyses, elevated E2F1, E2F3, E2F5, E2F7 and E2F8 expression levels were significantly associated with lower overall survival, relapse-free survival (RFS), distant metastasis-free survival (DMFS) or post-progression survival for patients with breast cancer. Furthermore, high expression of E2F4 indicated improved RFS but reduced DMFS. Subgroup analyses based on four clinicopathological factors further revealed that E2Fs were associated with the prognosis of patients with breast cancer in an estrogen receptor-, progesterone receptor-, human epidermal growth factor 2- and lymph node status-specific manner. These data indicated that E2Fs may serve as promising biomarkers and therapeutic targets for breast cancer.

Feature genes in metastatic breast cancer identified by MetaDE and SVM classifier methods

The aim of the present study was to investigate the feature genes in metastatic breast cancer samples. A total of 5 expression profiles of metastatic breast cancer samples were downloaded from the Gene Expression Omnibus database, which were then analyzed using the MetaQC and MetaDE packages in R language. The feature genes between metastasis and non‑metastasis samples were screened under the threshold of P<0.05. Based on the protein‑protein interactions (PPIs) in the Biological General Repository for Interaction Datasets, Human Protein Reference Database and Biomolecular Interaction Network Database, the PPI network of the feature genes was constructed. The feature genes identified by topological characteristics were then used for support vector machine (SVM) classifier training and verification. The accuracy of the SVM classifier was then evaluated using another independent dataset from The Cancer Genome Atlas database. Finally, function and pathway enrichment analyses for genes in the SVM classifier were performed. A total of 541 feature genes were identified between metastatic and non‑metastatic samples. The top 10 genes with the highest betweenness centrality values in the PPI network of feature genes were Nuclear RNA Export Factor 1, cyclin‑dependent kinase 2 (CDK2), myelocytomatosis proto‑oncogene protein (MYC), Cullin 5, SHC Adaptor Protein 1, Clathrin heavy chain, Nucleolin, WD repeat domain 1, proteasome 26S subunit non‑ATPase 2 and telomeric repeat binding factor 2. The cyclin‑dependent kinase inhibitor 1A (CDKN1A), E2F transcription factor 1 (E2F1), and MYC interacted with CDK2. The SVM classifier constructed by the top 30 feature genes was able to distinguish metastatic samples from non‑metastatic samples [correct rate, specificity, positive predictive value and negative predictive value >0.89; sensitivity >0.84; area under the receiver operating characteristic curve (AUROC) >0.96]. The verification of the SVM classifier in an independent dataset (35 metastatic samples and 143 non‑metastatic samples) revealed an accuracy of 94.38% and AUROC of 0.958. Cell cycle associated functions and pathways were the most significant terms of the 30 feature genes. A SVM classifier was constructed to assess the possibility of breast cancer metastasis, which presented high accuracy in several independent datasets. CDK2, CDKN1A, E2F1 and MYC were indicated as the potential feature genes in metastatic breast cancer.

p21 Restricts HIV-1 in Monocyte-Derived Dendritic Cells through the Reduction of Deoxynucleoside Triphosphate Biosynthesis and Regulation of SAMHD1 Antiviral Activity

HIV-1 infection of noncycling cells, such as dendritic cells (DCs), is impaired due to limited availability of deoxynucleoside triphosphates (dNTPs), which are needed for HIV-1 reverse transcription. The levels of dNTPs are tightly regulated during the cell cycle and depend on the balance between dNTP biosynthesis and degradation. SAMHD1 potently blocks HIV-1 replication in DCs, although the underlying mechanism is still unclear. SAMHD1 has been reported to be able to degrade dNTPs and viral nucleic acids, which may both hamper HIV-1 reverse transcription. The relative contribution of these activities may differ in cycling and noncycling cells. Here, we show that inhibition of HIV-1 replication in monocyte-derived DCs (MDDCs) is associated with an increased expression of p21cip1/waf, a cell cycle regulator that is involved in the differentiation and maturation of DCs. Induction of p21 in MDDCs decreases the pool of dNTPs and increases the antiviral active isoform of SAMHD1. Although both processes are complementary in inhibiting HIV-1 replication, the antiviral activity of SAMHD1 in our primary cell model appears to be, at least partially, independent of its dNTPase activity. The reduction in the pool of dNTPs in MDDCs appears rather mostly due to a p21-mediated suppression of several enzymes involved in dNTP synthesis (i.e., RNR2, TYMS, and TK-1). These results are important to better understand the interplay between HIV-1 and DCs and may inform the design of new therapeutic approaches to decrease viral dissemination and improve immune responses against HIV-1.IMPORTANCE DCs play a key role in the induction of immune responses against HIV. However, HIV has evolved ways to exploit these cells, facilitating immune evasion and virus dissemination. We have found that the expression of p21, a cyclin-dependent kinase inhibitor involved in cell cycle regulation and monocyte differentiation and maturation, potentially can contribute to the inhibition of HIV-1 replication in monocyte-derived DCs through multiple mechanisms. p21 decreased the size of the intracellular dNTP pool. In parallel, p21 prevented SAMHD1 phosphorylation and promoted SAMHD1 dNTPase-independent antiviral activity. Thus, induction of p21 resulted in conditions that allowed the effective inhibition of HIV-1 replication through complementary mechanisms. Overall, p21 appears to be a key regulator of HIV infection in myeloid cells.

Information theoretic sub-network mining characterizes breast cancer subtypes in terms of cancer core mechanisms

A breast cancer subtype classification scheme, PAM50, based on genetic information is widely accepted for clinical applications. On the other hands, experimental cancer biology studies have been successful in revealing the mechanisms of breast cancer and now the hallmarks of cancer have been determined to explain the core mechanisms of tumorigenesis. Thus, it is important to understand how the breast cancer subtypes are related to the cancer core mechanisms, but multiple studies are yet to address the hallmarks of breast cancer subtypes. Therefore, a new approach that can explain the differences among breast cancer subtypes in terms of cancer hallmarks is needed.

We developed an information theoretic sub-network mining algorithm, differentially expressed sub-network and pathway analysis (DeSPA), that retrieves tumor-related genes by mining a gene regulatory network (GRN) of transcription factors and miRNAs. With extensive experiments of the cancer genome atlas (TCGA) breast cancer sequencing data, we showed that our approach was able to select genes that belong to cancer core pathways such as DNA replication, cell cycle, p53 pathways while keeping the accuracy of breast cancer subtype classification comparable to that of PAM50. In addition, our method produces a regulatory network of TF, miRNA, and their target genes that distinguish breast cancer subtypes, which is confirmed by experimental studies in the literature.

Portraying breast cancers with long noncoding RNAs

Evidence is emerging that long noncoding RNAs (lncRNAs) may play a role in cancer development, but this role is not yet clear. We performed a genome-wide transcriptional survey to explore the lncRNA landscape across 995 breast tissue samples. We identified 215 lncRNAs whose genes are aberrantly expressed in breast tumors, as compared to normal samples. Unsupervised hierarchical clustering of breast tumors on the basis of their lncRNAs revealed four breast cancer subgroups that correlate tightly with PAM50-defined mRNA-based subtypes. Using multivariate analysis, we identified no less than 210 lncRNAs prognostic of clinical outcome. By analyzing the coexpression of lncRNA genes and protein-coding genes, we inferred potential functions of the 215 dysregulated lncRNAs. We then associated subtype-specific lncRNAs with key molecular processes involved in cancer. A correlation was observed, on the one hand, between luminal A-specific lncRNAs and the activation of phosphatidylinositol 3-kinase, fibroblast growth factor, and transforming growth factor-β pathways and, on the other hand, between basal-like-specific lncRNAs and the activation of epidermal growth factor receptor (EGFR)-dependent pathways and of the epithelial-to-mesenchymal transition. Finally, we showed that a specific lncRNA, which we called CYTOR, plays a role in breast cancer. We confirmed its predicted functions, showing that it regulates genes involved in the EGFR/mammalian target of rapamycin pathway and is required for cell proliferation, cell migration, and cytoskeleton organization. Overall, our work provides the most comprehensive analyses for lncRNA in breast cancers. Our findings suggest a wide range of biological functions associated with lncRNAs in breast cancer and provide a foundation for functional investigations that could lead to new therapeutic approaches.

The Prognostic Role and Relationship between E2F1 and SV40 in Diffuse Large B-Cell Lymphoma of Egyptian Patients

Diffuse large B-cell lymphoma (DLBCL) is the most common type of lymphomas worldwide. The pathogenesis of lymphomas is not yet well understood. SV40 induces malignant transformation by the large T-antigen (L-TAG) and promotes transformation by binding and inactivating p53 and pRb. L-TAG can bind pRb promoting the activation of the E2F1 transcription factor, thus inducing the expression of genes required for the entry to the S phase and leading to cell transformation. This immunohistochemical study was conducted to assess the prognostic role and relationship of SV40 L-TAG and E2F1 in diffuse large B-cell lymphoma (DLBCL) of Egyptian patients. This retrospective study was conducted on 105 tissue specimens including 20 follicular hyperplasia and 85 DLBCL cases. SV40 L-TAG was identified in 3/85 (4%) of DLBCL. High Ki-67 labeling index (Ki-67 LI) and apoptotic count were associated with high E2F1 expression (p<0.001 for all). No significant association was reached between E2F1 and SV40. E2F1 expression proved to be the most and first independent prognostic factor on overall survival of DLBCL patients (HR = 5.79, 95% CI = 2.3–14.6, and p<0.001). Upregulation of E2F1 has been implicated in oncogenesis, prognosis, and prediction of therapeutic response but is not seemingly to have a relationship with the accused SV40.

Homeostasis of redox status derived from glucose metabolic pathway could be the key to understanding the Warburg effect

Glucose metabolism in mitochondria through oxidative phosphorylation (OXPHOS) for generation of adenosine triphosphate (ATP) is vital for cell function. However, reactive oxygen species (ROS), a by-product from OXPHOS, is a major source of endogenously produced toxic stressors on the genome. In fact, ATP could be efficiently produced in a high throughput manner without ROS generation in cytosol through glycolysis, which could be a unique and critical metabolic pathway to prevent spontaneous mutation during DNA replication. Therefore glycolysis is dominant in robust proliferating cells. Indeed, aerobic glycolysis, or the Warburg effect, in normal proliferating cells is an example of homeostasis of redox status by transiently shifting metabolic flux from OXPHOS to glycolysis to avoid ROS generation during DNA synthesis and protect genome integrity. The process of maintaining redox homeostasis is driven by genome wide transcriptional clustering with mitochondrial retrograde signaling and coupled with the glucose metabolic pathway and cell division cycle. On the contrary, the Warburg effect in cancer cells is the results of the alteration of redox status from a reprogramed glucose metabolic pathway caused by the dysfunctional OXPHOS. Mutations in mitochondrial DNA (mtDNA) and nuclear DNA (nDNA) disrupt mitochondrial structural integrity, leading to reduced OXPHOS capacity, sustained glycolysis and excessive ROS leak, all of which are responsible for tumor initiation, progression and metastasis. A "plumbing model" is used to illustrate how redox status could be regulated through glucose metabolic pathway and provide a new insight into the understanding of the Warburg effect in both normal and cancer cells.

Homeostasis of redox status derived from glucose metabolic pathway could be the key to understanding the Warburg effect

Glucose metabolism in mitochondria through oxidative phosphorylation (OXPHOS) for generation of adenosine triphosphate (ATP) is vital for cell function. However, reactive oxygen species (ROS), a by-product from OXPHOS, is a major source of endogenously produced toxic stressors on the genome. In fact, ATP could be efficiently produced in a high throughput manner without ROS generation in cytosol through glycolysis, which could be a unique and critical metabolic pathway to prevent spontaneous mutation during DNA replication. Therefore glycolysis is dominant in robust proliferating cells. Indeed, aerobic glycolysis, or the Warburg effect, in normal proliferating cells is an example of homeostasis of redox status by transiently shifting metabolic flux from OXPHOS to glycolysis to avoid ROS generation during DNA synthesis and protect genome integrity. The process of maintaining redox homeostasis is driven by genome wide transcriptional clustering with mitochondrial retrograde signaling and coupled with the glucose metabolic pathway and cell division cycle. On the contrary, the Warburg effect in cancer cells is the results of the alteration of redox status from a reprogramed glucose metabolic pathway caused by the dysfunctional OXPHOS. Mutations in mitochondrial DNA (mtDNA) and nuclear DNA (nDNA) disrupt mitochondrial structural integrity, leading to reduced OXPHOS capacity, sustained glycolysis and excessive ROS leak, all of which are responsible for tumor initiation, progression and metastasis. A "plumbing model" is used to illustrate how redox status could be regulated through glucose metabolic pathway and provide a new insight into the understanding of the Warburg effect in both normal and cancer cells.

E2F1 impairs all-trans retinoic acid-induced osteogenic differentiation of osteosarcoma via promoting ubiquitination-mediated degradation of RARα

All-trans retinoic acid (ATRA) is a widely used differentiation drug that can effectively induce osteogenic differentiation of osteosarcoma cells, but the underlying mechanism remains elusive, which limits the clinical application for ATRA in osteosarcoma patients. In this study, we identified E2F1 as a novel regulator involved in ATRA-induced osteogenic differentiation of osteosarcoma cells. We observed that osteosarcoma cells are coupled with individual differences in the expression levels of E2F1 in patients, and E2F1 impairs ATRA-induced differentiation of osteosarcoma cells. Moreover, remarkable anti-proliferative and differentiation-inducing effects of ATRA treatment are only observed in E2F1 low to negative expressed primary osteosarcoma cultures. These results strongly suggested that E2F1 may serve as a potent indicator for the effectiveness of ATRA treatment in osteosarcoma. Interestingly, E2F1 is found to downregulate retinoic acid receptor α (RARα), a key factor determines the effectiveness of ATRA. E2F1 specifically binds to RARα and promotes its ubiquitination-mediated degradation; as a consequence, RARα-mediated differentiation is inhibited in osteosarcoma. Therefore, our studies present E2F1 as a potent biomarker, as well as a therapeutic target for ATRA-based differentiation therapeutics, and raise the hope of using differentiation-based approaches for osteosarcoma patients.

Upregulation of E2F transcription factor 3 is associated with poor prognosis in hepatocellular carcinoma

E2F transcription factor 3 (E2F3), a member of the E2F transcription factor family and a member of the genes involved in the regulation of cell cycle, is an oncogene with strong proliferative potential. E2F3 is involved in many processes and plays important roles in the development of several types of cancer, while its relationship with prognosis in hepatocellular carcinoma (HCC) has yet to be reported. In the present study, based on 4 independent microarray data sets which covered 385 cases of HCC and 327 cases of normal livers retrieved from the Oncomine database, we demonstrated that E2F3 was upregulated at least 1.5-fold and on average 2.3-fold in HCC when compared with normal controls. Comprehensive bioinformatics analysis consisting of protein-protein interaction, gene co-occurrence, microRNA-mRNA interaction and biological process annotation indicated that E2F3 interacted with a large number of genes, proteins and microRNAs which were all associated with poor prognosis in patients with HCC and other types of cancer, suggesting that E2F3 may also serve as a biomarker for poor prognosis. Taken together, for the first time, we show that the overexpression of E2F3 may be associated with unfavorable prognosis in HCC.

Eugenol triggers apoptosis in breast cancer cells through E2F1/survivin down-regulation

Background

Breast cancer is a major health problem that threatens the lives of millions of women worldwide each year. Most of the chemotherapeutic agents that are currently used to treat this complex disease are highly toxic with long-term side effects. Therefore, novel generation of anti-cancer drugs with higher efficiency and specificity are urgently needed.

Methods

Breast cancer cell lines were treated with eugenol and cytotoxicity was measured using the WST-1 reagent, while propidium iodide/annexinV associated with flow cytometry was utilized in order to determine the induced cell death pathway. The effect of eugenol on apoptotic and pro-carcinogenic proteins, both in vitro and in tumor xenografts was assessed by immunoblotting. While RT-PCR was used to determine eugenol effect on the E2F1 and survivin mRNA levels. In addition, we tested the effect of eugenol on cell proliferation using the real-time cell electronic sensing system.

Results

Eugenol at low dose (2 μM) has specific toxicity against different breast cancer cells. This killing effect was mediated mainly through inducing the internal apoptotic pathway and strong down-regulation of E2F1 and its downstream antiapoptosis target survivin, independently of the status of p53 and ERα. Eugenol inhibited also several other breast cancer related oncogenes, such as NF-κB and cyclin D1. Moreover, eugenol up-regulated the versatile cyclin-dependent kinase inhibitor p21^WAF1 protein, and inhibited the proliferation of breast cancer cells in a p53-independent manner. Importantly, these anti-proliferative and pro-apoptotic effects were also observed in vivo in xenografted human breast tumors.

Conclusion

Eugenol exhibits anti-breast cancer properties both in vitro and in vivo, indicating that it could be used to consolidate the adjuvant treatment of breast cancer through targeting the E2F1/survivin pathway, especially for the less responsive triple-negative subtype of the disease.

Fine-scale mapping of the FGFR2 breast cancer risk locus: putative functional variants differentially bind FOXA1 and E2F1

The 10q26 locus in the second intron of FGFR2 is the locus most strongly associated with estrogen-receptor-positive breast cancer in genome-wide association studies. We conducted fine-scale mapping in case-control studies genotyped with a custom chip (iCOGS), comprising 41 studies (n = 89,050) of European ancestry, 9 Asian ancestry studies (n = 13,983), and 2 African ancestry studies (n = 2,028) from the Breast Cancer Association Consortium. We identified three statistically independent risk signals within the locus. Within risk signals 1 and 3, genetic analysis identified five and two variants, respectively, highly correlated with the most strongly associated SNPs. By using a combination of genetic fine mapping, data on DNase hypersensitivity, and electrophoretic mobility shift assays to study protein-DNA binding, we identified rs35054928, rs2981578, and rs45631563 as putative functional SNPs. Chromatin immunoprecipitation showed that FOXA1 preferentially bound to the risk-associated allele (C) of rs2981578 and was able to recruit ERα to this site in an allele-specific manner, whereas E2F1 preferentially bound the risk variant of rs35054928. The risk alleles were preferentially found in open chromatin and bound by Ser5 phosphorylated RNA polymerase II, suggesting that the risk alleles are associated with changes in transcription. Chromatin conformation capture demonstrated that the risk region was able to interact with the promoter of FGFR2, the likely target gene of this risk region. A role for FOXA1 in mediating breast cancer susceptibility at this locus is consistent with the finding that the FGFR2 risk locus primarily predisposes to estrogen-receptor-positive disease.

Reconstruction of transcriptional regulatory networks by stability-based network component analysis

Reliable inference of transcription regulatory networks is a challenging task in computational biology. Network component analysis (NCA) has become a powerful scheme to uncover regulatory networks behind complex biological processes. However, the performance of NCA is impaired by the high rate of false connections in binding information. In this paper, we integrate stability analysis with NCA to form a novel scheme, namely stability-based NCA (sNCA), for regulatory network identification. The method mainly addresses the inconsistency between gene expression data and binding motif information. Small perturbations are introduced to prior regulatory network, and the distance among multiple estimated transcript factor (TF) activities is computed to reflect the stability for each TF's binding network. For target gene identification, multivariate regression and t-statistic are used to calculate the significance for each TF-gene connection. Simulation studies are conducted and the experimental results show that sNCA can achieve an improved and robust performance in TF identification as compared to NCA. The approach for target gene identification is also demonstrated to be suitable for identifying true connections between TFs and their target genes. Furthermore, we have successfully applied sNCA to breast cancer data to uncover the role of TFs in regulating endocrine resistance in breast cancer.

Antitumor and modeling studies of a penetratin-peptide that targets E2F-1 in small cell lung cancer

E2F-1, a key transcription factor necessary for cell growth, DNA repair, and differentiation, is an attractive target for development of anticancer drugs in tumors that are E2F "oncogene addicted". We identified a peptide isolated from phage clones that bound tightly to the E2F-1 promoter consensus sequence. The peptide was coupled to penetratin to enhance cellular uptake. Modeling of the penetratin-peptide (PEP) binding to the DNA E2F-1 promoter demonstrated favorable interactions that also involved the participation of most of the penetratin sequence. The penetratin-peptide (PEP) demonstrated potent in vitro cytotoxic effects against a range of cancer cell lines, particularly against Burkitt lymphoma cells and small cell lung cancer (SCLC) cells. Further studies in the H-69 SCLC cell line showed that the PEP inhibited transcription of E2F-1 and also several important E2F-regulated enzymes involved in DNA synthesis, namely, thymidylate synthase, thymidine kinase, and ribonucleotide reductase. As the PEP was found to be relatively unstable in serum, it was encapsulated in PEGylated liposomes for in vivo studies. Treatment of mice bearing the human small cell lung carcinoma H-69 with the PEP encapsulated in PEGylated liposomes (PL-PEP) caused tumor regression without significant toxicity. The liposome encapsulated PEP has promise as an antitumor agent, alone or in combination with inhibitors of DNA synthesis.

Senescence sensitivity of breast cancer cells is defined by positive feedback loop between CIP2A and E2F1

Senescence induction contributes to cancer therapy responses and is crucial for p53-mediated tumor suppression. However, whether p53 inactivation actively suppresses senescence induction has been unclear. Here, we show that E2F1 overexpression, due to p53 or p21 inactivation, promotes expression of human oncoprotein CIP2A, which in turn, by inhibiting PP2A activity, increases stabilizing serine 364 phosphorylation of E2F1. Several lines of evidence show that increased activity of E2F1-CIP2A feedback renders breast cancer cells resistant to senescence induction. Importantly, mammary tumorigenesis is impaired in a CIP2A-deficient mouse model, and CIP2A-deficient tumors display markers of senescence induction. Moreover, high CIP2A expression predicts for poor prognosis in a subgroup of patients with breast cancer treated with senescence-inducing chemotherapy. Together, these results implicate the E2F1-CIP2A feedback loop as a key determinant of breast cancer cell sensitivity to senescence induction. This feedback loop also constitutes a promising prosenescence target for therapy of cancers with an inactivated p53-p21 pathway.

SIGNIFICANCE

It has been recently realized that most currently used chemotherapies exert their therapeutic effect at least partly by induction of terminal cell arrest, senescence. However, the mechanisms by which cell-intrinsic senescence sensitivity is determined are poorly understood. Results of this study identify the E2F1-CIP2A positive feedback loop as a key determinant of breast cancer cell sensitivity to senescence and growth arrest induction. Our data also indicate that this newly characterized interplay between 2 frequently overexpressed oncoproteins constitutes a promising prosenescence target for therapy of cancers with inactivated p53 and p21. Finally, these results may also facilitate novel stratification strategies for selection of patients to receive senescence-inducing cancer therapies.

Cancer develops, progresses and responds to therapies through restricted perturbation of the protein-protein interaction network

The products of genes mutated or differentially expressed in cancer tend to occupy central positions within the network of protein-protein interactions, or the interactome network. Integration of different types of gene and protein relationships has considerably increased the understanding of the mechanisms of carcinogenesis, while also enhancing the applicability of expression signatures. In this scenario, however, it remains unknown how cancer develops, progresses and responds to therapies in a potentially controlled manner at the systems level. Here, by applying the concepts of load transfer and cascading failures in power grids, we examine the impact and transmission of cancer-related gene expression changes in the interactome network. Relative to random perturbations, this study reveals topological robustness associated with all cancer conditions. In addition, experimental perturbation of a central cancer node, which consists of over-expression of the α-synuclein (SNCA) protein in MCF7 breast cancer cells, also reveals robustness. Conversely, a search for proteins with an opposite topological impact identifies the autophagy pathway. Mechanistically, the existence of smaller shortest paths among cancer-related proteins appears to be a topological feature that partially contributes to the restricted perturbation of the network. Together, the results of this study suggest that cancer develops, progresses and responds to therapies following controlled, restricted perturbation of the interactome network.

Rbf1 degron dysfunction enhances cellular DNA replication

The E2F family of transcription factors contributes to oncogenesis through activation of multiple genes involved in cellular proliferation, a process that is opposed by the Retinoblastoma tumor suppressor protein (RB). RB also increases E2F1 stability by inhibiting its proteasome-mediated degradation, but the consequences of this post-translational regulation of E2F1 remain unknown. To better understand the mechanism of E2F stabilization and its physiological relevance, we examined the streamlined Rbf1-dE2F1 network in Drosophila. During embryonic development, Rbf1 is insulated from ubiquitin-mediated turnover by the COP9 signalosome, a multi-protein complex that modulates E3 ubiquitin ligase activity. Here, we report that the COP9 signalosome also protects the Cullin4-E3 ligase that is responsible for dE2F1 proteasome-mediated destruction. This dual role of the COP9 signalosome may serve to buffer E2F levels, enhancing its turnover via Cul4 protection and its stabilization through protection of Rbf1. We further show that Rbf1-mediated stabilization of dE2F1 and repression of dE2F1 cell cycle-target genes are distinct properties. Removal of an evolutionarily conserved Rbf1 C terminal degron disabled Rbf1 repression without affecting dE2F1 stabilization. This mutant form of Rbf1 also enhanced G(1)-to-S phase progression when expressed in Rbf1-containing S2 embryonic cells, suggesting that such mutations may generate gain-of-function properties relevant to cellular transformation. Consistent with this idea, several studies have identified mutations in the homologous C terminal domains of RB and p130 in human cancer.

Interplay between AP-1 and estrogen receptor α in regulating gene expression and proliferation networks in breast cancer cells

Estrogen receptor α (ERα) is a ligand-dependent transcription factor that plays an important role in breast cancer. Estrogen-dependent gene regulation by ERα can be mediated by interaction with other DNA-binding proteins, such as activator protein-1 (AP-1). The nature of such interactions in mediating the estrogen response in breast cancer cells remains unclear. Here we show that knockdown of c-Fos, a component of the transcription factor AP-1, attenuates the expression of 37% of all estrogen-regulated genes, suggesting that c-Fos is a fundamental factor for ERα-mediated transcription. Additionally, knockdown of c-Fos affected the expression of a number of genes that were not regulated by estrogen. Pathway analysis reveals that silencing of c-Fos downregulates an E2F1-dependent proproliferative gene network. Thus, modulation of the E2F1 pathway by c-Fos represents a novel mechanism by which c-Fos enhances breast cancer cell proliferation. Furthermore, we show that c-Fos and ERα can cooperate in regulating E2F1 gene expression by binding to regulatory elements in the E2F1 promoter. To start to dissect the molecular details of the cross talk between AP-1 and estrogen signaling, we identify a novel ERα/AP-1 target, PKIB (cAMP-dependent protein kinase inhibitor-β), which is overexpressed in ERα-positive breast cancer tissues. Knockdown of PKIB results in robust growth suppression of breast cancer cells. Collectively, our findings support c-Fos as a critical factor that governs estrogen-dependent gene expression and breast cancer proliferation programs.

Pathway-based classification of cancer subtypes

Background

Molecular markers based on gene expression profiles have been used in experimental and clinical settings to distinguish cancerous tumors in stage, grade, survival time, metastasis, and drug sensitivity. However, most significant gene markers are unstable (not reproducible) among data sets. We introduce a standardized method for representing cancer markers as 2-level hierarchical feature vectors, with a basic gene level as well as a second level of (more stable) pathway markers, for the purpose of discriminating cancer subtypes. This extends standard gene expression arrays with new pathway-level activation features obtained directly from off-the-shelf gene set enrichment algorithms such as GSEA. Such so-called pathway-based expression arrays are significantly more reproducible across datasets. Such reproducibility will be important for clinical usefulness of genomic markers, and augment currently accepted cancer classification protocols.

Results

The present method produced more stable (reproducible) pathway-based markers for discriminating breast cancer metastasis and ovarian cancer survival time. Between two datasets for breast cancer metastasis, the intersection of standard significant gene biomarkers totaled 7.47% of selected genes, compared to 17.65% using pathway-based markers; the corresponding percentages for ovarian cancer datasets were 20.65% and 33.33% respectively. Three pathways, consisting of Type_1_diabetes mellitus, Cytokine-cytokine_receptor_interaction and Hedgehog_signaling (all previously implicated in cancer), are enriched in both the ovarian long survival and breast non-metastasis groups. In addition, integrating pathway and gene information, we identified five (ID4, ANXA4, CXCL9, MYLK, FBXL7) and six (SQLE, E2F1, PTTG1, TSTA3, BUB1B, MAD2L1) known cancer genes significant for ovarian and breast cancer respectively.

Conclusions

Standardizing the analysis of genomic data in the process of cancer staging, classification and analysis is important as it has implications for both pre-clinical as well as clinical studies. The paradigm of diagnosis and prediction using pathway-based biomarkers as features can be an important part of the process of biomarker-based cancer analysis, and the resulting canonical (clinically reproducible) biomarkers can be important in standardizing genomic data. We expect that identification of such canonical biomarkers will improve clinical utility of high-throughput datasets for diagnostic and prognostic applications.

Reviewers

This article was reviewed by John McDonald (nominated by I. King Jordon), Eugene Koonin, Nathan Bowen (nominated by I. King Jordon), and Ekaterina Kotelnikova (nominated by Mikhail Gelfand).

A target based approach identifies genomic predictors of breast cancer patient response to chemotherapy

Background

The efficacy of chemotherapy regimens in breast cancer patients is variable and unpredictable. Whether individual patients either achieve long-term remission or suffer recurrence after therapy may be dictated by intrinsic properties of their breast tumors including genetic lesions and consequent aberrant transcriptional programs. Global gene expression profiling provides a powerful tool to identify such tumor-intrinsic transcriptional programs, whose analyses provide insight into the underlying biology of individual patient tumors. For example, multi-gene expression signatures have been identified that can predict the likelihood of disease reccurrence, and thus guide patient prognosis. Whereas such prognostic signatures are being introduced in the clinical setting, similar signatures that predict sensitivity or resistance to chemotherapy are not currently clinically available.

Methods

We used gene expression profiling to identify genes that were co-expressed with genes whose transcripts encode the protein targets of commonly used chemotherapeutic agents.

Results

Here, we present target based expression indices that predict breast tumor response to anthracycline and taxane based chemotherapy. Indeed, these signatures were independently predictive of chemotherapy response after adjusting for standard clinic-pathological variables such as age, grade, and estrogen receptor status in a cohort of 488 breast cancer patients treated with adriamycin and taxotere/taxol.

Conclusions

Importantly, our findings suggest the practicality of developing target based indices that predict response to therapeutics, as well as highlight the possibility of using gene signatures to guide the use of chemotherapy during treatment of breast cancer patients.

Core module biomarker identification with network exploration for breast cancer metastasis

BACKGROUND

In a complex disease, the expression of many genes can be significantly altered, leading to the appearance of a differentially expressed "disease module". Some of these genes directly correspond to the disease phenotype, (i.e. "driver" genes), while others represent closely-related first-degree neighbours in gene interaction space. The remaining genes consist of further removed "passenger" genes, which are often not directly related to the original cause of the disease. For prognostic and diagnostic purposes, it is crucial to be able to separate the group of "driver" genes and their first-degree neighbours, (i.e. "core module") from the general "disease module".

RESULTS

We have developed COMBINER: COre Module Biomarker Identification with Network ExploRation. COMBINER is a novel pathway-based approach for selecting highly reproducible discriminative biomarkers. We applied COMBINER to three benchmark breast cancer datasets for identifying prognostic biomarkers. COMBINER-derived biomarkers exhibited 10-fold higher reproducibility than other methods, with up to 30-fold greater enrichment for known cancer-related genes, and 4-fold enrichment for known breast cancer susceptible genes. More than 50% and 40% of the resulting biomarkers were cancer and breast cancer specific, respectively. The identified modules were overlaid onto a map of intracellular pathways that comprehensively highlighted the hallmarks of cancer. Furthermore, we constructed a global regulatory network intertwining several functional clusters and uncovered 13 confident "driver" genes of breast cancer metastasis.

CONCLUSIONS

COMBINER can efficiently and robustly identify disease core module genes and construct their associated regulatory network. In the same way, it is potentially applicable in the characterization of any disease that can be probed with microarrays.

TFRank: network-based prioritization of regulatory associations underlying transcriptional responses

MOTIVATION

Uncovering mechanisms underlying gene expression control is crucial to understand complex cellular responses. Studies in gene regulation often aim to identify regulatory players involved in a biological process of interest, either transcription factors coregulating a set of target genes or genes eventually controlled by a set of regulators. These are frequently prioritized with respect to a context-specific relevance score. Current approaches rely on relevance measures accounting exclusively for direct transcription factor-target interactions, namely overrepresentation of binding sites or target ratios. Gene regulation has, however, intricate behavior with overlapping, indirect effect that should not be neglected. In addition, the rapid accumulation of regulatory data already enables the prediction of large-scale networks suitable for higher level exploration by methods based on graph theory. A paradigm shift is thus emerging, where isolated and constrained analyses will likely be replaced by whole-network, systemic-aware strategies.

RESULTS

We present TFRank, a graph-based framework to prioritize regulatory players involved in transcriptional responses within the regulatory network of an organism, whereby every regulatory path containing genes of interest is explored and incorporated into the analysis. TFRank selected important regulators of yeast adaptation to stress induced by quinine and acetic acid, which were missed by a direct effect approach. Notably, they reportedly confer resistance toward the chemicals. In a preliminary study in human, TFRank unveiled regulators involved in breast tumor growth and metastasis when applied to genes whose expression signatures correlated with short interval to metastasis.

E2F1 and KIAA0191 expression predicts breast cancer patient survival

BACKGROUND

Gene expression profiling of human breast tumors has uncovered several molecular signatures that can divide breast cancer patients into good and poor outcome groups. However, these signatures typically comprise many genes (~50-100), and the prognostic tests associated with identifying these signatures in patient tumor specimens require complicated methods, which are not routinely available in most hospital pathology laboratories, thus limiting their use. Hence, there is a need for more practical methods to predict patient survival.

METHODS

We modified a feature selection algorithm and used survival analysis to derive a 2-gene signature that accurately predicts breast cancer patient survival.

RESULTS

We developed a tree based decision method that segregated patients into various risk groups using KIAA0191 expression in the context of E2F1 expression levels. This approach led to highly accurate survival predictions in a large cohort of breast cancer patients using only a 2-gene signature.

CONCLUSIONS

Our observations suggest a possible relationship between E2F1 and KIAA0191 expression that is relevant to the pathogenesis of breast cancer. Furthermore, our findings raise the prospect that the practicality of patient prognosis methods may be improved by reducing the number of genes required for analysis. Indeed, our E2F1/KIAA0191 2-gene signature would be highly amenable for an immunohistochemistry based test, which is commonly used in hospital laboratories.

Biological reprogramming in acquired resistance to endocrine therapy of breast cancer

Endocrine therapies targeting the proliferative effect of 17β-estradiol through estrogen receptor α (ERα) are the most effective systemic treatment of ERα-positive breast cancer. However, most breast tumors initially responsive to these therapies develop resistance through molecular mechanisms that are not yet fully understood. The long-term estrogen-deprived (LTED) MCF7 cell model has been proposed to recapitulate acquired resistance to aromatase inhibitors in postmenopausal women. To elucidate this resistance, genomic, transcriptomic and molecular data were integrated into the time course of MCF7-LTED adaptation. Dynamic and widespread genomic changes were observed, including amplification of the ESR1 locus consequently linked to an increase in ERα. Dynamic transcriptomic profiles were also observed that correlated significantly with genomic changes and were predicted to be influenced by transcription factors known to be involved in acquired resistance or cell proliferation (for example, interferon regulatory transcription factor 1 and E2F1, respectively) but, notably, not by canonical ERα transcriptional function. Consistently, at the molecular level, activation of growth factor signaling pathways by EGFR/ERBB/AKT and a switch from phospho-Ser118 (pS118)- to pS167-ERα were observed during MCF7-LTED adaptation. Evaluation of relevant clinical settings identified significant associations between MCF7-LTED and breast tumor transcriptome profiles that characterize ERα-negative status, early response to letrozole and tamoxifen, and recurrence after tamoxifen treatment. In accordance with these profiles, MCF7-LTED cells showed increased sensitivity to inhibition of FGFR-mediated signaling with PD173074. This study provides mechanistic insight into acquired resistance to endocrine therapies of breast cancer and highlights a potential therapeutic strategy.

Deregulated E2F and the AAA+ coregulator ANCCA drive proto-oncogene ACTR/AIB1 overexpression in breast cancer

The proto-oncogene ACTR/AIB1, a coactivator for transcription factors such as the nuclear receptors and E2Fs, is frequently overexpressed in various cancers including breast cancers. However, the underlying mechanism is poorly understood. Here, we identified several functional, noncanonical E2F binding sites in the ACTR first exon and intron that are critical for ACTR gene activation. We also found that the newly identified AAA+ coregulator AAA+ nuclear coregulator cancer associated (ANCCA) is recruited to the ACTR promoter and directly controls ACTR expression in breast cancer cells. Importantly, immunohistochemistry analysis indicated that ACTR overexpression is highly correlated with the expression of E2F1 and ANCCA in a cohort of human primary and lymph node-metastasized breast cancer specimens. Along with previous findings from us and others that ACTR is involved in its own gene regulation, these results suggest that one major mechanism of ACTR overexpression in cancer is the concerted, aberrant function of the nuclear coregulators such as ANCCA and ACTR, and they point to therapeutic strategies that target the Rb-E2F axis and/or the coregulator ANCCA for ACTR-overexpressing cancers.

Expression of estrogen receptor alpha with a Tet-off adenoviral system induces G0/G1 cell cycle arrest in SKBr3 breast cancer cells

Endocrine therapies targeting estrogen action are pivotal for the prevention and treatment of ER-positive breast cancers. Previous studies sought to recreate hormone responsiveness by the stable expression of ERalpha in the ER-negative MDA-MB-231 breast cancer cells. Paradoxically, estrogen inhibits breast cancer cell growth when an exogenous ERalpha is expressed. In this study, we have built on previous studies by developing a Tet-off adenoviral system to express ERalpha in the ER-negative SKBr3 breast cancer cells that over-express both EGFR and HER2. This system efficiently delivers ERalpha and the expression level of ERalpha is controlled by doxycycline in a concentration-dependent manner. The growth of SKBr3 was inhibited by ERalpha expression and further inhibited in the presence of 1 nM 17beta-estradiol. SKBr3 cells were arrested at G0/G1 cell cycle upon ERalpha expression, which corresponded to an increase of p21Cip1/Waf1, hypo-phosphorylation of pRb and decrease of E2F1. Estrogen also reduced EGFR and HER2 expression in SKBr3 cells after ERalpha was expressed. Given that estrogen-induced increase of p21Cip1/Waf1 and decrease of E2F1 was also observed in MDA-MB-231 cells stably transfected with ERalpha, our results suggest that a common pathway might be shared by different breast cancer cell lines whose growth is suppressed by ectopic ERalpha and estrogen.

Pathway analysis using random forests with bivariate node-split for survival outcomes

MOTIVATION

There is great interest in pathway-based methods for genomics data analysis in the research community. Although machine learning methods, such as random forests, have been developed to correlate survival outcomes with a set of genes, no study has assessed the abilities of these methods in incorporating pathway information for analyzing microarray data. In general, genes that are identified without incorporating biological knowledge are more difficult to interpret. Correlating pathway-based gene expression with survival outcomes may lead to biologically more meaningful prognosis biomarkers. Thus, a comprehensive study on how these methods perform in a pathway-based setting is warranted.

RESULTS

In this article, we describe a pathway-based method using random forests to correlate gene expression data with survival outcomes and introduce a novel bivariate node-splitting random survival forests. The proposed method allows researchers to identify important pathways for predicting patient prognosis and time to disease progression, and discover important genes within those pathways. We compared different implementations of random forests with different split criteria and found that bivariate node-splitting random survival forests with log-rank test is among the best. We also performed simulation studies that showed random forests outperforms several other machine learning algorithms and has comparable results with a newly developed component-wise Cox boosting model. Thus, pathway-based survival analysis using machine learning tools represents a promising approach in dissecting pathways and for generating new biological hypothesis from microarray studies.

AVAILABILITY

R package Pwayrfsurvival is available from URL: http://www.duke.edu/~hp44/pwayrfsurvival.htm.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Biological convergence of cancer signatures

Gene expression profiling has identified cancer prognostic and predictive signatures with superior performance to conventional histopathological or clinical parameters. Consequently, signatures are being incorporated into clinical practice and will soon influence everyday decisions in oncology. However, the slight overlap in the gene identity between signatures for the same cancer type or condition raises questions about their biological and clinical implications. To clarify these issues, better understanding of the molecular properties and possible interactions underlying apparently dissimilar signatures is needed. Here, we evaluated whether the signatures of 24 independent studies are related at the genome, transcriptome or proteome levels. Significant associations were consistently observed across these molecular layers, which suggest the existence of a common cancer cell phenotype. Convergence on cell proliferation and death supports the pivotal involvement of these processes in prognosis, metastasis and treatment response. In addition, functional and molecular associations were identified with the immune response in different cancer types and conditions that complement the contribution of cell proliferation and death. Examination of additional, independent, cancer datasets corroborated our observations. This study proposes a comprehensive strategy for interpreting cancer signatures that reveals common design principles and systems-level properties.

Biological processes, properties and molecular wiring diagrams of candidate low-penetrance breast cancer susceptibility genes

Background

Recent advances in whole-genome association studies (WGASs) for human cancer risk are beginning to provide the part lists of low-penetrance susceptibility genes. However, statistical analysis in these studies is complicated by the vast number of genetic variants examined and the weak effects observed, as a result of which constraints must be incorporated into the study design and analytical approach. In this scenario, biological attributes beyond the adjusted statistics generally receive little attention and, more importantly, the fundamental biological characteristics of low-penetrance susceptibility genes have yet to be determined.

Methods

We applied an integrative approach for identifying candidate low-penetrance breast cancer susceptibility genes, their characteristics and molecular networks through the analysis of diverse sources of biological evidence.

Results

First, examination of the distribution of Gene Ontology terms in ordered WGAS results identified asymmetrical distribution of Cell Communication and Cell Death processes linked to risk. Second, analysis of 11 different types of molecular or functional relationships in genomic and proteomic data sets defined the "omic" properties of candidate genes: i/ differential expression in tumors relative to normal tissue; ii/ somatic genomic copy number changes correlating with gene expression levels; iii/ differentially expressed across age at diagnosis; and iv/ expression changes after BRCA1 perturbation. Finally, network modeling of the effects of variants on germline gene expression showed higher connectivity than expected by chance between novel candidates and with known susceptibility genes, which supports functional relationships and provides mechanistic hypotheses of risk.

Conclusion

This study proposes that cell communication and cell death are major biological processes perturbed in risk of breast cancer conferred by low-penetrance variants, and defines the common omic properties, molecular interactions and possible functional effects of candidate genes and proteins.

Identification of a gene signature in cell cycle pathway for breast cancer prognosis using gene expression profiling data

Background

Numerous studies have used microarrays to identify gene signatures for predicting cancer patient clinical outcome and responses to chemotherapy. However, the potential impact of gene expression profiling in cancer diagnosis, prognosis and development of personalized treatment may not be fully exploited due to the lack of consensus gene signatures and poor understanding of the underlying molecular mechanisms.

Methods

We developed a novel approach to derive gene signatures for breast cancer prognosis in the context of known biological pathways. Using unsupervised methods, cancer patients were separated into distinct groups based on gene expression patterns in one of the following pathways: apoptosis, cell cycle, angiogenesis, metastasis, p53, DNA repair, and several receptor-mediated signaling pathways including chemokines, EGF, FGF, HIF, MAP kinase, JAK and NF-κB. The survival probabilities were then compared between the patient groups to determine if differential gene expression in a specific pathway is correlated with differential survival.

Results

Our results revealed expression of cell cycle genes is strongly predictive of breast cancer outcomes. We further confirmed this observation by building a cell cycle gene signature model using supervised methods. Validated in multiple independent datasets, the cell cycle gene signature is a more accurate predictor for breast cancer clinical outcome than the previously identified Amsterdam 70-gene signature that has been developed into a FDA approved clinical test MammaPrint^®.

Conclusion

Taken together, the gene expression signature model we developed from well defined pathways is not only a consistently powerful prognosticator but also mechanistically linked to cancer biology. Our approach provides an alternative to the current methodology of identifying gene expression markers for cancer prognosis and drug responses using the whole genome gene expression data.

Re-expression of ABI3-binding protein suppresses thyroid tumor growth by promoting senescence and inhibiting invasion

Loss of ABI gene family member 3-binding protein (ABI3BP) expression may be functionally involved in the pathogenesis of cancer. Previous reports have indicated a loss of expression in lung cancer and a presumed role in inducing cellular senescence. We show here that ABI3BP expression is significantly decreased in most malignant thyroid tumors of all types. To better understand ABI3BP's role, we created a model by re-expressing ABI3BP in two thyroid cancer cell lines. Re-expression of ABI3BP in thyroid cells resulted in a decrease in transforming activity, cell growth, cell viability, migration, invasion, and tumor growth in nude mice. ABI3BP re-expression appears to trigger cellular senescence through the p21 pathway. Additionally, ABI3BP induced formation of heterochromatin 1-binding protein gamma-positive senescence-associated (SA) heterochromatin foci and accumulation of SA beta-galactosidase. The combination of a decrease in cell growth, invasion, and other effects upon ABI3BP re-expression in vitro helps to explain the large reduction in tumor growth that we observed in nude mice. Together, our data provide evidence that the loss of ABI3BP expression could play a functional role in thyroid tumorigenesis. Activation of ABI3BP or its pathway may represent a possible basis for targeted therapy of certain cancers.