E2F4 regulatory program predicts patient survival prognosis in breast cancer

ESR1 and p53 interactome alteration defines mechanisms of tamoxifen response in luminal breast cancer

The canonical mechanism behind tamoxifen's therapeutic effect on estrogen receptor α/ESR1+ breast cancers is inhibition of ESR1-dependent estrogen signaling. Although ESR1+ tumors expressing wild-type p53 were reported to be more responsive to tamoxifen (Tam) therapy, p53 has not been factored into choice of this therapy and the mechanism underlying the role of p53 in Tam response remains unclear. In a window-of-opportunity trial on patients with newly diagnosed stage I-III ESR1+/HER2/wild-type p53 breast cancer who were randomized to arms with or without Tam prior to surgery, we reveal that the ESR1-p53 interaction in tumors was inhibited by Tam. This resulted in functional reactivation of p53 leading to transcriptional reprogramming that favors tumor-suppressive signaling, as well as downregulation of oncogenic pathways. These findings illustrating the convergence of ESR1 and p53 signaling during Tam therapy enrich mechanistic understanding of the impact of p53 on the response to Tam therapy.

The pRb/RBL2-E2F1/4-GCN5 axis regulates cancer stem cell formation and G0 phase entry/exit by paracrine mechanisms

The lethality, chemoresistance and metastatic characteristics of cancers are associated with phenotypically plastic cancer stem cells (CSCs). How the non-cell autonomous signalling pathways and cell-autonomous transcriptional machinery orchestrate the stem cell-like characteristics of CSCs is still poorly understood. Here we use a quantitative proteomic approach for identifying secreted proteins of CSCs in pancreatic cancer. We uncover that the cell-autonomous E2F1/4-pRb/RBL2 axis balances non-cell-autonomous signalling in healthy ductal cells but becomes deregulated upon KRAS mutation. E2F1 and E2F4 induce whereas pRb/RBL2 reduce WNT ligand expression (e.g. WNT7A, WNT7B, WNT10A, WNT4) thereby regulating self-renewal, chemoresistance and invasiveness of CSCs in both PDAC and breast cancer, and fibroblast proliferation. Screening for epigenetic enzymes identifies GCN5 as a regulator of CSCs that deposits H3K9ac onto WNT promoters and enhancers. Collectively, paracrine signalling pathways are controlled by the E2F-GCN5-RB axis in diverse cancers and this could be a therapeutic target for eliminating CSCs.

E2F4 transcription factor is a prognostic biomarker related to immune infiltration of head and neck squamous cell carcinoma

To investigate the relationship between the transcription factor, E2F4, and head and neck squamous cell carcinoma (HNSCC), and to preliminarily explore the signaling pathways and immunological role of E2F4. The mRNA expression of E2F4 in HNSCC was evaluated by searching Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets. E2F4 protein expression was analyzed by immunohistochemistry using the CMU1h-ENT database. The association between E2F4 expression and tumor infiltration of immune cells was analyzed. Intracellular signaling by E2F4 was explored using KEGG and GO analysis. The correlation of E2F4 expression with clinical characteristics and its prognostic role were validated and analyzed in TCGA database. From the analysis of GEO and TCGA data, E2F4 expression was found to be up-regulated in HNSCC tumor tissues, and its level was associated with T, Grade, and M staging. Kaplan–Meier curve and Cox analyses indicated that the high expression of E2F4 was related to a poor prognosis. Thus, E2F4 was considered a potential prognostic factor for HNSCC. Immunohistochemical staining showed that E2F4 was mainly localized in the cell nucleus; it was highly expressed in HNSCC tissues, with a significant difference noted from that in pericancerous mucosa tissues. A correlation was observed between the differential expression of E2F4 and the immune infiltration of HNSCC. As revealed by KEGG and GO analysis, differential enrichment was found in the cell cycle, spliceosome, meiosis, microbial polysaccharide synthesis, and WNT signaling pathway, as well as in cyclic adenosine monophosphate, ERBB2, VEGF, GCNP and MYC pathways. E2F4 plays an important role in tumor progression and may be a critical biological prognostic factor for HNSCC. In addition, it functions in the nucleus as a transcription factor, regulates immune cells, and could be a promising molecular target for the diagnosis and treatment of HNSCC.

Statistics and network-based approaches to identify molecular mechanisms that drive the progression of breast cancer

Breast cancer (BC) is one of the most malignant tumors and the leading cause of cancer-related death in women worldwide. So, an in-depth investigation on the molecular mechanisms of BC progression is required for diagnosis, prognosis and therapies. In this study, we identified 127 common differentially expressed genes (cDEGs) between BC and control samples by analyzing five gene expression profiles with NCBI accession numbers GSE139038, GSE62931, GSE45827, GSE42568 and GSE54002, based-on two statistical methods LIMMA and SAM. Then we constructed protein-protein interaction (PPI) network of cDEGs through the STRING database and selected top-ranked 7 cDEGs (BUB1, ASPM, TTK, CCNA2, CENPF, RFC4, and CCNB1) as a set of key genes (KGs) by cytoHubba plugin in Cytoscape. Several BC-causing crucial biological processes, molecular functions, cellular components, and pathways were significantly enriched by the estimated cDEGs including at-least one KGs. The multivariate survival analysis showed that the proposed KGs have a strong prognosis power of BC. Moreover, we detected some transcriptional and post-transcriptional regulators of KGs by their regulatory network analysis. Finally, we suggested KGs-guided three repurposable candidate-drugs (Trametinib, selumetinib, and RDEA119) for BC treatment by using the GSCALite online web tool and validated them through molecular docking analysis, and found their strong binding affinities. Therefore, the findings of this study might be useful resources for BC diagnosis, prognosis and therapies.

A Pan-Cancer Analysis of the Oncogenic Role of Nuclear Transport Factor 2 in Human Cancers

Nuclear transport factor 2 (NUTF2) is a GDP-binding protein that participates in the nucleocytoplasmic transport process. The role of NUTF2 in cancer development is largely unknown and lacks systemic assessment across human cancers. In this study, we performed a pan-cancer analysis of NUTF2 in human cancers. Out of 33 types of cancers, 19 types had significantly different expression of NUTF2 between tumor and normal tissues. Meanwhile, survival analysis showed that NUTF2 could be an independent prognostic factor in several tumor types. Further analysis suggested that the expression of NUTF2 expression was correlated with the infiltration of immune cells, such as CD8⁺ T cells, effector memory CD4⁺ T cells, and cancer-associated fibroblasts in kidney renal clear cell carcinoma. Moreover, co-expression analysis showed the positive association between NUTF2 and cell proliferation biomarkers (MKI67and PCNA) and epithelial–mesenchymal transition markers (VIM, TWIST1, SNAI1, SNAI2, FN1, and CDH2), suggesting that NUTF2 plays important roles in regulating cancer proliferation and metastasis. This pan-cancer analysis of NUTF2 provides a systemic understanding of its oncogenic role across different types of cancers.

A framework to predict the applicability of Oncotype DX, MammaPrint, and E2F4 gene signatures for improving breast cancer prognostic prediction

To improve cancer precision medicine, prognostic and predictive biomarkers are critically needed to aid physicians in deciding treatment strategies in a personalized fashion. Due to the heterogeneous nature of cancer, most biomarkers are expected to be valid only in a subset of patients. Furthermore, there is no current approach to determine the applicability of biomarkers. In this study, we propose a framework to improve the clinical application of biomarkers. As part of this framework, we develop a clinical outcome prediction model (CPM) and a predictability prediction model (PPM) for each biomarker and use these models to calculate a prognostic score (P-score) and a confidence score (C-score) for each patient. Each biomarker’s P-score indicates its association with patient clinical outcomes, while each C-score reflects the biomarker applicability of the biomarker’s CPM to a patient and therefore the confidence of the clinical prediction. We assessed the effectiveness of this framework by applying it to three biomarkers, Oncotype DX, MammaPrint, and an E2F4 signature, which have been used for predicting patient response, pathologic complete response versus residual disease to neoadjuvant chemotherapy (a classification problem), and recurrence-free survival (a Cox regression problem) in breast cancer, respectively. In both applications, our analyses indicated patients with higher C scores were more likely to be correctly predicted by the biomarkers, indicating the effectiveness of our framework. This framework provides a useful approach to develop and apply biomarkers in the context of cancer precision medicine.

A comprehensive long-read isoform analysis platform and sequencing resource for breast cancer

Tumors display widespread transcriptome alterations, but the full repertoire of isoform-level alternative splicing in cancer is unknown. We developed a long-read (LR) RNA sequencing and analytical platform that identifies and annotates full-length isoforms and infers tumor-specific splicing events. Application of this platform to breast cancer samples identifies thousands of previously unannotated isoforms; ~30% affect protein coding exons and are predicted to alter protein localization and function. We performed extensive cross-validation with -omics datasets to support transcription and translation of novel isoforms. We identified 3059 breast tumor–specific splicing events, including 35 that are significantly associated with patient survival. Of these, 21 are absent from GENCODE and 10 are enriched in specific breast cancer subtypes. Together, our results demonstrate the complexity, cancer subtype specificity, and clinical relevance of previously unidentified isoforms and splicing events in breast cancer that are only annotatable by LR-seq and provide a rich resource of immuno-oncology therapeutic targets.

Emerging Role of E2F Family in Cancer Stem Cells

The E2F family of transcription factors (E2Fs) consist of eight genes in mammals. These genes encode ten proteins that are usually classified as transcriptional activators or transcriptional repressors. E2Fs are important for many cellular processes, from their canonical role in cell cycle regulation to other roles in angiogenesis, the DNA damage response and apoptosis. A growing body of evidence demonstrates that cancer stem cells (CSCs) are key players in tumor development, metastasis, drug resistance and recurrence. This review focuses on the role of E2Fs in CSCs and notes that many signals can regulate the activities of E2Fs, which in turn can transcriptionally regulate many different targets to contribute to various biological characteristics of CSCs, such as proliferation, self-renewal, metastasis, and drug resistance. Therefore, E2Fs may be promising biomarkers and therapeutic targets associated with CSCs pathologies. Finally, exploring therapeutic strategies for E2Fs may result in disruption of CSCs, which may prevent tumor growth, metastasis, and drug resistance.

Dissecting Breast Cancer Circulating Tumor Cells Competence via Modelling Metastasis in Zebrafish

BACKGROUND

Cancer metastasis is a deathly process, and a better understanding of the different steps is needed. The shedding of circulating tumor cells (CTCs) and CTC-cluster from the primary tumor, its survival in circulation, and homing are key events of the metastasis cascade. In vitro models of CTCs and in vivo models of metastasis represent an excellent opportunity to delve into the behavior of metastatic cells, to gain understanding on how secondary tumors appear.

METHODS

Using the zebrafish embryo, in combination with the mouse and in vitro assays, as an in vivo model of the spatiotemporal development of metastases, we study the metastatic competency of breast cancer CTCs and CTC-clusters and the molecular mechanisms.

RESULTS

CTC-clusters disseminated at a lower frequency than single CTCs in the zebrafish and showed a reduced capacity to invade. A temporal follow-up of the behavior of disseminated CTCs showed a higher survival and proliferation capacity of CTC-clusters, supported by their increased resistance to fluid shear stress. These data were corroborated in mouse studies. In addition, a differential gene signature was observed, with CTC-clusters upregulating cell cycle and stemness related genes.

CONCLUSIONS

The zebrafish embryo is a valuable model system to understand the biology of breast cancer CTCs and CTC-clusters.

Knockdown of E2F4 suppresses the growth of ovarian cancer cells through the cell cycle pathway

Ovarian cancer remains one of the major causes of death from gynecologic cancer in developed countries. The E2F family has been shown to have a central role in the control of cell proliferation, differentiation, and cell cycle progression in various types of cancer. Despite advances in cancer research, the carcinogenic role of E2F transcription factor 4 (E2F4) in ovarian cancer remains unclear. In this study, we investigated the underlying molecular mechanism of E2F4 in human ovarian cancer cells (OCC). E2F4 expression was demonstrated by quantitative real time polymerase chain reaction (qRT-PCR) in OCC. The alterations of expression values were determined using 2^(-ΔΔCt) method. The effects of suppressing E2F4 on cell proliferation, migration, and differentiation were evaluated by cell proliferation assay, colony formation assay and wound healing assay in vitro. Overexpression of E2F4 was found at both mRNA and protein levels in OCC. Small interfering RNA was used to suppress E2F4 expression. Depletion of E2F4 inhibited cell proliferation and suppressed the cell migration and colony formation ability compared to controls. The expression of cell cycle machinery including cyclin A, cyclin D and cyclin dependent kinase 2 (CDK2) was increased after E2F4 knockdown. E2F4 modulates ovarian cancer cell proliferation and migration through cell cycle components including cyclin A, cyclin D, and CDK2. Our findings indicate that E2F4 may serve as a valuable candidate and therapeutic target for ovarian cancer treatment in regard to cell cycle control.

Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery

In this study, we integrate the outcomes of co-expression network analysis with the human interactome network to predict novel putative disease genes and modules. We first apply the SWItch Miner (SWIM) methodology, which predicts important (switch) genes within the co-expression network that regulate disease state transitions, then map them to the human protein-protein interaction network (PPI, or interactome) to predict novel disease-disease relationships (i.e., a SWIM-informed diseasome). Although the relevance of switch genes to an observed phenotype has been recently assessed, their performance at the system or network level constitutes a new, potentially fascinating territory yet to be explored. Quantifying the interplay between switch genes and human diseases in the interactome network, we found that switch genes associated with specific disorders are closer to each other than to other nodes in the network, and tend to form localized connected subnetworks. These subnetworks overlap between similar diseases and are situated in different neighborhoods for pathologically distinct phenotypes, consistent with the well-known topological proximity property of disease genes. These findings allow us to demonstrate how SWIM-based correlation network analysis can serve as a useful tool for efficient screening of potentially new disease gene associations. When integrated with an interactome-based network analysis, it not only identifies novel candidate disease genes, but also may offer testable hypotheses by which to elucidate the molecular underpinnings of human disease and reveal commonalities between seemingly unrelated diseases.

Transcription factor E2F4 is an indicator of poor prognosis and is related to immune infiltration in hepatocellular carcinoma

Background: Recent studies have shown that the transcription factor E2F4 is involved in the progression of various tumors, but its expression and influence on immune cell infiltration and biological functions are largely unknown in hepatocellular carcinoma (HCC).

Methods: The Cancer Genome Atlas (TCGA) database, the Tumor Immune Estimation Resource (TIMER) and related online tools as well as a tissue microarray (TMA) were used for analyses in our study.

Results: E2F4 expression was elevated in HCC tumor tissue compared with adjacent normal tissue at both the mRNA and protein levels. Overexpression of E2F4 was markedly related to a poor prognosis in HCC patients. In addition, positively and negatively correlated significant genes of E2F4 were identified in HCC. Pathway enrichment analyses revealed that the top 100 positively correlated significant genes of E2F4 were closely related to nuclear splicing and degradation-related pathways. Furthermore, nine hub genes correlated with E2F4 expression were validated based on a protein-protein interaction (PPI) network. It was also demonstrated that E2F4 expression was negatively correlated to immune purity and positively correlated to immune cell infiltration.

Conclusion: E2F4 could serve as a novel biomarker for HCC diagnosis and prognosis prediction.

Transcriptional E2F1/2/3/6 as potential prognostic biomarkers in cutaneous melanoma

Although the abnormal expression of members of the E2F family has been reported to participate in carcinogenesis in many human types of cancer, the bioinformatics role of the E2F family in melanoma is unknown. This research was designed to detect the expression, methylation, prognostic value and potential effects of the E2F family in melanoma. We investigated E2F family mRNA expression from the Oncomine and GEPIA databases and their methylation status in the MethHC database. Meanwhile, we detected the relative E2F family expression levels by qPCR and immunohistochemistry. Kaplan-Meier Plotter was used to draw survival analysis charts, and gene functional enrichment analyses were applied through cBioPortal database analysis. E2F1/2/3/4/5/6 mRNA and proteins were clearly upregulated in cutaneous melanoma patients, and high expression levels of E2F1/2/3/6 were statistically related to high methylation levels. Increased mRNA expression of E2F1/2/3/6 was related to lower overall survival rates (OS) and disease-free survival (DFS) in cutaneous melanoma cases. Meanwhile, E2F1/2/3/6 carried out these effects through regulating multiple signaling pathways, including the MAPK, PI3K-Akt and p53 signaling pathways. Taking together, our findings suggest that E2F1/2/3/6 could act as potential targets for precision therapy in cutaneous melanoma patients.

Cross-talk between myeloid-derived suppressor cells and Mucin1 in breast cancer vaccination: On the verge of a breakthrough

Although breast cancer is one of the leading troublesome cancers, the available therapeutic options have not fulfilled the desired outcomes. Immune-based therapy has gained special attention for breast cancer treatment. Although this approach is highly tolerable, its low response rate has rendered it as an undesirable approach. This review aims to describe the essential oncogenic pathways involved in breast cancer, elucidate the immunosuppression and oncogenic effect of Mucin1, and introduce myeloid-derived suppressor cells, which are the main culprits of anti-tumoral immune response attenuation. The various auto-inductive loops between Mucin1 and myeloid-derived suppressor cells are focal in the suppression of anti-tumoral immune responses in patients with breast cancer. These cross-talks between the Mucin1 and myeloid-derived suppressor cells can be the underlying causes of immunotherapy's impotence for patients with breast cancer. This approach can pave the road for the development of a potent vaccine for patients with breast cancer and is translated into clinical settings.

Computational STAT3 activity inference reveals its roles in the pancreatic tumor microenvironment

Transcription factor (TF) STAT3 contributes to pancreatic cancer progression through its regulatory roles in both tumor cells and the tumor microenvironment (TME). In this study, we performed a systematic analysis of all TFs in patient-derived gene expression datasets and confirmed STAT3 as a critical regulator in the pancreatic TME. Importantly, we developed a novel framework that is based on TF target gene expression to distinguish between environmental- and tumor-specific STAT3 activities in gene expression studies. Using this framework, our results novelly showed that compartment-specific STAT3 activities, but not STAT3 mRNA, have prognostications towards clinical values within pancreatic cancer datasets. In addition, high TME-derived STAT3 activity correlates with an immunosuppressive TME in pancreatic cancer, characterized by CD4 T cell and monocyte infiltration and high copy number variation burden. Where environmental-STAT3 seemed to play a dominant role at primary pancreatic sites, tumor-specific STAT3 seemed dominant at metastatic sites where its high activity persisted. In conclusion, by combining compartment-specific inference with other tumor characteristics, including copy number variation and immune-related gene expression, we demonstrate our method’s utility as a tool to generate novel hypotheses about TFs in tumor biology.

Characterization of the human E2F4 promoter region and its response to 12-O-tetradecanoylphorbol-13-acetate

The E2F transcription factors (TFs), which control the progression of the cell cycle in response to DNA-damage and various stresses, are known to interact with a tumour suppressor, Retinoblastoma 1 (RB1). We previously showed that the response of the human RB1 promoter to a 12-O-tetradecanoylphorbol-13-acetate (TPA) in HL-60 cells is mediated by a duplicated GGAA motif, which is also present in the 5'-upstream of the E2F family genes. The motifs are especially rich in the 5'-upstream of the E2F4 gene. In the present study, we constructed luciferase (Luc) expression vectors containing a 466 bp of the 5'-upstream of the human E2F4 gene. The transfection of this plasmid and deletion/mutation-introduced derivatives into HL-60 cells and a Luc reporter assay showed that duplicated and triplicated GGAA (TTCC) motifs in the E2F4 promoter respond to TPA. As expected, electrophoretic mobility shift assay indicated that SPI1 (PU.1) binds to the GGAA motif-containing element. A quantitative RT-PCR and western blotting showed that the E2F4 transcripts and its encoding proteins accumulate during the differentiation of HL-60 into macrophage-like cells. In contrast, the expression of the E2F1 gene and the protein, which possibly acts as a cell cycle accelerator, was greatly diminished.

Identification of Alternatively-Activated Pathways between Primary Breast Cancer and Liver Metastatic Cancer Using Microarray Data

Alternatively-activated pathways have been observed in biological experiments in cancer studies, but the concept had not been fully explored in computational cancer system biology. Therefore, an alternatively-activated pathway identification method was proposed and applied to primary breast cancer and breast cancer liver metastasis research using microarray data. Interestingly, the results show that cytokine-cytokine receptor interaction and calcium signaling were significantly enriched under both conditions. TGF beta signaling was found to be the hub in network topology analysis. In total, three types of alternatively-activated pathways were recognized. In the cytokine-cytokine receptor interaction pathway, four active alteration patterns in gene pairs were noticed. Thirteen cytokine-cytokine receptor pairs with inverse activity changes of both genes were verified by the literature. The second type was that some sub-pathways were active under only one condition. For the third type, nodes were significantly active in both conditions, but with different active genes. In the calcium signaling and TGF beta signaling pathways, node E2F5 and E2F4 were significantly active in primary breast cancer and metastasis, respectively. Overall, our study demonstrated the first time using microarray data to identify alternatively-activated pathways in breast cancer liver metastasis. The results showed that the proposed method was valid and effective, which could be helpful for future research for understanding the mechanism of breast cancer metastasis.

Hypoxia-Induced VISTA Promotes the Suppressive Function of Myeloid-Derived Suppressor Cells in the Tumor Microenvironment

Tumor hypoxia is a negative prognostic factor that is implicated in oncogenic signal activation, immune escape, and resistance to treatment. Identifying the mechanistic role of hypoxia in immune escape and resistance to immune-checkpoint inhibitors may aid the identification of therapeutic targets. We and others have shown that V-domain Ig suppressor of T-cell activation (VISTA), a negative checkpoint regulator in the B7 family, is highly expressed in the tumor microenvironment in tumor models and primary human cancers. In this study, we show that VISTA and HIF1α activity are correlated in a cohort of colorectal cancer patients. High VISTA expression was associated with worse overall survival. We used the CT26 colon cancer model to investigate the regulation of VISTA by hypoxia. Compared with less hypoxic tumor regions or draining lymph nodes, regions of profound hypoxia in the tumor microenvironment were associated with increased VISTA expression on tumor-infiltrating myeloid-derived suppressor cells (MDSC). Using chromatin immunoprecipitation and genetic silencing, we show that hypoxia-inducible factor (HIF)-1α binding to a conserved hypoxia response element in the VISTA promoter upregulated VISTA on myeloid cells. Further, antibody targeting or genetic ablation of VISTA under hypoxia relieved MDSC-mediated T-cell suppression, revealing VISTA as a mediator of MDSC function. Collectively, these data suggest that targeting VISTA may mitigate the deleterious effects of hypoxia on antitumor immunity.

Applications of ENCODE data to Systematic Analyses via Data Integration

Large-scale genomic data have been utilized to generate unprecedented biological findings and new hypotheses. To delineate functional elements in the human genome, the Encyclopedia of DNA Elements (ENCODE) project has generated an enormous amount of genomic data, yielding around 7,000 data profiles in different cell and tissue types. In this article, we reviewed the systematic analyses that have integrated ENCODE data with other data sources to reveal new biological insights, ranging from human genome annotation to the identification of new candidate drugs. These analyses demonstrate the critical impact of ENCODE data on basic biology and translational research.

Mining Featured Biomarkers Linked with Epithelial Ovarian CancerBased on Bioinformatics

Epithelial ovarian cancer (EOC) is the18th most common cancer worldwide and the 8th most common in women. The aim of this study was to diagnose the potential importance of, as well as novel genes linked with, EOC and to provide valid biological information for further research. The gene expression profiles of E-MTAB-3706 which contained four high-grade ovarian epithelial cancer samples, four normal fallopian tube samples and four normal ovarian epithelium samples were downloaded from the ArrayExpress database. Pathway enrichment and Gene Ontology (GO) enrichment analysis of differentially expressed genes (DEGs) were performed, and protein-protein interaction (PPI) network, microRNA-target gene regulatory network and TFs (transcription factors) -target gene regulatory network for up- and down-regulated were analyzed using Cytoscape. In total, 552 DEGs were found, including 276 up-regulated and 276 down-regulated DEGs. Pathway enrichment analysis demonstrated that most DEGs were significantly enriched in chemical carcinogenesis, urea cycle, cell adhesion molecules and creatine biosynthesis. GO enrichment analysis showed that most DEGs were significantly enriched in translation, nucleosome, extracellular matrix organization and extracellular matrix. From protein-protein interaction network (PPI) analysis, modules, microRNA-target gene regulatory network and TFs-target gene regulatory network for up- and down-regulated, and the top hub genes such as E2F4, SRPK2, A2M, CDH1, MAP1LC3A, UCHL1, HLA-C (major histocompatibility complex, class I, C), VAT1, ECM1 and SNRPN (small nuclear ribonucleoprotein polypeptide N) were associated in pathogenesis of EOC. The high expression levels of the hub genes such as CEBPD (CCAAT enhancer binding protein delta) and MID2 in stages 3 and 4 were validated in the TCGA (The Cancer Genome Atlas) database. CEBPD andMID2 were associated with the worst overall survival rates in EOC. In conclusion, the current study diagnosed DEGs between normal and EOC samples, which could improve our understanding of the molecular mechanisms in the progression of EOC. These new key biomarkers might be used as therapeutic targets for EOC.

E2F2/5/8 Serve as Potential Prognostic Biomarkers and Targets for Human Ovarian Cancer

E2Fs are a family of pivotal transcription factors. Accumulative evidence indicates that aberrant expression or activation of E2Fs is a common phenomenon in malignances, and significant associations have been noted between E2Fs and tumorigenesis or progression in a wide range of cancers. However, the expression patterns and exact roles of each E2F contributing to tumorigenesis and progression of ovarian cancer (OC) have not yet been elucidated. In this study, we investigated the distinct expression and prognostic value of E2Fs in patients with OC by analyzing a series of databases, including ONCOMINE, GEPIA, cBioPortal, Metascape, and Kaplan–Meier plotter. The mRNA expression levels of E2F1/3/5/8 were found to be significantly upregulated in patients with OC and were obviously associated with tumor stage for OC. Aberrant expression of E2F2/5/7/8 was found to be associated with the clinical outcomes of patients with OC. These results suggest that E2F2/5/8 might serve as potential prognostic biomarkers and targets for OC. However, future studies are required to validate our findings and promote the clinical utility of E2Fs in OC.

A data-driven interactome of synergistic genes improves network-based cancer outcome prediction

Robustly predicting outcome for cancer patients from gene expression is an important challenge on the road to better personalized treatment. Network-based outcome predictors (NOPs), which considers the cellular wiring diagram in the classification, hold much promise to improve performance, stability and interpretability of identified marker genes. Problematically, reports on the efficacy of NOPs are conflicting and for instance suggest that utilizing random networks performs on par to networks that describe biologically relevant interactions. In this paper we turn the prediction problem around: instead of using a given biological network in the NOP, we aim to identify the network of genes that truly improves outcome prediction. To this end, we propose SyNet, a gene network constructed ab initio from synergistic gene pairs derived from survival-labelled gene expression data. To obtain SyNet, we evaluate synergy for all 69 million pairwise combinations of genes resulting in a network that is specific to the dataset and phenotype under study and can be used to in a NOP model. We evaluated SyNet and 11 other networks on a compendium dataset of >4000 survival-labelled breast cancer samples. For this purpose, we used cross-study validation which more closely emulates real world application of these outcome predictors. We find that SyNet is the only network that truly improves performance, stability and interpretability in several existing NOPs. We show that SyNet overlaps significantly with existing gene networks, and can be confidently predicted (~85% AUC) from graph-topological descriptions of these networks, in particular the breast tissue-specific network. Due to its data-driven nature, SyNet is not biased to well-studied genes and thus facilitates post-hoc interpretation. We find that SyNet is highly enriched for known breast cancer genes and genes related to e.g. histological grade and tamoxifen resistance, suggestive of a role in determining breast cancer outcome.

Prognostic values of E2F mRNA expression in human gastric cancer

Gastric cancer (GC) is the second most frequent cause of cancer-related mortality in the world, with Eastern Asia having the highest incidence rates. E2F is a family of transcription factor proteins that has a variety of functions, which include control of cell cycle, cell differentiation, DNA damage response and cell death. E2F transcription factors are divided into two subfamilies: transcription activators (E2F transcription factors 1 (E2F1), 2 (E2F2) and 3a (E2F3a)) and repressors (E2F3b, E2F transcription factors 4 (E2F4), 5 (E2F5), 6 (E2F6), 7 (E2F7) and 8 (E2F8)). Studies have demonstrated that E2F had prognostic significance in a number of cancers. However, the entirety of the prognostic roles of E2F mRNA expression in GC has not yet been apparently determined. In the present study, the prognostic value of individual family members of E2F mRNA expression for overall survival (OS) was evaluated by using online Kaplan-Meier Plotter (KM Plotter) database. Our result demonstrated that high expressions of three family members of E2F (E2F1, E2F3, E2F4) mRNA were significantly associated with unfavourable OS in all GC patients. However, increased expressions of E2F2, E2F5, E2F6 and E2F7 were significantly associated with favourable OS, especially for higher clinical stages in GC patients. These results provided a better insight into the prognostic functions of E2F mRNA genes in GC. Although the results should be further verified in clinical trials, our findings may be a favourable prognostic predictor for the development of newer therapeutic drugs in the treatment of GC.

REPA: Applying Pathway Analysis to Genome-Wide Transcription Factor Binding Data

Pathway analysis has been extensively applied to aid in the interpretation of the results of genome-wide transcription profiling studies, and has been shown to successfully find associations between the biological phenomena under study and biological pathways. There are two widely used approaches of pathway analysis: over-representation analysis, and gene set analysis. Recently genome-wide transcription factor binding data has become widely available allowing for the application of pathway analysis to this type of data. In this work, we developed regulatory enrichment pathway analysis (REPA) to apply gene set analysis to genome-wide transcription factor binding data to infer associations between transcription factors and biological pathways. We used the transcription factor binding data generated by the ENCODE project, and gene sets from the Molecular Signatures and KEGG databases. Our results showed that 54 percent of the predictions examined have literature support and that REPA's recall is roughly 54 percent. This level of precision is promising as several of REPA's predictions are expected to be novel and can be used to guide new research avenues. In addition, the results of our case studies showed that REPA enhances the interpretation of genome-wide transcription profiling studies by suggesting putative regulators behind the observed transcriptional responses.

ER+ Breast Cancers Resistant to Prolonged Neoadjuvant Letrozole Exhibit an E2F4 Transcriptional Program Sensitive to CDK4/6 Inhibitors

Purpose: This study aimed to identify biomarkers of resistance to endocrine therapy in estrogen receptor-positive (ER+) breast cancers treated with prolonged neoadjuvant letrozole.Experimental Design: We performed targeted DNA and RNA sequencing in 68 ER+ breast cancers from patients treated with preoperative letrozole (median, 7 months).Results: Twenty-four tumors (35%) exhibited a PEPI score ≥4 and/or recurred after a median of 58 months and were considered endocrine resistant. Integration of the 47 most upregulated genes (log FC > 1, FDR < 0.03) in letrozole-resistant tumors with transcription-binding data showed significant overlap with 20 E2F4-regulated genes (P = 2.56E-15). In patients treated with the CDK4/6 inhibitor palbociclib before surgery, treatment significantly decreased expression of 24 of the 47 most upregulated genes in letrozole-resistant tumors, including 18 of the 20 E2F4 target genes. In long-term estrogen-deprived ER+ breast cancer cells, palbociclib also downregulated all 20 E2F4 target genes and P-RB levels, whereas the ER downregulator fulvestrant or paclitaxel only partially suppressed expression of this set of genes and had no effect on P-RB. Finally, an E2F4 activation signature was strongly associated with resistance to aromatase inhibitors in the ACOSOG Z1031B neoadjuvant trial and with an increased risk of relapse in adjuvant-treated ER+ tumors in METABRIC.Conclusions: In tumors resistant to prolonged neoadjuvant letrozole, we identified a gene expression signature of E2F4 target activation. CDK4/6 inhibition suppressed E2F4 target gene expression in estrogen-deprived ER+ breast cancer cells and in patients' ER+ tumors, suggesting a potential benefit of adjuvant CDK4/6 inhibitors in patients with ER+ breast cancer who fail to respond to preoperative estrogen deprivation. Clin Cancer Res; 24(11); 2517-29. ©2018 AACR.

Aberrant promoter methylation status is associated with upregulation of the E2F4 gene in breast cancer

E2F4 is an important basal transcription factor with the potential to promote tumor growth. Its upregulation in various types of cancer has been linked to numerous genetic factors; however, the nature of the involvement of epigenetic mechanisms, including DNA methylation, remains elusive. In the present study, E2F4 expression profiles were determined in 100 paired breast tumor and control samples, through RT-qPCR using the SYBR^® green method. Furthermore, the E2F4 promoter methylation status in each of these samples was assessed using methylation specific PCR, in order to evaluate its impact on gene expression. A two-fold increase in E2F4 gene expression was observed in the breast tumors compared with in their respective controls (P=0.022); of these tumors, ~72% were under-methylated. The change in methylation status was also significantly higher (P<0.001) in the tumor samples. Methylation status was negatively correlated (r=-30) with E2F4 expression profiles, indicating that a decrease in methylation may promote higher expression of E2F4. The two study cohorts (>45 and ≤45 years) had comparable methylation profiles, though they had significantly decreased methylation status compared with controls. Various histo-pathological types also have different methylation profiles, indicating the presence of a tissue specific methylation signature. The results of the present study demonstrated that E2F4 methylation status can have a notable influence on its expression, and that it may have prognostic value in breast carcinogenesis.

A P53-Deficiency Gene Signature Predicts Recurrence Risk of Patients with Early-Stage Lung Adenocarcinoma

Background: Lung cancer is associated with the highest mortality rate of all cancer types, and the most common histologic subtype of lung cancer is adenocarcinoma. To apply more effective therapeutic treatment, molecular markers that are able to predict the recurrence risk of patients with adenocarcinoma are critically needed. Mutations in TP53 tumor suppressor gene have been found in approximately 50% of lung adenocarcinoma cases, but the presence of a TP53 mutation does not always associate with increased mortality.Methods: The Cancer Genome Atlas RNA sequencing data of lung adenocarcinoma were used to define a novel gene signature for P53 deficiency. This signature was then used to calculate a sample-specific P53 deficiency score based on a patient's transcriptomic profile and tested in four independent lung adenocarcinoma microarray datasets.Results: In all datasets, P53 deficiency score was a significant predictor for recurrence-free survival where high P53 deficiency score was associated with poor survival. The score was prognostic even after adjusting for several key clinical variables including age, tumor stage, smoking status, and P53 mutation status. Furthermore, the score was able to predict recurrence-free survival in patients with stage I adenocarcinoma and was also associated with smoking status.Conclusions: The P53 deficiency score was a better predictor of recurrence-free survival compared with P53 mutation status and provided additional prognostic values to established clinical factors.Impact: The P53 deficiency score can be used to stratify early-stage patients into subgroups based on their risk of recurrence for aiding physicians to decide personalized therapeutic treatment. Cancer Epidemiol Biomarkers Prev; 27(1); 86-95. ©2017 AACR.

Therapeutic sensitivity to Rac GTPase inhibition requires consequential suppression of mTORC1, AKT, and MEK signaling in breast cancer

Rac GTPases have oncogenic roles in cell growth, survival, and migration. We tested response to the Rac inhibitor EHT1864 in a panel of breast cancer cell lines. EHT1864-induced growth inhibition was associated with dual inhibition of the PI3K/AKT/mTORC1 and MEK/ERK pathways. Breast cancer cells harboring PIK3CA mutations or HER2 overexpression were most sensitive to Rac inhibition, suggesting that such oncogenic alterations link Rac activation with PI3K/AKT/mTORC1 and MEK/ERK signaling. Interestingly, EHT1864 decreased activation of the mTORC1 substrate p70S6K earlier than AKT inhibition, suggesting that Rac may activate mTORC1/p70S6K independently of AKT. Comparison of the growth-inhibitory profile of EHT1864 to 137 other anti-cancer drugs across 656 cancer cell lines revealed significant correlation with the p70S6K inhibitor PF-4708671. We confirmed that Rac complexes contain MEK1/2 and ERK1/2, but also contain p70S6K; these interactions were disrupted by EHT1864. Pharmacokinetic profiles revealed that EHT1864 was present in mouse plasma at concentrations effective in vitro for approximately 1 h after intraperitoneal administration. EHT1864 suppressed growth of HER2+ tumors, and enhanced response to anti-estrogen treatment in ER+ tumors. Further therapeutic development of Rac inhibitors for HER2+ and PIK3CA-mutant cancers is warranted.

Cancer cell line specific co-factors modulate the FOXM1 cistrome

ChIP-seq has been commonly applied to identify genomic occupation of transcription factors (TFs) in a context-specific manner. It is generally assumed that a TF should have similar binding patterns in cells from the same or closely related tissues. Surprisingly, this assumption has not been carefully examined. To this end, we systematically compared the genomic binding of the cell cycle regulator FOXM1 in eight cell lines from seven different human tissues at binding signal, peaks and target genes levels. We found that FOXM1 binding in ER-positive breast cancer cell line MCF-7 are distinct comparing to those in not only other non-breast cell lines, but also MDA-MB-231, ER-negative breast cancer cell line. However, binding sites in MDA-MB-231 and non-breast cell lines were highly consistent. The recruitment of estrogen receptor alpha (ERα) caused the unique FOXM1 binding patterns in MCF-7. Moreover, the activity of FOXM1 in MCF-7 reflects the regulatory functions of ERα, while in MDA-MB-231 and non-breast cell lines, FOXM1 activities regulate cell proliferation. Our results suggest that tissue similarity, in some specific contexts, does not hold precedence over TF-cofactors interactions in determining transcriptional states and that the genomic binding of a TF can be dramatically affected by a particular co-factor under certain conditions.

The E2F4 prognostic signature predicts pathological response to neoadjuvant chemotherapy in breast cancer patients

Background

Neoadjuvant chemotherapy is a key component of breast cancer treatment regimens and pathologic complete response to this therapy varies among patients. This is presumably due to differences in the molecular mechanisms that underlie each tumor’s disease pathology. Developing genomic clinical assays that accurately categorize responders from non-responders can provide patients with the most effective therapy for their individual disease.

Methods

We applied our previously developed E2F4 genomic signature to predict neoadjuvant chemotherapy response in breast cancer. E2F4 individual regulatory activity scores were calculated for 1129 patient samples across 5 independent breast cancer neoadjuvant chemotherapy datasets. Accuracy of the E2F4 signature in predicting neoadjuvant chemotherapy response was compared to that of the Oncotype DX and MammaPrint predictive signatures.

Results

In all datasets, E2F4 activity level was an accurate predictor of neoadjuvant chemotherapy response, with high E2F4 scores predictive of achieving pathologic complete response and low scores predictive of residual disease. These results remained significant even after stratifying patients by estrogen receptor (ER) status, tumor stage, and breast cancer molecular subtypes. Compared to the Oncotype DX and MammaPrint signatures, our E2F4 signature achieved similar performance in predicting neoadjuvant chemotherapy response, though all signatures performed better in ER+ tumors compared to ER- ones. The accuracy of our signature was reproducible across datasets and was maintained when refined from a 199-gene signature down to a clinic-friendly 33-gene panel.

Conclusion

Overall, we show that our E2F4 signature is accurate in predicting patient response to neoadjuvant chemotherapy. As this signature is more refined and comparable in performance to other clinically available gene expression assays in the prediction of neoadjuvant chemotherapy response, it should be considered when evaluating potential treatment options.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3297-2) contains supplementary material, which is available to authorized users.

SWIM: a computational tool to unveiling crucial nodes in complex biological networks

SWItchMiner (SWIM) is a wizard-like software implementation of a procedure, previously described, able to extract information contained in complex networks. Specifically, SWIM allows unearthing the existence of a new class of hubs, called “fight-club hubs”, characterized by a marked negative correlation with their first nearest neighbors. Among them, a special subset of genes, called “switch genes”, appears to be characterized by an unusual pattern of intra- and inter-module connections that confers them a crucial topological role, interestingly mirrored by the evidence of their clinic-biological relevance. Here, we applied SWIM to a large panel of cancer datasets from The Cancer Genome Atlas, in order to highlight switch genes that could be critically associated with the drastic changes in the physiological state of cells or tissues induced by the cancer development. We discovered that switch genes are found in all cancers we studied and they encompass protein coding genes and non-coding RNAs, recovering many known key cancer players but also many new potential biomarkers not yet characterized in cancer context. Furthermore, SWIM is amenable to detect switch genes in different organisms and cell conditions, with the potential to uncover important players in biologically relevant scenarios, including but not limited to human cancer.

Contextual Refinement of Regulatory Targets Reveals Effects on Breast Cancer Prognosis of the Regulome

Gene expression regulators, such as transcription factors (TFs) and microRNAs (miRNAs), have varying regulatory targets based on the tissue and physiological state (context) within which they are expressed. While the emergence of regulator-characterizing experiments has inferred the target genes of many regulators across many contexts, methods for transferring regulator target genes across contexts are lacking. Further, regulator target gene lists frequently are not curated or have permissive inclusion criteria, impairing their use. Here, we present a method called iterative Contextual Transcriptional Activity Inference of Regulators (icTAIR) to resolve these issues. icTAIR takes a regulator’s previously-identified target gene list and combines it with gene expression data from a context, quantifying that regulator’s activity for that context. It then calculates the correlation between each listed target gene’s expression and the quantitative score of regulatory activity, removes the uncorrelated genes from the list, and iterates the process until it derives a stable list of refined target genes. To validate and demonstrate icTAIR’s power, we use it to refine the MSigDB c3 database of TF, miRNA and unclassified motif target gene lists for breast cancer. We then use its output for survival analysis with clinicopathological multivariable adjustment in 7 independent breast cancer datasets covering 3,430 patients. We uncover many novel prognostic regulators that were obscured prior to refinement, in particular NFY, and offer a detailed look at the composition and relationships among the breast cancer prognostic regulome. We anticipate icTAIR will be of general use in contextually refining regulator target genes for discoveries across many contexts. The icTAIR algorithm can be downloaded from https://github.com/icTAIR.

Gene expression regulators, such as transcription factors and microRNAs, are critical actors in cellular physiology and pathophysiology and act by modulating the expression levels of sets of target genes. Given their significance, numerous experiments have sought to characterize the specific target genes of specific regulators, which in turn has led to regulator target gene list databases. Unfortunately, these lists are plagued by poor curation and validation. Further, all lists suffer from the fundamental issue that regulator targets vary across tissue type and physiological state, or “context”, making them poor for conducting downstream, context-specific analyses. To address this issue, here we present a method called icTAIR that contextually-refines regulator target gene lists. To demonstrate its value, we use icTAIR to take the largest-available database of regulator target gene lists, refine it for the breast cancer context, and use both the pre-refined and refined lists for downstream survival analyses in over 3,400 tumors. We find that icTAIR improves the statistical power of the analyses by multiple orders of magnitude. This in turn lets us map the relational network of breast cancer regulators and identify regulators with prognostic effects even after clinicopathological adjustment. We anticipate icTAIR will be broadly useful in regulator studies.

The pan-cancer pathological regulatory landscape

Dysregulation of the normal gene expression program is the cause of a broad range of diseases, including cancer. Detecting the specific perturbed regulators that have an effect on the generation and the development of the disease is crucial for understanding the disease mechanism and for taking decisions on efficient preventive and curative therapies. Moreover, detecting such perturbations at the patient level is even more important from the perspective of personalized medicine. We applied the Transcription Factor Target Enrichment Analysis, a method that detects the activity of transcription factors based on the quantification of the collective transcriptional activation of their targets, to a large collection of 5607 cancer samples covering eleven cancer types. We produced for the first time a comprehensive catalogue of altered transcription factor activities in cancer, a considerable number of them significantly associated to patient’s survival. Moreover, we described several interesting TFs whose activity do not change substantially in the cancer with respect to the normal tissue but ultimately play an important role in patient prognostic determination, which suggest they might be promising therapeutic targets. An additional advantage of this method is that it allows obtaining personalized TF activity estimations for individual patients.

Targeting the RB-E2F pathway in breast cancer

Mutations of the retinoblastoma tumor-suppressor gene (RB1) or components regulating the CDK-RB-E2F pathway have been identified in nearly every human malignancy. Re-establishing cell cycle control through cyclin-dependent kinase (CDK) inhibition has therefore emerged as an attractive option in the development of targeted cancer therapy. The most successful example of this today is the use of the CDK4/6 inhibitor palbociclib combined with aromatase inhibitors for the treatment of estrogen receptor-positive breast cancers. Multiple studies have demonstrated that the CDK-RB-E2F pathway is critical for the control of cell proliferation. More recently, studies have highlighted additional roles of this pathway, especially E2F transcription factors themselves, in tumor progression, angiogenesis and metastasis. Specific E2Fs also have prognostic value in breast cancer, independent of clinical parameters. We discuss here recent advances in understanding of the RB-E2F pathway in breast cancer. We also discuss the application of genome-wide genetic screening efforts to gain insight into synthetic lethal interactions of CDK4/6 inhibitors in breast cancer for the development of more effective combination therapies.

iTAR: a web server for identifying target genes of transcription factors using ChIP-seq or ChIP-chip data

Background

Chromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP-seq) or microarray hybridization (ChIP-chip) has been widely used to determine the genomic occupation of transcription factors (TFs). We have previously developed a probabilistic method, called TIP (Target Identification from Profiles), to identify TF target genes using ChIP-seq/ChIP-chip data. To achieve high specificity, TIP applies a conservative method to estimate significance of target genes, with the trade-off being a relatively low sensitivity of target gene identification compared to other methods. Additionally, TIP’s output does not render binding-peak locations or intensity, information highly useful for visualization and general experimental biological use, while the variability of ChIP-seq/ChIP-chip file formats has made input into TIP more difficult than desired.

Description

To improve upon these facets, here we present are fined TIP with key extensions. First, it implements a Gaussian mixture model for p-value estimation, increasing target gene identification sensitivity and more accurately capturing the shape of TF binding profile distributions. Second, it enables the incorporation of TF binding-peak data by identifying their locations in significant target gene promoter regions and quantifies their strengths. Finally, for full ease of implementation we have incorporated it into a web server (http://syslab3.nchu.edu.tw/iTAR/) that enables flexibility of input file format, can be used across multiple species and genome assembly versions, and is freely available for public use. The web server additionally performs GO enrichment analysis for the identified target genes to reveal the potential function of the corresponding TF.

Conclusions

The iTAR web server provides a user-friendly interface and supports target gene identification in seven species, ranging from yeast to human. To facilitate investigating the quality of ChIP-seq/ChIP-chip data, the web server generates the chart of the characteristic binding profiles and the density plot of normalized regulatory scores. The iTAR web server is a useful tool in identifying TF target genes from ChIP-seq/ChIP-chip data and discovering biological insights.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2963-0) contains supplementary material, which is available to authorized users.

Analysis of TRRAP as a Potential Molecular Marker and Therapeutic Target for Breast Cancer

PURPOSE

This study was designed to assess the protein levels of transformation/transcription domain-associated protein (TRRAP) in invasive ductal breast carcinomas, and investigated the association between TRRAP and the clinicopathological features of breast cancer.

METHODS

We examined TRRAP protein expression in 470 breast cancer tissues and normal breast tissues by tissue microarray to study the correlation between TRRAP expression and clinicopathological features. This was analyzed using the chi-square test. Kaplan-Meier survival curves and log-rank tests were applied to analyze the survival status. Cox regression was applied for multivariate analysis of prognosis.

RESULTS

The data demonstrated that expression of TRRAP was significantly lower in breast carcinomas (36.6%) than in corresponding normal breast tissues (50.8%). In addition, TRRAP protein levels negatively correlated with tumor size, and indicated poor differentiation, increased nodal involvement, and low p53-positive rates. Analysis of survival revealed that lower TRRAP expression correlated with shorter survival time. Univariate analyses identified TRRAP and progesterone receptor as independent protective factors for breast cancer prognosis. However, Ki-67, tumor size, and nodal involvement appeared to be independent risk factors.

CONCLUSION

The findings indicate a significant correlation between TRRAP protein levels and adverse prognosis in breast cancer. Therefore, TRRAP could be a prognostic biomarker for breast cancer. In addition, TRRAP is also a predictive biomarker of breast cancer treatment.

Integrative Genomic Analyses Yield Cell-Cycle Regulatory Programs with Prognostic Value

Liposarcoma is the second most common form of sarcoma, which has been categorized into four molecular subtypes, which are associated with differential prognosis of patients. However, the transcriptional regulatory programs associated with distinct histologic and molecular subtypes of liposarcoma have not been investigated. This study uses integrative analyses to systematically define the transcriptional regulatory programs associated with liposarcoma. Likewise, computational methods are used to identify regulatory programs associated with different liposarcoma subtypes, as well as programs that are predictive of prognosis. Further analysis of curated gene sets was used to identify prognostic gene signatures. The integration of data from a variety of sources, including gene expression profiles, transcription factor-binding data from ChIP-Seq experiments, curated gene sets, and clinical information of patients, indicated discrete regulatory programs (e.g., controlled by E2F1 and E2F4), with significantly different regulatory activity in one or multiple subtypes of liposarcoma with respect to normal adipose tissue. These programs were also shown to be prognostic, wherein liposarcoma patients with higher E2F4 or E2F1 activity associated with unfavorable prognosis. A total of 259 gene sets were significantly associated with patient survival in liposarcoma, among which > 50% are involved in cell cycle and proliferation.

IMPLICATIONS

These integrative analyses provide a general framework that can be applied to investigate the mechanism and predict prognosis of different cancer types.

The Three Paralogous MicroRNA Clusters in Development and Disease, miR-17-92, miR-106a-363, and miR-106b-25

MicroRNAs (miRNAs) form a class of noncoding RNA genes whose products are small single-stranded RNAs that are involved in the regulation of translation and degradation of mRNAs. There is a fine balance between deregulation of normal developmental programs and tumor genesis. An increasing body of evidence suggests that altered expression of miRNAs is entailed in the pathogenesis of human cancers. Studies in mouse and human cells have identified the miR-17-92 cluster as a potential oncogene. The miR-17-92 cluster is often amplified or overexpressed in human cancers and has recently emerged as the prototypical oncogenic polycistron miRNA. The functional analysis of miR-17-92 is intricate by the existence of two paralogues: miR-106a-363 and miR-106b-25. During early evolution of vertebrates, it is likely that the three clusters commenced via a series of duplication and deletion occurrences. As miR-106a-363 and miR-106b-25 contain miRNAs that are very similar, and in some cases identical, to those encoded by miR-17-92, it is feasible that they regulate a similar set of genes and have overlapping functions. Further understanding of these three clusters and their functions will increase our knowledge about cancer progression. The present review discusses the characteristics and functions of these three miRNA clusters.

E2F4 Program Is Predictive of Progression and Intravesical Immunotherapy Efficacy in Bladder Cancer

Bladder cancer is a common malignant disease, with non-muscle-invasive bladder cancer (NMIBC) representing the majority of tumors. This cancer subtype is typically treated by transurethral resection. In spite of treatment, up to 70% of patients show local recurrences. Intravesical BCG (Bacillus Calmette-Guerin) immunotherapy has been widely used to treat NMIBC, but it fails to suppress recurrence of bladder tumors in up to 40% of patients. Therefore, the development of prognostic markers is needed to predict the progression of bladder cancer and the efficacy of intravesical BCG treatment. This study demonstrates the effectiveness of an E2F4 signature for prognostic prediction of bladder cancer. E2F4 scores for each sample in a bladder cancer expression dataset were calculated by summarizing the relative expression levels of E2F4 target genes identified by ChIP-seq, and then the scores were used to stratify patients into good- and poor-outcome groups. The molecular signature was investigated in a single bladder cancer dataset and then its effectiveness was confirmed in two meta-bladder datasets consisting of specimens from multiple independent studies. These results were consistent in different datasets and demonstrate that the E2F4 score is predictive of clinical outcomes in bladder cancer, with patients whose tumors exhibit an E2F4 score >0 having significantly shorter survival times than those with an E2F4 score <0, in both non-muscle-invasive, and muscle-invasive bladder cancer. Furthermore, although intravesical BCG immunotherapy can significantly improve the clinical outcome of NMIBC patients with positive E2F4 scores (E2F4>0 group), it does not show significant treatment effect for those with negative scores (E2F4<0 group).

IMPLICATIONS

The E2F4 signature can be applied to predict the progression/recurrence and the responsiveness of patients to intravesical BCG immunotherapy in bladder cancer.

Integrative analysis of survival-associated gene sets in breast cancer

BACKGROUND

Patient gene expression information has recently become a clinical feature used to evaluate breast cancer prognosis. The emergence of prognostic gene sets that take advantage of these data has led to a rich library of information that can be used to characterize the molecular nature of a patient's cancer. Identifying robust gene sets that are consistently predictive of a patient's clinical outcome has become one of the main challenges in the field.

METHODS

We inputted our previously established BASE algorithm with patient gene expression data and gene sets from MSigDB to develop the gene set activity score (GSAS), a metric that quantitatively assesses a gene set's activity level in a given patient. We utilized this metric, along with patient time-to-event data, to perform survival analyses to identify the gene sets that were significantly correlated with patient survival. We then performed cross-dataset analyses to identify robust prognostic gene sets and to classify patients by metastasis status. Additionally, we created a gene set network based on component gene overlap to explore the relationship between gene sets derived from MSigDB. We developed a novel gene set based on this network's topology and applied the GSAS metric to characterize its role in patient survival.

RESULTS

Using the GSAS metric, we identified 120 gene sets that were significantly associated with patient survival in all datasets tested. The gene overlap network analysis yielded a novel gene set enriched in genes shared by the robustly predictive gene sets. This gene set was highly correlated to patient survival when used alone. Most interestingly, removal of the genes in this gene set from the gene pool on MSigDB resulted in a large reduction in the number of predictive gene sets, suggesting a prominent role for these genes in breast cancer progression.

CONCLUSIONS

The GSAS metric provided a useful medium by which we systematically investigated how gene sets from MSigDB relate to breast cancer patient survival. We used this metric to identify predictive gene sets and to construct a novel gene set containing genes heavily involved in cancer progression.