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.

Protein mistranslation protects bacteria against oxidative stress.

Accurate flow of genetic information from DNA to protein requires faithful translation. An increased level of translational errors (mistranslation) has therefore been widely considered harmful to cells. Here we demonstrate that surprisingly, moderate levels of mistranslation indeed increase tolerance to oxidative stress in Escherichia coli. Our RNA sequencing analyses revealed that two antioxidant genes katE and osmC, both controlled by the general stress response activator RpoS, were upregulated by a ribosomal error-prone mutation. Mistranslation-induced tolerance to hydrogen peroxide required rpoS, katE and osmC. We further show that both translational and post-translational regulation of RpoS contribute to peroxide tolerance in the error-prone strain, and a small RNA DsrA, which controls translation of RpoS, is critical for the improved tolerance to oxidative stress through mistranslation. Our work thus challenges the prevailing view that mistranslation is always detrimental, and provides a mechanism by which mistranslation benefits bacteria under stress conditions.

Regulators associated with clinical outcomes revealed by DNA methylation data in breast cancer.

The regulatory architecture of breast cancer is extraordinarily complex and gene misregulation can occur at many levels, with transcriptional malfunction being a major cause. This dysfunctional process typically involves additional regulatory modulators including DNA methylation. Thus, the interplay between transcription factor (TF) binding and DNA methylation are two components of a cancer regulatory interactome presumed to display correlated signals. As proof of concept, we performed a systematic motif-based in silico analysis to infer all potential TFs that are involved in breast cancer prognosis through an association with DNA methylation changes. Using breast cancer DNA methylation and clinical data derived from The Cancer Genome Atlas (TCGA), we carried out a systematic inference of TFs whose misregulation underlie different clinical subtypes of breast cancer. Our analysis identified TFs known to be associated with clinical outcomes of p53 and ER (estrogen receptor) subtypes of breast cancer, while also predicting new TFs that may also be involved. Furthermore, our results suggest that misregulation in breast cancer can be caused by the binding of alternative factors to the binding sites of TFs whose activity has been ablated. Overall, this study provides a comprehensive analysis that links DNA methylation to TF binding to patient prognosis.

DPRP: a database of phenotype-specific regulatory programs derived from transcription factor binding data.

Gene expression profiling has been extensively used in the past decades, resulting in an enormous amount of expression data available in public databases. These data sets are informative in elucidating transcriptional regulation of genes underlying various biological and clinical conditions. However, it is usually difficult to identify transcription factors (TFs) responsible for gene expression changes directly from their own expression, as TF activity is often regulated at the posttranscriptional level. In recent years, technical advances have made it possible to systematically determine the target genes of TFs by ChIP-seq experiments. To identify the regulatory programs underlying gene expression profiles, we constructed a database of phenotype-specific regulatory programs (DPRP, http://syslab.nchu.edu.tw/DPRP/) derived from the integrative analysis of TF binding data and gene expression data. DPRP provides three methods: the Fisher's Exact Test, the Kolmogorov-Smirnov test and the BASE algorithm to facilitate the application of gene expression data for generating new hypotheses on transcriptional regulatory programs in biological and clinical studies.

Big data bioinformatics.

Recent technological advances allow for high throughput profiling of biological systems in a cost-efficient manner. The low cost of data generation is leading us to the "big data" era. The availability of big data provides unprecedented opportunities but also raises new challenges for data mining and analysis. In this review, we introduce key concepts in the analysis of big data, including both "machine learning" algorithms as well as "unsupervised" and "supervised" examples of each. We note packages for the R programming language that are available to perform machine learning analyses. In addition to programming based solutions, we review webservers that allow users with limited or no programming background to perform these analyses on large data compendia.

E2F4 regulatory program predicts patient survival prognosis in breast cancer.

INTRODUCTION: Genetic and molecular signatures have been incorporated into cancer prognosis prediction and treatment decisions with good success over the past decade. Clinically, these signatures are usually used in early-stage cancers to evaluate whether they require adjuvant therapy following surgical resection. A molecular signature that is prognostic across more clinical contexts would be a useful addition to current signatures. METHODS: We defined a signature for the ubiquitous tissue factor, E2F4, based on its shared target genes in multiple tissues. These target genes were identified by chromatin immunoprecipitation sequencing (ChIP-seq) experiments using a probabilistic method. We then computationally calculated the regulatory activity score (RAS) of E2F4 in cancer tissues, and examined how E2F4 RAS correlates with patient survival. RESULTS: Genes in our E2F4 signature were 21-fold more likely to be correlated with breast cancer patient survival time compared to randomly selected genes. Using eight independent breast cancer datasets containing over 1,900 unique samples, we stratified patients into low and high E2F4 RAS groups. E2F4 activity stratification was highly predictive of patient outcome, and our results remained robust even when controlling for many factors including patient age, tumor size, grade, estrogen receptor (ER) status, lymph node (LN) status, whether the patient received adjuvant therapy, and the patient's other prognostic indices such as Adjuvant! and the Nottingham Prognostic Index scores. Furthermore, the fractions of samples with positive E2F4 RAS vary in different intrinsic breast cancer subtypes, consistent with the different survival profiles of these subtypes. CONCLUSIONS: We defined a prognostic signature, the E2F4 regulatory activity score, and showed it to be significantly predictive of patient outcome in breast cancer regardless of treatment status and the states of many other clinicopathological variables. It can be used in conjunction with other breast cancer classification methods such as Oncotype DX to improve clinical outcome prediction.

Transcription factor binding profiles reveal cyclic expression of human protein-coding genes and non-coding RNAs.

Cell cycle is a complex and highly supervised process that must proceed with regulatory precision to achieve successful cellular division. Despite the wide application, microarray time course experiments have several limitations in identifying cell cycle genes. We thus propose a computational model to predict human cell cycle genes based on transcription factor (TF) binding and regulatory motif information in their promoters. We utilize ENCODE ChIP-seq data and motif information as predictors to discriminate cell cycle against non-cell cycle genes. Our results show that both the trans- TF features and the cis- motif features are predictive of cell cycle genes, and a combination of the two types of features can further improve prediction accuracy. We apply our model to a complete list of GENCODE promoters to predict novel cell cycle driving promoters for both protein-coding genes and non-coding RNAs such as lincRNAs. We find that a similar percentage of lincRNAs are cell cycle regulated as protein-coding genes, suggesting the importance of non-coding RNAs in cell cycle division. The model we propose here provides not only a practical tool for identifying novel cell cycle genes with high accuracy, but also new insights on cell cycle regulation by TFs and cis-regulatory elements.

Pan-cancer systematic identification of lncRNAs associated with cancer prognosis.

BACKGROUND: The "dark matter" of the genome harbors several non-coding RNA species including Long non-coding RNAs (lncRNAs), which have been implicated in neoplasia but remain understudied. RNA-seq has provided deep insights into the nature of lncRNAs in cancer but current RNA-seq data are rarely accompanied by longitudinal patient survival information. In contrast, a plethora of microarray studies have collected these clinical metadata that can be leveraged to identify novel associations between gene expression and clinical phenotypes. METHODS: In this study, we developed an analysis framework that computationally integrates RNA-seq and microarray data to systematically screen 9,463 lncRNAs for association with mortality risk across 20 cancer types. RESULTS: In total, we identified a comprehensive list of associations between lncRNAs and patient survival and demonstrate that these prognostic lncRNAs are under selective pressure and may be functional. Our results provide valuable insights that facilitate further exploration of lncRNAs and their potential as cancer biomarkers and drug targets.

A Transcriptionally Definable Subgroup of Triple-Negative Breast and Ovarian Cancer Samples Shows Sensitivity to HSP90 Inhibition.

PURPOSE: We hypothesized that integrated analysis of cancer types from different lineages would reveal novel molecularly defined subgroups with unique therapeutic vulnerabilities. On the basis of the molecular similarities between subgroups of breast and ovarian cancers, we analyzed these cancers as a single cohort to test our hypothesis. EXPERIMENTAL DESIGN: Identification of transcriptional subgroups of cancers and drug sensitivity analyses were performed using mined data. Cell line sensitivity to Hsp90 inhibitors (Hsp90i) was tested in vitro. The ability of a transcriptional signature to predict Hsp90i sensitivity was validated using cell lines, and cell line- and patient-derived xenograft (PDX) models. Mechanisms of Hsp90i sensitivity were uncovered using immunoblot and RNAi. RESULTS: Transcriptomic analyses of breast and ovarian cancer cell lines uncovered two mixed subgroups comprised primarily of triple-negative breast and multiple ovarian cancer subtypes. Drug sensitivity analyses revealed that cells of one mixed subgroup are significantly more sensitive to Hsp90i compared with cells from all other cancer lineages evaluated. A gene expression classifier was generated that predicted Hsp90i sensitivity in vitro, and in cell line- and PDXs. Cells from the Hsp90i-sensitive subgroup underwent apoptosis mediated by Hsp90i-induced upregulation of the proapoptotic proteins Bim and PUMA. CONCLUSIONS: Our findings identify Hsp90i as a potential therapeutic strategy for a transcriptionally defined subgroup of ovarian and breast cancers. This study demonstrates that gene expression profiles may be useful to identify therapeutic vulnerabilities in tumor types with limited targetable genetic alterations, and to identify molecularly definable cancer subgroups that transcend lineage.

An integrative ENCODE resource for cancer genomics.

ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNA-binding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a well-known oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource.

CD36-Mediated Metabolic Rewiring of Breast Cancer Cells Promotes Resistance to HER2-Targeted Therapies.

Although it is established that fatty acid (FA) synthesis supports anabolic growth in cancer, the role of exogenous FA uptake remains elusive. Here we show that, during acquisition of resistance to HER2 inhibition, metabolic rewiring of breast cancer cells favors reliance on exogenous FA uptake over de novo FA synthesis. Through cDNA microarray analysis, we identify the FA transporter CD36 as a critical gene upregulated in cells with acquired resistance to the HER2 inhibitor lapatinib. Accordingly, resistant cells exhibit increased exogenous FA uptake and metabolic plasticity. Genetic or pharmacological inhibition of CD36 suppresses the growth of lapatinib-resistant but not lapatinib-sensitive cells in vitro and in vivo. Deletion of Cd36 in mammary tissues of MMTV-neu mice significantly attenuates tumorigenesis. In breast cancer patients, CD36 expression increases following anti-HER2 therapy, which correlates with a poor prognosis. Our results define CD36-mediated metabolic rewiring as an essential survival mechanism in HER2-positive breast cancer.

Statins associate with improved mortality among patients with certain histological subtypes of lung cancer.

OBJECTIVES: To measure the association between statin exposure and mortality in lung cancer patients belonging to different categories of histological subtype. MATERIALS AND METHODS: A cohort of 19,974 individuals with incident lung cancer between 2007 and 2011 was identified using the SEER-Medicare linked database. Statin exposure both pre- and post-diagnosis was analyzed to identify a possible association with cancer-specific mortality in patients stratified by histological subtype. Intention-to-treat analyses and time-dependent Cox regression models were used to calculate hazard ratios and 95% confidence intervals (95% CIs) corresponding to statin exposure both pre- and post-diagnosis, respectively. RESULTS: Overall baseline statin exposure was associated with a decrease in mortality risk for squamous-cell carcinoma patients (HR = 0.89, 95% CI = 0.82-0.96) and adenocarcinoma patients (HR = 0.87, 95% CI = 0.82-0.94), but not among those with small-cell lung cancer. Post-diagnostic statin exposure was associated with prolonged survival in squamous-cell carcinoma patients (HR = 0.68, 95% CI = 0.59-0.79) and adenocarcinoma patients (HR = 0.78, 95% CI = 0.68-0.89) in a dose-dependent manner. CONCLUSION: There is consistent evidence indicating that baseline or post-diagnostic exposure to simvastatin and atorvastatin is associated with extended survival in non-small-cell lung cancer subtypes. These results warrant further randomized clinical trials to evaluate subtype-specific effects of certain statins in patient cohorts with characteristics similar to those examined in this study.

Integrity, standards, and QC-related issues with big data in pre-clinical drug discovery.

The tremendous expansion of data analytics and public and private big datasets presents an important opportunity for pre-clinical drug discovery and development. In the field of life sciences, the growth of genetic, genomic, transcriptomic and proteomic data is partly driven by a rapid decline in experimental costs as biotechnology improves throughput, scalability, and speed. Yet far too many researchers tend to underestimate the challenges and consequences involving data integrity and quality standards. Given the effect of data integrity on scientific interpretation, these issues have significant implications during preclinical drug development. We describe standardized approaches for maximizing the utility of publicly available or privately generated biological data and address some of the common pitfalls. We also discuss the increasing interest to integrate and interpret cross-platform data. Principles outlined here should serve as a useful broad guide for existing analytical practices and pipelines and as a tool for developing additional insights into therapeutics using big data.

Computational Investigation of Homologous Recombination DNA Repair Deficiency in Sporadic Breast Cancer.

BRCAness has important implications in the management and treatment of patients with breast and ovarian cancer. In this study, we propose a computational framework to measure the BRCAness of breast and ovarian tumor samples based on their gene expression profiles. We define a characteristic profile for BRCAness by comparing gene expression differences between BRCA1/2 mutant familial tumors and sporadic breast cancer tumors while adjusting for relevant clinical factors. With this BRCAness profile, our framework calculates sample-specific BRCA scores, which indicates homologous recombination (HR)-mediated DNA repair pathway activity of samples. We found that in sporadic breast cancer high BRCAness score is associated with aberrant copy number of HR genes rather than somatic mutation and other genomic features. Moreover, we observed significant correlations of BRCA score with genome instability and neoadjuvant chemotherapy. More importantly, BRCA score provides significant prognostic value in both breast and ovarian cancers after considering established clinical variables. In summary, the inferred BRCAness from our framework can be used as a robust biomarker for the prediction of prognosis and treatment response in breast and ovarian cancers.

MYC Mediates mRNA Cap Methylation of Canonical Wnt/ß-Catenin Signaling Transcripts By Recruiting CDK7 and RNA Methyltransferase.

MYC is a pleiotropic transcription factor that activates and represses a wide range of target genes and is frequently deregulated in human tumors. While much is known about the role of MYC in transcriptional activation and repression, MYC can also regulate mRNA cap methylation through a mechanism that has remained poorly understood. Here, it is reported that MYC enhances mRNA cap methylation of transcripts globally, specifically increasing mRNA cap methylation of genes involved in Wnt/ß-catenin signaling. Elevated mRNA cap methylation of Wnt signaling transcripts in response to MYC leads to augmented translational capacity, elevated protein levels, and enhanced Wnt signaling activity. Mechanistic evidence indicates that MYC promotes recruitment of RNA methyltransferase (RNMT) to Wnt signaling gene promoters by enhancing phosphorylation of serine 5 on the RNA polymerase II carboxy-terminal domain, mediated in part through an interaction between the TIP60 acetyltransferase complex and TFIIH. IMPLICATIONS: MYC enhances mRNA cap methylation above and beyond transcriptional induction. Mol Cancer Res; 15(2); 213-24. ©2016 AACR.

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.

Insulin-Mediated Signaling Facilitates Resistance to PDGFR Inhibition in Proneural hPDGFB-Driven Gliomas.

Despite abundant evidence implicating receptor tyrosine kinases (RTK), including the platelet-derived growth factor receptor (PDGFR), in the pathogenesis of glioblastoma (GBM), the clinical use of RTK inhibitors in this disease has been greatly compromised by the rapid emergence of therapeutic resistance. To study the resistance of proneural gliomas that are driven by a PDGFR-regulated pathway to targeted tyrosine kinase inhibitors, we utilized a mouse model of proneural glioma in which mice develop tumors that become resistant to PDGFR inhibition. We found that tumors resistant to PDGFR inhibition required the expression and activation of the insulin receptor (IR)/insulin growth-like factor receptor (IGF1R) for tumor cell proliferation and survival. Cotargeting IR/IGF1R and PDGFR decreased the emergence of resistant clones in vitro Our findings characterize a novel model of glioma recurrence that implicates the IR/IGF1R signaling axis in mediating the development of resistance to PDGFR inhibition and provide evidence that IR/IGF1R signaling is important in the recurrence of the proneural subtype of glioma in which PDGF/PDGFR is most commonly expressed at a high level. Mol Cancer Ther; 16(4); 705-16. ©2017 AACR.

In silico frameworks for systematic pre-clinical screening of potential anti-leukemia therapeutics.

INTRODUCTION: Leukemia is a collection of highly heterogeneous cancers that arise from neoplastic transformation and clonal expansion of immature hematopoietic cells. Post-treatment recurrence is high, especially among elderly patients, thus necessitating more effective treatment modalities. Development of novel anti-leukemic compounds relies heavily on traditional in vitro screens which require extensive resources and time. Therefore, integration of in silico screens prior to experimental validation can improve the efficiency of pre-clinical drug development. Areas covered: This article reviews different methods and frameworks used to computationally screen for anti-leukemic agents. In particular, three approaches are discussed including molecular docking, transcriptomic integration, and network analysis. Expert opinion: Today's data deluge presents novel opportunities to develop computational tools and pipelines to screen for likely therapeutic candidates in the treatment of leukemia. Formal integration of these methodologies can accelerate and improve the efficiency of modern day anti-leukemic drug discovery and ease the economic and healthcare burden associated with it.

Integrative analysis of cancer genes in a functional interactome.

The post-genomic era has resulted in the accumulation of high-throughput cancer data from a vast array of genomic technologies including next-generation sequencing and microarray. As such, the large amounts of germline variant and somatic mutation data that have been generated from GWAS and sequencing projects, respectively, show great promise in providing a systems-level view of these genetic aberrations. In this study, we analyze publicly available GWAS, somatic mutation, and drug target data derived from large databanks using a network-based approach that incorporates directed edge information under a randomized network hypothesis testing procedure. We show that these three classes of disease-associated nodes exhibit non-random topological characteristics in the context of a functional interactome. Specifically, we show that drug targets tend to lie upstream of somatic mutations and disease susceptibility germline variants. In addition, we introduce a new approach to measuring hierarchy between drug targets, somatic mutants, and disease susceptibility genes by utilizing directionality and path length information. Overall, our results provide new insight into the intrinsic relationships between these node classes that broaden our understanding of cancer. In addition, our results align with current knowledge on the therapeutic actionability of GWAS and somatic mutant nodes, while demonstrating relationships between node classes from a global network perspective.

Application of pharmacologically induced transcriptomic profiles to interrogate PI3K-Akt-mTOR pathway activity associated with cancer patient prognosis.

The PI3K-Akt-mTOR signaling pathway has been identified as a key driver of carcinogenesis in several cancer types. As such, a major area of focus in cancer biology is the development of genomic biomarkers that can measure the activity level of the PI3K-Akt-mTOR pathway. In this study, we systematically estimate PI3K-Akt-mTOR pathway activity in breast primary tumor samples using transcriptomic profiles derived from drug treatment in MCF7 cell lines. We demonstrate that gene expression profiles derived from chemically-induced protein inhibition allows us to measure PI3K-Akt-mTOR pathway activity in patient tumor samples. With this approach, we predict prognosis and response to chemotherapy in cancer patients, and screen for potential pharmacological modulators of PI3K-Akt-mTOR pathway inhibitors.