Functional characterization of somatic mutations in cancer using network-based inference of protein activity

RNA-seq from archival FFPE breast cancer samples: molecular pathway fidelity and novel discovery

BACKGROUND

Formalin-fixed, paraffin-embedded (FFPE) tissues for RNA-seq have advantages over fresh frozen tissue including abundance and availability, connection to rich clinical data, and association with patient outcomes. However, FFPE-derived RNA is highly degraded and chemically modified, which impacts its utility as a faithful source for biological inquiry.

METHODS

True archival FFPE breast cancer cases (n = 58), stored at room temperature for 2-23 years, were utilized to identify key steps in tissue selection, RNA isolation, and library choice. Gene expression fidelity was evaluated by comparing FFPE data to public data obtained from fresh tissues, and by employing single-gene, gene set and transcription network-based regulon analyses.

RESULTS

We report a single 10 μm section of breast tissue yields sufficient RNA for RNA-seq, and a relationship between RNA quality and block age that was not linear. We find single-gene analysis is limiting with FFPE tissues, while targeted gene set approaches effectively distinguish ER+ from ER- breast cancers. Novel utilization of regulon analysis identified the transcription factor KDM4B to associate with ER+ disease, with KDM4B regulon activity and gene expression having prognostic significance in an independent cohort of ER+ cases.

CONCLUSION

Our results, which outline a robust FFPE-RNA-seq pipeline for broad use, support utilizing FFPE tissues to address key questions in the breast cancer field, including the delineation between indolent and life-threatening disease, biological stratification and molecular mechanisms of treatment resistance.

A Human Adult Stem Cell Signature Marks Aggressive Variants across Epithelial Cancers

Cancer progression to an aggressive phenotype often co-opts aspects of stem cell biology. Here, we developed gene signatures for normal human stem cell populations to understand the relationship between epithelial cancers and stem cell transcriptional programs. Using a pan-cancer approach, we reveal that aggressive epithelial cancers are enriched for a transcriptional signature shared by epithelial adult stem cells. The adult stem cell signature selected for epithelial cancers with worse overall survival and alterations of oncogenic drivers. Lethal small cell neuroendocrine lung, prostate, and bladder cancers transcriptionally converged onto the adult stem cell signature and not other stem cell signatures tested. We found that DNA methyltransferase expression correlated with adult stem cell signature status and was enriched in small cell neuroendocrine cancers. DNA methylation analysis uncovered a shared epigenomic profile between small cell neuroendocrine cancers. These pan-cancer findings establish a molecular link between human adult stem cells and aggressive epithelial cancers.

Signatures of cell death and proliferation in perturbation transcriptomics data-from confounding factor to effective prediction

Transcriptional perturbation signatures are valuable data sources for functional genomics. Linking perturbation signatures to screenings opens the possibility to model cellular phenotypes from expression data and to identify efficacious drugs. We linked perturbation transcriptomics data from the LINCS-L1000 project with cell viability information upon genetic (Achilles project) and chemical (CTRP screen) perturbations yielding more than 90 000 signature–viability pairs. An integrated analysis showed that the cell viability signature is a major factor underlying perturbation signatures. The signature is linked to transcription factors regulating cell death, proliferation and division time. We used the cell viability–signature relationship to predict viability from transcriptomics signatures, and identified and validated compounds that induce cell death in tumor cell lines. We showed that cellular toxicity can lead to unexpected similarity of signatures, confounding mechanism of action discovery. Consensus compound signatures predicted cell-specific drug sensitivity, even if the signature is not measured in the same cell line, and outperformed conventional drug-specific features. Our results can help in understanding mechanisms behind cell death and removing confounding factors of transcriptomic perturbation screens. To interactively browse our results and predict cell viability in new gene expression samples, we developed CEVIChE (CEll VIability Calculator from gene Expression; https://saezlab.shinyapps.io/ceviche/).

Master Regulators of Signaling Pathways: An Application to the Analysis of Gene Regulation in Breast Cancer

Analysis of gene regulatory networks allows the identification of master transcriptional factors that control specific groups of genes. In this work, we inferred a gene regulatory network from a large dataset of breast cancer samples to identify the master transcriptional factors that control the genes within signal transduction pathways. The focus in a particular subset of relevant genes constitutes an extension of the original Master Regulator Inference Algorithm (MARINa) analysis. This modified version of MARINa utilizes a restricted molecular signature containing genes from the 25 human pathways in KEGG's signal transduction category. Our breast cancer RNAseq expression dataset consists of 881 samples comprising tumors and normal mammary gland tissue. The top 10 master transcriptional factors found to regulate signal transduction pathways in breast cancer we identified are: TSHZ2, HOXA2, MEIS2, HOXA3, HAND2, HOXA5, TBX18, PEG3, GLI2, and CLOCK. The functional enrichment of the regulons of these master transcriptional factors showed an important proportion of processes related to morphogenesis. Our results suggest that, as part of the aberrant regulation of signaling pathways in breast cancer, pathways similar to the regulation of cell differentiation, cardiovascular system development, and vasculature development may be dysregulated and co-opted in favor of tumor development through the action of these transcription factors.

Holland C, Zaza A, Murad W, Hassan R, A. Ahmed Y, Abbas T, A. Abdelrahim E, Schneider KM, Matz-Soja M, Reinders J, Gebhardt R, Berres ML, Hatting M, Drasdo D, Saez-Rodriguez J, Trautwein C, G. Hengstler J. Influence of Liver Fibrosis on Lobular Zonation

Little is known about how liver fibrosis influences lobular zonation. To address this question, we used three mouse models of liver fibrosis, repeated CCl4 administration for 2, 6 and 12 months to induce pericentral damage, as well as bile duct ligation (21 days) and mdr2-/- mice to study periportal fibrosis. Analyses were performed by RNA-sequencing, immunostaining of zonated proteins and image analysis. RNA-sequencing demonstrated a significant enrichment of pericentral genes among genes downregulated by CCl4; vice versa, periportal genes were enriched among the upregulated genes. Immunostaining showed an almost complete loss of pericentral proteins, such as cytochrome P450 enzymes and glutamine synthetase, while periportal proteins, such as arginase 1 and CPS1 became expressed also in pericentral hepatocytes. This pattern of fibrosis-associated 'periportalization' was consistently observed in all three mouse models and led to complete resistance to hepatotoxic doses of acetaminophen (200 mg/kg). Characterization of the expression response identified the inflammatory pathways TGFβ, NFκB, TNFα, and transcription factors NFKb1, Stat1, Hif1a, Trp53, and Atf1 among those activated, while estrogen-associated pathways, Hnf4a and Hnf1a, were decreased. In conclusion, liver fibrosis leads to strong alterations of lobular zonation, where the pericentral region adopts periportal features. Beside adverse consequences, periportalization supports adaptation to repeated doses of hepatotoxic compounds.

MEK-ERK signaling is a therapeutic target in metastatic castration resistant prostate cancer

BACKGROUND

Metastatic castration resistant prostate cancer (mCRPC) is incurable and progression after drugs that target the androgen receptor-signaling axis is inevitable. Thus, there is an urgent need to develop more effective treatments beyond hormonal manipulation. We sought to identify activated kinases in mCRPC as therapeutic targets for existing, approved agents, with the goal of identifying candidate drugs for rapid translation into proof of concept Phase II trials in mCRPC.

METHODS

To identify evidence of activation of druggable kinases in these patients, we compared mRNA expression from metastatic biopsies of patients with mCRPC (n = 101) to mRNA expression in localized prostate from TCGA and used this analysis to infer differential kinase activity. In addition, we assessed the differential phosphorylation levels for key MAPK pathway kinases between mCRPC and localized prostate cancers.

RESULTS

Transcriptomic profiling of 101 patients with mCRPC as compared to patients with localized prostate cancer identified evidence of hyperactive ERK1, and whole genome sequencing revealed frequent amplifications of members of the MAPK pathway in 32% of this cohort. Next, we confirmed elevated levels of phosphorylated ERK1/2 in castration resistant prostate cancer as compared to untreated primary prostate cancer. We observed that the presence of detectable phosphorylated ERK1/2 in the primary tumor is associated with biochemical failure after radical prostatectomy independent of clinicopathologic features. ERK1 is the immediate downstream target of MEK1/2, which is druggable with trametinib, an approved therapeutic for melanoma. Trametinib elicited a profound biochemical and clinical response in a patient who had failed multiple prior treatments for mCRPC.

CONCLUSIONS

We conclude that pharmacologic targeting of the MEK/ERK pathway may be a viable treatment strategy for patients with refractory metastatic prostate cancer. An ongoing Phase II trial tests this hypothesis.

Ontogenic Changes in Hematopoietic Hierarchy Determine Pediatric Specificity and Disease Phenotype in Fusion Oncogene-Driven Myeloid Leukemia

Fusion oncogenes are prevalent in several pediatric cancers, yet little is known about the specific associations between age and phenotype. We observed that fusion oncogenes, such as ETO2-GLIS2, are associated with acute megakaryoblastic or other myeloid leukemia subtypes in an age-dependent manner. Analysis of a novel inducible transgenic mouse model showed that ETO2-GLIS2 expression in fetal hematopoietic stem cells induced rapid megakaryoblastic leukemia whereas expression in adult bone marrow hematopoietic stem cells resulted in a shift toward myeloid transformation with a strikingly delayed in vivo leukemogenic potential. Chromatin accessibility and single-cell transcriptome analyses indicate ontogeny-dependent intrinsic and ETO2-GLIS2-induced differences in the activities of key transcription factors, including ERG, SPI1, GATA1, and CEBPA. Importantly, switching off the fusion oncogene restored terminal differentiation of the leukemic blasts. Together, these data show that aggressiveness and phenotypes in pediatric acute myeloid leukemia result from an ontogeny-related differential susceptibility to transformation by fusion oncogenes. SIGNIFICANCE: This work demonstrates that the clinical phenotype of pediatric acute myeloid leukemia is determined by ontogeny-dependent susceptibility for transformation by oncogenic fusion genes. The phenotype is maintained by potentially reversible alteration of key transcription factors, indicating that targeting of the fusions may overcome the differentiation blockage and revert the leukemic state.See related commentary by Cruz Hernandez and Vyas, p. 1653.This article is highlighted in the In This Issue feature, p. 1631.

A Functional Landscape of CKD Entities From Public Transcriptomic Data

Introduction

To develop effective therapies and identify novel early biomarkers for chronic kidney disease, an understanding of the molecular mechanisms orchestrating it is essential. We here set out to understand how differences in chronic kidney disease (CKD) origin are reflected in gene expression. To this end, we integrated publicly available human glomerular microarray gene expression data for 9 kidney disease entities that account for most of CKD worldwide. Our primary goal was to demonstrate the possibilities and potential on data analysis and integration to the nephrology community.

Methods

We integrated data from 5 publicly available studies and compared glomerular gene expression profiles of disease with that of controls from nontumor parts of kidney cancer nephrectomy tissues. A major challenge was the integration of the data from different sources, platforms, and conditions that we mitigated with a bespoke stringent procedure.

Results

We performed a global transcriptome-based delineation of different kidney disease entities, obtaining a transcriptomic diffusion map of their similarities and differences based on the genes that acquire a consistent differential expression between each kidney disease entity and nephrectomy tissue. We derived functional insights by inferring the activity of signaling pathways and transcription factors from the collected gene expression data and identified potential drug candidates based on expression signature matching. We validated representative findings by immunostaining in human kidney biopsies indicating, for example, that the transcription factor FOXM1 is significantly and specifically expressed in parietal epithelial cells in rapidly progressive glomerulonephritis (RPGN) whereas not expressed in control kidney tissue. Furthermore, we found drug candidates by matching the signature on expression of drugs to that of the CKD entities, in particular, the Food and Drug Administration-approved drug nilotinib.

Conclusion

These results provide a foundation to comprehend the specific molecular mechanisms underlying different kidney disease entities that can pave the way to identify biomarkers and potential therapeutic targets. To facilitate further use, we provide our results as a free interactive Web application: https://saezlab.shinyapps.io/ckd_landscape/. However, because of the limitations of the data and the difficulties in its integration, any specific result should be considered with caution. Indeed, we consider this study rather an illustration of the value of functional genomics and integration of existing data.

From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL

While gene expression profiling is commonly used to gain an overview of cellular processes, the identification of upstream processes that drive expression changes remains a challenge. To address this issue, we introduce CARNIVAL, a causal network contextualization tool which derives network architectures from gene expression footprints. CARNIVAL (CAusal Reasoning pipeline for Network identification using Integer VALue programming) integrates different sources of prior knowledge including signed and directed protein-protein interactions, transcription factor targets, and pathway signatures. The use of prior knowledge in CARNIVAL enables capturing a broad set of upstream cellular processes and regulators, leading to a higher accuracy when benchmarked against related tools. Implementation as an integer linear programming (ILP) problem guarantees efficient computation. As a case study, we applied CARNIVAL to contextualize signaling networks from gene expression data in IgA nephropathy (IgAN), a condition that can lead to chronic kidney disease. CARNIVAL identified specific signaling pathways and associated mediators dysregulated in IgAN including Wnt and TGF-β, which we subsequently validated experimentally. These results demonstrated how CARNIVAL generates hypotheses on potential upstream alterations that propagate through signaling networks, providing insights into diseases.

Chromatin regulators mediate anthracycline sensitivity in breast cancer

Anthracyclines are a highly effective component of curative breast cancer chemotherapy, but are associated with significant morbidity^1,2. Since anthracyclines work in part via inhibition of topoisomerase-II (TOP2) on accessible DNA^3,4, we hypothesized that chromatin regulatory genes (CRGs) that mediate DNA accessibility might predict anthracycline response. We elucidate the role of CRGs in anthracycline sensitivity in breast cancer through integrative analysis of patient and cell line data. We identify a consensus set of 38 CRGs associated with anthracycline response across ten cell line datasets. Evaluating the interaction between expression and treatment in predicting survival in a metacohort of 1006 early-stage breast cancer patients, we identify 54 CRGs whose expression levels dictate anthracycline benefit across the clinical subgroups, 12 of which overlapped with those identified in vitro. CRGs that promote DNA accessibility, including Trithorax complex members, were associated with anthracycline sensitivity when highly expressed, whereas CRGs that reduce accessibility such as Polycomb complex proteins, were associated with decreased anthracycline sensitivity. We show that KDM4B modulates TOP2 accessibility to chromatin, elucidating a mechanism of TOP2 inhibitor sensitivity. These findings indicate that CRGs mediate anthracycline benefit by modulating DNA accessibility with implications for breast cancer patient stratification and treatment decision making.

RTNsurvival: an R/Bioconductor package for regulatory network survival analysis

MOTIVATION

Transcriptional networks are models that allow the biological state of cells or tumours to be described. Such networks consist of connected regulatory units known as regulons, each comprised of a regulator and its targets. Inferring a transcriptional network can be a helpful initial step in characterizing the different phenotypes within a cohort. While the network itself provides no information on molecular differences between samples, the per-sample state of each regulon, i.e. the regulon activity, can be used for describing subtypes in a cohort. Integrating regulon activities with clinical data and outcomes would extend this characterization of differences between subtypes.

RESULTS

We describe RTNsurvival, an R/Bioconductor package that calculates regulon activity profiles using transcriptional networks reconstructed by the RTN package, gene expression data, and a two-tailed Gene Set Enrichment Analysis. Given regulon activity profiles across a cohort, RTNsurvival can perform Kaplan-Meier analyses and Cox Proportional Hazards regressions, while also considering confounding variables. The Supplementary Information provides two case studies that use data from breast and liver cancer cohorts and features uni- and multivariate regulon survival analysis.

AVAILABILITY AND IMPLEMENTATION

RTNsurvival is written in the R language, and is available from the Bioconductor project at http://bioconductor.org/packages/RTNsurvival/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Gene regulatory network inference resources: A practical overview

Transcriptional regulation is a fundamental molecular mechanism involved in almost every aspect of life, from homeostasis to development, from metabolism to behavior, from reaction to stimuli to disease progression. In recent years, the concept of Gene Regulatory Networks (GRNs) has grown popular as an effective applied biology approach for describing the complex and highly dynamic set of transcriptional interactions, due to its easy-to-interpret features. Since cataloguing, predicting and understanding every GRN connection in all species and cellular contexts remains a great challenge for biology, researchers have developed numerous tools and methods to infer regulatory processes. In this review, we catalogue these methods in six major areas, based on the dominant underlying information leveraged to infer GRNs: Coexpression, Sequence Motifs, Chromatin Immunoprecipitation (ChIP), Orthology, Literature and Protein-Protein Interaction (PPI) specifically focused on transcriptional complexes. The methods described here cover a wide range of user-friendliness: from web tools that require no prior computational expertise to command line programs and algorithms for large scale GRN inferences. Each method for GRN inference described herein effectively illustrates a type of transcriptional relationship, with many methods being complementary to others. While a truly holistic approach for inferring and displaying GRNs remains one of the greatest challenges in the field of systems biology, we believe that the integration of multiple methods described herein provides an effective means with which experimental and computational biologists alike may obtain the most complete pictures of transcriptional relationships. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Biological process activity transformation of single cell gene expression for cross-species alignment

The maintenance and transition of cellular states are controlled by biological processes. Here we present a gene set-based transformation of single cell RNA-Seq data into biological process activities that provides a robust description of cellular states. Moreover, as these activities represent species-independent descriptors, they facilitate the alignment of single cell states across different organisms.

Single cell RNA-Seq data can report on cellular types and states, but low signal-to noise and sparse data can make interpretation of cellular state difficult. Here the authors propose a transformation strategy to map RNA-Seq data to biological process activities that are species-agnostic and allow for comparison across species.

Gene networks in cancer are biased by aneuploidies and sample impurities

Gene regulatory network inference is a standard technique for obtaining structured regulatory information from, for instance, gene expression measurements. Methods performing this task have been extensively evaluated on synthetic, and to a lesser extent real data sets. In contrast to these test evaluations, applications to gene expression data of human cancers are often limited by fewer samples and more potential regulatory links, and are biased by copy number aberrations as well as cell mixtures and sample impurities. Here, we take networks inferred from TCGA cohorts as an example to show that (1) transcription factor annotations are essential to obtain reliable networks, and (2) even for state of the art methods, we expect that between 20 and 80% of edges are caused by copy number changes and cell mixtures rather than transcription factor regulation.

Using R and Bioconductor in Clinical Genomics and Transcriptomics

Bioinformatics pipelines are essential in the analysis of genomic and transcriptomic data generated by next-generation sequencing (NGS). Recent guidelines emphasize the need for rigorous validation and assessment of robustness, reproducibility, and quality of NGS analytic pipelines intended for clinical use. Software tools written in the R statistical language and, in particular, the set of tools available in the Bioconductor repository are widely used in research bioinformatics; and these frameworks offer several advantages for use in clinical bioinformatics, including the breath of available tools, modular nature of software packages, ease of installation, enforcement of interoperability, version control, and short learning curve. This review provides an introduction to R and Bioconductor software, its advantages and limitations for clinical bioinformatics, and illustrative examples of tools that can be used in various steps of NGS analysis.

Chromatin-informed inference of transcriptional programs in gynecologic and basal breast cancers

Chromatin accessibility data can elucidate the developmental origin of cancer cells and reveal the enhancer landscape of key oncogenic transcriptional regulators. We develop a computational strategy called PSIONIC (patient-specific inference of networks informed by chromatin) to combine chromatin accessibility data with large tumor expression data and model the effect of enhancers on transcriptional programs in multiple cancers. We generate a new ATAC-seq data profiling chromatin accessibility in gynecologic and basal breast cancer cell lines and apply PSIONIC to 723 patient and 96 cell line RNA-seq profiles from ovarian, uterine, and basal breast cancers. Our computational framework enables us to share information across tumors to learn patient-specific TF activities, revealing regulatory differences between and within tumor types. PSIONIC-predicted activity for MTF1 in cell line models correlates with sensitivity to MTF1 inhibition, showing the potential of our approach for personalized therapy. Many identified TFs are significantly associated with survival outcome. To validate PSIONIC-derived prognostic TFs, we perform immunohistochemical analyses in 31 uterine serous tumors for ETV6 and 45 basal breast tumors for MITF and confirm that the corresponding protein expression patterns are also significantly associated with prognosis.

NeTFactor, a framework for identifying transcriptional regulators of gene expression-based biomarkers

Biological and regulatory mechanisms underlying many multi-gene expression-based disease biomarkers are often not readily evident. We describe an innovative framework, NeTFactor, that combines network analyses with gene expression data to identify transcription factors (TFs) that significantly and maximally regulate such a biomarker. NeTFactor uses a computationally-inferred context-specific gene regulatory network and applies topological, statistical, and optimization methods to identify regulator TFs. Application of NeTFactor to a multi-gene expression-based asthma biomarker identified ETS translocation variant 4 (ETV4) and peroxisome proliferator-activated receptor gamma (PPARG) as the biomarker's most significant TF regulators. siRNA-based knock down of these TFs in an airway epithelial cell line model demonstrated significant reduction of cytokine expression relevant to asthma, validating NeTFactor's top-scoring findings. While PPARG has been associated with airway inflammation, ETV4 has not yet been implicated in asthma, thus indicating the possibility of novel, disease-relevant discovery by NeTFactor. We also show that NeTFactor's results are robust when the gene regulatory network and biomarker are derived from independent data. Additionally, our application of NeTFactor to a different disease biomarker identified TF regulators of interest. These results illustrate that the application of NeTFactor to multi-gene expression-based biomarkers could yield valuable insights into regulatory mechanisms and biological processes underlying disease.

Non-coding RNA regulatory networks

It is well established that the vast majority of human RNA transcripts do not encode for proteins and that non-coding RNAs regulate cell physiology and shape cellular functions. A subset of them is involved in gene regulation at different levels, from epigenetic gene silencing to post-transcriptional regulation of mRNA stability. Notably, the aberrant expression of many non-coding RNAs has been associated with aggressive pathologies. Rapid advances in network biology indicates that the robustness of cellular processes is the result of specific properties of biological networks such as scale-free degree distribution and hierarchical modularity, suggesting that regulatory network analyses could provide new insights on gene regulation and dysfunction mechanisms. In this study we present an overview of public repositories where non-coding RNA-regulatory interactions are collected and annotated, we discuss unresolved questions for data integration and we recall existing resources to build and analyse networks.

Deconvolution of transcriptional networks identifies TCF4 as a master regulator in schizophrenia

Applying tissue-specific deconvolution of transcriptional networks to identify their master regulators (MRs) in neuropsychiatric disorders has been largely unexplored. Here, using two schizophrenia (SCZ) case-control RNA-seq datasets, one on postmortem dorsolateral prefrontal cortex (DLPFC) and another on cultured olfactory neuroepithelium, we deconvolved the transcriptional networks and identified TCF4 as a top candidate MR that may be dysregulated in SCZ. We validated TCF4 as a MR through enrichment analysis of TCF4-binding sites in induced pluripotent stem cell (hiPSC)-derived neurons and in neuroblastoma cells. We further validated the predicted TCF4 targets by knocking down TCF4 in hiPSC-derived neural progenitor cells (NPCs) and glutamatergic neurons (Glut_Ns). The perturbed TCF4 gene network in NPCs was more enriched for pathways involved in neuronal activity and SCZ-associated risk genes, compared to Glut_Ns. Our results suggest that TCF4 may serve as a MR of a gene network dysregulated in SCZ at early stages of neurodevelopment.

Oral Selinexor-Dexamethasone for Triple-Class Refractory Multiple Myeloma

BACKGROUND

Selinexor, a selective inhibitor of nuclear export compound that blocks exportin 1 (XPO1) and forces nuclear accumulation and activation of tumor suppressor proteins, inhibits nuclear factor κB, and reduces oncoprotein messenger RNA translation, is a potential novel treatment for myeloma that is refractory to current therapeutic options.

METHODS

We administered oral selinexor (80 mg) plus dexamethasone (20 mg) twice weekly to patients with myeloma who had previous exposure to bortezomib, carfilzomib, lenalidomide, pomalidomide, daratumumab, and an alkylating agent and had disease refractory to at least one proteasome inhibitor, one immunomodulatory agent, and daratumumab (triple-class refractory). The primary end point was overall response, defined as a partial response or better, with response assessed by an independent review committee. Clinical benefit, defined as a minimal response or better, was a secondary end point.

RESULTS

A total of 122 patients in the United States and Europe were included in the modified intention-to-treat population (primary analysis), and 123 were included in the safety population. The median age was 65 years, and the median number of previous regimens was 7; a total of 53% of the patients had high-risk cytogenetic abnormalities. A partial response or better was observed in 26% of patients (95% confidence interval, 19 to 35), including two stringent complete responses; 39% of patients had a minimal response or better. The median duration of response was 4.4 months, median progression-free survival was 3.7 months, and median overall survival was 8.6 months. Fatigue, nausea, and decreased appetite were common and were typically grade 1 or 2 (grade 3 events were noted in up to 25% of patients, and no grade 4 events were reported). Thrombocytopenia occurred in 73% of the patients (grade 3 in 25% and grade 4 in 33%). Thrombocytopenia led to bleeding events of grade 3 or higher in 6 patients.

CONCLUSIONS

Selinexor-dexamethasone resulted in objective treatment responses in patients with myeloma refractory to currently available therapies. (Funded by Karyopharm Therapeutics; STORM ClinicalTrials.gov number, NCT02336815.).

CR2Cancer: a database for chromatin regulators in human cancer

Chromatin regulators (CRs) can dynamically modulate chromatin architecture to epigenetically regulate gene expression in response to intrinsic and extrinsic signalling cues. Somatic alterations or misexpression of CRs might reprogram the epigenomic landscape of chromatin, which in turn lead to a wide range of common diseases, notably cancer. Here, we present CR2Cancer, a comprehensive annotation and visualization database for CRs in human cancer constructed by high throughput data analysis and literature mining. We collected and integrated genomic, transcriptomic, proteomic, clinical and functional information for over 400 CRs across multiple cancer types. We also built diverse types of CR-associated relations, including cancer type dependent (CR-target and miRNA-CR) and independent (protein-protein interaction and drug-target) ones. Furthermore, we manually curated around 6000 items of aberrant molecular alterations and interactions of CRs in cancer development from 5007 publications. CR2Cancer provides a user-friendly web interface to conveniently browse, search and download data of interest. We believe that this database would become a valuable resource for cancer epigenetics investigation and potential clinical application. CR2Cancer is freely available at http://cis.hku.hk/CR2Cancer.

Benchmark and integration of resources for the estimation of human transcription factor activities

The prediction of transcription factor (TF) activities from the gene expression of their targets (i.e., TF regulon) is becoming a widely used approach to characterize the functional status of transcriptional regulatory circuits. Several strategies and data sets have been proposed to link the target genes likely regulated by a TF, each one providing a different level of evidence. The most established ones are (1) manually curated repositories, (2) interactions derived from ChIP-seq binding data, (3) in silico prediction of TF binding on gene promoters, and (4) reverse-engineered regulons from large gene expression data sets. However, it is not known how these different sources of regulons affect the TF activity estimations and, thereby, downstream analysis and interpretation. Here we compared the accuracy and biases of these strategies to define human TF regulons by means of their ability to predict changes in TF activities in three reference benchmark data sets. We assembled a collection of TF-target interactions for 1541 human TFs and evaluated how different molecular and regulatory properties of the TFs, such as the DNA-binding domain, specificities, or mode of interaction with the chromatin, affect the predictions of TF activity. We assessed their coverage and found little overlap on the regulons derived from each strategy and better performance by literature-curated information followed by ChIP-seq data. We provide an integrated resource of all TF-target interactions derived through these strategies, with confidence scores, as a resource for enhanced prediction of TF activities.

Fast, Quantitative and Variant Enabled Mapping of Peptides to Genomes

Current tools for visualization and integration of proteomics with other omics datasets are inadequate for large-scale studies and capture only basic sequence identity information. Furthermore, the frequent reformatting of annotations for reference genomes required by these tools is known to be highly error prone. We developed PoGo for mapping peptides identified through mass spectrometry to overcome these limitations. PoGo reduced runtime and memory usage by 85% and 20%, respectively, and exhibited overall superior performance over other tools on benchmarking with large-scale human tissue and cancer phosphoproteome datasets comprising ∼3 million peptides. In addition, extended functionality enables representation of single-nucleotide variants, post-translational modifications, and quantitative features. PoGo has been integrated in established frameworks such as the PRIDE tool suite and OpenMS, as well as a standalone tool with user-friendly graphical interface. With the rapid increase of quantitative high-resolution datasets capturing proteomes and global modifications to complement orthogonal genomics platforms, PoGo provides a central utility enabling large-scale visualization and interpretation of transomics datasets.

The number of titrated microRNA species dictates ceRNA regulation

microRNAs (miRNAs) play key roles in cancer, but their propensity to couple their targets as competing endogenous RNAs (ceRNAs) has only recently emerged. Multiple models have studied ceRNA regulation, but these models did not account for the effects of co-regulation by miRNAs with many targets. We modeled ceRNA and simulated its effects using established parameters for miRNA/mRNA interaction kinetics while accounting for co-regulation by multiple miRNAs with many targets. Our simulations suggested that co-regulation by many miRNA species is more likely to produce physiologically relevant context-independent couplings. To test this, we studied the overlap of inferred ceRNA networks from four tumor contexts-our proposed pan-cancer ceRNA interactome (PCI). PCI was composed of interactions between genes that were co-regulated by nearly three-times as many miRNAs as other inferred ceRNA interactions. Evidence from expression-profiling datasets suggested that PCI interactions are predictive of gene expression in 12 independent tumor- and non-tumor contexts. Biochemical assays confirmed ceRNA couplings for two PCI subnetworks, including oncogenes CCND1, HIF1A and HMGA2, and tumor suppressors PTEN, RB1 and TP53. Our results suggest that PCI is enriched for context-independent interactions that are coupled by many miRNA species and are more likely to be context independent.

Pan-Cancer and Single-Cell Modeling of Genomic Alterations Through Gene Expression

Cancer is a disease often characterized by the presence of multiple genomic alterations, which trigger altered transcriptional patterns and gene expression, which in turn sustain the processes of tumorigenesis, tumor progression, and tumor maintenance. The links between genomic alterations and gene expression profiles can be utilized as the basis to build specific molecular tumorigenic relationships. In this study, we perform pan-cancer predictions of the presence of single somatic mutations and copy number variations using machine learning approaches on gene expression profiles. We show that gene expression can be used to predict genomic alterations in every tumor type, where some alterations are more predictable than others. We propose gene aggregation as a tool to improve the accuracy of alteration prediction models from gene expression profiles. Ultimately, we show how this principle can be beneficial in intrinsically noisy datasets, such as those based on single-cell sequencing.

Pan-cancer Convergence to a Small-Cell Neuroendocrine Phenotype that Shares Susceptibilities with Hematological Malignancies

Small-cell neuroendocrine cancers (SCNCs) are an aggressive cancer subtype. Transdifferentiation toward an SCN phenotype has been reported as a resistance route in response to targeted therapies. Here, we identified a convergence to an SCN state that is widespread across epithelial cancers and is associated with poor prognosis. More broadly, non-SCN metastases have higher expression of SCN-associated transcription factors than non-SCN primary tumors. Drug sensitivity and gene dependency screens demonstrate that these convergent SCNCs have shared vulnerabilities. These common vulnerabilities are found across unannotated SCN-like epithelial cases, small-round-blue cell tumors, and unexpectedly in hematological malignancies. The SCN convergent phenotype and common sensitivity profiles with hematological cancers can guide treatment options beyond tissue-specific targeted therapies.

Estimation of Transcription Factor Activity in Knockdown Studies

Numerous methods have been developed trying to infer actual regulatory events in a sample. A prominent class of methods model genome-wide gene expression as linear equations derived from a transcription factor (TF) – gene network and optimizes parameters to fit the measured expression intensities. We apply four such methods on experiments with a TF-knockdown (KD) in human and E. coli. The transcriptome data provides clear expression signals and thus represents an extremely favorable test setting. The methods estimate activity changes of all TFs, which we expect to be highest in the KD TF. However, only in 15 out of 54 cases, the KD TFs ranked in the top 5%. We show that this poor overall performance cannot be attributed to a low effectiveness of the knockdown or the specific regulatory network provided as background knowledge. Further, the ranks of regulators related to the KD TF by the network or pathway are not significantly different from a random selection. In general, the result overlaps of different methods are small, indicating that they draw very different conclusions when presented with the same, presumably simple, inference problem. These results show that the investigated methods cannot yield robust TF activity estimates in knockdown schemes.

ChEA3: transcription factor enrichment analysis by orthogonal omics integration

Identifying the transcription factors (TFs) responsible for observed changes in gene expression is an important step in understanding gene regulatory networks. ChIP-X Enrichment Analysis 3 (ChEA3) is a transcription factor enrichment analysis tool that ranks TFs associated with user-submitted gene sets. The ChEA3 background database contains a collection of gene set libraries generated from multiple sources including TF-gene co-expression from RNA-seq studies, TF-target associations from ChIP-seq experiments, and TF-gene co-occurrence computed from crowd-submitted gene lists. Enrichment results from these distinct sources are integrated to generate a composite rank that improves the prediction of the correct upstream TF compared to ranks produced by individual libraries. We compare ChEA3 with existing TF prediction tools and show that ChEA3 performs better. By integrating the ChEA3 libraries, we illuminate general transcription factor properties such as whether the TF behaves as an activator or a repressor. The ChEA3 web-server is available from https://amp.pharm.mssm.edu/ChEA3.

Master regulator analysis of paragangliomas carrying SDHx, VHL, or MAML3 genetic alterations

BACKGROUND

Succinate dehydrogenase (SDH) loss and mastermind-like 3 (MAML3) translocation are two clinically important genetic alterations that correlate with increased rates of metastasis in subtypes of human paraganglioma and pheochromocytoma (PPGL) neuroendocrine tumors. Although hypotheses propose that succinate accumulation after SDH loss poisons dioxygenases and activates pseudohypoxia and epigenomic hypermethylation, it remains unclear whether these mechanisms account for oncogenic transcriptional patterns. Additionally, MAML3 translocation has recently been identified as a genetic alteration in PPGL, but is poorly understood. We hypothesize that a key to understanding tumorigenesis driven by these genetic alterations is identification of the transcription factors responsible for the observed oncogenic transcriptional changes.

METHODS

We leverage publicly-available human tumor gene expression profiling experiments (N = 179) to reconstruct a PPGL tumor-specific transcriptional network. We subsequently use the inferred transcriptional network to perform master regulator analyses nominating transcription factors predicted to control oncogenic transcription in specific PPGL molecular subtypes. Results are validated by analysis of an independent collection of PPGL tumor specimens (N = 188). We then perform a similar master regulator analysis in SDH-loss mouse embryonic fibroblasts (MEFs) to infer aspects of SDH loss master regulator response conserved across species and tissue types.

RESULTS

A small number of master regulator transcription factors are predicted to drive the observed subtype-specific gene expression patterns in SDH loss and MAML3 translocation-positive PPGL. Interestingly, although EPAS1 perturbation is detectible in SDH-loss and VHL-loss tumors, it is by no means the most potent factor driving observed patterns of transcriptional dysregulation. Analysis of conserved SDH-loss master regulators in human tumors and MEFs implicated ZNF423, a known modulator of retinoic acid response in neuroblastoma. Subsequent functional analysis revealed a blunted cell death response to retinoic acid in SDH-loss MEFs and blunted differentiation response in SDH-inhibited SH-SY5Y neuroblastoma cells.

CONCLUSIONS

The unbiased analyses presented here nominate specific transcription factors that are likely drivers of oncogenic transcription in PPGL tumors. This information has the potential to be exploited for targeted therapy. Additionally, the observation that SDH loss or inhibition results in blunted retinoic acid response suggests a potential developmental etiology for this tumor subtype.

Cross-Species Single-Cell Analysis of Pancreatic Ductal Adenocarcinoma Reveals Antigen-Presenting Cancer-Associated Fibroblasts

Cancer-associated fibroblasts (CAF) are major players in the progression and drug resistance of pancreatic ductal adenocarcinoma (PDAC). CAFs constitute a diverse cell population consisting of several recently described subtypes, although the extent of CAF heterogeneity has remained undefined. Here we use single-cell RNA sequencing to thoroughly characterize the neoplastic and tumor microenvironment content of human and mouse PDAC tumors. We corroborate the presence of myofibroblastic CAFs and inflammatory CAFs and define their unique gene signatures in vivo. Moreover, we describe a new population of CAFs that express MHC class II and CD74, but do not express classic costimulatory molecules. We term this cell population "antigen-presenting CAFs" and find that they activate CD4⁺ T cells in an antigen-specific fashion in a model system, confirming their putative immune-modulatory capacity. Our cross-species analysis paves the way for investigating distinct functions of CAF subtypes in PDAC immunity and progression. SIGNIFICANCE: Appreciating the full spectrum of fibroblast heterogeneity in pancreatic ductal adenocarcinoma is crucial to developing therapies that specifically target tumor-promoting CAFs. This work identifies MHC class II-expressing CAFs with a capacity to present antigens to CD4⁺ T cells, and potentially to modulate the immune response in pancreatic tumors.See related commentary by Belle and DeNardo, p. 1001.This article is highlighted in the In This Issue feature, p. 983.

YY1-Induced Upregulation of Long Noncoding RNA ARAP1-AS1 Promotes Cell Migration and Invasion in Colorectal Cancer Through the Wnt/β-Catenin Signaling Pathway

Introduction: It has been reported that long noncoding RNAs (lncRNAs) are crucial regulators in progression of human cancers, including colorectal cancer (CRC). However, the function of lncRNA ARAP1 antisense RNA 1 (ARAP1-AS1) in CRC remains unclear. Aim: The aim of this study was to investigate the function and molecular mechanism of lncRNA ARAP1-AS1 in CRC. Results: ARAP1-AS1 was highly expressed in CRC tissues and cell lines. ARAP1-AS1 knockdown suppressed cell migration, invasion, and epithelial-mesenchymal transition (EMT). YY1 transcription factor (YY1) enhanced the transcription activity of ARAP1-AS1. The YY1/ARAP1-AS1 axis promoted CRC cell migration and invasion. YY1/ARAP1-AS1 could regulate the Wnt/β-catenin signaling pathway. Conclusions: This study revealed that YY1-induced upregulation of ARAP1-AS1 promoted cell migration, invasion, and EMT process in CRC through the Wnt/β-catenin signaling pathway.

The LL-100 panel: 100 cell lines for blood cancer studies

For many years, immortalized cell lines have been used as model systems for cancer research. Cell line panels were established for basic research and drug development, but did not cover the full spectrum of leukemia and lymphoma. Therefore, we now developed a novel panel (LL-100), 100 cell lines covering 22 entities of human leukemia and lymphoma including T-cell, B-cell and myeloid malignancies. Importantly, all cell lines are unequivocally authenticated and assigned to the correct tissue. Cell line samples were proven to be free of mycoplasma and non-inherent virus contamination. Whole exome sequencing and RNA-sequencing of the 100 cell lines were conducted with a uniform methodology to complement existing data on these publicly available cell lines. We show that such comprehensive sequencing data can be used to find lymphoma-subtype-characteristic copy number aberrations, mRNA isoforms, transcription factor activities and expression patterns of NKL homeobox genes. These exemplary studies confirm that the novel LL-100 panel will be useful for understanding the function of oncogenes and tumor suppressor genes and to develop targeted therapies.

Footprint-based functional analysis of multiomic data

Omic technologies allow us to generate extensive data, including transcriptomic, proteomic, phosphoproteomic and metabolomic. These data can be used to study signal transduction, gene regulation and metabolism. In this review, we summarise resources and methods to analysis these types of data. We focus on methods developed to recover functional insights using footprints. Footprints are signatures defined by the effect of molecules or processes of interest. They integrate information from multiple measurements whose abundances are under the influence of a common regulator. For example, transcripts controlled by a transcription factor or peptides phosphorylated by a kinase. Footprints can also be generalised across multiple types of omic data. Thus, we also present methods to integrate multiple types of omic data and features (such as the ones derived from footprints) together. We highlight some examples of studies that leverage such approaches to discover new biological mechanisms.

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Functional information on signalling pathways, metabolism and gene regulation can be found across multiple types of omic data.

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One way to extract such information is to consider these data as the footprint of the activity of enzymes and pathways.

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Information on enzyme/pathway activities and omic data can be integrated together to contextualise multi-scale networks.

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Such an approach can lead to the discovery of regulatory events spanning across multiple biological processes.

A framework for identifying dysregulated chromatin regulators as master regulators in human cancer

MOTIVATION

Chromatin regulators (CRs) are frequently dysregulated to reprogram the epigenetic landscape of the cancer genome. However, the underpinnings of the dysregulation of CRs and their downstream effectors remain to be elucidated.

RESULTS

Here, we designed an integrated framework based on multi-omics data to identify candidate master regulatory CRs affected by genomic alterations across eight cancer types in The Cancer Genome Atlas. Most of them showed consistent activated or repressed (i.e. oncogenic or tumor-suppressive) roles in cancer initiation and progression. In order to further explore the insight mechanism of the dysregulated CRs, we developed an R package ModReg based on differential connectivity to identify CRs as modulators of transcription factors (TFs) involved in tumorigenesis. Our analysis revealed that the connectivity between TFs and their target genes (TGs) tended to be disrupted in the patients who had a high expression of oncogenic CRs or low-expression of tumor-suppressive CRs. As a proof-of-principle study, 14 (82.4%) of the top-ranked 17 driver CRs in liver cancer were able to be validated by literature mining or experiments including shRNA knockdown and dCas9-based epigenetic editing. Moreover, we confirmed that CR SIRT7 physically interacted with TF NFE2L2, and positively modulated the transcriptional program of NFE2L2 by affecting ∼64% of its TGs.

AVAILABILITY AND IMPLEMENTATION

ModReg is freely accessible at http://cis.hku.hk/software/ModReg.tar.gz.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Linking aberrant chromatin features in chronic lymphocytic leukemia to transcription factor networks

In chronic lymphocytic leukemia (CLL), a diverse set of genetic mutations is embedded in a deregulated epigenetic landscape that drives cancerogenesis. To elucidate the role of aberrant chromatin features, we mapped DNA methylation, seven histone modifications, nucleosome positions, chromatin accessibility, binding of EBF1 and CTCF, as well as the transcriptome of B cells from CLL patients and healthy donors. A globally increased histone deacetylase activity was detected and half of the genome comprised transcriptionally downregulated partially DNA methylated domains demarcated by CTCF CLL samples displayed a H3K4me3 redistribution and nucleosome gain at promoters as well as changes of enhancer activity and enhancer linkage to target genes. A DNA binding motif analysis identified transcription factors that gained or lost binding in CLL at sites with aberrant chromatin features. These findings were integrated into a gene regulatory enhancer containing network enriched for B-cell receptor signaling pathway components. Our study predicts novel molecular links to targets of CLL therapies and provides a valuable resource for further studies on the epigenetic contribution to the disease.

Optimal control nodes in disease-perturbed networks as targets for combination therapy

Most combination therapies are developed based on targets of existing drugs, which only represent a small portion of the human proteome. We introduce a network controllability-based method, OptiCon, for de novo identification of synergistic regulators as candidates for combination therapy. These regulators jointly exert maximal control over deregulated genes but minimal control over unperturbed genes in a disease. Using data from three cancer types, we show that 68% of predicted regulators are either known drug targets or have a critical role in cancer development. Predicted regulators are depleted for known proteins associated with side effects. Predicted synergy is supported by disease-specific and clinically relevant synthetic lethal interactions and experimental validation. A significant portion of genes regulated by synergistic regulators participate in dense interactions between co-regulated subnetworks and contribute to therapy resistance. OptiCon represents a general framework for systemic and de novo identification of synergistic regulators underlying a cellular state transition.

Synergistic interactions may arise between regulators in complex molecular networks. Here, the authors develop OptiCon, a computational method for de novo identification of synergistic key regulators and investigate their potential roles as candidate targets for combination therapy.

VULCAN integrates ChIP-seq with patient-derived co-expression networks to identify GRHL2 as a key co-regulator of ERa at enhancers in breast cancer

BACKGROUND

VirtUaL ChIP-seq Analysis through Networks (VULCAN) infers regulatory interactions of transcription factors by overlaying networks generated from publicly available tumor expression data onto ChIP-seq data. We apply our method to dissect the regulation of estrogen receptor-alpha activation in breast cancer to identify potential co-regulators of the estrogen receptor's transcriptional response.

RESULTS

VULCAN analysis of estrogen receptor activation in breast cancer highlights the key components of the estrogen receptor complex alongside a novel interaction with GRHL2. We demonstrate that GRHL2 is recruited to a subset of estrogen receptor binding sites and regulates transcriptional output, as evidenced by changes in estrogen receptor-associated eRNA expression and stronger estrogen receptor binding at active enhancers after GRHL2 knockdown.

CONCLUSIONS

Our findings provide new insight into the role of GRHL2 in regulating eRNA transcription as part of estrogen receptor signaling. These results demonstrate VULCAN, available from Bioconductor, as a powerful predictive tool.

Senescence and Defense Pathways Contribute to Heterosis

Hybrids are used extensively in agriculture due to their superior performance in seed yield and plant growth, yet the molecular mechanisms underpinning hybrid performance are not well understood. Recent evidence has suggested that a decrease in basal defense response gene expression regulated by reduced levels of salicylic acid (SA) may be important for vigor in certain hybrid combinations. Decreasing levels of SA in the Arabidopsis (Arabidopsis thaliana) accession C24 through the introduction of the SA catabolic enzyme salicylate1 hydroxylase (NahG) increases plant size, phenocopying the large-sized C24/Landsberg erecta (Ler) F1 hybrids. C24♀ × Ler♂ F1 hybrids and C24 NahG lines shared differentially expressed genes and pathways associated with plant defense and leaf senescence including decreased expression of SA biosynthetic genes and SA response genes. The expression of TL1 BINDING TRANSCRIPTION FACTOR1, a key regulator in resource allocation between growth and defense, was decreased in both the F1 hybrid and the C24 NahG lines, which may promote growth. Both C24 NahG lines and the F1 hybrids showed decreased expression of the key senescence-associated transcription factors WRKY53, NAC-CONTAINING PROTEIN29, and ORESARA1 with a delayed onset of senescence compared to C24 plants. The delay in senescence resulted in an extension of the photosynthetic period in the leaves of F1 hybrids compared to the parental lines, potentially allowing each leaf to contribute more resources toward growth.

Estimating Sample-Specific Regulatory Networks

Biological systems are driven by intricate interactions among molecules. Many methods have been developed that draw on large numbers of expression samples to infer connections between genes (or their products). The result is an aggregate network representing a single estimate for the likelihood of each interaction, or "edge," in the network. Although informative, aggregate models fail to capture population heterogeneity. Here we propose a method to reverse engineer sample-specific networks from aggregate networks. We demonstrate our approach in several contexts, including simulated, yeast microarray, and human lymphoblastoid cell line RNA sequencing data. We use these sample-specific networks to study changes in network topology across time and to characterize shifts in gene regulation that were not apparent in the expression data. We believe that generating sample-specific networks will greatly facilitate the application of network methods to large, complex, and heterogeneous multi-omic datasets, supporting the emerging field of precision network medicine.

BET bromodomain inhibition blocks the function of a critical AR-independent master regulator network in lethal prostate cancer

BET bromodomain inhibitors block prostate cancer cell growth at least in part through c-Myc and androgen receptor (AR) suppression. However, little is known about other transcriptional regulators whose suppression contributes to BET bromodomain inhibitor anti-tumor activity. Moreover, the anti-tumor activity of BET bromodomain inhibition in AR-independent castration-resistant prostate cancers (CRPC), whose frequency is increasing, is also unknown. Herein, we demonstrate that BET bromodomain inhibition blocks growth of a diverse set of CRPC cell models, including those that are AR-independent or in which c-Myc is not suppressed. To identify transcriptional regulators whose suppression accounts for these effects, we treated multiple CRPC cell lines with the BET bromodomain inhibitor JQ1 and then performed RNA-sequencing followed by Master Regulator computational analysis. This approach identified several previously unappreciated transcriptional regulators that are highly expressed in CRPC and whose suppression, via both transcriptional or post-translational mechanisms, contributes to the anti-tumor activity of BET bromodomain inhibitors.

Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers

At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumor’s genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses—all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the center of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation; therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community.

Encircling the regions of the pharmacogenomic landscape that determine drug response

BACKGROUND

The integration of large-scale drug sensitivity screens and genome-wide experiments is changing the field of pharmacogenomics, revealing molecular determinants of drug response without the need for previous knowledge about drug action. In particular, transcriptional signatures of drug sensitivity may guide drug repositioning, prioritize drug combinations, and point to new therapeutic biomarkers. However, the inherent complexity of transcriptional signatures, with thousands of differentially expressed genes, makes them hard to interpret, thus giving poor mechanistic insights and hampering translation to clinics.

METHODS

To simplify drug signatures, we have developed a network-based methodology to identify functionally coherent gene modules. Our strategy starts with the calculation of drug-gene correlations and is followed by a pathway-oriented filtering and a network-diffusion analysis across the interactome.

RESULTS

We apply our approach to 189 drugs tested in 671 cancer cell lines and observe a connection between gene expression levels of the modules and mechanisms of action of the drugs. Further, we characterize multiple aspects of the modules, including their functional categories, tissue-specificity, and prevalence in clinics. Finally, we prove the predictive capability of the modules and demonstrate how they can be used as gene sets in conventional enrichment analyses.

CONCLUSIONS

Network biology strategies like module detection are able to digest the outcome of large-scale pharmacogenomic initiatives, thereby contributing to their interpretability and improving the characterization of the drugs screened.

Visible Machine Learning for Biomedicine

A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Here, we argue for "visible" approaches that guide model structure with experimental biology.

Systems immunology: Integrating multi-omics data to infer regulatory networks and hidden drivers of immunity

The immune system is a highly complex and dynamic biological system. It operates through intracellular molecular networks and intercellular (cell-cell) interaction networks. Systems immunology is an emerging discipline that applies systems biology approaches of integrating high-throughput multi-omics measurements with computational network modeling to better understand immunity at various scales. In this review, we summarize key omics technologies and computational approaches used for immunological studies at both population and single-cell levels. We highlight the hidden driver analysis based on data-driven networks and comment on the potential of translating systems immunology discoveries to immunotherapy of cancer and other human diseases.

Exploiting rare driver mutations for precision cancer medicine

Catalyzed by the ability to develop precision therapies targeting the unique genetic changes that drive individual tumors, sequencing patients' tumor genomes is an increasingly common practice in oncology. In most cancer types, however, a limited number of common mutations are accompanied by a plethora of low-frequency variants whose functional consequences and clinical actionability are often unknown. We here illustrate that this 'long tail' of infrequent molecular alterations includes oncogenic drivers of biological significance that can be the genetic basis of extraordinary responses to systemic cancer therapies. Furthermore, we review current strategies to identify, prioritize, and experimentally validate novel long-tail driver mutations, efforts that will likely provide new insights into the clinically actionable genome and improve outcomes for patients.

MR4Cancer: a web server prioritizing master regulators for cancer

MOTIVATION

During cancer stage transition, a master regulator (MR) refers to the key gene controlling cancer initiation and progression by orchestrating the associated target genes (termed as its regulon). Due to their inherent importance, MRs can serve as critical biomarkers for cancer diagnosis and prognosis, and therapeutic targets. However, it is challenging to infer key MRs that might explain gene expression profile changes between two groups due to lack of context-specific regulons, whose expression level can collectively reflect the activity of likely MRs. There is also a need to design an easy-to-use tool of MR identification for research community.

RESULTS

First, we generated cancer-specific regulons for 26 cancer types by analyzing high-throughput omics data from TCGA, and extracted noncancer-specific regulons from public databases. We subsequently developed a web server MR4Cancer, integrating the regulons with statistical inference to identify and prioritize MRs driving a phenotypic divergence of interest. Based on the input gene list (e.g. differentially expressed genes) or expression profile with two groups, MR4Cancer outputs ranked MRs by enrichment testing against the predefined regulons. Gene Ontology and canonical pathway analyses are also conducted to elucidate the function of likely MRs. Moreover, MR4Cancer provides dynamic network visualization for MR-target relations, and users can interactively interrogate the network to produce new hypotheses and high-quality figures for publication. Finally, the presented case studies highlighted the performance of MR4Cancer. We expect this user-friendly and powerful web tool will provide researchers novel insights into tumorigenesis and therapeutic intervention.

AVAILABILITY AND IMPLEMENTATION

http://cis.hku.hk/MR4Cancer.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Circ-ZNF609 regulates G1-S progression in rhabdomyosarcoma

Circular RNAs (circRNAs) represent a class of covalently closed RNAs, derived from non-canonical splicing events, which are expressed in all eukaryotes and often conserved among different species. We previously showed that the circRNA originating from the ZNF609 locus (circ-ZNF609) acts as a crucial regulator of human primary myoblast growth: indeed, the downregulation of the circRNA, and not of its linear counterpart, strongly reduced the proliferation rate of in vitro cultured myoblasts. To deepen our knowledge about circ-ZNF609 role in cell cycle regulation, we studied its expression and function in rhabdomyosarcoma (RMS), a pediatric skeletal muscle malignancy. We found that circ-ZNF609 is upregulated in biopsies from the two major RMS subtypes, embryonal (ERMS) and alveolar (ARMS). Moreover, we discovered that in an ERMS-derived cell line circ-ZNF609 knock-down induced a specific block at the G1-S transition, a strong decrease of p-Akt protein level and an alteration of the pRb/Rb ratio. Regarding p-Akt, we were able to show that circ-ZNF609 acts by counteracting p-Akt proteasome-dependent degradation, thus working as a new regulator of cell proliferation-related pathways. As opposed to ERMS-derived cells, the circRNA depletion had no cell cycle effects in ARMS-derived cells. Since in these cells the p53 gene resulted downregulated, with a concomitant upregulation of its cell cycle-related target genes, we suggest that this could account for the lack of circ-ZNF609 effect in ARMS.

Elucidating synergistic dependencies in lung adenocarcinoma by proteome-wide signaling-network analysis

To understand drug combination effect, it is necessary to decipher the interactions between drug targets-many of which are signaling molecules. Previously, such signaling pathway models are largely based on the compilation of literature data from heterogeneous cellular contexts. Indeed, de novo reconstruction of signaling interactions from large-scale molecular profiling is still lagging, compared to similar efforts in transcriptional and protein-protein interaction networks. To address this challenge, we introduce a novel algorithm for the systematic inference of protein kinase pathways, and applied it to published mass spectrometry-based phosphotyrosine profile data from 250 lung adenocarcinoma (LUAD) samples. The resulting network includes 43 TKs and 415 inferred, LUAD-specific substrates, which were validated at >60% accuracy by SILAC assays, including "novel' substrates of the EGFR and c-MET TKs, which play a critical oncogenic role in lung cancer. This systematic, data-driven model supported drug response prediction on an individual sample basis, including accurate prediction and validation of synergistic EGFR and c-MET inhibitor activity in cells lacking mutations in either gene, thus contributing to current precision oncology efforts.