A modular master regulator landscape controls cancer transcriptional identity.

Despite considerable efforts, the mechanisms linking genomic alterations to the transcriptional identity of cancer cells remain elusive. Integrative genomic analysis, using a network-based approach, identified 407 master regulator (MR) proteins responsible for canalizing the genetics of individual samples from 20 cohorts in The Cancer Genome Atlas (TCGA) into 112 transcriptionally distinct tumor subtypes. MR proteins could be further organized into 24 pan-cancer, master regulator block modules (MRBs), each regulating key cancer hallmarks and predictive of patient outcome in multiple cohorts. Of all somatic alterations detected in each individual sample, >50% were predicted to induce aberrant MR activity, yielding insight into mechanisms linking tumor genetics and transcriptional identity and establishing non-oncogene dependencies. Genetic and pharmacological validation assays confirmed the predicted effect of upstream mutations and MR activity on downstream cellular identity and phenotype. Thus, co-analysis of mutational and gene expression profiles identified elusive subtypes and provided testable hypothesis for mechanisms mediating the effect of genetic alterations.

Identification of causal genetic drivers of human disease through systems-level analysis of regulatory networks.

Identification of driver mutations in human diseases is often limited by cohort size and availability of appropriate statistical models. We propose a framework for the systematic discovery of genetic alterations that are causal determinants of disease, by prioritizing genes upstream of functional disease drivers, within regulatory networks inferred de novo from experimental data. We tested this framework by identifying the genetic determinants of the mesenchymal subtype of glioblastoma. Our analysis uncovered KLHL9 deletions as upstream activators of two previously established master regulators of the subtype, C/EBPß and C/EBPδ. Rescue of KLHL9 expression induced proteasomal degradation of C/EBP proteins, abrogated the mesenchymal signature, and reduced tumor viability in vitro and in vivo. Deletions of KLHL9 were confirmed in > 50% of mesenchymal cases in an independent cohort, thus representing the most frequent genetic determinant of the subtype. The method generalized to study other human diseases, including breast cancer and Alzheimer's disease.

The relative impact of parental and current environment on plant transcriptomes depends on type of stress and genotype.

Through developmental plasticity, an individual organism integrates influences from its immediate environment with those due to the environment of its parents. While both effects on phenotypes are well documented, their relative impact has been little studied in natural systems, especially at the level of gene expression. We examined this issue in four genotypes of the annual plant Persicaria maculosa by varying two key resources-light and soil moisture-in both generations. Transcriptomic analyses showed that the relative effects of parent and offspring environment on gene expression (i.e. the number of differentially expressed transcripts, DETs) varied both for the two types of resource stress and among genotypes. For light, immediate environment induced more DETs than parental environment for all genotypes, although the precise proportion of parental versus immediate DETs varied among genotypes. By contrast, the relative effect of soil moisture varied dramatically among genotypes, from 8-fold more DETs due to parental than immediate conditions to 10-fold fewer. These findings provide evidence at the transcriptomic level that the relative impacts of parental and immediate environment on the developing organism may depend on the environmental factor and vary strongly among genotypes, providing potential for the interplay of these developmental influences to evolve.

Inhibition of the autocrine IL-6-JAK2-STAT3-calprotectin axis as targeted therapy for HR-/HER2+ breast cancers.

HER2-positive (HER2(+)) breast adenocarcinomas are a heterogeneous group in which hormone receptor (HR) status influences therapeutic decisions and patient outcome. By combining genome-wide RNAi screens with regulatory network analysis, we identified STAT3 as a critically activated master regulator of HR(-)/HER2(+) tumors, eliciting tumor dependency in these cells. Mechanistically, HR(-)/HER2(+) cells secrete high levels of the interleukin-6 (IL-6) cytokine, inducing the activation of STAT3, which in turn promotes a second autocrine stimulus to increase S100A8/9 complex (calprotectin) production and secretion. Increased calprotectin levels activate signaling pathways involved in proliferation and resistance. Importantly, we demonstrated that inhibition of the IL-6-Janus kinase 2 (JAK2)-STAT3-calprotectin axis with FDA-approved drugs, alone and in combination with HER2 inhibitors, reduced the tumorigenicity of HR(-)/HER2(+) breast cancers, opening novel targeted therapeutic opportunities.

Inheritance of DNA methylation differences in the mangrove Rhizophora mangle.

The capacity to respond to environmental challenges ultimately relies on phenotypic variation which manifests from complex interactions of genetic and nongenetic mechanisms through development. While we know something about genetic variation and structure of many species of conservation importance, we know very little about the nongenetic contributions to variation. Rhizophora mangle is a foundation species that occurs in coastal estuarine habitats throughout the neotropics where it provides critical ecosystem functions and is potentially threatened by anthropogenic environmental changes. Several studies have documented landscape-level patterns of genetic variation in this species, but we know virtually nothing about the inheritance of nongenetic variation. To assess one type of nongenetic variation, we examined the patterns of DNA sequence and DNA methylation in maternal plants and offspring from natural populations of R. mangle from the Gulf Coast of Florida. We used a reduced representation bisulfite sequencing approach (epi-genotyping by sequencing; epiGBS) to address the following questions: (a) What are the levels of genetic and epigenetic diversity in natural populations of R. mangle? (b) How are genetic and epigenetic variation structured within and among populations? (c) How faithfully is epigenetic variation inherited? We found low genetic diversity but high epigenetic diversity from natural populations of maternal plants in the field. In addition, a large portion (up to ~25%) of epigenetic differences among offspring grown in common garden was explained by maternal family. Therefore, epigenetic variation could be an important source of response to challenging environments in the genetically depauperate populations of this foundation species.

Fimepinostat (CUDC-907) in patients with relapsed/refractory diffuse large B cell and high-grade B-cell lymphoma: report of a phase 2 trial and exploratory biomarker analyses.

Fimepinostat (CUDC-907), a first-in-class oral small-molecule inhibitor of histone deacetylase and phosphatidylinositol 3-kinase, demonstrated efficacy in a phase 1 study of patients with relapsed/refractory (R/R) diffuse large and high-grade B-cell lymphomas (DLBCL/HGBL), particularly those with increased MYC protein expression and/or MYC gene rearrangement/copy number gain (MYC-altered disease). Therefore, a phase 2 study of fimepinostat was conducted in this patient population with 66 eligible patients treated. The primary end-point of overall response (OR) rate for patients with MYC-IHC ≥40% (n = 46) was 15%. Subsequently, exploratory pooled analyses were performed including patients treated on both the phase 1 and 2 studies based upon the presence of MYC-altered disease as well as a biomarker identified by Virtual Inference of Protein activity by Enriched Regulon analysis (VIPER). For these patients with MYC-altered disease (n = 63), the overall response (OR) rate was 22% with seven responding patients remaining on treatment for approximately two years or longer, and VIPER yielded a three-protein biomarker classification with positive and negative predictive values of ≥85%. Prolonged durations of response were achieved by patients with MYC-altered R/R DLBCL/HGBL treated with single-agent fimepinostat. Combination therapies and/or biomarker-based patient selection strategies may lead to higher response rates in future clinical trials.

Patterns of population genomic diversity in the invasive Japanese knotweed species complex.

PREMISE: Invasive species are expected to undergo a reduction in genetic diversity due to founder effects, which should limit their ability to adapt to new habitats. Still, many invasive species achieve widespread distributions and dense populations. This paradox of invasions could potentially be overcome through multiple introductions or hybridization, both of which increase genetic diversity. We conducted a population genomics study of Japanese knotweed (Reynoutria japonica), which is a polyploid, clonally reproducing invasive species that has been notoriously successful worldwide despite supposedly low genetic diversity. METHODS: We used genotyping by sequencing to collect 12,912 SNP markers from 88 samples collected at 38 locations across North America for the species complex. We used alignment-free k-mer hashing analysis in addition to traditional population genetic analyses to account for the challenges of genotyping polyploids. RESULTS: Genotypes conformed to three genetic clusters, likely representing Japanese knotweed, giant knotweed, and hybrid bohemian knotweed. We found that, contrary to previous findings, the Japanese knotweed cluster had substantial genetic diversity, though it had no apparent genetic structure across the landscape. In contrast, giant knotweed and hybrids showed distinct population groups. We did not find evidence of isolation by distance in the species complex, likely reflecting the stochastic introduction history of this species complex. CONCLUSIONS: The results indicate that clonal invasive species can show substantial genetic diversity and can be successful at colonizing a variety of habitats without showing evidence of local adaptation or genetic structure.

Transcriptome response of the foundation plant Spartina alterniflora to the Deepwater Horizon oil spill.

Despite the severe impacts of the Deepwater Horizon oil spill, the foundation plant species Spartina alterniflora proved resilient to heavy oiling, providing an opportunity to identify mechanisms of response to the anthropogenic stress of crude oil exposure. We assessed plants from oil-affected and unaffected populations using a custom DNA microarray to identify genomewide transcription patterns and gene expression networks that respond to crude oil exposure. In addition, we used T-DNA insertion lines of the model grass Brachypodium distachyon to assess the contribution of four novel candidate genes to crude oil response. Responses in S. alterniflora to hydrocarbon exposure across the transcriptome as well as xenobiotic specific response pathways had little overlap with those previously identified in the model plant Arabidopsis thaliana. Among T-DNA insertion lines of B. distachyon, we found additional support for two candidate genes, one (ATTPS21) involved in volatile production, and the other (SUVH5) involved in epigenetic regulation of gene expression, that may be important in the response to crude oil. The architecture of crude oil response in S. alterniflora is unique from that of the model species A. thaliana, suggesting that xenobiotic response may be highly variable across plant species. In addition, further investigations of regulatory networks may benefit from more information about epigenetic response pathways.

Systematic, network-based characterization of therapeutic target inhibitors.

A large fraction of the proteins that are being identified as key tumor dependencies represent poor pharmacological targets or lack clinically-relevant small-molecule inhibitors. Availability of fully generalizable approaches for the systematic and efficient prioritization of tumor-context specific protein activity inhibitors would thus have significant translational value. Unfortunately, inhibitor effects on protein activity cannot be directly measured in systematic and proteome-wide fashion by conventional biochemical assays. We introduce OncoLead, a novel network based approach for the systematic prioritization of candidate inhibitors for arbitrary targets of therapeutic interest. In vitro and in vivo validation confirmed that OncoLead analysis can recapitulate known inhibitors as well as prioritize novel, context-specific inhibitors of difficult targets, such as MYC and STAT3. We used OncoLead to generate the first unbiased drug/regulator interaction map, representing compounds modulating the activity of cancer-relevant transcription factors, with potential in precision medicine.

Detection and removal of spatial bias in multiwell assays.

MOTIVATION: Multiplex readout assays are now increasingly being performed using microfluidic automation in multiwell format. For instance, the Library of Integrated Network-based Cellular Signatures (LINCS) has produced gene expression measurements for tens of thousands of distinct cell perturbations using a 384-well plate format. This dataset is by far the largest 384-well gene expression measurement assay ever performed. We investigated the gene expression profiles of a million samples from the LINCS dataset and found that the vast majority (96%) of the tested plates were affected by a significant 2D spatial bias. RESULTS: Using a novel algorithm combining spatial autocorrelation detection and principal component analysis, we could remove most of the spatial bias from the LINCS dataset and show in parallel a dramatic improvement of similarity between biological replicates assayed in different plates. The proposed methodology is fully general and can be applied to any highly multiplexed assay performed in multiwell format. CONTACT: ac2248@columbia.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DIGGIT: a Bioconductor package to infer genetic variants driving cellular phenotypes.

UNLABELLED: Identification of driver mutations in human diseases is often limited by cohort size and availability of appropriate statistical models. We propose a method for the systematic discovery of genetic alterations that are causal determinants of disease, by prioritizing genes upstream of functional disease drivers, within regulatory networks inferred de novo from experimental data. Here we present the implementation of Driver-gene Inference by Genetical-Genomic Information Theory as an R-system package. AVAILABILITY AND IMPLEMENTATION: The diggit package is freely available under the GPL-2 license from Bioconductor (http://www.bioconductor.org).

Interrogation of a context-specific transcription factor network identifies novel regulators of pluripotency.

The predominant view of pluripotency regulation proposes a stable ground state with coordinated expression of key transcription factors (TFs) that prohibit differentiation. Another perspective suggests a more complexly regulated state involving competition between multiple lineage-specifying TFs that define pluripotency. These contrasting views were developed from extensive analyses of TFs in pluripotent cells in vitro. An experimentally validated, genome-wide repertoire of the regulatory interactions that control pluripotency within the in vivo cellular contexts is yet to be developed. To address this limitation, we assembled a TF interactome of adult human male germ cell tumors (GCTs) using the Algorithm for the Accurate Reconstruction of Cellular Pathways (ARACNe) to analyze gene expression profiles of 141 tumors comprising pluripotent and differentiated subsets. The network (GCT(Net)) comprised 1,305 TFs, and its ingenuity pathway analysis identified pluripotency and embryonal development as the top functional pathways. We experimentally validated GCT(Net) by functional (silencing) and biochemical (ChIP-seq) analysis of the core pluripotency regulatory TFs POU5F1, NANOG, and SOX2 in relation to their targets predicted by ARACNe. To define the extent of the in vivo pluripotency network in this system, we ranked all TFs in the GCT(Net) according to sharing of ARACNe-predicted targets with those of POU5F1 and NANOG using an odds-ratio analysis method. To validate this network, we silenced the top 10 TFs in the network in H9 embryonic stem cells. Silencing of each led to downregulation of pluripotency and induction of lineage; 7 of the 10 TFs were identified as pluripotency regulators for the first time.

The transcriptional network for mesenchymal transformation of brain tumours.

The inference of transcriptional networks that regulate transitions into physiological or pathological cellular states remains a central challenge in systems biology. A mesenchymal phenotype is the hallmark of tumour aggressiveness in human malignant glioma, but the regulatory programs responsible for implementing the associated molecular signature are largely unknown. Here we show that reverse-engineering and an unbiased interrogation of a glioma-specific regulatory network reveal the transcriptional module that activates expression of mesenchymal genes in malignant glioma. Two transcription factors (C/EBPbeta and STAT3) emerge as synergistic initiators and master regulators of mesenchymal transformation. Ectopic co-expression of C/EBPbeta and STAT3 reprograms neural stem cells along the aberrant mesenchymal lineage, whereas elimination of the two factors in glioma cells leads to collapse of the mesenchymal signature and reduces tumour aggressiveness. In human glioma, expression of C/EBPbeta and STAT3 correlates with mesenchymal differentiation and predicts poor clinical outcome. These results show that the activation of a small regulatory module is necessary and sufficient to initiate and maintain an aberrant phenotypic state in cancer cells.

Ten years of transcriptomics in wild populations: what have we learned about their ecology and evolution?

Molecular ecology has moved beyond the use of a relatively small number of markers, often noncoding, and it is now possible to use whole-genome measures of gene expression with microarrays and RNAseq (i.e. transcriptomics) to capture molecular response to environmental challenges. While transcriptome studies are shedding light on the mechanistic basis of traits as complex as personality or physiological response to catastrophic events, these approaches are still challenging because of the required technical expertise, difficulties with analysis and cost. Still, we found that in the last 10 years, 575 studies used microarrays or RNAseq in ecology. These studies broadly address three questions that reflect the progression of the field: (i) How much variation in gene expression is there and how is it structured? (ii) How do environmental stimuli affect gene expression? (iii) How does gene expression affect phenotype? We discuss technical aspects of RNAseq and microarray technology, and a framework that leverages the advantages of both. Further, we highlight future directions of research, particularly related to moving beyond correlation and the development of additional annotation resources. Measuring gene expression across an array of taxa in ecological settings promises to enrich our understanding of ecology and genome function.

Quantitative modeling of the terminal differentiation of B cells and mechanisms of lymphomagenesis.

Mature B-cell exit from germinal centers is controlled by a transcriptional regulatory module that integrates antigen and T-cell signals and, ultimately, leads to terminal differentiation into memory B cells or plasma cells. Despite a compact structure, the module dynamics are highly complex because of the presence of several feedback loops and self-regulatory interactions, and understanding its dysregulation, frequently associated with lymphomagenesis, requires robust dynamical modeling techniques. We present a quantitative kinetic model of three key gene regulators, BCL6, IRF4, and BLIMP, and use gene expression profile data from mature human B cells to determine appropriate model parameters. The model predicts the existence of two different hysteresis cycles that direct B cells through an irreversible transition toward a differentiated cellular state. By synthetically perturbing the interactions in this network, we can elucidate known mechanisms of lymphomagenesis and suggest candidate tumorigenic alterations, indicating that the model is a valuable quantitative tool to simulate B-cell exit from the germinal center under a variety of physiological and pathological conditions.

Regulation of extra-embryonic endoderm stem cell differentiation by Nodal and Cripto signaling.

The signaling pathway for Nodal, a ligand of the TGFß superfamily, plays a central role in regulating the differentiation and/or maintenance of stem cell types that can be derived from the peri-implantation mouse embryo. Extra-embryonic endoderm stem (XEN) cells resemble the primitive endoderm of the blastocyst, which normally gives rise to the parietal and the visceral endoderm in vivo, but XEN cells do not contribute efficiently to the visceral endoderm in chimeric embryos. We have found that XEN cells treated with Nodal or Cripto (Tdgf1), an EGF-CFC co-receptor for Nodal, display upregulation of markers for visceral endoderm as well as anterior visceral endoderm (AVE), and can contribute to visceral endoderm and AVE in chimeric embryos. In culture, XEN cells do not express Cripto, but do express the related EGF-CFC co-receptor Cryptic (Cfc1), and require Cryptic for Nodal signaling. Notably, the response to Nodal is inhibited by the Alk4/Alk5/Alk7 inhibitor SB431542, but the response to Cripto is unaffected, suggesting that the activity of Cripto is at least partially independent of type I receptor kinase activity. Gene set enrichment analysis of genome-wide expression signatures generated from XEN cells under these treatment conditions confirmed the differing responses of Nodal- and Cripto-treated XEN cells to SB431542. Our findings define distinct pathways for Nodal and Cripto in the differentiation of visceral endoderm and AVE from XEN cells and provide new insights into the specification of these cell types in vivo.

Improving breast cancer survival analysis through competition-based multidimensional modeling.

Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually. As with most cancers, it is a heterogeneous disease and different breast cancer subtypes are treated differently. Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease. In this work, we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission. We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble. We also find that model scores are highly consistent across multiple independent evaluations. This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models.

Ecological epigenetics.

Biologists have assumed that heritable variation due to DNA sequence differences (i.e., genetic variation) allows populations of organisms to be both robust and adaptable to extreme environmental conditions. Natural selection acts on the variation among different genotypes and ultimately changes the genetic composition of the population. While there is compelling evidence about the importance of genetic polymorphisms, evidence is accumulating that epigenetic mechanisms (e.g., chromatin modifications, DNA methylation) can affect ecologically important traits, even in the absence of genetic variation. In this chapter, we review this evidence and discuss the consequences of epigenetic variation in natural populations. We begin by defining the term epigenetics, providing a brief overview of various epigenetic mechanisms, and noting the potential importance of epigenetics in the study of ecology. We continue with a review of the ecological epigenetics literature to demonstrate what is currently known about the amount and distribution of epigenetic variation in natural populations. Then, we consider the various ecological contexts in which epigenetics has proven particularly insightful and discuss the potential evolutionary consequences of epigenetic variation. Finally, we conclude with suggestions for future directions of ecological epigenetics research.