Sven Nelander

Variants in ELL2 influencing immunoglobulin levels associate with multiple myeloma

Multiple myeloma (MM) is characterized by an uninhibited, clonal growth of plasma cells. While first-degree relatives of patients with MM show an increased risk of MM, the genetic basis of inherited MM susceptibility is incompletely understood. Here we report a genome-wide association study in the Nordic region identifying a novel MM risk locus at ELL2 (rs56219066T; odds ratio (OR)=1.25; P=9.6 × 10−10). This gene encodes a stoichiometrically limiting component of the super-elongation complex that drives secretory-specific immunoglobulin mRNA production and transcriptional regulation in plasma cells. We find that the MM risk allele harbours a Thr298Ala missense variant in an ELL2 domain required for transcription elongation. Consistent with a hypomorphic effect, we find that the MM risk allele also associates with reduced levels of immunoglobulin A (IgA) and G (IgG) in healthy subjects (P=8.6 × 10−9 and P=6.4 × 10−3, respectively) and, potentially, with an increased risk of bacterial meningitis (OR=1.30; P=0.0024).

Subtype-specific genomic alterations define new targets for soft tissue sarcoma therapy

Soft-tissue sarcomas, which result in approximately 10,700 diagnoses and 3,800 deaths per year in the United States, show remarkable histologic diversity, with more than 50 recognized subtypes. However, knowledge of their genomic alterations is limited. We describe an integrative analysis of DNA sequence, copy number and mRNA expression in 207 samples encompassing seven major subtypes. Frequently mutated genes included TP53 (17% of pleomorphic liposarcomas), NF1 (10.5% of myxofibrosarcomas and 8% of pleomorphic liposarcomas) and PIK3CA (18% of myxoid/round-cell liposarcomas, or MRCs). PIK3CA mutations in MRCs were associated with Akt activation and poor clinical outcomes. In myxofibrosarcomas and pleomorphic liposarcomas, we found both point mutations and genomic deletions affecting the tumor suppressor NF1. Finally, we found that short hairpin RNA (shRNA)-based knockdown of several genes amplified in dedifferentiated liposarcoma, including CDK4 and YEATS4, decreased cell proliferation. Our study yields a detailed map of molecular alterations across diverse sarcoma subtypes and suggests potential subtype-specific targets for therapy.

Models from experiments: combinatorial drug perturbations of cancer cells

We present a novel method for deriving network models from molecular profiles of perturbed cellular systems. The network models aim to predict quantitative outcomes of combinatorial perturbations, such as drug pair treatments or multiple genetic alterations. Mathematically, we represent the system by a set of nodes, representing molecular concentrations or cellular processes, a perturbation vector and an interaction matrix. After perturbation, the system evolves in time according to differential equations with built‐in nonlinearity, similar to Hopfield networks, capable of representing epistasis and saturation effects. For a particular set of experiments, we derive the interaction matrix by minimizing a composite error function, aiming at accuracy of prediction and simplicity of network structure. To evaluate the predictive potential of the method, we performed 21 drug pair treatment experiments in a human breast cancer cell line (MCF7) with observation of phospho‐proteins and cell cycle markers. The best derived network model rediscovered known interactions and contained interesting predictions. Possible applications include the discovery of regulatory interactions, the design of targeted combination therapies and the engineering of molecular biological networks.

Clinically Significant Copy Number Alterations and Complex Rearrangements of MYB and NFIB in Head and Neck Adenoid Cystic Carcinoma

Adenoid cystic carcinoma (ACC) of the head and neck is a malignant tumor with poor long‐term prognosis. Besides the recently identified MYB–NFIB fusion oncogene generated by a t(6;9) translocation, little is known about other genetic alterations in ACC. Using high‐resolution, array‐based comparative genomic hybridization, and massively paired‐end sequencing, we explored genomic alterations in 40 frozen ACCs. Eighty‐six percent of the tumors expressed MYB–NFIB fusion transcripts and 97% overexpressed MYB mRNA, indicating that MYB activation is a hallmark of ACC. Thirty‐five recurrent copy number alterations (CNAs) were detected, including losses involving 12q, 6q, 9p, 11q, 14q, 1p, and 5q and gains involving 1q, 9p, and 22q. Grade III tumors had on average a significantly higher number of CNAs/tumor compared to Grade I and II tumors (P = 0.007). Losses of 1p, 6q, and 15q were associated with high‐grade tumors, whereas losses of 14q were exclusively seen in Grade I tumors. The t(6;9) rearrangements were associated with a complex pattern of breakpoints, deletions, insertions, inversions, and for 9p also gains. Analyses of fusion‐negative ACCs using high‐resolution arrays and massively paired‐end sequencing revealed that MYB may also be deregulated by other mechanisms in addition to gene fusion. Our studies also identified several down‐regulated candidate tumor suppressor genes (CTNNBIP1, CASP9, PRDM2, and SFN) in 1p36.33‐p35.3 that may be of clinical significance in high‐grade tumors. Further, studies of these and other potential target genes may lead to the identification of novel driver genes in ACC.

Network modeling of the transcriptional effects of copy number aberrations in glioblastoma

DNA copy number aberrations (CNAs) are a hallmark of cancer genomes. However, little is known about how such changes affect global gene expression. We develop a modeling framework, EPoC (Endogenous Perturbation analysis of Cancer), to (1) detect disease‐driving CNAs and their effect on target mRNA expression, and to (2) stratify cancer patients into long‐ and short‐term survivors. Our method constructs causal network models of gene expression by combining genome‐wide DNA‐ and RNA‐level data. Prognostic scores are obtained from a singular value decomposition of the networks. By applying EPoC to glioblastoma data from The Cancer Genome Atlas consortium, we demonstrate that the resulting network models contain known disease‐relevant hub genes, reveal interesting candidate hubs, and uncover predictors of patient survival. Targeted validations in four glioblastoma cell lines support selected predictions, and implicate the p53‐interacting protein Necdin in suppressing glioblastoma cell growth. We conclude that large‐scale network modeling of the effects of CNAs on gene expression may provide insights into the biology of human cancer. Free software in MATLAB and R is provided.

Off-target effects dominate a large-scale RNAi screen for modulators of the TGF-β pathway and reveal microRNA regulation of TGFBR2

BackgroundRNA interference (RNAi) screens have been used to identify novel components of signal-transduction pathways in a variety of organisms. We performed a small interfering (si)RNA screen for novel members of the transforming growth factor (TGF)-β pathway in a human keratinocyte cell line. The TGF-β pathway is integral to mammalian cell proliferation and survival, and aberrant TGF-β responses have been strongly implicated in cancer.ResultsWe assayed how strongly single siRNAs targeting each of 6,000 genes affect the nuclear translocation of a green fluorescent protein (GFP)-SMAD2 reporter fusion protein. Surprisingly, we found no novel TGF-β pathway members, but we did find dominant off-target effects. All siRNA hits, whatever their intended direct target, reduced the mRNA levels of two known upstream pathway components, the TGF-β receptors 1 and 2 (TGFBR1 and TGFBR2), via micro (mi)RNA-like off-target effects. The scale of these off-target effects was remarkable, with at least 1% of the sequences in the unbiased siRNA library having measurable off-target effects on one of these two genes. It seems that relatively minor reductions of message levels via off-target effects can have dominant effects on an assay, if the pathway output is very dose-sensitive to levels of particular pathway components. In search of mechanistic details, we identified multiple miRNA-like sequence characteristics that correlated with the off-target effects. Based on these results, we identified miR-20a, miR-34a and miR-373 as miRNAs that inhibit TGFBR2 expression.ConclusionsOur findings point to potential improvements for miRNA/siRNA target prediction methods, and suggest that the type II TGF-β receptor is regulated by multiple miRNAs. We also conclude that the risk of obtaining misleading results in siRNA screens using large libraries with single-assay readout is substantial. Control and rescue experiments are essential in the interpretation of such screens, and improvements to the methods to reduce or predict RNAi off-target effects would be beneficial.

Predictive screening for regulators of conserved functional gene modules (gene batteries) in mammals

BackgroundThe expression of gene batteries, genomic units of functionally linked genes which are activated by similar sets of cis- and trans-acting regulators, has been proposed as a major determinant of cell specialization in metazoans. We developed a predictive procedure to screen the mouse and human genomes and transcriptomes for cases of gene-battery-like regulation.ResultsIn a screen that covered ~40 per cent of all annotated protein-coding genes, we identified 21 co-expressed gene clusters with statistically supported sharing of cis-regulatory sequence elements. 66 predicted cases of over-represented transcription factor binding motifs were validated against the literature and fell into three categories: (i) previously described cases of gene battery-like regulation, (ii) previously unreported cases of gene battery-like regulation with some support in a limited number of genes, and (iii) predicted cases that currently lack experimental support. The novel predictions include for example Sox 17 and RFX transcription factor binding sites that were detected in ~10% of all testis specific genes, and HNF-1 and 4 binding sites that were detected in ~30% of all kidney specific genes respectively. The results are publicly available at http://www.wlab.gu.se/lindahl/genebatteries.Conclusion21 co-expressed gene clusters were enriched for a total of 66 shared cis-regulatory sequence elements. A majority of these predictions represent novel cases of potential co-regulation of functionally coupled proteins. Critical technical parameters were evaluated, and the results and the methods provide a valuable resource for future experimental design.

The Impact of Phenotypic Switching on Glioblastoma Growth and Invasion

The brain tumour glioblastoma is characterised by diffuse and infiltrative growth into surrounding brain tissue. At the macroscopic level, the progression speed of a glioblastoma tumour is determined by two key factors: the cell proliferation rate and the cell migration speed. At the microscopic level, however, proliferation and migration appear to be mutually exclusive phenotypes, as indicated by recent in vivo imaging data. Here, we develop a mathematical model to analyse how the phenotypic switching between proliferative and migratory states of individual cells affects the macroscopic growth of the tumour. For this, we propose an individual-based stochastic model in which glioblastoma cells are either in a proliferative state, where they are stationary and divide, or in motile state in which they are subject to random motion. From the model we derive a continuum approximation in the form of two coupled reaction-diffusion equations, which exhibit travelling wave solutions whose speed of invasion depends on the model parameters. We propose a simple analytical method to predict progression rate from the cell-specific parameters and demonstrate that optimal glioblastoma growth depends on a non-trivial trade-off between the phenotypic switching rates. By linking cellular properties to an in vivo outcome, the model should be applicable to designing relevant cell screens for glioblastoma and cytometry-based patient prognostics.

Bridging the gaps in systems biology

Abstract Systems biology aims at creating mathematical models, i.e., computational reconstructions of biological systems and processes that will result in a new level of understanding—the elucidation of the basic and presumably conserved “design” and “engineering” principles of biomolecular systems. Thus, systems biology will move biology from a phenomenological to a predictive science. Mathematical modeling of biological networks and processes has already greatly improved our understanding of many cellular processes. However, given the massive amount of qualitative and quantitative data currently produced and number of burning questions in health care and biotechnology needed to be solved is still in its early phases. The field requires novel approaches for abstraction, for modeling bioprocesses that follow different biochemical and biophysical rules, and for combining different modules into larger models that still allow realistic simulation with the computational power available today. We have identified and discussed currently most prominent problems in systems biology: (1) how to bridge different scales of modeling abstraction, (2) how to bridge the gap between topological and mechanistic modeling, and (3) how to bridge the wet and dry laboratory gap. The future success of systems biology largely depends on bridging the recognized gaps.

DNA microarray study on gene expression profiles in co-cultured endothelial and smooth muscle cells in response to 4- and 24-h shear stress

Shear stress, a major hemodynamic force acting on the vessel wall, plays an important role in physiological processes such as cell growth, differentiation, remodelling, metabolism, morphology, and gene expression. We investigated the effect of shear stress on gene expression profiles in co-cultured vascular endothelial cells (ECs) and smooth muscle cells (SMCs). Human aortic ECs were cultured as a confluent monolayer on top of confluent human aortic SMCs, and the EC side of the co-culture was exposed to a laminar shear stress of 12 dyn/cm2 for 4 or 24 h. After shearing, the ECs and SMCs were separated and RNA was extracted from the cells. The RNA samples were labelled and hybridized with cDNA array slides that contained 8694 genes. Statistical analysis showed that shear stress caused the differential expression (p ≤ 0.05) of a total of 1151 genes in ECs and SMCs. In the co-cultured ECs, shear stress caused the up-regulation of 403 genes and down-regulation of 470. In the co-cultured SMCs, shear stress caused the up-regulation of 152 genes and down-regulation of 126 genes. These results provide new information on the gene expression profile and its potential functional consequences in co-cultured ECs and SMCs exposed to a physiological level of laminar shear stress. Although the effects of shear stress on gene expression in monocultured and co-cultured EC are generally similar, the response of some genes to shear stress is opposite between these two types of culture (e.g., ICAM-1 is up-regulated in monoculture and down-regulated in co-culture), which strongly indicates that EC–SMC interactions affect EC responses to shear stress.