Temporal change of DNA methylation subclasses between matched newly diagnosed and recurrent glioblastoma.

The longitudinal transition of phenotypes is pivotal in glioblastoma treatment resistance and DNA methylation emerged as an important tool for classifying glioblastoma phenotypes. We aimed to characterize DNA methylation subclass heterogeneity during progression and assess its clinical impact. Matched tissues from 47 glioblastoma patients were subjected to DNA methylation profiling, including CpG-site alterations, tissue and serum deconvolution, mass spectrometry, and immunoassay. Effects of clinical characteristics on temporal changes and outcomes were studied. Among 47 patients, 8 (17.0%) had non-matching classifications at recurrence. In the remaining 39 cases, 28.2% showed dominant DNA methylation subclass transitions, with 72.7% being a mesenchymal subclass. In general, glioblastomas with a subclass transition showed upregulated metabolic processes. Newly diagnosed glioblastomas with mesenchymal transition displayed increased stem cell-like states and decreased immune components at diagnosis and exhibited elevated immune signatures and cytokine levels in serum. In contrast, tissue of recurrent glioblastomas with mesenchymal transition showed increased immune components but decreased stem cell-like states. Survival analyses revealed comparable outcomes for patients with and without subclass transitions. This study demonstrates a temporal heterogeneity of DNA methylation subclasses in 28.2% of glioblastomas, not impacting patient survival. Changes in cell state composition associated with subclass transition may be crucial for recurrent glioblastoma targeted therapies.

Epigenetic neural glioblastoma enhances synaptic integration and predicts therapeutic vulnerability.

Neural-tumor interactions drive glioma growth as evidenced in preclinical models, but clinical validation is nascent. We present an epigenetically defined neural signature of glioblastoma that independently affects patients' survival. We use reference signatures of neural cells to deconvolve tumor DNA and classify samples into low- or high-neural tumors. High-neural glioblastomas exhibit hypomethylated CpG sites and upregulation of genes associated with synaptic integration. Single-cell transcriptomic analysis reveals high abundance of stem cell-like malignant cells classified as oligodendrocyte precursor and neural precursor cell-like in high-neural glioblastoma. High-neural glioblastoma cells engender neuron-to-glioma synapse formation in vitro and in vivo and show an unfavorable survival after xenografting. In patients, a high-neural signature associates with decreased survival as well as increased functional connectivity and can be detected via DNA analytes and brain-derived neurotrophic factor in plasma. Our study presents an epigenetically defined malignant neural signature in high-grade gliomas that is prognostically relevant.

Epigenetic encoding, heritability and plasticity of glioma transcriptional cell states.

Single-cell RNA sequencing has revealed extensive transcriptional cell state diversity in cancer, often observed independently of genetic heterogeneity, raising the central question of how malignant cell states are encoded epigenetically. To address this, here we performed multiomics single-cell profiling-integrating DNA methylation, transcriptome and genotype within the same cells-of diffuse gliomas, tumors characterized by defined transcriptional cell state diversity. Direct comparison of the epigenetic profiles of distinct cell states revealed key switches for state transitions recapitulating neurodevelopmental trajectories and highlighted dysregulated epigenetic mechanisms underlying gliomagenesis. We further developed a quantitative framework to directly measure cell state heritability and transition dynamics based on high-resolution lineage trees in human samples. We demonstrated heritability of malignant cell states, with key differences in hierarchal and plastic cell state architectures in IDH-mutant glioma versus IDH-wild-type glioblastoma, respectively. This work provides a framework anchoring transcriptional cancer cell states in their epigenetic encoding, inheritance and transition dynamics.

Evolution of fibroblasts in the lung metastatic microenvironment is driven by stage-specific transcriptional plasticity.

Mortality from breast cancer is almost exclusively a result of tumor metastasis, and lungs are one of the main metastatic sites. Cancer-associated fibroblasts are prominent players in the microenvironment of breast cancer. However, their role in the metastatic niche is largely unknown. In this study, we profiled the transcriptional co-evolution of lung fibroblasts isolated from transgenic mice at defined stage-specific time points of metastases formation. Employing multiple knowledge-based platforms of data analysis provided powerful insights on functional and temporal regulation of the transcriptome of fibroblasts. We demonstrate that fibroblasts in lung metastases are transcriptionally dynamic and plastic, and reveal stage-specific gene signatures that imply functional tasks, including extracellular matrix remodeling, stress response, and shaping the inflammatory microenvironment. Furthermore, we identified Myc as a central regulator of fibroblast rewiring and found that stromal upregulation of Myc transcriptional networks is associated with disease progression in human breast cancer.

Inferring cancer progression from Single-Cell Sequencing while allowing mutation losses.

MOTIVATION: In recent years, the well-known Infinite Sites Assumption has been a fundamental feature of computational methods devised for reconstructing tumor phylogenies and inferring cancer progressions. However, recent studies leveraging single-cell sequencing (SCS) techniques have shown evidence of the widespread recurrence and, especially, loss of mutations in several tumor samples. While there exist established computational methods that infer phylogenies with mutation losses, there remain some advancements to be made. RESULTS: We present Simulated Annealing Single-Cell inference (SASC): a new and robust approach based on simulated annealing for the inference of cancer progression from SCS datasets. In particular, we introduce an extension of the model of evolution where mutations are only accumulated, by allowing also a limited amount of mutation loss in the evolutionary history of the tumor: the Dollo-k model. We demonstrate that SASC achieves high levels of accuracy when tested on both simulated and real datasets and in comparison with some other available methods. AVAILABILITY AND IMPLEMENTATION: The SASC tool is open source and available at https://github.com/sciccolella/sasc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Electrical and synaptic integration of glioma into neural circuits.

High-grade gliomas are lethal brain cancers whose progression is robustly regulated by neuronal activity. Activity-regulated release of growth factors promotes glioma growth, but this alone is insufficient to explain the effect that neuronal activity exerts on glioma progression. Here we show that neuron and glioma interactions include electrochemical communication through bona fide AMPA receptor-dependent neuron-glioma synapses. Neuronal activity also evokes non-synaptic activity-dependent potassium currents that are amplified by gap junction-mediated tumour interconnections, forming an electrically coupled network. Depolarization of glioma membranes assessed by in vivo optogenetics promotes proliferation, whereas pharmacologically or genetically blocking electrochemical signalling inhibits the growth of glioma xenografts and extends mouse survival. Emphasizing the positive feedback mechanisms by which gliomas increase neuronal excitability and thus activity-regulated glioma growth, human intraoperative electrocorticography demonstrates increased cortical excitability in the glioma-infiltrated brain. Together, these findings indicate that synaptic and electrical integration into neural circuits promotes glioma progression.

An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma.

Diverse genetic, epigenetic, and developmental programs drive glioblastoma, an incurable and poorly understood tumor, but their precise characterization remains challenging. Here, we use an integrative approach spanning single-cell RNA-sequencing of 28 tumors, bulk genetic and expression analysis of 401 specimens from the The Cancer Genome Atlas (TCGA), functional approaches, and single-cell lineage tracing to derive a unified model of cellular states and genetic diversity in glioblastoma. We find that malignant cells in glioblastoma exist in four main cellular states that recapitulate distinct neural cell types, are influenced by the tumor microenvironment, and exhibit plasticity. The relative frequency of cells in each state varies between glioblastoma samples and is influenced by copy number amplifications of the CDK4, EGFR, and PDGFRA loci and by mutations in the NF1 locus, which each favor a defined state. Our work provides a blueprint for glioblastoma, integrating the malignant cell programs, their plasticity, and their modulation by genetic drivers.

A systematic approach to orient the human protein-protein interaction network.

The protein-protein interaction (PPI) network of an organism serves as a skeleton for its signaling circuitry, which mediates cellular response to environmental and genetic cues. Understanding this circuitry could improve the prediction of gene function and cellular behavior in response to diverse signals. To realize this potential, one has to comprehensively map PPIs and their directions of signal flow. While the quality and the volume of identified human PPIs improved dramatically over the last decade, the directions of these interactions are still mostly unknown, thus precluding subsequent prediction and modeling efforts. Here we present a systematic approach to orient the human PPI network using drug response and cancer genomic data. We provide a diffusion-based method for the orientation task that significantly outperforms existing methods. The oriented network leads to improved prioritization of cancer driver genes and drug targets compared to the state-of-the-art unoriented network.

Simultaneous Integration of Multi-omics Data Improves the Identification of Cancer Driver Modules.

The identification of molecular pathways driving cancer progression is a fundamental challenge in cancer research. Most approaches to address it are limited in the number of data types they employ and perform data integration in a sequential manner. Here, we describe ModulOmics, a method to de novo identify cancer driver pathways, or modules, by integrating protein-protein interactions, mutual exclusivity of mutations and copy number alterations, transcriptional coregulation, and RNA coexpression into a single probabilistic model. To efficiently search and score the large space of candidate modules, ModulOmics employs a two-step optimization procedure that combines integer linear programming with stochastic search. Applied across several cancer types, ModulOmics identifies highly functionally connected modules enriched with cancer driver genes, outperforming state-of-the-art methods and demonstrating the power of using multiple omics data types simultaneously. On breast cancer subtypes, ModulOmics proposes unexplored connections supported by an independent patient cohort and independent proteomic and phosphoproteomic datasets.

Principles of Systems Biology, No. 31.

This month: selected work from the 2018 RECOMB meeting, organized by Ecole Polytechnique and held last April in Paris.

ANAT 2.0: reconstructing functional protein subnetworks.

BACKGROUND: ANAT is a graphical, Cytoscape-based tool for the inference of protein networks that underlie a process of interest. The ANAT tool allows the user to perform network reconstruction under several scenarios in a number of organisms including yeast and human. RESULTS: Here we report on a new version of the tool, ANAT 2.0, which introduces substantial code and database updates as well as several new network reconstruction algorithms that greatly extend the applicability of the tool to biological data sets. CONCLUSIONS: ANAT 2.0 is an up-to-date network reconstruction tool that addresses several reconstruction challenges across multiple species.

Cell-Specific Computational Modeling of the PIM Pathway in Acute Myeloid Leukemia.

Personalized therapy is a major goal of modern oncology, as patient responses vary greatly even within a histologically defined cancer subtype. This is especially true in acute myeloid leukemia (AML), which exhibits striking heterogeneity in molecular segmentation. When calibrated to cell-specific data, executable network models can reveal subtle differences in signaling that help explain differences in drug response. Furthermore, they can suggest drug combinations to increase efficacy and combat acquired resistance. Here, we experimentally tested dynamic proteomic changes and phenotypic responses in diverse AML cell lines treated with pan-PIM kinase inhibitor and fms-related tyrosine kinase 3 (FLT3) inhibitor as single agents and in combination. We constructed cell-specific executable models of the signaling axis, connecting genetic aberrations in FLT3, tyrosine kinase 2 (TYK2), platelet-derived growth factor receptor alpha (PDGFRA), and fibroblast growth factor receptor 1 (FGFR1) to cell proliferation and apoptosis via the PIM and PI3K kinases. The models capture key differences in signaling that later enabled them to accurately predict the unique proteomic changes and phenotypic responses of each cell line. Furthermore, using cell-specific models, we tailored combination therapies to individual cell lines and successfully validated their efficacy experimentally. Specifically, we showed that cells mildly responsive to PIM inhibition exhibited increased sensitivity in combination with PIK3CA inhibition. We also used the model to infer the origin of PIM resistance engineered through prolonged drug treatment of MOLM16 cell lines and successfully validated experimentally our prediction that this resistance can be overcome with AKT1/2 inhibition. Cancer Res; 77(4); 827-38. ©2016 AACR.

INFERENCE OF PERSONALIZED DRUG TARGETS VIA NETWORK PROPAGATION.

We present a computational strategy to simulate drug treatment in a personalized setting. The method is based on integrating patient mutation and differential expression data with a protein-protein interaction network. We test the impact of in-silico deletions of different proteins on the flow of information in the network and use the results to infer potential drug targets. We apply our method to AML data from TCGA and validate the predicted drug targets using known targets. To benchmark our patient-specific approach, we compare the personalized setting predictions to those of the conventional setting. Our predicted drug targets are highly enriched with known targets from DrugBank and COSMIC (p < 10(-5) outperforming the non-personalized predictions. Finally, we focus on the largest AML patient subgroup (~30%) which is characterized by an FLT3 mutation, and utilize our prediction score to rank patient sensitivity to inhibition of each predicted target, reproducing previous findings of in-vitro experiments.

Network orientation via shortest paths.

UNLABELLED: The graph orientation problem calls for orienting the edges of a graph so as to maximize the number of pre-specified source-target vertex pairs that admit a directed path from the source to the target. Most algorithmic approaches to this problem share a common preprocessing step, in which the input graph is reduced to a tree by repeatedly contracting its cycles. Although this reduction is valid from an algorithmic perspective, the assignment of directions to the edges of the contracted cycles becomes arbitrary, and the connecting source-target paths may be arbitrarily long. In the context of biological networks, the connection of vertex pairs via shortest paths is highly motivated, leading to the following problem variant: given a graph and a collection of source-target vertex pairs, assign directions to the edges so as to maximize the number of pairs that are connected by a shortest (in the original graph) directed path. This problem is NP-complete and hard to approximate to within sub-polynomial factors. Here we provide a first polynomial-size integer linear program formulation for this problem, which allows its exact solution in seconds on current networks. We apply our algorithm to orient protein-protein interaction networks in yeast and compare it with two state-of-the-art algorithms. We find that our algorithm outperforms previous approaches and can orient considerable parts of the network, thus revealing its structure and function. AVAILABILITY AND IMPLEMENTATION: The source code is available at www.cs.tau.ac.il/∼roded/shortest.zip. CONTACT: roded@post.tau.ac.il.

Optimally orienting physical networks.

In a network orientation problem, one is given a mixed graph, consisting of directed and undirected edges, and a set of source-target vertex pairs. The goal is to orient the undirected edges so that a maximum number of pairs admit a directed path from the source to the target. This NP-complete problem arises in the context of analyzing physical networks of protein-protein and protein-DNA interactions. While the latter are naturally directed from a transcription factor to a gene, the direction of signal flow in protein-protein interactions is often unknown or cannot be measured en masse. One then tries to infer this information by using causality data on pairs of genes such that the perturbation of one gene changes the expression level of the other gene. Here we provide a first polynomial-size ILP formulation for this problem, which can be efficiently solved on current networks. We apply our algorithm to orient protein-protein interactions in yeast and measure our performance using edges with known orientations. We find that our algorithm achieves high accuracy and coverage in the orientation, outperforming simplified algorithmic variants that do not use information on edge directions. The obtained orientations can lead to a better understanding of the structure and function of the network.