A prognostic neural epigenetic signature in high-grade glioma

Neural-tumor interactions drive glioma growth as evidenced in preclinical models, but clinical validation is limited. We present an epigenetically defined neural signature of glioblastoma that independently predicts 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 a high abundance of malignant stemcell-like cells in high-neural glioblastoma, primarily of the neural lineage. These cells are further classified as neural-progenitor-cell-like, astrocyte-like and oligodendrocyte-progenitor-like, alongside oligodendrocytes and excitatory neurons. In line with these findings, 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 is associated with decreased overall and progression-free survival. High-neural tumors also exhibit increased functional connectivity in magnetencephalography and resting-state magnet resonance imaging and can be detected via DNA analytes and brain-derived neurotrophic factor in patients' plasma. The prognostic importance of the neural signature was further validated in patients diagnosed with diffuse midline glioma. Our study presents an epigenetically defined malignant neural signature in high-grade gliomas that is prognostically relevant. High-neural gliomas likely require a maximized surgical resection approach for improved outcomes.

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.

EPCO-14. DECIPHERING DIFFERENTIATION HIERARCHIES, HERITABILITY AND PLASTICITY IN HUMAN GLIOMAS VIA SINGLE-CELL MULTI-OMICS

Human diffuse gliomas are incurable malignancies, where cellular state diversity fuels tumor progression and resistance to therapy. Single-cell RNA-sequencing (scRNAseq) studies recently charted the cellular states of the two major categories of human gliomas, IDH-mutant gliomas (IDH-MUT) and IDH-wildtype glioblastoma (GBM), showing that malignant cells partly recapitulate neurodevelopmental trajectories. This raises the central questions of how cell states are encoded epigenetically and whether unidirectional hierarchies or more plastic state transitions govern glioma cellular architectures. To address these questions, we generated multi-omics single-cell profiling, integrating DNA methylation (DNAme), transcriptome and genotyping of 1,728 cells from 11 GBM and IDH-MUT primary patient samples. Direct comparison of the methylomes of distinct glioma cell states revealed Polycomb repressive complex 2 (PRC2) targets DNAme as a key switch in the differentiation of malignant GBM cells. In contrast, dissecting aberrant circuits of hyper-methylation and gene expression in IDH-MUT gliomas, we observed a decoupling of the promoter methylation-expression relationship, with disruption of CTCF insulation and enhancer vulnerability which increases with cellular differentiation. To define cell state transition dynamics directly in patient samples, we generated high-resolution lineage histories of glioma cells using heritable DNAme changes, and projected the scRNAseq-derived cell states onto the lineage trees. This analysis demonstrated that cell states are heritable across malignant gliomas and, while in IDH-MUT differentiation far outpaces de-differentiation, GBM harbors a higher degree of cell state plasticity allowing reversion of GBM cells from a differentiated to a stem-like state. Overall, our work provides detailed insights into gliomagenesis, dissecting the epigenetic encoding, regulatory programs, and dynamics of the cellular states that drive human gliomas. Importantly, it also carries significant translational implication, as the high degree of de-differentiation in GBM challenges the paradigm of therapeutically targeting glioma stem-like cells to deprive tumors of their ability to regenerate.

Abstract PO-019: Deciphering differentiation hierarchies, heritability and plasticity in human gliomas via single-cell multi-omics

Human diffuse gliomas are incurable brain tumors, where cellular state diversity fuels tumor progression and resistance to therapy. Single-cell RNA-sequencing (scRNAseq) studies recently charted the cellular states of the two major categories of human gliomas, IDH-mutant gliomas (IDH-MUT) and IDH-wildtype glioblastoma (GBM), showing that malignant cells partly recapitulate neurodevelopmental trajectories. This raises the central questions of how cell states are encoded epigenetically and whether unidirectional hierarchies or more plastic state transitions govern glioma cellular architectures. To address these questions, we generated multi-omics single-cell profiling, integrating DNA methylation (DNAme), transcriptome and genotyping of 1,728 cells from 11 GBM and IDH-MUT primary patient samples. The assessment of DNAme intra-tumoral heterogeneity of malignant cells revealed that single-cell DNAme profiles within tumors span multiple bulk subtypes, are associated with important biological features of malignant cells, and may be confounded by the tumor micro-environment. Such sources of intra-tumoral heterogeneity in bulk profiles are important to recognize, as DNAme profiling is increasingly being utilized for bulk clinical brain tumor classification. The direct comparison of the methylomes of distinct glioma cellular states revealed Polycomb repressive complex 2 (PRC2) targets DNAme as a key switch in the differentiation of malignant GBM cells. In contrast, dissecting aberrant circuits of hypermethylation and gene expression in IDH-MUT gliomas, we observed a decoupling of the promoter methylation-expression relationship, with disruption of CTCF insulation and enhancer vulnerability which increases with cellular differentiation. To define cell state transition dynamics directly in patient samples, we generated high-resolution lineage histories of glioma cells using heritable DNAme changes, and projected the scRNAseq-derived cell states onto the lineage trees. This analysis demonstrated that cell states are heritable across malignant gliomas and, while in IDH-MUT differentiation far outpaces de-differentiation, GBM harbors a higher degree of cellular state plasticity allowing reversion of GBM cells from a differentiated to a stem-like state. Overall, our work provides detailed insights into gliomagenesis, dissecting the epigenetic encoding, regulatory programs, and dynamics of the cellular states that drive human gliomas. Importantly, it also carries significant translational implication, as the high degree of de-differentiation in GBM challenges the paradigm of therapeutically targeting glioma stem-like cells to deprive tumors of their ability to regenerate.

Citation Format: Federico Gaiti, Ronan Chaligne, Dana Silverbush, Joshua S. Schiffman, Hannah R. Weisman, Lloyd Kluegel, Simon Gritsch, Sunil D. Deochand, Alyssa R. Richman, Johanna Klughammer, Tommaso Biancalani, Christoph Muus, Caroline Sheridan, Alicia Alonso, Franco Izzo, Orit Rozenblatt-Rosen, Aviv Regev, Mario L. Suvà, Dan A. Landau. Deciphering differentiation hierarchies, heritability and plasticity in human gliomas via single-cell multi-omics [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-019.

TMIC-18. ELECTRICAL CIRCUIT INTEGRATION OF GLIOMA THROUGH NEURON-GLIOMA SYNAPSES AND POTASSIUM CURRENTS

High-grade gliomas are a lethal group of cancers whose progression is robustly regulated by neuronal activity. Activity-regulated release of growth factors into the tumor microenvironment represents part of the mechanism by which neuronal activity influences glioma growth, but this alone is insufficient to explain the magnitude of the effect that activity exerts on glioma progression. Here, we report that neuron-glioma interactions include electrochemical communication through both bona fide synapses and activity-dependent potassium flux. Single cell transcriptomic analyses revealed unambiguous expression of synaptic genes by malignant glioma cells, and neuron to glioma synaptic structures were evident by electron microscopy. Whole cell patch clamp electrophysiology demonstrated AMPAR-mediated excitatory neurotransmission between pre-synaptic neurons and post-synaptic glioma cells. Millisecond timescale excitatory post-synaptic currents (EPSCs) were found in a subpopulation of glioma cells, reminiscent of the axon-glial synapses between neurons and normal oligodendrocyte precursor cells (OPCs). Neuronal activity also evokes a second, non-synaptic electrophysiological response characterized by a prolonged (>1 sec) depolarization in a subpopulation of glioma cells. These longer duration currents are blocked by tetrodotoxin or barium and induced by potassium, indicating neuronal activity-dependent potassium flux reminiscent of astrocyte currents. The amplitude of the prolonged currents is reduced by gap junction inhibitors, supporting the concept that gap junction-mediated tumor interconnections can function to amplify evoked potassium currents in an electrically coupled network. As membrane depolarization of normal neural precursor cells can regulate proliferation, differentiation and survival, and glioma cells exhibit two distinct mechanisms of neuronal activity-evoked membrane depolarization, we tested the hypothesis that membrane depolarization promotes glioma growth. Using in vivooptogenetic techniques to depolarize xenografted glioma cells, we found that glioma membrane depolarization robustly promoted proliferation, while pharmacologically or genetically blocking electrochemical signaling inhibited glioma xenograft growth and extended mouse survival. Together, these findings indicate that electrical circuit integration promotes glioma progression.

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.

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.

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

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.