Mukesh Bansal

How to infer gene networks from expression profiles

Inferring, or ‘reverse‐engineering’, gene networks can be defined as the process of identifying gene interactions from experimental data through computational analysis. Gene expression data from microarrays are typically used for this purpose. Here we compared different reverse‐engineering algorithms for which ready‐to‐use software was available and that had been tested on experimental data sets. We show that reverse‐engineering algorithms are indeed able to correctly infer regulatory interactions among genes, at least when one performs perturbation experiments complying with the algorithm requirements. These algorithms are superior to classic clustering algorithms for the purpose of finding regulatory interactions among genes, and, although further improvements are needed, have reached a discreet performance for being practically useful.

Inference of gene regulatory networks and compound mode of action from time course gene expression profiles

MOTIVATION Time series expression experiments are an increasingly popular method for studying a wide range of biological systems. Here we developed an algorithm that can infer the local network of gene-gene interactions surrounding a gene of interest. This is achieved by a perturbation of the gene of interest and subsequently measuring the gene expression profiles at multiple time points. We applied this algorithm to computer simulated data and to experimental data on a nine gene network in Escherichia coli. RESULTS In this paper we show that it is possible to recover the gene regulatory network from a time series data of gene expression following a perturbation to the cell. We show this both on simulated data and on a nine gene subnetwork part of the DNA-damage response pathway (SOS pathway) in the bacteria E. coli. CONTACT dibernardo@tigem.it SUPLEMENTARY INFORMATION: Supplementary data are available at http://dibernado.tigem.it

A community effort to assess and improve drug sensitivity prediction algorithms

Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.

A community computational challenge to predict the activity of pairs of compounds

Recent therapeutic successes have renewed interest in drug combinations, but experimental screening approaches are costly and often identify only small numbers of synergistic combinations. The DREAM consortium launched an open challenge to foster the development of in silico methods to computationally rank 91 compound pairs, from the most synergistic to the most antagonistic, based on gene-expression profiles of human B cells treated with individual compounds at multiple time points and concentrations. Using scoring metrics based on experimental dose-response curves, we assessed 32 methods (31 community-generated approaches and SynGen), four of which performed significantly better than random guessing. We highlight similarities between the methods. Although the accuracy of predictions was not optimal, we find that computational prediction of compound-pair activity is possible, and that community challenges can be useful to advance the field of in silico compound-synergy prediction.

Disruption of KMT2D perturbs germinal center B cell development and promotes lymphomagenesis

Mutations in the gene encoding the KMT2D (or MLL2) methyltransferase are highly recurrent and occur early during tumorigenesis in diffuse large B cell lymphoma (DLBCL) and follicular lymphoma (FL). However, the functional consequences of these mutations and their role in lymphomagenesis are unknown. Here we show that FL- and DLBCL-associated KMT2D mutations impair KMT2D enzymatic activity, leading to diminished global H3K4 methylation in germinal-center (GC) B cells and DLBCL cells. Conditional deletion of Kmt2d early during B cell development, but not after initiation of the GC reaction, results in an increase in GC B cells and enhances B cell proliferation in mice. Moreover, genetic ablation of Kmt2d in mice overexpressing Bcl2 increases the incidence of GC-derived lymphomas resembling human tumors. These findings suggest that KMT2D acts as a tumor suppressor gene whose early loss facilitates lymphomagenesis by remodeling the epigenetic landscape of the cancer precursor cells. Eradication of KMT2D-deficient cells may thus represent a rational therapeutic approach for targeting early tumorigenic events.

Aberrant epithelial GREM1 expression initiates colonic tumorigenesis from cells outside the stem cell niche

Hereditary mixed polyposis syndrome (HMPS) is characterized by the development of mixed-morphology colorectal tumors and is caused by a 40-kb genetic duplication that results in aberrant epithelial expression of the gene encoding mesenchymal bone morphogenetic protein antagonist, GREM1. Here we use HMPS tissue and a mouse model of the disease to show that epithelial GREM1 disrupts homeostatic intestinal morphogen gradients, altering cell fate that is normally determined by position along the vertical epithelial axis. This promotes the persistence and/or reacquisition of stem cell properties in Lgr5-negative progenitor cells that have exited the stem cell niche. These cells form ectopic crypts, proliferate, accumulate somatic mutations and can initiate intestinal neoplasia, indicating that the crypt base stem cell is not the sole cell of origin of colorectal cancer. Furthermore, we show that epithelial expression of GREM1 also occurs in traditional serrated adenomas, sporadic premalignant lesions with a hitherto unknown pathogenesis, and these lesions can be considered the sporadic equivalents of HMPS polyps.

MEF2B mutations lead to deregulated expression of the oncogene BCL6 in diffuse large B cell lymphoma.

MEF2B encodes a transcriptional activator and is mutated in ∼11% of diffuse large B cell lymphomas (DLBCLs) and ∼12% of follicular lymphomas (FLs). Here we found that MEF2B directly activated the transcription of the proto-oncogene BCL6 in normal germinal-center (GC) B cells and was required for DLBCL proliferation. Mutation of MEF2B resulted in enhanced transcriptional activity of MEF2B either through disruption of its interaction with the corepressor CABIN1 or by rendering it insensitive to inhibitory signaling events mediated by phosphorylation and sumoylation. Consequently, the transcriptional activity of Bcl-6 was deregulated in DLBCLs with MEF2B mutations. Thus, somatic mutations of MEF2B may contribute to lymphomagenesis by deregulating BCL6 expression, and MEF2B may represent an alternative target for blocking Bcl-6 activity in DLBCLs.

Elucidating Compound Mechanism of Action by Network Perturbation Analysis

Genome-wide identification of the mechanism of action (MoA) of small-molecule compounds characterizing their targets, effectors, and activity modulators represents a highly relevant yet elusive goal, with critical implications for assessment of compound efficacy and toxicity. Current approaches are labor intensive and mostly limited to elucidating high-affinity binding target proteins. We introduce a regulatory network-based approach that elucidates genome-wide MoA proteins based on the assessment of the global dysregulation of their molecular interactions following compound perturbation. Analysis of cellular perturbation profiles identified established MoA proteins for 70% of the tested compounds and elucidated novel proteins that were experimentally validated. Finally, unknown-MoA compound analysis revealed altretamine, an anticancer drug, as an inhibitor of glutathione peroxidase 4 lipid repair activity, which was experimentally confirmed, thus revealing unexpected similarity to the activity of sulfasalazine. This suggests that regulatory network analysis can provide valuable mechanistic insight into the elucidation of small-molecule MoA and compound similarity.

A Molecular Signature Predictive of Indolent Prostate Cancer

A three-gene panel derived from mechanistic models of cell senescence predicts outcome of low Gleason score prostate tumors. To Treat or Not to Treat...* ...That is often the question for prostate cancer patients and their caretakers. Now, Irshad et al. describe a gene signature that may guide treatment choices when prognosis is unclear. Along with other clinical and molecular parameters, pathologists use the Gleason grading system to stage prostate cancers and predict patient prognosis. A Gleason score is assigned to a cancer on the basis of its microscopic features and is directly related to tumor aggressiveness and poor prognosis. Most newly diagnosed prostate cancers with low Gleason scores require no treatment intervention and are monitored with active surveillance (indolent tumors). However, the pinpointing of tumors that are aggressive and lethal despite having low Gleason scores is a clinical challenge. In these cases, new tools are needed to answer the title question. Irshad and colleagues show that low Gleason score prostate tumors can be separated into distinct indolent and aggressive subgroups on the basis of their expression of aging and senescence genes. Using patient tissue samples and gene expression data along with computational biology techniques, including a decision tree learning model, the authors identified three genes—FGFR1, PMP22, and CDKN1A—that predicted the clinical outcome of low Gleason score prostate tumors. The prognostic power of the three-gene signature was validated in independent patient cohorts, and expression of the FGFR1, PMP22, and CDKN1A proteins in biopsy samples identified Gleason 6 patients who had failed surveillance over a 10-year period. Just as Hamlet laments in his famous soliloquy, oncologists and patients need more information about the unknown before making a decision. The new signature might aid in the choice between “bear[ing] those ills [they] have” with active surveillance or actively treating—and hopefully thwarting—aggressive tumors. *Paraphrased from the “To be, or not to be” soliloquy in Hamlet by William Shakespeare. Many newly diagnosed prostate cancers present as low Gleason score tumors that require no treatment intervention. Distinguishing the many indolent tumors from the minority of lethal ones remains a major clinical challenge. We now show that low Gleason score prostate tumors can be distinguished as indolent and aggressive subgroups on the basis of their expression of genes associated with aging and senescence. Using gene set enrichment analysis, we identified a 19-gene signature enriched in indolent prostate tumors. We then further classified this signature with a decision tree learning model to identify three genes—FGFR1, PMP22, and CDKN1A—that together accurately predicted outcome of low Gleason score tumors. Validation of this three-gene panel on independent cohorts confirmed its independent prognostic value as well as its ability to improve prognosis with currently used clinical nomograms. Furthermore, protein expression of this three-gene panel in biopsy samples distinguished Gleason 6 patients who failed surveillance over a 10-year period. We propose that this signature may be incorporated into prognostic assays for monitoring patients on active surveillance to facilitate appropriate courses of treatment.

An ID2-dependent mechanism for VHL inactivation in cancer

Mechanisms that maintain cancer stem cells are crucial to tumour progression. The ID2 protein supports cancer hallmarks including the cancer stem cell state. HIFα transcription factors, most notably HIF2α (also known as EPAS1), are expressed in and required for maintenance of cancer stem cells (CSCs). However, the pathways that are engaged by ID2 or drive HIF2α accumulation in CSCs have remained unclear. Here we report that DYRK1A and DYRK1B kinases phosphorylate ID2 on threonine 27 (Thr27). Hypoxia downregulates this phosphorylation via inactivation of DYRK1A and DYRK1B. The activity of these kinases is stimulated in normoxia by the oxygen-sensing prolyl hydroxylase PHD1 (also known as EGLN2). ID2 binds to the VHL ubiquitin ligase complex, displaces VHL-associated Cullin 2, and impairs HIF2α ubiquitylation and degradation. Phosphorylation of Thr27 of ID2 by DYRK1 blocks ID2–VHL interaction and preserves HIF2α ubiquitylation. In glioblastoma, ID2 positively modulates HIF2α activity. Conversely, elevated expression of DYRK1 phosphorylates Thr27 of ID2, leading to HIF2α destabilization, loss of glioma stemness, inhibition of tumour growth, and a more favourable outcome for patients with glioblastoma.