Gonzalo Lopez

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.

firestar—advances in the prediction of functionally important residues

firestar is a server for predicting catalytic and ligand-binding residues in protein sequences. Here, we present the important developments since the first release of firestar. Previous versions of the server required human interpretation of the results; the server is now fully automatized. firestar has been implemented as a web service and can now be run in high-throughput mode. Prediction coverage has been greatly improved with the extension of the FireDB database and the addition of alignments generated by HHsearch. Ligands in FireDB are now classified for biological relevance. Many of the changes have been motivated by the critical assessment of techniques for protein structure prediction (CASP) ligand-binding prediction experiment, which provided us with a framework to test the performance of firestar. URL: http://firedb.bioinfo.cnio.es/Php/FireStar.php.

ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information

Summary: The accurate reconstruction of gene regulatory networks from large scale molecular profile datasets represents one of the grand challenges of Systems Biology. The Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) represents one of the most effective tools to accomplish this goal. However, the initial Fixed Bandwidth (FB) implementation is both inefficient and unable to deal with sample sets providing largely uneven coverage of the probability density space. Here, we present a completely new implementation of the algorithm, based on an Adaptive Partitioning strategy (AP) for estimating the Mutual Information. The new AP implementation (ARACNe-AP) achieves a dramatic improvement in computational performance (200× on average) over the previous methodology, while preserving the Mutual Information estimator and the Network inference accuracy of the original algorithm. Given that the previous version of ARACNe is extremely demanding, the new version of the algorithm will allow even researchers with modest computational resources to build complex regulatory networks from hundreds of gene expression profiles. Availability and Implementation: A JAVA cross-platform command line executable of ARACNe, together with all source code and a detailed usage guide are freely available on Sourceforge (http://sourceforge.net/projects/aracne-ap). JAVA version 8 or higher is required. Contact: califano@c2b2.columbia.edu Supplementary information: Supplementary data are available at Bioinformatics online.

HAUSP deubiquitinates and stabilizes N-Myc in neuroblastoma

The MYCN proto-oncogene is amplified in a number of advanced-stage human tumors, such as neuroblastomas. Similar to other members of the MYC family of oncoproteins, MYCN (also known as N-Myc) is a transcription factor, and its stability and activity are tightly controlled by ubiquitination-dependent proteasome degradation. Although numerous studies have demonstrated that N-Myc is a driver of neuroblastoma tumorigenesis, therapies that directly suppress N-Myc activity in human tumors are limited. Here we have identified ubiquitin-specific protease 7 (USP7; also known as HAUSP) as a regulator of N-Myc function in neuroblastoma. HAUSP interacts with N-Myc, and HAUSP expression induces deubiquitination and subsequent stabilization of N-Myc. Conversely, RNA interference (RNAi)-mediated knockdown of USP7 in neuroblastoma cancer cell lines, or genetic ablation of Usp7 in the mouse brain, destabilizes N-Myc, which leads to inhibition of N-Myc function. Notably, HAUSP is more abundant in patients with neuroblastoma who have poorer prognosis, and HAUSP expression substantially correlates with N-Myc transcriptional activity. Furthermore, small-molecule inhibitors of HAUSPs deubiquitinase activity markedly suppress the growth of MYCN-amplified human neuroblastoma cell lines in xenograft mouse models. Taken together, our findings demonstrate a crucial role of HAUSP in regulating N-Myc function in vivo and suggest that HAUSP inhibition is a potential therapy for MYCN-amplified tumors.

Inferring protein modulation from gene expression data using conditional mutual information.

Systematic, high-throughput dissection of causal post-translational regulatory dependencies, on a genome wide basis, is still one of the great challenges of biology. Due to its complexity, however, only a handful of computational algorithms have been developed for this task. Here we present CINDy (Conditional Inference of Network Dynamics), a novel algorithm for the genome-wide, context specific inference of regulatory dependencies between signaling protein and transcription factor activity, from gene expression data. The algorithm uses a novel adaptive partitioning methodology to accurately estimate the full Condition Mutual Information (CMI) between a transcription factor and its targets, given the expression of a signaling protein. We show that CMI analysis is optimally suited to dissecting post-translational dependencies. Indeed, when tested against a gold standard dataset of experimentally validated protein-protein interactions in signal transduction networks, CINDy significantly outperforms previous methods, both in terms of sensitivity and precision.

Identification of GPC2 as an Oncoprotein and Candidate Immunotherapeutic Target in High-Risk Neuroblastoma

We developed an RNA-sequencing-based pipeline to discover differentially expressed cell-surface molecules in neuroblastoma that meet criteria for optimal immunotherapeutic target safety and efficacy. Here, we show that GPC2 is a strong candidate immunotherapeutic target in this childhood cancer. We demonstrate high GPC2 expression in neuroblastoma due to MYCN transcriptional activation and/or somatic gain of the GPC2 locus. We confirm GPC2 to be highly expressed on most neuroblastomas, but not detectable at appreciable levels in normal childhood tissues. In addition, we demonstrate that GPC2 is required for neuroblastoma proliferation. Finally, we develop a GPC2-directed antibody-drug conjugate that is potently cytotoxic to GPC2-expressing neuroblastoma cells. Collectively, these findings validate GPC2 as a non-mutated neuroblastoma oncoprotein and candidate immunotherapeutic target.

Cross-cohort analysis identifies a TEAD4 ↔ MYCN positive-feedback loop as the core regulatory element of high-risk neuroblastoma

High-risk neuroblastomas show a paucity of recurrent somatic mutations at diagnosis. As a result, the molecular basis for this aggressive phenotype remains elusive. Recent progress in regulatory network analysis helped us elucidate disease-driving mechanisms downstream of genomic alterations, including recurrent chromosomal alterations. Our analysis identified three molecular subtypes of high-risk neuroblastomas, consistent with chromosomal alterations, and identified subtype-specific master regulator proteins that were conserved across independent cohorts. A 10-protein transcriptional module-centered around a TEAD4-MYCN positive feedback loop-emerged as the regulatory driver of the high-risk subtype associated with MYCN amplification. Silencing of either gene collapsed MYCN-amplified (MYCNAmp) neuroblastoma transcriptional hallmarks and abrogated viability in vitro and in vivo Consistently, TEAD4 emerged as a robust prognostic marker of poor survival, with activity independent of the canonical Hippo pathway transcriptional coactivators YAP and TAZ. These results suggest novel therapeutic strategies for the large subset of MYCN-deregulated neuroblastomas.Significance: Despite progress in understanding of neuroblastoma genetics, little progress has been made toward personalized treatment. Here, we present a framework to determine the downstream effectors of the genetic alterations sustaining neuroblastoma subtypes, which can be easily extended to other tumor types. We show the critical effect of disrupting a 10-protein module centered around a YAP/TAZ-independent TEAD4-MYCN positive feedback loop in MYCNAmp neuroblastomas, nominating TEAD4 as a novel candidate for therapeutic intervention. Cancer Discov; 8(5); 582-99. ©2018 AACR.This article is highlighted in the In This Issue feature, p. 517.

Transcription factor activating protein 4 is synthetically lethal and a master regulator of MYCN- amplified neuroblastoma

Despite the identification of MYCN amplification as an adverse prognostic marker in neuroblastoma, MYCN inhibitors have yet to be developed. Here, by integrating evidence from a whole-genome shRNA library screen and the computational inference of master regulator proteins, we identify transcription factor activating protein 4 (TFAP4) as a critical effector of MYCN amplification in neuroblastoma, providing a novel synthetic lethal target. We demonstrate that TFAP4 is a direct target of MYCN in neuroblastoma cells, and that its expression and activity strongly negatively correlate with neuroblastoma patient survival. Silencing TFAP4 selectively inhibits MYCN-amplified neuroblastoma cell growth both in vitro and in vivo, in xenograft mouse models. Mechanistically, silencing TFAP4 induces neuroblastoma differentiation, as evidenced by increased neurite outgrowth and upregulation of neuronal markers. Taken together, our results demonstrate that TFAP4 is a key regulator of MYCN-amplified neuroblastoma and may represent a valuable novel therapeutic target.

Abstract 4384: Selective cross-cohort discovery of transcriptional mechanisms presiding over high-risk neuroblastoma subtype state maintenance

BACKGROUND: Neuroblastoma (NBL) is the most common extracranial solid tumor in children. High-risk NBLs progress to metastatic disease and have 5-year survival of only ∼40%, despite intensive multimodal therapy. This malignancy is characterized by significant heterogeneity, both clinical and molecular, which is still poorly understood. Rather than focusing on its initiating genetic events, which are highly idiosyncratic, we focused on the core regulatory machinery responsible for implementation and maintenance of tumor state. This approach led to elucidating three molecularly distinct subtypes of high-risk NBLs, as well as the core regulatory machinery responsible for their implementation and stability, including canalization and integration of mutational events and regulation of the genetic programs that represent the hallmarks of this disease. METHODS: We dissected large-scale gene expression profiling data available from TARGET and NRC Consortium by clustering algorithm and established three subtypes of high-risk NBL, followed by identification of master regulators (MR)s of each subtype by Master Regulator Inference algorithm (Lefebvre, C. et al, 2010). We performed extensive experimental validation of MRs by both in-vitro and in-vivo RNAi mediated screening, using cell viability as readout. We then used a variety of experimental assays to elucidate the modular logic controlling disease state and to identify novel NBL subtype specific dependencies. RESULTS: We identified unique MR protein modules for three distinct molecular subtypes of high-risk NBL, which were conserved across independent cohorts. Experimental MR validation identified a TEAD4-MYCN positive feedback loop as the key NBL state maintenance mechanisms in the MYCN amplification associated subtype. Jointly, MYCN and TEAD4 regulate 90% of inferred MR proteins and causally implement 70% of the subtype gene expression signature. Biologically, MYCN repressed differentiation and TEAD4 activated proliferation, two hallmarks of MYCN-amplified NBL. Specifically, TEAD4 was shown to induce MYCN-independent proliferation by transactivating key genes implicated in high-risk NBL pathogenesis, including cyclin-dependent kinases, cyclins, E2Fs, DNA replication factors, checkpoint kinases and ubiquitin ligases. Consistently, TEAD4 inhibition induced loss of NBL cell viability, thus providing novel therapeutic targets. TEAD4 activity was an outstanding predictor of survival, independent of outcome-related variables. CONCLUSION: Our results show that the inference of transcriptional regulators driving distinct molecular subgroups when combined with functional analyses is valuable to uncover the regulatory modules required for sustaining the tumor subtypes. This approach can be used to successfully identify the functional bottlenecks of other cancer subtypes. Citation Format: Presha Rajbhandari, Gonzalo Lopez, Jiyang Yu, Ruth Rodriguez-Barrueco, Mariano Alvarez, Daniel Martinez, Mark Yarmarkovich, Jo Vandesompele, Pieter Mestdagh, Jose M. Silva, Anna Lasorella, Antonio Iavarone, John M. Maris, Andrea Califano. Selective cross-cohort discovery of transcriptional mechanisms presiding over high-risk neuroblastoma subtype state maintenance. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4384.

Abstract PR10: Oncogenic dysregulations in neuroblastoma are associated with distal large chromosomal aberrations

Neuroblastoma (NB) is a prenatal malignancy diagnosed in infants, arising from neural crest cells, with heterogeneous etiology and prognosis. High risk tumors harbor large chromosomal alterations that substantially impact the expression of approximately one quarter of the genome; this challenges the distinction between driver and passenger copy number mutations, hindering the discovery of new therapeutic targets. Our lab has established a paradigm known as the bottleneck hypothesis in which multiple disease driver genetic elements integrate their aberrant signal through regulatory bottleneck, typically formed by a few transcriptional regulators, responsible of maintaining aggressive phenotypes. A new algorithm developed in our lab DIGGIT (Driver-Gene Inference by Genetical-Genomic Information Theory) has been able to successfully identify the impact of deletions in KLHL9 on the transcriptional activity of C/EBPβ and C/EBPδ, established master regulators of mesenchymal subtype in glioblastoma(1). To gain understanding of the association between genetics and the molecular phenotype which drives NB disease we analyze genome wide expression and copy number profiles from primary tumors from two independent cohorts with clinical information available; TARGET (n=250) and SIOPEN (n2=278); first, we identify genomic regions that prevalently suffer gain/loss aberrations which genetic dosage is associated with patient survival using a Cox hazards model. The main covariates are chromosome 1p (P=2.3e-9), 3p (P = 1.1e-2), 6q (P=), 11q (P=9.7e-7), 17p(P=5.3e-4) and 17q(P=4.5e-3). Also, we observed a multiplicative affect of chromosome 17 imbalance between p and q arms (P=7.8e-6). All measured with independence of MYCN amplification (the main hallmark of NB aggressive tumors). We used the above-mentioned dosage of these regions as trait loci to perform trans-aQTL analysis using the algorithm DIGGIT. We also included p and q arms combined cox linear model of chromosomes 11 and 17 to study the imbalance effect. The LIM domain only 1 protein (LMO1) is a validated NB oncogene which expression is increased in 9% of patients due to duplication events of its chromosome 11p15 locus. These events only partially explain LMO1 de-regulation. Our findings show that deletions on the distal arm 11q are implicated in LMO1 increased activity (P=4.3e-8), this effect is significantly stronger than its own loci duplication acting in cis (P=7.07e-5). The linear combination of p and q arms strikes with a p-value P=7.8e-10 supporting the additive effect of chromosome 11 imbalance. In addition to this finding, which confirms the oncogenic role of LMO1 in this disease, our integrated analysis also identified a plethora of additional findings providing plausible hypotheses for genetic alterations that contribute to dysregulation of driver genes in NB. The approach presented here is especially well suited to study tumors characterized by genomic instabilities leading to large chromosomal rearrangements, despite a paucity of recurrent point mutations. 1. James C. Chen, et al. Identification of Causal Genetic Drivers of Human Disease through Systems-Level Analysis of Regulatory Networks. Cell, Volume 159, Issue 2, p402–414, 9 October 2014. Citation Format: Gonzalo Lopez, Mariano Alvarez, James Chen, Presha Rajbhandari, Kristina A. Cole, Edward F. Attiyeh, Sharon Diskin, Pieter Mestdagh, Jo Vandesompele, John M. Maris, Andrea Califano. Oncogenic dysregulations in neuroblastoma are associated with distal large chromosomal aberrations. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr PR10.