Presha Rajbhandari

A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers

Assembly of a transcriptional and post‐translational molecular interaction network in B cells, the human B‐cell interactome (HBCI), reveals a hierarchical, transcriptional control module, where MYB and FOXM1 act as synergistic master regulators of proliferation in the germinal center (GC). Eighty percent of genes jointly regulated by these transcription factors are activated in the GC, including those encoding proteins in a complex regulating DNA pre‐replication, replication, and mitosis. These results indicate that the HBCI analysis can be used for the identification of determinants of major human cell phenotypes and provides a paradigm of general applicability to normal and pathologic tissues.

Genome-wide Identification of Post-translational Modulators of Transcription Factor Activity in Human B-Cells

The ability of a transcription factor (TF) to regulate its targets is modulated by a variety of genetic and epigenetic mechanisms, resulting in highly context-dependent regulatory networks. However, high-throughput methods for the identification of proteins that affect TF activity are still largely unavailable. Here we introduce an algorithm, modulator inference by network dynamics (MINDy), for the genome-wide identification of post-translational modulators of TF activity within a specific cellular context. When used to dissect the regulation of MYC activity in human B lymphocytes, the approach inferred novel modulators of MYC function, which act by distinct mechanisms, including protein turnover, transcription complex formation and selective enzyme recruitment. MINDy is generally applicable to study the post-translational modulation of mammalian TFs in any cellular context. As such it can be used to dissect context-specific signaling pathways and combinatorial transcriptional regulation.

STK38 is a Critical Upstream Regulator of MYC’s Oncogenic Activity in Human B-cell lymphoma

The MYC protooncogene is associated with the pathogenesis of most human neoplasia. Conversely, its experimental inactivation elicits oncogene addiction. Besides constituting a formidable therapeutic target, MYC also has an essential function in normal physiology, thus creating the need for context-specific targeting strategies. The analysis of post-translational MYC activity modulation yields novel targets for MYC inactivation. Specifically, following regulatory network analysis in human B-cells, we identify a novel role of the STK38 kinase as a regulator of MYC activity and a candidate target for abrogating tumorigenesis in MYC-addicted lymphoma. We found that STK38 regulates MYC protein stability and turnover in a kinase activity-dependent manner. STK38 kinase inactivation abrogates apoptosis following B-cell receptor activation, whereas its silencing significantly decreases MYC levels and increases apoptosis. Moreover, STK38 knockdown suppresses growth of MYC-addicted tumors in vivo, thus providing a novel viable target for treating these malignancies.

A Systems Biology Approach to Transcription Factor Binding Site Prediction

Background The elucidation of mammalian transcriptional regulatory networks holds great promise for both basic and translational research and remains one the greatest challenges to systems biology. Recent reverse engineering methods deduce regulatory interactions from large-scale mRNA expression profiles and cross-species conserved regulatory regions in DNA. Technical challenges faced by these methods include distinguishing between direct and indirect interactions, associating transcription regulators with predicted transcription factor binding sites (TFBSs), identifying non-linearly conserved binding sites across species, and providing realistic accuracy estimates. Methodology/Principal Findings We address these challenges by closely integrating proven methods for regulatory network reverse engineering from mRNA expression data, linearly and non-linearly conserved regulatory region discovery, and TFBS evaluation and discovery. Using an extensive test set of high-likelihood interactions, which we collected in order to provide realistic prediction-accuracy estimates, we show that a careful integration of these methods leads to significant improvements in prediction accuracy. To verify our methods, we biochemically validated TFBS predictions made for both transcription factors (TFs) and co-factors; we validated binding site predictions made using a known E2F1 DNA-binding motif on E2F1 predicted promoter targets, known E2F1 and JUND motifs on JUND predicted promoter targets, and a de novo discovered motif for BCL6 on BCL6 predicted promoter targets. Finally, to demonstrate accuracy of prediction using an external dataset, we showed that sites matching predicted motifs for ZNF263 are significantly enriched in recent ZNF263 ChIP-seq data. Conclusions/Significance Using an integrative framework, we were able to address technical challenges faced by state of the art network reverse engineering methods, leading to significant improvement in direct-interaction detection and TFBS-discovery accuracy. We estimated the accuracy of our framework on a human B-cell specific test set, which may help guide future methodological development.

Correlating measurements across samples improves accuracy of large-scale expression profile experiments

Gene expression profiling technologies suffer from poor reproducibility across replicate experiments. However, when analyzing large datasets, probe-level expression profile correlation can help identify flawed probes and lead to the construction of truer probe sets with improved reproducibility. We describe methods to eliminate uninformative and flawed probes, account for dependence between probes, and address variability due to transcript-isoform mixtures. We test and validate our approach on Affymetrix microarrays and outline their future adaptation to other technologies.

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.