Master Regulator Proteins Research Articles

Abstract 6227: A systems biology approach for identifying targetable vulnerabilities in Ewing sarcoma

Abstract Background: Targeting fusion oncoproteins like the canonical EWS/ETS fusions in Ewing sarcoma remain an elusive challenge. To circumvent these challenges, we have utilized transcriptome-based systems biology approaches that identify targetable Master Regulator proteins (MRs) critical to maintaining the cancer cell state. OncoTarget analysis generates a rank order list of targetable MRs while OncoTreat analysis uses cognate cell lines matched to patient tumors to identify drugs that invert the MR signature. We used OncoTarget and OncoTreat analyses to identify existing drugs which could be repurposed to target MRs active in Ewing sarcoma. Methods: A gene regulatory interactome was developed from 122 pediatric sarcoma samples to enable identification of MR targets using OncoTarget. To enable OncoTreat analysis, 2 cognate cell lines were subjected to high-throughput drug perturbational screens. Cells were treated with sub-lethal concentrations of 335 compounds (FDA approved or investigational drugs). Ex vivo drug screens using PDX-derived cells were performed as a complementary assay to validate predictions. In vivo validation was performed in Ewing sarcoma PDXs. Results: The MR architecture of 3 Ewing sarcoma PDX models was evaluated using OncoTarget. OncoTreat analysis identified 43 drugs predicted to be active in at least 1 Ewing sarcoma PDX while OncoTarget analysis identified 20 MR targets predicted in at least 1 Ewing sarcoma PDX. DOT1L and AURKB were predicted targets across multiple Ewing sarcoma PDXs. Ex vivo screening of dissociated cells from 2 Ewing sarcoma PDXs was performed to select the most potent agents for in vivo validation. Three OncoTreat (clarithromycin, tivozanib and obataclax) and two OncoTarget (DOT1L targeted with EPZ-5676 and AURKB targeted with baracertib) predicted drugs were evaluated in a STAG2 mutant Ewing sarcoma PDX and all demonstrated significant abrogation of tumor growth including complete regression in baracertib-treated animals (p<0.01). Conclusion: These systems biology approaches can identify non-oncogene encoded vulnerabilities in Ewing sarcoma and hold the potential for expanding the number of therapeutic options for tumors driven by untargetable oncoproteins. Citation Format: Filemon S. Dela Cruz, Prabhjot S. Mundi, Armaan Siddiquee, Kristina Guillan, Daoqi You, Glorymar Ibanez Sanchez, Andoyo Ndengu, Andrea Califano, Andrew L. Kung. A systems biology approach for identifying targetable vulnerabilities in Ewing sarcoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6227.

Abstract 6545: Reversing lineage plasticity and drug resistance in lethal prostate cancer

Abstract Although major mechanisms of resistance to targeted therapies include mutation of the drug target, recent evidence suggests cell-adaptive mechanisms as alternative avenues for escaping treatment. In several cancers, tumor cells become plastic in response to target inhibition, changing their identity and molecular programs without acquiring further mutations. In the case of castration-resistant prostate cancer (CRPC) treated with standard-of-care, next-generation anti-androgens enzalutamide, tumor cells often switch lineage to survive drug pressure, acquiring neuroendocrine (NE) features and thereby transforming into neuroendocrine prostate cancer (CRPC-NE). Existing targeted therapies for CRPC and CRPC-NE provide limited benefit, representing an unmet clinical need. Hence, the elucidation of tumor plasticity and consequent heterogeneity that contribute to drug resistance is of paramount importance to identify novel therapeutic opportunities. We have recently developed a network-based precision cancer medicine framework to characterize molecular and drug sensitivity profiles of ~100 Genetically-Engineered Mouse Models (GEMM)-derived tumors of prostate cancer (Vasciaveo, et al. 2023). This study revealed that tumors derived from GEMMs with the loss of function of both Pten and Trp53 (NPp53) align with CRPC patients and exhibit high heterogeneity in regulatory programs and histopathologic features, including NE differentiation. We then adopted the NPp53 model to generate several 3D organoid lines in order to study tumor plasticity and heterogeneity. To elucidate their tumors’ regulatory programs, we reverse-engineered genome-wide regulatory networks using multimodal single-cell RNASeq and ATACSeq data to identify Master Regulator (MR) proteins implementing tumor cell identities and characterize their epigenetic landscape. Our network-based regulatory analysis identified several distinct cell populations, including cells activating epithelial-to-mesenchymal (EMT) programs and NE differentiation, all resistant to enzalutamide treatment. Cross-species analysis aligned these cells to human CRPC and CRPC-NE tumors. In particular, our analyses have shed light into the roles of NSD2, a known MR of lethal prostate cancer, whose ablation restored sensitivity to enzalutamide. In summary, we leveraged single-cell, multimodal data from CRPC and CRPC-NE 3D mouse organoids to elucidate regulatory programs of drug resistant, lethal prostate cancer and characterize its heterogeneity. Importantly, we also identified a mechanism that can be exploited for therapeutic intervention to restore tumor cells’ sensitivity Citation Format: Alessandro Vasciaveo, Jia J. Li, Dimitrios Karagiannis, Xiao Chen, Andrea Califano, Chao Lu, Michael M. Shen. Reversing lineage plasticity and drug resistance in lethal prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6545.

Abstract PR01: Identification and pharmacological targeting of treatment-resistant, stem-like breast cancer cells for combination therapy

Abstract Marker-based isolation of Cancer Stem-Like Cells (CSLCs), which can display preferential resistance to standard-of-care chemotherapy, has proven challenging, due to their very small frequency in breast cancer tissue, thus impeding their characterization, as well as identification and targeting of mechanistic dependencies. To address this challenge, we leveraged a systems biology framework for the protein-activity based characterization of CSLC biology at the single cell level and for the identification of drugs that could reprogram them to a differentiated, chemotherapy-sensitive state. To enrich for CSLCs we performed flow cytometry-based sorting of cells dissociated from 7 metastatic breast cancer patients, both TNBC and HR+, using the EpCAM and CD49f markers, followed by RNA-seq profiling. Protein activity-based clustering, using the VIPER algorithm (Alvarez et al. Nat Genet 2017), identified complementary cell states associated with high activity of either established CSLC markers (BMI1, NOTCH1, etc.) or markers of differentiated cells (GATA3, KRT18, etc.). VIPER-inferred Master Regulator (MR) proteins, identified by comparing highest versus lowest stemness score cells in each patient, were virtually identical across patients and independent of hormone receptor status. Following MR validation by pooled, CRISPR/Cas9-mediated KO followed by single cell RNA-seq profiling (CROP-seq) in cell lines comprising both CLSC and differentiated BRCA cells, we used the OncoTreat algorithm (Alvarez et al. Nat Genet 2018) to identify small molecule compounds capable of inverting their activity (MR-inverter drugs), thus potentially inducing differentiation or cell death. For this purpose, we leveraged gene expression profiles of BT-20 cells—which significantly recapitulate the CSLC MR signature—following treatment with a repertoire of 91 clinically relevant drugs. Albendazole, the top drug identified by OncoTreat, depleted CSLC population (p = 2.0 × 10−4, by Fisher’s exact test, w.r.t. CSLCs to differentiated cells ratio) without noticeable cytotoxicity in the experiment of single cell-based assays in a TNBC PDX model, at 14 days after treatment. In contrast, paclitaxel, a commonly used chemotherapeutic, significantly increase the ratio of CSLC to differentiated cells (p = 2.6 × 10−4, by Fisher’s exact test), suggesting that alternating treatment with the two drugs may abrogate the tumor-initiating potential of paclitaxel-resistant cells, while also preventing uncontrolled tumor growth. This rationale of combination therapy was confirmed by preclinical study, where treatment with multiple cycles of albendazole and paclitaxel displayed significant synergy (p = 0.00869), compared to the corresponding monotherapies. In conclusion, our systems biology framework is a proof-of-concept that drugs can be effectively repurposed to enhance the therapeutic activity of anti-tumor agents used in conventional chemotherapy, and this method can be generalizable to address the challenge of other cancers with heterogeneity. Citation Format: Jeremy Worley, Heeju Noh, Daoqi You, Mikko M Turunen, Hongxu Ding, Evan Paull, Aaron T Griffin, Adina Grunn, Kristina Guillan, Erin C Bush, Samantha J Brosius, Prabhjot S Mundi, Peter Sims, Piero Dalerba, Filemon S Dela Cruz, Andrew L Kung, Andrea Califano. Identification and pharmacological targeting of treatment-resistant, stem-like breast cancer cells for combination therapy [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr PR01.

Psychoneurological Disorders in Children with Congenital Aniridia and <i>PAX6</i>-Associated Syndromes

Congenital aniridia manifests with total or partial absence of the iris. The association of the disease with the PAX6 gene has been proven. Changes in the PAX6 structure lead to intrauterine pathology, visual organ malformation, malformation of master regulator proteins of organogenesis affecting various cells’ differentiation (central nervous system cells included). Such disorders result into the development of PAX6-associated syndromes with various brain malformations, neurological disorders, and systemic pathology (thyroid pathology, Wilms tumor, glucose intolerance). Isolated congenital aniridia is also accompanied by psychoneurological disorders. It can be associated with brain structures’ disorders during embryogenesis and with impact of external stress factors on the child (frequent medical checkup, surgical treatments). The psychoneurological disorders’ pathogenesis as well as congenital aniridia’s genetic mechanisms remain unclear. Thus, it is crucial to review new relevant data within the context of previously obtained information to gather full picture of the clinical signs of the disease and to improve the management of children with congenital aniridia.

DIPG-49. A SYSTEMS BIOLOGY APPROACH TO DEFINING AND TARGETING CELL STATE-SPECIFIC MASTER REGULATOR DEPENDENCIES IN DIFFUSE MIDLINE GLIOMA

Abstract Diffuse Midline Glioma (DMG) are fatal pediatric brain tumors. We leveraged network-based methodologies to dissect the heterogeneity of DMG tumors and to discover Master Regulator (MR) proteins representing pharmacologically accessible, mechanistic determinants of molecularly distinct cell states. We produced a DMG regulatory network from 122 publicly available RNAseq profiles with ARACNe, and inferred sample-specific MR protein activity with VIPER. A CRISPR/Cas9 KO screen across 3 DMG patient cell lines identified a set of 73/77 essential genes that were enriched in the MR signature of 80% of patient samples (GSEA p=0.000034). FOXM1 emerged as an essential MR, significantly activated across virtually all patients. We then generated RNAseq profiles following perturbation with ~300 oncology drugs in 2 DMG cell lines most representative of patient MR signatures, and used this to identify drugs that invert patient MR activity profiles using the NYS/CA Dept.of Health approved OncoTreat algorithm. OncoTreat predicted sensitivity to HDAC, MEK, CDK, PI3K, and proteosome inhibitors in subsets of patients. 80%of OncoTreat-predicted drugs (p<10-5) from 3 DMG patient tumor biopsies showed in vitro sensitivity in cultured tumor cells from the respective patients, with overall 68% accuracy among 223 drugs evaluated by both OncoTreat and in vitro (Fisher’s Exact Test p=0.0449). Further analysis of DMG intra-tumor heterogeneity via protein activity inference from published scRNAseq profiles identified 6 tumor clusters with unique MR signatures representing distinct cellular states. Targetable MRs and OncoTreat-predicted drugs were distinct between these states. Bulk RNAseq analysis recapitulated predictions seen in the more prevalent Oligodendrocyte progenitor cell-like states, but failed to capture MR and drug predictions for the Astrocyte-like states. Ongoing validations of cell state-specific drug predictions in vivo in subcutaneous patient-derived xenograft and orthotopic syngeneic DMG models have already shown tumor volume and subpopulation differences (e.g. Trametinib-treated). This provides a platform to nominate much-needed novel drugs to treat DMG.

Abstract 1116: Identifying neuroendocrine prostate cancer drivers using in vivo forward genetics

Abstract Prostate cancer (PC) is the malignancy with the highest incidence worldwide in males. Although localized disease can be effectively treated and usually has a favorable prognosis, progression to metastasis results in high mortality rates as current treatment modalities are not yet curative. The standard of care for advanced PC is hormonal therapy, termed Androgen Deprivation Therapy (ADT). Unfortunately, ADT typically elicits the emergence of a lethal phenotype, termed Castration-Resistant Prostate Cancer (CRPC), which is treatment-resistant and frequently gives rise to a highly aggressive variant with features of neuroendocrine (NE) differentiation, known as Neuroendocrine PC (NEPC). There is no cure for NEPC, thus identifying drivers that lead to its emergence is of paramount importance. We used the Sleeping Beauty (SB) transposon system to perform a forward genetic screening on Genetically-Engineered Mouse Models (GEMMs) of CRPC that are known to develop NEPC under ADT pressure. SB transposon activation generated tumors with an accelerated growth phenotype and increased metastasis incidence compared to non-activated controls. Notably, several tumors presented histological features of NEPC. We sequenced SB transposon integration sites on tumors’ genomes to identify recurrent patterns suggesting genes responsible for phenotype acceleration and NEPC differentiation. Recently, we generated and assembled a set of >100 GEMM-derived tumors, that recapitulate a broad spectrum of PC disease states, to reverse-engineer a network of regulatory proteins. We used this network on RNA-Seq data collected from SB-activated tumors to identify Master Regulator (MR) proteins that are specifically associated with NEPC emergence. Cross-species network analysis of patient-specific MRs activity conservation between patient and our mouse cohorts, aligned NEPC patients to mice tumors with NE features, corroborating our study's clinical relevance. We performed an integrative network analysis that links genomic events as generated by SB transposon integrations to downstream regulatory programs implemented by MR proteins. This analysis prioritized Sirt1 as upstream modulator of NEPC MRs activity. Activation of SIRT1 in human adenocarcinoma PC cells induced NE-like phenotype, while SIRT1 inhibitor in NEPC cells reduced tumor aggressiveness. In summary, we used in vivo models of prostate cancer to elucidate molecular drivers of NEPC, a lethal variant that is treatment-resistant. Integrative network data analysis prioritized Sirt1 as driver of NEPC emergence and was validated in vivo. Citation Format: Alessandro Vasciaveo, Francisca Nunes De Almeida, Min Zou, Matteo Di Bernardo, Andrea Califano, Cory Abate-Shen. Identifying neuroendocrine prostate cancer drivers using in vivo forward genetics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1116.

Abstract 5773: A forward genetic screen identifies SIRT1 as a driver of neuroendocrine prostate cancer

Abstract Neuroendocrine prostate cancer (NEPC) is a deadly variant of prostate cancer for which no cure is yet available. Up to 20 percent of the patients with castration resistant prostate cancer (CRPC), will progress to NEPC after treatment with standard-of-care therapies, such as abiraterone and enzalutamide, that target the androgen receptor (AR) pathway. However, the molecular mechanisms that drive the emergence of NEPC are largely unknown, contributing to a lack of treatment options for patients with NEPC. We implemented a forward genetic approach to identify novel drivers of NEPC, by using the Sleeping Beauty (SB) transposon-based forward genetic mutagenesis screening system in our Pten-, Trp53-deficient mouse model that is primed to develop NEPC at a low frequency. We generated a mouse strain (NPp53-SB) in which the Nkx3.1 promoter regulates the expression of tamoxifen-inducible Cre specifically in the mouse prostate epithelium, resulting in transposition of the T2Onc2 transposable element and conditional deletion of Pten and Trp53 in mouse prostate epithelium. We observed an increased NEPC incidence in our NPp53-SB mouse model compared to control mice without the transposon T2Onc2, suggesting that the SB-mediated insertional mutagenesis events are driving the development of NEPC. To elucidate the genetic events that are driving the observed NEPC phenotype, we identified the common insertion sites (CISs) of the transposon insertions, which revealed a total of 330 CIS-associated genes. We further performed an integrative analysis using RNA-seq data from our SB cohort, a second mouse dataset and a metastatic CRPC patient cohort, which allowed us to identify the Master Regulator (MR) proteins that are associated with NEPC, as well as the upstream genomic events that modulate these MRs and the emergence of NEPC. Our integrative analysis identified the deacetylase Sirt1 as a candidate driver of NEPC. Using CRISPR activation (CRISPRa) technology in vivo we observed that SIRT1 activation induced an NEPC-like phenotype in the adenocarcinoma LNCaP cell line, while Sirt1 drug-mediated inhibition in an NEPC cell line resulted in less aggressive tumors.In summary, we have developed a Sleeping Beauty forward genetic screen in our mouse model of CRPC, which not only accelerates disease progression, but also increases the incidence of NEPC. Our integrative analysis identified a panel of CIS-associated genes that are likely to be functionally involved in progression to NEPC. In particular, Sirt1 was shown to be a promising candidate, which may shed light on mechanisms that this aggressive disease uses to evade treatments and may inform the development of novel therapies. Citation Format: Francisca Nunes de Almeida, Alessandro Vasciaveo, Min Zou, Matteo Di Bernardo, Andrea Califano, Cory Abate-Shen. A forward genetic screen identifies SIRT1 as a driver of neuroendocrine prostate cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5773.

Abstract 4304: Network-based inference identifies cell state-specific drugs targeting master regulator vulnerabilities in diffuse midline glioma

Abstract Diffuse Midline Glioma (DMG) are fatal pediatric brain tumors with no therapies. We leveraged network-based methodologies to dissect the heterogeneity of DMG tumors and to discover Master Regulator (MR) proteins representing pharmacologically accessible, mechanistic determinants of molecularly distinct cell states. We produced the first DMG regulatory network from 122 publicly available RNAseq profiles with ARACNe (Basso et al. Nat Genet 2005), and inferred sample-specific MR protein activity with VIPER (Alvarez et al. Nat Genet 2016) based on the differential expression of their targets. 7 of the top 25 most active MRs found comprise a well-characterized MR block (MRB2) (Paull et al.Cell 2021), frequently activated across aggressive tumors, and enriched in DMG patient MR signatures (Fisher’s Exact Test p=4.4 × 10−18). A CRISPR/Cas9 KO screen across 3 DMG patient cell lines identified a set of 73/77 essential genes that were enriched in the MR signature of 80% of patient samples (GSEA p=0.000034). FOXM1 emerged as an essential MR, significantly activated across virtually all patients. We then generated RNAseq profiles following perturbation with ~300 oncology drugs in 2 DMG cell lines most representative of patient MR signatures, and used this to identify drugs that invert patient MR activity profiles using the NYS/CA Dept. of Health approved OncoTreat algorithm (Alvarez et al. Nat Genet 2018). OncoTreat predicted sensitivity to HDAC, MEK, CDK, PI3K, and proteosome inhibitors in subsets of patients, overlapping with published DMG drug screens. Importantly, 80% of OncoTreat-predicted drugs (p<10−5) from 3 DMG patient tumor biopsies showed in vitro sensitivity in cultured tumor cells from the respective patients, with overall 68% accuracy among 223 drugs evaluated by both OncoTreat and in vitro (Fisher’s Exact Test p=0.0449). Further analysis of DMG intra-tumor heterogeneity via protein activity inference across DMG single cells from 6 published scRNAseq profiles identified 6 tumor clusters with unique MR signatures co-existing in virtually all patients representing distinct cellular states (2 astrocyte-, 1 oligodendrocyte-, and 3 oligodendrocyte precursor cell-like states). Targetable MRs and OncoTreat-predicted drugs were distinct between these states. Bulk RNAseq analysis recapitulated predictions seen in the more prevalent OPC-like states, but failed to capture MR and drug predictions for the AC-like states (e.g. JAK1/Ruxolitinib and STAT3/Napabucasin). We are currently validating cell state-specific drug predictions in vivo at single-cell resolution in subcutaneous patient-derived xenograft and orthotopic syngeneic DMG models that we have shown recapitulate patient tumor heterogeneity, including with focused ultrasound-mediated drug delivery. This provides a platform to nominate much-needed novel drugs and drug combinations to treat DMG. Citation Format: Ester Calvo Fernandez, Junqiang Wang, Xu Zhang, Hong-Jian Wei, Hanna E. Minns, Aaron T. Griffin, Lukas Vlahos, Timothy J. Martins, Pamela S. Becker, John Crawford, Robyn D. Gartrell, Luca Szalontay, Stergios Zacharoulis, Zhiguo Zhang, Robert Wechsler-Reya, Cheng-Chia Wu, Andrea Califano, Jovana Pavisic. Network-based inference identifies cell state-specific drugs targeting master regulator vulnerabilities in diffuse midline glioma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4304.

Single-cell protein activity analysis reveals a novel subpopulation of chondrocytes and the corresponding key master regulator proteins associated with anti-senescence and OA progression.

Osteoarthritis (OA) is a prevalent senescence-related disease with substantial joint pain, loss of joint function, and cartilage degeneration. Because of the paucity of single-cell studies of OA and the gene dropout problem of single-cell RNA sequencing, it is difficult to acquire an in-depth understanding of the molecular characteristics of various chondrocyte clusters. Here, we aimed to provide new insights into chondrocyte senescence and a rationale for the development of effective intervention strategies for OA by using published single-cell RNA-sequencing data sets and the metaVIPER algorithm (Virtual Inference of Protein activity by Enriched Regulon). This algorithm was employed to present a proteome catalog of 62,449 chondrocytes from the cartilage of healthy individuals and OA patients at single-cell resolution. Furthermore, histopathologic analysis was carried out in cartilage samples from clinical patients and experimental mouse models of OA to validate above results. We identified 16 protein-activity-based chondrocyte clusters as well as the underlying master regulators in each cluster. By assessing the enrichment score of each cluster in bulk RNA-sequencing data, followed by gene-set variation analysis, we preliminarily identified a novel subpopulation of chondrocytes (cluster 3). This clinically relevant cluster was predicted to be the main chondrocyte cluster responsible for maintaining cellular homeostasis and anti-senescence. Specifically, we uncovered a set of the key leading-edge proteins of cluster 3 by validating the robustness of the above results using another human chondrocyte single-cell RNA-sequencing data set, consisting of 24,675 chondrocytes. Furthermore, cartilage samples from clinical patients and experimental mouse models of OA were used to evaluate the expression patterns of these leading-edge proteins, and the results indicated that NDRG2, TSPYL2, JMJD6 and HMGB2 are closely associated with OA pathogenesis and might play critical roles in modulating cellular homeostasis and anti-senescence in chondrocytes. Our study revealed a novel subpopulation of chondrocytes that are critical for anti-progression of OA and the corresponding master regulator proteins, which might serve as therapeutic targets in OA.

TEAD4 is a master regulator of high-risk nasopharyngeal carcinoma

The molecular basis underlying nasopharyngeal carcinoma (NPC) remains unclear. Recent progress in transcriptional regulatory network analysis helps identify the master regulator (MR) proteins that transcriptionally define malignant tumor phenotypes. Here, we investigated transcription factor-target interactions and identified TEA domain transcription factor 4 (TEAD4) as an MR of high-risk NPC. Precisely, TEAD4 promoted NPC migration, invasion and cisplatin resistance, depending on its autopalmitoylation. Mechanistically, YTHDF2 (YTH domain family 2) recognized WTAP (Wilms tumor 1-associating protein)-mediated TEAD4 m6A methylation to facilitate its stability and led to aberrant up-regulation of TEAD4. Up-regulated TEAD4 further drove NPC progression by transcriptionally activating BZW2 (basic leucine zipper and W2 domains 2) to induce the oncogenic AKT pathway. Moreover, the transcriptional activity of TEAD4 was independent of its canonical coactivators YAP/TAZ. Clinically, TEAD4 serves as an independent predictor of unfavorable prognosis and cisplatin response in NPC. Our data revealed the crucial role of TEAD4 in driving tumor malignancy, thus, may provide therapeutic vulnerability in NPC.

Efficient Inhibition of Bacterial Biofilm Through Interference of Protein-Protein Interaction of Master Regulator Proteins: a Proof of Concept Study with SinR- SinI Complex of Bacillus subtilis.

Biofilm-associated microbial growth is a major cause of environmental, industrial, and public health concern. Therefore, there is a pressing need to discover and develop efficient antibiofilm strategies. Regulatory proteins vital for biofilm formation might be ideal targets for developing novel antibiofilm therapeutics. Their activities often depend on protein-protein interactions. Therefore, such targets present unique opportunities and challenges to drug discovery. In Bacillus subtilis, a model organism for studying biofilms, SinR acts as the master regulator of the biofilm formation cascade. Under favourable growth conditions, it represses the epsA-O and tapA-sipW-tasA operons, which encode for essential structural components of biofilms. Under unfavourable growth conditions, SinI, an agonist protein, inactivates SinR by forming a heterotrimeric complex. This results in derepression of epsA-O and tapA-sipW-tasA operons and leads to the phenotypic switch from planktonic to biofilm-associated form. We hypothesized that inhibiting SinR-SinI interaction might warrant repression of epsA-O and tapA-sipW-tasA operons and inhibit biofilm formation. To evaluate this hypothesis, we carried out a drug repurposing study for identifying potential inhibitors of SinI. Cefoperazone and itraconazole were identified as potential inhibitors with virtual screening. The stability of their interaction with SinI was assessed in extended MD performed over 100ns. Both cefoperazone and itraconazole showed stable interaction. In in vitro studies, cefoperazone hindered the interaction of purified recombinant SinI and SinR. In the whole cell-based biofilm inhibition assays also cefoperazone was found to efficiently inhibited biofilm formation. These results provide proof of concept for targeting protein-protein interaction of master regulators as potential target for discovery and development of antibiofilm therapeutics. We propose that similar drug repurposing studies targeting key regulators of biofilm formation cascade could be an efficient approach for discovering novel anti-biofilm therapeutics against priority pathogens.

A model for network-based identification and pharmacological targeting of aberrant, replication-permissive transcriptional programs induced by viral infection

SARS-CoV-2 hijacks the host cell transcriptional machinery to induce a phenotypic state amenable to its replication. Here we show that analysis of Master Regulator proteins representing mechanistic determinants of the gene expression signature induced by SARS-CoV-2 in infected cells revealed coordinated inactivation of Master Regulators enriched in physical interactions with SARS-CoV-2 proteins, suggesting their mechanistic role in maintaining a host cell state refractory to virus replication. To test their functional relevance, we measured SARS-CoV-2 replication in epithelial cells treated with drugs predicted to activate the entire repertoire of repressed Master Regulators, based on their experimentally elucidated, context-specific mechanism of action. Overall, 15 of the 18 drugs predicted to be effective by this methodology induced significant reduction of SARS-CoV-2 replication, without affecting cell viability. This model for host-directed pharmacological therapy is fully generalizable and can be deployed to identify drugs targeting host cell-based Master Regulator signatures induced by virtually any pathogen.

Abstract 2127: Master regulator analysis of the tumor microenvironment and the distinctive tumor sub-populations in pancreatic ductal adenocarcinoma

Abstract By 2030, pancreatic ductal adenocarcinoma (PDAC) is projected to become the second leading cause of cancer-associated mortality in the United States. However, current strategies in targeting key driver mutations such as KRAS, CDKN2A, TP53, and SMAD4, have found limited translational success. As a result, the standard of care for PDAC patients continues to rely on cytotoxic chemotherapies. Master regulator (MR) proteins integrate upstream genomic alterations to generate a common downstream transcriptional signature characteristic of a tumor cell population. Importantly, aberrant MR protein activity is requisite to implementing and maintaining a pro-tumor state. Therefore, MR proteins serve as critical tumor dependencies and represent a promising class of therapeutic targets for cancer treatment. Here, we propose a novel, network-based approach to identify top MR proteins by utilizing a combination of ARACNe (Margolin et al, 2006) and VIPER (Alvarez et al, 2016) algorithms for the analysis of single-nuclei transcriptomic data. Specifically, we used VIPER to investigate the heterogeneity of the tumor microenvironment (TME) and to characterize the tumor sub-populations that co-exist in a patient PDAC specimen. Clustering analysis based on protein activity revealed six populations in the TME. The SingleR classifier subsequently annotated these clusters as myeloid and lymphoid immune, fibroblast, neuroendocrine, epithelial, and putative tumor cells. Next, we validated the putative tumor cells by performing InferCNV which detected widespread chromosomal copy number variations. We identified two distinct tumor subpopulations which were found to be enriched by the MR signatures of the Oncogenic Precursor (OP) and Lineage subtypes, previously demonstrated to represent more and less differentiated tumor cell populations, respectively (Laise et al, 2020). Interestingly, top MR proteins identified for the putative tumor cells using the single-nuclei transcriptomics closely corresponded with those identified by single-cell and previous bulk transcriptomics, providing orthogonal validation for the inferred protein activities. We characterized the diverse cell populations in the PDAC TME, delineated two distinct tumor cell subtypes, and demonstrated high concordance of inferred MR protein activity at the single-cell, single-nuclei, and bulk levels. These results demonstrate the feasibility of applying systems biology-based methods to identify MR proteins in an N = 1 context and represent a novel precision medicine approach in the treatment of PDAC patients. Future lines of inquiry center on identifying treatments against each tumor subpopulation via OncoTreat, an algorithm that leverages high-throughput drug perturbation data to predict effective therapies based on inferred ability to invert MR protein activity (Alvarez, 2018). Citation Format: Jinjie Ling, Lorenzo Tomassoni, Alvaro Curiel, Kenneth Olive, Andrea Califano. Master regulator analysis of the tumor microenvironment and the distinctive tumor sub-populations in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2127.

Abstract 3165: Elucidation and pharmacological targeting of master regulator proteins representing mechanistic determinants of breast cancer stem-like tumor initiating cell state

Abstract Although breast cancer Stem-Like Tumor Initiating Cells (SLTIC) represent only a minute fraction of the total tumor mass, they are resistant to standard of care treatment and play a key role in tumor initiation, maintenance, and progression. Unequivocal SLTIC isolation, using surface markers, has proven highly elusive, thus impeding characterization and targeting of their mechanistic dependencies. To address this challenge, we applied a systems biology approach to effectively characterize SLTIC biology and to prioritize drugs that can reprogram them to a more differentiated state that is sensitive to chemotherapy. To isolate breast cancer cells enriched for SLTICs, we performed flow cytometry-based sorting of tumor cells from 7 metastatic breast cancer patients, based on the expression of Epcam and CD49F, which are established epithelial and SLTIC-enriched cell markers, respectively. Activity-based clustering of single cell RNASeq profiles using the VIPER (Alvarez et al. Nat Genet 2017) algorithm identified two cell states, comprising cells presenting high activity of either SLTIC (i.e., BMI1, NOTCH1, etc.) or differentiated, proliferative epithelial cell markers (i.e., PCNA, CCNB1, etc.). Analysis of RNASeq profiles of BT20 cells treated with ~400 FDA approved and late-stage experimental drugs identified albendazole as the drug inducing the most significant activity inversion of SLTIC Master Regulator proteins (Alvarez et al. Nat Genet 2018) (p=4.21x10-5). This was experimentally confirmed in vivo by single cell analysis of metastatic TNBC PDX models at 14 days after treatment with albendazole, paclitaxel (a drug known to kill differentiated but not SLTIC cells), and vehicle control. As expected, paclitaxel induced dramatic decrease of the differentiated vs. SLTIC cell ratio, while albendazole had the opposite effect, inducing equally dramatic increase in that ratio, as assessed by a combination of CytoTRACE and established SLTIC marker analysis. Sequential therapy, based on a 30-day treatment with albendazole with 3 rounds of paclitaxel at day 15, 22, and 30, repeated after a 15-day drug holiday produced highly synergistic tumor volume reduction, compared to individual monotherapies (p=0.00869 by Bliss independence analysis). Indeed, while albendazole had little effect compared to vehicle control, as monotherapy, it induced >50% additional tumor viability reduction when combined with paclitaxel. The approach is highly generalizable and can be used to identify potential combination therapy approaches for any tumor in which molecular distinct subpopulations co-exist with different drug sensitivities, thus offering a practical solution to addressing tumor heterogeneity. Citation Format: Jeremy Worley, Hongxu Ding, Heeju Noh, Evan Paull, Aaron T. Griffin, Adina Grunn, Daoqi You, Kristina Guillan, Erin Bush, Piero Dalerba, Peter Sims, Filemon S. Dela Cruz, Andrew L. Kung, Andrea Califano. Elucidation and pharmacological targeting of master regulator proteins representing mechanistic determinants of breast cancer stem-like tumor initiating cell state [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3165.

Abstract 486: A systems biology approach to defining tumor heterogeneity, prognostic and targetable master regulator protein signatures from bulk and single cell RNA-seq in diffuse midline glioma (DMG)

Abstract Despite major advances in molecular profiling and numerous clinical trials, diffuse midline glioma (DMG) remains a fatal disease with median survival of only ~9 months and no identified effective drugs. To address this challenge, we leveraged network-based methodologies to dissect the heterogeneity of DMG tumors and to discover Master Regulator (MR) proteins representing pharmacologically accessible, mechanistic determinants of molecularly distinct DMG cell states. The study has produced the first DMG-specific regulatory network, reverse-engineered from 122 publicly available pediatric DMG RNA-seq profiles with ARACNe (Basso et al. Nat Genet 2005). Using this network, we measured protein activity for each sample via VIPER (Alvarez et al., Nat Genet 2016). Activity-based clustering identified 2 clusters, characterized by significant overall survival difference (>1 year, p-val=0.02 by χ2 analysis). Protein activity signatures were not significantly associated with tumor location and Histone3 mutation status. The most aberrantly activated MR proteins across all DMG patients (i.e., TOP2A, CENPF, BUB1B, FOXM1, GTSE1, MKI67, and E2F8), relative to normal caudate tissue from GTEx, were highly enriched in cell cycle regulation members, with samples in the worst outcome cluster showing significantly higher activity. Pharmacologically accessible MRs found to be significantly activated in subsets of patients (p-val<10E-5) include TOP2A, CHEK1, CDK2, and EZH2. To dissect DMG intra-tumor heterogeneity, we measured protein activity from published single-cell RNA-seq profiles of 6 DMG patients, using single-cell based regulatory networks. Activity-based analysis of tumor cells identified 8 clusters representing distinct differentiation and proliferation stages—i.e. astrocyte-like, oligodendrocyte-like, and multiple oligodendrocyte precursor cell (OPC)-like subpopulations. Consistent with bulk-based findings, these included an OPC-like-cycling population presenting highly aberrant activity of proliferative MRs (i.e. TOP2A, CENPF, FOXM1, E2F8, ZWINT, and CCNA2), suggesting this as a key DMG regulatory module (Tumor Checkpoint). We are working to define targetable MRs in these subpopulations, and generating RNA-seq profiles of SU-DIPG-VI and SF8628, two DMG cell lines showing protein activity similarity to >95% of patient samples by enrichment analysis (p-val < 10E-5), following perturbation with ~400 oncology drugs. This will allow us to identify drugs capable of inducing tumor demise by inverting the activity of the MRs of each tumor subpopulation, using the NY Dept. of Health approved OncoTreat algorithm (Alvarez et al., Nat Genet 2018). We are currently finalizing and validating both MR and drug predictions to nominate novel, much-needed therapeutic strategies. Citation Format: Ester Calvo Fernández, Junqiang Wang, Aaron T. Griffin, Luca Szalontay, Stergios Zacharoulis, Jovana Pavisic, Andrea Califano. A systems biology approach to defining tumor heterogeneity, prognostic and targetable master regulator protein signatures from bulk and single cell RNA-seq in diffuse midline glioma (DMG) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 486.

Abstract 488: A systems biology approach to defining tumor heterogeneity and prognostic and targetable master regulator protein signatures from bulk and single-cell RNAseq in osteosarcoma

Abstract Despite many clinical trials, outcome for osteosarcoma (OS) patients remains poor, especially for those with metastatic or relapsed/refractory disease. We leveraged network-based systems biology approaches to discover Master Regulator (MR) proteins representing pharmacologically accessible, mechanistic determinants of OS cell state, and to dissect tumor transcriptional heterogeneity. MRs were identified by interrogating an OS regulatory network—generated from 87 RNAseq profiles in the TARGET OS cohort—with gene expression signatures from 149 diagnostic OS samples, using the VIPER algorithm. RNAseq profiles were generated in four sarcoma cell lines following perturbation with ~400 oncology drugs, and used to identify drugs capable of inverting MR activity profiles with the OncoTreat algorithm. Unsupervised, protein activity-based clustering of the samples identified two clusters, characterized by a significant overall survival difference (76% vs 38%, log-rank p-val 0.0041). Patients in the high-risk cluster presented aberrant activity of proteins involved in cell cycle (FOXM1, CENPF, TOP2A) and epigenetic remodeling (EZH2, KDM1A), while those in the low-risk cluster had aberrant activity of proteins involved in immune response (VAV1, CD86, CEBPE), interferon gama signaling (IRF5, MNDA), and senescence control (CREG1, TFEC). We further identified patients with metastatic disease and particularly poor outcome (<15% survival) showing high activity in stem cell-related proteins NANOG, ZSCAN4, and LIN28A. To address intra-tumor heterogeneity and immune infiltration (40-60% in the samples) in bulk RNAseq data, we inferred protein activity in previously published single-cell RNAseq profiles from 11 OS patients, using regulatory networks also produced by single-cell analysis. We identified three tumor subpopulations, co-existing in all the samples, with lineage similarity to osteoblasts, chondroblasts, and fibroblasts. The most active MR proteins (p-val < 10E-30) were highly distinct across these subpopulations: NANOG, REL, IGF2, TOP2A (osteoblasts), SSTR3, WNT1, KDM6B, EZH2 (chondroblasts), and NOD1, ERBB3, PRKDC, INSR (fibroblasts). Pharmacologically accessible MRs significantly active across the three subpopulations and in subsets of bulk RNAseq samples included EZH2, CDK2, TOP2A, HDAC5, and PRKDC. OncoTreat analysis predicted sensitivity to camptothecin, seliciclib, galunisertib, and rigosertib in both fibroblast- and chondroblast-like subpopulations. In conclusion, using network-based systems biology approaches in OS, we identified pharmacologically accessible MR subtypes differentiating survival at the bulk tissue level, and defining three distinct tumor subpopulations at the single-cell level with unique predicted drug sensitivities. Citation Format: Somnath Tagore, Jovana Pavisic, Aaron T. Griffin, Katherine Janeway, Andrew L. Kung, Filemon Dela Cruz, Alejandro Sweet-Cordero, Inge Behroozfard, Stanley Leung, Alex Lee, Darrell Yamashiro, Julia Glade Bender, Andrea Califano. A systems biology approach to defining tumor heterogeneity and prognostic and targetable master regulator protein signatures from bulk and single-cell RNAseq in osteosarcoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 488.

Abstract 1905: Accelerating clinically-translatable discoveries using a network- and RNA-based precision-oncology framework

Abstract Despite recent advances, prioritizing therapy at the individual patient level remains challenging. In fact, inter-patient tumor heterogeneity remains one of the major challenges in cancer therapy, making it difficult to optimize available treatments on an individual patient basis. Likewise, the systematic prediction of drug sensitivity in vivo is still a major challenge in translational research, where targeted therapeutics are currently selected based on the presence of either actionable oncogene dependencies or aberrant cellular mechanisms. A further challenge is the limited availability of models that faithfully recapitulate the biology, complexity, and heterogeneity of human tumors, including their interaction with a conserved microenvironment and a competent immune system. To address these challenges, we introduce OncoLoop, a highly-generalizable, network-based precision medicine framework to triangulate between available mouse models, human tumors, and large-scale drug perturbational assays with in vivo validation to predict personalized treatment. OncoLoop requires only transcriptomic data (i.e., RNA-seq profiles) and leverages regulatory network analysis to (a) identify cognate models based on conservation of patient-specific Master Regulator (MR) proteins and (b) prioritize drugs based on their ability to invert the activity of MR proteins (MR-inverters), using drug perturbation profiles in cognate cell lines. As proof-of-concept, we applied OncoLoop to prostate cancer using a series of genetically engineered mouse models (GEMMs) that capture a broad range of phenotypes, including metastatic, castration-resistant and neuroendocrine disease. Indeed, >70% of patients in published cohorts had at least one high-fidelity matched GEMM. Drugs targeting shared Master Regulator dependencies of a patient and its cognate GEMM(s) were predicted using perturbational profiles of >300 drugs in MR-matched cell lines, resulting in an 80% validation rate in GEMM allografts and human xenografts. This network-based approach is highly generalized and can be applied to both cancer and non-cancer-related contexts. Citation Format: Alessandro Vasciaveo, Min Zou, Juan M. Arriaga, Francisca Nunes de Almeida, Eugene F. Douglass, Michael Shen, Andrea Califano, Cory Abate-Shen. Accelerating clinically-translatable discoveries using a network- and RNA-based precision-oncology framework [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1905.

Abstract 3612: Elucidating and targeting master regulators of tumor infiltrating regulatory T cells

Abstract Regulatory T cells (Tregs) are highly attractive targets for immunotherapy development, however, there remains a critical need for clinically actionable targeting strategies that specifically inhibit human tumor-infiltrating Tregs (TI-Tregs) while preserving function of cytotoxic effectors and peripheral non-tumor Tregs (P-Tregs). To this end, we sought to identify, validate, and target novel master regulators of TI-Tregs by leveraging a suite of next-generation bioinformatic tools and rigorous ex vivo and in vivo screening and validation methodologies. Specifically, we performed VIPER (Virtual Inference of Protein Activity) analysis on a large dataset of T cell transcriptional profiles from matched peripheral blood and tumors of 36 human patients, yielding 17 master regulator (MR) proteins predicted to uniquely drive the TI-Treg phenotype across cancers. To identify putative therapeutics that modulate TI-Treg MRs, we performed a systematic ex vivo drug screen with an unbiased panel of 1,554 FDA-approved and experimental compounds coupled to RNA sequencing (PLATE-Seq) on human TI-Tregs and P-Tregs. Drug candidates with preferential cytotoxic activity on TI-Tregs versus P-Tregs that also reversed MR transcriptional activity were thoroughly validated in vivo in the MC38 tumor model. In parallel, we performed a pooled in vivo CRISPR/Cas9 screen via the CHIME (CHimeric IMmune Editing) system to identify candidate MRs that regulate TI-Treg recruitment to and/or retention within MC38 tumors. By these approaches, we successfully validated an as-yet poorly described TI-Treg MR, TRPS1 (Transcriptional Repressor GATA Binding 1), which after genetic deletion across the hematopoietic compartment diminished TI-Tregs while preserving P-Tregs in MC38-bearing mice, and led to enhanced spontaneous control of MCA205 sarcomas. In addition, we found the widely used nucleoside analog chemotherapeutic Gemcitabine exhibits preferential inhibitory activity against human TI-Tregs versus P-Tregs, and validated in mice that at sub-clinical dose levels Gemcitabine exhibits immune-dependent therapeutic activity that significantly potentiates checkpoint blockade control of late-stage MC38 tumors. By single cell RNA sequencing of TI- and P-Tregs flow-sorted from Gemcitabine-treated mice, we observe specific depletion of a TI-Treg subset enriched for expression of TI-Treg MRs, including TRPS1. Together, these studies reveal new putative regulators of human TI-Tregs and identify a readily available clinical therapeutic that exhibits inhibitory activity against them. These findings specifically warrant future investigation into the role of TRPS1 in TI-Treg activity and clinical evaluation of low-dose Gemcitabine as a Treg-targeting strategy capable of enhancing checkpoint blockade immunotherapy. Citation Format: Casey R. Ager, Aleksandar Obradovic, Mikko Turunen, Mohsen Khosravi-Maharlooei, Charles Karan, Andrea Califano, Charles G. Drake. Elucidating and targeting master regulators of tumor infiltrating regulatory T cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3612.

Abstract 2359: Identifying synergistic master regulators of pancreatic ductal adenocarcinoma tumorigenesis through CRISPR/Cas9 combinatorial genetic screens

Abstract Pancreatic ductal adenocarcinoma (PDAC) is the third-leading cause of cancer-related mortality and is highly resistant to cytotoxic, targeted, and immune therapies. Compared to other cancers, PDAC is remarkable for its relatively uniform set of DNA alterations; unfortunately, these hallmark events are not currently targetable and other mutations known to confer sensitivity to specific drugs are uncommon. Consequently, cytotoxic combinations remain the standard of care, with most patients quickly exhibiting primary or secondary chemoresistance. Previous studies have shown that transcriptional regulatory proteins can form small, highly interconnected and autoregulated modules that can initiate and maintain malignant tumor cell state, and that the Master Regulator (MR) protein they comprise represent a novel class of non-oncogene dependencies. We used the VIPER algorithm to identify MR proteins representing mechanistic regulators of the transcriptional state for 200 laser microdissected PDAC epithelial compartment samples, collected at Columbia University Irving Medical Center (CUIMC). To systematically identify synergistic pairs of MRs, we designed a combinatorial CRISPR-Cas9 screen based on the Big Papi dual SaCas9/SpCas9 system which was used to systematically target all possible pairs of the top 50 MRs identified by VIPER analysis across 6 PDAC cell lines (KP4, PATU8988S, PANC1, HPAFII, BXPC3, MIAPACA2) with three guide-RNAs per gene, thus differentiating between potentially idiosyncratic, cell line-specific and highly conserved genetic interactions. We also developed a novel nonparametric statistical method for inferring genetic interactions from this data based on the use of conditional Mann-Whitney U tests; our new analytical method significantly improved over a previously developed parametric model of synergy inference, demonstrating increased correlation between genetic interactions inferred from biological replicates of the same cell line while simultaneously prioritizing four established synthetic lethal gene pairs in the top 10 gene pairs following integration across all 6 cell lines: BCL2L1-MCL1, BCL2L1-BCL2L2, MAPK1-MAPK3, and SEC23A-SEC23B. The methods also identified 314 additional highly significant (FDR < 0.05), yet never previously reported synthetic lethal interactions that are being validated using an array-based, low-throughput screening platform. We expect that the results of this screen will help elucidate mechanisms controlling the state of PDAC tumor cells and identify potentially druggable gene pairs. Citation Format: Aaron T. Griffin, Mikko Turunen, Lorenzo Tomassoni, Kenneth P. Olive, Pasquale Laise, Andrea Califano. Identifying synergistic master regulators of pancreatic ductal adenocarcinoma tumorigenesis through CRISPR/Cas9 combinatorial genetic screens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2359.

DIPG-57. A systems biology approach to defining and targeting master regulator dependencies from bulk and single-Cell RNA-seq in diffuse midline glioma (DMG)

Abstract Diffuse midline glioma (DMG) are fatal pediatric brain tumors with no effective systemic therapies. Molecular profiling demonstrates epigenetic dysregulation and heterogeneity, and novel approaches are needed to identify promising drugs and drug combinations. We used network-based computational analysis of RNA-seq to discover Master Regulator (MR) proteins that represent targetable, mechanistic determinants of distinct DMG cell states. We reverse-engineered the first DMG-specific regulatory network from 122 publicly available DMG RNA-seq profiles with ARACNe. Using this network, we measured sample-specific protein activity based on differential expression of their targets via VIPER. Activity-based clustering identified two clusters showing a trend in survival differences (>1 year, by χ2). The most activated MRs (i.e., TOP2A, CENPF, BUB1B, FOXM1, GTSE1, MKI67, E2F8), relative to normal caudate tissue, were enriched in cell cycle regulation members. The cluster with worse outcomes had significantly higher activity. Targetable MRs activated in subsets of patients () included TOP2A, CHEK1, CDK2, and EZH2. RNA-seq profiles were generated in two DMG cell lines following perturbation with ~400 oncology drugs, and used to identify drugs that invert MR activity profiles with the OncoTreat algorithm. We identified four pharmacotypes, and predicted sensitivity to HDAC, proteosome, EGFR, MEK, and PI3K inhibitors (), amongst others, consistent with published DMG high-throughput drug screens. To dissect intra-tumor heterogeneity, we measured protein activity from published single-cell RNA-seq profiles of 6 DMG patients, using single-cell based regulatory networks. Preliminary activity-based analysis identified 6 cell states representing distinct stages of differentiation/proliferation, including an oligodendrocyte precursor cell-like proliferative state whose MRs overlapped with bulk data (i.e. TOP2A, CENPF, FOXM1, E2F8, ZWINT, CCNA2), suggesting this as a key regulatory module. We are identifying cell state-specific MR-inverter drugs with OncoTreat to ultimately suggest drugs and drug combinations that collapse the transcriptional program and induce tumor kill for validation in MR-matched preclinical DMG models.