Elucidation and Pharmacologic Targeting of Master Regulator Dependencies in Coexisting Diffuse Midline Glioma Subpopulations.

Diffuse Midline Gliomas (DMGs) are universally fatal, primarily pediatric malignancies affecting the midline structures of the central nervous system. Despite decades of clinical trials, treatment remains limited to palliative radiation therapy. A major challenge is the coexistence of molecularly distinct malignant cell states with potentially orthogonal drug sensitivities. To address this challenge, we leveraged established network-based methodologies to elucidate Master Regulator (MR) proteins representing mechanistic, non-oncogene dependencies of seven coexisting subpopulations identified by single-cell analysis-whose enrichment in essential genes was validated by pooled CRISPR/Cas9 screens. Perturbational profiles of 372 clinically relevant drugs helped identify those able to invert the activity of subpopulation-specific MRs for follow-up in vivo validation. While individual drugs predicted to target individual subpopulations-including avapritinib, larotrectinib, and ruxolitinib-produced only modest tumor growth reduction in orthotopic models, systemic co-administration induced significant survival extension, making this approach a valuable contribution to the rational design of combination therapy.

SMARCAL1 is a dual regulator of innate immune signaling and PD-L1 expression that promotes tumor immune evasion.

Genomic instability can trigger cancer-intrinsic innate immune responses that promote tumor rejection. However, cancer cells often evade these responses by overexpressing immune checkpoint regulators, such as PD-L1. Here, we identify the SNF2-family DNA translocase SMARCAL1 as a factor that favors tumor immune evasion by a dual mechanism involving both the suppression of innate immune signaling and the induction of PD-L1-mediated immune checkpoint responses. Mechanistically, SMARCAL1 limits endogenous DNA damage, thereby suppressing cGAS-STING-dependent signaling during cancer cell growth. Simultaneously, it cooperates with the AP-1 family member JUN to maintain chromatin accessibility at a PD-L1 transcriptional regulatory element, thereby promoting PD-L1 expression in cancer cells. SMARCAL1 loss hinders the ability of tumor cells to induce PD-L1 in response to genomic instability, enhances anti-tumor immune responses and sensitizes tumors to immune checkpoint blockade in a mouse melanoma model. Collectively, these studies uncover SMARCAL1 as a promising target for cancer immunotherapy.

Tumor Explants Elucidate a Cascade of Paracrine SHH, WNT, and VEGF Signals Driving Pancreatic Cancer Angiosuppression.

The sparse vascularity of pancreatic ductal adenocarcinoma (PDAC) presents a mystery: What prevents this aggressive malignancy from undergoing neoangiogenesis to counteract hypoxia and better support growth? An incidental finding from prior work on paracrine communication between malignant PDAC cells and fibroblasts revealed that inhibition of the Hedgehog (HH) pathway partially relieved angiosuppression, increasing tumor vascularity through unknown mechanisms. Initial efforts to study this phenotype were hindered by difficulties replicating the complex interactions of multiple cell types in vitro. Here we identify a cascade of paracrine signals between multiple cell types that act sequentially to suppress angiogenesis in PDAC. Malignant epithelial cells promote HH signaling in fibroblasts, leading to inhibition of noncanonical WNT signaling in fibroblasts and epithelial cells, thereby limiting VEGFR2-dependent activation of endothelial hypersprouting. This cascade was elucidated using human and murine PDAC explant models, which effectively retain the complex cellular interactions of native tumor tissues. SIGNIFICANCE: We present a key mechanism of tumor angiosuppression, a process that sculpts the physiologic, cellular, and metabolic environment of PDAC. We further present a computational and experimental framework for the dissection of complex signaling cascades that propagate among multiple cell types in the tissue environment. This article is featured in Selected Articles from This Issue, p. 201.

Subtype-selective prenylated isoflavonoids disrupt regulatory drivers of MYCN-amplified cancers.

Transcription factors have proven difficult to target with small molecules because they lack pockets necessary for potent binding. Disruption of protein expression can suppress targets and enable therapeutic intervention. To this end, we developed a drug discovery workflow that incorporates cell-line-selective screening and high-throughput expression profiling followed by regulatory network analysis to identify compounds that suppress regulatory drivers of disease. Applying this approach to neuroblastoma (NBL), we screened bioactive molecules in cell lines representing its MYC-dependent (MYCNA) and mesenchymal (MES) subtypes to identify selective compounds, followed by PLATESeq profiling of treated cells. This revealed compounds that disrupt a sub-network of MYCNA-specific regulatory proteins, resulting in MYCN degradation in vivo. The top hit was isopomiferin, a prenylated isoflavonoid that inhibited casein kinase 2 (CK2) in cells. Isopomiferin and its structural analogs inhibited MYC and MYCN in NBL and lung cancer cells, highlighting the general MYC-inhibiting potential of this unique scaffold.

Kinases Controlling Stability of the Oncogenic MYCN Protein.

We previously identified the natural products isopomiferin and pomiferin as powerful, indirect MYCN-ablating agents. In this work, we expand on their mechanism of action and find that casein kinase 2 (CK2), phosphoinositide 3-kinase (PI3K), checkpoint kinase 1 (CHK1) and serine/threonine protein kinase 38-like (STK38L), as well as STK38, work synchronously to create a field effect that maintains MYCN stability. By systematically inhibiting these kinases, we degraded MYCN and induced cell death. Additionally, we synthesized and tested several simpler and more cost-effective pomiferin analogues, which successfully emulated the compound's MYCN ablating activity. Our work identified and characterized key kinases that can be targeted to interfere with the stability of the MYCN protein in NBL cells, demonstrating the efficacy of an indirect approach to targeting "undruggable" cancer drivers.

Activating Transcription Factor 5 Promotes Neuroblastoma Metastasis by Inducing Anoikis Resistance.

MYCN-amplified neuroblastoma often presents as a highly aggressive metastatic disease with a poor prognosis. Activating transcription factor 5 (ATF5) is implicated in neural cell differentiation and cancer cell survival. Here, we show that ATF5 is highly expressed in patients with stage 4 high-risk neuroblastoma, with increased expression correlating with a poorer prognosis. We demonstrated that ATF5 promotes the metastasis of neuroblastoma cell lines in vivo. Functionally, ATF5 depletion significantly reduced xenograft tumor growth and metastasis of neuroblastoma cells to the bone marrow and liver. Mechanistically, ATF5 endows tumor cells with resistance to anoikis, thereby increasing their survival in systemic circulation and facilitating metastasis. We identified the proapoptotic BCL-2 modifying factor (BMF) as a critical player in ATF5-regulated neuroblastoma anoikis. ATF5 suppresses BMF under suspension conditions at the transcriptional level, promoting anoikis resistance, whereas BMF knockdown significantly prevents ATF5 depletion-induced anoikis. Therapeutically, we showed that a cell-penetrating dominant-negative ATF5 peptide, CP-d/n-ATF5, inhibits neuroblastoma metastasis to the bone marrow and liver by inducing anoikis sensitivity in circulating tumor cells. Our study identified ATF5 as a metastasis promoter and CP-d/n-ATF5 as a potential antimetastatic therapeutic agent for neuroblastoma. SIGNIFICANCE: This study shows that resistance to anoikis in neuroblastoma is mediated by ATF5 and offers a rationale for targeting ATF5 to treat metastatic neuroblastoma.

Anti-PD-1 immunotherapy with androgen deprivation therapy induces robust immune infiltration in metastatic castration-sensitive prostate cancer.

When compared to other malignancies, the tumor microenvironment (TME) of primary and castration-resistant prostate cancer (CRPC) is relatively devoid of immune infiltrates. While androgen deprivation therapy (ADT) induces a complex immune infiltrate in localized prostate cancer, the composition of the TME in metastatic castration-sensitive prostate cancer (mCSPC), and the effects of ADT and other treatments in this context are poorly understood. Here, we perform a comprehensive single-cell RNA sequencing (scRNA-seq) profiling of metastatic sites from patients participating in a phase 2 clinical trial (NCT03951831) that evaluated standard-of-care chemo-hormonal therapy combined with anti-PD-1 immunotherapy. We perform a longitudinal, protein activity-based analysis of TME subpopulations, revealing immune subpopulations conserved across multiple metastatic sites. We also observe dynamic changes in these immune subpopulations in response to treatment and a correlation with clinical outcomes. Our study uncovers a therapy-resistant, transcriptionally distinct tumor subpopulation that expands in cell number in treatment-refractory patients.

Elucidating Compound Mechanism of Action and Polypharmacology with a Large-scale Perturbational Profile Compendium.

The Mechanism of Action (MoA) of a drug is generally represented as a small, non-tissue-specific repertoire of high-affinity binding targets. Yet, drug activity and polypharmacology are increasingly associated with a broad range of off-target and tissue-specific effector proteins. To address this challenge, we have implemented an efficient integrative experimental and computational framework leveraging the systematic generation and analysis of drug perturbational profiles representing >700 FDA-approved and experimental oncology drugs, in cell lines selected as high-fidelity models of 23 aggressive tumor subtypes. Protein activity-based analyses revealed highly reproducible, drug-mediated modulation of tissue-specific targets, leading to generation of a proteome-wide polypharmacology map, characterization of MoA-related drug clusters and off-target effects, and identification and experimental validation of novel, tissue-specific inhibitors of undruggable oncoproteins. The proposed framework, which is easily extended to elucidating the MoA of novel small-molecule libraries, could help support more systematic and quantitative approaches to precision oncology.

Tff2 defines transit-amplifying pancreatic acinar progenitors that lack regenerative potential and are protective against Kras-driven carcinogenesis.

While adult pancreatic stem cells are thought not to exist, it is now appreciated that the acinar compartment harbors progenitors, including tissue-repairing facultative progenitors (FPs). Here, we study a pancreatic acinar population marked by trefoil factor 2 (Tff2) expression. Long-term lineage tracing and single-cell RNA sequencing (scRNA-seq) analysis of Tff2-DTR-CreERT2-targeted cells defines a transit-amplifying progenitor (TAP) population that contributes to normal homeostasis. Following acute and chronic injury, Tff2+ cells, distinct from FPs, undergo depopulation but are eventually replenished. At baseline, oncogenic KrasG12D-targeted Tff2+ cells are resistant to PDAC initiation. However, KrasG12D activation in Tff2+ cells leads to survival and clonal expansion following pancreatitis and a cancer stem/progenitor cell-like state. Selective ablation of Tff2+ cells prior to KrasG12D activation in Mist1+ acinar or Dclk1+ FP cells results in enhanced tumorigenesis, which can be partially rescued by adenoviral Tff2 treatment. Together, Tff2 defines a pancreatic TAP population that protects against Kras-driven carcinogenesis.

UHRF1 is a mediator of KRAS driven oncogenesis in lung adenocarcinoma.

KRAS is a frequent driver in lung cancer. To identify KRAS-specific vulnerabilities in lung cancer, we performed RNAi screens in primary spheroids derived from a Kras mutant mouse lung cancer model and discovered an epigenetic regulator Ubiquitin-like containing PHD and RING finger domains 1 (UHRF1). In human lung cancer models UHRF1 knock-out selectively impaired growth and induced apoptosis only in KRAS mutant cells. Genome-wide methylation and gene expression analysis of UHRF1-depleted KRAS mutant cells revealed global DNA hypomethylation leading to upregulation of tumor suppressor genes (TSGs). A focused CRISPR/Cas9 screen validated several of these TSGs as mediators of UHRF1-driven tumorigenesis. In vivo, UHRF1 knock-out inhibited tumor growth of KRAS-driven mouse lung cancer models. Finally, in lung cancer patients high UHRF1 expression is anti-correlated with TSG expression and predicts worse outcomes for patients with KRAS mutant tumors. These results nominate UHRF1 as a KRAS-specific vulnerability and potential target for therapeutic intervention.

NSD2 maintains lineage plasticity and castration-resistance in neuroendocrine prostate cancer.

The clinical use of potent androgen receptor (AR) inhibitors has promoted the emergence of novel subtypes of metastatic castration-resistant prostate cancer (mCRPC), including neuroendocrine prostate cancer (CRPC-NE), which is highly aggressive and lethal 1 . These mCRPC subtypes display increased lineage plasticity and often lack AR expression 2-5 . Here we show that neuroendocrine differentiation and castration-resistance in CRPC-NE are maintained by the activity of Nuclear Receptor Binding SET Domain Protein 2 (NSD2) 6 , which catalyzes histone H3 lysine 36 dimethylation (H3K36me2). We find that organoid lines established from genetically-engineered mice 7 recapitulate key features of human CRPC-NE, and can display transdifferentiation to neuroendocrine states in culture. CRPC-NE organoids express elevated levels of NSD2 and H3K36me2 marks, but relatively low levels of H3K27me3, consistent with antagonism of EZH2 activity by H3K36me2. Human CRPC-NE but not primary NEPC tumors expresses high levels of NSD2, consistent with a key role for NSD2 in lineage plasticity, and high NSD2 expression in mCRPC correlates with poor survival outcomes. Notably, CRISPR/Cas9 targeting of NSD2 or expression of a dominant-negative oncohistone H3.3K36M mutant results in loss of neuroendocrine phenotypes and restores responsiveness to the AR inhibitor enzalutamide in mouse and human CRPC-NE organoids and grafts. Our findings indicate that NSD2 inhibition can reverse lineage plasticity and castration-resistance, and provide a potential new therapeutic target for CRPC-NE.

Therapeutic blood-brain barrier modulation and stroke treatment by a bioengineered FZD4-selective WNT surrogate in mice.

Derangements of the blood-brain barrier (BBB) or blood-retinal barrier (BRB) occur in disorders ranging from stroke, cancer, diabetic retinopathy, and Alzheimer's disease. The Norrin/FZD4/TSPAN12 pathway activates WNT/ß-catenin signaling, which is essential for BBB and BRB function. However, systemic pharmacologic FZD4 stimulation is hindered by obligate palmitoylation and insolubility of native WNTs and suboptimal properties of the FZD4-selective ligand Norrin. Here, we develop L6-F4-2, a non-lipidated, FZD4-specific surrogate which significantly improves subpicomolar affinity versus native Norrin. In Norrin knockout (NdpKO) mice, L6-F4-2 not only potently reverses neonatal retinal angiogenesis deficits, but also restores BRB and BBB function. In adult C57Bl/6J mice, post-stroke systemic delivery of L6-F4-2 strongly reduces BBB permeability, infarction, and edema, while improving neurologic score and capillary pericyte coverage. Our findings reveal systemic efficacy of a bioengineered FZD4-selective WNT surrogate during ischemic BBB dysfunction, with potential applicability to adult CNS disorders characterized by an aberrant blood-brain barrier.

Systematic Pan-cancer Functional Inference and Validation of Hyper, Hypo and Neomorphic Mutations.

While the functional effects of many recurrent cancer mutations have been characterized, the TCGA repository comprises more than 10M non-recurrent events, whose function is unknown. We propose that the context specific activity of transcription factor (TF) proteins-as measured by expression of their transcriptional targets-provides a sensitive and accurate reporter assay to assess the functional role of oncoprotein mutations. Analysis of differentially active TFs in samples harboring mutations of unknown significance-compared to established gain (GOF/hypermorph) or loss (LOF/hypomorph) of function-helped functionally characterize 577,866 individual mutational events across TCGA cohorts, including identification of mutations that are either neomorphic (gain of novel function) or phenocopy other mutations ( mutational mimicry ). Validation using mutation knock-in assays confirmed 15 out of 15 predicted gain and loss of function mutations and 15 of 20 predicted neomorphic mutations. This could help determine targeted therapy in patients with mutations of unknown significance in established oncoproteins.

Genomic and transcriptomic analysis of checkpoint blockade response in advanced non-small cell lung cancer.

Anti-PD-1/PD-L1 agents have transformed the treatment landscape of advanced non-small cell lung cancer (NSCLC). To expand our understanding of the molecular features underlying response to checkpoint inhibitors in NSCLC, we describe here the first joint analysis of the Stand Up To Cancer-Mark Foundation cohort, a resource of whole exome and/or RNA sequencing from 393 patients with NSCLC treated with anti-PD-(L)1 therapy, along with matched clinical response annotation. We identify a number of associations between molecular features and outcome, including (1) favorable (for example, ATM altered) and unfavorable (for example, TERT amplified) genomic subgroups, (2) a prominent association between expression of inducible components of the immunoproteasome and response and (3) a dedifferentiated tumor-intrinsic subtype with enhanced response to checkpoint blockade. Taken together, results from this cohort demonstrate the complexity of biological determinants underlying immunotherapy outcomes and reinforce the discovery potential of integrative analysis within large, well-curated, cancer-specific cohorts.

Systematic elucidation and pharmacological targeting of tumor-infiltrating regulatory T cell master regulators.

Due to their immunosuppressive role, tumor-infiltrating regulatory T cells (TI-Tregs) represent attractive immuno-oncology targets. Analysis of TI vs. peripheral Tregs (P-Tregs) from 36 patients, across four malignancies, identified 17 candidate master regulators (MRs) as mechanistic determinants of TI-Treg transcriptional state. Pooled CRISPR-Cas9 screening in vivo, using a chimeric hematopoietic stem cell transplant model, confirmed the essentiality of eight MRs in TI-Treg recruitment and/or retention without affecting other T cell subtypes, and targeting one of the most significant MRs (Trps1) by CRISPR KO significantly reduced ectopic tumor growth. Analysis of drugs capable of inverting TI-Treg MR activity identified low-dose gemcitabine as the top prediction. Indeed, gemcitabine treatment inhibited tumor growth in immunocompetent but not immunocompromised allografts, increased anti-PD-1 efficacy, and depleted MR-expressing TI-Tregs in vivo. This study provides key insight into Treg signaling, specifically in the context of cancer, and a generalizable strategy to systematically elucidate and target MR proteins in immunosuppressive subpopulations.

Ras-dependent activation of BMAL2 regulates hypoxic metabolism in pancreatic cancer.

To identify novel drivers of malignancy in pancreatic ductal adenocarcinoma (PDAC), we employed regulatory network analysis, which calculates the activity of transcription factors and other regulatory proteins based on the integrated expression of their positive and negative target genes. We generated a regulatory network for the malignant epithelial cells of human PDAC using gene expression data from a set of 197 laser capture microdissected human PDAC samples and 45 low-grade precursors, for which we had matched histopathological, clinical, and epidemiological annotation. We then identified the most highly activated and repressed regulatory proteins (e.g. master regulators or MRs) associated with four malignancy phenotypes: precursors vs. PDAC (initiation), low-grade vs. high grade histopathology (progression), survival post resection, and association with KRAS activity. Integrating across these phenotypes, the top MR of PDAC malignancy was found to be BMAL2, a member of the PAS family of bHLH transcription factors. Although the canonical function of BMAL2 is linked to the circadian rhythm protein CLOCK, annotation of BMAL2 target genes highlighted a potential role in hypoxia response. We previously demonstrated that PDAC is hypovascularized and hypoperfused, and here show that PDAC from the genetically engineered KPC model exists in a state of extreme hypoxia, with a partial oxygen pressure of <1mmHg. Given the close homology of BMAL2 to HIF1ß (ARNT) and its potential to heterodimerize with HIF1A and HIF2A, we investigated whether BMAL2 plays a role in the hypoxic response of PDAC. Indeed, BMAL2 controlled numerous hypoxia response genes and could be inhibited following treatment with multiple RAF, MEK, and ERK inhibitors, validating its association with RAS activity. Knockout of BMAL2 in four human PDAC cell lines led to defects in growth and invasion in the setting of hypoxia. Strikingly, BMAL2 null cells failed to induce glycolysis upon exposure to severe hypoxia and this was associated with a loss of expression of the glycolytic enzyme LDHA. Moreover, HIF1A was no longer stabilized under hypoxia in BMAL2 knockout cells. By contrast, HIF2A was hyper-stabilized under hypoxia, indicating a dysregulation of hypoxia metabolism in response to BMAL2 loss. We conclude that BMAL2 is a master regulator of hypoxic metabolism in PDAC, serving as a molecular switch between the disparate metabolic roles of HIF1A- and HIF2A-dependent hypoxia responses.

NaRnEA: An Information Theoretic Framework for Gene Set Analysis.

Gene sets are being increasingly leveraged to make high-level biological inferences from transcriptomic data; however, existing gene set analysis methods rely on overly conservative, heuristic approaches for quantifying the statistical significance of gene set enrichment. We created Nonparametric analytical-Rank-based Enrichment Analysis (NaRnEA) to facilitate accurate and robust gene set analysis with an optimal null model derived using the information theoretic Principle of Maximum Entropy. By measuring the differential activity of ~2500 transcriptional regulatory proteins based on the differential expression of each protein's transcriptional targets between primary tumors and normal tissue samples in three cohorts from The Cancer Genome Atlas (TCGA), we demonstrate that NaRnEA critically improves in two widely used gene set analysis methods: Gene Set Enrichment Analysis (GSEA) and analytical-Rank-based Enrichment Analysis (aREA). We show that the NaRnEA-inferred differential protein activity is significantly correlated with differential protein abundance inferred from independent, phenotype-matched mass spectrometry data in the Clinical Proteomic Tumor Analysis Consortium (CPTAC), confirming the statistical and biological accuracy of our approach. Additionally, our analysis crucially demonstrates that the sample-shuffling empirical null models leveraged by GSEA and aREA for gene set analysis are overly conservative, a shortcoming that is avoided by the newly developed Maximum Entropy analytical null model employed by NaRnEA.

Network-based elucidation of colon cancer drug resistance by phosphoproteomic time-series analysis.

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. By leveraging progress in proteomic technologies and network-based methodologies, over the past decade, we developed VESPA-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and used it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogation of tumor-specific enzyme/substrate interactions accurately inferred kinase and phosphatase activity, based on their inferred substrate phosphorylation state, effectively accounting for signal cross-talk and sparse phosphoproteome coverage. The analysis elucidated time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring that was experimentally confirmed by CRISPRko assays, suggesting broad applicability to cancer and other diseases.

OncoLoop: A Network-Based Precision Cancer Medicine Framework.

Prioritizing treatments for individual patients with cancer remains challenging, and performing coclinical studies using patient-derived models in real time is often unfeasible. To circumvent these challenges, we introduce OncoLoop, a precision medicine framework that predicts drug sensitivity in human tumors and their preexisting high-fidelity (cognate) model(s) by leveraging drug perturbation profiles. As a proof of concept, we applied OncoLoop to prostate cancer using genetically engineered mouse models (GEMM) that recapitulate a broad spectrum of disease states, including castration-resistant, metastatic, and neuroendocrine prostate cancer. Interrogation of human prostate cancer cohorts by Master Regulator (MR) conservation analysis revealed that most patients with advanced prostate cancer were represented by at least one cognate GEMM-derived tumor (GEMM-DT). Drugs predicted to invert MR activity in patients and their cognate GEMM-DTs were successfully validated in allograft, syngeneic, and patient-derived xenograft (PDX) models of tumors and metastasis. Furthermore, OncoLoop-predicted drugs enhanced the efficacy of clinically relevant drugs, namely, the PD-1 inhibitor nivolumab and the AR inhibitor enzalutamide. SIGNIFICANCE: OncoLoop is a transcriptomic-based experimental and computational framework that can support rapid-turnaround coclinical studies to identify and validate drugs for individual patients, which can then be readily adapted to clinical practice. This framework should be applicable in many cancer contexts for which appropriate models and drug perturbation data are available. This article is highlighted in the In This Issue feature, p. 247.