Rarγ -Foxa1 signaling promotes luminal identity in prostate progenitors and is disrupted in prostate cancer.

Retinoic acid (RA) signaling is a master regulator of vertebrate development with crucial roles in directing body axis orientation and tissue differentiation, including in the reproductive system. However, a mechanistic understanding of how RA signaling promotes cell lineage identity in different tissues is often missing. Here, leveraging prostate organoid technology, we demonstrated that RA signaling orchestrates the commitment of adult mouse prostate progenitors to glandular identity, epithelial barrier integrity, and ultimately, proper specification of the prostatic lumen. Mechanistically, RA-dependent RARγ activation promotes the expression of the pioneer factor Foxa1, which synergizes with the androgen pathway for proper luminal expansion, cytoarchitecture and function. FOXA1 nucleotide variants are common in human prostate and breast cancers and considered driver mutations, though their pathogenic mechanism is incompletely understood. Combining functional genetics experiments with structural modeling of FOXA1 folding and chromatin binding analyses, we discovered that FOXA1 F254E255 is a loss-of-function mutation leading to compromised transcriptional function and lack of luminal fate commitment of prostate progenitors. Overall, we define RA as a crucial instructive signal for glandular identity in adult prostate progenitors. We propose deregulation of vitamin A metabolism as a risk factor for benign and malignant prostate disease, and identified cancer associated FOXA1 indels affecting residue F254 as loss-of-function mutations promoting dedifferentiation of adult prostate progenitors. Summary: Retinoic acid signaling orchestrates luminal differentiation of adult prostate progenitors.

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.

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.

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.

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.

HER3 Is an Actionable Target in Advanced Prostate Cancer.

It has been recognized for decades that ERBB signaling is important in prostate cancer, but targeting ERBB receptors as a therapeutic strategy for prostate cancer has been ineffective clinically. However, we show here that membranous HER3 protein is commonly highly expressed in lethal prostate cancer, associating with reduced time to castration resistance (CR) and survival. Multiplex immunofluorescence indicated that the HER3 ligand NRG1 is detectable primarily in tumor-infiltrating myelomonocytic cells in human prostate cancer; this observation was confirmed using single-cell RNA sequencing of human prostate cancer biopsies and murine transgenic prostate cancer models. In castration-resistant prostate cancer (CRPC) patient-derived xenograft organoids with high HER3 expression as well as mouse prostate cancer organoids, recombinant NRG1 enhanced proliferation and survival. Supernatant from murine bone marrow-derived macrophages and myeloid-derived suppressor cells promoted murine prostate cancer organoid growth in vitro, which could be reversed by a neutralizing anti-NRG1 antibody and ERBB inhibition. Targeting HER3, especially with the HER3-directed antibody-drug conjugate U3-1402, exhibited antitumor activity against HER3-expressing prostate cancer. Overall, these data indicate that HER3 is commonly overexpressed in lethal prostate cancer and can be activated by NRG1 secreted by myelomonocytic cells in the tumor microenvironment, supporting HER3-targeted therapeutic strategies for treating HER3-expressing advanced CRPC. SIGNIFICANCE: HER3 is an actionable target in prostate cancer, especially with anti-HER3 immunoconjugates, and targeting HER3 warrants clinical evaluation in prospective trials.

A modular master regulator landscape controls cancer transcriptional identity.

Despite considerable efforts, the mechanisms linking genomic alterations to the transcriptional identity of cancer cells remain elusive. Integrative genomic analysis, using a network-based approach, identified 407 master regulator (MR) proteins responsible for canalizing the genetics of individual samples from 20 cohorts in The Cancer Genome Atlas (TCGA) into 112 transcriptionally distinct tumor subtypes. MR proteins could be further organized into 24 pan-cancer, master regulator block modules (MRBs), each regulating key cancer hallmarks and predictive of patient outcome in multiple cohorts. Of all somatic alterations detected in each individual sample, >50% were predicted to induce aberrant MR activity, yielding insight into mechanisms linking tumor genetics and transcriptional identity and establishing non-oncogene dependencies. Genetic and pharmacological validation assays confirmed the predicted effect of upstream mutations and MR activity on downstream cellular identity and phenotype. Thus, co-analysis of mutational and gene expression profiles identified elusive subtypes and provided testable hypothesis for mechanisms mediating the effect of genetic alterations.

Systematic elucidation of neuron-astrocyte interaction in models of amyotrophic lateral sclerosis using multi-modal integrated bioinformatics workflow.

Cell-to-cell communications are critical determinants of pathophysiological phenotypes, but methodologies for their systematic elucidation are lacking. Herein, we propose an approach for the Systematic Elucidation and Assessment of Regulatory Cell-to-cell Interaction Networks (SEARCHIN) to identify ligand-mediated interactions between distinct cellular compartments. To test this approach, we selected a model of amyotrophic lateral sclerosis (ALS), in which astrocytes expressing mutant superoxide dismutase-1 (mutSOD1) kill wild-type motor neurons (MNs) by an unknown mechanism. Our integrative analysis that combines proteomics and regulatory network analysis infers the interaction between astrocyte-released amyloid precursor protein (APP) and death receptor-6 (DR6) on MNs as the top predicted ligand-receptor pair. The inferred deleterious role of APP and DR6 is confirmed in vitro in models of ALS. Moreover, the DR6 knockdown in MNs of transgenic mutSOD1 mice attenuates the ALS-like phenotype. Our results support the usefulness of integrative, systems biology approach to gain insights into complex neurobiological disease processes as in ALS and posit that the proposed methodology is not restricted to this biological context and could be used in a variety of other non-cell-autonomous communication mechanisms.

Mesenchymal subtype neuroblastomas are addicted to TGF-ßR2/HMGCR-driven protein geranylgeranylation.

The identification of targeted agents with high therapeutic index is a major challenge for cancer drug discovery. We found that screening chemical libraries across neuroblastoma (NBL) tumor subtypes for selectively-lethal compounds revealed metabolic dependencies that defined each subtype. Bioactive compounds were screened across cell models of mesenchymal (MESN) and MYCN-amplified (MYCNA) NBL subtypes, which revealed the mevalonate and folate biosynthetic pathways as MESN-selective dependencies. Treatment with lovastatin, a mevalonate biosynthesis inhibitor, selectively inhibited protein prenylation and induced apoptosis in MESN cells, while having little effect in MYCNA lines. Statin sensitivity was driven by HMGCR expression, the rate-limiting enzyme for cholesterol synthesis, which correlated with statin sensitivity across NBL cell lines, thus providing a drug sensitivity biomarker. Comparing expression profiles from sensitive and resistant cell lines revealed a TGFBR2 signaling axis that regulates HMGCR, defining an actionable addiction in that leads to MESN-subtype-dependent apoptotic cell death.

Engineering of human cardiac muscle electromechanically matured to an adult-like phenotype.

The application of tissue-engineering approaches to human induced pluripotent stem (hiPS) cells enables the development of physiologically relevant human tissue models for in vitro studies of development, regeneration, and disease. However, the immature phenotype of hiPS-derived cardiomyocytes (hiPS-CMs) limits their utility. We have developed a protocol to generate engineered cardiac tissues from hiPS cells and electromechanically mature them toward an adult-like phenotype. This protocol also provides optimized methods for analyzing these tissues' functionality, ultrastructure, and cellular properties. The approach relies on biological adaptation of cultured tissues subjected to biomimetic cues, applied at an increasing intensity, to drive accelerated maturation. hiPS cells are differentiated into cardiomyocytes and used immediately after the first contractions are observed, when they still have developmental plasticity. This starting cell population is combined with human dermal fibroblasts, encapsulated in a fibrin hydrogel and allowed to compact under passive tension in a custom-designed bioreactor. After 7 d of tissue formation, the engineered tissues are matured for an additional 21 d by increasingly intense electromechanical stimulation. Tissue properties can be evaluated by measuring contractile function, responsiveness to electrical stimuli, ultrastructure properties (sarcomere length, mitochondrial density, networks of transverse tubules), force-frequency and force-length relationships, calcium handling, and responses to ß-adrenergic agonists. Cell properties can be evaluated by monitoring gene/protein expression, oxidative metabolism, and electrophysiology. The protocol takes 4 weeks and requires experience in advanced cell culture and machining methods for bioreactor fabrication. We anticipate that this protocol will improve modeling of cardiac diseases and testing of drugs.

A Graph Based Framework to Model Virus Integration Sites.

With next generation sequencing thousands of virus and viral vector integration genome targets are now under investigation to uncover specific integration preferences and to define clusters of integration, termed common integration sites (CIS), that may allow to assess gene therapy safety or to detect disease related genomic features such as oncogenes. Here, we addressed the challenge to: 1) define the notion of CIS on graph models, 2) demonstrate that the structure of CIS enters in the category of scale-free networks and 3) show that our network approach analyzes CIS dynamically in an integrated systems biology framework using the Retroviral Transposon Tagged Cancer Gene Database (RTCGD) as a testing dataset.

Common integration sites of published datasets identified using a graph-based framework.

With next-generation sequencing, the genomic data available for the characterization of integration sites (IS) has dramatically increased. At present, in a single experiment, several thousand viral integration genome targets can be investigated to define genomic hot spots. In a previous article, we renovated a formal CIS analysis based on a rigid fixed window demarcation into a more stretchy definition grounded on graphs. Here, we present a selection of supporting data related to the graph-based framework (GBF) from our previous article, in which a collection of common integration sites (CIS) was identified on six published datasets. In this work, we will focus on two datasets, ISRTCGD and ISHIV, which have been previously discussed. Moreover, we show in more detail the workflow design that originates the datasets.

An extended gene protein/products Boolean network model including post-transcriptional regulation.

BACKGROUND: Networks Biology allows the study of complex interactions between biological systems using formal, well structured, and computationally friendly models. Several different network models can be created, depending on the type of interactions that need to be investigated. Gene Regulatory Networks (GRN) are an effective model commonly used to study the complex regulatory mechanisms of a cell. Unfortunately, given their intrinsic complexity and non discrete nature, the computational study of realistic-sized complex GRNs requires some abstractions. Boolean Networks (BNs), for example, are a reliable model that can be used to represent networks where the possible state of a node is a boolean value (0 or 1). Despite this strong simplification, BNs have been used to study both structural and dynamic properties of real as well as randomly generated GRNs. RESULTS: In this paper we show how it is possible to include the post-transcriptional regulation mechanism (a key process mediated by small non-coding RNA molecules like the miRNAs) into the BN model of a GRN. The enhanced BN model is implemented in a software toolkit (EBNT) that allows to analyze boolean GRNs from both a structural and a dynamic point of view. The open-source toolkit is compatible with available visualization tools like Cytoscape and allows to run detailed analysis of the network topology as well as of its attractors, trajectories, and state-space. In the paper, a small GRN built around the mTOR gene is used to demonstrate the main capabilities of the toolkit. CONCLUSIONS: The extended model proposed in this paper opens new opportunities in the study of gene regulation. Several of the successful researches done with the support of BN to understand high-level characteristics of regulatory networks, can now be improved to better understand the role of post-transcriptional regulation for example as a network-wide noise-reduction or stabilization mechanisms.