Paralog knockout profiling identifies DUSP4 and DUSP6 as a digenic dependence in MAPK pathway-driven cancers

Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells

BACKGROUND

Cells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and conditions. How the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear.

RESULTS

We analyze existing bulk and single-cell transcriptomic datasets to uncover the prevalence of transcriptional adaptation in mammalian systems across diverse contexts and cell types. We perform regulon gene expression analyses of transcription factor target sets in both bulk and pooled single-cell genetic perturbation datasets. Our results reveal greater robustness in expression of regulons of transcription factors exhibiting transcriptional adaptation compared to those of transcription factors that do not. Stochastic mathematical modeling of minimal compensatory gene networks qualitatively recapitulates several aspects of transcriptional adaptation, including paralog upregulation and robustness to mutation. Combined with machine learning analysis of network features of interest, our framework offers potential explanations for which regulatory steps are most important for transcriptional adaptation.

CONCLUSIONS

Our integrative approach identifies several putative hits-genes demonstrating possible transcriptional adaptation-to follow-up on experimentally and provides a formal quantitative framework to test and refine models of transcriptional adaptation.

DUSP4 enhances therapeutic sensitivity in HER2-positive breast cancer by inhibiting the G6PD pathway and ROS metabolism by interacting with ALDOB

BACKGROUND

Breast cancer (BC) poses a global threat, with HER2-positive BC being a particularly hazardous subtype. Despite the promise shown by neoadjuvant therapy (NAT) in improving prognosis, resistance in HER2-positive BC persists despite emerging targeted therapies. The objective of this study is to identify markers that promote therapeutic sensitivity and unravel the underlying mechanisms.

METHODS

We conducted an analysis of 86 HER2-positive BC biopsy samples pre-NAT using RNA-seq. Validation was carried out using TCGA, Kaplan‒Meier Plotter, and Oncomine databases. Phenotype verification utilized IC50 assays, and prognostic validation involved IHC on tissue microarrays. RNA-seq was performed on wild-type/DUSP4-KO cells, while RT‒qPCR assessed ROS pathway regulation. Mechanistic insights were obtained through IP and MS assays.

RESULTS

Our findings reveal that DUSP4 enhances therapeutic efficacy in HER2-positive BC by inhibiting the ROS pathway. Elevated DUSP4 levels correlate with increased sensitivity to HER2-targeted therapies and improved clinical outcomes. DUSP4 independently predicts disease-free survival (DFS) and overall survival (OS) in HER2-positive BC. Moreover, DUSP4 hinders G6PD activity via ALDOB dephosphorylation, with a noteworthy association with heightened ROS levels.

CONCLUSIONS

In summary, our study unveils a metabolic reprogramming paradigm in BC, highlighting DUSP4's role in enhancing therapeutic sensitivity in HER2-positive BC cells. DUSP4 interacts with ALDOB, inhibiting G6PD activity and the ROS pathway, establishing it as an independent prognostic predictor for HER2-positive BC patients.

Dual-Specificity Phosphatase 4 Promotes Malignant Features in Colorectal Cancer Through Cyclic-AMP Response Element Binding Protein/Protein Kinase CAMP-Activated Catalytic Subunit Beta Activation

INTRODUCTION

Previous studies have demonstrated that Dual-specificity phosphatase 4 (DUSP4) plays an important role in the progression of different tumor types. However, the role and mechanism of DUSP4 in colorectal cancer (CRC) remain unclear.

AIMS

We investigate the role and mechanisms of DUSP4 in CRC.

METHODS

Immunohistochemistry was used to investigate DUSP4 expression in CRC tissues. Cell proliferation, apoptosis and migration assays were used to validate DUSP4 function in vitro and in vivo. RNA-sequence assay was used to identify the target genes of DUSP4. Human phosphokinase array and inhibitor assays were used to explore the downstream signaling of DUSP4.

RESULTS

DUSP4 expression was upregulated in CRC tissues relative to normal colorectal tissues, and DUSP4 expression showed a significant positive correlation with CRC stage. Consistently, we found that DUSP4 was highly expressed in colorectal cancer cells compared to normal cells. DUSP4 knockdown inhibits CRC cell proliferation, migration and promotes apoptosis. Furthermore, the ectopic expression of DUSP4 enhanced CRC cell proliferation, migration and diminished apoptosis in vitro and in vivo. Human phosphokinase array data showed that ectopic expression of DUSP4 promotes CREB activation. RNA-sequencing data showed that PRKACB acts as a downstream target gene of DUSP4/CREB and enhances CREB activation through PKA/cAMP signaling. In addition, xenograft model results demonstrated that DUSP4 promotes colorectal tumor progression via PRKACB/CREB activation in vivo.

CONCLUSION

These findings suggest that DUSP4 promotes CRC progression. Therefore, it may be a promising therapeutic target for CRC.

Mutant IDH1 inhibition induces dsDNA sensing to activate tumor immunity

Isocitrate dehydrogenase 1 (IDH1) is the most commonly mutated metabolic gene across human cancers. Mutant IDH1 (mIDH1) generates the oncometabolite (R)-2-hydroxyglutarate, disrupting enzymes involved in epigenetics and other processes. A hallmark of IDH1-mutant solid tumors is T cell exclusion, whereas mIDH1 inhibition in preclinical models restores antitumor immunity. Here, we define a cell-autonomous mechanism of mIDH1-driven immune evasion. IDH1-mutant solid tumors show selective hypermethylation and silencing of the cytoplasmic double-stranded DNA (dsDNA) sensor CGAS, compromising innate immune signaling. mIDH1 inhibition restores DNA demethylation, derepressing CGAS and transposable element (TE) subclasses. dsDNA produced by TE-reverse transcriptase (TE-RT) activates cGAS, triggering viral mimicry and stimulating antitumor immunity. In summary, we demonstrate that mIDH1 epigenetically suppresses innate immunity and link endogenous RT activity to the mechanism of action of a US Food and Drug Administration-approved oncology drug.

Sensitive detection of synthetic response to cancer immunotherapy driven by gene paralog pairs

Emerging immunotherapies such as immune checkpoint blockade (ICB) and chimeric antigen receptor T-cell (CAR-T) therapy have revolutionized cancer treatment and have improved the survival of patients with multiple cancer types. Despite this success many patients are unresponsive to these treatments or relapse following treatment. CRISPR activation and knockout (KO) screens have been used to identify novel single gene targets that can enhance effector T cell function and promote immune cell targeting and eradication of tumors. However, cancer cells often employ multiple genes to promote an immunosuppressive pathway and thus modulating individual genes often has a limited effect. Paralogs are genes that originate from common ancestors and retain similar functions. They often have complex effects on a particular phenotype depending on factors like gene family similarity, each individual gene's expression and the physiological or pathological context. Some paralogs exhibit synthetic lethal interactions in cancer cell survival; however, a thorough investigation of paralog pairs that could enhance the efficacy of cancer immunotherapy is lacking. Here we introduce a sensitive computational approach that uses sgRNA sets enrichment analysis to identify cancer-intrinsic paralog pairs which have the potential to synergistically enhance T cell-mediated tumor destruction. We have further developed an ensemble learning model that uses an XGBoost classifier and incorporates features such as gene characteristics, sequence and structural similarities, protein-protein interaction (PPI) networks, and gene coevolution data to predict paralog pairs that are likely to enhance immunotherapy efficacy. We experimentally validated the functional significance of these predicted paralog pairs using double knockout (DKO) of identified paralog gene pairs as compared to single gene knockouts (SKOs). These data and analyses collectively provide a sensitive approach to identify previously undetected paralog pairs that can enhance cancer immunotherapy even when individual genes within the pair has a limited effect.

Paradoxical Activation of Oncogenic Signaling as a Cancer Treatment Strategy

Cancer homeostasis depends on a balance between activated oncogenic pathways driving tumorigenesis and engagement of stress response programs that counteract the inherent toxicity of such aberrant signaling. Although inhibition of oncogenic signaling pathways has been explored extensively, there is increasing evidence that overactivation of the same pathways can also disrupt cancer homeostasis and cause lethality. We show here that inhibition of protein phosphatase 2A (PP2A) hyperactivates multiple oncogenic pathways and engages stress responses in colon cancer cells. Genetic and compound screens identify combined inhibition of PP2A and WEE1 as synergistic in multiple cancer models by collapsing DNA replication and triggering premature mitosis followed by cell death. This combination also suppressed the growth of patient-derived tumors in vivo. Remarkably, acquired resistance to this drug combination suppressed the ability of colon cancer cells to form tumors in vivo. Our data suggest that paradoxical activation of oncogenic signaling can result in tumor-suppressive resistance. Significance: A therapy consisting of deliberate hyperactivation of oncogenic signaling combined with perturbation of the stress responses that result from this is very effective in animal models of colon cancer. Resistance to this therapy is associated with loss of oncogenic signaling and reduced oncogenic capacity, indicative of tumor-suppressive drug resistance.

Rebound growth of BRAF mutant pediatric glioma cells after MAPKi withdrawal is associated with MAPK reactivation and secretion of microglia-recruiting cytokines

INTRODUCTION

Patients with pediatric low-grade gliomas (pLGGs), the most common primary brain tumors in children, can often benefit from MAPK inhibitor (MAPKi) treatment. However, rapid tumor regrowth, also referred to as rebound growth, may occur once treatment is stopped, constituting a significant clinical challenge.

METHODS

Four patient-derived pediatric glioma models were investigated to model rebound growth in vitro based on viable cell counts in response to MAPKi treatment and withdrawal. A multi-omics dataset (RNA sequencing and LC-MS/MS based phospho-/proteomics) was generated to investigate possible rebound-driving mechanisms. Following in vitro validation, putative rebound-driving mechanisms were validated in vivo using the BT-40 orthotopic xenograft model.

RESULTS

Of the tested models, only a BRAFV600E-driven model (BT-40, with additional CDKN2A/Bdel) showed rebound growth upon MAPKi withdrawal. Using this model, we identified a rapid reactivation of the MAPK pathway upon MAPKi withdrawal in vitro, also confirmed in vivo. Furthermore, transient overactivation of key MAPK molecules at transcriptional (e.g. FOS) and phosphorylation (e.g. pMEK) levels, was observed in vitro. Additionally, we detected increased expression and secretion of cytokines (CCL2, CX3CL1, CXCL10 and CCL7) upon MAPKi treatment, maintained during early withdrawal. While increased cytokine expression did not have tumor cell intrinsic effects, presence of these cytokines in conditioned media led to increased attraction of microglia cells in vitro.

CONCLUSION

Taken together, these data indicate rapid MAPK reactivation upon MAPKi withdrawal as a tumor cell intrinsic rebound-driving mechanism. Furthermore, increased secretion of microglia-recruiting cytokines may play a role in treatment response and rebound growth upon withdrawal, warranting further evaluation.

Protein tyrosine phosphatases: emerging role in cancer therapy resistance

BACKGROUND

Tyrosine phosphorylation of intracellular proteins is a post-translational modification that plays a regulatory role in signal transduction during cellular events. Dephosphorylation of signal transduction proteins caused by protein tyrosine phosphatases (PTPs) contributed their role as a convergent node to mediate cross-talk between signaling pathways. In the context of cancer, PTP-mediated pathways have been identified as signaling hubs that enabled cancer cells to mitigate stress induced by clinical therapy. This is achieved by the promotion of constitutive activation of growth-stimulatory signaling pathways or modulation of the immune-suppressive tumor microenvironment. Preclinical evidences suggested that anticancer drugs will release their greatest therapeutic potency when combined with PTP inhibitors, reversing drug resistance that was responsible for clinical failures during cancer therapy.

AREAS COVERED

This review aimed to elaborate recent insights that supported the involvement of PTP-mediated pathways in the development of resistance to targeted therapy and immune-checkpoint therapy.

EXPERT OPINION

This review proposed the notion of PTP inhibition in anticancer combination therapy as a potential strategy in clinic to achieve long-term tumor regression. Ongoing clinical trials are currently underway to assess the safety and efficacy of combination therapy in advanced-stage tumors.

Genetic dependencies associated with transcription factor activities in human cancer cell lines

Transcription factors (TFs) are important mediators of aberrant transcriptional programs in cancer cells. In this study, we focus on TF activity (TFa) as a biomarker for cell-line-selective anti-proliferative effects, in that high TFa predicts sensitivity to loss of function of a given gene (i.e., genetic dependencies [GDs]). Our linear-regression-based framework identifies 3,047 pan-cancer and 3,952 cancer-type-specific candidate TFa-GD associations from cell line data, which are then cross-examined for impact on survival in patient cohorts. One of the most prominent biomarkers is TEAD1 activity, whose associations with its predicted GDs are validated through experimental evidence as proof of concept. Overall, these TFa-GD associations represent an attractive resource for identifying innovative, biomarker-driven hypotheses for drug discovery programs in oncology.

Efficient gene knockout and genetic interaction screening using the in4mer CRISPR/Cas12a multiplex knockout platform

Genetic interactions mediate the emergence of phenotype from genotype, but technologies for combinatorial genetic perturbation in mammalian cells are challenging to scale. Here, we identify background-independent paralog synthetic lethals from previous CRISPR genetic interaction screens, and find that the Cas12a platform provides superior sensitivity and assay replicability. We develop the in4mer Cas12a platform that uses arrays of four independent guide RNAs targeting the same or different genes. We construct a genome-scale library, Inzolia, that is ~30% smaller than a typical CRISPR/Cas9 library while also targeting ~4000 paralog pairs. Screens in cancer cells demonstrate discrimination of core and context-dependent essential genes similar to that of CRISPR/Cas9 libraries, as well as detection of synthetic lethal and masking/buffering genetic interactions between paralogs of various family sizes. Importantly, the in4mer platform offers a fivefold reduction in library size compared to other genetic interaction methods, substantially reducing the cost and effort required for these assays.

Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes

BACKGROUND

Aneuploidy, an abnormal number of chromosomes within a cell, is a hallmark of cancer. Patterns of aneuploidy differ across cancers, yet are similar in cancers affecting closely related tissues. The selection pressures underlying aneuploidy patterns are not fully understood, hindering our understanding of cancer development and progression.

RESULTS

Here, we apply interpretable machine learning methods to study tissue-selective aneuploidy patterns. We define 20 types of features corresponding to genomic attributes of chromosome-arms, normal tissues, primary tumors, and cancer cell lines (CCLs), and use them to model gains and losses of chromosome arms in 24 cancer types. To reveal the factors that shape the tissue-specific cancer aneuploidy landscapes, we interpret the machine learning models by estimating the relative contribution of each feature to the models. While confirming known drivers of positive selection, our quantitative analysis highlights the importance of negative selection for shaping aneuploidy landscapes. This is exemplified by tumor suppressor gene density being a better predictor of gain patterns than oncogene density, and vice versa for loss patterns. We also identify the importance of tissue-selective features and demonstrate them experimentally, revealing KLF5 as an important driver for chr13q gain in colon cancer. Further supporting an important role for negative selection in shaping the aneuploidy landscapes, we find compensation by paralogs to be among the top predictors of chromosome arm loss prevalence and demonstrate this relationship for one paralog interaction. Similar factors shape aneuploidy patterns in human CCLs, demonstrating their relevance for aneuploidy research.

CONCLUSIONS

Our quantitative, interpretable machine learning models improve the understanding of the genomic properties that shape cancer aneuploidy landscapes.

Histone lysine acetyltransferase inhibitors: an emerging class of drugs for cancer therapy

Lysine acetyltransferases (KATs) are a family of epigenetic enzymes involved in the regulation of gene expression; they represent a promising class of emerging drug targets. The frequent molecular dysregulation of these enzymes, as well as their mechanistic links to biological functions that are crucial to cancer, have led to exploration around the development of small-molecule inhibitors against KATs. Despite early challenges, recent advances have led to the development of potent and selective enzymatic and bromodomain (BRD) KAT inhibitors. In this review we discuss the discovery and development of new KAT inhibitors and their application as oncology therapeutics. Additionally, new chemically induced proximity approaches are presented, offering opportunities for unique target selectivity profiles and tissue-specific targeting of KATs. Emerging clinical data for CREB binding protein (CREBBP)/EP300 BRD inhibitors and KAT6 catalytic inhibitors indicate the promise of this target class in cancer therapeutics.

A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization

Genetic screens in cancer cell lines inform gene function and drug discovery. More comprehensive screen datasets with multi-omics data are needed to enhance opportunities to functionally map genetic vulnerabilities. Here, we construct a second-generation map of cancer dependencies by annotating 930 cancer cell lines with multi-omic data and analyze relationships between molecular markers and cancer dependencies derived from CRISPR-Cas9 screens. We identify dependency-associated gene expression markers beyond driver genes, and observe many gene addiction relationships driven by gain of function rather than synthetic lethal effects. By combining clinically informed dependency-marker associations with protein-protein interaction networks, we identify 370 anti-cancer priority targets for 27 cancer types, many of which have network-based evidence of a functional link with a marker in a cancer type. Mapping these targets to sequenced tumor cohorts identifies tractable targets in different cancer types. This target prioritization map enhances understanding of gene dependencies and identifies candidate anti-cancer targets for drug development.

SLKB: synthetic lethality knowledge base

Emerging CRISPR-Cas9 technology permits synthetic lethality (SL) screening of large number of gene pairs from gene combination double knockout (CDKO) experiments. However, the poor integration and annotation of CDKO SL data in current SL databases limit their utility, and diverse methods of calculating SL scores prohibit their comparison. To overcome these shortcomings, we have developed SL knowledge base (SLKB) that incorporates data of 11 CDKO experiments in 22 cell lines, 16,059 SL gene pairs and 264,424 non-SL gene pairs. Additionally, within SLKB, we have implemented five SL calculation methods: median score with and without background control normalization (Median-B/NB), sgRNA-derived score (sgRNA-B/NB), Horlbeck score, GEMINI score and MAGeCK score. The five scores have demonstrated a mere 1.21% overlap among their top 10% SL gene pairs, reflecting high diversity. Users can browse SL networks and assess the impact of scoring methods using Venn diagrams. The SL network generated from all data in SLKB shows a greater likelihood of SL gene pair connectivity with other SL gene pairs than non-SL pairs. Comparison of SL networks between two cell lines demonstrated greater likelihood to share SL hub genes than SL gene pairs. SLKB website and pipeline can be freely accessed at https://slkb.osubmi.org and https://slkb.docs.osubmi.org/, respectively.

Drug dependence in cancer is exploitable by optimally constructed treatment holidays

Cancers with acquired resistance to targeted therapy can become simultaneously dependent on the presence of the targeted therapy drug for survival, suggesting that intermittent therapy may slow resistance. However, relatively little is known about which tumours are likely to become dependent and how to schedule intermittent therapy optimally. Here we characterized drug dependence across a panel of over 75 MAPK-inhibitor-resistant BRAFV600E mutant melanoma models at the population and single-clone levels. Melanocytic differentiated models exhibited a much greater tendency to give rise to drug-dependent progeny than their dedifferentiated counterparts. Mechanistically, acquired loss of microphthalmia-associated transcription factor in differentiated melanoma models drives ERK-JunB-p21 signalling to enforce drug dependence. We identified the optimal scheduling of 'drug holidays' using simple mathematical models that we validated across short and long timescales. Without detailed knowledge of tumour characteristics, we found that a simple adaptive therapy protocol can produce near-optimal outcomes using only measurements of total population size. Finally, a spatial agent-based model showed that optimal schedules derived from exponentially growing cells in culture remain nearly optimal in the context of tumour cell turnover and limited environmental carrying capacity. These findings may guide the implementation of improved evolution-inspired treatment strategies for drug-dependent cancers.

Chemical-genetics refines transcription factor regulatory circuits

Transcriptional dysregulation is a key step in oncogenesis, but our understanding of transcriptional control has relied on genetic approaches that are slow and allow for compensation. Chemical-genetic approaches have shortened the time frame for the analysis of transcription factors from days or weeks to minutes. These studies show that while DNA-binding proteins bind to thousands of sites, they are directly required to regulate only a small cadre of genes. Moreover, these transcriptional control networks are far more distinct, with much less overlap and interconnectivity than predicted from DNA binding. The identified direct targets can then be used to dissect the mechanism of action of these factors, which could identify ways to therapeutically manipulate these oncogenic transcriptional control networks.

Extracellular Signal-Regulated Kinases: One Pathway, Multiple Fates

This comprehensive review delves into the multifaceted aspects of ERK signaling and the intricate mechanisms underlying distinct cellular fates. ERK1 and ERK2 (ERK) govern proliferation, transformation, epithelial-mesenchymal transition, differentiation, senescence, or cell death, contingent upon activation strength, duration, and context. The biochemical mechanisms underlying these outcomes are inadequately understood, shaped by signaling feedback and the spatial localization of ERK activation. Generally, ERK activation aligns with the Goldilocks principle in cell fate determination. Inadequate or excessive ERK activity hinders cell proliferation, while balanced activation promotes both cell proliferation and survival. Unraveling the intricacies of how the degree of ERK activation dictates cell fate requires deciphering mechanisms encompassing protein stability, transcription factors downstream of ERK, and the chromatin landscape.

ARID1A loss activates MAPK signaling via DUSP4 downregulation

BACKGROUND

ARID1A, a tumor suppressor gene encoding BAF250, a protein participating in chromatin remodeling, is frequently mutated in endometrium-related malignancies, including ovarian or uterine clear cell carcinoma (CCC) and endometrioid carcinoma (EMCA). However, how ARID1A mutations alter downstream signaling to promote tumor development is yet to be established.

METHODS

We used RNA-sequencing (RNA-seq) to explore transcriptomic changes in isogenic human endometrial epithelial cells after deleting ARID1A. Chromatin immunoprecipitation sequencing (ChIP-seq) was employed to assess the active or repressive histone marks on DUSP4 promoter and regulatory regions. We validated our findings using genetically engineered murine endometroid carcinoma models, human endometroid carcinoma tissues, and in silico approaches.

RESULTS

RNA-seq revealed the downregulation of the MAPK phosphatase dual-specificity phosphatase 4 (DUSP4) in ARID1A-deficient cells. ChIP-seq demonstrated decreased histone acetylation marks (H3K27Ac, H3K9Ac) on DUSP4 regulatory regions as one of the causes for DUSP4 downregulation in ARID1A-deficient cells. Ectopic DUSP4 expression decreased cell proliferation, and pharmacologically inhibiting the MAPK pathway significantly mitigated tumor formation in vivo.

CONCLUSIONS

Our findings suggest that ARID1A protein transcriptionally modulates DUSP4 expression by remodeling chromatin, subsequently inactivating the MAPK pathway, leading to tumor suppression. The ARID1A-DUSP4-MAPK axis may be further considered for developing targeted therapies against ARID1A-mutated cancers.

Complex synthetic lethality in cancer

The concept of synthetic lethality has been widely applied to identify therapeutic targets in cancer, with varying degrees of success. The standard approach normally involves identifying genetic interactions between two genes, a driver and a target. In reality, however, most cancer synthetic lethal effects are likely complex and also polygenic, being influenced by the environment in addition to involving contributions from multiple genes. By acknowledging and delineating this complexity, we describe in this article how the success rate in cancer drug discovery and development could be improved.

miR-133a-3p regulates the growth of hair follicle stem cells in white goats from the Yangtze River Delta

The Yangtze River Delta white goats are the sole goat breed producing brush hair of high quality. Owing to the particularities of its wool production, a higher demand is placed on breeding efforts for this animal. Studies on the developmental mechanisms of the aligned hair follicle stem cells (HFSCs) provide a theoretical basis for molecular breeding. In the present study, HFSCs were isolated using the technique of immunohistochemistry from the cervical spinal skin tissue samples from the fetal sheep, and the miR-133a-3p expression was confirmed using quantitative reverse-transcription PCR (RT-qPCR) and western blotting experiments from the isolated HFSCs. Additionally, the effects on the proliferation and apoptosis of HFSCs were detected using flow cytometry and 5-ethynyl-2'-deoxyuridine assays, along with other methods, following the overexpression of miR-133a-3p or its inhibition. The experimental results revealed that miR-133a-3p overexpressed could inhibit the proliferation of HFSCs and promote apoptosis by specifically targeting DUSP6. While the miR-133a-3p knockdown could promote the proliferation but inhibit the apoptosis of the HFSCs. Meanwhile, the miR-133a-3p knockdown experiments showed opposite outcomes. These results illustrate the presence of a relevant network between DUSP6 and miR-133a-3p, which regulates the production of superior-quality brush hair.

Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells

Cells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene can trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, to date, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and contexts. Moreover, how the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear. Here we combine computational analysis of existing datasets with stochastic mathematical modeling and machine learning to uncover the widespread prevalence of transcriptional adaptation in mammalian systems and the diverse single-cell manifestations of minimal compensatory gene networks. Regulon gene expression analysis of a pooled single-cell genetic perturbation dataset recapitulates important model predictions. Our integrative approach uncovers several putative hits-genes demonstrating possible transcriptional adaptation-to follow up on experimentally, and provides a formal quantitative framework to test and refine models of transcriptional adaptation.

Identification of DUSP4/6 overexpression as a potential rheostat to NRAS-induced hepatocarcinogenesis

BACKGROUND

Upregulation of the mitogen-activated protein kinase (MAPK) cascade is common in hepatocellular carcinoma (HCC). Neuroblastoma RAS viral oncogene homolog (NRAS) is mutated in a small percentage of HCC and is hitherto considered insufficient for hepatocarcinogenesis. We aimed to characterize the process of N-Ras-dependent carcinogenesis in the liver and to identify potential therapeutic vulnerabilities.

METHODS

NRAS V12 plasmid was delivered into the mouse liver via hydrodynamic tail vein injection (HTVI). The resulting tumours, preneoplastic lesions, and normal tissue were characterized by NanoString® gene expression analysis, Western Blot, and Immunohistochemistry (IHC). The results were further confirmed by in vitro analyses of HCC cell lines.

RESULTS

HTVI with NRAS V12 plasmid resulted in the gradual formation of preneoplastic and neoplastic lesions in the liver three months post-injection. These lesions mostly showed characteristics of HCC, with some exceptions of spindle cell/ cholangiocellular differentiation. Progressive upregulation of the RAS/RAF/MEK/ERK signalling was detectable in the lesions by Western Blot and IHC. NanoString® gene expression analysis of preneoplastic and tumorous tissue revealed a gradual overexpression of the cancer stem cell marker CD133 and Dual Specificity Phosphatases 4 and 6 (DUSP4/6). In vitro, transfection of HCC cell lines with NRAS V12 plasmid resulted in a coherent upregulation of DUSP4 and DUSP6. Paradoxically, this upregulation in PLC/PRF/5 cells was accompanied by a downregulation of phosphorylated extracellular-signal-regulated kinase (pERK), suggesting an overshooting compensation. Silencing of DUSP4 and DUSP6 increased proliferation in HCC cell lines.

CONCLUSIONS

Contrary to prior assumptions, the G12V NRAS mutant form is sufficient to elicit hepatocarcinogenesis in the mouse. Furthermore, the upregulation of the MAPK cascade was paralleled by the overexpression of DUSP4, DUSP6, and CD133 in vivo and in vitro. Therefore, DUSP4 and DUSP6 might fine-tune the excessive MAPK activation, a mechanism that can potentially be harnessed therapeutically.

Acute Endoplasmic Reticulum Stress Suppresses Hepatic Gluconeogenesis by Stimulating MAPK Phosphatase 3 Degradation

Drug-induced liver injury (DILI) is a widespread and harmful disease, and is closely linked to acute endoplasmic reticulum (ER) stress. Previous reports have shown that acute ER stress can suppress hepatic gluconeogenesis and even leads to hypoglycemia. However, the mechanism is still unclear. MAPK phosphatase 3 (MKP-3) is a positive regulator for gluconeogenesis. Thus, this study was conducted to investigate the role of MKP-3 in the suppression of gluconeogenesis by acute ER stress, as well as the regulatory role of acute ER stress on the expression of MKP-3. Results showed that acute ER stress induced by tunicamycin significantly suppressed gluconeogenesis in both hepatocytes and mouse liver, reduced glucose production level in hepatocytes, and decreased fasting blood glucose level in mice. Additionally, the protein level of MKP-3 was reduced by acute ER stress in both hepatocytes and mouse liver. Mkp-3 deficiency eliminated the inhibitory effect of acute ER stress on gluconeogenesis in hepatocytes. Moreover, the reduction effect of acute ER stress on blood glucose level and hepatic glucose 6-phosphatase (G6pc) expression was not observed in the liver-specific Mkp-3 knockout mice. Furthermore, activation of protein kinase R-like ER kinase (PERK) decreased the MKP-3 protein level, while inactivation of PERK abolished the reduction effect of acute ER stress on the MKP-3 protein level in hepatocytes. Taken together, our study suggested that acute ER stress could suppress hepatic gluconeogenesis by stimulating MKP-3 degradation via PERK, at least partially. Thus, MKP-3 might be a therapeutic target for DILI-related hypoglycemia.

Discovery of a highly potent, selective, orally bioavailable inhibitor of KAT6A/B histone acetyltransferases with efficacy against KAT6A-high ER+ breast cancer

KAT6A, and its paralog KAT6B, are histone lysine acetyltransferases (HAT) that acetylate histone H3K23 and exert an oncogenic role in several tumor types including breast cancer where KAT6A is frequently amplified/overexpressed. However, pharmacologic targeting of KAT6A to achieve therapeutic benefit has been a challenge. Here we describe identification of a highly potent, selective, and orally bioavailable KAT6A/KAT6B inhibitor CTx-648 (PF-9363), derived from a benzisoxazole series, which demonstrates anti-tumor activity in correlation with H3K23Ac inhibition in KAT6A over-expressing breast cancer. Transcriptional and epigenetic profiling studies show reduced RNA Pol II binding and downregulation of genes involved in estrogen signaling, cell cycle, Myc and stem cell pathways associated with CTx-648 anti-tumor activity in ER-positive (ER+) breast cancer. CTx-648 treatment leads to potent tumor growth inhibition in ER+ breast cancer in vivo models, including models refractory to endocrine therapy, highlighting the potential for targeting KAT6A in ER+ breast cancer.

Using graph-based model to identify cell specific synthetic lethal effects

Synthetic lethal (SL) pairs are pairs of genes whose simultaneous loss-of-function results in cell death, while a damaging mutation of either gene alone does not affect the cell's survival. This makes SL pairs attractive targets for precision cancer therapies, as targeting the unimpaired gene of the SL pair can selectively kill cancer cells that already harbor the impaired gene. Limited by the difficulty of finding true SL pairs, especially on specific cell types, current computational approaches provide only limited insights because of overlooking the crucial aspects of cellular context dependency and mechanistic understanding of SL pairs. As a result, the identification of SL targets still relies on expensive, time-consuming experimental approaches. In this work, we applied cell-line specific multi-omics data to a specially designed deep learning model to predict cell-line specific SL pairs. Through incorporating multiple types of cell-specific omics data with a self-attention module, we represent gene relationships as graphs. Our approach achieves the prediction of SL pairs in a cell-specific manner and demonstrates the potential to facilitate the discovery of cell-specific SL targets for cancer therapeutics, providing a tool to unearth mechanisms underlying the origin of SL in cancer biology. The code and data of our approach can be found at https://github.com/promethiume/SLwise.

ERK mediates interferon gamma-induced melanoma cell death

BACKGROUND

Interferon-gamma (IFNγ) exerts potent growth inhibitory effects on a wide range of cancer cells through unknown signaling pathways. We pursued complementary screening approaches to characterize the growth inhibition pathway.

METHODS

We performed chemical genomics and whole genome targeting CRISPR/Cas9 screens using patient-derived melanoma lines to uncover essential nodes in the IFNγ-mediated growth inhibition pathway. We used transcriptomic profiling to identify cell death pathways activated upon IFNγ exposure. Live imaging experiments coupled with apoptosis assays confirmed the involvement of these pathways in IFNγ-mediated cell death.

RESULTS

We show that IFNγ signaling activated ERK. Blocking ERK activation rescued IFNγ-mediated apoptosis in 17 of 23 (~ 74%) cell lines representing BRAF, NRAS, NF1 mutant, and triple wild type subtypes of cutaneous melanoma. ERK signaling induced a stress response, ultimately leading to apoptosis through the activity of DR5 and NOXA proteins.

CONCLUSIONS

Our results provide a new understanding of the IFNγ growth inhibition pathway, which will be crucial in defining mechanisms of immunotherapy response and resistance.

Precision Oncology Comes of Age: Designing Best-in-Class Small Molecules by Integrating Two Decades of Advances in Chemistry, Target Biology, and Data Science

Small-molecule drugs have enabled the practice of precision oncology for genetically defined patient populations since the first approval of imatinib in 2001. Scientific and technology advances over this 20-year period have driven the evolution of cancer biology, medicinal chemistry, and data science. Collectively, these advances provide tools to more consistently design best-in-class small-molecule drugs against known, previously undruggable, and novel cancer targets. The integration of these tools and their customization in the hands of skilled drug hunters will be necessary to enable the discovery of transformational therapies for patients across a wider spectrum of cancers.

SIGNIFICANCE

Target-centric small-molecule drug discovery necessitates the consideration of multiple approaches to identify chemical matter that can be optimized into drug candidates. To do this successfully and consistently, drug hunters require a comprehensive toolbox to avoid following the "law of instrument" or Maslow's hammer concept where only one tool is applied regardless of the requirements of the task. Combining our ever-increasing understanding of cancer and cancer targets with the technological advances in drug discovery described below will accelerate the next generation of small-molecule drugs in oncology.

Systematic profiling of conditional pathway activation identifies context-dependent synthetic lethalities

The paradigm of cancer-targeted therapies has focused largely on inhibition of critical pathways in cancer. Conversely, conditional activation of signaling pathways as a new source of selective cancer vulnerabilities has not been deeply characterized. In this study, we sought to systematically identify context-specific gene-activation-induced lethalities in cancer. To this end, we developed a method for gain-of-function genetic perturbations simultaneously across ~500 barcoded cancer cell lines. Using this approach, we queried the pan-cancer vulnerability landscape upon activating ten key pathway nodes, revealing selective activation dependencies of MAPK and PI3K pathways associated with specific biomarkers. Notably, we discovered new pathway hyperactivation dependencies in subsets of APC-mutant colorectal cancers where further activation of the WNT pathway by APC knockdown or direct β-catenin overexpression led to robust antitumor effects in xenograft and patient-derived organoid models. Together, this study reveals a new class of conditional gene-activation dependencies in cancer.

Optimization of Cas12a for multiplexed genome-scale transcriptional activation

Cas12a CRISPR technology, unlike Cas9, allows for facile multiplexing of guide RNAs from a single transcript, simplifying combinatorial perturbations. While Cas12a has been implemented for multiplexed knockout genetic screens, it has yet to be optimized for CRISPR activation (CRISPRa) screens in human cells. Here, we develop a new Cas12a-based transactivation domain (TAD) recruitment system using the ALFA nanobody and demonstrate simultaneous activation of up to four genes. We screen a genome-wide library to identify modulators of growth and MEK inhibition, and we compare these results with those obtained with open reading frame (ORF) overexpression and Cas9-based CRISPRa. We find that the activity of multiplexed arrays is largely predictable from the best-performing guide and provide criteria for selecting active guides. We anticipate that these results will greatly accelerate the exploration of gene function and combinatorial phenotypes at scale.

Efficient gene knockout and genetic interactions: the IN4MER CRISPR/Cas12a multiplex knockout platform

Genetic interactions mediate the emergence of phenotype from genotype, but initial technologies for combinatorial genetic perturbation in mammalian cells suffer from inefficiency and are challenging to scale. Recent focus on paralog synthetic lethality in cancer cells offers an opportunity to evaluate different approaches and improve on the state of the art. Here we report a meta-analysis of CRISPR genetic interactions screens, identifying a candidate set of background-independent paralog synthetic lethals, and find that the Cas12a platform provides superior sensitivity and assay replicability. We demonstrate that Cas12a can independently target up to four genes from a single guide array, and we build on this knowledge by constructing a genome-scale library that expresses arrays of four guides per clone, a platform we call 'in4mer'. Our genome-scale human library, with only 49k clones, is substantially smaller than a typical CRISPR/Cas9 monogenic library while also targeting more than four thousand paralog pairs, triples, and quads. Proof of concept screens in four cell lines demonstrate discrimination of core and context-dependent essential genes similar to that of state-of-the-art CRISPR/Cas9 libraries, as well as detection of synthetic lethal and masking/buffering genetic interactions between paralogs of various family sizes, a capability not offered by any extant library. Importantly, the in4mer platform offers a fivefold reduction in the number of clones required to assay genetic interactions, dramatically improving the cost and effort required for these studies.

A knowledge graph approach to predict and interpret disease-causing gene interactions

BACKGROUND

Understanding the impact of gene interactions on disease phenotypes is increasingly recognised as a crucial aspect of genetic disease research. This trend is reflected by the growing amount of clinical research on oligogenic diseases, where disease manifestations are influenced by combinations of variants on a few specific genes. Although statistical machine-learning methods have been developed to identify relevant genetic variant or gene combinations associated with oligogenic diseases, they rely on abstract features and black-box models, posing challenges to interpretability for medical experts and impeding their ability to comprehend and validate predictions. In this work, we present a novel, interpretable predictive approach based on a knowledge graph that not only provides accurate predictions of disease-causing gene interactions but also offers explanations for these results.

RESULTS

We introduce BOCK, a knowledge graph constructed to explore disease-causing genetic interactions, integrating curated information on oligogenic diseases from clinical cases with relevant biomedical networks and ontologies. Using this graph, we developed a novel predictive framework based on heterogenous paths connecting gene pairs. This method trains an interpretable decision set model that not only accurately predicts pathogenic gene interactions, but also unveils the patterns associated with these diseases. A unique aspect of our approach is its ability to offer, along with each positive prediction, explanations in the form of subgraphs, revealing the specific entities and relationships that led to each pathogenic prediction.

CONCLUSION

Our method, built with interpretability in mind, leverages heterogenous path information in knowledge graphs to predict pathogenic gene interactions and generate meaningful explanations. This not only broadens our understanding of the molecular mechanisms underlying oligogenic diseases, but also presents a novel application of knowledge graphs in creating more transparent and insightful predictors for genetic research.

Collateral lethality between HDAC1 and HDAC2 exploits cancer-specific NuRD complex vulnerabilities

Transcriptional co-regulators have been widely pursued as targets for disrupting oncogenic gene regulatory programs. However, many proteins in this target class are universally essential for cell survival, which limits their therapeutic window. Here we unveil a genetic interaction between histone deacetylase 1 (HDAC1) and HDAC2, wherein each paralog is synthetically lethal with hemizygous deletion of the other. This collateral synthetic lethality is caused by recurrent chromosomal deletions that occur in diverse solid and hematological malignancies, including neuroblastoma and multiple myeloma. Using genetic disruption or dTAG-mediated degradation, we show that targeting HDAC2 suppresses the growth of HDAC1-deficient neuroblastoma in vitro and in vivo. Mechanistically, we find that targeted degradation of HDAC2 in these cells prompts the degradation of several members of the nucleosome remodeling and deacetylase (NuRD) complex, leading to diminished chromatin accessibility at HDAC2-NuRD-bound sites of the genome and impaired control of enhancer-associated transcription. Furthermore, we reveal that several of the degraded NuRD complex subunits are dependencies in neuroblastoma and multiple myeloma, providing motivation to develop paralog-selective HDAC1 or HDAC2 degraders that could leverage HDAC1/2 synthetic lethality to target NuRD vulnerabilities. Altogether, we identify HDAC1/2 collateral synthetic lethality as a potential therapeutic target and reveal an unexplored mechanism for targeting NuRD-associated cancer dependencies.

Paralog-based synthetic lethality: rationales and applications

Tumor cells can result from gene mutations and over-expression. Synthetic lethality (SL) offers a desirable setting where cancer cells bearing one mutated gene of an SL gene pair can be specifically targeted by disrupting the function of the other genes, while leaving wide-type normal cells unharmed. Paralogs, a set of homologous genes that have diverged from each other as a consequence of gene duplication, make the concept of SL feasible as the loss of one gene does not affect the cell's survival. Furthermore, homozygous loss of paralogs in tumor cells is more frequent than singletons, making them ideal SL targets. Although high-throughput CRISPR-Cas9 screenings have uncovered numerous paralog-based SL pairs, the unclear mechanisms of targeting these gene pairs and the difficulty in finding specific inhibitors that exclusively target a single but not both paralogs hinder further clinical development. Here, we review the potential mechanisms of paralog-based SL given their function and genetic combination, and discuss the challenge and application prospects of paralog-based SL in cancer therapeutic discovery.

Network Biology-Inspired Machine Learning Features Predict Cancer Gene Targets and Reveal Target Coordinating Mechanisms

Anticipating and understanding cancers' need for specific gene activities is key for novel therapeutic development. Here we utilized DepMap, a cancer gene dependency screen, to demonstrate that machine learning combined with network biology can produce robust algorithms that both predict what genes a cancer is dependent on and what network features coordinate such gene dependencies. Using network topology and biological annotations, we constructed four groups of novel engineered machine learning features that produced high accuracies when predicting binary gene dependencies. We found that in all examined cancer types, F1 scores were greater than 0.90, and model accuracy remained robust under multiple hyperparameter tests. We then deconstructed these models to identify tumor type-specific coordinators of gene dependency and identified that in certain cancers, such as thyroid and kidney, tumors' dependencies are highly predicted by gene connectivity. In contrast, other histologies relied on pathway-based features such as lung, where gene dependencies were highly predictive by associations with cell death pathway genes. In sum, we show that biologically informed network features can be a valuable and robust addition to predictive pharmacology models while simultaneously providing mechanistic insights.

Optimized metrics for orthogonal combinatorial CRISPR screens

CRISPR-based gene perturbation enables unbiased investigations of single and combinatorial genotype-to-phenotype associations. In light of efforts to map combinatorial gene dependencies at scale, choosing an efficient and robust CRISPR-associated (Cas) nuclease is of utmost importance. Even though SpCas9 and AsCas12a are widely used for single, combinatorial, and orthogonal screenings, side-by-side comparisons remain sparse. Here, we systematically compared combinatorial SpCas9, AsCas12a, and CHyMErA in hTERT-immortalized retinal pigment epithelial cells and extracted performance-critical parameters for combinatorial and orthogonal CRISPR screens. Our analyses identified SpCas9 to be superior to enhanced and optimized AsCas12a, with CHyMErA being largely inactive in the tested conditions. Since AsCas12a contains RNA processing activity, we used arrayed dual-gRNAs to improve AsCas12a and CHyMErA applications. While this negatively influenced the effect size range of combinatorial AsCas12a applications, it enhanced the performance of CHyMErA. This improved performance, however, was limited to AsCas12a dual-gRNAs, as SpCas9 gRNAs remained largely inactive. To avoid the use of hybrid gRNAs for orthogonal applications, we engineered the multiplex SpCas9-enAsCas12a approach (multiSPAS) that avoids RNA processing for efficient orthogonal gene editing.

Targeting synthetic lethal paralogs in cancer

Synthetic lethal interactions, where mutation of one gene renders cells sensitive to inhibition of another gene, can be exploited for the development of targeted therapeutics in cancer. Pairs of duplicate genes (paralogs) often share common functionality and hence are a potentially rich source of synthetic lethal interactions. Because the majority of human genes have paralogs, exploiting such interactions could be a widely applicable approach for targeting gene loss in cancer. Moreover, existing small-molecule drugs may exploit synthetic lethal interactions by inhibiting multiple paralogs simultaneously. Consequently, the identification of synthetic lethal interactions between paralogs could be extremely informative for drug development. Here we review approaches to identify such interactions and discuss some of the challenges of exploiting them.

The journey from melanocytes to melanoma

Over the past decade, melanoma has led the field in new cancer treatments, with impressive gains in on-treatment survival but more modest improvements in overall survival. Melanoma presents heterogeneity and transcriptional plasticity that recapitulates distinct melanocyte developmental states and phenotypes, allowing it to adapt to and eventually escape even the most advanced treatments. Despite remarkable advances in our understanding of melanoma biology and genetics, the melanoma cell of origin is still fiercely debated because both melanocyte stem cells and mature melanocytes can be transformed. Animal models and high-throughput single-cell sequencing approaches have opened new opportunities to address this question. Here, we discuss the melanocytic journey from the neural crest, where they emerge as melanoblasts, to the fully mature pigmented melanocytes resident in several tissues. We describe a new understanding of melanocyte biology and the different melanocyte subpopulations and microenvironments they inhabit, and how this provides unique insights into melanoma initiation and progression. We highlight recent findings on melanoma heterogeneity and transcriptional plasticity and their implications for exciting new research areas and treatment opportunities. The lessons from melanocyte biology reveal how cells that are present to protect us from the damaging effects of ultraviolet radiation reach back to their origins to become a potentially deadly cancer.

TAF1D promotes proliferation by transcriptionally activating G2/M phase-related genes in MYCN-amplified neuroblastoma

High-risk neuroblastoma (HR-NB) is an aggressive childhood cancer that responds poorly to currently available therapies and is associated with only about a 50% 5-year survival rate. MYCN amplification is a critical driver of these aggressive tumors, but so far there have not been any approved treatments to effectively treat HR-NB by targeting MYCN or its downstream effectors. Thus, the identification of novel molecular targets and therapeutic strategies to treat children diagnosed with HR-NB represents an urgent unmet medical need. Here, we conducted a targeted siRNA screening and identified TATA box-binding protein-associated factor RNA polymerase I subunit D, TAF1D, as a critical regulator of the cell cycle and proliferation in HR-NB cells. Analysis of three independent primary NB cohorts determined that high TAF1D expression correlated with MYCN-amplified, high-risk disease and poor clinical outcomes. TAF1D knockdown more robustly inhibited cell proliferation in MYCN-amplified NB cells compared with MYCN-non-amplified NB cells, as well as suppressed colony formation and inhibited tumor growth in a xenograft mouse model of MYCN-amplified NB. RNA-seq analysis revealed that TAF1D knockdown downregulates the expression of genes associated with the G2/M transition, including the master cell-cycle regulator, cell-cycle-dependent kinase 1 (CDK1), resulting in cell-cycle arrest at G2/M. Our findings demonstrate that TAF1D is a key oncogenic regulator of MYCN-amplified HR-NB and suggest that therapeutic targeting of TAF1D may be a viable strategy to treat HR-NB patients by blocking cell-cycle progression and the proliferation of tumor cells.

A global collaboRAtive study of CIC-rearranged, BCOR::CCNB3-rearranged and other ultra-rare unclassified undifferentiated small round cell sarcomas (GRACefUl)

BACKGROUND

Undifferentiated small round cell sarcomas (URCSs) represent a diagnostic challenge, and their optimal treatment is unknown. We aimed to define the clinical characteristics, treatment, and outcome of URCS patients.

METHODS

URCS patients treated from 1983 to 2019 at 21 worldwide sarcoma reference centres were retrospectively identified. Based on molecular assessment, cases were classified as follows: (1) CIC-rearranged round cell sarcomas, (2) BCOR::CCNB3-rearranged round cell sarcomas, (3) unclassified URCSs. Treatment, prognostic factors and outcome were reviewed.

RESULTS

In total, 148 patients were identified [88/148 (60%) CIC-rearranged sarcoma (median age 32 years, range 7-78), 33/148 (22%) BCOR::CCNB3-rearranged (median age 17 years, range 5-91), and 27/148 (18%) unclassified URCSs (median age 37 years, range 4-70)]. One hundred-one (68.2%) cases presented with localised disease; 47 (31.8%) had metastases at diagnosis. Male prevalence, younger age, bone primary site, and a low rate of synchronous metastases were observed in BCOR::CCNB3-rearranged cases. Local treatment was surgery in 67/148 (45%) patients, and surgery + radiotherapy in 52/148 (35%). Chemotherapy was given to 122/148 (82%) patients. At a 42.7-month median follow-up, the 3-year overall survival (OS) was 92.2% (95% CI 71.5-98.0) in BCOR::CCNB3 patients, 39.6% (95% CI 27.7-51.3) in CIC-rearranged sarcomas, and 78.7% in unclassified URCSs (95% CI 56.1-90.6; p < 0.0001).

CONCLUSIONS

This study is the largest conducted in URCS and confirms major differences in outcomes between URCS subtypes. A full molecular assessment should be undertaken when a diagnosis of URCS is suspected. Prospective studies are needed to better define the optimal treatment strategy in each URCS subtype.

The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks

Inference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection of open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, and explore the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages and between methods to allow better integration of these tools into analytical pipelines. We demonstrate the utility using multi-omic data from the Cancer Cell Line Encyclopedia. We will continue to expand the netZoo to incorporate additional methods.

Chromatin complex dependencies reveal targeting opportunities in leukemia

Chromatin regulators are frequently mutated in human cancer and are attractive drug targets. They include diverse proteins that share functional domains and assemble into related multi-subunit complexes. To investigate functional relationships among these regulators, here we apply combinatorial CRISPR knockouts (KOs) to test over 35,000 gene-gene pairings in leukemia cells, using a library of over 300,000 constructs. Top pairs that demonstrate either compensatory non-lethal interactions or synergistic lethality enrich for paralogs and targets that occupy the same protein complex. The screen highlights protein complex dependencies not apparent in single KO screens, for example MCM histone exchange, the nucleosome remodeling and deacetylase (NuRD) complex, and HBO1 (KAT7) complex. We explore two approaches to NuRD complex inactivation. Paralog and non-paralog combinations of the KAT7 complex emerge as synergistic lethal and specifically nominate the ING5 PHD domain as a potential therapeutic target when paired with other KAT7 complex member losses. These findings highlight the power of combinatorial screening to provide mechanistic insight and identify therapeutic targets within redundant networks.

Multi-layered chromatin proteomics identifies cell vulnerabilities in DNA repair

The DNA damage response (DDR) is essential to maintain genome stability, and its deregulation predisposes to carcinogenesis while encompassing attractive targets for cancer therapy. Chromatin governs the DDR via the concerted interplay among different layers, including DNA, histone post-translational modifications (hPTMs) and chromatin-associated proteins. Here, we employ multi-layered proteomics to characterize chromatin-mediated functional interactions of repair proteins, signatures of hPTMs and the DNA-bound proteome during DNA double-strand break (DSB) repair at high temporal resolution. Our data illuminate the dynamics of known and novel DDR-associated factors both at chromatin and at DSBs. We functionally attribute novel chromatin-associated proteins to repair by non-homologous end-joining (NHEJ), homologous recombination (HR) and DSB repair pathway choice. We reveal histone reader ATAD2, microtubule organizer TPX2 and histone methyltransferase G9A as regulators of HR and involved in poly-ADP-ribose polymerase-inhibitor sensitivity. Furthermore, we distinguish hPTMs that are globally induced by DNA damage from those specifically acquired at sites flanking DSBs (γH2AX foci-specific) and profiled their dynamics during the DDR. Integration of complementary chromatin layers implicates G9A-mediated monomethylation of H3K56 in DSBs repair via HR. Our data provide a dynamic chromatin-centered view of the DDR that can be further mined to identify novel mechanistic links and cell vulnerabilities in DSB repair.

Identification of common molecular signatures of SARS-CoV-2 infection and its influence on acute kidney injury and chronic kidney disease

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the main cause of COVID-19, causing hundreds of millions of confirmed cases and more than 18.2 million deaths worldwide. Acute kidney injury (AKI) is a common complication of COVID-19 that leads to an increase in mortality, especially in intensive care unit (ICU) settings, and chronic kidney disease (CKD) is a high risk factor for COVID-19 and its related mortality. However, the underlying molecular mechanisms among AKI, CKD, and COVID-19 are unclear. Therefore, transcriptome analysis was performed to examine common pathways and molecular biomarkers for AKI, CKD, and COVID-19 in an attempt to understand the association of SARS-CoV-2 infection with AKI and CKD. Three RNA-seq datasets (GSE147507, GSE1563, and GSE66494) from the GEO database were used to detect differentially expressed genes (DEGs) for COVID-19 with AKI and CKD to search for shared pathways and candidate targets. A total of 17 common DEGs were confirmed, and their biological functions and signaling pathways were characterized by enrichment analysis. MAPK signaling, the structural pathway of interleukin 1 (IL-1), and the Toll-like receptor pathway appear to be involved in the occurrence of these diseases. Hub genes identified from the protein-protein interaction (PPI) network, including DUSP6, BHLHE40, RASGRP1, and TAB2, are potential therapeutic targets in COVID-19 with AKI and CKD. Common genes and pathways may play pathogenic roles in these three diseases mainly through the activation of immune inflammation. Networks of transcription factor (TF)-gene, miRNA-gene, and gene-disease interactions from the datasets were also constructed, and key gene regulators influencing the progression of these three diseases were further identified among the DEGs. Moreover, new drug targets were predicted based on these common DEGs, and molecular docking and molecular dynamics (MD) simulations were performed. Finally, a diagnostic model of COVID-19 was established based on these common DEGs. Taken together, the molecular and signaling pathways identified in this study may be related to the mechanisms by which SARS-CoV-2 infection affects renal function. These findings are significant for the effective treatment of COVID-19 in patients with kidney diseases.

Investigating the Role of DUSP4 in Uveal Melanoma

Purpose

Dual-specificity phosphatase 4 (DUSP4) inactivates factors in the mitogen-activated protein kinase (MAPK) signaling cascade, activated in uveal melanoma (UM) by mutations in upstream G-protein α subunits GNAQ/11 in >90% cases. This study examined whether DUSP4 (1) protein expression in primary UM (pUM) was a biomarker of metastatic risk and (2) knockdown sensitized UM cells to therapeutic agents, selumetinib or doxorubicin.

Methods

DUSP4 mRNA data from The Cancer Genome Atlas and DUSP4 protein expression examined using immunohistochemistry in 28 cases of pUM were evaluated for association with clinical, genetic, and histological features. In vitro cytotoxic drug assays tested the efficacy of selumetinib and doxorubicin in UM cell lines with/without small interfering RNA DUSP4 gene silencing.

Results

DUSP4 protein expression was observed in 93% of cases, with strong nuclear positivity in 79%. Despite higher DUSP4 messenger RNA levels in disomy 3/wild-type BAP1 UM, there was no significant association of nDUSP4 protein with these metastatic risk predictors or outcome. DUSP4 expression in UM cell lines varied. DUSP4 silencing in Mel202, MP46, and MP41 cells did not affect ERK1/2 or phospho-ERK levels. Despite increased phospho-ERK levels in Mel285, no cell line showed enhanced sensitivity to selumetinib/doxorubicin.

Conclusions

DUSP4 protein expression is not a biomarker of UM metastatic risk. DUSP4 plays a complex role in oncogenesis, as reported in other cancers, and further work is required to fully understand its functional role in the MAPK pathway.

Translational Relevance

Understanding the role of phosphatases, such as DUSP4, in the control of intracellular signaling cascades will facilitate our ability to identify successful treatment options.

Synthetic lethal gene pairs: Experimental approaches and predictive models

Synthetic lethality (SL) refers to a genetic interaction in which the simultaneous perturbation of two genes leads to cell or organism death, whereas viability is maintained when only one of the pair is altered. The experimental exploration of these pairs and predictive modeling in computational biology contribute to our understanding of cancer biology and the development of cancer therapies. We extensively reviewed experimental technologies, public data sources, and predictive models in the study of synthetic lethal gene pairs and herein detail biological assumptions, experimental data, statistical models, and computational schemes of various predictive models, speculate regarding their influence on individual sample- and population-based synthetic lethal interactions, discuss the pros and cons of existing SL data and models, and highlight potential research directions in SL discovery.

Paralog Explorer: A resource for mining information about paralogs in common research organisms

Paralogs are genes which arose via gene duplication, and when such paralogs retain overlapping or redundant function, this poses a challenge to functional genetics research. Recent technological advancements have made it possible to systematically probe gene function for redundant genes using dual or multiplex gene perturbation, and there is a need for a simple bioinformatic tool to identify putative paralogs of a gene(s) of interest. We have developed Paralog Explorer (https://www.flyrnai.org/tools/paralogs/), an online resource that allows researchers to quickly and accurately identify candidate paralogous genes in the genomes of the model organisms D. melanogaster, C. elegans, D. rerio, M. musculus, and H. sapiens. Paralog Explorer deploys an effective between-species ortholog prediction software, DIOPT, to analyze within-species paralogs. Paralog Explorer allows users to identify candidate paralogs, and to navigate relevant databases regarding gene co-expression, protein-protein and genetic interaction, as well as gene ontology and phenotype annotations. Altogether, this tool extends the value of current ortholog prediction resources by providing sophisticated features useful for identification and study of paralogous genes.

MEK inhibitor resistance in lung adenocarcinoma is associated with addiction to sustained ERK suppression

MEK inhibitors (MEKi) have limited efficacy in KRAS mutant lung adenocarcinoma (LUAD) patients, and this is attributed to both intrinsic and adaptive mechanisms of drug resistance. While many studies have focused on the former, there remains a dearth of data regarding acquired resistance to MEKi in LUAD. We established trametinib-resistant KRAS mutant LUAD cells through dose escalation and performed targeted MSK-IMPACT sequencing to identify drivers of MEKi resistance. Comparing resistant cells to their sensitive counterparts revealed alteration of genes associated with trametinib response. We describe a state of "drug addiction" in resistant cases where cells are dependent on continuous culture in trametinib for survival. We show that dependence on ERK2 suppression underlies this phenomenon and that trametinib removal hyperactivates ERK, resulting in ER stress and apoptosis. Amplification of KRAS^G12C occurs in drug-addicted cells and blocking mutant-specific activity with AMG 510 rescues the lethality associated with trametinib withdrawal. Furthermore, we show that increased KRAS^G12C expression is lethal to other KRAS mutant LUAD cells, consequential to ERK hyperactivation. Our study determines the drug-addicted phenotype in lung cancer is associated with KRAS amplification and demonstrates that toxic acquired genetic changes can develop de novo in the background of MAPK suppression with MEK inhibitors. We suggest that the presence of mutant KRAS amplification in patients may identify those that may benefit from a "drug holiday" to circumvent drug resistance. These findings demonstrate the toxic potential of hyperactive ERK signaling and highlight potential therapeutic opportunities in patients bearing KRAS mutations.

Druggable Metabolic Vulnerabilities Are Exposed and Masked during Progression to Castration Resistant Prostate Cancer

There is an urgent need for exploring new actionable targets other than androgen receptor to improve outcome from lethal castration-resistant prostate cancer. Tumor metabolism has reemerged as a hallmark of cancer that drives and supports oncogenesis. In this regard, it is important to understand the relationship between distinctive metabolic features, androgen receptor signaling, genetic drivers in prostate cancer, and the tumor microenvironment (symbiotic and competitive metabolic interactions) to identify metabolic vulnerabilities. We explore the links between metabolism and gene regulation, and thus the unique metabolic signatures that define the malignant phenotypes at given stages of prostate tumor progression. We also provide an overview of current metabolism-based pharmacological strategies to be developed or repurposed for metabolism-based therapeutics for castration-resistant prostate cancer.

Principles and functions of metabolic compartmentalization

Metabolism has historically been studied at the levels of whole cells, whole tissues and whole organisms. As a result, our understanding of how compartmentalization-the spatial and temporal separation of pathways and components-shapes organismal metabolism remains limited. At its essence, metabolic compartmentalization fulfils three important functions or 'pillars': establishing unique chemical environments, providing protection from reactive metabolites and enabling the regulation of metabolic pathways. However, how these pillars are established, regulated and maintained at both the cellular and systemic levels remains unclear. Here we discuss how the three pillars are established, maintained and regulated within the cell and discuss the consequences of dysregulation of metabolic compartmentalization in human disease. Organelles are increasingly emerging as 'command-and-control centres' and the increased understanding of metabolic compartmentalization is revealing new aspects of metabolic homeostasis, with this knowledge being translated into therapies for the treatment of cancer and certain neurodegenerative diseases.