Tyrosine phosphorylation of CARM1 promotes its enzymatic activity and alters its target specificity.

An important epigenetic component of tyrosine kinase signaling is the phosphorylation of histones, and epigenetic readers, writers, and erasers. Phosphorylation of protein arginine methyltransferases (PRMTs), have been shown to enhance and impair their enzymatic activity. In this study, we show that the hyperactivation of Janus kinase 2 (JAK2) by the V617F mutation phosphorylates tyrosine residues (Y149 and Y334) in coactivator-associated arginine methyltransferase 1 (CARM1), an important target in hematologic malignancies, increasing its methyltransferase activity and altering its target specificity. While non-phosphorylatable CARM1 methylates some established substrates (e.g. BAF155 and PABP1), only phospho-CARM1 methylates the RUNX1 transcription factor, on R223 and R319. Furthermore, cells expressing non-phosphorylatable CARM1 have impaired cell-cycle progression and increased apoptosis, compared to cells expressing phosphorylatable, wild-type CARM1, with reduced expression of genes associated with G2/M cell cycle progression and anti-apoptosis. The presence of the JAK2-V617F mutant kinase renders acute myeloid leukemia (AML) cells less sensitive to CARM1 inhibition, and we show that the dual targeting of JAK2 and CARM1 is more effective than monotherapy in AML cells expressing phospho-CARM1. Thus, the phosphorylation of CARM1 by hyperactivated JAK2 regulates its methyltransferase activity, helps select its substrates, and is required for the maximal proliferation of malignant myeloid cells.

Kinome-Wide Virtual Screening by Multi-Task Deep Learning.

Deep learning is a machine learning technique to model high-level abstractions in data by utilizing a graph composed of multiple processing layers that experience various linear and non-linear transformations. This technique has been shown to perform well for applications in drug discovery, utilizing structural features of small molecules to predict activity. Here, we report a large-scale study to predict the activity of small molecules across the human kinome-a major family of drug targets, particularly in anti-cancer agents. While small-molecule kinase inhibitors exhibit impressive clinical efficacy in several different diseases, resistance often arises through adaptive kinome reprogramming or subpopulation diversity. Polypharmacology and combination therapies offer potential therapeutic strategies for patients with resistant diseases. Their development would benefit from a more comprehensive and dense knowledge of small-molecule inhibition across the human kinome. Leveraging over 650,000 bioactivity annotations for more than 300,000 small molecules, we evaluated multiple machine learning methods to predict the small-molecule inhibition of 342 kinases across the human kinome. Our results demonstrated that multi-task deep neural networks outperformed classical single-task methods, offering the potential for conducting large-scale virtual screening, predicting activity profiles, and bridging the gaps in the available data.

Automation, live-cell imaging, and endpoint cell viability for prostate cancer drug screens.

Androgen deprivation therapy (ADT) is the standard of care for high risk and advanced prostate cancer; however, disease progression from androgen-dependent prostate cancer (ADPC) to lethal and incurable castration-resistant prostate cancer (CRPC) and (in a substantial minority of cases) neuroendocrine prostate cancer (NEPC) is common. Identifying effective targeted therapies is challenging because of acquired resistance to established treatments and the vast heterogeneity of advanced prostate cancer (PC). To streamline the identification of potentially active prostate cancer therapeutics, we have developed an adaptable semi-automated protocol which optimizes cell growth and leverages automation to enhance robustness, reproducibility, and throughput while integrating live-cell imaging and endpoint viability assays to assess drug efficacy in vitro. In this study, culture conditions for 72-hr drug screens in 96-well plates were established for a large, representative panel of human prostate cell lines including: BPH-1 and RWPE-1 (non-tumorigenic), LNCaP and VCaP (ADPC), C4-2B and 22Rv1 (CRPC), DU 145 and PC3 (androgen receptor-null CRPC), and NCI-H660 (NEPC). The cell growth and 72-hr confluence for each cell line was optimized for real-time imaging and endpoint viability assays prior to screening for novel or repurposed drugs as proof of protocol validity. We demonstrated effectiveness and reliability of this pipeline through validation of the established finding that the first-in-class BET and CBP/p300 dual inhibitor EP-31670 is an effective compound in reducing ADPC and CRPC cell growth. In addition, we found that insulin-like growth factor-1 receptor (IGF-1R) inhibitor linsitinib is a potential pharmacological agent against highly lethal and drug-resistant NEPC NCI-H660 cells. This protocol can be employed across other cancer types and represents an adaptable strategy to optimize assay-specific cell growth conditions and simultaneously assess drug efficacy across multiple cell lines.

Smoking induces WEE1 expression to promote docetaxel resistance in esophageal adenocarcinoma.

Esophageal adenocarcinoma (EAC) patients have poor clinical outcomes, with an overall 5-year survival rate of 20%. Smoking is a significant risk factor for EAC. The role of WEE1, a nuclear kinase that negatively regulates the cell cycle in normal conditions, in EAC tumorigenesis and drug resistance is not fully understood. Immunohistochemistry staining shows significant WEE1 overexpression in human EAC tissues. Nicotine, nicotine-derived nitrosamine ketone, or 2% cigarette smoke extract treatment induces WEE1 protein expression in EAC, detected by western blot and immunofluorescence staining. qRT-PCR and reporter assay indicates that smoking induces WEE1 expression through miR-195-5p downregulation in EAC. ATP-Glo cell viability and clonogenic assay confirmed that WEE1 inhibition sensitizes EAC cells to docetaxel treatment in vitro. A TE-10 smoking machine with EAC patient-derived xenograft mouse model demonstrated that smoking induces WEE1 protein expression and resistance to docetaxel in vivo. MK-1775 and docetaxel combined treatment improves EAC patient-derived xenograft mouse survival in vivo. Our findings demonstrate, for the first time, that smoking-induced WEE1 overexpression through miRNA dysregulation in EAC plays an essential role in EAC drug resistance. WEE1 inhibition is a promising therapeutic method to overcome drug resistance and target treatment refractory cancer cells.

Proteogenomic analysis of chemo-refractory high-grade serous ovarian cancer.

To improve the understanding of chemo-refractory high-grade serous ovarian cancers (HGSOCs), we characterized the proteogenomic landscape of 242 (refractory and sensitive) HGSOCs, representing one discovery and two validation cohorts across two biospecimen types (formalin-fixed paraffin-embedded and frozen). We identified a 64-protein signature that predicts with high specificity a subset of HGSOCs refractory to initial platinum-based therapy and is validated in two independent patient cohorts. We detected significant association between lack of Ch17 loss of heterozygosity (LOH) and chemo-refractoriness. Based on pathway protein expression, we identified 5 clusters of HGSOC, which validated across two independent patient cohorts and patient-derived xenograft (PDX) models. These clusters may represent different mechanisms of refractoriness and implicate putative therapeutic vulnerabilities.

Correction: The Genetic Landscape of Ocular Adnexa MALT Lymphoma Reveals Frequent Aberrations in NFAT and MEF2B Signaling Pathways.

[This corrects the article DOI: 10.1158/2767-9764.CRC-21-0022.][This corrects the article DOI: 10.1158/2767-9764.CRC-21-0022.].

AI-Assisted chemical probe discovery for the understudied Calcium-Calmodulin Dependent Kinase, PNCK.

PNCK, or CAMK1b, is an understudied kinase of the calcium-calmodulin dependent kinase family which recently has been identified as a marker of cancer progression and survival in several large-scale multi-omics studies. The biology of PNCK and its relation to oncogenesis has also begun to be elucidated, with data suggesting various roles in DNA damage response, cell cycle control, apoptosis and HIF-1-alpha related pathways. To further explore PNCK as a clinical target, potent small-molecule molecular probes must be developed. Currently, there are no targeted small molecule inhibitors in pre-clinical or clinical studies for the CAMK family. Additionally, there exists no experimentally derived crystal structure for PNCK. We herein report a three-pronged chemical probe discovery campaign which utilized homology modeling, machine learning, virtual screening and molecular dynamics to identify small molecules with low-micromolar potency against PNCK activity from commercially available compound libraries. We report the discovery of a hit-series for the first targeted effort towards discovering PNCK inhibitors that will serve as the starting point for future medicinal chemistry efforts for hit-to-lead optimization of potent chemical probes.

Reversal of Lactate and PD-1-mediated Macrophage Immunosuppression Controls Growth of PTEN/p53-deficient Prostate Cancer.

PURPOSE: Phosphatase and tensin homolog (PTEN) loss of function occurs in approximately 50% of patients with metastatic castrate-resistant prostate cancer (mCRPC), and is associated with poor prognosis and responsiveness to standard-of-care therapies and immune checkpoint inhibitors. While PTEN loss of function hyperactivates PI3K signaling, combinatorial PI3K/AKT pathway and androgen deprivation therapy (ADT) has demonstrated limited anticancer efficacy in clinical trials. Here, we aimed to elucidate mechanism(s) of resistance to ADT/PI3K-AKT axis blockade, and to develop rational combinatorial strategies to effectively treat this molecular subset of mCRPC. EXPERIMENTAL DESIGN: Prostate-specific PTEN/p53-deficient genetically engineered mice (GEM) with established 150-200 mm3 tumors, as assessed by ultrasound, were treated with either ADT (degarelix), PI3K inhibitor (copanlisib), or anti-PD-1 antibody (aPD-1), as single agents or their combinations, and tumors were monitored by MRI and harvested for immune, transcriptomic, and proteomic profiling, or ex vivo co-culture studies. Single-cell RNA sequencing on human mCRPC samples was performed using 10X Genomics platform. RESULTS: Coclinical trials in PTEN/p53-deficient GEM revealed that recruitment of PD-1-expressing tumor-associated macrophages (TAM) thwarts ADT/PI3Ki combination-induced tumor control. The addition of aPD-1 to ADT/PI3Ki combination led to TAM-dependent approximately 3-fold increase in anticancer responses. Mechanistically, decreased lactate production from PI3Ki-treated tumor cells suppressed histone lactylation within TAM, resulting in their anticancer phagocytic activation, which was augmented by ADT/aPD-1 treatment and abrogated by feedback activation of Wnt/ß-catenin pathway. Single-cell RNA-sequencing analysis in mCRPC patient biopsy samples revealed a direct correlation between high glycolytic activity and TAM phagocytosis suppression. CONCLUSIONS: Immunometabolic strategies that reverse lactate and PD-1-mediated TAM immunosuppression, in combination with ADT, warrant further investigation in patients with PTEN-deficient mCRPC.

A novel chemical attack on Notch-mediated transcription by targeting the NACK ATPase.

Notch activation complex kinase (NACK) is a component of the Notch transcriptional machinery critical for the Notch-mediated tumorigenesis. However, the mechanism through which NACK regulates Notch-mediated transcription is not well understood. Here, we demonstrate that NACK binds and hydrolyzes ATP and that only ATP-bound NACK can bind to the Notch ternary complex (NTC). Considering this, we sought to identify inhibitors of this ATP-dependent function and, using computational pipelines, discovered the first small-molecule inhibitor of NACK, Z271-0326, that directly blocks the activity of Notch-mediated transcription and shows potent antineoplastic activity in PDX mouse models. In conclusion, we have discovered the first inhibitor that holds promise for the efficacious treatment of Notch-driven cancers by blocking the Notch activity downstream of the NTC.

Cellular and molecular effects of PNCK, a non-canonical kinase target in renal cell carcinoma.

Renal cell carcinoma (RCC) is a fatal disease when advanced. While immunotherapy and tyrosine kinase inhibitor-based combinations are associated with improved survival, the majority of patients eventually succumb to the disease. Through a comprehensive pan-cancer, pan-kinome analysis of the Cancer Genome Atlas (TCGA), pregnancy-upregulated non-ubiquitous calcium-calmodulin-dependent kinase (PNCK), was identified as the most differentially overexpressed kinase in RCC. PNCK overexpression correlated with tumor stage, grade and poor survival. PNCK overexpression in RCC cells was associated with increased CREB phosphorylation, increased cell proliferation, and cell cycle progression. PNCK down-regulation, conversely, was associated with the opposite, in addition to increased apoptosis. Pathway analyses in PNCK knockdown cells showed significant down-regulation of hypoxia and angiogenesis pathways, as well as the modulation of the cell cycle, DNA damage, and apoptosis pathways. These results demonstrate for the first time the biological role of PNCK, an understudied kinase, in RCC and validate PNCK as a druggable target.

Connecting omics signatures and revealing biological mechanisms with iLINCS.

There are only a few platforms that integrate multiple omics data types, bioinformatics tools, and interfaces for integrative analyses and visualization that do not require programming skills. Here we present iLINCS ( http://ilincs.org ), an integrative web-based platform for analysis of omics data and signatures of cellular perturbations. The platform facilitates mining and re-analysis of the large collection of omics datasets (>34,000), pre-computed signatures (>200,000), and their connections, as well as the analysis of user-submitted omics signatures of diseases and cellular perturbations. iLINCS analysis workflows integrate vast omics data resources and a range of analytics and interactive visualization tools into a comprehensive platform for analysis of omics signatures. iLINCS user-friendly interfaces enable execution of sophisticated analyses of omics signatures, mechanism of action analysis, and signature-driven drug repositioning. We illustrate the utility of iLINCS with three use cases involving analysis of cancer proteogenomic signatures, COVID 19 transcriptomic signatures and mTOR signaling.

A multiparametric pharmacogenomic strategy for drug repositioning predicts therapeutic efficacy for glioblastoma cell lines.

BACKGROUND: Poor prognosis of glioblastoma patients and the extensive heterogeneity of glioblastoma at both the molecular and cellular level necessitates developing novel individualized treatment modalities via genomics-driven approaches. METHODS: This study leverages numerous pharmacogenomic and tissue databases to examine drug repositioning for glioblastoma. RNA-seq of glioblastoma tumor samples from The Cancer Genome Atlas (TCGA, n = 117) were compared to "normal" frontal lobe samples from Genotype-Tissue Expression Portal (GTEX, n = 120) to find differentially expressed genes (DEGs). Using compound gene expression data and drug activity data from the Library of Integrated Network-Based Cellular Signatures (LINCS, n = 66,512 compounds) CCLE (71 glioma cell lines), and Chemical European Molecular Biology Laboratory (ChEMBL) platforms, we employed a summarized reversal gene expression metric (sRGES) to "reverse" the resultant disease signature for GBM and its subtypes. A multiparametric strategy was employed to stratify compounds capable of blood-brain barrier penetrance with a favorable pharmacokinetic profile (CNS-MPO). RESULTS: Significant correlations were identified between sRGES and drug efficacy in GBM cell lines in both ChEMBL(r = 0.37, P < .001) and Cancer Therapeutic Response Portal (CTRP) databases (r = 0.35, P < 0.001). Our multiparametric algorithm identified two classes of drugs with highest sRGES and CNS-MPO: HDAC inhibitors (vorinostat and entinostat) and topoisomerase inhibitors suitable for drug repurposing. CONCLUSIONS: Our studies suggest that reversal of glioblastoma disease signature correlates with drug potency for various GBM subtypes. This multiparametric approach may set the foundation for an early-phase personalized -omics clinical trial for glioblastoma by effectively identifying drugs that are capable of reversing the disease signature and have favorable pharmacokinetic and safety profiles.

Proteomic cellular signatures of kinase inhibitor-induced cardiotoxicity.

Drug Toxicity Signature Generation Center (DToxS) at the Icahn School of Medicine at Mount Sinai is one of the centers for the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. Its key aim is to generate proteomic and transcriptomic signatures that can predict cardiotoxic adverse effects of kinase inhibitors approved by the Food and Drug Administration. Towards this goal, high throughput shotgun proteomics experiments (308 cell line/drug combinations +64 control lysates) have been conducted. Using computational network analyses, these proteomic data can be integrated with transcriptomic signatures, generated in tandem, to identify cellular signatures of cardiotoxicity that may predict kinase inhibitor-induced toxicity and enable possible mitigation. Both raw and processed proteomics data have passed several quality control steps and been made publicly available on the PRIDE database. This broad protein kinase inhibitor-stimulated human cardiomyocyte proteomic data and signature set is valuable for prediction of drug toxicities.

A novel strategy for combination of clofarabine and pictilisib is synergistic in gastric cancer.

Gastric cancer (GC) is frequently characterized by resistance to standard chemotherapeutic regimens and poor clinical outcomes. We aimed to identify a novel therapeutic approach using drug sensitivity testing (DST) and our computational SynerySeq pipeline. DST of GC cell lines was performed with a library of 215 Federal Drug Administration (FDA) approved compounds and identified clofarabine as a potential therapeutic agent. RNA-sequencing (RNAseq) of clofarabine treated GC cells was analyzed according to our SynergySeq pipeline and identified pictilisib as a potential synergistic agent. Clonogenic survival and Annexin V assays demonstrated increased cell death with clofarabine and pictilisib combination treatment (P<0.01). The combination induced double strand breaks (DSB) as indicated by phosphorylated H2A histone family member X (γH2AX) immunofluorescence and western blot analysis (P<0.01). Pictilisib treatment inhibited the protein kinase B (AKT) cell survival pathway and promoted a pro-apoptotic phenotype as evidenced by quantitative real time polymerase chain reaction (qRT-PCR) analysis of the B-cell lymphoma 2 (BCL2) protein family members (P<0.01). Patient derived xenograft (PDX) data confirmed that the combination is more effective in abrogating tumor growth with prolonged survival than single-agent treatment (P<0.01). The novel combination of clofarabine and pictilisib in GC promotes DNA damage and inhibits key cell survival pathways to induce cell death beyond single-agent treatment.

Molecular Dynamics Simulations Identify Tractable Lead-like Phenyl-Piperazine Scaffolds as eIF4A1 ATP-competitive Inhibitors.

eIF4A1 is an ATP-dependent RNA helicase whose overexpression and activity have been tightly linked to oncogenesis in a number of malignancies. An understanding of the complex kinetics and conformational changes of this translational enzyme is necessary to map out all targetable binding sites and develop novel, chemically tractable inhibitors. We herein present a comprehensive quantitative analysis of eIF4A1 conformational changes using protein-ligand docking, homology modeling, and extended molecular dynamics simulations. Through this, we report the discovery of a novel, biochemically active phenyl-piperazine pharmacophore, which is predicted to target the ATP-binding site and may serve as the starting point for medicinal chemistry optimization efforts. This is the first such report of an ATP-competitive inhibitor for eiF4A1, which is predicted to bind in the nucleotide cleft. Our novel interdisciplinary pipeline serves as a framework for future drug discovery efforts for targeting eiF4A1 and other proteins with complex kinetics.

Discovery of an eIF4A Inhibitor with a Novel Mechanism of Action.

Increased protein synthesis is a requirement for malignant growth, and as a result, translation has become a pharmaceutical target for cancer. The initiation of cap-dependent translation is enzymatically driven by the eukaryotic initiation factor (eIF)4A, an ATP-powered DEAD-box RNA-helicase that unwinds the messenger RNA secondary structure upstream of the start codon, enabling translation of downstream genes. A screen for inhibitors of eIF4A ATPase activity produced an intriguing hit that, surprisingly, was not ATP-competitive. A medicinal chemistry campaign produced the novel eIF4A inhibitor 28, which decreased BJAB Burkitt lymphoma cell viability. Biochemical and cellular studies, molecular docking, and functional assays uncovered that 28 is an RNA-competitive, ATP-uncompetitive inhibitor that engages a novel pocket in the RNA groove of eIF4A and inhibits unwinding activity by interfering with proper RNA binding and suppressing ATP hydrolysis. Inhibition of eIF4A through this unique mechanism may offer new strategies for targeting this promising intersection point of many oncogenic pathways.

A proteogenomic portrait of lung squamous cell carcinoma.

Lung squamous cell carcinoma (LSCC) remains a leading cause of cancer death with few therapeutic options. We characterized the proteogenomic landscape of LSCC, providing a deeper exposition of LSCC biology with potential therapeutic implications. We identify NSD3 as an alternative driver in FGFR1-amplified tumors and low-p63 tumors overexpressing the therapeutic target survivin. SOX2 is considered undruggable, but our analyses provide rationale for exploring chromatin modifiers such as LSD1 and EZH2 to target SOX2-overexpressing tumors. Our data support complex regulation of metabolic pathways by crosstalk between post-translational modifications including ubiquitylation. Numerous immune-related proteogenomic observations suggest directions for further investigation. Proteogenomic dissection of CDKN2A mutations argue for more nuanced assessment of RB1 protein expression and phosphorylation before declaring CDK4/6 inhibition unsuccessful. Finally, triangulation between LSCC, LUAD, and HNSCC identified both unique and common therapeutic vulnerabilities. These observations and proteogenomics data resources may guide research into the biology and treatment of LSCC.

The Genetic Landscape of Ocular Adnexa MALT Lymphoma Reveals Frequent Aberrations in NFAT and MEF2B Signaling Pathways.

A comprehensive constellation of somatic non-silent mutations and copy number (CN) variations in ocular adnexa marginal zone lymphoma (OAMZL) is unknown. By utilizing whole-exome sequencing in 69 tumors we define the genetic landscape of OAMZL. Mutations and CN changes in CABIN1 (30%), RHOA (26%), TBL1XR1 (22%), and CREBBP (17%) and inactivation of TNFAIP3 (26%) were among the most common aberrations. Candidate cancer driver genes cluster in the B-cell receptor (BCR), NFkB, NOTCH and NFAT signaling pathways. One of the most commonly altered genes is CABIN1, a calcineurin inhibitor acting as a negative regulator of the NFAT and MEF2B transcriptional activity. CABIN1 deletions enhance BCR-stimulated NFAT and MEF2B transcriptional activity, while CABIN1 mutations enhance only MEF2B transcriptional activity by impairing binding of mSin3a to CABIN1. Our data provide an unbiased identification of genetically altered genes that may play a role in the molecular pathogenesis of OAMZL and serve as therapeutic targets.

Machine and deep learning approaches for cancer drug repurposing.

Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced "omics" coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targets and pathways critical to those processes that may be amenable to pharmacologic modulation. However, the current anti-cancer therapeutic armamentarium continues to lag behind. As the cost of developing a new drug remains prohibitively expensive, repurposing of existing approved and investigational drugs is sought after given known safety profiles and reduction in the cost barrier. Notably, successes in oncologic drug repurposing have been infrequent. Computational in-silico strategies have been developed to aid in modeling biological processes to find new disease-relevant targets and discovering novel drug-target and drug-phenotype associations. Machine and deep learning methods have especially enabled leaps in those successes. This review will discuss these methods as they pertain to cancer biology as well as immunomodulation for drug repurposing opportunities in oncologic diseases.