The novel driver gene ASAP2 is a potential druggable target in pancreatic cancer

Identification of new potential candidates to inhibit EGF via machine learning algorithm

One of the cost-effective alternative methods to find new inhibitors has been the repositioning approach of existing drugs. The advantage of computational drug repositioning method is saving time and cost to remove the pre-clinical step and accelerate the drug discovery process. Hence, an ensemble computational-experimental approach, consisting of three steps, a machine learning model, simulation of drug-target interaction and experimental characterization, was developed. The machine learning type used here was a different tree classification method, which is one of the best randomize machine learning model to identify potential inhibitors from weak inhibitors. This model was trained more than one-hundred times, and forty top trained models were extracted for the drug repositioning step. The machine learning step aimed to discover the approved drugs with the highest possible success rate in the experimental step. Therefore, among all the identified molecules with more than 0.9 probability in more than 70% of the models, nine compounds, were selected. Besides, out of the nine chosen drugs, seven compounds have been confirmed to inhibit EGF in the published articles since 2019. Hence, two identified compounds, in addition to gefitinib, as a positive control, five weak-inhibitors and one neutral, were considered via molecular docking study. Finally, the eight proposed drugs, including gefitinib, were investigated using MTT assay and In-Cell ELISA to characterize the drugs' effect on A431 cell growth and EGF-signaling. From our experiments, we could conclude that salicylic acid and piperazine could play an EGF-inhibitor role like gefitinib.

Copy Number Variations in Pancreatic Cancer: From Biological Significance to Clinical Utility

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, characterized by high tumor heterogeneity and a poor prognosis. Inter- and intra-tumoral heterogeneity in PDAC is a major obstacle to effective PDAC treatment; therefore, it is highly desirable to explore the tumor heterogeneity and underlying mechanisms for the improvement of PDAC prognosis. Gene copy number variations (CNVs) are increasingly recognized as a common and heritable source of inter-individual variation in genomic sequence. In this review, we outline the origin, main characteristics, and pathological aspects of CNVs. We then describe the occurrence of CNVs in PDAC, including those that have been clearly shown to have a pathogenic role, and further highlight some key examples of their involvement in tumor development and progression. The ability to efficiently identify and analyze CNVs in tumor samples is important to support translational research and foster precision oncology, as copy number variants can be utilized to guide clinical decisions. We provide insights into understanding the CNV landscapes and the role of both somatic and germline CNVs in PDAC, which could lead to significant advances in diagnosis, prognosis, and treatment. Although there has been significant progress in this field, understanding the full contribution of CNVs to the genetic basis of PDAC will require further research, with more accurate CNV assays such as single-cell techniques and larger cohorts than have been performed to date.

Epigenetic regulation during cancer transitions across 11 tumour types

Chromatin accessibility is essential in regulating gene expression and cellular identity, and alterations in accessibility have been implicated in driving cancer initiation, progression and metastasis1-4. Although the genetic contributions to oncogenic transitions have been investigated, epigenetic drivers remain less understood. Here we constructed a pan-cancer epigenetic and transcriptomic atlas using single-nucleus chromatin accessibility data (using single-nucleus assay for transposase-accessible chromatin) from 225 samples and matched single-cell or single-nucleus RNA-sequencing expression data from 206 samples. With over 1 million cells from each platform analysed through the enrichment of accessible chromatin regions, transcription factor motifs and regulons, we identified epigenetic drivers associated with cancer transitions. Some epigenetic drivers appeared in multiple cancers (for example, regulatory regions of ABCC1 and VEGFA; GATA6 and FOX-family motifs), whereas others were cancer specific (for example, regulatory regions of FGF19, ASAP2 and EN1, and the PBX3 motif). Among epigenetically altered pathways, TP53, hypoxia and TNF signalling were linked to cancer initiation, whereas oestrogen response, epithelial-mesenchymal transition and apical junction were tied to metastatic transition. Furthermore, we revealed a marked correlation between enhancer accessibility and gene expression and uncovered cooperation between epigenetic and genetic drivers. This atlas provides a foundation for further investigation of epigenetic dynamics in cancer transitions.

ASAP2 interrupts c-MET-CIN85 interaction to sustain HGF/c-MET-induced malignant potentials in hepatocellular carcinoma

BACKGROUND

Sustained activation of hepatocyte growth factor (HGF)/c-MET signaling is a major driver of hepatocellular carcinoma (HCC) progression, but underlying mechanism is unclear. ArfGAP With SH3 Domain, Ankyrin Repeat And PH Domain 2 (ASAP2) can reportedly activate GTPases and promote receptor tyrosine kinase signaling. However, the exact role of ASAP2 in HCC, especially for c-MET activation, also remains elusive.

METHODS

ASAP2 expression levels in HCC tissues and cells were quantified using qRT-PCR, western blot (WB) analysis, and immunohistochemistry staining. Cell counting kit-8 (CCK-8) and colony formation assays were performed to evaluate cell proliferation rates. Flow cytometry assays were conducted to assess apoptosis rates. Wound healing and Transwell assays were performed to determine cell migration and invasion capacities. Epithelial-mesenchymal transition (EMT)-related marker expression levels were also examined. Subcutaneous implantation and tail vein injection models were applied for in vivo growth and metastasis evaluations, respectively. Bioinformatics analyses of The Cancer Genome Atlas and STRING datasets were performed to explore ASAP2 downstream signaling. Co-immunoprecipitation and Cycloheximide chasing experiments were performed to assess protein-protein interactions and protein half-life, respectively.

RESULTS

ASAP2 had higher expression levels in HCC tissues than in normal liver, and also predicted poor prognosis. Knocking down ASAP2 significantly impaired cell proliferation, migration, and invasion capacities, but promoted apoptosis in HCC cells in vitro. However, overexpression of ASAP2 achieved the opposite effects. In vivo experiments confirmed that ASAP2 could promote HCC cell growth and facilitate lung metastasis. Interestingly, ASAP2 was essential for triggering EMT. Gene Set Enrichment Analysis demonstrated that c-MET signaling was greatly enriched in ASAP2-high HCC cases. Additionally, c-MET signaling activity was significantly decreased following ASAP knockdown, evidenced by reduced c-MET, p-AKT, and p-ERK1/2 protein levels. Importantly, ASAP2 knockdown effectively attenuated HGF/c-MET signaling-induced malignant phenotypes. c-MET and ASAP2 expression levels were positively correlated in our cohort. Mechanistically, ASAP2 can directly bind to CIN85, thereby disrupting its interaction with c-MET, and can thus antagonize CIN85-induced c-MET internalization and lysosome-mediated degradation. Notably, knocking down CIN85 can rescue the observed inhibitory effects caused by ASAP2 knockdown.

CONCLUSIONS

This study highlights the importance of ASAP2 in sustaining c-MET signaling, which can facilitate HCC progression.

Identify AGAP2 as prognostic biomarker in clear cell renal cell carcinoma based on bioinformatics and IHC staining

Background

Arf GTPase-activating proteins are aberrantly expressed in a variety of tumors, but their role in clear cell renal cell carcinoma (ccRCC) was unclear. Exploring the biological role of Arf GAP with GTP binding protein like domain, Ankyrin repeat and PH domain 2 (AGAP2) in ccRCC could improve our understanding on the aggressiveness and immune relevance of ccRCC.

Methods

The expression of AGAP2 was analyzed based on the Cancer Genome Atlas (TCGA) database and verified in ccRCC samples using immunohistochemistry. The association between AGAP2 and clinical cancer stages was explored by TCGA dataset and UALCAN. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to analyze the biological functions of AGAP2-related genes. Moreover, the relationship between AGAP2 and immune cell infiltration was investigated with TIME and TCGA dataset.

Results

Compared to normal tissues, AGAP2 was upregulated in ccRCC tissues. Higher expression of AGAP2 was associated with clinical cancer stages, TNM stages, pathologic stages, and status. Prognostic analysis on AGAP2 showed that AGAP2 overexpression was associated with KIRC overall survival (OS) reduction (P = 0.019). However, higher expression of AGAP2 may improve the OS of CESC (P = 0.002), THYM (P = 0.006) and UCEC (P = 0.049). GO and KEGG analysis showed that AGAP2-related genes was related to T cell activation, immune activity and PD-L1 expression and PD-1 checkpoint pathway. Furthermore, our study showed that AGAP2 were significantly associated with T cells, Cytotoxic cells, Treg, Th1 cells, CD8 T cells, T helper cells. And AGAP2 expression level affected the abundance of immune cells infiltration. The infiltrating level of immune cells was different between the AGAP2 high-expression and low-expression groups.

Conclusion

The expression of AGAP2 in ccRCC was higher than that in normal kidney tissues. It was significantly associated with clinical stage, poor prognosis, and immune cell infiltration. Therefore, AGAP2 may become an important component for ccRCC patients who receive precision cancer therapy and may be a promising prognostic biomarker.

How I Treat TP53-Mutated Acute Myeloid Leukemia and Myelodysplastic Syndromes

TP53-mutated acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS) are among the myeloid malignancies with the poorest prognosis. In this review, we analyze the prognosis of these two diseases, focussing particularly on the extent of the mono or biallelic mutation status of TP53 mutation, which is largely correlated with cytogenetic complexity. We discuss the possible/potential improvement in outcome based on recent results obtained with new drugs (especially eprenetapopt and magrolimab). We also focus on the impact of allogeneic hematopoietic stem cell transplantation (aHSCT) including post aHSCT treatment.

SEMA3C Supports Pancreatic Cancer Progression by Regulating the Autophagy Process and Tumor Immune Microenvironment

To date, driver genes for pancreatic cancer treatment are difficult to pursue therapeutically. Targeting mutated KRAS, the most renowned driver gene in pancreatic cancer, is an active area of study. We discovered a gene named SEMA3C was highly expressed in pancreatic cancer cell lines and patients with a G12D mutation in KRAS. High expression of SEMA3C in patients was significantly associated with the decreased survival of pancreatic cancer patients based on the TCGA database. In pancreatic cancer cells, SEMA3C knockdown or inhibition exhibited growth/colony inhibition and cell cycle arrest. In addition, SEMA3C inhibition sensitized KRAS or MEK1/2 inhibition in pancreatic cancer cells. Overexpression of SEMA3C resulted in the induction of autophagy, whereas depletion of SEMA3C compromised induction of autophagy. SEMA3C modified the PD-L1 expression in tumor and immune cells and is correlated with the M2-like macrophage marker ARG1/CD163 expression, which could reshape the tumor microenvironment. Inhibition of SEMA3C decreased tumor formation in the xenograft model in vivo. Taken together, our data suggest that SEMA3C plays a substantial role in promoting cancer cell survival by regulating the autophagy process and impacting the tumor environment immune response. SEMA3C can be used as a novel target or marker with therapeutic or diagnostic potential in pancreatic cancer especially in tumors harboring the specific KRAS G12D mutation.

Nephrotoxicity evaluation and proteomic analysis in kidneys of rats exposed to thioacetamide

Thioacetamide (TAA) was administered orally at 0, 10, and 30 mg/kg body weight (BW) daily to Sprague–Dawley rats aged 6–7 weeks for 28 consecutive days. Nephrotoxicity and proteomics were evaluated in the kidneys of rats exposed to TAA. The BW decreased, however, the relative kidneys weight increased. No significant histopathologic abnormalities were found in the kidneys. The numbers of monocytes and platelets were significantly increased. However, the mean corpuscular volume and hematocrit values were decreased significantly in rats exposed to 30 mg/kg BW TAA. The expression levels of Kim-1 and NGAL were increased 4 to 5-fold in the kidneys, resulting in significant nephrotoxicity. Proteomic analysis was conducted and a total of 5221 proteins spots were resolved. Of these, 3 and 21 protein spots were up- and downregulated, respectively. The validation of seven proteins was performed by Western blot analysis. The expression level of ASAP2 was significantly upregulated, whereas RGS14, MAP7Dl, IL-3Rα, Tmod1, NQO2, and MUP were reduced. Sixteen isoforms of MUP were found by the 2DE immunoblot assay and were significantly downregulated with increasing exposure to TAA. MUP isoforms were compared in the liver, kidneys, and urine of untreated rats and a total of 43 isoforms were found.

Chemical-induced gene expression ranking and its application to pancreatic cancer drug repurposing

Chemical-induced gene expression profiles provide critical information of chemicals in a biological system, thus offering new opportunities for drug discovery. Despite their success, large-scale analysis leveraging gene expressions is limited by time and cost. Although several methods for predicting gene expressions were proposed, they only focused on imputation and classification settings, which have limited applications to real-world scenarios of drug discovery. Therefore, a chemical-induced gene expression ranking (CIGER) framework is proposed to target a more realistic but more challenging setting in which overall rankings in gene expression profiles induced by de novo chemicals are predicted. The experimental results show that CIGER significantly outperforms existing methods in both ranking and classification metrics. Furthermore, a drug screening pipeline based on CIGER is proposed to identify potential treatments of drug-resistant pancreatic cancer. Our predictions have been validated by experiments, thereby showing the effectiveness of CIGER for phenotypic compound screening of precision medicine.

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A new deep-learning method (CIGER) for chemical-induced gene expression ranking

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CIGER can predict gene expression for de novo chemicals from chemical structures

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We discovered drugs for the treatment of drug-resistant pancreatic cancer

In recent years, a phenotype-based drug discovery approach using chemical-induced gene expressions has shown to be effective in drug discovery and precision medicine. However, it is not feasible to experimentally determine chemical-induced gene expressions for all available chemicals of interest, thereby hindering the application of gene expression-based compound screening on a large scale. Thus, it is crucial to design a computational approach that can generate gene expression information for any chemicals. We proposed a new, deep-learning framework named chemical-induced gene expression ranking (CIGER) to predict a landmark gene expression profile (i.e., gene ranking) induced by de novo chemicals based on their chemical structures. Leveraging CIGER, we predicted and experimentally validated that several existing drugs can increase the therapeutic response on drug-resistant pancreatic cancer. Our results demonstrated the effectiveness of CIGER for precision drug discovery in practice.

The Unique Genetic and Histological Characteristics of DMBA-Induced Mammary Tumors in an Organoid-Based Carcinogenesis Model

Here, we report a model system using in vitro 7,12-dimethylbenz[a]anthracene (DMBA; 0.6 μM)-treated mammary tissue-derived organoids generated from heterozygous BALB/c-Trp53 knockout mice to induce tumors after injection into the nude mouse subcutis. In parallel, a single oral dose of DMBA (50 mg/kg bodyweight) to the same murine strain induced mammary adenocarcinomas, characterized by biphasic structures differentiated into luminal and myoepithelial lineages and frequent Hras mutations at codon 61. In the present study, the genetic and histological characteristics of DMBA-induced tumors in the organoid-based model were evaluated to validate its similarities to the in vivo study. The organoid-derived tumors were low-grade adenocarcinomas composed of luminal and basal/myoepithelial cells. When the organoid-derived carcinomas were passaged to other nude mice, they partly progressed to squamous cell carcinomas (SCCs). Whole exome sequencing revealed no mutations at Hras codon 61 in the organoid-derived tumors. However, various mutations were detected in other genes such as Tusc3 and Tgfbr2, which have been reported as cancer-associated or homeostatic squamous cell genes. The most common mutational pattern observed in these genes were the G:C to T:A transversions and G:C to A:T transitions, which are not typical of the mutations caused by DMBA treatment. In conclusion, DMBA exhibited carcinogenicity in the both the ex vivo and in vivo mammary carcinogenesis models, albeit with distinct histological and genetical alterations. Further studies are needed to clarify whether organoid-based carcinogenesis models generated following chemical treatment in vitro could be applied to the clarification of the novel mode of action of chemical carcinogenesis.

Annotation of pituitary neuroendocrine tumors with genome-wide expression analysis

Pituitary neuroendocrine tumors (PitNETs) are common, generally benign tumors with complex clinical characteristics related to hormone hypersecretion and/or growing sellar tumor mass. PitNETs can be classified based on the expression pattern of anterior pituitary hormones and three main transcriptions factors (TF), SF1, PIT1 and TPIT that regulate differentiation of adenohypophysial cells. Here, we have extended this classification based on the global transcriptomics landscape using tumor tissue from a well-defined cohort comprising 51 PitNETs of different clinical and histological types. The molecular profiles were compared with current classification schemes based on immunohistochemistry. Our results identified three main clusters of PitNETs that were aligned with the main pituitary TFs expression patterns. Our analyses enabled further identification of specific genes and expression patterns, including both known and unknown genes, that could distinguish the three different classes of PitNETs. We conclude that the current classification of PitNETs based on the expression of SF1, PIT1 and TPIT reflects three distinct subtypes of PitNETs with different underlying biology and partly independent from the expression of corresponding hormones. The transcriptomic analysis reveals several potentially targetable tumor-driving genes with previously unknown role in pituitary tumorigenesis.

GET4 is a novel driver gene in colorectal cancer that regulates the localization of BAG6, a nucleocytoplasmic shuttling protein

Colorectal cancer (CRC) is one of the most common types of cancer and a significant cause of cancer mortality worldwide. Further improvements of CRC therapeutic approaches are needed. BCL2‐associated athanogene 6 (BAG6), a multifunctional scaffold protein, plays an important role in tumor progression. However, regulation of BAG6 in malignancies remains unclear. This study showed that guided entry of tail‐anchored proteins factor 4 (GET4), a component of the BAG6 complex, regulates the intercellular localization of BAG6 in CRC. Furthermore, GET4 was identified as a candidate driver gene on the short arm of chromosome 7, which is often amplified in CRC, by our bioinformatics approach using the CRC dataset from The Cancer Genome Atlas. Clinicopathologic and prognostic analyses using CRC datasets showed that GET4 was overexpressed in tumor cells due to an increased DNA copy number. High GET4 expression was an independent poor prognostic factor in CRC, whereas BAG6 was mainly overexpressed in the cytoplasm of tumor cells without gene alteration. The biological significance of GET4 was examined using GET4 KO CRC cells generated with CRISPR‐Cas9 technology or transfected CRC cells. In vitro and in vivo analyses showed that GET4 promoted tumor growth. It appears to facilitate cell cycle progression by cytoplasmic enrichment of BAG6‐mediated p53 acetylation followed by reduced p21 expression. In conclusion, we showed that GET4 is a novel driver gene and a prognostic biomarker that promotes CRC progression by inducing the cytoplasmic transport of BAG6. GET4 could be a promising therapeutic molecular target in CRC.

TRIOMPHE: Transcriptome-Based Inference and Generation of Molecules with Desired Phenotypes by Machine Learning

One of the most challenging tasks in the drug-discovery process is the efficient identification of small molecules with desired phenotypes. In this study, we propose a novel computational method for omics-based de novo drug design, which we call TRIOMPHE (transcriptome-based inference and generation of molecules with desired phenotypes). We investigated the correlation between chemically induced transcriptome profiles (reflecting cellular responses to compound treatment) and genetically perturbed transcriptome profiles (reflecting cellular responses to gene knock-down or gene overexpression of target proteins) in terms of ligand-target interactions. Subsequently, we developed novel machine learning methods to generate the chemical structures of new molecules with desired transcriptome profiles in the framework of a variational autoencoder. The use of desired transcriptome profiles enables the automatic design of molecules that are likely to have bioactivities for target proteins of interest. We showed that our methods can generate chemically valid molecules that are likely to have biological activities on 10 target proteins; moreover, they can outperform previous methods that had the same objective. Our omics-based structure generator is expected to be useful for the de novo design of drugs for a variety of target proteins.

Repurposing Niclosamide for Targeting Pancreatic Cancer by Inhibiting Hh/Gli Non-Canonical Axis of Gsk3β

Simple Summary

The current obstacles for discovering new drugs for cancer therapy have necessitated the development of the alternative strategy of drug repurposing, the identification of new uses for approved or investigational drugs for new therapeutic purposes. Niclosamide (Nic) is a Food and Drug Administration (FDA)-approved anti-helminthic drug, reported to have anti-cancer effects, and is being assessed in various clinical trials. In the current study, we assessed the therapeutic efficacy of Nic on pancreatic cancer (PC) in vitro. Our results revealed mitochondrial stress and mTORC1-dependent autophagy as the predominant players of Nic-induced PC cell death. This study provided a novel mechanistic insight for anti-cancer efficacy of Nic by increasing p-Gsk3β that modulates molecular signaling(s), including inhibition of hedgehog (Hh) signaling-mediated cellular proliferation and increased apoptosis through mTORC1-dependent autophagy may prove helpful for the development of novel PC therapies.

Abstract

Niclosamide (Nic), an FDA-approved anthelmintic drug, is reported to have anti-cancer efficacy and is being assessed in clinical trials for various solid tumors. Based on its ability to target multiple signaling pathways, in the present study, we evaluated the therapeutic efficacy of Nic on pancreatic cancer (PC) in vitro. We observed an anti-cancerous effect of this drug as shown by the G0/G1 phase cell cycle arrest, inhibition of PC cell viability, colony formation, and migration. Our results revealed the involvement of mitochondrial stress and mTORC1-dependent autophagy as the predominant players of Nic-induced PC cell death. Significant reduction of Nic-induced reactive oxygen species (ROS) and cell death in the presence of a selective autophagy inhibitor spautin-1 demonstrated autophagy as a major contributor to Nic-mediated cell death. Mechanistically, Nic inhibited the interaction between BCL2 and Beclin-1 that supported the crosstalk of autophagy and apoptosis. Further, Nic treatment resulted in Gsk3β inactivation by phosphorylating its Ser-9 residue leading to upregulation of Sufu and Gli3, thereby negatively impacting hedgehog signaling and cell survival. Nic induced autophagic cell death, and p-Gsk3b mediated Sufu/Gli3 cascade was further confirmed by Gsk3β activator, LY-294002, by rescuing inactivation of Hh signaling upon Nic treatment. These results suggested the involvement of a non-canonical mechanism of Hh signaling, where p-Gsk3β acts as a negative regulator of Hh/Gli1 cascade and a positive regulator of autophagy-mediated cell death. Overall, this study established the therapeutic efficacy of Nic for PC by targeting p-Gsk3β mediated non-canonical Hh signaling and promoting mTORC1-dependent autophagy and cell death.

Pan-cancer characterization of metabolism-related biomarkers identifies potential therapeutic targets

Background

Generally, cancer cells undergo metabolic reprogramming to adapt to energetic and biosynthetic requirements that support their uncontrolled proliferation. However, the mutual relationship between two critical metabolic pathways, glycolysis and oxidative phosphorylation (OXPHOS), remains poorly defined.

Methods

We developed a “double-score” system to quantify glycolysis and OXPHOS in 9668 patients across 33 tumor types from The Cancer Genome Atlas and classified them into four metabolic subtypes. Multi-omics bioinformatical analyses was conducted to detect metabolism-related molecular features.

Results

Compared with patients with low glycolysis and high OXPHOS (LGHO), those with high glycolysis and low OXPHOS (HGLO) were consistently associated with worse prognosis. We identified common dysregulated molecular features between different metabolic subgroups across multiple cancers, including gene, miRNA, transcription factor, methylation, and somatic alteration, as well as investigated their mutual interfering relationships.

Conclusion

Overall, this work provides a comprehensive atlas of metabolic heterogeneity on a pan-cancer scale and identified several potential drivers of metabolic rewiring, suggesting corresponding prognostic and therapeutic utility.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02889-0.

The novel driver gene ASAP2 is a potential druggable target in pancreatic cancer

Targeting mutated oncogenes is an effective approach for treating cancer. The 4 main driver genes of pancreatic ductal adenocarcinoma (PDAC) are KRAS, TP53, CDKN2A, and SMAD4, collectively called the "big 4" of PDAC, however they remain challenging therapeutic targets. In this study, ArfGAP with SH3 domain, ankyrin repeat and PH domain 2 (ASAP2), one of the ArfGAP family, was identified as a novel driver gene in PDAC. Clinical analysis with PDAC datasets showed that ASAP2 was overexpressed in PDAC cells based on increased DNA copy numbers, and high ASAP2 expression contributed to a poor prognosis in PDAC. The biological roles of ASAP2 were investigated using ASAP2-knockout PDAC cells generated with CRISPR-Cas9 technology or transfected PDAC cells. In vitro and in vivo analyses showed that ASAP2 promoted tumor growth by facilitating cell cycle progression through phosphorylation of epidermal growth factor receptor (EGFR). A repositioned drug targeting the ASAP2 pathway was identified using a bioinformatics approach. The gene perturbation correlation method showed that niclosamide, an antiparasitic drug, suppressed PDAC growth by inhibition of ASAP2 expression. These data show that ASAP2 is a novel druggable driver gene that activates the EGFR signaling pathway. Furthermore, niclosamide was identified as a repositioned therapeutic agent for PDAC possibly targeting ASAP2.