TNK2 as a key drug target for the treatment of metastatic colorectal cancer

A comprehensive multi-omics study reveals potential prognostic and diagnostic biomarkers for colorectal cancer

BACKGROUND AND OBJECTIVE

Colorectal cancer (CRC) is a complex disease with diverse genetic alterations and causes 10 % of cancer-related deaths worldwide. Understanding its molecular mechanisms is essential for identifying potential biomarkers and therapeutic targets for its effective management.

METHODS

We integrated copy number alterations (CNA) and mutation data via their differentially expressed genes termed as candidate genes (CGs) computed using bioinformatics approaches. Then, using the CGs, we perform Weighted correlation network analysis (WGCNA) and utilise several hazard models such as Univariate Cox, Least Absolute Shrinkage and Selection Operator (LASSO) Cox and multivariate Cox to identify the key genes involved in CRC progression. We used different machine-learning models to demonstrate the discriminative power of selected hub genes among normal and CRC (early and late-stage) samples.

RESULTS

The integration of CNA with mRNA expression identified over 3000 CGs, including CRC-specific driver genes like MYC and APC. In addition, pathway analysis revealed that the CGs are mainly enriched in endocytosis, cell cycle, wnt signalling and mTOR signalling pathways. Hazard models identified four key genes, CASP2, HCN4, LRRC69 and SRD5A1, that were significantly associated with CRC progression and predicted the 1-year, 3-years, and 5-years survival times. WGCNA identified seven hub genes: DSCC1, ETV4, KIAA1549, NOP56, RRS1, TEAD4 and ANKRD13B, which exhibited strong predictive performance in distinguishing normal from CRC (early and late-stage) samples.

CONCLUSIONS

Integrating regulatory information with gene expression improved early versus late-stage prediction. The identified potential prognostic and diagnostic biomarkers in this study may guide us in developing effective therapeutic strategies for CRC management.

Improvement of ACK1-targeted therapy efficacy in lung adenocarcinoma using chloroquine or bafilomycin A1

BACKGROUND

Activated Cdc42-associated kinase 1 (ACK1) is a promising druggable target for cancer, but its inhibitors only showed moderate effects in clinical trials. The study aimed to investigate the underlying mechanisms and improve the antitumor efficacy of ACK1 inhibitors.

METHODS

RNA-seq was performed to determine the downstream pathways of ACK. Using Lasso Cox regression analysis, we built a risk signature with ACK1-related autophagy genes in the lung adenocarcinoma (LUAD) patients from The Cancer Genome Atlas (TCGA) project. The performance of the signature in predicting the tumor immune environment and response to immunotherapy and chemotherapy were assessed in LUAD. CCK8, mRFP-GFP-LC3 assay, western blot, colony formation, wound healing, and transwell migration assays were conducted to evaluate the effects of the ACK1 inhibitor on lung cancer cells. A subcutaneous NSCLC xenograft model was used for in vivo study.

RESULTS

RNA-seq revealed the regulatory role of ACK1 in autophagy. Furthermore, the risk signature separated LUAD patients into low- and high-risk groups with significantly different prognoses. The two groups displayed different tumor immune environments regarding 28 immune cell subsets. The low-risk groups showed high immune scores, high CTLA4 expression levels, high immunophenoscore, and low DNA mismatch repair capacity, suggesting a better response to immunotherapy. This signature also predicted sensitivity to commonly used chemotherapy and targeted drugs. In vitro, the ACK1 inhibitors (AIM-100 and Dasatinib) appeared to trigger adaptive autophagy-like response to protect lung cancer cells from apoptosis and activated the AMPK/mTOR signaling pathway, partially explaining its moderate antitumor efficacy. However, blocking lysosomal degradation with chloroquine/Bafilamycine A1 or inhibiting AMPK signaling with compound C/shPRKAA1 enhanced the ACK1 inhibitor's cytotoxic effects on lung cancer cells. The efficacy of the combined therapy was also verified using a mouse xenograft model.

CONCLUSIONS

The resulting signature from ACK1-related autophagy genes robustly predicted survival and drug sensitivity in LUAD. The lysosomal degradation inhibition improved the therapeutic effects of the ACK1 inhibitor, suggesting a potential role for autophagy in therapy evasion.

Machine learning approaches and their applications in drug discovery and design

This review is focused on several machine learning approaches used in chemoinformatics. Machine learning approaches provide tools and algorithms to improve drug discovery. Many physicochemical properties of drugs like toxicity, absorption, drug-drug interaction, carcinogenesis, and distribution have been effectively modeled by QSAR techniques. Machine learning is a subset of artificial intelligence, and this technique has shown tremendous potential in the field of drug discovery. Techniques discussed in this review are capable of modeling non-linear datasets, as well as big data of increasing depth and complexity. Various machine learning-based approaches are being used for drug target prediction, modeling the structure of drug target, binding site prediction, ligand-based similarity searching, de novo designing of ligands with desired properties, developing scoring functions for molecular docking, building QSAR model for biological activity prediction, and prediction of pharmacokinetic and pharmacodynamic properties of ligands. In recent years, these predictive tools and models have achieved good accuracy. By the use of more related input data, relevant parameters, and appropriate algorithms, the accuracy of these predictions can be further improved.

Small Molecules Targeting Activated Cdc42-Associated Kinase 1 (ACK1/TNK2) for the Treatment of Cancers

Activated Cdc42-associated kinase 1 (ACK1/TNK2) is a nonreceptor tyrosine kinase with a unique structure. It not only can act as an activated transmembrane effector of receptor tyrosine kinases (RTKs) to transmit various RTK signals but also can play a corresponding role in epigenetic regulation. A number of studies have shown that ACK1 is a carcinogenic factor. Blockage of ACK1 has been proven to be able to inhibit cancer cell survival, proliferation, migration, and radiation resistance. Thus, ACK1 is a promising potential antitumor target. To date, despite many efforts to develop ACK1 inhibitors, no specific small molecule inhibitors have entered clinical trials. This Perspective provides an overview of the structural features, biological functions, and association with diseases of ACK1 and in vitro and in vivo activities, selectivity, and therapeutic potential of small molecule ACK1 inhibitors with different chemotypes.

Significant Gene Biomarker Tyrosine Kinase Non-receptor 2 Mediated Cell Proliferation and Invasion in Colon Cancer

Objective: This study aimed to investigate the expression and biological functions of TNK2 and miR-125a-3p in colon cancer.

Materials and methods: The expression of TNK2 and miR-125a-3p in colon cancer tissues was analyzed using data deposited on public databases including UALCAN and ONCOMINE. We verified their expression in colon cancer cell lines by RT-qPCR and western blotting. By regulating the expression of TNK2 and miR-125a-3p in colon cancer cells, their functions and potential mechanisms were explored.

Results:TNK2 was overexpressed in colon cancer cell lines, and it was found to directly bind to miR-125a-3p, which was downregulated in these cell lines. Their expression affected the proliferation and invasion of colon cancer cells. Additionally, colon cancer patients with lower TNK2 expression had better prognoses than those with higher TNK2 expression.

Conclusion: Our results indicated that TNK2 and miR-125a-3p play critical roles in colon cancer, and could also serve as biomarkers for the diagnosis and prognosis of this malignant disease.

Kinome Array Profiling of Patient-Derived Pancreatic Ductal Adenocarcinoma Identifies Differentially Active Protein Tyrosine Kinases

Pancreatic cancer remains one of the most difficult malignancies to treat. Minimal improvements in patient outcomes and persistently abysmal patient survival rates underscore the great need for new treatment strategies. Currently, there is intense interest in therapeutic strategies that target tyrosine protein kinases. Here, we employed kinome arrays and bioinformatic pipelines capable of identifying differentially active protein tyrosine kinases in different patient-derived pancreatic ductal adenocarcinoma (PDAC) cell lines and wild-type pancreatic tissue to investigate the unique kinomic networks of PDAC samples and posit novel target kinases for pancreatic cancer therapy. Consistent with previously described reports, the resultant peptide-based kinome array profiles identified increased protein tyrosine kinase activity in pancreatic cancer for the following kinases: epidermal growth factor receptor (EGFR), fms related receptor tyrosine kinase 4/vascular endothelial growth factor receptor 3 (FLT4/VEGFR-3), insulin receptor (INSR), ephrin receptor A2 (EPHA2), platelet derived growth factor receptor alpha (PDGFRA), SRC proto-oncogene kinase (SRC), and tyrosine kinase non receptor 2 (TNK2). Furthermore, this study identified increased activity for protein tyrosine kinases with limited prior evidence of differential activity in pancreatic cancer. These protein tyrosine kinases include B lymphoid kinase (BLK), Fyn-related kinase (FRK), Lck/Yes-related novel kinase (LYN), FYN proto-oncogene kinase (FYN), lymphocyte cell-specific kinase (LCK), tec protein kinase (TEC), hemopoietic cell kinase (HCK), ABL proto-oncogene 2 kinase (ABL2), discoidin domain receptor 1 kinase (DDR1), and ephrin receptor A8 kinase (EPHA8). Together, these results support the utility of peptide array kinomic analyses in the generation of potential candidate kinases for future pancreatic cancer therapeutic development.

Comprehensive Analysis of the Immune Implication of ACK1 Gene in Non-small Cell Lung Cancer

Activated Cdc42-associated kinase1 (ACK1), a non-receptor tyrosine kinase, has been considered as an oncogene and therapeutic target in various cancers. However, its contribution to cancer immunity remains uncertain. Here we first compared the profiles of immune cells in cancerous and normal tissues in The Cancer Genome Atlas (TCGA) lung cancer cohorts. Next, we found that the immune cell infiltration levels were associated with the ACK1 gene copy numbers in lung cancer. Consistently, our RNA-seq data unveiled that the silencing of ACK1 upregulated several immune pathways in lung cancer cells, including the T cell receptor signaling pathway. The impacts of ACK1 on immune activity were validated by Gene Set Enrichment Analysis of RNA-seq data of 188 lung cancer cell lines from the public database. A pathway enrichment analysis of 35 ACK1-associated immunomodulators and 50 tightly correlated genes indicated the involvement of the PI3K-Akt and Ras signaling pathways. Based on ACK1-associated immunomodulators, we established multiple-gene risk prediction signatures using the Cox regression model. The resulting risk scores were an independent prognosis predictor in the TCGA lung cohorts. We also accessed the prognostic accuracy of the risk scores with a receiver operating characteristic methodology. Finally, a prognostic nomogram, accompanied by a calibration curve, was constructed to predict individuals' 3- and 5-year survival probabilities. Our findings provided evidence of ACK1's implication in tumor immunity, suggesting that ACK1 may be a potential immunotherapeutic target for non-small cell lung cancer (NSCLC). The nominated immune signature is a promising prognostic biomarker in NSCLC.

miRDRN-miRNA disease regulatory network: a tool for exploring disease and tissue-specific microRNA regulatory networks

BACKGROUND

MicroRNA (miRNA) regulates cellular processes by acting on specific target genes, and cellular processes proceed through multiple interactions often organized into pathways among genes and gene products. Hundreds of miRNAs and their target genes have been identified, as are many miRNA-disease associations. These, together with huge amounts of data on gene annotation, biological pathways, and protein-protein interactions are available in public databases. Here, using such data we built a database and web service platform, miRNA disease regulatory network (miRDRN), for users to construct disease and tissue-specific miRNA-protein regulatory networks, with which they may explore disease related molecular and pathway associations, or find new ones, and possibly discover new modes of drug action.

METHODS

Data on disease-miRNA association, miRNA-target association and validation, gene-tissue association, gene-tumor association, biological pathways, human protein interaction, gene ID, gene ontology, gene annotation, and product were collected from publicly available databases and integrated. A large set of miRNA target-specific regulatory sub-pathways (RSPs) having the form (T, G 1, G 2) was built from the integrated data and stored, where T is a miRNA-associated target gene, G 1 (G 2) is a gene/protein interacting with T (G 1). Each sequence (T, G 1, G 2) was assigned a p-value weighted by the participation of the three genes in molecular interactions and reaction pathways.

RESULTS

A web service platform, miRDRN (http://mirdrn.ncu.edu.tw/mirdrn/), was built. The database part of miRDRN currently stores 6,973,875 p-valued RSPs associated with 116 diseases in 78 tissue types built from 207 diseases-associated miRNA regulating 389 genes. miRDRN also provides facilities for the user to construct disease and tissue-specific miRNA regulatory networks from RSPs it stores, and to download and/or visualize parts or all of the product. User may use miRDRN to explore a single disease, or a disease-pair to gain insights on comorbidity. As demonstrations, miRDRN was applied: to explore the single disease colorectal cancer (CRC), in which 26 novel potential CRC target genes were identified; to study the comorbidity of the disease-pair Alzheimer's disease-Type 2 diabetes, in which 18 novel potential comorbid genes were identified; and, to explore possible causes that may shed light on recent failures of late-phase trials of anti-AD, BACE1 inhibitor drugs, in which genes downstream to BACE1 whose suppression may affect signal transduction were identified.