Pharmacological targeting of Axin2 suppresses cell growth and metastasis in colorectal cancer

BACKGROUND AND PURPOSE

The scaffold molecule Axin2 is constitutively activated in colorectal cancer (CRC) and functions as a potent promoter of CRC behaviour. Pharmacological targeting of Axin2 may therefore exert a therapeutic effect in patients with CRC. Here, we discovered a potent small-molecule inhibitor of Axin2, based on the mechanism by which Axin2 is regulated post-translationally, and investigated its antitumour effects.

EXPERIMENTAL APPROACH

Compound discovery and its inhibitory action on Axin2 protein were revealed by microscale thermophoresis, in vitro kinase assay, quantitative kinetic assay, immunoblotting/immunoprecipitation, RT-qPCR and cycloheximide pulse-chase assay. Compound antitumour effects and the underlying mechanisms were evaluated in multiple cell-based assays and mouse models.

KEY RESULTS

We discovered that glycogen synthase kinase 3β (GSK3β) phosphorylates Axin2 at two consensus motifs and coupled Axin2 phosphorylation to its ubiquitination (mediated by the E3 ligase β-Trcp2) and proteasomal degradation. The binding of Axin2 to GSK3β in CRC cells is faint, which enables most of the Axin2 protein to maintain an unphosphorylated status and thereby permits the cells to preserve high levels of Axin2. Importantly, we identified a small-molecule compound CW85319 that enhances Axin2's interaction with GSK3β via forming a high affinity for Axin2. Treatment of CRC cells with CW85319 enhanced Axin2 binding with GSK3β, thereby promoting Axin2 phosphorylation, subsequent ubiquitination, and degradation. Furthermore, we demonstrated that CW85319 efficiently suppressed Axin2-driven CRC growth and metastasis, without eliciting side toxicity.

CONCLUSIONS AND IMPLICATIONS

These findings suggest that pharmacological targeting of Axin2 by CW85319 may provide therapeutic benefits against certain human cancers, especially CRC.

Two antisense RNAs-AFAP1-AS1 and MLK7-AS1-promote colorectal cancer progression by sponging miR-149-5p and miR-485-5p

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths. Antisense RNAs (asRNAs) are closely associated with cancer malignancy. This study aimed to identify the action mechanism of asRNAs in controlling CRC malignancy. Analysis of the RNA sequencing data revealed that AFAP1-AS1 and MLK7-AS1 were upregulated in CRC patients and cell lines. High levels of both asRNAs were associated with poor prognosis in patients with CRC. Both in vitro and in vivo experiments revealed that the knockdown of the two asRNAs decreased the proliferative and metastatic abilities of CRC cells. Mechanistically, AFAP1-AS1 and MLK7-AS1 decreased the levels of miR-149-5p and miR-485-5p by functioning as ceRNAs. Overexpression of miRNAs by introducing miRNA mimics suppressed the expression of SHMT2 and IGFBP5 by directly binding to the 3' UTR of their mRNA. Knockdown of both asRNAs decreased the expression of SHMT2 and IGFBP5, which was reversed by inhibition of both miRNAs by miRNA inhibitors. In vivo pharmacological targeting of both asRNAs by small interfering RNA-loaded nanoparticles showed that knockdown of asRNAs significantly reduced tumor growth and metastasis. Our findings demonstrate that AFAP1-AS1 and MLK7-AS1 promote CRC progression by sponging the tumor-suppressing miRNAs miR-149-5p and miR-485-5p, thus upregulating SHMT2 and IGFBP5.

Gene-Mutation-Based Algorithm for Prediction of Treatment Response in Colorectal Cancer Patients

Purpose: Despite the high mortality of metastatic colorectal cancer (mCRC), no new biomarker tools are available for predicting treatment response. We developed gene-mutation-based algorithms as a biomarker classifier to predict treatment response with better precision than the current predictive factors. Methods: Random forest machine learning (ML) was applied to identify the candidate algorithms using the MSK Cohort (n = 471) as a training set and validated in the TCGA Cohort (n = 221). Logistic regression, progression-free survival (PFS), and univariate/multivariate Cox proportional hazard analyses were performed and the performance of the candidate algorithms was compared with the established risk parameters. Results: A novel 7-Gene Algorithm based on mutation profiles of seven KRAS-associated genes was identified. The algorithm was able to distinguish non-progressed (responder) vs. progressed (non-responder) patients with AUC of 0.97 and had predictive power for PFS with a hazard ratio (HR) of 16.9 (p < 0.001) in the MSK cohort. The predictive power of this algorithm for PFS was more pronounced in mCRC (HR = 16.9, p < 0.001, n = 388). Similarly, in the TCGA validation cohort, the algorithm had AUC of 0.98 and a significant predictive power for PFS (p < 0.001). Conclusion: The novel 7-Gene Algorithm can be further developed as a biomarker model for prediction of treatment response in mCRC patients to improve personalized therapies.