In Silico Optimized Stapled Peptides Targeting WASF3 in Breast Cancer

Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine

Personalized interventions are deemed vital given the intricate characteristics, advancement, inherent genetic composition, and diversity of cardiovascular diseases (CVDs). The appropriate utilization of artificial intelligence (AI) and machine learning (ML) methodologies can yield novel understandings of CVDs, enabling improved personalized treatments through predictive analysis and deep phenotyping. In this study, we proposed and employed a novel approach combining traditional statistics and a nexus of cutting-edge AI/ML techniques to identify significant biomarkers for our predictive engine by analyzing the complete transcriptome of CVD patients. After robust gene expression data pre-processing, we utilized three statistical tests (Pearson correlation, Chi-square test, and ANOVA) to assess the differences in transcriptomic expression and clinical characteristics between healthy individuals and CVD patients. Next, the recursive feature elimination classifier assigned rankings to transcriptomic features based on their relation to the case-control variable. The top ten percent of commonly observed significant biomarkers were evaluated using four unique ML classifiers (Random Forest, Support Vector Machine, Xtreme Gradient Boosting Decision Trees, and k-Nearest Neighbors). After optimizing hyperparameters, the ensembled models, which were implemented using a soft voting classifier, accurately differentiated between patients and healthy individuals. We have uncovered 18 transcriptomic biomarkers that are highly significant in the CVD population that were used to predict disease with up to 96% accuracy. Additionally, we cross-validated our results with clinical records collected from patients in our cohort. The identified biomarkers served as potential indicators for early detection of CVDs. With its successful implementation, our newly developed predictive engine provides a valuable framework for identifying patients with CVDs based on their biomarker profiles.

WASF3 overexpression affects the expression of circular RNA hsa-circ-0100153, which promotes breast cancer progression by sponging hsa-miR-31, hsa-miR-767-3p, and hsa-miR-935

Background

The WASF3 gene has been linked to promoting metastasis in breast cancer (BC) cells, and low expression reduces invasion potential. Circular RNAs (circRNAs) function as microRNA (miRNA) modulators and are involved in cancer progression, but the relationship between these factors remains unclear.

Methods

This study used bioinformatics methods and a computational approach to investigate the role of circRNAs and miRNAs in the context of WASF3 overexpression. Differentially expressed mRNAs, circRNAs, and miRNAs were identified using Gene Expression Omnibus (GEO) datasets. A competing endogenous RNA (ceRNA) network was constructed based on circRNA-miRNA pairs and miRNA-mRNA pairs. Functional and pathway enrichment analyses were predicted using a circRNA-miRNA-mRNA network.

Results

RNA expression patterns were significantly different between normal and tumor samples. A total of 190 circRNAs, 76 miRNAs, and 678 mRNAs were differentially expressed. The analysis of the circRNA-miRNA-mRNA regulatory network revealed interactions between hsa-circ-0100153, hsa-miR-31, hsa-miR-767-3p, and hsa-miR-935 with WASF3 in cancer. These interactions primarily function in DNA replication and the cell cycle.

Conclusions

This study reveals a mechanism by which WASF3 overexpression affects the expression of circRNAs hsa-circ-0100153, promoting BC progression by sponging hsa-miR-31/hsa-miR-767-3p /hsa-miR-935. This mechanism may increase the invasive potential of cancers, in addition to other reported molecular mechanisms involving the WASF3 gene.

The WAVE3/β-catenin oncogenic signaling regulates chemoresistance in triple negative breast cancer

BACKGROUND

Metastatic breast cancer is responsible for the death of the majority of breast cancer patients. In fact, metastatic BC is the 2nd leading cause of cancer-related deaths in women in the USA and worldwide. Triple negative breast cancer (TNBC), which lacks expression of hormone receptors (ER-α and PR) and ErbB2/HER2, is especially lethal due to its highly metastatic behavior, propensity to recur rapidly, and for its resistance to standard of care therapies, through mechanisms that remain incompletely understood. WAVE3 has been established as a promoter of TNBC development and metastatic progression. In this study, we investigated the molecular mechanisms whereby WAVE3 promotes therapy-resistance and cancer stemness in TNBC, through the regulation of β-catenin stabilization.

METHODS

The Cancer Genome Atlas dataset was used to assess the expression of WAVE3 and β-catenin in breast cancer tumors. Kaplan-Meier Plotter analysis was used to correlate expression of WAVE3 and β-catenin with breast cancer patients' survival probability. MTT assay was used to quantify cell survival. CRISPR/Cas9-mediated gene editing, 2D and 3D tumorsphere growth and invasion assays, Immunofluorescence, Western blotting, Semi-quantitative and real-time quantitative PCR analyses were applied to study the WAVE3/β-catenin oncogenic signaling in TNBC. Tumor xenograft assays were used to study the role of WAVE3 in mediating chemotherapy resistance of TNBC tumors.

RESULTS

Genetic inactivation of WAVE3 in combination of chemotherapy resulted in inhibition of 2D growth and 3D tumorsphere formation and invasion of TNBC cells in vitro, as well as tumor growth and metastasis in vivo. In addition, while re-expression of phospho-active WAVE3 in the WAVE3-deficient TNBC cells restored the oncogenic activity of WAVE3, re-expression of phospho-mutant WAVE3 did not. Further studies revealed that dual blocking of WAVE3 expression or phosphorylation in combination with chemotherapy treatment inhibited the activity and expression and stabilization of β-catenin. Most importantly, the combination of WAVE3-deficiency or WAVE3-phospho-deficiency and chemotherapy suppressed the oncogenic behavior of chemoresistant TNBC cells, both in vitro and in vivo.

CONCLUSION

We identified a novel WAVE3/β-catenin oncogenic signaling axis that modulates chemoresistance of TNBC. This study suggests that a targeted therapeutic strategy against WAVE3 could be effective for the treatment of chemoresistant TNBC tumors.

Targeting the WASF3 complex to suppress metastasis

Wiskott-Aldrich syndrome protein family members (WASF) regulate the dynamics of the actin cytoskeleton, which plays an instrumental role in cancer metastasis and invasion. WASF1/2/3 forms a hetero-pentameric complex with CYFIP1/2, NCKAP1/1 L, Abi1/2/3 and BRK1 called the WASF Regulatory Complex (WRC), which cooperatively regulates actin nucleation by WASF1/2/3. Activation of the WRC enables actin networking and provides the mechanical force required for the formation of lamellipodia and invadopodia. Although the WRC drives cell motility essential for several routine physiological functions, its aberrant deployment is observed in cancer metastasis and invasion. WASF3 expression is correlated with metastatic potential in several cancers and inversely correlates with overall progression-free survival. Therefore, disruption of the WRC may serve as a novel strategy for targeting metastasis. Given the complexity involved in the formation of the WRC which is largely comprised of large protein-protein interfaces, there are currently no inhibitors for WASF3. However, several constrained peptide mimics of the various protein-protein interaction interfaces within the WRC were found to successfully disrupt WASF3-mediated migration and invasion. This review explores the role of the WASF3 WRC in driving metastasis and how it may be selectively targeted for suppression of metastasis.