Integrated drug response prediction models pinpoint repurposed drugs with effectiveness against rhabdomyosarcoma.

Targeted therapies for inhibiting the growth of cancer cells or inducing apoptosis are urgently needed for effective rhabdomyosarcoma (RMS) treatment. However, identifying cancer-targeting compounds with few side effects, among the many potential compounds, is expensive and time-consuming. A computational approach to reduce the number of potential candidate drugs can facilitate the discovery of attractive lead compounds. To address this and obtain reliable predictions of novel cell-line-specific drugs, we apply prediction models that have the potential to improve drug discovery approaches for RMS treatment. The results of two prediction models were ensemble and validated via in vitro experiments. The computational models were trained using data extracted from the Genomics of Drug Sensitivity in Cancer database and tested on two RMS cell lines to select potential RMS drug candidates. Among 235 candidate drugs, 22 were selected following the result of the computational approach, and three candidate drugs were identified (NSC207895, vorinostat, and belinostat) that showed selective effectiveness in RMS cell lines in vitro via the induction of apoptosis. Our in vitro experiments have demonstrated that our proposed methods can effectively identify and repurpose drugs for treating RMS.

Machine learning with in silico analysis markedly improves survival prediction modeling in colon cancer patients.

BACKGROUND: Predicting the survival of cancer patients provides prognostic information and therapeutic guidance. However, improved prediction models are needed for use in diagnosis and treatment. OBJECTIVE: This study aimed to identify genomic prognostic biomarkers related to colon cancer (CC) based on computational data and to develop survival prediction models. METHODS: We performed machine-learning (ML) analysis to screen pathogenic survival-related driver genes related to patient prognosis by integrating copy number variation and gene expression data. Moreover, in silico system analysis was performed to clinically assess data from ML analysis, and we identified RABGAP1L, MYH9, and DRD4 as candidate genes. These three genes and tumor stages were used to generate survival prediction models. Moreover, the genes were validated by experimental and clinical analyses, and the theranostic application of the survival prediction models was assessed. RESULTS: RABGAP1L, MYH9, and DRD4 were identified as survival-related candidate genes by ML and in silico system analysis. The survival prediction model using the expression of the three genes showed higher predictive performance when applied to predict the prognosis of CC patients. A series of functional analyses revealed that each knockdown of three genes reduced the protumor activity of CC cells. In particular, validation with an independent cohort of CC patients confirmed that the coexpression of MYH9 and DRD4 gene expression reflected poorer clinical outcomes in terms of overall survival and disease-free survival. CONCLUSIONS: Our survival prediction approach will contribute to providing information on patients and developing a therapeutic strategy for CC patients.

Prediction of survival and recurrence in patients with pancreatic cancer by integrating multi-omics data.

Predicting the prognosis of pancreatic cancer is important because of the very low survival rates of patients with this particular cancer. Although several studies have used microRNA and gene expression profiles and clinical data, as well as images of tissues and cells, to predict cancer survival and recurrence, the accuracies of these approaches in the prediction of high-risk pancreatic adenocarcinoma (PAAD) still need to be improved. Accordingly, in this study, we proposed two biological features based on multi-omics datasets to predict survival and recurrence among patients with PAAD. First, the clonal expansion of cancer cells with somatic mutations was used to predict prognosis. Using whole-exome sequencing data from 134 patients with PAAD from The Cancer Genome Atlas (TCGA), we found five candidate genes that were mutated in the early stages of tumorigenesis with high cellular prevalence (CP). CDKN2A, TP53, TTN, KCNJ18, and KRAS had the highest CP values among the patients with PAAD, and survival and recurrence rates were significantly different between the patients harboring mutations in these candidate genes and those harboring mutations in other genes (p = 2.39E-03, p = 8.47E-04, respectively). Second, we generated an autoencoder to integrate the RNA sequencing, microRNA sequencing, and DNA methylation data from 134 patients with PAAD from TCGA. The autoencoder robustly reduced the dimensions of these multi-omics data, and the K-means clustering method was then used to cluster the patients into two subgroups. The subgroups of patients had significant differences in survival and recurrence (p = 1.41E-03, p = 4.43E-04, respectively). Finally, we developed a prediction model for prognosis using these two biological features and clinical data. When support vector machines, random forest, logistic regression, and L2 regularized logistic regression were used as prediction models, logistic regression analysis generally revealed the best performance for both disease-free survival (DFS) and overall survival (OS) (accuracy [ACC] = 0.762 and area under the curve [AUC] = 0.795 for DFS; ACC = 0.776 and AUC = 0.769 for OS). Thus, we could classify patients with a high probability of recurrence and at a high risk of poor outcomes. Our study provides insights into new personalized therapies on the basis of mutation status and multi-omics data.