Integrating molecular interactions and gene expression to identify biomarkers to predict response to tumor necrosis factor inhibitor therapies in rheumatoid arthritis patients.

BACKGROUND: Targeted therapy using anti-TNF (tumor necrosis factor) is the first option for patients with rheumatoid arthritis (RA). Anti-TNF therapy, however, does not lead to meaningful clinical improvement in many RA patients. To predict which patients will not benefit from anti-TNF therapy, clinical tests should be performed prior to treatment beginning. OBJECTIVE: Although various efforts have been made to identify biomarkers and pathways that may be helpful to predict the response to anti-TNF treatment, gaps remain in clinical use due to the low predictive power of the selected biomarkers. METHODS: In this paper, we used a network-based computational method to identify the select the predictive biomarkers to guide the treatment of RA patients. RESULTS: We select 69 genes from peripheral blood expression data from 46 subjects using a sparse network-based method. The result shows that the selected 69 genes might influence biological processes and molecular functions related to the treatment. CONCLUSIONS: Our approach advances the predictive power of anti-TNF therapy response and provides new genetic markers and pathways that may influence the treatment.

Beyond the limitation of targeted therapy: Improve the application of targeted drugs combining genomic data with machine learning.

Precision oncology involves effectively selecting drugs for cancer patients and planning an effective treatment regimen. However, for Molecular targeted drug, using genomic state of the drug target to select drugs has limitations. Many patients who could benefit from molecularly targeted drugs, but they are being missed due to the insufficient labelling ability of the existing target genes. For non-specific chemotherapy drugs, most of the first-line anticancer drugs do not have biomarkers to guide doctor make treatment regimen. Furthermore, it is important to determine a long-term treatment plan based on the patient's genomic data during tumor evolution. Therefore, it is necessary to establish a tumor drug sensitivity prediction model, which can assist doctors in designing a personalized tumor treatment regimen. This paper proposed a novel model to predict tumor drug sensitivity including targeted drugs and non-specific chemotherapy drugs. This model uses statistical methods based on Bimodal distribution to select multimodal genetic data to solve dimensional challenges and reduce noise and to establish a classification model to predict the effectiveness of the drug in the tumor cell line using machine learning. The experimental test 87 molecular targeted drugs and non-specific chemotherapy drugs. The results show that the method can effectively predict the sensitivity of tumor drugs with an average sensitivity of 0.98 and specificity of 0.97. This model is worth to promotion. If it can be successfully used in clinical trials, it will effectively assist doctors to develop personalized cancer treatment programs and expand the application of molecularly targeted drugs.