Molecular docking and machine learning analysis of Abemaciclib in colon cancer

Abstract

Background: The main challenge in cancer research is the identification of different omic variables that present a prognostic value and personalised diagnosis for each tumour. The fact that the diagnosis is personalised opens the doors to the design and discovery of new specific treatments for each patient. In this context, this work offers new ways to reuse existing databases and work to create added value in research. Three published signatures with significante prognostic value in Colon Adenocarcinoma (COAD) were indentified. These signatures were combined in a new meta-signature and validated with main Machine Learning (ML) and conventional statistical techniques. In addition, a drug repurposing experiment was carried out through Molecular Docking (MD) methodology in order to identify new potential treatments in COAD. Results: The prognostic potential of the signature was validated by means of ML algorithms and differential gene expression analysis. The results obtained supported the possibility that this meta-signature could harbor genes of interest for the prognosis and treatment of COAD. We studied drug repurposing following a molecular docking (MD) analysis, where the different protein data bank (PDB) structures of the genes of the meta-signature (in total 155) were confronted with 81 anti-cancer drugs approved by the FDA. We observed four interactions of interest: GLTP-Nilotinib, PTPRN-Venetoclax, VEGFA-Venetoclax and FABP6-Abemaciclib. The FABP6 gene and its role within different metabolic pathways were studied in tumour and normal tissue and we observed the capability of the FABP6 gene to be a therapeutic target. Our in silico results showed a significant specificity of the union of the protein products of the FABP6 gene as well as the known action of Abemaciclib as an inhibitor of the CDK4/6 protein and therefore, of the cell cycle. Conclusions: The results of our ML and differential expression experiments have first shown the FABP6 gene as a possible new cancer biomarker due to its specificity in colonic tumour tissue and no expression in healthy adjacent tissue. Next, the MD analysis showed that the drug Abemaciclib characteristic affinity for the different protein

Cyclin-Dependent Kinase 4/6 Inhibitors: A Potential Breakthrough Therapy for Malignancies of Gastrointestinal Tract

“…The synergistic effect was pronounced when abemaciclib was combined with LY3009120, a pan-raf inhibitor (64). Using machine learning to identify the prognostic signature of colon adenocarcinoma, Linares-Blanco et al found that several genes are potential candidates for therapeutic targeting including fatty acid binding protein 6 (FABP6) (65). The in silico molecular docking revealed that FABP6 strongly interacted with abemaciclib.…”

Systems-level biomarkers identification and drug repositioning in colorectal cancer

“…However, the FABP6 gene has greater specificity for abemaciclib when compared with others. Therefore, this study suggested that abemaciclib targeting FABP6 has the potential to be a therapeutic target and needs further experimental validation[ 99 ]. Consequently, our research group applied a DR strategy by targeting the coexpression network of the protein Multidrug Resistance 1 (MDR1) expressed by the ABCB1 protein that causes chemotherapy failure.…”

Characterizing Immune Responses in Whole Slide Images of Cancer With Digital Pathology and Pathomics

“…Current advances in Pathomics tissue analytics are focused on predicting the biological behavior of human disease and cancer for precision medicine applications. One of the main challenges in Pathomics is to identify the types of data products that can be useful to identify patterns, relationships, or biomarkers to provide clinically actionable information [42]. Current Pathomics applications are incredibly powerful and useful for understanding how and why the dynamic microscopic landscapes of tissues change during the initiation and progression of disease.…”