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 structures of the FABP6 gene. Therefore, in silico experiments have shown a new opportunity that should be validated experimentally, thus helping to reduce the cost and speed of drug screening. For these reasons, we propose the validation of the drug Abemaciclib for the treatment of colon cancer.

Keywords: Abemaciclib; Colon cancer; Drug repurposing; FABP6; Machine learning; Molecular docking; Prognosis; TCGA.

Fig. 1

Classification according to the stage of the patients. A comparative experiment was carried out with different datasets and different algorithms for the classification of patients according to their stage. The classification consisted of a binary classification, grouping the patients in two classes (stage I-II and stage III-IV)

Fig. 2

Classification by metastatic stage of patients. A comparative experiment was carried out with different datasets and different algorithms for the classification of patients according to their metastatic stage of lymphatic node. The classification consisted of a binary classification, grouping patients into two classes (stage n0 and stage 1-3)

Fig. 3

Results of analyisis prediction from tumor and helath tissues. a) A comparative ML task was carried out with three different signatures (Random signature, Meta signature and Drivers Intogen) to predict between tumor and helth tissues. TCGA expression values of these three signatures were the input in training phase for two ML algorithms (Random Forest and glmnet). The accuracy of the models for each signature is shown. b) Mean difference plot after differencial gene expressión is shown. Up and Down expression genes are highlighted in red and blue respectively. FABP6 and CDH3 were the genes with major gene expression differences. c) Comparative variable importance for metasignature in Random Forest and glment algorithms. Values were scaled for comparative analysis. d) Pie chart with intersections of same genes obtained by two ML approaches and differential gene expression. The three approaches obtained very similar conclusions

Fig. 4

Percentage of 3D PDB structures for each gene obtained

Fig. 5

Box diagram of the expression of the four genes between healthy and diseased tissues of the COAD cohort of the TCGA

Fig. 6

Three FABP structures (white ribbon) with the natural ligands (violet lines) and Abemaciclib (blue-green sticks and balls): 1O1V a, 2MM3 b, and 5L8N c

Fig. 7

Box plot panel with the comparision between tumour and control samples through 21 tumour s types from TCGA

Fig. 8

Survival curve according to the number of copies of the FABP6 gene. Extracted from [52]