Discovery of Novel Gene Variants in a Cohort of Qatari Breast Cancer Patients and Ensuing Personalised Drug Selection

Abstract

Background: The advent of personalised medicine promises a deeper understanding of mechanisms and therefore therapies. However, the connection between genomic sequences and clinical treatments is often unclear. Methods: We studied 50 breast cancer patients belonging to a population-cohort in the state of Qatar. The effect of these mutations on drug treatment in the protein target encoded by ESR1, namely the estrogen receptor, was achieved via rapid and accurate protein-ligand binding affinity interaction studies which were performed for the selected drugs and the natural ligand estrogen. Findings: From Sanger sequencing, we identified several new deleterious mutations in the estrogen receptor 1 gene (ESR1). Four nonsynonymous mutations in the ligand binding domain were subjected to molecular dynamics simulation using absolute and relative binding free energy methods, leading to the ranking of the efficacy of six selected drugs for patients with the mutations. Interpretation: Our study shows that a personalised clinical decision system can be created by integrating an individual patient’s genomic data at the molecular level within a computational pipeline which ranks the efficacy of binding of particular drugs to variant proteins. Funding Statement: This work was supported by Qatar National Research Fund (7-1083-1-191), the UK Medical Research Council for funding the Medical Bioinformatics project (MR/L016311/1), EU H2020 projects ComPat (671564), CompBioMed and CompBioMed2 (675451 and 823712), NSF Award (1713749) and special funding to PVC from the UCL Provost. Declaration of Interests: The authors have no conflicts of interest to declare. Ethics Approval Statement: This study was approved by the ethical committee of Hamad Medical Corporation.