Cancer type clusters

<div><p>The P-glycoprotein efflux pump, encoded by the <i>MDR1</i> gene, is an ATP-driven transporter capable of expelling a diverse array of compounds from cells. Overexpression of this protein is implicated in the multi-drug resistant phenotype observed in various cancers. Numerous studies have attempted to decipher the impact of genetic variants within <i>MDR1</i> on P-glycoprotein expression, functional activity, and clinical outcomes in cancer patients. Among these, three specific single nucleotide polymorphisms—<i>T1236C</i>, <i>T2677G</i>, and <i>T3435C</i> - have been the focus of extensive research efforts, primarily through in vitro cell line models and clinical cohort analyses. However, the findings from these studies have been remarkably contradictory. In this study, we employ a computational, data-driven approach to systematically evaluate the effects of these three variants on principal stages of the gene expression process. Leveraging current knowledge of gene regulatory mechanisms, we elucidate potential mechanisms by which these variants could modulate P-glycoprotein levels and function. Our findings suggest that all three variants significantly change the mRNA folding in their vicinity. This change in mRNA structure is predicted to increase local translation elongation rates, but not to change the protein expression. Nonetheless, the increased translation rate near <i>T3435C</i> is predicted to affect the protein’s co-translational folding trajectory in the region of the second ATP binding domain. This potentially impacts P-glycoprotein conformation and function. Our study demonstrates the value of computational approaches in elucidating the functional consequences of genetic variants. This framework provides new insights into the molecular mechanisms of <i>MDR1</i> variants and their potential impact on cancer prognosis and treatment resistance. Furthermore, we introduce an approach which can be systematically applied to identify mutations potentially affecting mRNA folding in pathology. We demonstrate the utility of this approach on both ClinVar and TCGA and identify hundreds of disease related variants that modify mRNA folding at essential positions.</p></div>