A mutation-induced drug resistance database (MdrDB)

Site-specific mutagenesis screening in KRASG12D mutant library to uncover resistance mechanisms to KRASG12D inhibitors

KRAS plays a crucial role in regulating cell survival and proliferation and is one of the most commonly mutated oncogenes in human cancers. The novel KRASG12D inhibitor, MRTX1133, demonstrates promising antitumor efficacy in vitro and in vivo. However, the development of acquired resistance in treated patients presents a considerable challenge to sustained therapeutic effectiveness. In response to this challenge, we conducted site-specific mutagenesis screening to identify potential secondary mutations that could induce resistance to MRTX1133. We screened a range of KRASG12D variants harboring potential secondary mutations, and 44 representative variants were selected for in-depth validation of the pooled screening outcomes. We identified eight variants (G12D with V9E, V9W, V9Q, G13P, T58Y, R68G, Y96W, and Q99L) that exhibited substantial resistance, with V9W showing notable resistance, and downstream signaling analyses and structural modeling were conducted. We observed that secondary mutations in KRASG12D can lead to acquired resistance to MRTX1133 and BI-2865, a novel pan-KRAS inhibitor, in human cancer cell lines. This evidence is critical for devising new strategies to counteract resistance mechanisms and, ultimately, enhance treatment outcomes in patients with KRASG12D-mutant cancers.

CAP-RNAseq: an integrated pipeline for functional annotation and prioritization of co-expression clusters

Cluster analysis is one of the most widely used exploratory methods for visualization and grouping of gene expression patterns across multiple samples or treatment groups. Although several existing online tools can annotate clusters with functional terms, there is no all-in-one webserver to effectively prioritize genes/clusters using gene essentiality as well as congruency of mRNA-protein expression. Hence, we developed CAP-RNAseq that makes possible (1) upload and clustering of bulk RNA-seq data followed by identification, annotation and network visualization of all or selected clusters; and (2) prioritization using DepMap gene essentiality and/or dependency scores as well as the degree of correlation between mRNA and protein levels of genes within an expression cluster. In addition, CAP-RNAseq has an integrated primer design tool for the prioritized genes. Herein, we showed using comparisons with the existing tools and multiple case studies that CAP-RNAseq can uniquely aid in the discovery of co-expression clusters enriched with essential genes and prioritization of novel biomarker genes that exhibit high correlations between their mRNA and protein expression levels. CAP-RNAseq is applicable to RNA-seq data from different contexts including cancer and available at http://konulabapps.bilkent.edu.tr:3838/CAPRNAseq/ and the docker image is downloadable from https://hub.docker.com/r/konulab/caprnaseq.