Paul A Stewart | Search PubMed

PubMed2026

iModMix: integrative module analysis for multi-omics data.

SUMMARY: Integrative Module Analysis for Multi-omics Data (iModMix) is a biology-agnostic framework that enables the discovery of novel associations across any type of quantitative abundance data, including but not limited to transcriptomics, proteomics, and metabolomics. Instead of relying on pathway annotations or prior biological knowledge, iModMix constructs data-driven modules using graphical lasso to estimate sparse networks from omics features. These modules are summarized into eigenfeatures and correlated across datasets for horizontal integration, while preserving the distinct feature sets and interpretability of each omics type. iModMix operates directly on matrices containing expression or abundances for a wide range of features, including but not limited to genes, proteins, and metabolites. Because it does not rely on annotations (e.g., KEGG identifiers), it can seamlessly incorporate both identified and unidentified metabolites, addressing a key limitation of many existing metabolomics tools. iModMix is available as a user-friendly R Shiny application requiring no programming expertise (https://imodmix.moffitt.org), and as a Bioconductor R package for advanced users (https://bioconductor.org/packages/release/bioc/html/iModMix.html). The tool includes several public and in-house datasets to illustrate its utility in identifying novel multi-omics relationships in diverse biological contexts. AVAILABILITY AND IMPLEMENTATION: iModMix is freely available from Bioconductor (https://bioconductor.org/packages/release/bioc/html/iModMix.html), and the example dataset package (iModMixData) is also available from Bioconductor (https://bioconductor.org/packages/release/ data/experiment/html/iModMixData.html). The R package source code and Docker are available from GitHub: https://github.com/biodatalab/iModMix. Shiny application can be accessed at: https://imodmix.moffitt.org.

Multiomics↗

PubMed2026

RAS-GTP Inhibition Overcomes Acquired Resistance to KRASG12C Inhibitors Mediated by Oncogenic and Wild-Type RAS Activation in Non-Small Cell Lung Cancer.

UNLABELLED: Small-molecule KRASG12C(OFF) inhibitors that bind to the inactive GDP-bound state of KRAS have demonstrated efficacy in patients with KRASG12C-mutant tumors, yet responses tend to be transient because of emergence of on-treatment resistance. Recently, RAS(ON) G12C-selective inhibitors, which bind to the active GTP-bound state of RAS, were described, and elironrasib is undergoing evaluation in multiple clinical trials. In this study, we generated resistant cell lines and patient-derived xenograft models to KRASG12C(OFF) and RAS(ON) G12C-selective inhibitors and interrogated resistance mechanisms using a multiomics strategy consisting of phosphoproteomics, whole-exome sequencing, and RNA sequencing combined with functional testing using small-molecule and CRISPR screens and RAS(ON) inhibitors being evaluated in clinical trials. Two models reactivated RAS signaling, either via KRASG12C gene amplification or NRASG13R mutation, and were vulnerable to dual inhibition by RAS(ON) G12C-selective and RAS(ON) multiselective inhibitors, RMC-4998 and RMC-7977. Two models, which lacked any discernable genomic alteration, acquired resistance associated with increased receptor tyrosine kinase activity and downstream persistent RAS activity and were sensitive to RAS-GTP inhibition by RMC-7977. Finally, one model displayed epithelial-mesenchymal transition, loss of RAS dependence, and acquired reliance on cell-cycle kinases and proteins associated with DNA damage response. This work highlights KRASG12C-selective inhibitor resistant states that parallel and complement clinical findings and demonstrate that a large subset could be overcome with a RAS(ON) multi-selective inhibitor as a stand-alone agent or in combination with other therapies. SIGNIFICANCE: Multi-omic characterization of resistance mechanisms to KRASG12C-selective inhibitors in non-small cell lung cancer provides insights that could inform precision medicine-based therapeutic approaches for improving the treatment of KRASG12C mutant tumors. See related article by Stern et al., p. 485.

Humans↗