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Unveiling mutational dynamics in non-small cell lung cancer patients by quantitative EGFR profiling in vesicular RNA.

ABSTRACT: The mutational status of the epidermal growth factor receptor (EGFR) guides the stratification of non-small cell lung cancer (NSCLC) patients for treatment with tyrosine kinase inhibitors (TKIs). A liquid biopsy test on cell-free DNA is recommended as a clinical decision-supporting tool, although it has limited sensitivity. Here, we comparatively investigated the extracellular vesicle (EV)-RNA as an independent source for multidimensional and longitudinal EGFR profiling in a cohort of 27 NSCLC patients. We introduced and validated a new rapid, highly specific EV-RNA test with wild-type (WT) and mutant-sensitive probes (E746-A750del, L858R, and T790M). We included a cohort of 20 NSCLC patients with EGFR WT tumor tissues and systematically performed molecular EV-RNA and circulating tumor DNA analyses with clinical data statistics and biophysical profiles of EVs. At the single-patient level, we detected variegated tumor heterogeneity dynamics supported by combinations of driver EGFR mutations. EV-RNA-based mutation analysis showed an unprecedented sensitivity of over 90%. The resistance-associated mutation T790M frequently pre-existed at baseline with a gained EV-transcript copy number at progression, while the general mutational burden was mostly decreasing during the intermediate follow-up. The biophysical profile of EVs and the quantitative assessment of T790M revealed an association with tumor size determined by the sum of the longest diameters in target lesions. Vesicular RNA provides a validated tool suitable for use in clinical practice to investigate the dynamics of common driver EGFR mutations in NSCLC patients receiving TKIs.

SUBMITTER: Pasini L

PROVIDER: S-EPMC8410558 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related deaths worldwide. Within the molecular scope of NCSLC, a complex landscape of dysregulated cellular signaling has emerged, defined largely by mutations in select mediators of signal transduction, including the epidermal growth factor receptor (EGFR) and anaplastic lymphoma (ALK) kinases. Consequently, these mutant kinases become constitutively activated and targets for chemotherapeutic intervention. Encouragingly, small molecule inhibitors of these pathways have shown promise in clinical trials or are approved for clinical use. However, many protein kinases are dysregulated in NSCLC without genetic mutations. To quantify differences in tumor cell signaling that are transparent to genomic methods, we established a super-SILAC internal standard derived from NSCLC cell lines grown in vitro and labeled with heavy lysine and arginine, and deployed them in a phosphoproteomic workflow. We identified 9019 and 8753 phosphorylation sites in two separate tumors. Relative quantification of phosphopeptide abundance between tumor samples allowed for the determination of specific hubs and pathways differing between each tumor. Sites downstream of Ras showed decreased inhibitory phosphorylation (Raf/Mek) and increased activating phosphorylation (Erk1/2) in one tumor versus another. In this way, we were able to quantitatively access oncogenic kinase signaling in primary human tumors.Through the use of quantitative proteomics, we demonstrated the feasibility and coverage that large scale mass spectrometry can leverage for understanding kinase networks in cancer. By incorporating Super-SILAC based quantitation into a typical pathology workflow, we were able to access and compare tumors from multiple patients in this analysis with high accuracy and dynamic range. We analyzed tumors from patients diagnosed with non-small cell lung cancer and were able to detect comprehensive phosphorylation networks relaying through known hubs of oncogenesis in lung cancer. We hereby show that it is possible to track changes to phosphorylation networks across multiple tumors, opening up the possibility that drug susceptibility and patient-specific stratification can be implemented downstream of classical pathology.

Dataset Information

Unveiling mutational dynamics in non-small cell lung cancer patients by quantitative EGFR profiling in vesicular RNA.

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