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Van IJcken, W.

Publications and source records attributed to Van IJcken, W..

2 recordsLinked to original sources

Unraveling Tyrosine-Kinase Inhibitor Resistance in NSCLC Cells via Same-cell Measurement of RNA, Protein, and Morphological Responses

Non-small cell lung cancer (NSCLC) frequently develops resistance to tyrosine kinase inhibitors (TKIs), limiting the long-term success of targeted therapies. A deeper understanding of resistance mechanisms at the molecular and cellular levels may enable the development of more effective treatment strategies. Here, we applied the Teton detection assay on the AVITI24 platform to measure concurrently RNA, protein, and cellular morphology in NSCLC cell lines treated with the TKIs gefitinib and osimertinib. This single-cell, multiomic analysis revealed distinct expression and morphological profiles between drug-sensitive and resistant cells, including differences in MAPK-related pathway activity. Stratifying responses at the single-cell level uncovered subtle responses not detectable in bulk measurements. We identified CDK4/6 activity as a route of cell survival under TKI treatment and demonstrated that co-treatment with the CDK4/6 inhibitor palbociclib enhanced TKI efficacy. The ability to measure multiomics and cellular morphology in the same cells opens new avenues for future studies aimed at improving personalized treatment strategies in NSCLC and overcoming the obstacles posed by drug resistance.

genomics↗

DNA methylation database for gynecological cancer detection, classification and assay development

Changes in the genome wide DNA methylation landscape are hallmarks of cancer cells and precursor lesions of cancers. To capitalize on utilizing DNA methylation for detection and classification of cancer, we generated a DNA methylation database of gynecological cancers and associated healthy tissues using Methylated DNA sequencing (MeD-seq). We show that target cell enrichment to generate the database is crucial for marker discovery and report a wide range of novel biomarkers for classification and tissue of origin determination of gynecological cancers. We developed a subset of these novel biomarkers, both intragenic and intergenic, into a qMSP assays that detect all gynecological cancers at once or specific gynecological cancer subtypes, as well as cancers that are not part of our database. The database generated in this study not only provides the foundation for cancer detection, classification and biomarker discovery, but also for treatment monitoring of cancers using MeD-seq on liquid biopsies.

cancer biology↗