bioRxivCancer BiologyPreprint
Patient-Specific Pharmacogenomics unveils xCT Regulation Pathways in Colon Cancer
Marco StreckeriD, Keren ZohariD, Martin BöttcheriD, Thomas WartmanniD, Henry Freudenstein, Maximilian Doelling, Mihailo AndriciD, Wenjie ShiiD, Or KakhloniD, Katrin Hippe, Beatrix Jahnke, Dimitrios MougiakakosiD, Franziska BaenkeiD, Daniel E. StangeiD, Roland S. CroneriD, Michal Linial BiD, Ulf D. KahlertiD
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Abstract
Colorectal cancer (CRC) represents the third-leading cause of cancer-related deaths. Knowledge covering diverse cellular and molecular data from individual patients has become valuable for diagnosis, prognosis, and treatment selection. Here, we present an in-depth comparative mRNA-seq and microRNA-seq analysis of tissue samples from 32 CRC, pairing tumors with adjacent healthy tissues. The differential expression gene (DEG) analysis revealed an interconnection between nutrients, metabolic programs, and cell cycle pathways. We focused on the impact of overexpressed SLC7A11 (xCT) and SLC3A2 genes which compose the cystine/glutamate transporter (Xc-) system. To assess the oncogenic potency of the Xc-system in a cellular setting, we applied a knowledge-based approach for analyzing gene perturbations from CRISPR screens across various cell types as well as using a variety of functional assays in five primary patient-derived organoid cell models to functionally verify our hypothesis. We identified a previously undescribed cell surface protein signature predicting chemotherapy resistance and further highlighted the causality and potential of pharmacological blockage of ferroptosis as promising avenue for cancer therapy. Biological processes such as redox homeostasis, ion/amino acid transporters and de novo nucleotide synthesis were associated with these co-dependent genes in patient specimens. This study highlighted a number of overlooked genes as potential clinical targets for CRC and promotes stem cell-based, patient-individual in vitro model systems as a versatile partner platform to functionally validate in silico predictions, with focus on SLC7A11 and its associated genes in tumorigenesis.
- organism: human
- assay: bulk RNA-seq
- research focus: drug response / drug discovery
- research focus: cell type annotation
- approach: deep learning
- approach: statistical model
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