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Budczies, J.

Publications and source records attributed to Budczies, J..

2 recordsLinked to original sources

Exploiting TGF-β-mediated Stromal Programming in Homologous Recombination-Deficient Pancreatic Cancer

The tumor microenvironment (TME) actively contributes to pancreatic ductal adenocarcinoma (PDAC) pathogenesis through dynamic bidirectional tumor-stroma interactions. Here, we demonstrate that homologous recombination-defective (HRD) tumor epithelium reprograms the TME in a genotype-specific manner to enhance cancer aggressiveness. Using genetically engineered mouse models, pancreatic stellate cell (PSC) and cancer-associated fibroblast (CAF) co-culture systems, single-nucleus multiomics, and human PDAC models, we show that tumoral loss of ATM serine/threonine kinase drives CAFs toward SMA+ myofibroblastic differentiation, independently of P53 status. These myCAFs, in turn, promote cancer aggressiveness and chemoresistance. Mechanistically, ATM deficiency increases reactive oxygen species and contractility signaling, enhancing TGF-{beta}1 secretion. Pharmacological TGF-{beta} inhibition reverses myCAF differentiation, sensitizes tumors to chemotherapy, and impairs tumor progression in both murine and human ATM-null models. Our findings reveal that ATM-deficient tumors shape a cancer-promoting niche via TGF-{beta} signaling and identify dual targeting of intrinsic and extrinsic vulnerabilities as a promising precision oncology strategy. SIGNIFICANCEHRD pancreatic cancers reprogram the tumor microenvironment in a genotype-specific manner through TGF-{beta}-driven myCAF-enrichment. Targeting this stromal axis alongside platinum-based chemotherapy improves therapeutic efficacy in ATM-deficient models. These findings highlight the need to integrate epithelial genotype and stromal context for truly personalized treatment strategies in PDAC.

cancer biology↗

Prioritization of metabolic genes as novel therapeutic targets in estrogen-receptor negative breast tumors using multi-omics data and text mining

Estrogen-receptor negative (ERneg) breast cancer is an aggressive breast cancer subtype in the need for new therapeutic options. We have analyzed metabolomics, proteomics and transcriptomics data for a cohort of 276 breast tumors (MetaCancer study) and nine public transcriptomics datasets using univariate statistics, meta-analysis, Reactome pathway analysis, biochemical network mapping and text mining of metabolic genes. In the MetaCancer cohort, a total of 29% metabolites, 21% proteins and 33% transcripts were significantly different (raw p < 0.05) between ERneg and ERpos breast tumors. In the nine public transcriptomics datasets, on average 23% of all genes were significantly different (raw p < 0.05). Specifically, up to 60% of the metabolic genes were significantly different (meta-analysis raw p < 0.05) across the transcriptomics datasets. Reactome pathway analysis of all omics showed that energy metabolism, and biosynthesis of nucleotides, amino acids, and lipids were associated with ERneg status. Text mining revealed that several significant metabolic genes and enzymes have been rarely reported to date, including PFKP, GART, PLOD1, ASS1, NUDT12, FAR1, PDE7A, FAHD1, ITPK1, SORD, HACD3, CDS2 and PDSS1. Metabolic processes associated with ERneg tumors were identified by multi-omics integration analysis of metabolomics, proteomics and transcriptomics data. Overall results suggested that TCA anaplerosis, proline biosynthesis, synthesis of complex lipids and mechanisms for recycling substrates were activated in ERneg tumors. Under-reported genes were revealed by text mining which may serve as novel candidates for drug targets in cancer therapies. The workflow presented here can also be used for other tumor types.

bioinformatics↗