Direct genetic transformation bypasses tumor-associated DNA methylation alterations

Abstract Background Tumors represent dynamically evolving populations of mutant cells, and many advances have been made in understanding the biology of their progression. However, there are key unresolved questions about the conditions that support a cell’s initial transformation, which cannot be ea...

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Main Authors: Sara Hetzel, Eran Hodis, Elena Torlai Triglia, Alexander Kovacsovics, Kathleen Steinmann, Andreas Gnirke, Meiying Cui, Daniel McQuaid, Raha Weigert, Georg Pohl, Mandar D. Muzumdar, Serge Leyvraz, Ulrich Keilholz, Marie-Laure Yaspo, Aviv Regev, Helene Kretzmer, Zachary D. Smith, Alexander Meissner
Format: Article
Language:English
Published: BMC 2025-07-01
Series:Genome Biology
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Online Access:https://doi.org/10.1186/s13059-025-03650-2
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Summary:Abstract Background Tumors represent dynamically evolving populations of mutant cells, and many advances have been made in understanding the biology of their progression. However, there are key unresolved questions about the conditions that support a cell’s initial transformation, which cannot be easily captured in patient populations and are instead modeled using transgenic cellular or animal systems. Results Here, we use extensive patient atlas data to define common features of the tumor DNA methylation landscape as they compare to healthy human cells and apply this benchmark to evaluate 21 engineered human and mouse models for their ability to reproduce these patterns. Notably, we find that genetically induced cellular transformation rarely recapitulates the widespread de novo methylation of Polycomb regulated promoter sequences as found in clinical samples, but can trigger global changes in DNA methylation levels that are consistent with extensive proliferation in vitro. Conclusions Our results raise pertinent questions about the relationship between genetic and epigenetic aspects of tumorigenesis as well as provide an important molecular reference for evaluating existing and emerging tumor models.
ISSN:1474-760X