Jennifer Derrien1, Catherine Guérin-Charbonnel1,2, Victor Gaborit1,3, Loïc Campion1,2, Magali Devic1,4, Elise Douillard1,4, Nathalie Roi1,4, Hervé Avet-Loiseau5, Olivier Decaux6, Thierry Facon7, Jan-Philipp Mallm8, Roland Eils9,10,11, Nikhil C Munshi12, Philippe Moreau1,4, Carl Herrmann9, Florence Magrangeas1,4, Stéphane Minvielle13,14. 1. Université de Nantes, CNRS, INSERM, CRCINA, Nantes, F-44000, France. 2. Institut de Cancérologie de l'Ouest, Nantes-Saint Herblain, France. 3. LS2N, CNRS, Université de Nantes, Nantes, France. 4. Centre Hospitalier Universitaire, Nantes, France. 5. Institut Universitaire du Cancer, CHU, Centre de Recherche en Cancérologie de Toulouse, INSERM 1037, Toulouse, France. 6. Centre Hospitalier Universitaire, Rennes, France. 7. Centre Hospitalier Universitaire, Lilles, France. 8. Research Group Genome Organization & Function, DKFZ, and BioQuant Heidelberg, Heidelberg, 69120, Germany. 9. Health Data Science Unit, Medical Faculty Heidelberg and BioQuant, Heidelberg, 69120, Germany. 10. Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt- Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, Berlin, 10117, Germany. 11. Berlin Institute of Health (BIH), Center for Digital Health, Anna-Louisa-Karsch-Strasse 2, Berlin, 10178, Germany. 12. Dana-Farber Cancer Institute, Harvard Medical School, LeBow Institute for Myeloma Therapeutics and Jerome Lipper Center for Multiple Myeloma Research, Boston, MA, United States. 13. Université de Nantes, CNRS, INSERM, CRCINA, Nantes, F-44000, France. stephane.minvielle@univ-nantes.fr. 14. Centre Hospitalier Universitaire, Nantes, France. stephane.minvielle@univ-nantes.fr.
Abstract
BACKGROUND: Cancer evolution depends on epigenetic and genetic diversity. Historically, in multiple myeloma (MM), subclonal diversity and tumor evolution have been investigated mostly from a genetic perspective. METHODS: Here, we performed an analysis of 42 MM samples from 21 patients by using enhanced reduced representation bisulfite sequencing (eRRBS). We combined several metrics of epigenetic heterogeneity to analyze DNA methylation heterogeneity in MM patients. RESULTS: We show that MM is characterized by the continuous accumulation of stochastic methylation at the promoters of development-related genes. High combinatorial entropy change is associated with poor outcomes in our pilot study and depends predominantly on partially methylated domains (PMDs). These PMDs, which represent the major source of inter- and intrapatient DNA methylation heterogeneity in MM, are linked to other key epigenetic aberrations, such as CpG island (CGI)/transcription start site (TSS) hypermethylation and H3K27me3 redistribution as well as 3D organization alterations. In addition, transcriptome analysis revealed that intratumor methylation heterogeneity was associated with low-level expression and high variability. CONCLUSIONS: We propose that disrupted DNA methylation in MM is responsible for high epigenetic and transcriptomic instability allowing tumor cells to adapt to environmental changes by tapping into a pool of evolutionary trajectories.
BACKGROUND: Cancer evolution depends on epigenetic and genetic diversity. Historically, in multiple myeloma (MM), subclonal diversity and tumor evolution have been investigated mostly from a genetic perspective. METHODS: Here, we performed an analysis of 42 MM samples from 21 patients by using enhanced reduced representation bisulfite sequencing (eRRBS). We combined several metrics of epigenetic heterogeneity to analyze DNA methylation heterogeneity in MM patients. RESULTS: We show that MM is characterized by the continuous accumulation of stochastic methylation at the promoters of development-related genes. High combinatorial entropy change is associated with poor outcomes in our pilot study and depends predominantly on partially methylated domains (PMDs). These PMDs, which represent the major source of inter- and intrapatient DNA methylation heterogeneity in MM, are linked to other key epigenetic aberrations, such as CpG island (CGI)/transcription start site (TSS) hypermethylation and H3K27me3 redistribution as well as 3D organization alterations. In addition, transcriptome analysis revealed that intratumor methylation heterogeneity was associated with low-level expression and high variability. CONCLUSIONS: We propose that disrupted DNA methylation in MM is responsible for high epigenetic and transcriptomic instability allowing tumor cells to adapt to environmental changes by tapping into a pool of evolutionary trajectories.
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