bioRxivCancer BiologyPreprint
Replication timing alterations in leukemia reflect stable clinically-relevant changes in genome architecture
J. C. Rivera-Mulia, T. Sasaki, C. Trevilla-Garcia, N. Nakamichi, D. J. H. F. Knapp, C. Hammond, B. Chang, J. W. Tyner, M. Devidas, J. Zimmerman, K. Klein, V. Somasundaram, B. Druker, T. Gruber, A. Koren, C. J. Eaves, D. M. Gilbert
Abstract
Human B-lineage precursor acute lymphoid leukemias (BCP-ALLs) comprise a group of genetically and clinically distinct disease entities with features of differentiation arrest at known stages of normal B-lineage differentiation. We previously showed BCP-ALL cells display unique and clonally heritable DNA-replication timing (RT) programs; i.e., programs describing the variable order of replication of megabase-scale chromosomal units of DNA in different cell types. To determine the extent to which BCP-ALL RT programs mirror or deviate from specific stages of normal human B-cell differentiation, we transplanted immunodeficient mice with quiescent normal human CD34+ cord blood cells and obtained RT signatures of the regenerating B-lineage populations. We then compared these with RT signatures for leukemic cells from a large cohort of BCP-ALL patients. The results identify BCP-ALL subtype-specific features that resemble specific stages of B-cell differentiation and features that appear associated with relapse. These results suggest the genesis of BCP-ALL involves alterations in RT that reflect clinically relevant leukemia-specific genetic and/or epigenetic changes. SUMMARYGenome-wide DNA replication timing profiles of >100 pediatric leukemic samples and normally differentiating human B-lineage cells isolated from xenografted immunodeficient mice were generated. Comparison of these identified potentially clinically relevant features that both match and deviate from the normal profiles.
- organism: human
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
- approach: statistical model
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