Mutational patterns and clonal evolution from diagnosis to relapse in pediatric acute lymphoblastic leukemia

Abstract The mechanisms driving clonal heterogeneity and evolution in relapsed pediatric acute lymphoblastic leukemia (ALL) are not fully understood. We performed whole genome sequencing of samples collected at diagnosis, relapse(s) and remission from 29 Nordic patients. Somatic point mutations and...

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Autores principales: Shumaila Sayyab, Anders Lundmark, Malin Larsson, Markus Ringnér, Sara Nystedt, Yanara Marincevic-Zuniga, Katja Pokrovskaja Tamm, Jonas Abrahamsson, Linda Fogelstrand, Mats Heyman, Ulrika Norén-Nyström, Gudmar Lönnerholm, Arja Harila-Saari, Eva C. Berglund, Jessica Nordlund, Ann-Christine Syvänen
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/df7f00d635d847359cec38029b48a552
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Sumario:Abstract The mechanisms driving clonal heterogeneity and evolution in relapsed pediatric acute lymphoblastic leukemia (ALL) are not fully understood. We performed whole genome sequencing of samples collected at diagnosis, relapse(s) and remission from 29 Nordic patients. Somatic point mutations and large-scale structural variants were called using individually matched remission samples as controls, and allelic expression of the mutations was assessed in ALL cells using RNA-sequencing. We observed an increased burden of somatic mutations at relapse, compared to diagnosis, and at second relapse compared to first relapse. In addition to 29 known ALL driver genes, of which nine genes carried recurrent protein-coding mutations in our sample set, we identified putative non-protein coding mutations in regulatory regions of seven additional genes that have not previously been described in ALL. Cluster analysis of hundreds of somatic mutations per sample revealed three distinct evolutionary trajectories during ALL progression from diagnosis to relapse. The evolutionary trajectories provide insight into the mutational mechanisms leading relapse in ALL and could offer biomarkers for improved risk prediction in individual patients.