Making sense of genomic complexity and nonsense-RNA in hematologic malignancies

Open Access
Authors
  • A.C. Leeksma
Supervisors
Cosupervisors
  • J.E.J. Guikema
Award date 18-10-2019
ISBN
  • 9789463753432
Number of pages 194
Organisations
  • Faculty of Medicine (AMC-UvA)
Abstract
The advent of novel efficacious but also expensive targeted agents has created an increased demand for more in-depth characterization and prognostication in chronic lymphocytic leukemia (CLL). Next-generation sequencing (NGS) analyses identified the splicing factor SF3B1 to be frequently mutated in cancer. Mutations in SF3B1 are characterized by increased expression of alternative transcripts. However, the pathobiological mechanism of these mutations is still unclear. This thesis aims to identify (i) the prognostic value of genomic aberrations and (ii) potential pathobiology of SF3B1 mutations in CLL and inhibition of the mRNA degradation pathway, nonsense-mediated decay (NMD), in hematologic malignancies. Studying large CLL patient groups using targeted-NGS within a comprehensive genomic profiling approach identified mutations not previously described in CLL. Additionally, clonal diversity was found to be predictive for adverse outcome. Genomic arrays were highlighted as an accurate diagnostic tool and identified high genomic complexity defined as five or more chromosomal aberrations detected by genomic arrays as an independent prognostic factor in CLL. Analysis of the functional effects of SF3B1 mutations revealed an intact TP53/ATM response in untreated SF3B1 mutated CLL cells, and SF3B1 mutation associated alternative transcripts appeared to be sensitive to NMD inhibition. Inhibition of the NMD regulator SMG1 was discovered as an exploitable clinical target with potent antitumor activity in a subset of tumors including multiple myeloma (MM). Finally, we identified biological correlates of response to SMG1 inhibition. Increased knowledge of splicing factors and NMD regulation will undoubtedly result in novel therapies.
Document type PhD thesis
Language English
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