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An ultra-deep sequencing strategy to detect sub-clonal TP53 mutations in presentation chronic lymphocytic leukaemia cases using multiple polymerases

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Baskaran,  P       
Department Integrative Evolutionary Biology, Max Planck Institute for Developmental Biology, Max Planck Society;
Evolutionary Genomics and Bioinformatics Group, Department Integrative Evolutionary Biology, Max Planck Institute for Developmental Biology, Max Planck Society;

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Citation

Worrillow, L., Baskaran, P., Care, M., Varghese, A., Munir, T., Evans, P., et al. (2016). An ultra-deep sequencing strategy to detect sub-clonal TP53 mutations in presentation chronic lymphocytic leukaemia cases using multiple polymerases. Oncogene, 35(40), 5328-5336. doi:10.1038/onc.2016.73.


Cite as: https://hdl.handle.net/21.11116/0000-000B-8582-B
Abstract
Chronic lymphocytic leukaemia (CLL) is the most common clonal B-cell disorder characterized by clonal diversity, a relapsing and remitting course, and in its aggressive forms remains largely incurable. Current front-line regimes include agents such as fludarabine, which act primarily via the DNA damage response pathway. Key to this is the transcription factor p53. Mutations in the TP53 gene, altering p53 functionality, are associated with genetic instability, and are present in aggressive CLL. Furthermore, the emergence of clonal TP53 mutations in relapsed CLL, refractory to DNA-damaging therapy, suggests that accurate detection of sub-clonal TP53 mutations prior to and during treatment may be indicative of early relapse. In this study, we describe a novel deep sequencing workflow using multiple polymerases to generate sequencing libraries (MuPol-Seq), facilitating accurate detection of TP53 mutations at a frequency as low as 0.3%, in presentation CLL cases tested. As these mutations were mostly clustered within the regions of TP53 encoding DNA-binding domains, essential for DNA contact and structural architecture, they are likely to be of prognostic relevance in disease progression. The workflow described here has the potential to be implemented routinely to identify rare mutations across a range of diseases.