Title: Predicting DNA mutations during cancer evolution

Authors: Juan Carlos Martinez; Nelson Lopez-Jimenez; Tao Meng; S.S. Iyengar

Addresses: School of Computing and Information Sciences, Florida International University, Miami, Florida, USA ' Department of Computer Science, University of Miami, Coral Gables, Florida, USA ' Department of Computer Science, University of Miami, Coral Gables, Florida, USA ' School of Computing and Information Sciences, Florida International University, Miami, Florida, USA

Abstract: Bio-systems are inherently complex information processing systems. Their physiological complexities limit the formulation and testing of a hypothesis for their behaviour. Our goal here was to test a computational framework utilising published data from a longitudinal study of patients with acute myeloid leukaemia (AML), whose DNA from both normal and malignant tissues were subjected to NGS analysis at various points in time. By processing the sequencing data before relapse time, we tested our framework by predicting the regions of the genome to be mutated at relapse time and, later, by comparing our results with the actual regions that showed mutations (discovered by genome sequencing at relapse time). After a detailed statistical analysis, the resulting correlation coefficient (degree of matching of proposed framework with real data) is 0.9816 ± 0.009 at 95% confidence interval. This high performance from our proposed framework opens new research opportunities for bioinformatics researchers and clinical doctors.

Keywords: NGS; acute myeloid leukaemia; AML; DNA mutations; cancer evolution; mutation prediction; genome sequencing; bioinformatics.

DOI: 10.1504/IJBRA.2015.069186

International Journal of Bioinformatics Research and Applications, 2015 Vol.11 No.3, pp.200 - 218

Received: 11 Nov 2013
Accepted: 11 Dec 2013
Published online: 05 May 2015 *

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