Abstract

Combining alignment-free methods for phylogenetic analysis with multi-regional sampling using next-generation sequencing can provide an assessment of intra-patient tumour heterogeneity. From multi-regional sampling divergent branching, we validated two different lesions within a patient’s prostate. Where multi-regional sampling has not been used, a single sample from one of these areas could misguide as to which drugs or therapies would best benefit this patient, due to the fact these tumours appear to be genetically different. This application has the power to render, in a fraction of the time used by other approaches, intra-patient heterogeneity and decipher aberrant biomarkers. Another alignment-free method for calling single-nucleotide variants from raw next-generation sequencing samples has determined possible variants and genomic locations that may be able to characterize the differences between the two main branching patterns. Alignment-free approaches have been applied to relevant clinical multi-regional samples and may be considered as a valuable option for comparing and determining heterogeneity to help deliver personalized medicine through more robust efforts in identifying targetable pathways and therapeutic strategies. Our study highlights the application these tools could have on patient-aligned treatment indications.

Details

Title
Prostate cancer heterogeneity assessment with multi-regional sampling and alignment-free methods
Author
Murphy, Ross G 1   VIAFID ORCID Logo  ; Roddy, Aideen C 1 ; Srivastava, Shambhavi 1 ; Baena, Esther 2 ; Waugh, David J 1 ; Joe M O’Sullivan 1 ; McArt, Darragh G 1 ; Jain, Suneil 1 ; LaBonte, Melissa J 1 

 Movember FASTMAN Centre of Excellence, Patrick G Johnston Centre for Cancer Research, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast , Belfast BT9 7AE, UK 
 Belfast–Manchester Movember Centre of Excellence, Cancer Research UK Manchester Institute, The University of Manchester , Alderley Park SK10 4TG, UK 
Publication year
2020
Publication date
Sep 2020
Publisher
Oxford University Press
e-ISSN
26319268
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3170915333
Copyright
© The Author(s) 2019. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. This work is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.