Quantification of within-sample genetic heterogeneity from SNP-array data

Abstract Intra-tumour genetic heterogeneity (ITH) fosters drug resistance and is a critical hurdle to clinical treatment. ITH can be well-measured using multi-region sampling but this is costly and challenging to implement. There is therefore a need for tools to estimate ITH in individual samples, u...

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Autores principales:	Pierre Martinez, Christopher Kimberley, Nicolai J. BirkBak, Andrea Marquard, Zoltan Szallasi, Trevor A. Graham
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9d1bae01394948b38cd85e38d0687611
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spelling	oai:doaj.org-article:9d1bae01394948b38cd85e38d06876112021-12-02T16:06:23ZQuantification of within-sample genetic heterogeneity from SNP-array data10.1038/s41598-017-03496-02045-2322https://doaj.org/article/9d1bae01394948b38cd85e38d06876112017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-03496-0https://doaj.org/toc/2045-2322Abstract Intra-tumour genetic heterogeneity (ITH) fosters drug resistance and is a critical hurdle to clinical treatment. ITH can be well-measured using multi-region sampling but this is costly and challenging to implement. There is therefore a need for tools to estimate ITH in individual samples, using standard genomic data such as SNP-arrays, that could be implemented routinely. We designed two novel scores S and R, respectively based on the Shannon diversity index and Ripley’s L statistic of spatial homogeneity, to quantify ITH in single SNP-array samples. We created in-silico and in-vitro mixtures of tumour clones, in which diversity was known for benchmarking purposes. We found significant but highly-variable associations of our scores with diversity in-silico (p < 0.001) and moderate associations in–vitro (p = 0.015 and p = 0.085). Our scores were also correlated to previous ITH estimates from sequencing data but heterogeneity in the fraction of tumour cells present across samples hampered accurate quantification. The prognostic potential of both scores was moderate but significantly predictive of survival in several tumour types (corrected p = 0.03). Our work thus shows how individual SNP-arrays reveal intra-sample clonal diversity with moderate accuracy.Pierre MartinezChristopher KimberleyNicolai J. BirkBakAndrea MarquardZoltan SzallasiTrevor A. GrahamNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Pierre Martinez Christopher Kimberley Nicolai J. BirkBak Andrea Marquard Zoltan Szallasi Trevor A. Graham Quantification of within-sample genetic heterogeneity from SNP-array data
description	Abstract Intra-tumour genetic heterogeneity (ITH) fosters drug resistance and is a critical hurdle to clinical treatment. ITH can be well-measured using multi-region sampling but this is costly and challenging to implement. There is therefore a need for tools to estimate ITH in individual samples, using standard genomic data such as SNP-arrays, that could be implemented routinely. We designed two novel scores S and R, respectively based on the Shannon diversity index and Ripley’s L statistic of spatial homogeneity, to quantify ITH in single SNP-array samples. We created in-silico and in-vitro mixtures of tumour clones, in which diversity was known for benchmarking purposes. We found significant but highly-variable associations of our scores with diversity in-silico (p < 0.001) and moderate associations in–vitro (p = 0.015 and p = 0.085). Our scores were also correlated to previous ITH estimates from sequencing data but heterogeneity in the fraction of tumour cells present across samples hampered accurate quantification. The prognostic potential of both scores was moderate but significantly predictive of survival in several tumour types (corrected p = 0.03). Our work thus shows how individual SNP-arrays reveal intra-sample clonal diversity with moderate accuracy.
format	article
author	Pierre Martinez Christopher Kimberley Nicolai J. BirkBak Andrea Marquard Zoltan Szallasi Trevor A. Graham
author_facet	Pierre Martinez Christopher Kimberley Nicolai J. BirkBak Andrea Marquard Zoltan Szallasi Trevor A. Graham
author_sort	Pierre Martinez
title	Quantification of within-sample genetic heterogeneity from SNP-array data
title_short	Quantification of within-sample genetic heterogeneity from SNP-array data
title_full	Quantification of within-sample genetic heterogeneity from SNP-array data
title_fullStr	Quantification of within-sample genetic heterogeneity from SNP-array data
title_full_unstemmed	Quantification of within-sample genetic heterogeneity from SNP-array data
title_sort	quantification of within-sample genetic heterogeneity from snp-array data
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/9d1bae01394948b38cd85e38d0687611
work_keys_str_mv	AT pierremartinez quantificationofwithinsamplegeneticheterogeneityfromsnparraydata AT christopherkimberley quantificationofwithinsamplegeneticheterogeneityfromsnparraydata AT nicolaijbirkbak quantificationofwithinsamplegeneticheterogeneityfromsnparraydata AT andreamarquard quantificationofwithinsamplegeneticheterogeneityfromsnparraydata AT zoltanszallasi quantificationofwithinsamplegeneticheterogeneityfromsnparraydata AT trevoragraham quantificationofwithinsamplegeneticheterogeneityfromsnparraydata
_version_	1718385041368678400

Quantification of within-sample genetic heterogeneity from SNP-array data

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