Biopsy bacterial signature can predict patient tissue malignancy

Abstract Considerable recent research has indicated the presence of bacteria in a variety of human tumours and matched normal tissue. Rather than focusing on further identification of bacteria within tumour samples, we reversed the hypothesis to query if establishing the bacterial profile of a tissu...

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Autores principales:	Glenn Hogan, Julia Eckenberger, Neegam Narayanen, Sidney P. Walker, Marcus J. Claesson, Mark Corrigan, Deirdre O’Hanlon, Mark Tangney
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8a3d7410ec474e9e912ec35e3f000fb3
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spelling	oai:doaj.org-article:8a3d7410ec474e9e912ec35e3f000fb32021-12-02T18:50:03ZBiopsy bacterial signature can predict patient tissue malignancy10.1038/s41598-021-98089-32045-2322https://doaj.org/article/8a3d7410ec474e9e912ec35e3f000fb32021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98089-3https://doaj.org/toc/2045-2322Abstract Considerable recent research has indicated the presence of bacteria in a variety of human tumours and matched normal tissue. Rather than focusing on further identification of bacteria within tumour samples, we reversed the hypothesis to query if establishing the bacterial profile of a tissue biopsy could reveal its histology / malignancy status. The aim of the present study was therefore to differentiate between malignant and non-malignant fresh breast biopsy specimens, collected specifically for this purpose, based on bacterial sequence data alone. Fresh tissue biopsies were obtained from breast cancer patients and subjected to 16S rRNA gene sequencing. Progressive microbiological and bioinformatic contamination control practices were imparted at all points of specimen handling and bioinformatic manipulation. Differences in breast tumour and matched normal tissues were probed using a variety of statistical and machine-learning-based strategies. Breast tumour and matched normal tissue microbiome profiles proved sufficiently different to indicate that a classification strategy using bacterial biomarkers could be effective. Leave-one-out cross-validation of the predictive model confirmed the ability to identify malignant breast tissue from its bacterial signature with 84.78% accuracy, with a corresponding area under the receiver operating characteristic curve of 0.888. This study provides proof-of-concept data, from fit-for-purpose study material, on the potential to use the bacterial signature of tissue biopsies to identify their malignancy status.Glenn HoganJulia EckenbergerNeegam NarayanenSidney P. WalkerMarcus J. ClaessonMark CorriganDeirdre O’HanlonMark TangneyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Glenn Hogan Julia Eckenberger Neegam Narayanen Sidney P. Walker Marcus J. Claesson Mark Corrigan Deirdre O’Hanlon Mark Tangney Biopsy bacterial signature can predict patient tissue malignancy
description	Abstract Considerable recent research has indicated the presence of bacteria in a variety of human tumours and matched normal tissue. Rather than focusing on further identification of bacteria within tumour samples, we reversed the hypothesis to query if establishing the bacterial profile of a tissue biopsy could reveal its histology / malignancy status. The aim of the present study was therefore to differentiate between malignant and non-malignant fresh breast biopsy specimens, collected specifically for this purpose, based on bacterial sequence data alone. Fresh tissue biopsies were obtained from breast cancer patients and subjected to 16S rRNA gene sequencing. Progressive microbiological and bioinformatic contamination control practices were imparted at all points of specimen handling and bioinformatic manipulation. Differences in breast tumour and matched normal tissues were probed using a variety of statistical and machine-learning-based strategies. Breast tumour and matched normal tissue microbiome profiles proved sufficiently different to indicate that a classification strategy using bacterial biomarkers could be effective. Leave-one-out cross-validation of the predictive model confirmed the ability to identify malignant breast tissue from its bacterial signature with 84.78% accuracy, with a corresponding area under the receiver operating characteristic curve of 0.888. This study provides proof-of-concept data, from fit-for-purpose study material, on the potential to use the bacterial signature of tissue biopsies to identify their malignancy status.
format	article
author	Glenn Hogan Julia Eckenberger Neegam Narayanen Sidney P. Walker Marcus J. Claesson Mark Corrigan Deirdre O’Hanlon Mark Tangney
author_facet	Glenn Hogan Julia Eckenberger Neegam Narayanen Sidney P. Walker Marcus J. Claesson Mark Corrigan Deirdre O’Hanlon Mark Tangney
author_sort	Glenn Hogan
title	Biopsy bacterial signature can predict patient tissue malignancy
title_short	Biopsy bacterial signature can predict patient tissue malignancy
title_full	Biopsy bacterial signature can predict patient tissue malignancy
title_fullStr	Biopsy bacterial signature can predict patient tissue malignancy
title_full_unstemmed	Biopsy bacterial signature can predict patient tissue malignancy
title_sort	biopsy bacterial signature can predict patient tissue malignancy
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8a3d7410ec474e9e912ec35e3f000fb3
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_version_	1718377516442320896

Biopsy bacterial signature can predict patient tissue malignancy

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