Biopsy bacterial signature can predict patient tissue malignancy

• Published in: Scientific reports Vol. 11; no. 1; p. 18535
• Main Authors: Hogan, Glenn, Eckenberger, Julia, Narayanen, Neegam, Walker, Sidney P., Claesson, Marcus J., Corrigan, Mark, O’Hanlon, Deirdre, Tangney, Mark
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17.09.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/114/1305; 631/114/2397; 631/114/2413; 631/114/2785; 631/114/794; 631/326; 631/326/2522; 631/326/41; 631/67; 631/67/1347; 631/67/1857; 692/53; 692/53/2421; 692/53/2423; Bacteria; Biopsy; Breast cancer; Contamination; Histology; Humanities and Social Sciences; Learning algorithms; Machine learning; Malignancy; Microbiomes; multidisciplinary; Pollution control; Prediction models; rRNA 16S; Science; Science (multidisciplinary); Tissues; Tumors
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-98089-3

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.