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 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.

The involvement of oncobiosis and bacterial metabolite signaling in metastasis formation in breast cancer

“…A good example of such a study design can be found in the paper of Plaza-Diaz and colleagues [273]. Along the same line, protocols for identifying external contaminations to the microbiome are very important [8,69,274], especially in samples with low biomass.…”

“…Multiple microbial compartments undergo oncobiotic transformation during breast cancer, including breast tissue [55,56], milk ducts [57], the inherent microbiome of the breast carcinoma [54,[58][59][60][61][62][63][64][65][66][67][68][69][70][71][72], the distal gut [51,52,[73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89], and the urinary microbiome [54,90]. However, no differences in the microbiome of the nipple [57,69] and the oral microbiome [54] between healthy individuals and breast cancer patients have been detected. Besides bacteria, viruses (parapoxviruses [91], human papillomavirus [92], Herpesviridae, Retroviridae, Parapoxviridae, Polyomaviridae, Papillomaviridae [64]), fungi, and p...…”

“…Proteobacteria , Firmicutes , and Actinobacteria can be found in breast tumors. Differences in ER + and ER- breast tumors [ 67 ] 21 female and 2 male BC patients 16S rRNA gene sequencing Compared to normal breast tissue, the abundance of Proteobacteria increased in tumor tissue [ 69 ] 95–105 FFPE samples for each BC subtype, 86 controls Pathochip Large set of viruses, parasites, and fungi were detected in FFPE sections of breast cancer. The least of these were detected in TNBC cases [ 70 ] 16 healthy controls, 32 breast cancer patients 16S rRNA gene sequencing The abundance of Corynebacterium, Prevotella, and Gammaproteobacteria (unclassified) decreased, while Acinetobacter increased in BC tissue [ 71 ] Changes to the gut microbiome 48 postmenopausal BC patients (most stages 0–I), vs. 48 control patients 16S rRNA gene sequencing Breast cancer patients had a higher abundance of Clostridium , Faecalibacterium , and Ruminococcus (all Clostridiales ) and a lower abundance of Dorea and Lachnospiraceae Lower number of observed species, Chao1 and PD whole tree indices in breast cancer patients [ 73 ] 30 BC and 36 control patients Classical bacterial culture Fecal bile acid levels were lower in breast cancer patients.…”

Characterizing and Exploiting Tumor Microenvironments to Optimize Treatment Outcomes

“…Since then, multiple labs have reported similar findings in various tumor types, but the need for improvements in methodology became apparent due to the difficult nature of ‘low bacterial biomass’ sample types involved and the need to eliminate potential sources of contamination. Prof. Tangney presented details from several recent papers from his group aimed at tackling this problem, through improvements in bioinformatics [ 10 ], sequencing [ 11 ], and sample processing aspects [ 12 , 13 ], in addition to improved protocols for sample acquisition [ 14 ].…”

“…The rationale for the title of his talk quickly became apparent, as he described ‘What’ as the characterization of the tumor microbiome [ 15 ], and ‘So What’ as studies on potential implications of bacterial presence, such as his group’s finding in 2015 that such bacteria enzymatically altered commonly used chemotherapeutic drugs in mouse models [ 16 ]. Finally, an example of ‘Now What’ was given with a description of exploitation of such findings in a biomarker context, describing a new study from his group where the development of a machine-learning strategy enabled the team to use the bacterial signature of fresh tissue biopsies to predict their malignancy status [ 14 ].…”