Department of Otorhinolaryngology-Head and Neck Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA.
Cell Death Dis. 2021 Sep 4;12(9):831. doi: 10.1038/s41419-021-04092-x.
Alterations to the natural microbiome are linked to different diseases, and the presence or absence of specific microbes is directly related to disease outcomes. We performed a comprehensive analysis with unique cohorts of the four subtypes of breast cancer (BC) characterized by their microbial signatures, using a pan-pathogen microarray strategy. The signature (includes viruses, bacteria, fungi, and parasites) of each tumor subtype was correlated with clinical data to identify microbes with prognostic potential. The subtypes of BC had specific viromes and microbiomes, with ER+ and TN tumors showing the most and least diverse microbiome, respectively. The specific microbial signatures allowed discrimination between different BC subtypes. Furthermore, we demonstrated correlations between the presence and absence of specific microbes in BC subtypes with the clinical outcomes. This study provides a comprehensive map of the oncobiome of BC subtypes, with insights into disease prognosis that can be critical for precision therapeutic intervention strategies.
微生物组的改变与不同的疾病有关,特定微生物的存在与否与疾病结果直接相关。我们使用泛病原体微阵列策略,对具有微生物特征的四种乳腺癌 (BC) 亚型的独特队列进行了全面分析。每个肿瘤亚型的特征(包括病毒、细菌、真菌和寄生虫)与临床数据相关联,以确定具有预后潜力的微生物。BC 的亚型具有特定的病毒组和微生物组,ER+ 和 TN 肿瘤的微生物组分别具有最大和最小的多样性。特定的微生物特征可区分不同的 BC 亚型。此外,我们还证明了 BC 亚型中特定微生物的存在与否与临床结果之间存在相关性。这项研究提供了 BC 亚型的肿瘤微生物组的综合图谱,深入了解疾病预后,这对精准治疗干预策略至关重要。