The human microbiome constitutes a complex multikingdom community that symbiotically interacts with the host across multiple body sites. Host-microbiome interactions impact multiple physiological processes and a variety of multifactorial disease conditions. In the past decade, microbiome communities have been suggested to influence the development, progression, metastasis formation, and treatment response of multiple cancer types. While causal evidence of microbial impacts on cancer biology is only beginning to be unraveled, enhanced molecular understanding of such cancer-modulating interactions and impacts on cancer treatment are considered of major scientific importance and clinical relevance. In this review, we describe the molecular pathogenic mechanisms shared throughout microbial niches that contribute to the initiation and progression of cancer. We highlight advances, limitations, challenges, and prospects in understanding how the microbiome may causally impact cancer and its treatment responsiveness, and how microorganisms or their secreted bioactive metabolites may be potentially harnessed and targeted as precision cancer therapeutics.

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Background At the entry site of respiratory virus infections, the oropharyngeal microbiome has been proposed as a major hub integrating viral and host immune signals. Early studies suggested that infections with Coronavirus 2 (SARS-CoV-2) are associated with changes of the upper and lower airway microbiome, and that specific microbial signatures may predict COVID-19 illness. However, the results are not conclusive, as critical illness can drastically alter a patient’s microbiome through multiple confounders. Methods To study oropharyngeal microbiome profiles in SARS-CoV-2 infection, clinical confounders, and prediction models in COVID-19, we performed a multi-center, cross-sectional clinical study analyzing oropharyngeal microbial metagenomes in healthy adults, patients with non-SARS-CoV-2 infections, or with mild, moderate and severe COVID-19 (n=322 participants). Results In contrast to mild infections, patients admitted to a hospital with moderate or severe COVID-19 showed dysbiotic microbial configurations, which were significantly pronounced in patients treated with broad-spectrum antibiotics, receiving invasive mechanical ventilation, or when sampling was performed during prolonged hospitalization. In contrast, specimens collected early after admission allowed us to segregate microbiome features predictive of hospital COVID-19 mortality utilizing machine learning models. Taxonomic signatures were found to perform better than models utilizing clinical variables with Neisseria and Haemophilus species abundances as most important features. Conclusion In addition to the infection per se, several factors shape the oropharyngeal microbiome of severely affected COVID-19 patients and deserve consideration in the interpretation of the role of the microbiome in severe COVID-19. Nevertheless, we were able to extract microbial features that can help to predict clinical outcomes.