Maureen Feucherolles

The role of the microbiome in cancer metastasis has emerged as a critical area of research, with growing evidence suggesting that microbial composition and interactions within the tumour microenvironment may significantly influence metastatic progression. This review explores the role of the microbiome in cancer metastasis, as well as potential key bacteria and their mechanisms through which they could impact tumour dissemination, seeding and growth. Biological models used to study metastasis are discussed to provide context for the further investigation of these interactions. In order to answer unresolved questions regarding the microbiome’s involvement in metastatic dissemination, recent advancements in spatial biology techniques are examined, including spatial genomics, transcriptomics, proteomics and metabolomics, which enable the spatial mapping of microbial interactions within the tumour microenvironment. Additionally, multimodal-omics imaging approaches are highlighted for their potential to integrate multiple molecular layers, offering comprehensive insights into the microbiome’s role in cancer metastasis. The review also addresses the challenges and limitations of these techniques, underscoring the complexity of studying microbiome-tumour interactions and offering directions for future research to better explore and target the microbiological landscape in metastatic cancer.

While MALDI-TOF mass spectrometry (MS) is widely considered as the reference method for the rapid and inexpensive identification of microorganisms in routine laboratories, less attention has been addressed to its ability for detection of antimicrobial resistance (AMR). Recently, some studies assessed its potential application together with machine learning for the detection of AMR in clinical pathogens. The scope of this study was to investigate MALDI-TOF MS protein mass spectra combined with a prediction approach as an AMR screening tool for relevant foodborne pathogens, such as Campylobacter coli and Campylobacter jejuni. A One-Health panel of 224 C. jejuni and 116 C. coli strains was phenotypically tested for seven antimicrobial resistances, i.e., ciprofloxacin, erythromycin, tetracycline, gentamycin, kanamycin, streptomycin, and ampicillin, independently, and were submitted, after an on- and off-plate protein extraction, to MALDI Biotyper analysis, which yielded one average spectra per isolate and type of extraction. Overall, high performance was observed for classifiers detecting susceptible as well as ciprofloxacin- and tetracycline-resistant isolates. A maximum sensitivity and a precision of 92.3 and 81.2%, respectively, were reached. No significant prediction performance differences were observed between on- and off-plate types of protein extractions. Finally, three putative AMR biomarkers for fluoroquinolones, tetracyclines, and aminoglycosides were identified during the current study. Combination of MALDI-TOF MS and machine learning could be an efficient and inexpensive tool to swiftly screen certain AMR in foodborne pathogens, which may enable a rapid initiation of a precise, targeted antibiotic treatment.