Association of Skin Microbiome Dynamics With Radiodermatitis in Patients With Breast Cancer.

Importance: The interindividual differences in severity of acute radiation dermatitis are not well understood. To date, the pathomechanism and interplay of microbiome and radiodermatitis before and during treatment remain largely unknown. Objective: To assess the association of skin microbiome baseline composition and dynamics with severity of radiodermatitis in patients undergoing adjuvant radiotherapy for breast cancer. Design, Setting, and Participants: A longitudinal prospective pilot observational study was conducted between January 2017 and January 2019. Sequencing results were received in March 2021, and the data were analyzed from August 2021 to March 2023. This study was performed at an urban academic university cancer center. A total of 21 female patients with breast cancer after surgery were consecutively approached, of which 1 patient withdrew consent before the study started. Exposure: Adjuvant radiotherapy for breast cancer for 7 weeks. Main Outcomes and Measures: The main outcome was the association of baseline skin microbiome composition and its dynamics with the severity of radiodermatitis. A total of 360 skin microbiome samples from patients were analyzed, taken before, during, and after radiotherapy, from both the treated and contralateral healthy sides. The skin microbiome samples were analyzed using 16S (V1-V3) amplicon sequencing and quantitative polymerase chain reaction bacterial enumeration. Results: Twenty female patients with breast cancer after surgery who underwent radiotherapy enrolled in the study had a median (range) age of 61 (37-81) years. The median (range) body mass index of the patients was 24.2 (17.6-38.4). The 16S sequencing revealed that low (<5%) relative abundance of commensal skin bacteria (Staphylococcus epidermidis, Staphylococcus hominis, Cutibacterium acnes) at baseline composition was associated with the development of severe radiodermatitis with an accuracy of 100% (sensitivity and specificity of 100%, P < .001). Furthermore, in patients with severe radiodermatitis, quantitative polymerase chain reaction bacterial enumeration revealed a general non-species-specific overgrowth of skin bacterial load before the onset of severe symptoms. Subsequently, the abundance of commensal bacteria increased in severe radiodermatitis, coinciding with a decline in total bacterial load. Conclusions and Relevance: The findings of this observational study indicated a potential mechanism associated with the skin microbiome for the pathogenesis of severe radiodermatitis, which may be a useful biomarker for personalized prevention of radiodermatitis in patients undergoing adjuvant radiotherapy for breast cancer.

The airway microbiota of neonates colonized with asthma-associated pathogenic bacteria.

Culture techniques have associated colonization with pathogenic bacteria in the airways of neonates with later risk of childhood asthma, whereas more recent studies utilizing sequencing techniques have shown the same phenomenon with specific anaerobic taxa. Here, we analyze nasopharyngeal swabs from 1 month neonates in the COPSAC2000 prospective birth cohort by 16S rRNA gene sequencing of the V3-V4 region in relation to asthma risk throughout childhood. Results are compared with previous culture results from hypopharyngeal aspirates from the same cohort and with hypopharyngeal sequencing data from the later COPSAC2010 cohort. Nasopharyngeal relative abundance values of Streptococcus pneumoniae, Haemophilus influenzae, and Moraxella catarrhalis are associated with the same species in the hypopharyngeal cultures. A combined pathogen score of these bacteria's abundance values is associated with persistent wheeze/asthma by age 7. No other taxa are associated. Compared to the hypopharyngeal aspirates from the COPSAC2010 cohort, the anaerobes Veillonella and Prevotella, which have previously been implicated in asthma development, are less commonly detected in the COPSAC2000 nasopharyngeal samples, but correlate with the pathogen score, hinting at latent community structures that bridge current and previous results. These findings have implications for future asthma prevention efforts.

Highly Predictive Model for a Protective Immune Response to the A(H1N1)pdm2009 Influenza Strain after Seasonal Vaccination.

Understanding the immune response after vaccination against new influenza strains is highly important in case of an imminent influenza pandemic and for optimization of seasonal vaccination strategies in high risk population groups, especially the elderly. Models predicting the best sero-conversion response among the three strains in the seasonal vaccine were recently suggested. However, these models use a large number of variables and/or information post- vaccination. Here in an exploratory pilot study, we analyzed the baseline immune status in young (<31 years, N = 17) versus elderly (≥50 years, N = 20) donors sero-negative to the newly emerged A(H1N1)pdm09 influenza virus strain and correlated it with the serological response to that specific strain after seasonal influenza vaccination. Extensive multi-chromatic FACS analysis (36 lymphocyte sub-populations measured) was used to quantitatively assess the cellular immune status before vaccination. We identified CD4+ T cells, and amongst them particularly naive CD4+ T cells, as the best correlates for a successful A(H1N1)pdm09 immune response. Moreover, the number of influenza strains a donor was sero-negative to at baseline (NSSN) in addition to age, as expected, were important predictive factors. Age, NSSN and CD4+ T cell count at baseline together predicted sero-protection (HAI≥40) to A(H1N1)pdm09 with a high accuracy of 89% (p-value = 0.00002). An additional validation study (N = 43 vaccinees sero-negative to A(H1N1)pdm09) has confirmed the predictive value of age, NSSN and baseline CD4+ counts (accuracy = 85%, p-value = 0.0000004). Furthermore, the inclusion of donors at ages 31-50 had shown that the age predictive function is not linear with age but rather a sigmoid with a midpoint at about 50 years. Using these results we suggest a clinically relevant prediction model that gives the probability for non-protection to A(H1N1)pdm09 influenza strain after seasonal multi-valent vaccination as a continuous function of age, NSSN and baseline CD4 count.