Update: The molecular spectrum of virus-associated high-grade B-cell non-Hodgkin lymphomas.

The vast spectrum of aggressive B-cell non-Hodgkin neoplasms (B-NHL) encompasses several infrequent entities occurring in association with viral infections, posing diagnostic challenges for practitioners. In the emerging era of precision oncology, the molecular characterization of malignancies has acquired paramount significance. The pathophysiological comprehension of specific entities and the identification of targeted therapeutic options have seen rapid development. However, owing to their rarity, not all entities have undergone exhaustive molecular characterization. Considerable heterogeneity exists in the extant body of work, both in terms of employed methodologies and the scale of cases studied. Presently, therapeutic strategies are predominantly derived from observations in diffuse large B-cell lymphoma (DLBCL), the most prevalent subset of aggressive B-NHL. Ongoing investigations into the molecular profiles of these uncommon virus-associated entities are progressively facilitating a clearer distinction from DLBCL, ultimately paving the way towards individualized therapeutic approaches. This review consolidates the current molecular insights into aggressive and virus-associated B-NHL, taking into consideration the recently updated 5th edition of the WHO classification of hematolymphoid tumors (WHO-5HAEM) and the International Consensus Classification (ICC). Additionally, potential therapeutically targetable susceptibilities are highlighted, offering a comprehensive overview of the present scientific landscape in the field.

Dysbiosis of skin microbiota with increased fungal diversity is associated with severity of disease in atopic dermatitis.

BACKGROUND: Atopic dermatitis (AD) is a multifactorial inflammatory skin disease and an altered skin microbiota with an increase of Staphylococcus aureus has been reported. However, the role of fungi remains poorly investigated. OBJECTIVES: We aimed to improve the understanding of the fungal skin microbiota, the mycobiota, in AD in relation to the bacterial colonization. METHODS: Skin swabs of 16 AD patients and 16 healthy controls (HC) from four different skin sites, that is antecubital crease, dorsal neck, glabella and vertex from multiple time points were analysed by DNA sequencing of the internal transcribed spacer region 1 (ITS1) and 16S rRNA gene for fungi and bacteria, respectively. RESULTS: Malassezia spp. were the predominant fungi in all subjects but with a decreased dominance in severe AD patients in favour of non-Malassezia fungi, for example Candida spp. For bacteria, a decrease of Cutibacterium spp. in AD patients in favour of Staphylococcus spp., particularly S. aureus, was observed. Further, both bacterial and fungal community compositions of severe AD patients significantly differed from mild-to-moderate AD patients and HC with the latter two having overall similar microbiota showing some distinctions in bacterial communities. CONCLUSIONS: We conclude that severe AD is associated with a pronounced dysbiosis of the microbiota with increased fungal diversity. Potentially infectious agents, for example Staphylococcus and Candida, were increased in severe AD.

Combined culture and metagenomic analyses reveal significant shifts in the composition of the cutaneous microbiome in psoriasis.

BACKGROUND: The treatment of psoriasis has been revolutionized by the development of biologic therapies. However, the pathogenesis of psoriasis, in particular the role of the cutaneous microbiome, remains incompletely understood. Moreover, skin microbiome studies have relied heavily on 16S rRNA sequencing data in the absence of bacterial culture. OBJECTIVES: To characterize and compare the cutaneous microbiome in 20 healthy controls and 23 patients with psoriasis using metagenomic analyses and to determine changes in the microbiome during treatment. METHODS: Swabs from lesional and nonlesional skin from patients with psoriasis, and from controls matched for site and skin microenvironment, were analysed using both 16S rRNA sequencing and traditional culture combined with mass spectrometry (MALDI-TOF) in a prospective study. RESULTS: Psoriasis was associated with an increased abundance of Firmicutes and a corresponding reduction in Actinobacteria, most marked in lesional skin, and at least partially reversed during systemic treatment. Shifts in bacterial community composition in lesional sites were reflected in similar changes in culturable bacteria, although changes in the microbiota over repeated swabbing were detectable only with sequencing. The composition of the microbial communities varied by skin site and microenvironment. Prevotella and Staphylococcus were significantly associated with lesional skin, and Anaerococcus and Propionibacterium with nonlesional skin. There were no significant differences in the amount of bacteria cultured from the skin of healthy controls and patients with psoriasis. CONCLUSIONS: Shifts in the cutaneous microbiome in psoriasis, particularly during treatment, may shed new light on the pathogenesis of the disease and may be clinically exploited to predict treatment response. What's already known about this topic? Alterations in the composition of the cutaneous microbiome have been described in psoriasis, although methodological differences in study design prevent direct comparison of results. To date, most cutaneous microbiome studies have focused on 16S rRNA sequencing data, including both living and dead bacteria. What does this study add? This prospective observational study confirms that changes in the composition of the cutaneous microbiome, detected by 16S rRNA sequencing, are consistent with those identified by bacterial culture and mass spectrometry. The changes in the microbiome during antipsoriasis therapy should be further investigated to determine whether these represent potential novel biomarkers of treatment response. What is the translational message? Characterization of cutaneous microbiota may ultimately move into the clinic to help facilitate treatment selection, not only by optimizing currently available treatments, but also by identifying new therapeutic targets.