A Descriptive Study of 103 Primary Cutaneous B-Cell Lymphomas: Clinical and Pathological Characteristics and Treatment from the Spanish Lymphoma Oncology Group (GOTEL).

Primary cutaneous B-cell lymphomas (PCBCLs) are B-cell lymphomas that can occur in the skin without evidence of extracutaneous involvement. The 2005 WHO/EORTC classification of cutaneous lymphomas and its 2018 update have distinguished three main categories based on clinicopathological, immunohistochemical, and genetic characteristics: primary cutaneous marginal zone lymphoma (PCMZL), primary cutaneous follicle centre lymphoma (PCFCL), and primary cutaneous diffuse large B-cell lymphoma, leg type (PCDLBCL-LT). PCMZL and PCFCL are clinically indolent, while PCDLBCL-LT is an aggressive lymphoma. Due to its low incidence and lack of prospective studies, it is difficult to establish a standard treatment for each subgroup. The objective of our study was to describe the clinical and pathological characteristics of 103 patients with cutaneous B-cell lymphoma from 12 centres belonging to the Spanish Lymphoma Oncology Group. The median age was 53 years (40-65). According to skin extension, 62% had single-site lymphoma, 17% had regional lymphoma, and 20% had multifocal lymphoma. Histology: 66% had PCMZL, 26% had PCFCL, and 8% had PCDLBCL-LT. Twenty-three percent of the patients were treated exclusively with surgery, 26% with radiotherapy only, 21% with surgery plus radiotherapy, 10% with polychemotherapy, and 5% with rituximab monotherapy. Overall, 96% of patients achieved a complete response, and 44% subsequently relapsed, most of them relapsing either locally or regionally. The 10-year OS was 94.5% for the entire cohort, 98% for the PCMZL cohort, 95% for the PCFCL cohort, and 85.7% for the PCDLBCL-LT cohort. Our data are comparable to those of other published series, except for the high frequency of PCMZL. The expected heterogeneity in therapeutic management has been observed.

Easy-Amanida: An R Shiny application for the meta-analysis of aggregate results in clinical metabolomics using Amanida and Webchem.

Meta-analysis is a useful tool in clinical research, as it combines the results of multiple clinical studies to improve precision when answering a particular scientific question. While there has been a substantial increase in publications using meta-analysis in various clinical research topics, the number of published meta-analyses in metabolomics is significantly lower compared to other omics disciplines. Metabolomics is the study of small chemical compounds in living organisms, which provides important insights into an organism's phenotype. However, the wide variety of compounds and the different experimental methods used in metabolomics make it challenging to perform a thorough meta-analysis. Additionally, there is a lack of consensus on reporting statistical estimates, and the high number of compound naming synonyms further complicates the process. Easy-Amanida is a new tool that combines two R packages, "amanida" and "webchem", to enable meta-analysis of aggregate statistical data, like p-value and fold-change, while ensuring the compounds naming harmonization. The Easy-Amanida app is implemented in Shiny, an R package add-on for interactive web apps, and provides a workflow to optimize the naming combination. This article describes all the steps to perform the meta-analysis using Easy-Amanida, including an illustrative example for interpreting the results. The use of aggregate statistics metrics extends the use of Easy-Amanida beyond the metabolomics field.

A Metabolites Merging Strategy (MMS): Harmonization to Enable Studies' Intercomparison.

Metabolomics encounters challenges in cross-study comparisons due to diverse metabolite nomenclature and reporting practices. To bridge this gap, we introduce the Metabolites Merging Strategy (MMS), offering a systematic framework to harmonize multiple metabolite datasets for enhanced interstudy comparability. MMS has three steps. Step 1: Translation and merging of the different datasets by employing InChIKeys for data integration, encompassing the translation of metabolite names (if needed). Followed by Step 2: Attributes' retrieval from the InChIkey, including descriptors of name (title name from PubChem and RefMet name from Metabolomics Workbench), and chemical properties (molecular weight and molecular formula), both systematic (InChI, InChIKey, SMILES) and non-systematic identifiers (PubChem, CheBI, HMDB, KEGG, LipidMaps, DrugBank, Bin ID and CAS number), and their ontology. Finally, a meticulous three-step curation process is used to rectify disparities for conjugated base/acid compounds (optional step), missing attributes, and synonym checking (duplicated information). The MMS procedure is exemplified through a case study of urinary asthma metabolites, where MMS facilitated the identification of significant pathways hidden when no dataset merging strategy was followed. This study highlights the need for standardized and unified metabolite datasets to enhance the reproducibility and comparability of metabolomics studies.