Motivation Despite of the fast development of highly effective vaccines to control the current COVID–19 pandemics, the unequal distribution and availability of these vaccines worldwide and the number of people infected in the world lead to the continuous emergence of SARS-CoV-2 (Severe Acute Respiratory Syndrome coronavirus 2) variants of concern. Therefore, it is likely that real-time genomic surveillance will be continuously needed as an unceasing monitoring tool, necessary to follow the spread of the disease and the evolution of the virus. In this context, new genomic variants of SARS-CoV-2, including variants refractory to current vaccines, makes genomic surveillance programs tools of utmost importance. Nevertheless, the lack of appropriate analytical tools to quickly and effectively access the viral composition in meta-transcriptomic sequencing data, including environmental surveillance, represent possible challenges that may impact the fast adoption of this approach to mitigate the spread and transmission of viruses. Results We propose a statistical model for the estimation of the relative frequencies of SARS-CoV-2 variants in pooled samples. This model is built by considering a previously defined selection of genomic polymorphisms that characterize SARS-CoV-2 variants. The methods described here support both raw sequencing reads for polymorphisms-based markers calling and predefined markers in the VCF format. Results obtained by using simulated data show that our method is quite effective in recovering the correct variant proportions. Further, results obtained by considering longitudinal data from wastewater samples of two locations in Switzerland agree well with those describing the epidemiological evolution of COVID-19 variants in clinical samples of these locations. Our results show that the described method can be a valuable tool for tracking the proportions of SARS-CoV-2 variants in complex mixtures such as waste water and environmental samples. Availability http://github.com/rvalieris/LCS Supplementary information Supplementary data are available at Bioinformatics online.

ObjectiveTo better understand the immune microenvironment of pancreatic ductal adenocarcinomas (PDACs), here we explored the relevance of T and B cell compartmentalisation into tertiary lymphoid structures (TLSs) for the generation of local antitumour immunity.DesignWe characterised the functional states and spatial organisation of PDAC-infiltrating T and B cells using single-cell RNA sequencing (scRNA-seq), flow cytometry, multicolour immunofluorescence, gene expression profiling of microdissected TLSs, as well as in vitro assays. In addition, we performed a pan-cancer analysis of tumour-infiltrating T cells using scRNA-seq and sc T cell receptor sequencing datasets from eight cancer types. To evaluate the clinical relevance of our findings, we used PDAC bulk RNA-seq data from The Cancer Genome Atlas and the PRINCE chemoimmunotherapy trial.ResultsWe found that a subset of PDACs harbours fully developed TLSs where B cells proliferate and differentiate into plasma cells. These mature TLSs also support T cell activity and are enriched with tumour-reactive T cells. Importantly, we showed that chronically activated, tumour-reactive T cells exposed to fibroblast-derived TGF-β may act as TLS organisers by producing the B cell chemoattractant CXCL13. Identification of highly similar subsets of clonally expandedCXCL13+tumour-infiltrating T cells across multiple cancer types further indicated a conserved link between tumour-antigen recognition and the allocation of B cells within sheltered hubs in the tumour microenvironment. Finally, we showed that the expression of a gene signature reflecting mature TLSs was enriched in pretreatment biopsies from PDAC patients with longer survival after receiving different chemoimmunotherapy regimens.ConclusionWe provided a framework for understanding the biological role of PDAC-associated TLSs and revealed their potential to guide the selection of patients for future immunotherapy trials.

Every two years groups worldwide participate in the Critical Assessment of Protein Structure Prediction (CASP) experiment to blindly test the strengths and weaknesses of their computational methods. CASP has significantly advanced the field but many hurdles still remain, which may require new ideas and collaborations. In 2012 a web-based effort called WeFold, was initiated to promote collaboration within the CASP community and attract researchers from other fields to contribute new ideas to CASP. Members of the WeFold coopetition (cooperation and competition) participated in CASP as individual teams, but also shared components of their methods to create hybrid pipelines and actively contributed to this effort. We assert that the scale and diversity of integrative prediction pipelines could not have been achieved by any individual lab or even by any collaboration among a few partners. The models contributed by the participating groups and generated by the pipelines are publicly available at the WeFold website providing a wealth of data that remains to be tapped. Here, we analyze the results of the 2014 and 2016 pipelines showing improvements according to the CASP assessment as well as areas that require further adjustments and research.

DNA mismatch repair deficiency (dMMR) is associated with the microsatellite instability (MSI) phenotype and leads to increased mutation load, which in turn may impact anti-tumor immune responses and treatment effectiveness. Various mutational signatures directly linked to dMMR have been described for primary cancers. To investigate which mutational signatures are associated with prognosis in gastric cancer, we performed a de novo extraction of mutational signatures in a cohort of 787 patients. We detected three dMMR-related signatures, one of which clearly discriminates tumors with MLH1 gene silencing caused by promoter hypermethylation (area under the curve = 98%). We then demonstrated that samples with the highest exposure of this signature share features related to better prognosis, encompassing clinical and molecular aspects and altered immune infiltrate composition. Overall, the assessment of the prognostic value and of the impact of modifications in MMR-related genes on shaping specific dMMR mutational signatures provides evidence that classification based on mutational signature exposure enables prognosis stratification.

Gastric cancer (GC) is the fifth most common type of cancer worldwide with high incidences in Asia, Central, and South American countries. This patchy distribution means that GC studies are neglected by large research centers from developed countries. The need for further understanding of this complex disease, including the local importance of epidemiological factors and the rich ancestral admixture found in Brazil, stimulated the implementation of the GE4GAC project. GE4GAC aims to embrace epidemiological, clinical, molecular and microbiological data from Brazilian controls and patients with malignant and pre-malignant gastric disease. In this letter, we summarize the main goals of the project, including subject and sample accrual and current findings.