TOSCA: an automated Tumor Only Somatic CAlling workflow for somatic mutation detection without matched normal samples

HSP and CD279 gene expression as candidate biomarkers in symptomatic LGLL patients

The clinical presentation of T-cell large granular lymphocytic leukemia (T-LGLL) is extremely variable: 30% of patients have neutropenia with no associated symptoms, others present with bacterial infections and sepsis may occur. Tools to predict patient outcome are lacking. Stemming from preliminary results obtained by single cell-RNAseq we investigated by qPCR HSP and IFIT gene families in 27 LGLL patients (23T-LGLL and 4 NK-LGLL), including 11 with neutropenia and/or thrombocytopenia and 16 asymptomatic for the disease. HSP90AA1 and HSPA1B, among HSP family and CD279 exhibited a significantly higher expression in CD3 + CD57 + sorted cells of symptomatic LGLL patients compared to asymptomatic patients and healthy controls. Also, monocytes derived from symptomatic LGLL patients expressed high levels of CCL3, CCL4 and CCL5 mRNA and of IL-1β, IL-6, TNF, and PD-L1 mRNA, thus confirming a pro-inflammatory cytokine profile reminiscent of a non-classical phenotype. Overall, these data provide a rationale for considering HSP and CD279 genes as potential biomarkers for distinguishing symptomatic LGLL patients from asymptomatic ones, emphasizing the importance of further research to explore their implications for targeted therapy development.

Scalable and efficient DNA sequencing analysis on different compute infrastructures aiding variant discovery

DNA variation analysis has become indispensable in many aspects of modern biomedicine, most prominently in the comparison of normal and tumor samples. Thousands of samples are collected in local sequencing efforts and public databases requiring highly scalable, portable, and automated workflows for streamlined processing. Here, we present nf-core/sarek 3, a well-established, comprehensive variant calling and annotation pipeline for germline and somatic samples. It is suitable for any genome with a known reference. We present a full rewrite of the original pipeline showing a significant reduction of storage requirements by using the CRAM format and runtime by increasing intra-sample parallelization. Both are leading to a 70% cost reduction in commercial clouds enabling users to do large-scale and cross-platform data analysis while keeping costs and CO2 emissions low. The code is available at https://nf-co.re/sarek.