Waise, S. | bioRxiv Science

bioRxiv2025

An optimised computational approach for the identification of somatic structural variants in cancer

Structural variants play a critical role in tumorigenesis. At present, these events are most commonly identified using short-read whole-genome sequencing data, and a number of computational tools are available for this purpose. Consensus approaches have been used to improve precision, but may reduce sensitivity. The optimal number and combination of callers remains unclear, in part due to the lack of gold standard real-world datasets for validation. Here, we benchmark the performance of Delly, GRIDSS, LUMPY, Manta and SvABA, using a validation set of consensus calls from the Pan-Cancer Analysis of Whole Genomes Consortium. Manta showed the best standalone performance, identifying 88% of the validation set calls, and was included in all of the best-performing caller combinations. A consensus approach comprising Delly, GRIDSS, Manta and SvABA was selected as the optimum approach from those tested. We provide a NextFlow implementation of our optimised consensus approach as a resource for the cancer genomics community.

genomics↗

bioRxiv2020

Spatially discrete signalling niches regulate fibroblast heterogeneity in human lung cancer

Fibroblasts are functionally heterogeneous cells, capable of promoting and suppressing tumour progression. Across cancer types, the extent and cause of this phenotypic diversity remains unknown. We used single-cell RNA sequencing and multiplexed immunohistochemistry to examine fibroblast heterogeneity in human lung and non-small cell lung cancer (NSCLC) samples. This identified seven fibroblast subpopulations: including inflammatory fibroblasts and myofibroblasts (representing terminal differentiation states), quiescent fibroblasts, proto-myofibroblasts (x2) and proto-inflammatory fibroblasts (x2). Fibroblast subpopulations were variably distributed throughout tissues but accumulated at discrete niches associated with differentiation status. Bioinformatics analyses suggested TGF-{beta}1 and IL-1 as key regulators of myofibroblastic and inflammatory differentiation respectively. However, in vitro analyses showed that whilst TGF-{beta}1 stimulation in combination with increased tissue tension could induce myofibroblast marker expression, it failed to fully re-capitulate ex-vivo phenotypes. Similarly, IL-1{beta} treatment only induced upregulation of a subset of inflammatory fibroblast marker genes. In silico modelling of ligand-receptor signalling identified additional pathways and cell interactions likely to be involved in fibroblast activation, which can be examined using publicly available R shiny applications (at the following links: myofibroblast activation and inflammatory fibroblast activation). This highlighted a potential role for IL-11 and IL-6 (among other ligands) in myofibroblast and inflammatory fibroblast activation respectively. This analysis provides valuable insight into fibroblast subtypes and differentiation mechanisms in NSCLC.

cancer biology↗