Melanoma patients treated with oncogenic BRAF inhibitors can develop cutaneous squamous cell carcinoma (cSCC) within weeks of treatment, driven by paradoxical RAS/RAF/MAPK pathway activation. Here we identify frequent TGFBR1 and TGFBR2 mutations in human vemurafenib-induced skin lesions and in sporadic cSCC. Functional analysis reveals these mutations ablate canonical TGFβ Smad signalling, which is localized to bulge stem cells in both normal human and murine skin. MAPK pathway hyperactivation (through Braf(V600E) or Kras(G12D) knockin) and TGFβ signalling ablation (through Tgfbr1 deletion) in LGR5(+ve) stem cells enables rapid cSCC development in the mouse. Mutation of Tp53 (which is commonly mutated in sporadic cSCC) coupled with Tgfbr1 deletion in LGR5(+ve) cells also results in cSCC development. These findings indicate that LGR5(+ve) stem cells may act as cells of origin for cSCC, and that RAS/RAF/MAPK pathway hyperactivation or Tp53 mutation, coupled with loss of TGFβ signalling, are driving events of skin tumorigenesis.

Cutaneous squamous cell carcinoma (cSCC) has a high tumour mutational burden (50 mutations per megabase DNA pair). Here, we combine whole-exome analyses from 40 primary cSCC tumours, comprising 20 well-differentiated and 20 moderately/poorly differentiated tumours, with accompanying clinical data from a longitudinal study of immunosuppressed and immunocompetent patients and integrate this analysis with independent gene expression studies. We identify commonly mutated genes, copy number changes and altered pathways and processes. Comparisons with tumour differentiation status suggest events which may drive disease progression. Mutational signature analysis reveals the presence of a novel signature (signature 32), whose incidence correlates with chronic exposure to the immunosuppressive drug azathioprine. Characterisation of a panel of 15 cSCC tumour-derived cell lines reveals that they accurately reflect the mutational signatures and genomic alterations of primary tumours and provide a valuable resource for the validation of tumour drivers and therapeutic targets.

Rheumatoid arthritis affects individuals commonly during the most productive years of adulthood. Poor response rates and high costs associated with treatment mandate the search for new therapies. Here we show that targeting a specific G-protein coupled receptor promotes senescence in synovial fibroblasts, enabling amelioration of joint inflammation. Following activation of the melanocortin type 1 receptor (MC1), synovial fibroblasts acquire a senescence phenotype characterized by arrested proliferation, metabolic re-programming and marked gene alteration resembling the remodeling phase of wound healing, with increased matrix metalloproteinase expression and reduced collagen production. This biological response is attained by selective agonism of MC1, not shared by non-selective ligands, and dependent on downstream ERK1/2 phosphorylation. In vivo, activation of MC1 leads to anti-arthritic effects associated with induction of senescence in the synovial tissue and cartilage protection. Altogether, selective activation of MC1 is a viable strategy to induce cellular senescence, affording a distinct way to control joint inflammation and arthritis.

As the scale of single-cell genomics experiments grows into the millions, the computational requirements to process this data are beyond the reach of many. Herein we present Scarf, a modularly designed Python package that seamlessly interoperates with other single-cell toolkits and allows for memory-efficient single-cell analysis of millions of cells on a laptop or low-cost devices like single-board computers. We demonstrate Scarf's memory and compute-time efficiency by applying it to the largest existing single-cell RNA-Seq and ATAC-Seq datasets. Scarf wraps memory-efficient implementations of a graph-based t-stochastic neighbour embedding and hierarchical clustering algorithm. Moreover, Scarf performs accurate reference-anchored mapping of datasets while maintaining memory efficiency. By implementing a subsampling algorithm, Scarf additionally has the capacity to generate representative sampling of cells from a given dataset wherein rare cell populations and lineage differentiation trajectories are conserved. Together, Scarf provides a framework wherein any researcher can perform advanced processing, subsampling, reanalysis, and integration of atlas-scale datasets on standard laptop computers. Scarf is available on Github: https://github.com/parashardhapola/scarf .