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Biology subjects

Cast, O.

Publications and source records attributed to Cast, O..

3 recordsLinked to original sources

Hedgehog signaling controls cytotoxic T cell migration in the tumour microenvironment.

Cytotoxic T lymphocytes effectively eliminate cancer cells. Their abundance in the tumour microenvironment is one of the strongest pan-cancer predictors of clinical response. Here, we show that Hedgehog (Hh) signaling regulates T cell migration into tumours. Using conditional knockout mouse models of central Hh signaling components Ihh, Smo and Gli1 in CD8 T cells, we show that Smo deletion greatly impairs the anti-tumour response in vivo due to diminished CD8 T cell migration into the tumour microenvironment. The migration defect is mediated exclusively by Smo, both in in vivo cancer models and in vitro migration assays. This effect is independent of the canonical Hh pathway and relies on the GPCR function of Smo to regulate the migration of murine and human CD8 T cells via RhoA. Hh signaling is critical during embryonic development and adult stem cell homeostasis, but is also amplified in multiple cancer types. Hh inhibitors targeting SMO have been clinically-approved and shown efficacy in the treatment of Hh-driven basal cell carcinoma and medulloblastoma but have failed in clinical trials in other solid cancers with upregulated Hh signaling. We demonstrate that SMO inhibitors specifically decrease CD8 T cell migration into the tumour microenvironment, both in murine cancer models and resected BCCs from patients treated with the SMO inhibitor vismodegib, providing the first mechanistic explanation as to why Hh inhibitors have failed in solid cancers. Our data establishes a novel link between Hh inhibition in vivo and the anti-tumour immune response and reveals a fundamental mechanism controlling T cell migration. The work provides the basis for improved Hh targeting approaches in the clinic and new entry points into enhancing migration in T cell therapies.

immunology↗

Multi-site clonality analyses uncovers pervasive subclonal heterogeneity and branching evolution across melanoma metastases

Metastatic melanoma carries a poor prognosis despite modern systemic therapies. Understanding the evolution of the disease could help inform patient management. Through whole-genome sequencing of 13 melanoma metastases sampled at autopsy from a treatment naive patient and by leveraging the analytical power of multi-sample analyses, we reveal that metastatic cells may depart the primary tumour very early in the disease course and follow a branched pattern of evolution. Truncal UV-induced mutations that often swamp downstream analyses of heterogeneity, were found to be replaced by APOBEC-associated mutations in the branches of the evolutionary tree. Multi-sample analyses from a further 7 patients confirmed that branched evolution was pervasive, representing an important mode of melanoma dissemination. Our analyses illustrate that combining cancer cell fraction estimates across multiple metastases provides higher resolution phylogenetic reconstructions relative to single sample analyses and highlights the limitations of accurately inferring inter-tumoural heterogeneity from a single biopsy.

genomics↗

Comprehensive Benchmarking and Integration of Tumour Microenvironment Cell Estimation Methods

The tumour microenvironment comprises complex cellular compositions and interactions between cancer, immune, and stromal components which all play crucial roles in cancer. Various computational approaches have been developed during the last decade that estimate the relative abundance of different cell types in an unbiased manner using bulk tumour RNA data. However, a comparison that objectively evaluates the performance of these approaches against one another has not been conducted. Here we benchmarked six widely used tools and gene sets: Bindea et al. gene sets, Davoli et al. gene sets, CIBERSORT, MCP-counter, TIMER, and xCell. We also introduce ConsensusTME, a consensus approach that uses the union of genes that the six tools used for cell estimation, and corrects for tumour-type specificity. We benchmarked the seven tools using TCGA DNA-derived purity scores (33 tumour-types), methylation-derived leukocyte scores (30 tumour-types), and H&E deep learning derived lymphocyte counts (13 tumour types), and individual benchmark data sets (PBMCs and 2 tumour types). Although none of the seven tools outperformed others in every benchmark, ConsensusTME ranked consistently well in all cancer-related benchmarks making it the top performing method overall. Computational methods that provide robust and accurate estimates of non-cancerous cell populations in the tumour microenvironment from tumour bulk expression data are important tools that can advance our understanding of tumour, immune, and stroma interactions, with potential clinical application if high accuracy estimates are achieved.

bioinformatics↗