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Biology subjects

Neeson, P. J.

Publications and source records attributed to Neeson, P. J..

3 recordsLinked to original sources

A novel transcriptional signature identifies T-cell infiltration in high-risk paediatric cancer

Molecular profiling of the tumour immune microenvironment (TIME) has enabled the rational choice of immunotherapies in some adult cancers. In contrast, the TIME of paediatric cancers is relatively unexplored. We speculated that a more refined appreciation of the TIME in childhood cancers, rather than a reliance on commonly used biomarkers such as tumour mutation burden (TMB), neoantigen load and PD-L1 expression, is an essential prerequisite for improved immunotherapies in childhood solid cancers. We combined immunohistochemistry (IHC) and molecular profiling to develop an alternative, expression-based signature associated with CD8+ T-cell infiltration of the TIME in high-risk paediatric tumours. Using this novel 15-gene immune signature, Immune Paediatric Signature Score (IPASS), we estimate up to 31% of high-risk cancers harbour infiltrating T-cells. Our data provides new insights into the variable immune-suppressive mechanisms dampening responses in paediatric solid cancers. Effective immune-based interventions in high-risk paediatric cancer will require individualised analysis of the TIME.

cancer biology↗

Tissue remodeling and cell signaling underpin changes in tumor microenvironment heterogeneity in glioma oncogenesis

Brain tumor cells thrive by adapting to the signals in their microenvironment. Understanding how the tumor microenvironment evolves during disease progression is crucial to deciphering the mechanisms underlying the functional behavior of cancer cells. To adapt, cancer cells activate signaling and transcriptional programs and migrate to establish micro-niches, in response to signals from neighboring cells and non-cellular stromal factors. Using multiple tissue analysis approaches to identify and measure immune cell infiltration and extracellular matrix deposition in brain tumors, we show that low-grade glioma is largely devoid of infiltrating immune cells and extracellular matrix proteins, while high-grade glioma exhibits abundant immune cell infiltration and activation, as well as extensive collagen deposition. Spatial analysis shows that most T-cells are sequestered in perivascular nests, but macrophages penetrate deep into tumor cell rich regions. High-grade gliomas exhibit heterogeneous PI3K and MAPK signaling, which correlates with distinct pathological hallmarks, including tumor angiogenesis, tumor cell density and extracellular matrix deposition. Our results also provide compelling evidence that tissue remodeling is an important element in glioma progression, and that targeting the extracellular matrix will be critical to improving GBM therapy.

cancer biology↗

SPIAT: An R package for the Spatial Image Analysis of Cells in Tissues

Spatial technologies that query the location of cells in tissues at single-cell resolution are gaining popularity and are likely to become commonplace. The resulting data includes the X, Y coordinates of millions of cells, cell phenotypes and marker or gene expression levels. However, to date, the tools for the analysis of this data are largely underdeveloped, making us severely underpowered in our ability to extract quantifiable information. We have developed SPIAT (Spatial Image Analysis of Tissues), an R package with a suite of data processing, quality control, visualization, data handling and data analysis tools. SPIAT includes our novel algorithms for the identification of cell clusters, cell margins and cell gradients, the calculation of neighbourhood proportions, and algorithms for the prediction of cell phenotypes. SPIAT also includes speedy implementations of the calculation of cell distances and detection of cell communities. This version of SPIAT is directly compatible with Opal multiplex immunohistochemistry images analysed through the HALO and InForm analysis software, but its intuitive implementation allows use with a diversity of platforms. We expect SPIAT to become a user-friendly and speedy go-to package for the spatial analysis of cells in tissues. SPIAT is available on Github: https://github.com/cancer-evolution/SPIAT

bioinformatics↗