Biology subjects

Matthews, T. J.

Publications and source records attributed to Matthews, T. J..

3 recordsLinked to original sources

bioRxiv2026

Characterization of tumor interactions with the immune system in an autochthonous mouse model of glioblastoma

BackgroundGlioblastoma is an aggressive and incurable brain tumor. Clinical trials of immune checkpoint inhibitors showed no clinical benefit in glioblastoma when given after surgery. However, a clinical trial in which PD1 inhibition was given prior to second surgery did show pharmacodynamic evidence for activity. This suggests the possibility that immune checkpoint inhibitors may be more effective in a setting where large tumors are present. Here we have studied immune responses to large tumors in an autochthonous mouse model of glioblastoma. MethodsGlioblastoma was induced by transfection with oncogenic plasmids injected directly into the lateral ventricle of neonatal mice. Immune responses were assessed using a combination of spectral flow cytometry and immunohistochemistry. ResultsThere was a marked immune response to large tumors, with significant increases in CD4 T cells and dendritic cells. T cell changes occurred primarily at leptomeningeal/perivascular border sites. A large proportion of CD4 T cells expressed PD1 and half of these were regulatory T cells. NK cells were also increased in mice with large tumors, but were predominantly in immature states. The mouse model accurately recapitulates the formation of palisading necroses. These contain apoptotic cells and avidly recruit myeloid cells that are induced to express large amounts of TGF{beta}. ConclusionsLarge glioblastoma tumors generate a border site population of PD1 positive T cells that may explain the pharmacodynamic response in neoadjuvant trials, and a palisading necrosis-driven immunosuppressive mechanism that may explain why responses are insufficient to provide a significant clinical benefit. KEY POINTSThe SB mouse model accurately recapitulates immune features of human glioblastoma Large tumors induce a significant border site immune response Palisading necroses in large tumors counter this with a strong immunosuppressive response IMPORTANCE OF STUDYImmune checkpoint inhibitors have not shown efficacy in glioblastoma when used post-surgery, but do show pharmacodynamic activity when used in patients prior to second surgery (i.e. neoadjuvant). This suggest the possibility that immune checkpoint inhibition is more effective when large tumors are present. Using a clinically-relevant autochthonous mouse model, we show here that large tumors induce an immune response that is evident in leptomeningeal border sites. Large tumors in this mouse model also generate palisading necroses, a well-known diagnostic feature in glioblastoma tumors. These palisading necroses generate large amounts of TGF{beta}, providing a mechanism by which large tumors can suppress border site immune responses. This further supports the concept that palisading necroses are drivers of glioblastoma malignancy and suggests novel strategies to enhance responses to immune checkpoint inhibition in this cancer.

cancer biology↗

bioRxiv2024

The biogeography of evolutionary radiations on oceanic archipelagos

Evolutionary radiations on oceanic archipelagos (ROAs) have long served as models for understanding evolutionary and ecological processes underlying species diversification. Yet, diversity patterns emerging from ROAs have received relatively little attention from biogeographers, even though characterizing the effect of key geo-environmental factors on island clades species distribution could be important for unraveling diversification dynamics. In this study, we conducted a comparative analysis using island-specific species richness values for approximately one hundred ROAs across major oceanic archipelagos (mostly Hawaii, Canary Islands, Galapagos and Fiji) and taxa (vascular plants, invertebrates and vertebrates). Our aim was to determine whether (1) ROA species richness patterns scale as a function of key geo-environmental factors including island area, geological age, environmental heterogeneity (elevation and topographic complexity) and inter-island isolation, and (2) whether the magnitude of the effects of these factors varies across archipelagos and taxa. Our results identified elevation as a key driver of ROA species richness patterns on islands, supporting existing theoretical and empirical work that highlighted the central role of environmental heterogeneity in driving diversification on oceanic islands. As importantly, we found that the influence of geo-environmental factors varies across archipelagos and taxa, suggesting that unique archipelagic dynamics and biological traits together shape diversification differently. Our findings emphasize the value of applying biogeographical modeling at the resolution of individual radiations to improve our understanding of evolutionary processes on oceanic archipelagos.

evolutionary biology↗

bioRxiv2024

The what, how and why of trait-based analyses in ecology

Functional diversity is increasingly used alongside taxonomic diversity to describe populations and communities in ecology. Indeed, functional diversity metrics allow researchers to summarize complex occupancy patterns in space and/or time (what is changing?) that lead to changes in communities and/or populations (the process; how is it changing?) in response to some stressors (the mechanism; why is it changing?). However, as the diversity of functional diversity metrics and methods increases, it is often not directly clear which metric is more readily appropriate for which question. We studied the ability of different functional diversity metrics to recover patterns and signals from different processes linked to common assembly mechanisms (environmental filtering, competitive exclusion, equalizing fitness, and facilitation) in community ecology. Using both simulated data and an empirical dataset affected by more complex and nuanced mechanisms, we tested the effectiveness of different space occupancy metrics to recover the simulated or empirical changes. We show that different metrics perform better for different tasks, emphasizing the importance of not using a one-size-fits-all metric. Instead, researchers should carefully consider and test whether a particular metric will be effective in capturing a pattern of interest.

ecology↗