Biology subjects

Thompson, E. J.

Publications and source records attributed to Thompson, E. J..

3 recordsLinked to original sources

bioRxiv2026

Deciphering the Genomic Landscape of Renal Cell Carcinoma Brain Metastases

Brain metastases from renal cell carcinoma (RCC) remain a major cause of morbidity and mortality, yet the genomic features associated with metastatic dissemination remain poorly understood. Whole-exome sequencing was performed on 72 RCC brain metastasis samples with matched normal. To identify candidate genomic alterations associated with brain metastasis, the genomic alterations detected in the brain metastases were compared against alterations in extracranial metastases from the MSK-ECM cohort (n=137) and primary RCC tumors from TCGA (n=432) by case-control analyses. Candidate alterations were also identified through matched-pair analyses comparing brain metastases with matched primary tumors or extracranial metastases from the same patient (n=25). A random survival forest model incorporating the candidate CNA events was developed to predict overall survival. The candidate CNAs were further evaluated using functional experimental data from MetMap and DepMap. Survival analyses were conducted to assess the prognostic relevance of these alterations. We identified recurrent CNAs enriched in RCC brain metastases, including 4q loss, 7p gain, 7q gain, 8p loss, 8q gain, 9p21.3 deletion, 12q15 amplification, and 14q loss. These alterations were associated with significantly poorer patient survival among RCC patients. A random survival forest model based on these CNA events stratified TCGA-KIRC patients into prognostically distinct risk groups (C-index = 0.64). Among the recurrent CNAs, 8p loss, 8q gain, 9p21.3 deletion were associated with increased incidence of brain metastases across multiple primary cancer types in xenograft mouse models. These alterations were also strongly associated with metastatic progression and poor prognosis across RCC, lung adenocarcinoma, breast cancer, and melanoma. These findings indicate a shared genomic basis for brain tropism and highlight the potential utility of copy-number alterations as biomarkers for risk stratification and clinical decision-making.

genetics↗

bioRxiv2024

ESPClust: Unsupervised identification of modifiers for the effect size profile in omics association studies

High-throughput omics technologies have revolutionised the identification of associations between individual traits and underlying biological characteristics, but still use one effect-size fits all approaches. While covariates are often used, their potential as effect modifiers often remains unexplored. To bridge this gap, we introduce ESPClust, a novel unsupervised method designed to identify covariates that modify the effect size of associations between sets of omics variables and outcomes. By extending the concept of moderators to encompass multiple exposures, ESPClust analyses the effect size profile (ESP) to identify regions in covariate space with different ESP, enabling the discovery of subpopulations with distinct associations. Applying ESPClust to insulin resistance and COVID-19 symptom manifestation, we demonstrate its versatility and ability to uncover nuanced effect size modifications that traditional analyses may overlook. By integrating information from multiple exposures, ESPClust identifies effect size modifiers in datasets that are too small for traditional univariate stratified analyses. This method provides a robust framework for understanding complex omics data and holds promise for personalised medicine.

bioinformatics↗

bioRxiv2024

Repix: reliable, reusable, versatile chronic Neuropixels implants using minimal components

Neuropixels probes represent the state-of-the-art for high-yield electrophysiology in neuroscience: the simultaneous recording of hundreds of neurons is now routinely carried out in head-restrained animals. In contrast, neural recording in unrestrained animals, as well as recording and tracking neurons over days, remains challenging, though it is possible using chronic implants. A major challenge is the availability of simple methods that can be implemented with limited or no prior experience with Neuropixels probes, while achieving reliable, reusable, versatile high-density electrophysiology. Here we developed, deployed, and evaluated the real-world performance of Repix, a chronic implantation system that permits the repeated re-use of Neuropixels probes. The lightweight system allows implanted animals to express a full range of natural behaviors, including social behaviors. We show that Repix allows the recording of hundreds of neurons across many months, up to a year, with implants across cortical and subcortical brain regions. Probes can be reused repeatedly with stable yield. Repix has been used by 16 researchers in 10 laboratories to date, and we evaluated the real-world performance of Repix in a variety of chronic recording paradigms in both mice and rats with a combined 209 implantations. We found that the key advantage of Repix is robustness and simplicity. Adopters of Repix became proficient at five procedures on average, regardless of prior experience with in vivo electrophysiology. With the companion protocol alongside this article, the performance and user-friendliness of Repix should facilitate a wide uptake of chronic Neuropixels recordings.

neuroscience↗