Biology subjects

Welsh, S. J.

Publications and source records attributed to Welsh, S. J..

5 recordsLinked to original sources

bioRxiv2026

Monoclonal nephritic factors reveal insights into C3 convertase dynamics and dysregulation

C3- and C5-nephritic factors are potent but poorly understood autoantibodies that dysregulate complement convertases. To date, their underlying mechanisms of action, epitopes, and sequences remain unknown. To address this knowledge gap, we immune profiled B cells from a nephritic factor-positive C3 glomerulopathy patient and identified the first monoclonal C3- and C5-nephritic factors. We present the structure of a C3-nephritic factor bound to a C3 convertase with the convertase protease domain unexpectedly rotated and inhibited. This rotation advances our understanding of complement convertase progression and decay, explains disease-associated convertase variants, and reveals the molecular mechanism by which nephritic factors can either activate or inhibit convertase activity. We also detail heterogeneity within and between C3- and C5-nephritic factors in terms of convertase binding, stabilizing capacity, regulator inhibition, fluid-phase activation, and disease contribution. These findings improve stratification of patients with C3 glomerulopathy, redefine basic C3 convertase dynamics, and provide insights into antibody-mediated modulation of the complement system.

immunology↗

bioRxiv2024

Angiogenic and Immune Predictors of Neoadjuvant Axitinib Response in Renal Cell Carcinoma with Venous Tumour Thrombus

Venous tumour thrombus (VTT), where the primary tumour invades the renal vein and inferior vena cava, affects 10-15% of renal cell carcinoma (RCC) patients. Curative surgery for VTT is high-risk, but neoadjuvant therapy may improve outcomes. The NAXIVA trial demonstrated a 35% VTT response rate after 8 weeks of neoadjuvant axitinib, a VEGFR-directed therapy. However, understanding non-response is critical for better treatment. We conducted a multiparametric investigation of samples collected during NAXIVA using digital pathology, flow cytometry, plasma cytokine profiling and RNA sequencing. Responders had higher baseline microvessel density and increased induction of VEGF-A and PlGF during treatment. A multi-modal machine learning model integrating features predicted response with an AUC of 0.868, improving to 0.945 when using features from week 3. Key predictive features included plasma CCL17 and IL-12. These findings may guide future treatment strategies for VTT, improving the clinical management of this challenging scenario. One Sentence SummaryA comprehensive multiparametric assessment of the effect of neoadjuvant axitinib in renal cell carcinoma patients with venous tumour thrombus was performed on tissue and peripheral blood, including an integrative machine learning model, which identified both angiogenic and immune determinants of response to therapy.

cancer biology↗

bioRxiv2021

Multi-regional characterisation of renal cell carcinoma and microenvironment at single cell resolution

Tumour behaviour is dependent on the oncogenic properties of cancer cells and their multicellular interactions. These dependencies were examined through 270,000 single cell transcriptomes and 100 micro-dissected whole exomes obtained from 12 patients with kidney tumours. Tissue was sampled from multiple regions of tumour core, tumour-normal interface, normal surrounding tissues, and peripheral blood. We found the principal spatial location of CD8+ T cell clonotypes largely defined exhaustion state, with clonotypic heterogeneity not explained by somatic intra-tumoural heterogeneity. De novo mutation calling from single cell RNA sequencing data allows us to lineagetrace and infer clonality of cells. We discovered six meta-programmes that distinguish tumour cell function. An epithelial-mesenchymal transition meta-programme, enriched at the tumour-normal interface appears modulated through macrophage expressed IL1B, potentially forming a therapeutic target. Single sentence summaryKidney cancer evolution, prognosis and therapy are revealed by a single cell multi-regional study of the microenvironment.

genomics↗

bioRxiv2019

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↗

bioRxiv2019

Comprehensive characterisation of cell-free tumour DNA in plasma and urine of patients with renal tumours

Cell-free tumour-derived DNA (ctDNA) allows non-invasive monitoring of cancers but its utility in renal cell cancer (RCC) has not been established. Here, untargeted and targeted sequencing methods, applied to two independent cohorts of renal tumour patients (n=90), were used to determine ctDNA content in plasma and urine. Our data revealed lower plasma ctDNA levels in RCC relative to other cancers, with untargeted detection of [~]33%. A sensitive personalised approach, applied to plasma and urine from select patients improved detection to [~]50%, including in patients with early-stage and even benign lesions.\n\nA machine-learning based model predicted detection, potentially offering a means of triaging samples for personalised analysis. In addition, with limited data we observed that plasma, and for the first time, urine ctDNA may better represent tumour heterogeneity than tissue biopsy. Furthermore, longitudinal sampling of >200 plasma samples revealed that ctDNA can track disease course. Additional datasets will be required to validate these findings.\n\nOverall, our data highlight RCC as a ctDNA-low malignancy, but indicate potential clinical utility provided improvement in detection approaches.\n\nOne sentence summaryComplementary sequencing methods show that cell-free tumour DNA levels are low in renal cancer though, via various strategies, may still be informative.

cancer biology↗