Biology subjects

Wall, I.

Publications and source records attributed to Wall, I..

4 recordsLinked to original sources

bioRxiv2026

Mural β3-integrin signposts Stroma6, a gene signature predicting early relapse in low-risk and chemoresistant breast cancer

The Oncotype DX (ODX) assay guides adjuvant chemotherapy in ER+/HER2- breast cancer but overlooks drivers from the tumour stroma, leaving some low-risk and chemotherapy-spared patients vulnerable to rapid relapse. Here, we show that perivascular mural {beta}3-integrin protein expression predicts early (3-year) relapse in ODX low-risk patients (HR = 15.80, P = 0.010), from which we derived a translatable 6-gene stromal signature, termed Stroma6. High Stroma6 expression predicted early recurrence in the ODX low-risk discovery cohort, with zero early relapses in the low-expression group. External validation in the METABRIC dataset confirmed that Stroma6 independently predicts rapid recurrence in ODX low-risk patients (HR = 11.68, P = 0.002). Furthermore, Stroma6 utility extends to clinical chemoresistance, independently predicting early events in a pan-subtype cohort with residual disease following neoadjuvant chemotherapy (HR = 2.29, P = 0.003). Ultimately, Stroma6 is a robust prognostic signature identifying patients at high risk of early recurrence across diverse clinical contexts.

cancer biology↗

bioRxiv2026

Extrafollicular plasma cells disable dendritic cell-T-cell priming in tumor-draining lymph nodes

How plasma cells (PCs) shape anti-tumor immunity is unclear. We hypothesized that conflicting prognostic associations reflect differences in immune context and PC ontogeny. We identify extrafollicular (EF)-PCs as an antibody-independent checkpoint that aborts priming by disabling the cDC1[->]CD8+ T-cell axis in tumor-draining lymph nodes (td-LNs). EF-PCs blunt cDC1 activation and CCR7-guided repositioning into T-cell zones, precluding formation of TCF1 stem-like CD8 T-cells. Depleting EF-PCs in vivo restores cDC1 trafficking, expands the stem-like reservoir, increases intratumoral CD8 infiltration, and restrains tumor growth; benefit is lost with CD8 T-cell ablation. Neither serum transfer nor Fc{gamma} receptor blockade reverses tumor control, supporting a non-canonical, antibody-independent mechanism. Across independent triple-negative breast cancer cohorts, we find EF-PC hyperplasia in td-LNs and tumors; and within immune-cold cases, EF-PC burden stratifies poor prognosis and metastatic risk. A cross-species EF-PC signature maps to a conserved PC-state across cancer types that is linked to poor outcome and immune-checkpoint blockade resistance. EF-PCs thus relocate the dominant failure point to td-LNs and offer a tractable upstream target to convert immune-cold tumors into immune-responsive disease.

immunology↗

bioRxiv2025

SMART: A Spatio-Molecular Atlas of Response Trajectories in Triple-Negative Breast Cancer

A major challenge in treating Triple-Negative Breast Cancer (TNBC) lies in its molecular, morphological and clinical heterogeneity, which hampers accurate prediction of responses to neoadjuvant treatment. To address this, we introduce SMART: Spatio-Molecular Atlas of Response Trajectories, a comprehensive, multimodal resource compiled from 129 TNBC samples across 89 patients, obtained before, during, and after neoadjuvant chemotherapy (NACT). SMART comprises of 5,096 high quality manually selected spatial transcriptomic profiles enriched for epithelial, immune, or stromal compartments; paralleled with histological annotations, imagebased network analysis and protein expression. Seven novel spatial epithelial archetypes (EAs), seven tumour-immune microenvironments (TIMEs) and their co-localisation patterns were defined, revealing an opposing prevalence of functionally divergent EAs between response groups and the prognostic significance of B-cell enriched TIMEs, in particular those surrounding histologically normal epithelium adjacent to the tumour. The SMART dataset and analytical tools are publicly available via the PharosAI platform, providing the research community with the most comprehensive, manually annotated spatio-molecular transcriptomics atlas of NACT-treated TNBC to date.

cancer biology↗

bioRxiv2024

NoButter: An R package for reducing transcript dis-persion in CosMx Spatial Molecular Imaging Data

MotivationAdvances in spatial transcriptomics technologies at single-cell resolution have high-lighted the need for innovative quality assessment approaches and improved analytical tools. Imaging-based spatial transcriptomics technologies, such as the CosMx Spatial Molecular Imager (SMI), provide the location and abundance of transcripts through multifocal imaging. Optical sections (or Z-slices) form a Z-stack that represents the tissue depth. Transcript dispersion can be observed across these Z-slice and introduce considerable levels of technical noise to the data that can negatively impact downstream analysis. Package FunctionalityNoButter is an R package designed to evaluate transcript dispersion in CosMx SMI spatial transcriptomics data. Using the raw data, the transcript distribution is assessed for each Z-slice of a Z-stack across multiple fields of views (FOVs). To systematically identify transcript dispersion, the percentage of transcripts located outside cell boundaries is calculated. Z-slices exhibiting high levels of transcript dispersion can be excluded, while high-confidence transcripts are preserved. Usage ScenarioTo demonstrate the functionalities of NoButter, spatial transcriptomics data was generated using the CosMx SMI for lymph node tissue, a lung sample, and two triple-negative breast cancers (TNBCs). Use cases illustrate substantial transcript dispersion in optical planes closer to the glass slide. In these Z-slices, on average, an additional 10% of the transcripts were discarded using NoButter. Cleaning such Z-slices with high dispersion rates reduces technical noise and improves the overall quality of the spatial transcriptomics data. AvailabilityThe package can be accessed at https://github.com/cancerbioinformatics/NoButter.

bioinformatics↗