Marinello, A. | Search bioRxiv

bioRxiv2025

STHELAR, a multi-tissue dataset linking spatial transcriptomics and histology for cell type annotation

Understanding the composition of the tumor microenvironment is critical for cancer research. Spatial transcriptomics profile gene expressions in spatial context, revealing tissue architecture and cellular heterogeneity, but its cost and technical complexity limit adoption. To address this issue, we introduce a pipeline to build STHELAR, a large-scale dataset that integrates spatial transcriptomics with Hematoxylin and Eosin (H&E) whole slide images for cell type annotation. The dataset comprises 31 human Xenium FFPE sections across 16 tissue types, for 22 cancerous and 9 non-cancerous patients. It contains over 11 million cells, each assigned to one of ten curated cell-type categories designed to accommodate a pan-cancer setting. Annotations were derived through Tangram-based alignment to single-cell reference atlases, followed by slide-specific clustering and differential expression analysis. Co-registered H&E images enabled extraction of over 500,000 patches with segmentation and classification masks. Quality control steps assessed segmentation accuracy, filtered out low-confidence regions, and verified annotation integrity. STHELAR provides a reference resource for developing models to predict cell-type annotations directly from histological images.

cell biology↗

bioRxiv2023

The PI3K-AKT-mTOR axis persists as a therapeutic dependency in KRASG12D-driven non-small cell lung cancer

IntroductionKRASG12C and KRASG12D inhibitors represent a major translational breakthrough for non-small cell lung cancer (NSCLC) and cancer in general by directly targeting its most mutated oncoprotein. However, resistance to these small molecules has highlighted the need for rational combination partners necessitating a critical understanding of signaling downstream of KRAS mutant isoforms. MethodsWe contrasted tumor development between KrasG12Cand KrasG12D genetically engineered mouse models (GEMMs). To corroborate findings and determine mutant subtype-specific dependencies, isogenic models of KrasG12C and KrasG12D initiation and adaptation were profiled by RNA sequencing. We also employed cell line models of established KRAS mutant NSCLC and determined therapeutic vulnerabilities through pharmacological inhibition. We analysed differences in survival outcomes for patients affected by advanced KRASG12C or KRASG12D-mutant NSCLC. ResultsKRASG12D exhibited higher potency in vivo, manifesting as more rapid lung tumor formation and reduced survival of KRASG12D GEMMs compared to KRASG12C. This increased potency, recapitulated in an isogenic initiation model, was associated with enhanced PI3K-AKT-mTOR signaling. However, KRASG12C oncogenicity and downstream pathway activation were comparable with KRASG12D at later stages of tumorigenesis in vitro and in vivo, consistent with similar clinical outcomes in patients. Despite this, established KRASG12D NSCLC models depended more on the PI3K-AKT-mTOR pathway, while KRASG12C models on the MAPK pathway. Specifically, KRASG12D inhibition was synergistically enhanced by AKT inhibition. ConclusionsOur data highlight a unique combination treatment vulnerability and suggest that patient selection strategies for combination approaches using direct KRAS inhibitors should be i) contextualised to individual RAS mutants, and ii) tailored to their downstream signaling.

cancer biology↗