# Long-Read Spatial Transcriptomics of Patient-Derived Clear Cell Renal Cell Carcinoma Organoids Identifies Heterogeneity and Transcriptional Remodelling Following NUC-7738 Treatment

## TL;DR

This study uses advanced sequencing to examine gene activity in kidney cancer organoids and finds treatment-induced changes in gene expression patterns.

## Contribution

Combines long-read spatial transcriptomics with patient-derived organoids to reveal spatial heterogeneity and drug response in kidney cancer.

## Key findings

- Distinct spatial patterns in genes related to protein synthesis and energy metabolism were identified in organoids.
- Treatment with NUC-7738 caused significant transcriptional remodelling, including changes in ribosomal and mitochondrial gene expression.
- Spatially variable expression of GLS isoforms (GAC and KGA) was observed across organoid regions.

## Abstract

Clear cell renal cell carcinoma is the most common form of kidney cancer. Recent advances in spatial and long-read sequencing now allow detailed examination of gene activity within tumours. We used non-passaged, patient-derived tumour organoid models which preserve the structure and cellular composition of the original tumours. Long-read spatial transcriptomics was then applied to study gene expression and transcript variants across organoid regions. We also examined how these patterns change following treatment with the experimental medicine NUC-7738. There are distinct spatial patterns in genes related to protein synthesis and energy metabolism, as well as region-specific differences in transcript isoforms. This work provides new insight into the molecular diversity of kidney cancer and demonstrates how advanced sequencing technologies can be used to study treatment effects in physiologically relevant tumour models.

Background: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer and is marked by pronounced intra-tumoural heterogeneity that complicates therapeutic response. Patient-derived organoids offer a physiologically relevant model to capture this diversity and evaluate treatment effects. When integrated with spatial transcriptomics, they might enable the mapping of spatially resolved transcriptional and isoform-level changes within the tumour microenvironment. Methods: We established a robust workflow for generating patient-derived ccRCC organoids, that are not passaged and retain original cellular components. These retain key features of the original tumours, including cancer cell, stromal, and immune components. Results: Spatial transcriptomic profiling revealed multiple transcriptionally distinct regions within and across organoids, reflecting the intrinsic heterogeneity of ccRCC. Isoform-level analysis identified spatially variable expression of glutaminase (GLS) isoforms, with heterogeneous distributions of both the GAC and KGA variants. Treatment with NUC-7738, a phosphoramidate derivative of 3′-deoxyadenosine, induced marked transcriptional remodelling of organoids, including alterations in ribosomal and mitochondrial gene expression. Conclusions: This study demonstrates that combining long-read spatial transcriptomics with patient-derived organoid models provides a powerful and scalable approach for dissecting gene and isoform-level heterogeneity in ccRCC and for elucidating spatially resolved transcriptional responses to novel therapeutics.