TY - JOUR
T1 - Long-read spatial transcriptomics of patient-derived clear cell renal cell carcinoma organoids identifies heterogeneity and transcriptional remodelling following NUC-7738 treatment
AU - Abdullah, Hazem
AU - Zhang, Ying
AU - Kirkwood, Kathryn
AU - Laird, Alexander
AU - Mullen, Peter
AU - Harrison, David
AU - Elshani, Mustafa
N1 - Funding: The authors acknowledge Research Computing at the James Hutton Institute for providing computational resources and technical support for the “UK’s Crop Diversity Bioinformatics HPC” (BBSRC grants BB/S019669/1 and BB/X019683/1), use of which has contributed to the results reported within this paper.
PY - 2026/1/14
Y1 - 2026/1/14
N2 - 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.
AB - 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.
KW - Clear cell renal cell carcinoma
KW - Patient-derived organoids
KW - Organoid spatial transcriptomics
KW - NUC-7738
KW - Heterogeneity
KW - Long read sequencing
KW - Transcript isoforms
U2 - 10.3390/cancers18020254
DO - 10.3390/cancers18020254
M3 - Article
SN - 2072-6694
VL - 18
JO - Cancers
JF - Cancers
IS - 2
M1 - 254
ER -