Extrachromosomal DNA (ecDNA) presents a promising target for cancer therapy; however, its spatial–temporal diversity and influence on tumor evolution and the immune microenvironment remain largely unclear. We apply computational methods to analyze ecDNA from whole-genome sequencing data of 595 patients with urothelial carcinoma. We demonstrate that ecDNA drives clonal evolution through structural rearrangements during malignant transformation and recurrence of urothelial carcinoma. This supports a model wherein tumors evolve via the selective expansion of ecDNA-bearing cells. Through multiregional sampling of tumors, we demonstrate that ecDNA contributes to the evolution of multifocality and increased intratumoral heterogeneity. ecDNA is present in 36% of urothelial carcinoma tumors and correlates with an immunosuppressive phenotype and poor prognosis. Single-cell RNA sequencing analyses reveal that ecDNA+ malignant cells exhibit diminished expression of MHC class I molecules, enabling them to evade T-cell immunity. Finally, we show that sequencing of urinary sediment–derived DNA has excellent specificity in detecting ecDNA.

Significance:

Our comprehensive analysis of ecDNA in urothelial carcinoma reveals its crucial role in driving the evolution and heterogeneity of multifocal cancer, as well as its early involvement in tumorigenesis. Moreover, this study sheds light on immune evasion mechanisms associated with ecDNA and offers valuable insights for developing targeted therapeutic strategies.

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©2025 The Authors; Published by the American Association for Cancer Research
2025
American Association for Cancer Research
This open access article is distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.
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First page of Spatial–Temporal Diversity of Extrachromosomal DNA Shapes Urothelial Carcinoma Evolution and the Tumor Immune Microenvironment<alt-title alt-title-type="short">ecDNA Shapes Urothelial Carcinoma Dynamics</alt-title>

Supplementary data

Supplementary Tables 1-24

Table_S1. Study cohort. Table_S2. The sample composition of each dataset. Table_S3. Patient information. Table_S4. Whole genome sequencing (WGS) datasets. 1,411 UC whole genomes. Table_S5. Whole exome sequencing (WES) datasets. Table_S6. RNA sequencing (RNA-seq) datasets. Table_S7. Circle-seq datasets. Table_S8. Single-cell RNA-seq (scRNA-seq) datasets. Table_S9. Amplicon classification. Table_S10. Amplicon similarity scores. Table_S11. ecDNA prevalence in human cancers. Table_S12. Genomic and sequence features of amplicons. Table_S13. Oncogene list. Table_S14. Genes encoded on all focal amplifications. Table_S15. ecDNA status and CNV ITH in tumor multi-region sampling cohort. Table_S16. Diver gene alterations in ecDNA- and ecDNA+ tumors. Table_S17. Summary of Single-cell RNA-seq (scRNA-seq) data. Table_S18. IHC evaluation of CD45 in 19 tumors with scRNA-seq data. Table_S19. Signature genes of 50 robust NMF programs. Table_S20. Pathway enriched in each NMF program. Table_S21. Top 30 signature genes of tumor meta-program. Table_S22. Differential pathways of malignant cells between ecDNA- and ecDNA+ tumors. Table_S23. Gene signature matrix for deconvolution analysis of bulk RNA-seq data. Table_S24. Software.

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Supplementary Figures 1-12

Supplementary Figure 1. Prevalence of ecDNA in human cancers. Supplementary Figure 2. Characteristics of ecDNA. Supplementary Figure 3. ecDNA drives massive oncogene expression in UC. Supplementary Figure 4. ecDNA can arise in flat urothelial lesions. Supplementary Figure 5. ecDNA sequence is rearranged during tumor progression. Supplementary Figure 6. Clonal ecDNA. Supplementary Figure 7. Subclonal ecDNA. Supplementary Figure 8. Timing analysis of ecDNA and driver genes. Supplementary Figure 9. Clinical and genetic features associated with ecDNA. Supplementary Figure 10. Tumor immune microenvironment associated with ecDNA. Supplementary Figure 11. Transcriptional programs associated with ecDNA. Supplementary Figure 12. Urinary ecDNA.

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