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| Status |
Public on Jul 17, 2022 |
| Title |
High grade serous ovarian cancer organoids as models of chromosomal instability |
| Organism |
Homo sapiens |
| Experiment type |
Expression profiling by high throughput sequencing
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| Summary |
High-grade serous ovarian carcinoma (HGSOC) is the most genomically complex cancer, characterised by ubiquitous TP53 mutation, profound structural variation and heterogeneity. Multiple mutational processes driving chromosomal instability can be distinguished by specific copy number signatures. To develop clinically relevant models of these mutational processes we derived 15 continuous HGSOC patient-derived organoids (PDOs) and provide detailed transcriptomic and genomic profiles using shallow whole genome sequencing single cell and bulk analysis. We show that PDOs comprise communities of different clonal populations and represent models of CCNE1 amplification, chromothripsis, tandem-duplicator phenotype and whole genome duplication. PDOs can also be used as exploratory tools to study transcriptional effects of copy number alterations as well as compound-sensitivity tests. In summary, HGSOC PDO cultures provide a genomic tool for studies of specific mutational processes and precision therapeutics.
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| Overall design |
Gene expression profiling analysis of bulk RNA-seq for organoid samples
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| Contributor(s) |
Vias M, Gavarró LM, Sauer CM, Sanders D, Piskorz AM, Couturier D, Ballereau S, Hernando B, Hall J, Correia-Martins F, Markowetz F, Macintyre G, Brenton JD |
| Citation(s) |
37166279 |
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| Submission date |
Jul 14, 2022 |
| Last update date |
Aug 04, 2023 |
| Contact name |
Lena Morrill Gavarró |
| E-mail(s) |
lm687@cam.ac.uk
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| Organization name |
Cancer Research UK Cambridge Institute, University of Cambridge
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| Street address |
Robinson Way
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| City |
Cambridge |
| ZIP/Postal code |
CB2 0RE |
| Country |
United Kingdom |
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| Platforms (1) |
| GPL20301 |
Illumina HiSeq 4000 (Homo sapiens) |
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| Samples (14)
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| Relations |
| BioProject |
PRJNA858797 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSE208216_ViasMorrill_RNASeq_raw_counts.txt.gz |
722.2 Kb |
(ftp)(http) |
TXT |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data are available on Series record |
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