Tumor Profiler Ovarian Study

The Tumor Profiler Study is an observational trial combining a prospective diagnostic approach to assess the relevance of in-depth tumor profiling to support clinical decision-making with an exploratory approach to improve the biological understanding of the disease. Technologies used are digital pathology, targeted NGS, single-cell RNA, single-cell DNA, bulk RNA, bulk proteotyping, single-cell CyTOF, imaging mass cytometry, pharmacoscopy, 4i Drug Response Profiling, and cell-free DNA. These technologies were applied to 240 tumor samples collected over 3 years across three cancer indications: metastatic melanoma, metastatic epithelial ovarian cancer, and acute myeloid leukemia (AML). Here, we publish the scRNA and scDNA data of the ovarian cancer cohort.

Type: Cancer Genomics
Archiver: European Genome-Phenome Archive (EGA)

5 Datasets

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID	Description	Technology	Samples
EGAD50000001290	The dataset contains 10x Chromium single-cell transcriptomics data from 43 samples from 38 different patients with ovarian cancer. 36 samples were collected from the tumor, 7 samples from ascites. Sequencing was performed in paired-end mode and sequencing data is provided in fastq format.	Illumina NovaSeq 6000 NextSeq 500	43
EGAD50000001291	The dataset contains 10x Chromium single-cell DNA sequencing data from 42 samples from 38 different patients with ovarian cancer. 35 samples were collected from the tumor, and 7 samples from ascites. Sequencing was performed in paired-end mode and sequencing data is provided in fastq format.	Illumina NovaSeq 6000 NextSeq 500	42
EGAD50000001292	The Tumor Profiler Study is an observational trial combining a prospective diagnostic approach to assess the relevance of in-depth tumor profiling to support clinical decision-making with an exploratory approach to improve the biological understanding of the disease. This dataset contains scDNA-seq data for 3 ovarian cancer samples from the Tumor Profiler Study used to validate LongSom. LongSom is a computational workflow leveraging high-quality long-read scRNA-seq data to call de novo somatic single-nucleotide variants (SNVs), including in mitochondria (mtSNVs), copy-number alterations (CNAs), and gene fusions, to reconstruct tumor clonal heterogeneity.	Illumina NovaSeq 6000	3
EGAD50000001293	The dataset contains 10x Chromium single-cell transcriptomics data from 6 samples from 4 different patients with ovarian cancer. 3 samples were collected from the tumor, 3 samples from ascites. Sequencing was performed in paired-end mode and sequencing data is provided in fastq format. This dataset contains additional samples from patients not classified as having high-grade serous adenocarcinoma.	Illumina NovaSeq 6000 NextSeq 500	6
EGAD50000001294	The dataset contains 10x Chromium single-cell DNA sequencing data from 6 samples from 5 different patients with ovarian cancer. 4 samples were collected from the tumor, and 2 samples from ascites. Sequencing was performed in paired-end mode and sequencing data is provided in fastq format. This dataset contains additional samples from patients not classified as having high-grade serous adenocarcinoma.	Illumina NovaSeq 6000 NextSeq 500	6