NBDC Research ID: hum0030.v2
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SUMMARY
Aims: To clarify the biological difference between clear cell carcinoma and other histological subtypes in ovarian carcinomas by comparing copy number variants, and to identify molecular subtypes and carcinogenesis in clear cell ovarian carcinomas by whole-exome sequencing and RNA-sequencing. Then, characterize high-grade serous carcinomas by NGS-based, integrative genomic analyses, with focus on homologous recombination deficiency, molecular subtypes, and prognostic factors.
Methods:
Copy Number Variation Analysis: Gene Chip Human Mapping 250K Nsp Arrays were used for detecting the signal intensity of about 260 thousands SNPs and intensities of ovarian clear cell carcinoma were compared to the ones of non-carcinoma.
Whole Exome sequencing and RNA-seq
Participants/Materials: Ovarian cancer: 57 cases (12 endometrioid carcinoma) + 111 cases
| Dataset ID | Type of Data | Criteria | Release Date |
|---|---|---|---|
| JGAS000022 | Copy Number Variations in cancer genome | Controlled-access (Type I) | 2015/04/21 |
| JGAS000560 | NGS (Exome, RNA-seq) | Controlled-access (Type I) | 2024/05/10 |
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MOLECULAR DATA
| Participants/Materials: |
Surgical samples were obtained from 57 patients (31 clear cell carcinomas, 14 serous adenocarcinomas, and 12 endometrioid adenocarcinomas) |
| Targets | genome wide CNVs |
| Target Loci for Capture Methods |
- |
| Platform | Affymetrix [GeneChip Human Mapping 250K Nsp Array] |
| Source | gDNAs extracted from ovarian cancer cells and peripheral blood cells |
| Cell Lines | - |
| Library Construction (kit name) | GeneChip Human Mapping 250K Nsp Array |
| Algorithm for detecting CNVs (software) | genome imbalance map (GIM) algorithm (doi:10.1016/j.bbrc.2005.06.040) |
| CNV number | 262,264 CNVs |
| Japanese Genotype-phenotype Archive Dataset ID | |
| Total Data Volume | 17.5 GB |
| Comments (Policies) | NBDC policy |
| Participants/Materials |
High-grade serous ovarian carcinoma (ICD10: C56.12): 78 cases (tumor tissue: 82 samples, non-tumor tissue: 78 samples) Ovarian clear cell adenocarcinoma (ICD10: C56.14): 78 cases (tumor tissue: 78 samples, non-tumor tissue: 78 samples) |
| Targets | Exome |
| Target Loci for Capture Methods | - |
| Platform | Illumina [HiSeq 2000] |
| Library Source | DNAs extracted from tumor tissues and non-tumor tissues (peripheral blood cells) |
| Cell Lines | - |
| Library Construction (kit name) | SureSelect Human All Exon kit v4, SureSelect Human All Exon kit v5 |
| Fragmentation Methods | Ultrasonic fragmentation (Covaris) |
| Spot Type | Paired-end |
| Read Length (without Barcodes, Adaptors, Primers, and Linkers) | 100 bp x 2 |
| Mapping Methods | BWA, Novoalign |
| Mapping Quality | MAPQ>20 |
| Reference Genome Sequence | hg19 |
| Coverage (Depth) | tumor 115.6, normall 108.4 |
| Japanese Genotype-phenotype Archive Dataset ID | JGAD000682 |
| Total Data Volume | 5.6 TB (fastq, bam) |
| Comments (Policies) | NBDC policy |
| Participants/Materials |
High-grade serous ovarian carcinoma (ICD10: C56.12): 77 cases (tumor tissue: 77 samples) |
| Targets | RNA-seq |
| Target Loci for Capture Methods | - |
| Platform | Illumina [HiSeq 2000] |
| Library Source | RNAs extracted from tumor tissues |
| Cell Lines | - |
| Library Construction (kit name) | TruSeq Stranded mRNA LT Sample Prep Kit |
| Fragmentation Methods | Including library prep kit protocol |
| Spot Type | Paired-end |
| Read Length (without Barcodes, Adaptors, Primers, and Linkers) | 100 bp x 2 |
| Mapping Methods | STAR (V.2.5.2a) |
| Mapping Quality | MAPQ>20 |
| Reference Genome Sequence | hg19 |
| Detecting method for read count (software) | Cufflinks (v2.1.1) |
| QC Methods | - |
| Gene Number | 38,515 genes |
| Japanese Genotype-phenotype Archive Dataset ID | JGAD000682 |
| Total Data Volume | 5.6 TB (fastq, bam, csv [FPKM]) |
| Comments (Policies) | NBDC policy |
DATA PROVIDER
Principal Investigator: Katsutoshi Oda
Affiliation: Department of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo
Project / Group Name: -
Funds / Grants (Research Project Number):
| Name | Title | Project Number |
|---|---|---|
|
KAKENHI Grant-in-Aid for Scientific Research (S) |
An Integrated Genomic Analysis on Evolution of Cancer Cell Population |
24221011 |
|
KAKENHI Grant-in-Aid for Scientific Research (C) |
Search for the New Molecular Targeted Therapies and Biomarkers Inducing Apoptosis in Endometrial Carcinoma and Ovarian Carcinoma |
26462515 |
|
KAKENHI Grant-in-Aid for Young Scientists (B) |
Search for the New Molecular Targeted Therapies Based on Genetic Profiles of Ovarian Clear Cell Carcinoma |
25861473 |
|
Project for Development of Innovative Research on Cancer Therapeutics (P-DIRECT) |
Development of the Intractable Cancer Therapies through the New Target Identification by the Molecular Profiling (The Identification of the Gene Variation to Regulate the Treatment Sensitivity of the Progressive Ovarian Cancer) |
11114014 |
PUBLICATIONS
| Title | DOI | Dataset ID | |
|---|---|---|---|
| 1 | Integrated Copy Number and Expression Analysis Identifies Profiles of Whole-Arm Chromosomal Alterations and Subgroups with Favorable Outcome in Ovarian Clear Cell Carcinomas | doi: 10.1371/journal.pone.0128066 | JGAD000022 |
| 2 | The frequency of neoantigens per somatic mutation rather than overall mutational load or number of predicted neoantigens per se is a prognostic factor in ovarian clear cell carcinoma | doi: 10.1080/2162402X.2017.1338996 | JGAD000682 |
| 3 | Neoantigen load and HLA-class I expression identify a subgroup of tumors with a T-cell-inflamed phenotype and favorable prognosis in homologous recombination-proficient high-grade serous ovarian carcinoma | doi: 10.1136/jitc-2019-000375 | JGAD000682 |
USERS (Controlled-access Data)
| Principal Investigator | Affiliation | Research Title | Data in Use (Dataset ID) | Period of Data Use |
|---|---|---|---|---|
| Ikuo Konishi | Kyoto University | JGAD000022 | 2015/07/13-2017/03/31 | |
| Masaki Mandai | Kyoto University Faculty of Medicene, department of Gynecology and Obstetrics | Integrated analyses of omics (genomics, transcriptomics, proteomics and metabolomics) associated with clinical variables for developing indivisualizedtreatment in gynecological malignancy | JGAD000022 | 2018/10/04-2025/03/31 |
