JHOS-4Homo sapiens (Human)Cancer cell line

Also known as: JHOS4

🤖 AI SummaryBased on 9 publications

Quick Overview

Human ovarian cancer cell line with high genomic similarity to HGSOC.

Detailed Summary

JHOS-4 is a human ovarian cancer cell line derived from high-grade serous ovarian cancer (HGSOC). It exhibits strong genomic and proteomic similarities to HGSOC tumors, making it a valuable model for studying this aggressive cancer subtype. The cell line shows characteristics consistent with HGSOC, including TP53 mutations and specific gene expression profiles. It has been identified as a suitable model for preclinical studies due to its close resemblance to HGSOC tumors in terms of copy-number alterations and mutational profiles. JHOS-4 is also noted for its potential in drug sensitivity studies, particularly in relation to targeted therapies and molecular profiling.

Research Applications

Genomic profilingProteomic analysisDrug sensitivity studiesMolecular characterization

Key Characteristics

High genomic similarity to HGSOCTP53 mutationsSpecific gene expression profilesSuitability for preclinical studies

Generated on 6/21/2025

Basic Information

Database ID	CVCL_4649
Species	Homo sapiens (Human)
Tissue Source	Peritoneum[UBERON:UBERON_0002358]

Donor Information

Age	44
Age Category	Adult
Sex	Female
Race	asian

Disease Information

Disease	High grade ovarian serous adenocarcinoma
Lineage	Ovary/Fallopian Tube
Subtype	High-Grade Serous Ovarian Cancer
OncoTree Code	HGSOC

DepMap Information

Source Type	RIKEN
Source ID	ACH-000584_source

Known Sequence Variations

Type	Gene/Protein	Description	Zygosity	Note	Source
MutationSimple	TP53	p.Val147Gly (c.440T>G)	Homozygous	-	Unknown

Haplotype Information (STR Profile)

Short Tandem Repeat (STR) profile for cell line authentication.

Amelogenin

CSF1PO

D13S317

D16S539

9,10

D21S11

29,31.2

D5S818

D7S820

TH01

TPOX

8,12

vWA

Gene Expression Profile

Gene expression levels and statistical distribution

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Full DepMap dataset with combined data across cell lines

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Publications

Pan-cancer proteomic map of 949 human cell lines.";

Robinson P.J., Zhong Q., Garnett M.J., Reddel R.R.

Cancer Cell 40:835-849.e8(2022).

DOI PubMed PMC

Comprehensive transcriptomic analysis of cell lines as models of primary tumors across 22 tumor types.

van 't Veer L.J., Butte A.J., Goldstein T., Sirota M.

Nat. Commun. 10:3574.1-3574.11(2019).

DOI PubMed PMC

Next-generation characterization of the Cancer Cell Line Encyclopedia.

Sellers W.R.

Nature 569:503-508(2019).

DOI PubMed PMC

Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens.

Stronach E.A., Saez-Rodriguez J., Yusa K., Garnett M.J.

Nature 568:511-516(2019).

DOI PubMed

An interactive resource to probe genetic diversity and estimated ancestry in cancer cell lines.

Dutil J., Chen Z.-H., Monteiro A.N.A., Teer J.K., Eschrich S.A.

Cancer Res. 79:1263-1273(2019).

DOI PubMed PMC

Integrated genomic, epigenomic, and expression analyses of ovarian cancer cell lines.

Velculescu V.E., Scharpf R.B.

Cell Rep. 25:2617-2633(2018).

DOI PubMed PMC

Characterization of human cancer cell lines by reverse-phase protein arrays.

Liang H.

Cancer Cell 31:225-239(2017).

DOI PubMed PMC

Integrative proteomic profiling of ovarian cancer cell lines reveals precursor cell associated proteins and functional status.

Tyanova S., Montag A., Lastra R.R., Lengyel E., Mann M.

Nat. Commun. 7:12645.1-12645.14(2016).

DOI PubMed PMC

A landscape of pharmacogenomic interactions in cancer.";

Wessels L.F.A., Saez-Rodriguez J., McDermott U., Garnett M.J.

Cell 166:740-754(2016).

DOI PubMed PMC

TCLP: an online cancer cell line catalogue integrating HLA type, predicted neo-epitopes, virus and gene expression.

Loewer M., Sahin U., Castle J.C.

Genome Med. 7:118.1-118.7(2015).

DOI PubMed PMC

Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies.

Golub T.R., Root D.E., Hahn W.C.

Sci. Data 1:140035-140035(2014).

DOI PubMed PMC

Evaluating cell lines as tumour models by comparison of genomic profiles.

Domcke S., Sinha R., Levine D.A., Sander C., Schultz N.

Nat. Commun. 4:2126.1-2126.10(2013).

DOI PubMed PMC

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity.

Morrissey M.P., Sellers W.R., Schlegel R., Garraway L.A.

Nature 483:603-607(2012).

DOI PubMed PMC

Web Resources

tcpaportal.org