PXD065781

PXD065781 is an original dataset announced via ProteomeXchange.

Title	Leveraging Patient-Derived Models to Identify Novel Treatment Options in High-Grade Serous Ovarian Cancer
Description	Ovarian cancer (OC) remains one of the leading causes of gynecological cancer related. Despite its histological and genetic heterogeneity, standard treatment has remained largely unchanged, relying on cytoreductive surgery followed by platinum-taxol chemotherapy. Although the introduction of PARP inhibitor has led to some therapeutic advancements, high relapse rates and acquired resistance to secondary treatments continue to contribute to poor 5-year survival outcome. These challenges highlight the need for improved and personalized treatment strategies. Hence in this study, we applied a functional precision medicine approach to systematically profile OC patients at both molecular and drug-response levels, aiming to identify novel candidate biomarkers and potential therapeutic opportunities. We developed a collection of patient-derived models (PDMs) representing 22 OC patients, including of 21 cancer and 14 fibroblasts cells. These models include 7 OC subtypes, predominantly high-grade serous OC. Models were successfully validated by matching genetic profile with original tumor or ascites samples, proving to recapitulate the key genetic aberrations. These models were compatible with high-throughput drug screening assay and were profiled against library of up to 527 oncology compounds and small molecules. Drug screening revealed patient-specific drug response profiles. Overall, approximately 85% of tested compounds demonstrated minimal toxicity in healthy bone marrow cells, indicating therapeutic selectivity. Our data identified EGFR/ERBB2, mTORC1 inhibitors, and BH3 mimetics as drug subclasses showing selective anti-cancer activity. The Bcl-xL inhibitor A-1331852 demonstrated heterogenous efficacy across patient models. To explore the mechanisms underlaying differential responses to A-1331852, we performed mass-spectrometry based proteomics. This analysis revealed upregulation in NOTCH signaling in resistant clones. Subsequent combination treatment using -secretase inhibitors, A-1331852, and Carboplatin induced lasting cytotoxic responses in long-term spheroid cultures and ex vivo patient spheroids. In conclusion, our study demonstrated the potential of PDMs to capture patient-specific molecular and functional heterogeneity in OC. This approach revealed new therapeutic strategies, including combination-based targeting of Bcl-xL and NOTCH pathways as a promising strategy to overcome treatment resistance in OC.
HostingRepository	PRIDE
AnnounceDate	2026-09-07
AnnouncementXML	Submission_2026-09-06_16:38:46.504.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Georgios Mermelekas
SpeciesList	scientific name: Homo sapiens (Human); NCBI TaxID: NEWT:9606;
ModificationList	monohydroxylated residue; iodoacetamide derivatized residue
Instrument	Q Exactive HF

Revision	Datetime	Status	ChangeLog Entry
0	2025-07-04 03:11:18	ID requested
⏵ 1	2026-09-06 16:38:47	announced

10.1038/s41698-026-01628-2;

Gudoityte G, Berkovska O, Orre LM, Moussaud-Lamodi, è, re E, Bergstr, ö, m R, Lindberg J, Haraldsson M, Nafeh SB, Louhaur M, Kallioniemi O, Fernebro J, Joneborg U, Seashore-Ludlow B, Functional profiling of ovarian cancer models reveals Bcl-xL/NOTCH targeting to overcome resistance. NPJ Precis Oncol, 10(1):(2026) [pubmed]

submitter keyword: LC-MS, drug resistance,TMT, HiRIEF, BCL-XL,Ovarian cancer

Brinton Seashore-Ludlow
contact affiliation	Department of oncology-pathology, Karolinska Institutet, Sweden
contact email	brinton.seashore-ludlow@ki.se
lab head
Georgios Mermelekas
contact affiliation	Karolinska Institutet
contact email	georgios.mermelekas@scilifelab.se
dataset submitter

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PRIDE project URI

• PRIDE
  1. PXD065781
     1. Label: PRIDE project
     2. Name: Leveraging Patient-Derived Models to Identify Novel Treatment Options in High-Grade Serous Ovarian Cancer