Evaluating Feature Extraction in Ovarian Cancer Cell Line Co-Cultures Using Deep Neural Networks
Data files
Data files
Citation and access
Citation and access
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Creator/Principal investigator(s):
Research principal:
Data contains personal data:
No
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Method and outcome
Method and outcome
Unit of analysis:
Population:
This dataset consists of images of ovarian cancer cell lines and fibroblasts cell lines grown together (2D coculture) in a 384 well microtiter plate (in vitro). In our analysis each single cell is an object. These cocultures are used to study how cancer and fibroblasts cells interact with each other and upon drug purturbation and how the morphology of cancer cells changes.
Time method:
Study design:
- Experimental study
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Administrative information
Administrative information
Responsible department/unit:
Department of Oncology-Pathology [K7]
Contributor(s):
- Greta Gudoitytė - Karolinska Institutet - Department of Oncology and Pathology
Funding
Funding
Funding agency:
- Swedish Research Council
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Award number:
2021-03420_VR
Award title:
Druging 3D-oids: Molecular subtypes and targeted therapies for ovarian cancer
Funding information:
Despite genome sequencing, molecular profiling and numerous new cancer drugs, most ovarian cancer (OvCa) patients are treated in the same way as 30 years ago. We do not understand cancer signaling dependences, cannot predict drug sensitivities nor create tailored drug combinations. Here, we apply ovarian cancer patient cell models and naturally occurring 3D spheroids (Oids) taken directly from patient ascites samples to determine the effects of >500 clinical and emerging oncology drugs and their combinations. We have established a translational flow of fresh (living) cancer samples from the clinic and a capability for high-throughput drug screening and multi-omics profiling. This research has already led to functional taxonomy of high-grade and low-grade OvCa and the discovery of critical pathways that are explored in more detail here. Our first aim is to identify synergies of Wee, SMAC and MEK signaling pathway inhibitors with other cancer drugs in the functional taxonomic subgroups of Ovca. Second, we will create a next-generation cancer drug testing technology using 3D-oids from ascites, where it will be possible to identify the impact of drugs on cancer-host/stroma interactions and therapeutic mechanisms involved. Finally, we will explore if further technology development could lead to the creation of a new diagnostic platform to better support the selection of drugs and drug combinations for each individual cancer patient.
Funding agency:
- Åke Wibergs Stiftelse
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Award number:
M19-0271
Funding agency:
- Karolinska Institutet
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Award number:
KID, 2020-01096
Funding information:
Karolinska Institutet Doctoral Student Funding, KID-funding
