Establishing efficient technologies for ovarian cancer organoid derivation from fresh tumor resections

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Biospecimen Science Technologies
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Not Applicable
PROJECT SUMMARYOvarian cancer is among the deadliest cancer types, with a 5-year survival rate of only 47%. Survival rates forwomen with ovarian cancer have not changed in the past 25 years. This is partly due to the high frequency ofpatient relapses (over 75%) with cancers exhibiting drug resistance, making these cancers extremely difficultto treat effectively. Exacerbating the issue is that ovarian carcinomas are especially heterogeneous withrespect to the cell of origin, genetics, and clinical evolution. These are major impediments to establishingeffective experimental models for laboratory research, which are critical to improve the understanding andtreatment of each patient's disease. Recent technological advances have enabled the development of`organoids' ? 3D self-organized tissue cultures ? from adult stem cells and subsequently from tumor samples.Tumor-derived organoids are arranged in a way that mimics the original tumor organization. Tumor-derivedorganoids that can be cultured long-term offer the advantage of extending the experimental lifetime oftumor resection samples, which are currently a limiting step in cancer research. Recent studies haveshown that these organoids faithfully recapitulate the genetics, histology, and drug responses of originaltumor samples from breast, colorectal, and pancreatic cancer patients, paving the way for `living biobanks' ofthese cancer types. Ovarian cancer organoids have been more challenging to establish, partly because oftheir heterogeneity and unique growth requirements ? but preliminary successes have now made it feasible todevelop technology for ovarian cancer organoid derivation from fresh tumor samples. This project aims tobuild on these recent advances in the development of stem cell culture and stem-cell-based organoidsto establish platform technology for ovarian cancer organoid generation. Numerous media conditionswill be tested, which in a proof of concept will incorporate tumor genetic information to define specific growthrequirements. The organoid models generated will be validated to ensure concordance with the original tumorgenetics and histology, modifying the protocols accordingly. This project presents a unique opportunity toinvestigate whether organoid drug sensitivities correspond to patient treatment outcomes, as patients will betreated with standard-of-care chemotherapies following surgery to take the tumor samples, and organoidswill be tested with the same agents. Finally, the protocols generated will be adapted for robotic automation sothat numerous samples can be processed and biobanked in parallel at large scale, facilitating future adoptionof these methods in a clinical setting. Taken together, this project will establish a new patient-specificpreclinical model system to accelerate basic and clinical ovarian cancer research, ranging fromdisease mechanisms to personalized medicine approaches that will help to prioritize the treatmentsmost likely to be effective for each patient.