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Patient Derived Organoids for the Screening of Therapeutics and Personalized Medicine in Pancreatic Cancer Patients

Patient Derived Organoids for the Screening of Therapeutics and Personalized Medicine in Pancreatic Cancer Patients

Despite the approval of 2 frontline treatment regimens for pancreatic cancer over the last decade, only one third of patients will demonstrate a RECIST defined response to first line therapy, another third will achieve stable disease, while the remaining third will progress through treatment. Even in patients who receive clinical benefit from therapy, the median progression free survival is only approximately 6 months and response rates to subsequent lines of therapy decline further.

In order to develop more efficacious therapeutics, better models systems are necessary. Currently pre-clinical testing is performed on a limited panel of cell lines or mouse models with homogeneous genetics and phenotypes.  Patient derived organoids (PDOs) are 3D culture systems that enable the generation, serial passaging and virtually unlimited expansion of tumor cell cultures from small amounts of tissue. Therefore, 3D organoids significantly increase the number of patients whose tumors can undergo in-depth evaluation, diversifying our model systems and enabling the identification and study of underrepresented groups. 

The Simeone Lab has extensive experience in organoid generation and is creating a large bank of pancreatic cancer organoids from patients with all stages of disease and extensive accompanying clinical data. We are using this growing bank to validate organoids as an appropriate model through comprehensive histopathologic, genomic and transcriptomic characterization of organoids and their paired primary tissue specimens. We are further utilizing the organoid bank to screen panels of approved and investigational therapeutics to compare patient clinical responses with those in organoids and obtain groundbreaking data about investigational therapeutics in heterogeneous patient samples with comprehensive genomic and gene expression data. We expect that these analyses will greatly advance personalized medicine and therapeutic development.

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