Faculty, Staff and Student Publications
Publication Date
12-1-2022
Journal
Heliyon
Abstract
BACKGROUND: Non-metastatic muscle invasive urothelial bladder cancer (MIBC) has a poor prognosis and standard of care (SOC) includes neoadjuvant cisplatin-based chemotherapy (NAC) combined with cystectomy. Patients receiving NAC have at best
METHODS: We optimized engraftment conditions for primary MIBC tumors using the CAM-PDX model and tested concordance between cisplatin-based chemotherapy response of patients to matching PDX tumors using tumor growth coupled with immunohistochemistry markers of proliferation and apoptosis. We also tested select kinase inhibitor response on chemotherapy-resistant bladder cancers on the CAM-PDX using tumor growth measurements and immuno-detection of proliferation marker, Ki-67.
RESULTS: Our results show primary, NAC-resistant, MIBC tumors grown on the CAM share histological characteristics along with cisplatin-based chemotherapy resistance observed in the clinic for matched parent human tumor specimens. Patient tumor specimens acquired after chemotherapy treatment (post-NAC) and exhibiting NAC resistance were engrafted successfully on the CAM and displayed decreased tumor growth size and proliferation in response to treatment with a dual EGFR and HER2 inhibitor, but had no significant response to either CDK4/6 or FGFR inhibition.
CONCLUSIONS: Our data suggests concordance between cisplatin-based chemotherapy resistance phenotypes in primary patient tumors and CAM-PDX models. Further, proteogenomic informed kinase inhibitor use on MIBC CAM-PDX models suggests a benefit from integration of rapid in vivo testing of novel therapeutics to inform more complex, pre-clinical mouse PDX experiments for more effective clinical trial design aimed at achieving optimal precision medicine for patients with limited treatment options.
Keywords
Bladder cancer, CAM, Proteomics, Genomics, Chemotherapy, Kinase inhibitor, PDX, Targeted therapy, EGFR, HER2, CDK4/6
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Medical Sciences Commons, Oncology Commons
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Associated Data
PMID: 36643309