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Biomarker-guided treatment strategies for ovarian cancer identified from a heterogeneous panel of patient-derived tumor xenografts

Authors :
Deborah Plana
GiNell Elliott
David A. Ruddy
Xiamei Zhang
Stacy Rivera
Joshua M. Korn
Jeffrey A. Engelman
Margaret E. McLaughlin
Ronald Meyer
John Green
Paul Fordjour
Hui Gao
Esther Kurth
Adam C. Palmer
Julie Goldovitz
Guizhi Yang
Caroline Bullock
William R. Sellers
Roberto Velazquez
Daniel P. Rakiec
Colleen Kowal
Peter K. Sorger
Juliet Williams
Hans Bitter
Alice Loo
Publication Year :
2020
Publisher :
Cold Spring Harbor Laboratory, 2020.

Abstract

Advanced ovarian cancers are a leading cause of cancer-related death in women. Such cancers are currently treated with surgery and chemotherapy which is often temporarily successful but exhibits a high rate of relapse after which treatment options are few. Here we assess the responses of a panel of patient-derived ovarian cancer xenografts (PDXs) to 19 mono and combination therapies, including small molecules and antibody-drug conjugates. The PDX panel aimed to mimic the heterogeneity of disease observed in patients, and exhibited a distribution of responsiveness to standard of care chemotherapy similar to human clinical data. Three monotherapies and one drug combination were found to be active in different subsets of PDXs. By analyzing gene expression data we identified gene expression biomarkers predictive of responsiveness to each of three novel targeted therapy regimens. While no single treatment had as high a response rate as chemotherapy, nearly 90% of PDXs were eligible for and responded to at least one biomarker-guided treatment, including tumors resistant to standard chemotherapy. Biomarker frequency was similar in human patients, suggesting the possibility of a new therapeutic approach to ovarian cancer and demonstrating the potential power of PDX-based trials in broadening the reach of precision cancer medicine.

Details

Language :
English
Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....be77d97372cd8af1ee4d3f4a5bfdc2bc
Full Text :
https://doi.org/10.1101/2020.01.08.898734