@article{c0661335dd6b4a248141e64327318f81,
title = "Machine-learning aided in situ drug sensitivity screening predicts treatment outcomes in ovarian PDX tumors",
abstract = "Long-term treatment outcomes for patients with high grade ovarian cancers have not changed despite innovations in therapies. There is no recommended assay for predicting patient response to second-line therapy, thus clinicians must make treatment decisions based on each individual patient. Patient-derived xenograft (PDX) tumors have been shown to predict drug sensitivity in ovarian cancer patients, but the time frame for intraperitoneal (IP) tumor generation, expansion, and drug screening is beyond that for tumor recurrence and platinum resistance to occur, thus results do not have clinical utility. We describe a drug sensitivity screening assay using a drug delivery microdevice implanted for 24 h in subcutaneous (SQ) ovarian PDX tumors to predict treatment outcomes in matched IP PDX tumors in a clinically relevant time frame. The SQ tumor response to local microdose drug exposure was found to be predictive of the growth of matched IP tumors after multi-week systemic therapy using significantly fewer animals (10 SQ vs 206 IP). Multiplexed immunofluorescence image analysis of phenotypic tumor response combined with a machine learning classifier could predict IP treatment outcomes against three second-line cytotoxic therapies with an average AUC of 0.91.",
keywords = "Drug delivery, Ovarian cancer, Patient derived xenograft, Personalized medicine",
author = "Cotler, {Max J.} and Ramadi, {Khalil B.} and Xiaonan Hou and Elena Christodoulopoulos and Sebastian Ahn and Ashvin Bashyam and Huiming Ding and Melissa Larson and Oberg, {Ann L.} and Charles Whittaker and Oliver Jonas and Kaufmann, {Scott H.} and Weroha, {S. John} and Cima, {Michael J.}",
note = "Funding Information: We thank P. Bursch, B. Miller, A. Lammers, K. Subramanyam, H. Montague-Alamin, M. Sherman, C. Frangieh, and all members of the Cima, Weroha, and Jonas Labs for technical discussions and support with this study. We thank B. Ferland and the Dana-Farber/Harvard Cancer Center in Boston, MA, for the use of the Specialized Histopathology Core and the Tissue Microarray Imaging Core, which provided histology, immunofluorescence, and imaging services. We also the Koch Institute Swanson Biotechnology Center's Hope Babette Tang (1983) Histology Facility (K. Cormier and C. Condon), Preclinical Imaging and Testing Core (M. Cornwall-Brady), and Barbara K. Ostrom (1978) Bioinformatics & Computing Facility (H. Ding and C. Whittaker). We also thank the women who donated their tumor tissue. Funding Information: National Cancer Institute Innovative Molecular Analysis Technologies program (R33 CA223904), Mayo Clinic-Koch Institute Cancer Solutions Team Grant, the Koch Institute Support (core) Grant P30-CA14051 from the National Cancer Institute, Dana-Farber/Harvard Cancer Center NCI Cancer Center Support Grant # NIH 5 P30 CA06516, Mayo Clinic Ovarian Cancer SPORE National Cancer Institute P50 CA136393, National Cancer Institute R01 CA184502, and the National Science Foundation Graduate Research Fellowship Program under Grant No. (1122374). Publisher Copyright: {\textcopyright} 2022",
year = "2022",
month = jul,
doi = "10.1016/j.tranon.2022.101427",
language = "English (US)",
volume = "21",
journal = "Translational Oncology",
issn = "1936-5233",
publisher = "Neoplasia Press",
}