Machine-learning aided in situ drug sensitivity screening predicts treatment outcomes in ovarian PDX tumors
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1-s2.0-S1936523322000869-main.pdf
Description
Published version
Size
6.02 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Cotler, Max J
Ramadi, Khalil B
Hou, Xiaonan
Christodoulopoulos, Elena
Ahn, Sebastian
Bashyam, Ashvin
Ding, Huiming
Larson, Melissa
Oberg, Ann L
Whittaker, Charles
Date Issued
July 2022
Journal
Translational Oncology
Publisher
Elsevier BV
Citation
Cotler, Max J, Ramadi, Khalil B, Hou, Xiaonan, Christodoulopoulos, Elena, Ahn, Sebastian et al. 2022. "Machine-learning aided in situ drug sensitivity screening predicts treatment outcomes in ovarian PDX tumors." Translational Oncology, 21.
Version
Final published version
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.
MIT Department
Koch Institute for Integrative Cancer Research at MIT
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Materials Science and Engineering
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licens
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DOI of Published Version
https://doi.org/10.1016/j.tranon.2022.101427
