Machine-learning aided in situ drug sensitivity screening predicts treatment outcomes in ovarian PDX tumors

Translational Oncology

Ann L. Oberg¹  Khalil B. Ramadi²  Elena Christodoulopoulos²  Sebastian Ahn³  Huiming Ding⁴  Scott H. Kaufmann⁵  Oliver Jonas⁵  Ashvin Bashyam⁶  Charles Whittaker⁶  Melissa Larson⁶  Max J. Cotler⁶  Xiaonan Hou⁶  Michael J. Cima⁶  S. John Weroha⁶ 

[1] Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;Harvard-MIT Program in Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;Department of Oncology, Mayo Clinic, Rochester, MN 55905, USA;Department of Radiology, Brigham and Women's Hospital, Boston, MA 02115, USA;Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA;The David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;

关键词: Drug delivery;    Ovarian cancer;    Personalized medicine;    Patient derived xenograft;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown