Quantitative Optical Imaging of Primary Tumor Organoid Metabolism Predicts Drug Response in Breast Cancer

被引:234
作者
Walsh, Alex J. [1 ]
Cook, Rebecca S. [2 ,4 ]
Sanders, Melinda E. [4 ,5 ]
Aurisicchio, Luigi [6 ]
Ciliberto, Gennaro [7 ]
Arteaga, Carlos L. [2 ,3 ,4 ]
Skala, Melissa C. [1 ]
机构
[1] Vanderbilt Univ, Dept Biomed Engn, Nashville, TN 37235 USA
[2] Vanderbilt Univ, Dept Canc Biol, Nashville, TN 37235 USA
[3] Vanderbilt Univ, Dept Med, Nashville, TN 37235 USA
[4] Vanderbilt Univ, Vanderbilt Ingram Canc Ctr, Breast Canc Res Program, Nashville, TN 37235 USA
[5] Vanderbilt Univ, Med Ctr, Dept Pathol Microbiol & Immunol, Nashville, TN 37235 USA
[6] Takis Biotech, Rome, Italy
[7] IRCCS Natl Canc Inst G Pascale, Naples, Italy
基金
美国国家科学基金会;
关键词
PHASE-II TRIAL; FLUORESCENCE LIFETIME; MULTIPHOTON MICROSCOPY; EPITHELIAL-CELLS; PI3K INHIBITORS; NADH; EXPRESSION; RECEPTOR; MICROENVIRONMENT; THERAPEUTICS;
D O I
10.1158/0008-5472.CAN-14-0663
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
There is a need for technologies to predict the efficacy of cancer treatment in individual patients. Here, we show that optical metabolic imaging of organoids derived from primary tumors can predict the therapeutic response of xenografts and measure antitumor drug responses in human tumor-derived organoids. Optical metabolic imaging quantifies the fluorescence intensity and lifetime of NADH and FAD, coenzymes of metabolism. As early as 24 hours after treatment with clinically relevant anticancer drugs, the optical metabolic imaging index of responsive organoids decreased (P < 0.001) and was further reduced when effective therapies were combined (P < 5 x 10(-6)), with no change in drug-resistant organoids. Drug response in xenograft-derived organoids was validated with tumor growth measurements in vivo and staining for proliferation and apoptosis. Heterogeneous cellular responses to drug treatment were also resolved in organoids. Optical metabolic imaging shows potential as a high-throughput screen to test the efficacy of a panel of drugs to select optimal drug combinations. (C)2014 AACR.
引用
收藏
页码:5184 / 5194
页数:11
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