Tumor tissue explant culture of patient-derived xenograft as potential prioritization tool for targeted therapy

Susmita Ghosh, Manu Prasad, Kiran Kundu, Limor Cohen, Ksenia M. Yegodayev, Jonathan Zorea, Ben Zion Joshua, Batel Lasry, Orr Dimitstein, Anat Bahat-Dinur, Aviram Mizrachi, Vladimir Lazar, Moshe Elkabets, Angel Porgador

نتاج البحث: نشر في مجلةمقالةمراجعة النظراء

ملخص

Despite of remarkable progress made in the head and neck cancer (HNC) therapy, the survival rate of this metastatic disease remain low. Tailoring the appropriate therapy to patients is a major challenge and highlights the unmet need to have a good preclinical model that will predict clinical response. Hence, we developed an accurate and time efficient drug screening method of tumor ex vivo analysis (TEVA) system, which can predict patient-specific drug responses. In this study, we generated six patient derived xenografts (PDXs) which were utilized for TEVA. Briefly, PDXs were cut into 2 × 2 × 2 mm3 explants and treated with clinically relevant drugs for 24 h. Tumor cell proliferation and death were evaluated by immunohistochemistry and TEVA score was calculated. Ex vivo and in vivo drug efficacy studies were performed on four PDXs and three drugs side-by-side to explore correlation between TEVA and PDX treatment in vivo. Efficacy of drug combinations was also ventured. Optimization of the culture timings dictated 24 h to be the time frame to detect drug responses and drug penetrates 2 × 2 × 2 mm3 explants as signaling pathways were significantly altered. Tumor responses to drugs in TEVA, significantly corresponds with the drug efficacy in mice. Overall, this low cost, robust, relatively simple and efficient 3D tissue-based method, employing material from one PDX, can bypass the necessity of drug validation in immune-incompetent PDX-bearing mice. Our data provides a potential rationale for utilizing TEVA to predict tumor response to targeted and chemo therapies when multiple targets are proposed.

اللغة الأصليةإنجليزيّة أمريكيّة
رقم المقال17
دوريةFrontiers in Oncology
مستوى الصوت9
رقم الإصدارJAN
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 1 يناير 2019

All Science Journal Classification (ASJC) codes

  • !!Oncology
  • !!Cancer Research

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