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<p>Patient-derived-organoids (PDOs) are valuable tools for predicting individual responses to cancer treatments. However, current screening methods require large numbers of PDOs, resulting in long turnaround times and limiting clinical use. This study aimed to streamline the process by automating PDO seeding with the Yamaha Cell Handler™ (YCH). We optimized the YCH to pick and place up to ten PDOs per well, significantly reducing sample requirements compared to conventional methods. Assay optimization included evaluating seeding densities, devices, readouts, and measurement techniques. We validated the miniaturized assay by comparing it to standard screens and correlating organoid responses with patient outcomes. Our proof-of-concept demonstrated that mCRC PDOs respond to chemotherapy and targeted treatments in a way that closely matches results from traditional assays. This miniaturized automated platform enables efficient, high-quality drug screening with fewer cells, offering promising potential for faster, personalized cancer treatment predictions in clinical settings.</p><p></p>

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Accelerating personalized medicine: miniaturized patient-derived organoid drug screening for predicting cancer treatment responses and beyond

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月20日

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  • Yasmine Abouleila,
  • Lidwien P. Smabers,
  • Timo Voskuilen,
  • Mayke Doorn,
  • Roel Verkerk,
  • Gakuro Harada,
  • Masahiko Watanabe,
  • Hideaki Kyan,
  • Takahiko Kumagai,
  • Yuichi Hikichi,
  • René Overmeer,
  • Jeanine M. L. Roodhart,
  • Kiyotaka Matsuno,
  • Carla S. Verissimo,
  • Sylvia F. Boj

摘要

Patient-derived-organoids (PDOs) are valuable tools for predicting individual responses to cancer treatments. However, current screening methods require large numbers of PDOs, resulting in long turnaround times and limiting clinical use. This study aimed to streamline the process by automating PDO seeding with the Yamaha Cell Handler™ (YCH). We optimized the YCH to pick and place up to ten PDOs per well, significantly reducing sample requirements compared to conventional methods. Assay optimization included evaluating seeding densities, devices, readouts, and measurement techniques. We validated the miniaturized assay by comparing it to standard screens and correlating organoid responses with patient outcomes. Our proof-of-concept demonstrated that mCRC PDOs respond to chemotherapy and targeted treatments in a way that closely matches results from traditional assays. This miniaturized automated platform enables efficient, high-quality drug screening with fewer cells, offering promising potential for faster, personalized cancer treatment predictions in clinical settings.