Journal of Experimental & Clinical Cancer Research (Feb 2024)

Organoids as a biomarker for personalized treatment in metastatic colorectal cancer: drug screen optimization and correlation with patient response

  • Lidwien P. Smabers,
  • Emerens Wensink,
  • Carla S. Verissimo,
  • Esmee Koedoot,
  • Katerina-Chara Pitsa,
  • Maarten A. Huismans,
  • Celia Higuera Barón,
  • Mayke Doorn,
  • Liselot B. Valkenburg-van Iersel,
  • Geert A. Cirkel,
  • Anneta Brousali,
  • René Overmeer,
  • Miriam Koopman,
  • Manon N. Braat,
  • Bas Penning de Vries,
  • Sjoerd G. Elias,
  • Robert G. Vries,
  • Onno Kranenburg,
  • Sylvia F. Boj,
  • Jeanine M. Roodhart

DOI
https://doi.org/10.1186/s13046-024-02980-6
Journal volume & issue
Vol. 43, no. 1
pp. 1 – 16

Abstract

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Abstract Background The inability to predict treatment response of colorectal cancer patients results in unnecessary toxicity, decreased efficacy and survival. Response testing on patient-derived organoids (PDOs) is a promising biomarker for treatment efficacy. The aim of this study is to optimize PDO drug screening methods for correlation with patient response and explore the potential to predict responses to standard chemotherapies. Methods We optimized drug screen methods on 5–11 PDOs per condition of the complete set of 23 PDOs from patients treated for metastatic colorectal cancer (mCRC). PDOs were exposed to 5-fluorouracil (5-FU), irinotecan- and oxaliplatin-based chemotherapy. We compared medium with and without N-acetylcysteine (NAC), different readouts and different combination treatment set-ups to capture the strongest association with patient response. We expanded the screens using the optimized methods for all PDOs. Organoid sensitivity was correlated to the patient’s response, determined by % change in the size of target lesions. We assessed organoid sensitivity in relation to prior exposure to chemotherapy, mutational status and sidedness. Results Drug screen optimization involved excluding N-acetylcysteine from the medium and biphasic curve fitting for 5-FU & oxaliplatin combination screens. CellTiter-Glo measurements were comparable with CyQUANT and did not affect the correlation with patient response. Furthermore, the correlation improved with application of growth rate metrics, when 5-FU & oxaliplatin was screened in a ratio, and 5-FU & SN-38 using a fixed dose of SN-38. Area under the curve was the most robust drug response curve metric. After optimization, organoid and patient response showed a correlation coefficient of 0.58 for 5-FU (n = 6, 95% CI -0.44,0.95), 0.61 for irinotecan- (n = 10, 95% CI -0.03,0.90) and 0.60 for oxaliplatin-based chemotherapy (n = 11, 95% CI -0.01,0.88). Median progression-free survival of patients with resistant PDOs to oxaliplatin-based chemotherapy was significantly shorter than sensitive PDOs (3.3 vs 10.9 months, p = 0.007). Increased resistance to 5-FU in patients with prior exposure to 5-FU/capecitabine was adequately reflected in PDOs (p = 0.003). Conclusions Our study emphasizes the critical impact of the screening methods for determining correlation between PDO drug screens and mCRC patient outcomes. Our 5-step optimization strategy provides a basis for future research on the clinical utility of PDO screens.

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