Translational Oncology (Jan 2021)

Rapid screening for individualized chemotherapy optimization of colorectal cancer: A novel conditional reprogramming technology-based functional diagnostic assay.

  • Yingjie Li,
  • Dagang Guo,
  • Yihong Zhang,
  • Lin Wang,
  • Tingting Sun,
  • Zhongwu Li,
  • Xiaoyan Zhang,
  • Shuai Wang,
  • Yiyou Chen,
  • Aiwen Wu

Journal volume & issue
Vol. 14, no. 1
p. 100935

Abstract

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Background: In vitro patient tumor models such as patient-derived organoids (PDO) and conditionally reprogrammed (CR) cell culture are important for translational research and pre-clinical drug testing. In this study we present a personalized drug sensitivity test for late stage, potentially operable colorectal cancer (CRC) using patient-derived primary tumor cells isolated with i-CR technology, an optimized CR method. We explored the clinical feasibility of using i-CR platform to guide CRC chemotherapy, and established the correlation between in vitro drug sensitivity and patient clinical response. Methods: Primary CRC tumor cells were isolated and cultured with the i-CR technology. NGS was performed and the WES and CNV results of i-CR cells were compared with that of the original patient tumor samples. In vitro drug screenings were done with guideline chemotherapy drugs for CRC. In vivo drug response was examined with paired PDX mouse models. A double-blind co-clinical cohort study was carried out and the clinical outcomes of the enrolled patients were compared with the i-CR results. Results: i-CR platform could be used to rapidly propagate primary colorectal tumor cells that represent individual patient tumors effectively by keeping the clonal heterogeneity and the genetic characteristics. Chemotherapy drug screenings with i-CR cells were comparable with that of PDX models. More importantly, i-CR results showed high accordance with the clinical outcomes of the enrolled CRC patients. Conclusion: i-CR platform was capable to test and optimize therapeutic regimens pre-clinically, study cancer cell biology, and model tumor re-emergence to identify new targeted therapeutics from an effective personalized medicine standpoint.

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