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Poster session 21

1568P - Establishment of a platform to predict radiation sensitivity in organoids derived from esophageal cancer patients

Date

21 Oct 2023

Session

Poster session 21

Topics

Clinical Research;  Laboratory Diagnostics

Tumour Site

Oesophageal Cancer

Presenters

Ga Yeon Kim

Citation

Annals of Oncology (2023) 34 (suppl_2): S852-S886. 10.1016/S0923-7534(23)01930-0

Authors

G.Y. Kim1, E. Jeong2, D.W. Lee3, B. Ku2, S.Y. Choi4, M.K. Chung5, D. Oh6

Author affiliations

  • 1 Precision Medical Dept., MBD Co., Ltd, 16229 - Suwon/KR
  • 2 Precision Medicine Laboratory, MBD Co., Ltd, 16229 - Suwon/KR
  • 3 Biomedical Engineering, Gachon University - Global Campus, 13120 - Seongnam/KR
  • 4 Otorhinolaryngology-head And Neck Surgery, Uijeongbu Eulji Medical Center, 11749 - UIJEONGBU/KR
  • 5 Otorhinolaryngology-head And Neck Surgery, Samsung Medical Center (SMC), 06351 - Seoul/KR
  • 6 Radiation Oncology, Samsung Medical Center (SMC), 06351 - Seoul/KR

Resources

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Abstract 1568P

Background

To increase the effectiveness of the treatment, radiotherapy (RT) is frequently combined with chemotherapy, immunotherapy, or radiosensitizer medications. Identifying a patient’s radiosensitivity can be important for determining patient’s therapy strategy. But there isn’t a reliable and pertinent radiation sensitivity prediction model for assessing radiation sensitivity. Here, we propose the cancer organoid-based platform for predicting the individual RT response evaluation and recurrence.

Methods

Eighteen Esophageal cancer biopsy tissues were dissociated to single cells and then disposed with BME on pillar plate (MBD co., Korea) to make the cancer organoids. After forming the organoids, these were exposed to radiation doses of 2, 4 and 8Gy. We are staining live organoid with calcein AM after radiation and obtain organoid viability. From viability, AUC (area under curve) and growth rate were calculated to predict patient’s radiation sensitivity. With these two variables, the patient's cancer stage score was also used to predict patient’s radiation sensitivity. CODRP (cancer organoid-based diagnosis reactivity prediction), radiosensitivity predication index, were calculated by patient’s AUC, growth rate, and cancer stage.

Results

Radiation sensitivity prediction model using each single-factor (patient’s AUC, growth rate and cancer stage) showed about 62% specificity, 72% sensitivity and positive predictive value (PPV) was 69%. However, when applying multi-factor model (CODRP), the specificity was 81.8%, the sensitivity was 85.7%, and the PPV was 90%. In the CODRP model, the radiation sensitive group showed 11.1% recurrence rate, while the radiation resistive group showed 72.7% recurrence rate. Therefore, there was a significant difference in recurrence-free survival rate between the sensitive group and the non-responder group based on the CODRP model.

Conclusions

Our proposed CODRP models using patient derived cancer organoid successfully predict clinical response of patient’s radiation treatment. Thus, this platform has promise as a novel prognostic indicator for people with esophageal cancer and can support precision treatment.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety.

Disclosure

All authors have declared no conflicts of interest.

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