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Poster session 02

312P - Prediction model of breast cancer patient’s neoadjuvant treatment outcome using breast cancer organoids cultured from core needle biopsies

Date

21 Oct 2023

Session

Poster session 02

Topics

Clinical Research;  Cancer Biology;  Cell-Based Therapy;  Cancer Diagnostics

Tumour Site

Breast Cancer

Presenters

Dong Woo Lee

Citation

Annals of Oncology (2023) 34 (suppl_2): S278-S324. 10.1016/S0923-7534(23)01258-9

Authors

D.W. Lee1, G.Y. Kim2, K.H. PARK2, B.J. Chae3, J.E. Lee3, S.W. Kim3, S.Y. Jang3, J.Y. Choi3, J.M. Ryu3, S.K. Lee3, J.H. Yu3, S.J. Nam3, B. Ku4

Author affiliations

  • 1 Biomedical Engineering, Gachon University - Global Campus, 13120 - Seongnam/KR
  • 2 Precision Medicine Research Institute, MBD Co., Ltd, 16229 - Suwon/KR
  • 3 Division Of Breast Surgery, Department Of Surgery, Samsung Medical Center (SMC)-Sungkyunkwan University School of Medicine, 135-710 - Seoul/KR
  • 4 Chief Executive Officer, MBD Co., Ltd, 16229 - Suwon/KR

Resources

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Abstract 312P

Background

The incidence of breast cancer is steadily increasing, and many patients receive adjuvant chemotherapy before or after surgery. In particular, neoadjuvant drug therapy is commonly used to suppress microinvasive before surgery. In this study, the patient-derived organoids (PDOs) from breast cancer patient’s biopsies were used for drug sensitivity test. To predict patient’s neoadjuvant treatment outcome, we propose a new drug sensitivity evaluation method based on multi-factors such as Area Under Curve (AUC) of dose response curve, organoid growth rate.

Methods

Patient cells were obtained from core needle biopsy before surgery and mechanically dissociated before being mixed with extracellular matrix (ECM) to generate PDOs. Anti-cancer drug sensitivity testing was performed on these PDOs using a pillar-based organoid system, which involved single or combination treatments with anti-cancer drug with 384 plate-based high throughput screening. Area Under Curve (AUC) values of the dose-response curves (DRC) and organoid growth rate were calculated by pillar-based organoid system.

Results

The proposed multi-factor prediction model was verified by comparing drug sensitivity test with patient’s neoadjuvant outcome. We performed drug sensitivity test of 79 breast cancer patients. So far, 13 patients have finally been evaluated for neoadjuvant treatment results. Multi-factor prediction model ( C ancer O rganoid-based D iagnosis R eactivity P rediction, CODRP) shows 83 % sensitivity and 85 % specificity while conventional prediction model using only AUC shows 50 % sensitivity and 71 % specificity.

Conclusions

Therefore, Multi-factor prediction model (CODRP) may provide useful supplementary diagnostic information for individual breast cancer patients by selecting optimal anticancer drug candidates for patient treatment.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Medical & Bio Decision Co., Ltd.

Disclosure

All authors have declared no conflicts of interest.

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