Related Experiment Video
Updated: Jul 16, 2025

Profiling Sensitivity to Targeted Therapies in EGFR-Mutant NSCLC Patient-Derived Organoids
Published on: November 22, 2021
Clonal evolution of long-term expanding head and neck cancer organoid: Impact on treatment response for personalized
Sung Yong Choi1, Joonho Shim2, Do-Eon Gu3
1Department of Otorhinolaryngology-Head and Neck Surgery, National Cancer Center, Goyang, Republic of Korea.
Objectives:
In biobanking based on patient-derived organoids (PDO), the genetic stability of organoid lines is critical for the clinical relevance of PDO with parental tumors. However, data on mutational heterogeneity and clonal evolution of PDO and their effects on treatment response are insufficient.
Methods:
To investigate whether head and neck cancer organoids (HNCOs) could maintain the genetic characteristics of their original tumors and elucidate the clonal evolution process during a long-term passage, we performed targeted sequencing, covering 377 cancer-related genes and adopted a sub-clonal fraction model. To explore therapeutic response variability between an early and late passage (>passage 6), we generated dose-response curves for drugs and radiation using two HNCO lines.
Results:
Using 3D ex vivo organoid culture protocol, we successfully established 27 HNCOs from 39 patients with an overall success rate of 70% (27/39). Their mutational profiles were highly concordant, with three of the HNCOs analyzed showing greater than 70% concordance. Only one HNCO displayed less than 50% concordance. However, many of these organoid lines displayed clonal evolution during serial passaging, although major cancer driver genes and VAF distributions were shared between early and later passages. We also found that all late passages of HNCOs tended to be more sensitive to radiation than early passages, similar to drug response results.
Conclusions:
We report the establishment of HNCO lines derived from 27 patients and demonstrate their genetic concordance with corresponding parental tumors. Furthermore, we show serial changes in mutational profiles of HNCO along with long passage culture and the impact of these clonal evolutions on response to radiotherapy.
Insights
Patient-derived organoids (PDO) are crucial for cancer research, but their genetic stability over time is key. This study shows head and neck cancer organoids (HNCOs) largely maintain genetic profiles but evolve clonally, impacting treatment sensitivity.
Area of Science:
- Oncology
- Genetics
- Biobanking
Background:
- Patient-derived organoids (PDO) are vital for cancer research, yet their genetic stability during biobanking is crucial for clinical relevance.
- Limited data exists on the mutational heterogeneity and clonal evolution of PDOs and their impact on treatment response.
Purpose of the Study:
- To assess the genetic stability of head and neck cancer organoids (HNCOs) compared to parental tumors.
- To investigate clonal evolution in HNCOs during long-term passaging.
- To determine the effect of clonal evolution on therapeutic response to drugs and radiation.
Main Methods:
- Established 27 head and neck cancer organoid (HNCO) lines from 39 patients using 3D ex vivo culture.
- Performed targeted sequencing of 377 cancer-related genes.
- Utilized a sub-clonal fraction model and generated dose-response curves for drug and radiation sensitivity across early and late passages.
Main Results:
- Achieved a 70% success rate in establishing HNCO lines with high mutational concordance to parental tumors.
- Observed clonal evolution in many HNCO lines during serial passaging, while major cancer driver genes remained consistent.
- Found that late-passage HNCOs exhibited increased sensitivity to radiation and drugs compared to early passages.
Conclusions:
- Successfully established and characterized HNCO lines, demonstrating high genetic concordance with primary tumors.
- Documented serial genetic changes in HNCOs during extended culture and their influence on radiotherapy response.
- Highlighted the importance of considering clonal evolution in PDOs for accurate treatment response prediction.

