Abstract 187P
Background
Determining the radiosensitivity of an individual patient is pivotal in formulating an effective treatment strategy. However, the challenge has been the lack of reliable and clinically relevant predictive models for assessing radiosensitivity. In this study, we introduce a novel cancer organoid-based model called OncoSensi (Organoid Growth-based Oncological Sensitivity Test), which aims to predict an individual's response to radiotherapy and evaluate the risk of recurrence in patients with pharyngeal and esophageal cancer.
Methods
Biopsy tissues from 18 esophageal cancer patients and 14 pharyngeal cancer patients were dissociated into single cells and cultured on pillar plates with extracellular matrix (ECM) to establish cancer organoids array for high throughput radiation screening. These organoids were subsequently exposed to radiation doses of 2, 4, and 8 Gy. Post-irradiation, viable organoids were stained with calcein AM to assess survival. The area under the curve (AUC) and growth rate were calculated from viability data to determine the radiation sensitivity of each patient's organoids. Additionally, the patient's cancer stage score was integrated with these two parameters to generate the OncoSensi model and radiosensitivity prediction index.
Results
When individual parameters, such as the patient's AUC (conventional method), were employed, the radiation sensitivity prediction model showed specificity and sensitivity ranging from 50% to 70%. However, the organoid growth-based Oncological Sensitivity Test (OncoSensi) notably improved specificity to over 80% and sensitivity to over 80% in patients with pharyngeal and esophageal cancer. Additionally, OncoSensi identified a radiation-resistant group with a recurrence rate of over 50% within one year, distinguishing it significantly from the radiosensitive group in both pharyngeal and esophageal cancer patients.
Conclusions
Hence, the proposed OncoSensi proves valuable in predicting radiation response and recurrence among patients before undergoing radiation therapy for pharyngeal and esophageal cancers. It holds promise for application in precision medical platforms.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
D.W. Lee.
Funding
Medical & Bio Decision.
Disclosure
All authors have declared no conflicts of interest.
Resources from the same session
182P - Multi-modal artificial intelligence outperforms image-based approaches for mutation prediction from H&E tissue images in colorectal cancer
Presenter: Marc Päpper
Session: Poster session 08
183P - Development of a cadherin-17 (CDH17) immunohistochemistry assay for use as a companion diagnostic for cabotamig in gastrointestinal cancers
Presenter: Dennis Wong
Session: Poster session 08
184P - From breast and gastric to beyond: Expanding HER2 detection in solid tumors using quantitative RNA and protein analysis
Presenter: Kristian Egebjerg
Session: Poster session 08
185P - Multi-omics profiling and clinical characterization of colon-like cancer of unknown primary (CUP)
Presenter: Maria Pouyiourou
Session: Poster session 08
186P - Differences in antigen and immune marker expression in lymphoepithelioma-like carcinoma (LELC) and nasopharyngeal carcinoma (NPC): A multiplex immunohistochemistry (mIHC), spatial transcriptomic and multiplex immunofluorescence (mIF)-based analysis
Presenter: Daniel Peh
Session: Poster session 08
188P - Integration of immunohistochemistry and transcriptomics reveals new insights into the immune landscape of soft-tissue sarcomas
Presenter: Giulia Petroni
Session: Poster session 08
189P - An image-based deep learning prediction model for characterization of the drug tolerant persister cell state
Presenter: Lauren Cech
Session: Poster session 08
190P - A large scale proteogenomics atlas for precision oncology research
Presenter: Timothy Anthony Yap
Session: Poster session 08
191P - Understanding and overcoming resistance to selective FGFR inhibitors across FGFR2-driven tumors
Presenter: Francesco Facchinetti
Session: Poster session 08
192P - Use of biosimulation to predict homologous recombination deficiency and PARPi benefit in patients with ovarian, pancreatic, prostate and triple negative breast cancers
Presenter: Daniel Palmer
Session: Poster session 08