Journal Article Preprint Epublish
PubMed: 40667087 DOI: 10.1101/2025.06.24.660824
Integrating Artificial Intelligence-Driven Digital Pathology and Genomics to Establish Patient-Derived Organoids as a Novel Alternative Model for Drug Response in Head and Neck Cancer.
bioRxiv
Authors: Rose Doerfler, Jie Chen, Carl Kim, Joshua D Smith, Micah Harris, Krishna B Singh, Brian Isett, Rebekah E Dadey, Daniel D Brown, Adrian V Lee, Xuefeng Wang, Matthew E Spector, Seungwon Kim, Shaum Sridharan, Kevin Contrera, Katelyn Smith, Carly Reeder, Maureen Lyons, Jianhua Luo, Silvia Liu, Dan P Zandberg, Heath D Skinner, Ioannis K Zervantonakis, Lazar Vujanović, Robert L Ferris, Raja R Seethala, José P Zevallos, Jason J Luke, Riyue Bao
Affiliations
- Hillman Cancer Center, UPMC, Pittsburgh, PA, USA.
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
- Cancer Bioinformatics Core, UPMC, Pittsburgh, PA, USA.
- Department of Otolaryngology, University of Pittsburgh, Pittsburgh, PA, USA.
- Institute for Precision Medicine, UPMC, Pittsburgh, PA, USA.
- Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA, USA.
- Biostatistics and Bioinformatics Program and Immuno-Oncology Program, Moffitt Cancer Center, Tampa, FL.
- Department of Immunology, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
- Organ Pathobiology and Therapeutics Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
- Department of Radiation Oncology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
- UNC Lineberger Comprehensive Cancer Center, Chapel Hill, NC, USA.
- Division of Hematology/Oncology, University of Pittsburgh, Pittsburgh, PA, USA.
Abstract
Patient-derived organoids (PDOs) are emerging as advanced 3D ex vivo novel alternative method (NAM) preclinical models, offering significant advantages over traditional cell lines and monolayer cultures for therapeutic development. In this study, we established PDOs from surgically resected fresh tissues of human papillomavirus (HPV)-negative head and neck squamous cell carcinoma (HNSCC) across anatomical sites, tumor T-categories, and sample types. These PDOs faithfully recapitulate the tumor's pathology, mutational profile, and drug response. To enable rapid classification of PDO identity, we developed a new convolutional neural network (CNN) model, TransferNet-PDO, which accurately distinguished tumor versus normal PDOs in culture using digital histopathology images (AUC≥0.88). PDOs maintained stable cultures and were cryopreserved between passages 5 and 12. Immunohistochemistry (IHC) staining (PanCK, p63, Cytokeratin 13, Ki67) confirmed squamous phenotype and histologic aggression of the original tumor. For tumors harboring TP53 mutations by whole-exome sequencing (WES), PDOs retained the corresponding p53 functional status as confirmed by IHC (enhanced or loss of expression). Somatic mutational landscape revealed that PDOs preserved driver somatic mutations, copy number variations (CNVs), and clonal architecture including low-prevalence subclones. Drug sensitivity assessment of PDOs showed that cisplatin reduced cell viability, whereas cetuximab and lenvatinib had minimal effects. Chemoradiation led to greater tumor organoid killing compared to radiation or chemotherapy alone. This study presents an integrated HNSCC PDO platform combining tissue biobanking, organoid establishment, multi-omics characterization, functional drug screening, and AI-driven histopathologic classification, providing a comprehensive and scalable system for translational cancer research.
MeSH
Cells, Telepathology, Intelligence, Genomics, Pharmaceutical Preparations, Neoplasms, Pathology, Classification, Squamous Cell Carcinoma Of Head And Neck, Organoids, Head And Neck Neoplasms, Alphapapillomavirus, Artificial Intelligence, Tissues, Mutation, Human Papillomavirus Viruses