Abstract:
Patient-derived organoids (PDOs) provide a unique opportunity to study cancer as an evolving, heterogeneous disease while retaining clinically relevant features of individual tumors. We are developing integrated organoid platforms spanning Barrett’s esophagus (BE), esophageal adenocarcinoma (EAC), and gastroesophageal malignancies to investigate how genetic susceptibility, acquired genomic alterations, epigenetic dysregulation, and structural genome evolution shape tumor initiation, progression, and therapeutic response.
These models enable long-term study of epithelial states that are hard to analyze in standard cell lines or bulk tumors. Combining PDOs of premalignant, primary, and advanced disease with genomic and epigenomic profiling allows us to see how early genetic risks and chromatin changes drive cancer- promoting transcriptional programs and therapy-resistant cell states. Functional and drug-response studies test whether these states create actionable dependencies.
Tumor heterogeneity is addressed by integrating organoid findings with single-cell, spatial transcriptomic, and epigenomic datasets from patient tissues. This maps organoid programs to native tissues, revealing epithelial states, stromal interactions, and signaling networks linked to malignant progression and metastasis. In advanced tumors, we investigate ecDNA as a source of oncogene amplification, copy- number plasticity, and intratumoral diversity. ecDNA-positive PDOs serve as systems to study their evolution under therapy and resistance.
Long-read sequencing adds an important complementary layer by resolving structural variants, complex amplicons, ecDNA architecture, haplotypes, and transcript isoforms that short-read approaches may incompletely capture. Integrating long-read genomics with spatial profiling, epigenetic mapping, and functional organoid assays enables molecular architecture to be connected directly with phenotype and therapeutic vulnerability.
This framework sees PDOs as patient-specific systems for reconstructing cancer evolution at multiple scales, aiding biomarker discovery, understanding tumor heterogeneity and resistance, and guiding personalized therapies to improve precision oncology. It connects mechanistic research, biomarker development, therapeutic testing, and disease modeling.
