The application of organoids in treatment decision-making for digestive system cancers: progress and challenges

Abstract

Digestive system cancers-including gastric, liver, colorectal, esophageal, and pancreatic malignancies-remain leading causes of cancer death, with treatment resistance posing major challenges in advanced disease. Patient-derived cancer organoids (PDCOs), 3D mini-tumors grown from patient biopsies, have revolutionized personalized oncology by faithfully replicating tumor biology and enabling predictive drug testing for chemotherapy, radiotherapy, targeted therapy, and immunotherapy. While demonstrating good predictive accuracy, current limitations include incomplete tumor microenvironments, variable establishment rates, and lengthy processing times. Emerging technologies like AI, organ-on-chip systems, and 3D bioprinting are addressing these challenges, while clinical trials explore applications in neoadjuvant therapy and real-time treatment guidance. This Review highlights key advances in PDCO technology and its transformative potential for treatment decision-making in digestive system cancers, bridging laboratory research with clinical care to enable truly personalized therapeutic strategies tailored to individual tumor biology.

Keywords: Digestive system cancers; Drug screening; Organoids; Personalized medicine; Precision oncology; Tumor microenvironment.

Fig. 1

Genetic mutations mediating morphological transformation of GC organoids. A Representative bright-field (top) and confoal (bottom) images of solid (left) and glandular organoid morphologies (right) with F-actin (green) and integrin-α6 (red) staining. B Pathological and morphological subtypes, and Y-27,632 requirement status of GC organoids, associated with CDH1 and RHOA alterations. GS-GC organoids are highlighted in green characters. C Sanger sequencing confirmation of CDH1 and RHOA KO in single KO organoids. Black frames show sgRNA targets. D CDH1 (red) and Ki67 (green) immunostaining validating CDH1 KO and the viability of CDH1KO organoids, respectively. E DGC-like morphological transformation by CDH1 KO and the retention of the cystic structure by RHOA single-KO and CDH1/RHOA DKO. F–H ROCK inhibitor (Y-27632) treatment on CDH1KO gastric organoids (F), DGC organoids (G) and CTNNA1KO gastric organoids (H). GC: Gastric cancer. GS: Genomically stable. KO: Knockout. DKO: Double knockout. DGC: Diffuse type GC.Reprinted from Cell, Vol 174/4, Nanki et al. Divergent Routes toward Wnt and R-spondin Niche Independency during Human Gastric Carcinogenesis, Pages No.5, Copyright 2018, with permission from Elsevier.

Fig. 2

Milestones in Organoid Development and Digestive System Cancer Organoid Models. Note: PSCs: Pluripotent Stem Cells. 3D: Three-dimensions. ESCs: Embryonic Stem Cells. IPSCs: Induced Pluripotent Stem Cells. This figure was created with Adobe Illustrator

Fig. 3

Therapeutic Applications of PDCOs in Digestive System Malignancies. a Chemotherapy: PDCOs are used for drug screening and testing to predict patient responses to cytotoxic agents like 5-FU and oxaliplatin. b Radiotherapy: Organoids assess tumor radiosensitivity and optimize radiation dosing. c Targeted Therapy: PDCOs identify effective targeted drugs (e.g., KRAS inhibitors) and resistance mechanisms. d Immunotherapy: Co-cultures with immune cells evaluate checkpoint inhibitors (e.g., anti-PD-1) and CAR-T/NK therapies. e Microbial-Assisted Therapy: Probiotics and microbiota interactions are tested for anti-tumor effects. f Photodynamic Therapy (PDT): Organoids model light-activated treatments combined with drugs or immunotherapy. This figure was created with Adobe Illustrator

Fig. 4

Patient-derived organoids recapitulate intra- and inter-patient heterogeneity in response to TAS-102. A PDOs were established from a patient (R-019) with mixed response to TAS-102. While the segment 2 metastasis rapidly progressed, the segment 5 one remained stable upon TAS-102 treatment (white arrows in the CT-scan indicate metastases; bars indicate pre- and post-treatment measurement of the indicated metastases). B Ex vivo dose-response curves in baseline (BL) and post-treatment (PD) multi-region PDOs from patient R-019 (with mixed response to TAS-102). N = independent experiments; viability values are expressed as mean ± SEM. C TK1 immunohistochemistry (IHC) expression in TAS-102 refractory (segment 2) and sensitive (segment 5) PDOs. BL = baseline; PD = post-treatment/progressive disease. D Cell viability (left) and TK1 mRNA expression (right) in PDOs from TAS-102 responsive and refractory patients. BL = baseline; PD = post-treatment/progressive disease. N indicates independent experiments; viability values are expressed as mean ± SEM. CT: Computed tomography. Reproduced from Vlachogiannis et al., Science, DOI: https://doi.org/10.1126/science.aao2774, copyright 2018, AAAS.

Fig. 5

The workflow of PDCOs-guided patient treatment. a Tumor Sample Collection: Biopsies or surgical samples are obtained from the patient. b PDCO Generation & Characterization: Tissue dissociation and culture in extracellular matrix (e.g., Matrigel) with tailored growth factors. Validation via omics (e.g., NGS) and histopathology (e.g., H&E staining). c Functional Testing: High-throughput drug screening (e.g., chemotherapy, targeted therapy). AI-driven prediction of drug response based on viability assays. d Data Integration & AI Analysis: Prioritize sensitive regimens and avoid resistant therapies. e Clinical Decision: Treatment selection informed by PDCO results (e.g., 91.7% concordance in gastric cancer). This figure was created with Biorender