Patient-derived tumor organoids to model drug response in gastric cancer

Gastric cancer poses diverse treatment challenges due to its high tumor heterogeneity. Through the use of patient-derived tumor organoid (PDO) models, new research1Zhao Y. Li S. Zhu L. Huang M. Xie Y. Song X. Chen Z. Lau H.C.-H. Sung J.J.-Y. Xu L. Personalized drug screening using patient-derived organoid and its clinical relevance in gastric cancer.Cell Rep. Med. 2024; 5: 101627https://doi.org/10.1016/j.xcrm.2024.101627Abstract Full Text Full Text PDF Google Scholar has identified genes and molecular signatures that are predictive of chemotherapeutic response, providing valuable insights for clinical management and translational advancements. Gastric cancer poses diverse treatment challenges due to its high tumor heterogeneity. Through the use of patient-derived tumor organoid (PDO) models, new research1Zhao Y. Li S. Zhu L. Huang M. Xie Y. Song X. Chen Z. Lau H.C.-H. Sung J.J.-Y. Xu L. Personalized drug screening using patient-derived organoid and its clinical relevance in gastric cancer.Cell Rep. Med. 2024; 5: 101627https://doi.org/10.1016/j.xcrm.2024.101627Abstract Full Text Full Text PDF Google Scholar has identified genes and molecular signatures that are predictive of chemotherapeutic response, providing valuable insights for clinical management and translational advancements. Gastric cancer (GC) presents a significant global health challenge, with over a million new cases diagnosed annually and approximately 800,000 deaths each year.2Bray F. Laversanne M. Sung H. Ferlay J. Siegel R.L. Soerjomataram I. Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA. Cancer J. Clin. 2024; 74: 229-263https://doi.org/10.3322/caac.21834Crossref PubMed Scopus (192) Google Scholar The majority of GC patients are diagnosed with locally advanced disease, for which surgical resection combined with perioperative chemotherapy (e.g., 5-fluorouracil 5-FU, cisplatin, and oxaliplatin) remains the primary curative option.3Cunningham D. Allum W.H. Stenning S.P. Thompson J.N. Van de Velde C.J.H. Nicolson M. Scarffe J.H. Lofts F.J. Falk S.J. Iveson T.J. et al.Perioperative chemotherapy versus surgery alone for resectable gastroesophageal cancer.N. Engl. J. Med. 2006; 355: 11-20https://doi.org/10.1056/NEJMoa055531Crossref PubMed Scopus (5292) Google Scholar Despite the establishment of histological classification4Laurén P. The Two Histological Main Types of Gastric Carcinoma: Diffuse and So-Called Intestinal-Type Carcinoma. An Attempt at a Histo-Clinical Classification.Acta Pathol. Microbiol. Scand. 1965; 64: 31-49https://doi.org/10.1111/apm.1965.64.1.31Crossref PubMed Scopus (5379) Google Scholar and molecular subtypes,5The Cancer Genome Atlas Research NetworkComprehensive molecular characterization of gastric adenocarcinoma.Nature. 2014; 513: 202-209https://doi.org/10.1038/nature13480Crossref PubMed Scopus (4718) Google Scholar the high variability in chemotherapy responses among GC patients remains poorly understood. Patient-derived tumor organoids (PDOs) preserve key genomic and epigenomic abnormalities of the parental tumors, maintaining histopathological characteristics of the modeled specimens.6Lo Y.-H. Karlsson K. Kuo C.J. Applications of Organoids for Cancer Biology and Precision Medicine.Nat. Cancer. 2020; 1: 761-773https://doi.org/10.1038/s43018-020-0102-yCrossref PubMed Scopus (83) Google Scholar These features make organoid modeling a valid tool for investigating treatment response in patient tumors.6Lo Y.-H. Karlsson K. Kuo C.J. Applications of Organoids for Cancer Biology and Precision Medicine.Nat. Cancer. 2020; 1: 761-773https://doi.org/10.1038/s43018-020-0102-yCrossref PubMed Scopus (83) Google Scholar Indeed, several studies have utilized organoid modeling to interrogate drug responsiveness among GC patients,7Yan H.H.N. Siu H.C. Law S. Ho S.L. Yue S.S.K. Tsui W.Y. Chan D. Chan A.S. Ma S. Lam K.O. et al.A Comprehensive Human Gastric Cancer Organoid Biobank Captures Tumor Subtype Heterogeneity and Enables Therapeutic Screening.Cell Stem Cell. 2018; 23: 882-897.e11https://doi.org/10.1016/j.stem.2018.09.016Abstract Full Text Full Text PDF PubMed Scopus (450) Google Scholar,8Seidlitz T. Merker S.R. Rothe A. Zakrzewski F. von Neubeck C. Grützmann K. Sommer U. Schweitzer C. Schölch S. Uhlemann H. et al.Human gastric cancer modelling using organoids.Gut. 2019; 68: 207-217https://doi.org/10.1136/gutjnl-2017-314549Crossref PubMed Scopus (219) Google Scholar,9Steele N.G. Chakrabarti J. Wang J. Biesiada J. Holokai L. Chang J. Nowacki L.M. Hawkins J. Mahe M. Sundaram N. et al.An Organoid-Based Preclinical Model of Human Gastric Cancer.Cell. Mol. Gastroenterol. Hepatol. 2019; 7: 161-184https://doi.org/10.1016/j.jcmgh.2018.09.008Abstract Full Text Full Text PDF PubMed Scopus (100) Google Scholar significantly advancing our understanding of GC biology and therapeutic resistance. Nevertheless, key questions remain to be addressed. (1) What are the genomic/transcriptomic determinants of drug sensitivity of GC? (2) Can gene signatures serve as predictive biomarkers for treatment response in GC patients? (3) Is ex vivo PDO drug response data reproducible in vivo, a key property for its translational application? New research published in Cell Reports Medicine1Zhao Y. Li S. Zhu L. Huang M. Xie Y. Song X. Chen Z. Lau H.C.-H. Sung J.J.-Y. Xu L. Personalized drug screening using patient-derived organoid and its clinical relevance in gastric cancer.Cell Rep. Med. 2024; 5: 101627https://doi.org/10.1016/j.xcrm.2024.101627Abstract Full Text Full Text PDF Google Scholar has specifically taken on these questions by performing histopathological, genomic, transcriptomic, and therapeutic analyses of a significant number of GC PDOs. The authors established 57 organoids from 73 GC patient specimens, covering multiple stomach sites and different TNM stages. A detailed histopathologic analysis confirmed that PDOs recapitulated the histologies of their original tumors in a subtype-specific manner. For example, PDOs derived from intestinal-type GC were characterized by single-layered or cribriform glandular morphologies, featuring cells arranged to form multiple luminal structures of variable dimensions. In comparison, those derived from diffuse-type GC presented with loosely-cohesive to solid cell clusters or cystic configurations. Despite some inconsistencies, these data underscore that organoid cultures maintain the histopathologic features of their parental GC tumors. The team then investigated the responsiveness of GC PDOs to 6 common chemotherapeutics for GC patients. The authors rigorously assayed drug sensitivity by independent measurements (AUC values and IC50 values) across 2 different passages of each organoid line and along 2 sets of drug concentrations. After ensuring the robustness of the assay, they analyzed RNA sequencing data to correlate gene expression changes with drug responsiveness variability in matched PDOs. Pathway enrichment analysis revealed upregulation of the p53 signaling pathway and cellular senescence in 5-FU-sensitive PDOs. By contrast, 5-FU-resistant PDOs showed strong enrichment of tumor-invasion- and stemness-related pathways, such as NOTCH signaling and WNT signaling. Similarly, signaling pathways enriched for PDOs with differential responses to oxaliplatin were also identified. From significantly enriched pathway genes, several were selected for functional experimentation to test for their contribution to drug responsiveness. Notably, knockout of MSMB or S1PR4 (5-FU-sensitive genes) by CRISPR-Cas9 editing significantly decreased the sensitivity of PDOs to 5-FU treatment. Conversely, knockout of FKBP10 (a 5-FU-resistant gene) conferred sensitivity to 5-FU treatment on PDOs. Likewise, genes involved in oxaliplatin response (MYO1A and NDUFA4L2) were also validated. These results are significant since they demonstrate the biological regulation of these genes on chemotherapeutic sensitivity in GC PDOs. Employing the recursive feature elimination method, the researchers explored gene-expression-based biomarkers that discriminated sensitive PDO lines from lines that were resistant to 5-FU treatment. A panel of 9 genes was identified and verified to have high discriminative AUC values. Similarly, a panel of 6 genes was established to predict responsiveness to oxaliplatin treatment. These predictive gene signatures were further validated using The Cancer Genome Atlas cohort, wherein patients treated with 5-FU or oxaliplatin were stratified into sensitive- and resistant-like groups based on gene signature scores. Notably, in the 5-FU-treated cohort, sensitive-like patients showed significantly better progression-free survival. This 5-FU gene signature also showed the highest AUC values for predicting drug sensitivity when compared against published gene signatures. To validate the drug response data obtained ex vivo, the authors established matched PDO xenograft (PDOX) in immunodeficient mice for chemotherapeutic treatment. Importantly, in each of the 5 PDO-PDOX paired models, drug responsiveness was highly consistent between in vitro and in vivo assays. The PDO drug testing results were further subjected to the last, perhaps most important, validation using real-life clinical outcome data from original GC patients. Specifically, 12 PDO-matched patients received peri- or postoperative adjuvant chemotherapy with oxaliplatin and 5-FU and were followed for up to 49 months. Strikingly, 11 out of 12 patients exhibited drug response results consistent with their corresponding PDO lines. These data highlight that PDOs are robust and valid tools for cancer drug screening and drug response prediction. Lastly, the authors explored the impact of the tumor microenvironment on drug responsiveness by co-culturing cancer-associated fibroblasts (CAFs) with autologous PDOs. Indeed, the presence of CAFs substantially increased the drug resistance of PDOs to 5-FU and oxaliplatin treatment. Interestingly, this pro-resistance effect was also seen when PDOs were co-cultured with heterologous CAF lines. These findings align with the recognized role of CAFs in enhancing drug resistance within the tumor microenvironment.10Saw P.E. Chen J. Song E. Targeting CAFs to overcome anticancer therapeutic resistance.Trends Cancer. 2022; 8: 527-555https://doi.org/10.1016/j.trecan.2022.03.001Abstract Full Text Full Text PDF PubMed Scopus (79) Google Scholar In summary, upon establishing a sizable PDO biobank for GC patients, this study identified and verified genes and molecular signatures that are predictive of chemotherapeutic response. Co-culturing with CAFs highlighted the role of the tumor microenvironment in mediating drug resistance. These results confirm the robustness of PDO models for cancer drug screening and response prediction. Moving forward, single-cell multi-omic analyses of PDOs will identify mechanisms underlying drug responsiveness in an unprecedented resolution. Moreover, single-cell CRISPR screening on PDO models will causally pinpoint genes responsible for drug resistance in an unbiased manner. These exciting new molecular technologies, together with sophisticated organoid modeling of the tumor microenvironment, will greatly improve precision medicine for cancer patients. D.-C.L. was supported by the Ming Hsieh Institute Research Award and the NIH under awards R37CA237022, R01DK135562, and R01DE033648. The authors declare no competing interests. Personalized drug screening using patient-derived organoid and its clinical relevance in gastric cancerZhao et al.Cell Reports MedicineJuly 3, 2024In BriefYi et al. present the establishment of a biobank of gastric cancer organoids derived from patients. These organoids mirror the complex dynamics of chemotherapy response. It may offer a valuable tool for the refinement of personalized treatments and the prediction of individual patient responses to chemotherapy. Full-Text PDF Open Access