Tumorigenicity and prediction of clinical prognosis of patient-derived gastric cancer organoids.
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ABSTRACT:
SUBMITTER: Wang T
PROVIDER: S-EPMC10870796 | biostudies-literature | 2024 Feb
REPOSITORIES: biostudies-literature
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Tumorigenicity and prediction of clinical prognosis of patient-derived gastric cancer organoids.
Wang Ting T Song Wanlu W Meng Qingyu Q Qu Chuanqing C Guo Shaohua S Wang Yalong Y Tan Ronghui R Jia Baoqing B Chen Ye-Guang YG
Clinical and translational medicine 20240201 2
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Project description:BackgroundRecently, two molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) have been proposed: the "Classical" and "Basal-like" subtypes, with the former showing better clinical outcomes than the latter. However, the "molecular" classification has not been applied in real-world clinical practice. This study aimed to establish patient-derived organoids (PDOs) for PDAC and evaluate their application in subtype classification and clinical outcome prediction.MethodsWe utilized tumor samples acquired through endoscopic ultrasound-guided fine-needle biopsy and established a PDO library for subsequent use in morphological assessments, RNA-seq analyses, and in vitro drug response assays. We also conducted a prospective clinical study to evaluate whether analysis using PDOs can predict treatment response and prognosis.ResultsPDOs of PDAC were established at a high efficiency (> 70%) with at least 100,000 live cells. Morphologically, PDOs were classified as gland-like structures (GL type) and densely proliferating inside (DP type) less than 2 weeks after tissue sampling. RNA-seq analysis revealed that the "morphological" subtype (GL vs. DP) corresponded to the "molecular" subtype ("Classical" vs. "Basal-like"). The "morphological" classification predicted the clinical treatment response and prognosis; the median overall survival of patients with GL type was significantly longer than that with DP type (P < 0.005). The GL type showed a better response to gemcitabine than the DP type in vitro, whereas the drug response of the DP type was improved by the combination of ERK inhibitor and chloroquine.ConclusionsPDAC PDOs help in subtype determination and clinical outcome prediction, thereby facilitating the bench-to-bedside precision medicine for PDAC.
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Project description:BackgroundA major obstacle to the development of personalized therapies for gastric cancer (GC) is the prevalent heterogeneity at the intra-tumor, intra-patient, and inter-patient levels. Although the pathological stage and histological subtype diagnosis can approximately predict prognosis, GC heterogeneity is rarely considered. The extracellular matrix (ECM), a major component of the tumor microenvironment (TME), extensively interacts with tumor and immune cells, providing a possible proxy to investigate GC heterogeneity. However, ECM consists of numerous protein components, and there are no suitable models to screen ECM-related genes contributing to tumor growth and prognosis. We constructed patient-derived tumor xenograft (PDTX) models to obtain robust ECM-related transcriptomic signatures to improve GC prognosis prediction and therapy design.MethodsOne hundred twenty two primary GC tumor tissues were collected to construct PDTX models. The tumorigenesis rate and its relationship with GC prognosis were investigated. Transcriptome profiling was performed for PDTX-originating tumors, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to extract prognostic ECM signatures and establish PDTX tumorigenicity-related gene (PTG) scores. The predictive ability of the PTG score was validated using two independent cohorts. Finally, we combined PTG score, age, and pathological stage information to establish a robust nomogram for GC prognosis prediction.ResultsWe found that PDTX tumorigenicity indicated a poor prognosis in patients with GC, even at the same pathological stage. Transcriptome profiling of PDTX-originating GC tissues and corresponding normal controls identified 383 differentially expressed genes, with enrichment of ECM-related genes. A robust prognosis prediction model using the PTG score showed robust performance in two validation cohorts. A high PTG score was associated with elevated M2 polarized macrophage and cancer-associated fibroblast infiltration. Finally, combining the PTG score with age and TNM stage resulted in a more effective prognostic model than age or TNM stage alone.ConclusionsWe found that ECM-related signatures may contribute to PDTX tumorigenesis and indicate a poor prognosis in GC. A feasible survival prediction model was built based on the PTG score, which was associated with immune cell infiltration. Together with patient ages and pathological TNM stages, PTG score could be a new approach for GC prognosis prediction.
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Project description:BackgroundPatient-derived organoids (PDOs) from advanced colorectal cancer (CRC) patients could be a key platform to predict drug response and discover new biomarkers. We aimed to integrate PDO drug response with multi-omics characterization beyond genomics.MethodsWe generated 29 PDO lines from 22 advanced CRC patients and provided a morphologic, genomic, and transcriptomic characterization. We performed drug sensitivity assays with a panel of both standard and non-standard agents in five long-term cultures, and integrated drug response with a baseline proteomic and transcriptomic characterization by SWATH-MS and RNA-seq analysis, respectively.ResultsPDOs were successfully generated from heavily pre-treated patients, including a paired model of advanced MSI high CRC deriving from pre- and post-chemotherapy liver metastasis. Our PDOs faithfully reproduced genomic and phenotypic features of original tissue. Drug panel testing identified differential response among PDOs, particularly to oxaliplatin and palbociclib. Proteotranscriptomic analyses revealed that oxaliplatin non-responder PDOs present enrichment of the t-RNA aminoacylation process and showed a shift towards oxidative phosphorylation pathway dependence, while an exceptional response to palbociclib was detected in a PDO with activation of MYC and enrichment of chaperonin T-complex protein Ring Complex (TRiC), involved in proteome integrity. Proteotranscriptomic data fusion confirmed these results within a highly integrated network of functional processes involved in differential response to drugs.ConclusionsOur strategy of integrating PDOs drug sensitivity with SWATH-mass spectrometry and RNA-seq allowed us to identify different baseline proteins and gene expression profiles with the potential to predict treatment response/resistance and to help in the development of effective and personalized cancer therapeutics.
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Project description:Gastric cancer (GC) is a challenging malignant disease among gastrointestinal tumors. The clinical treatments for GC include surgery, neoadjuvant therapy and adjuvant chemotherapy. As patients’ response to chemotherapeutic drugs varies, an approach to predict the efficacy of chemotherapeutic treatment is urgently needed. Here, a biorepository of 17 human gastric tumor organoids (GTOs) and 9 normal organoids (GNOs) derived from GC patients was established, which contains the intestinal, diffuse and mixed-type of GC. GTOs retained morphologic, histopathological and genetic features of the tumors from which they were derived, and patient-derived organoid xenograft model indicated tumorigenicity of organoids. GTOs responses to clinically respective chemotherapy treatment positively correlated with the individual patient’s clinical response. Meanwhile, GNOs, which maintained original characteristics of corresponding tissues, could be separated from GTOs. The use of GNOs predicted the cytotoxic effect of chemotherapy on the organism. Our study implies that patient-derived organoids predict GC patient responses to chemotherapy and provide a solution for personalized treatment.
2024-03-10 | GSE235912 | GEO
Project description:Two molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) have been proposed: the “Classical” and “Basal-like” subtypes. However, the “molecular” classification has not been applied in real-world clinical practice. This study aimed to establish patient-derived organoids (PDOs) for PDAC and evaluate their application in subtype classification and clinical outcome prediction. We constructed a PDO library for morphologic and RNA-seq analyses and drug response assays in vitro. PDOs of PDAC were established at a high efficiency (> 70%) with at least 100,000 live cells. Morphologically, PDOs were classified as gland-like structures (GL type) or densely proliferating inside (DP type). RNA-seq analysis revealed that the “morphological” subtype (GL vs. DP) roughly corresponded to the “molecular” subtype (“Classical” vs. “Basal-like”). The proposed “morphological” classification predicted clinical treatment response and prognosis; the median overall survival of patients with GL type were significantly longer than that of those with DP type (P < 0.005). The GL type showed a better response to gemcitabine than the DP type in vitro, whereas the drug response of the DP type was improved by the combination of ERK and autophagy inhibition. PDAC PDOs facilitated subtype determination and clinical outcome prediction, thereby advancing precision medicine for PDAC.
2024-04-11 | GSE228801 | GEO
Project description:Based on genomic analysis, 50% of high-grade serous ovarian cancers (HGSC) are predicted to have DNA repair defects. Whether this substantial subset of HGSCs actually have functional repair defects remains unknown. Here, we devise a platform for functional profiling of DNA repair in short-term patient-derived HGSC organoids. We tested 33 organoid cultures derived from 22 patients with HGSC for defects in homologous recombination (HR) and replication fork protection. Regardless of DNA repair gene mutational status, a functional defect in HR in the organoids correlated with PARP inhibitor sensitivity. A functional defect in replication fork protection correlated with carboplatin and CHK1 and ATR inhibitor sensitivity. Our results indicate that a combination of genomic analysis and functional testing of organoids allows for the identification of targetable DNA damage repair defects. Larger numbers of patient-derived organoids must be analyzed to determine whether these assays can reproducibly predict patient response in the clinic.Significance: Patient-derived ovarian tumor organoids grow rapidly and match the tumors from which they are derived, both genetically and functionally. These organoids can be used for DNA repair profiling and therapeutic sensitivity testing and provide a rapid means of assessing targetable defects in the parent tumor, offering more suitable treatment options. Cancer Discov; 8(11); 1404-21. ©2018 AACR. This article is highlighted in the In This Issue feature, p. 1333.
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Project description:BackgroundPerioperative chemotherapy has been accepted as one of the most common approaches for locally advanced gastric cancer. However, the efficacy of chemotherapy varies among patients, and there is no effective method to predict the chemotherapy efficacy currently. We previously established the first larval zebrafish patient-derived xenografts (zPDXs) of gastric cancer as a platform for the translational research and personalized treatment. The objective of this study was to investigate the feasibility of screening individualized chemotherapeutics using the zPDXs.MethodsWe further optimized this zPDXs platform including administration route, drug dosing, and rhythm to develop a stable and reliable protocol for chemotherapeutics screening. Using the novel platform, we investigated the chemosensitivity of 5-fluorouracil, cisplatin, docetaxel, and doxorubicin for gastric cancer patients.ResultsWe showed that the engrafted zebrafish retained the original prominent cell components of the corresponding human tumor tissues, and we successfully obtained the results of chemosensitivity of 5-fluorouracil, cisplatin, docetaxel, and doxorubicin for 28 patients with locally advanced gastric cancer. These patients underwent radical gastrectomy for curative intent and 27 cases received postoperative adjuvant chemotherapy. We revealed that the chemosensitivity obtained from zPDXs was consistent with the clinical responses in these patients (P = 0.029). More importantly, the responder drug(s) from zPDXs used or not was the only risk factor for early-stage recurrence in these 27 patients (P = 0.003).ConclusionOur study with the largest sample size so far suggests that larval zPDXs help to predict the chemotherapeutics response and to achieve precise chemotherapy for gastric cancer.
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2025-02-03 | PXD038149 | Pride
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Project description:BackgroundA major hurdle in translational endometrial cancer (EC) research is the lack of robust preclinical models that capture both inter- and intra-tumor heterogeneity. This has hampered the development of new treatment strategies for people with EC.MethodsEC organoids were derived from resected patient tumor tissue and expanded in a chemically defined medium. Established EC organoids were orthotopically implanted into female NSG mice. Patient tissue and corresponding models were characterized by morphological evaluation, biomarker and gene expression and by whole exome sequencing. A gene signature was defined and its prognostic value was assessed in multiple EC cohorts using Mantel-Cox (log-rank) test. Response to carboplatin and/or paclitaxel was measured in vitro and evaluated in vivo. Statistical difference between groups was calculated using paired t-test.ResultsWe report EC organoids established from EC patient tissue, and orthotopic organoid-based patient-derived xenograft models (O-PDXs). The EC organoids and O-PDX models mimic the tissue architecture, protein biomarker expression and genetic profile of the original tissue. Organoids show heterogenous sensitivity to conventional chemotherapy, and drug response is reproduced in vivo. The relevance of these models is further supported by the identification of an organoid-derived prognostic gene signature. This signature is validated as prognostic both in our local patient cohorts and in the TCGA endometrial cancer cohort.ConclusionsWe establish robust model systems that capture both the diversity of endometrial tumors and intra-tumor heterogeneity. These models are highly relevant preclinical tools for the elucidation of the molecular pathogenesis of EC and identification of potential treatment strategies.
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