Establishment of patient-derived tumor xenografts from Asian gastric adenocarcinoma is characterized by clonal selection and bias in molecular subtypes
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE115755 | GEO | 2020/07/15
REPOSITORIES: GEO
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Project description:Patient-derived xenografts (PDX) have emerged as an important translational research tool for understanding tumor biology and enabling drug efficacy testing. They are established by transfer of patient tumor into immune compromised mice with the intent of using them as Avatars; operating under the assumption that they closely resemble patient tumors. In this study, we established 27 PDX from 100 resected gastric cancers and studied their fidelity in histological and molecular subtypes. We show that the established PDX preserved histology and molecular subtypes of parental tumors. However, in depth investigation of the entire cohort revealed that not all histological and molecular subtypes are established. Also, for the established PDX models, genetic changes are selected at early passages and rare subclones can emerge in PDX. This study highlights the importance of considering the molecular and evolutionary characteristics of PDX for a proper use of such models, particularly for Avatar trials.
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Project description:Establishment of patient-derived tumor xenografts from Asian gastric adenocarcinoma is characterized by clonal selection and bias in molecular subtypes
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Project description:Gastric adenocarcinoma (GAC) is a lethal disease characterized by genomic and clinical heterogeneity. By integrating 8 previously established genomic signatures for GAC subtypes, we identified 6 clinically and molecularly distinct genomic consensus subtypes (CGSs). CGS1 have the poorest prognosis, very high stem cell characteristics, and high IGF1 expression, but low genomic alterations. CGS2 is enriched with canonical epithelial gene expression. CGS3 and CGS4 have high copy number alterations and low immune reactivity. However, CGS3 and CGS4 differ in that CGS3 has high HER2 activation, while CGS4 has high SALL4 and KRAS activation. CGS5 has the high mutation burden and moderately high immune reactivity that are characteristic of microsatellite instable tumors. Most CGS6 tumors are positive for Epstein Barr virus and show extremely high levels of methylation and high immune reactivity. In a systematic analysis of genomic and proteomic data, we estimated the potential response rate of each consensus subtype to standard and experimental treatments such as radiation therapy, targeted therapy, and immunotherapy. Interestingly, CGS3 was significantly associated with a benefit from chemoradiation therapy owing to its high basal level of ferroptosis. In addition, we also identified potential therapeutic targets for each consensus subtype. Thus, the consensus subtypes produced a robust classification and provide for additional characterizations for subtype-based customized interventions.
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Project description:The patient-derived xenograft (PDX) model is emerging as a promising translational platform to duplicate the characteristics of tumours. However, few studies have reported detailed histological and genomic analyses for model fidelity and for factors affecting successful model establishment of gastric cancer. Here, we generated PDX tumours surgically-derived from 62 gastric cancer patients. Fifteen PDX models were successfully established (24.2%, 15/62) and passaged to maintain tumours in immune-compromised mice. Diffuse type and low tumour cell percentage were negatively correlated with success rates (p = 0.005 and p = 0.025, respectively), while reducing ex vivo and overall procedure times were positively correlated with success rates (p = 0.003 and p = 0.01, respectively). The histology and genetic characteristics of PDX tumour models were stable over subsequent passages. Lymphoma transformation occurred in five cases (33.3%, 5/15), and all were in the NOG mouse, with none in the nude mouse. Together, the present study identified Lauren classification, tumour cell percentages, and ex vivo times along with overall procedure times, as key determinants for successful PDX engraftment. Furthermore, genetic and histological characteristics were highly consistent between primary and PDX tumours, which provide realistic paraclinical models, enabling personalised development of treatment options for gastric cancer.
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Project description:Patient-derived xenograft (PDX) models have been recognized as being more suitable for predicting therapeutic efficacy than cell-culture models. However, there are several limitations in applying PDX models in preclinical studies, including their availability-especially for cancers such as gastric cancer-that are not frequently encountered in Western countries. In addition, the differences in morphology between primary, PDX, and tumor cell line-derived xenograft (CDX) models have not been well established. In this study, we aimed to establish a series of gastric cancer PDXs and cell-lines from a relatively large number of gastric cancer patients. We also investigated the clinicopathological factors associated with the establishment of PDX and CDX models, and compared the histology between the primary tumor, PDX, and CDX that originated from the same patient. We engrafted 232 gastric cancer tissues into immune-deficient mice subcutaneously and successfully established 35 gastric cancer PDX models (15.1% success rate). Differentiated type adenocarcinomas (DAs, 19.4%) were more effectively established than poorly differentiated type adenocarcinomas (PDAs, 10.8%). For establishing CDXs, the success rate was less influenced by histological differentiation grade (DA vs. PDA, 12.1% vs. 9.8%). In addition, concordance of histological differentiation grade between primary tumors and PDXs was significant (p < 0.01), while concordance between primary tumors and CDXs was not. Among clinicopathological factors investigated, pathological nodal metastasis status (pN) was significantly associated with the success rate of PDX establishment. Although establishing cell lines from ascites fluid was more efficient (41.2%, 7/17) than resected tissues, it should be noted that all CDXs from ascites fluid had the PDA phenotype. In conclusion, we established 35 PDX and 32 CDX models from 249 gastric cancer patients; among them, 21 PDX/CDX models were established from the same patients. Our findings may provide helpful insights for establishing PDX and CDX models not only from gastric but from other cancer types, as well as select preclinical models for developing new therapeutics.
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Project description:Gastric cancer (GC) is a heterogeneous disease, so molecular classification is important for selecting the most appropriate treatment strategies for GC patients. To be applicable in the clinic, there is an urgent need for a platform that will allow screening real-life archival tissue specimens. For this purpose, we performed RNA sequencing of 50 samples from our Asian Cancer Research Group (ACRG) GC cohort to reproduce the molecular subtypes of GC using archival tissues with different platforms. We filtered out genes from the epithelial-to-mesenchymal transition (EMT) and microsatellite instability-high (MSI) signatures (coefficient ≤ 0.4) followed by the ACRG molecular subtype strategy. Overall accuracy of reproduction of ACRG subtype was 66% (33/50). Given the importance of EMT subtype in future clinical trials, we further developed the minimum number of genes (10 genes) for EMT signatures correlating highly with the original EMT signatures (correlation ≥ 0.65). Using our 10-gene model, we could classify EMT subtypes with high sensitivity (0.9576) and specificity (0.811). In conclusion, we reproduced ACRG GC subtypes using different platforms and could predict EMT subtypes with 10 genes and are now planning to use them in our prospective clinical study of precision oncology in GC.
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Project description:Background: Gastric adenocarcinoma (GAC) is a common clinical malignancy with a poor prognosis. Endoplasmic reticulum (ER) stress plays important roles in the progression, immune filtration, and chemoresistance of cancers. However, whether ER stress-related gene signatures can predict the prognosis of GAC patients remains unknown. Methods: GAC patient RNA-seq data downloaded from The Cancer Genome Atlas and gastric cancer patient microarray data from Gene Expression Omnibus datasets were analyzed using LASSO regression to construct an ER stress-related signature. Survival analysis, time-dependent receiver operating characteristic (ROC) curves, and Cox regression analysis were used to verify the efficacy of the signature. Immune infiltration, somatic mutation, immune checkpoint, and copy number variation analyses were utilized to explore the potential biological significance of the signature. Results: In the present study, eight ER stress-related gene signatures were constructed. Survival analysis showed that patients in the high-risk group had a significantly worse prognosis. The area under the time-dependent ROC curves was 0.65, 0.70, and 0.63 at 1, 3, and 5 years, respectively, in the training cohort. Cox regression analysis showed that the signature is an independent prognostic factor. To predict GAC patients' prognosis meeting individual needs, a nomogram was constructed with good accuracy. In addition, gene set enrichment and immune infiltration analyses showed that the ER stress-related signature is associated with cancer-related pathway activation and an immunosuppressive tumor microenvironment in GAC. Conclusion: In the current study, we established an ER stress-related signature. This prognostic signature has good predictive power and could facilitate the development of novel strategies for the clinical treatment of GAC.
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Project description:Lung adenocarcinoma (LUAD) patients in East Asia predominantly harbor oncogenic EGFR mutations. However, there remains a limited understanding of the biological characteristics and therapeutic vulnerabilities of the concurrent mutations of EGFR and other genes in LUAD. Here, we performed comprehensive bioinformatics analyses on 88 treatment-naïve East Asian LUAD patients. Based on somatic mutation clustering, we identified three somatic mutation subtypes: EGFR + TP53 co-mutation, EGFR mutation, and multiple-gene mutation. A proteogenomic analysis among subtypes revealed varying degrees of dysregulation in cell-cycle-related and immune-related processes. An immune-characteristic analysis revealed higher PDL1 protein expression in the EGFR + TP53 co-mutation subtype than in the EGFR mutation subtype, which may affect the therapeutic efficacy of anti-PD-L1 therapy. Moreover, integrating known and potential therapeutic target analysis reveals therapeutic vulnerabilities of specific subtypes and nominates candidate biomarkers for therapeutic intervention. This study provides new biological insight and therapeutic opportunities with respect to EGFR-mutant LUAD subtypes.
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Project description:BackgroundAT-rich interaction domain 1A (ARID1A) is an essential subunit of the switch/sucrose non-fermentable chromatin remodeling complex and is considered to be a tumor suppressor. The Cancer Genome Atlas (TCGA) molecular classification has deepened our understanding of gastric cancer at the molecular level. This study explored the significance of ARID1A expression in TCGA subtypes of gastric adenocarcinoma.MethodsWe collected 1248 postoperative patients with gastric adenocarcinoma, constructed tissue microarrays, performed immunohistochemistry for ARID1A, and obtained correlations between ARID1A and clinicopathological variables. We then carried out the prognostic analysis of ARID1A in TCGA subtypes. Finally, we screened patients by random sampling and propensity score matching method and performed multiplex immunofluorescence to explore the effects of ARID1A on CD4, CD8, and PD-L1 expression in TCGA subtypes.ResultsSeven variables independently associated with ARID1A were screened out: mismatch repair proteins, PD-L1, T stage, differentiation status, p53, E-cadherin, and EBER. The independent prognostic variables in the genomically stable (GS) subtype were N stage, M stage, T stage, chemotherapy, size, and ARID1A. PD-L1 expression was higher in the ARID1A negative group than in the ARID1A positive group in all TCGA subgroups. CD4 showed higher expression in the ARID1A negative group in most subtypes, while CD8 did not show the difference in most subtypes. When ARID1A was negative, PD-L1 expression was positively correlated with CD4/CD8 expression; while when ARID1A was positive, this correlation disappeared.ConclusionsThe negative expression of ARID1A occurred more frequently in the Epstein-Barr virus and microsatellite instability subtypes and was an independent adverse prognostic factor in the GS subtype. In the TCGA subtypes, ARID1A negative expression caused increased CD4 and PD-L1 expression, whereas CD8 expression appeared independent of ARID1A. The expression of CD4/CD8 induced by ARID1A negativity was accompanied by an increase in PD-L1 expression.
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Project description:Background: Although mRNA vaccines have been effective against multiple cancers, their efficacy against stomach adenocarcinoma (STAD) remains undefined. Immunotyping can indicate the comprehensive immune status in tumors and their immune microenvironment, which is closely associated with therapeutic response and vaccination potential. The aim of this study was to identify potential antigens in STAD for mRNA vaccine development, and further distinguish immune subtypes of STAD to construct an immune landscape for selecting suitable patients for vaccination. Methods: The gene expression and clinicopathological features of patients with gastric cancer were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression Program (GTEx). 729 samples from GSE66229 and GSE84437 were downloaded through GEO and were used as the validation cohorts. Differential gene expression, genetic alterations and prognosis were analyzed using the R package, cBioPortal program and Kaplan-Meier. The relationship between tumor antigens and immune cells was evaluated and plotted by TIMER. ConsensusClusterPlus was used for consistency matrix construction and data clustering, and graph learning-based dimensional reduction was used to depict immune landscape. WGCNA was used to estimate the relationship between the color modules and immune subtypes. Results: Two overexpressed and mutated tumor antigens associated with poor prognosis and infiltration of antigen presenting cells were identified in STAD, including RAI14 and NREP. The immune subtypes showed distinct molecular, cellular and clinical characteristics. IS1 and IS2 exhibited immune-activated phenotypes and correlated to better survival compared to IS3, while IS3 tumors was immunologically cold. Immunogenic cell death modulators, immune checkpoints, and CA125, and CEA were also differentially expressed among the three immune subtypes. Finally, the immune landscape of STAD showed a high degree of heterogeneity between individual patients. Conclusion: RAI14 and NREP are potential antigens for developing anti-STAD mRNA vaccine, and patients with IS1 and IS3 tumors may be suitable for vaccination.
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