Multiomic characterization of colorectal cancer organoids
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB57288 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| Other | ||||
| ERR10476882_1.fastq.gz | Fastqsanger.gz | |||
| ERR10476882_2.fastq.gz | Fastqsanger.gz | |||
| ERR10476885_1.fastq.gz | Fastqsanger.gz | |||
| ERR10476885_2.fastq.gz | Fastqsanger.gz |
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Project description:Understanding the phenotypic alterations that play a role in differential response to drugs will be pivotal in identifying predictive biomarkers and developing efficacious therapies for advanced CRC. The bulk of research to date has focused on the value of organoids as a predictive tool; some have characterized the transcriptomic profile in relation to drug sensitivity in an effort to identify predictive biomarkers, but only a limited number of studies have used quantitative proteomic analysis to characterize PDOs. Extensive literature indicates that transcriptomic and proteomic expression profiles lack correlation due to significant post-transcriptional regulation, which could explain why RNA signatures are rarely useful as drug response predictors in the clinic. In this study we generated PDO models from advanced CRC patients including heavily pre-treated patients and a model of microsatellite instable- (MSI) high CRC, for which pre- and post chemotherapy liver disease PDOs were established and characterized to ensure biological fidelity with the original tumors. Our main objective was to integrate functional drug assays on PDOs with proteotranscriptomic study to explore the mechanisms underlying drug response. To this end, we used SWATH-MS (sequential window acquisition of all theoretical mass spectrometry), a highly accurate proteomic quantification technique and a bulk RNA-seq analysis. This strategy could represent a useful tool to discover new therapeutic targets in advanced CRC and to guide personalized therapies.
2025-02-03 | PXD038149 | Pride
Project description:Despite advances in contemporary chemotherapeutic strategies, long term survival still remains elusive for patients with metastatic colorectal cancer. A better understanding of the molecular markers of drug sensitivity to match therapy with patient is needed to improve clinical outcomes. In this study, we used in vitro drug sensitivity data from the NCI-60 cell lines together with their Affymetrix microarray data to develop a gene expression signature to predict sensitivity to oxaliplatin. In order to validate our oxaliplatin sensitivity signature, Patient-Derived Colorectal Cancer Explants (PDCCEs) were developed in NOD-SCID mice from resected human colorectal tumors. Analysis of gene expression profiles found similarities between the PDCCEs and their parental human tumors, suggesting their utility to study drug sensitivity in vivo. The oxaliplatin sensitivity signature was then validated in vivo with response data from 14 PDCCEs treated with oxaliplatin and was found to have an accuracy of 92.9% (Sensitivity=87.5%; Specificity=100%). Our findings suggest that PDCCEs can be a novel source to study drug sensitivity in colorectal cancer. Furthermore, genomic-based analysis has the potential to be incorporated into future strategies to optimize individual therapy for patients with metastatic colorectal cancer. Fourty-two human tumors and murine explants of colorectal origin, both primary colon and of various metastatic sites, were processed for total RNA. The samples included RNA from 14 patient samples in addition to RNA from Patient-Derived Colorectal Cancer Explant (PDCCEs) generated from these 14 patient samples. The PDCCEs were processed as fresh frozen whole tumor in addition to formalin-fixed paraffin-embedded (FFPE) tumors.
2012-02-17 | E-GEOD-28691 | biostudies-arrayexpress
Project description:Despite advances in contemporary chemotherapeutic strategies, long term survival still remains elusive for patients with metastatic colorectal cancer. A better understanding of the molecular markers of drug sensitivity to match therapy with patient is needed to improve clinical outcomes. In this study, we used in vitro drug sensitivity data from the NCI-60 cell lines together with their Affymetrix microarray data to develop a gene expression signature to predict sensitivity to oxaliplatin. In order to validate our oxaliplatin sensitivity signature, Patient-Derived Colorectal Cancer Explants (PDCCEs) were developed in NOD-SCID mice from resected human colorectal tumors. Analysis of gene expression profiles found similarities between the PDCCEs and their parental human tumors, suggesting their utility to study drug sensitivity in vivo. The oxaliplatin sensitivity signature was then validated in vivo with response data from 14 PDCCEs treated with oxaliplatin and was found to have an accuracy of 92.9% (Sensitivity=87.5%; Specificity=100%). Our findings suggest that PDCCEs can be a novel source to study drug sensitivity in colorectal cancer. Furthermore, genomic-based analysis has the potential to be incorporated into future strategies to optimize individual therapy for patients with metastatic colorectal cancer.
2012-02-17 | GSE28691 | GEO
Project description:We tested transcript profile of colorectal cancer (CRC) organoid from CRC patients. We compared the transcripts profile change between drug resistant organoid and drug sensitive organoid.
2022-11-29 | GSE218558 | GEO
Project description:Solid tumors are complex organs comprising neoplastic cells and stroma, yet cancer cell lines remain widely used to study tumor biology, biomarkers and experimental therapy. Here, we performed a fully integrative analysis of global proteomic data comparing human colorectal cancer (CRC) cell lines to primary tumors and normal tissues. We found a significant, systematic difference between cell line and tumor proteomes, with a major contribution from tumor stroma proteomes. Nevertheless, cell lines overall mirrored the proteomic differences observed between tumors and normal tissues, in particular for genetic information processing and metabolic pathways, indicating that cell lines provide a system for the study of the intrinsic molecular programs in cancer cells. Intersection of cell line data with tumor data provided insights into tumor cell specific proteome alterations driven by genomic alterations. Our integration of cell line proteogenomic data with drug sensitivity data highlights the potential of proteomic data in predicting therapeutic response. We identified representative cell lines for the proteomic subtypes of primary tumors, and linked these to drug sensitivity data to identify subtype-specific drug candidates.
2016-12-01 | MSV000080374 | GNPS
Project description:Background: Patient derived organoids (PDOs) can be established from colorectal cancers as in vitro models to interrogate cancer biology and its clinical relevance. We applied mass spectrometry (MS) immunopeptidomics to investigate neoantigen presentation and whether this can be augmented through interferon gamma (IFN) or MEK-inhibitors. Methods: Four PDOs from chemotherapy refractory and one from a treatment naïve CRC were expanded to replicates with 100 million cells each, and HLA class I and class II peptide ligands were analysed by MS. Results: We identified an average of 9,936 unique peptides per PDO which compares favourably against published immunopeptidomics studies, suggesting high sensitivity. Loss of heterozygosity of the HLA locus was associated with low peptide diversity in one PDO. Peptides from genes without detectable expression by RNA-sequencing were rarely identified by MS. Only 3 out of 612 non-silent mutations encoded for neoantigens that were detected by MS. Treatment of four PDOs with IFN upregulated HLA class I expression and qualitatively changed the immunopeptidome, with increased presentation of IFN-inducible genes. HLA class II presented peptides increased on average 16-fold with IFN treatment. MEK-inhibitor treatment showed no consistent effect on class I or II HLA expression or the peptidome. Importantly, no additional HLA class I or II presented neoantigens became detectable with any treatment. Conclusions: Only 3 out of 612 non-synonymous mutations encoded for neoantigens that were detectable by MS. Although MS has sensitivity limits and biases, and likely underestimated the true neoantigen burden, this established a lower bound of the percentage of non-silent mutations that encode for presented neoantigens, which may be as low as 0.5%. This could be a reason for the poor responses of non-hypermutated CRCs to immune checkpoint inhibitors. MEK-inhibitors recently failed to improve checkpoint-inhibitor efficacy in CRC and the observed lack of HLA upregulation or improved peptide presentation may explain this.
2019-11-27 | PXD014017 | Pride
Project description:Solid tumors are complex organs comprising neoplastic cells and stroma, yet cancer cell lines remain widely used to study tumor biology, biomarkers and experimental therapy. Here, we performed a fully integrative analysis of global proteomic data comparing human colorectal cancer (CRC) cell lines to primary tumors and normal tissues. We found a significant, systematic difference between cell line and tumor proteomes, with a major contribution from tumor stroma proteomes. Nevertheless, cell lines overall mirrored the proteomic differences observed between tumors and normal tissues, in particular for genetic information processing and metabolic pathways, indicating that cell lines provide a system for the study of the intrinsic molecular programs in cancer cells. Intersection of cell line data with tumor data provided insights into tumor cell specific proteome alterations driven by genomic alterations. Our integration of cell line proteogenomic data with drug sensitivity data highlights the potential of proteomic data in predicting therapeutic response. We identified representative cell lines for the proteomic subtypes of primary tumors, and linked these to drug sensitivity data to identify subtype-specific drug candidates.
2016-12-01 | MSV000080374 | MassIVE
Project description:As metabolic rewiring is crucial for cancer cell proliferation, metabolic phenotyping of patient-derived organoids is desirable to identify drug-induced changes and trace metabolic vulnerabilities of tumor subtypes. We established a novel protocol for metabolomic and lipidomic profiling of colorectal cancer organoids by LC-QTOF-MS facing the challenge of capturing metabolic information from minimal sample amount (< 500 cells/injection) in the presence of extracellular matrix (ECM). The best procedure of the tested protocols included ultrasonic metabolite extraction with acetonitrile/methanol/water (2:2:1, v/v/v) without ECM removal. To eliminate ECM-derived background signals, we implemented a data filtering procedure based on p-value and fold change cut-offs which retained features with signal intensities >120% compared to matrix-derived signals present in blank samples. As a proof-of-concept, the method was applied to examine the early metabolic response of colorectal cancer organoids to 5-fluorouracil treatment. Statistical analysis revealed dose-dependent changes in the metabolic profiles of treated organoids including elevated levels of 2'-deoxyuridine, 2'-O-methylcytidin, inosine and 1-methyladenosine and depletion of 2'-deoxyadenosine and specific phospholipids. In accordance with the mechanism of action of 5-fluorouracil, changed metabolites are mainly involved in purine and pyrimidine metabolism. The novel protocol provides a first basis for the assessment of metabolic drug response phenotypes in 3D organoid models.
2021-10-12 | MTBLS2130 | MetaboLights
Project description:Proteotranscriptomic Characterization of Human Retinoblastoma
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