Similar Datasets
Project description:Rhabdomyosarcoma (RMS) is the main form of pediatric soft-tissue sarcoma. Its cure rate has not improved in the last 20 years notably following relapse, and the lack of reliable preclinical models has so far hampered the design of new therapies. This is particularly true for highly heterogeneous fusion-negative RMS (FNRMS). Although methods have been proposed to establish FNRMS organoids, their efficiency remains limited to date, both in terms of derivation rate and ability to accurately mimic the original tumor. Here, we present the development of a next-generation 3D-organoid model derived from relapsed adult and pediatric FNRMS. This model preserves the molecular features of the patients’ tumors and is expandable for several months in 3D, reinforcing its interest to drug combination screening with longitudinal efficacy monitoring. As a proof-of-concept, we demonstrated its preclinical relevance by reevaluating the therapeutic opportunities of targeting apoptosis in FNRMS from a streamlined approach based on transcriptomic data exploitation.
2023-12-19 | GSE248182 | GEO
Project description:Rhabdomyosarcoma (RMS) is the main form of pediatric soft-tissue sarcoma. Its cure rate has not improved in the last 20 years notably following relapse, and the lack of reliable preclinical models has so far hampered the design of new therapies. This is particularly true for highly heterogeneous fusion-negative RMS (FNRMS). Although methods have been proposed to establish FNRMS organoids, their efficiency remains limited to date, both in terms of derivation rate and ability to accurately mimic the original tumor. Here, we present the development of a next-generation 3D-organoid model derived from relapsed adult and pediatric FNRMS. This model preserves the molecular features of the patients’ tumors and is expandable for several months in 3D, reinforcing its interest to drug combination screening with longitudinal efficacy monitoring. As a proof-of-concept, we demonstrated its preclinical relevance by reevaluating the therapeutic opportunities of targeting apoptosis in FNRMS from a streamlined approach based on transcriptomic data exploitation.
2023-12-19 | GSE248177 | GEO
Project description:Rhabdomyosarcoma (RMS) is the main form of pediatric soft-tissue sarcoma. Its cure rate has not improved in the last 20 years notably following relapse, and the lack of reliable preclinical models has so far hampered the design of new therapies. This is particularly true for highly heterogeneous fusion-negative RMS (FNRMS). Although methods have been proposed to establish FNRMS organoids, their efficiency remains limited to date, both in terms of derivation rate and ability to accurately mimic the original tumor. Here, we present the development of a next-generation 3D-organoid model derived from relapsed adult and pediatric FNRMS. This model preserves the molecular features of the patients’ tumors and is expandable for several months in 3D, reinforcing its interest to drug combination screening with longitudinal efficacy monitoring. As a proof-of-concept, we demonstrated its preclinical relevance by reevaluating the therapeutic opportunities of targeting apoptosis in FNRMS from a streamlined approach based on transcriptomic data exploitation.
2023-12-19 | GSE248178 | GEO
Project description:Rhabdomyosarcoma (RMS) is the main form of pediatric soft-tissue sarcoma. Its cure rate has not improved in the last 20 years notably following relapse, and the lack of reliable preclinical models has so far hampered the design of new therapies. This is particularly true for highly heterogeneous fusion-negative RMS (FNRMS). Although methods have been proposed to establish FNRMS organoids, their efficiency remains limited to date, both in terms of derivation rate and ability to accurately mimic the original tumor. Here, we present the development of a next-generation 3D-organoid model derived from relapsed adult and pediatric FNRMS. This model preserves the molecular features of the patients’ tumors and is expandable for several months in 3D, reinforcing its interest to drug combination screening with longitudinal efficacy monitoring. As a proof-of-concept, we demonstrated its preclinical relevance by reevaluating the therapeutic opportunities of targeting apoptosis in FNRMS from a streamlined approach based on transcriptomic data exploitation.
2023-12-19 | GSE248176 | GEO
Project description:BACKGROUND:Organotypic cultures derived from pancreatic ductal adenocarcinoma (PDAC) termed pancreatic ductal cancer organoids (PDOs) recapitulate the primary cancer and can be derived from primary or metastatic biopsies. Although isolation and culture of patient-derived pancreatic organoids were established several years ago, pros and cons for individualized medicine have not been comprehensively investigated to date. METHODS:We conducted a feasibility study, systematically comparing head-to-head patient-derived xenograft tumor (PDX) and PDX-derived organoids by rigorous immunohistochemical and molecular characterization. Subsequently, a drug testing platform was set up and validated in vivo. Patient-derived organoids were investigated as well. RESULTS:First, PDOs faithfully recapitulated the morphology and marker protein expression patterns of the PDXs. Second, quantitative proteomes from the PDX as well as from corresponding organoid cultures showed high concordance. Third, genomic alterations, as assessed by array-based comparative genomic hybridization, revealed similar results in both groups. Fourth, we established a small-scale pharmacotyping platform adjusted to operate in parallel considering potential obstacles such as culture conditions, timing, drug dosing, and interpretation of the results. In vitro predictions were successfully validated in an in vivo xenograft trial. Translational proof-of-concept is exemplified in a patient with PDAC receiving palliative chemotherapy. CONCLUSION:Small-scale drug screening in organoids appears to be a feasible, robust and easy-to-handle disease modeling method to allow response predictions in parallel to daily clinical routine. Therefore, our fast and cost-efficient assay is a reasonable approach in a predictive clinical setting.
| S-EPMC7268941 | biostudies-literature
Project description:Effective design of combination therapies requires understanding the changes in cell physiology resulting from drug interactions. Here, we show that the genome-wide transcriptional response to combinations of two drugs, measured at a rigorously controlled growth rate, can predict higher-order antagonism with a third drug in Saccharomyces cerevisiae. Using isogrowth profiling, over 90% of the variation in cellular response can be decomposed into three principal components (PCs) that have clear biological interpretations. We demonstrate that the third PC captures emergent transcriptional programs that are dependent on both drugs and can predict antagonism with a third drug targeting the emergent pathway. We further show that emergent gene expression patterns are most pronounced at a drug ratio where the drug interaction is strongest, providing a guideline for future measurements. Our results provide a readily applicable recipe for uncovering emergent responses in other systems and for higher-order drug combinations.
2019-10-02 | GSE138256 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below.
2017-10-11 | GSE104279 | GEO
Project description:Molecular technologies have allowed laboratories to detect and establish the profiles of human cancers by identifying a variety of somatic variants. In order to improve personalized patient care, we have established a next-generation sequencing (NGS) test to screen for somatic variants in primary or advanced cancers. In this study, we describe the laboratory quality management program for NGS testing, and also provide an overview of the somatic variants identified in over 1000 patient samples as well as their implications in clinical practice.Over the past one-and-a-half years, our laboratory received a total of 1028 formalin-fixed, paraffin-embedded (FFPE) tumor tissues, which consisted of non-small-cell lung carcinomas (NSCLCs), colon adenocarcinomas, glioma/glioblastomas, melanomas, breast carcinomas, and other tumor types. During this time period, we implemented a series of quality control (QC) checks that included (1) pre-DNA extraction, (2) DNA quantification, (3) DNA quality, (4) library quantification, (5) post-emulsification PCR, and (6) post-sequencing metrics. At least 10 ng of genomic DNA (gDNA) were used to prepare barcoded libraries using the AmpliSeq CHPv2. Samples were multiplexed and sequenced on Ion Torrent 318 chips using the Ion PGM System. Variants were identified using the Variant Caller Plugin, and annotation and functional predictions were performed using the Golden Helix SVS.A total of 1005 samples passed QC1-3, and following additional library preparation QC checkpoints, 877 samples were sequenced. Samples were classified into two categories: wild-type (127) and positive for somatic variants (750). Somatic variants were classified into clinically actionable (60%) and non-actionable (40%).The use of NGS in routine clinical laboratory practice allowed for the detection of tumor profiles that are essential for the selection of targeted therapies and identification of applicable clinical trials, contributing to the improvement of personalized patient care in oncology.
| S-EPMC5506542 | biostudies-other
Project description:Drug combinatorial therapy is a promising strategy for combating complex diseases due to its fewer side effects, lower toxicity and better efficacy. However, it is not feasible to determine all the effective drug combinations in the vast space of possible combinations given the increasing number of approved drugs in the market, since the experimental methods for identification of effective drug combinations are both labor- and time-consuming. In this study, we conducted systematic analysis of various types of features to characterize pairs of drugs. These features included information about the targets of the drugs, the pathway in which the target protein of a drug was involved in, side effects of drugs, metabolic enzymes of the drugs, and drug transporters. The latter two features (metabolic enzymes and drug transporters) were related to the metabolism and transportation properties of drugs, which were not analyzed or used in previous studies. Then, we devised a novel improved naïve Bayesian algorithm to construct classification models to predict effective drug combinations by using the individual types of features mentioned above. Our results indicated that the performance of our proposed method was indeed better than the naïve Bayesian algorithm and other conventional classification algorithms such as support vector machine and K-nearest neighbor.
| S-EPMC5855689 | biostudies-other
Project description:Combinations of anti-cancer drugs can overcome resistance and provide new treatments1,2. The number of possible drug combinations vastly exceeds what could be tested clinically. Efforts to systematically identify active combinations and the tissues and molecular contexts in which they are most effective could accelerate the development of combination treatments. Here we evaluate the potency and efficacy of 2,025 clinically relevant two-drug combinations, generating a dataset encompassing 125 molecularly characterized breast, colorectal and pancreatic cancer cell lines. We show that synergy between drugs is rare and highly context-dependent, and that combinations of targeted agents are most likely to be synergistic. We incorporate multi-omic molecular features to identify combination biomarkers and specify synergistic drug combinations and their active contexts, including in basal-like breast cancer, and microsatellite-stable or KRAS-mutant colon cancer. Our results show that irinotecan and CHEK1 inhibition have synergistic effects in microsatellite-stable or KRAS-TP53 double-mutant colon cancer cells, leading to apoptosis and suppression of tumour xenograft growth. This study identifies clinically relevant effective drug combinations in distinct molecular subpopulations and is a resource to guide rational efforts to develop combinatorial drug treatments.
| S-EPMC8891012 | biostudies-literature