A Novel Strategy for Combination of Clofarabine and Pictilisib is Synergistic in Gastric Cancer
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PROVIDER: PRJNA729107 | ENA |
REPOSITORIES: ENA
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Project description:Purpose: We aimed to identify a novel therapeutic approach in gastric cancer using high-throughput screening and our computational SynerySeq pipeline. We investigated the therapeutic efficiency of clofarabine as a single agent and in combination with the PI3K inhibitor pictilisib in treating in vitro and in vivo models of gastric cancer.
2021-11-01 | GSE174288 | GEO
Project description:Transcriptomic analysis uncovers novel synergistic mechanisms in combination therapy for lupus nephritis
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Project description:A serum miRNA combination could be a powerful classifier for the detection of patients with early stage gastric cancer.
2021-06-23 | GSE164174 | GEO
Project description:Chemotherapeutic treatment regimens often take advantage of synergistic effects of drug combinations. Anticipating that synergistic effects on cell biological level likely manifest on proteome level, the analysis of proteome modulations represents an appropriate strategy to study drug combinations on molecular level. More specifically, the detection of single proteins exhibiting synergistic abundance changes could be helpful to shed light on key molecules, which contribute in mechanisms facilitating the synergistic interaction and therefore represent potential target for specific therapeutic approaches. In the reported study, we investigated the drug combination of cisplatin and the neddylation inhibitor MLN4924 in HCT-116 cells via cell biological analyses and mass spectrometry-based quantitative proteomics. From 1,789 proteins quantified with two unique peptides, activated RNA polymerase II transcriptional coactivator p15 (SUB1) was highlighted as most synergistically regulated protein using a synergistic scoring approach. Western blotting and analyses of cellular processes associated with this protein (DNA damage, oxidative stress and apoptosis) revealed supporting evidence for the synergistic regulation. Whereas the distinct role of SUB1 in the investigated drug combination needs to be elucidated in future studies, the presented results demonstrated the benefit and feasibility of synergistic scoring of proteome alterations to highlight proteins that likely contribute to the underlying molecular mechanisms of synergistic effects.
2018-10-02 | PXD009185 | Pride
Project description:A novel combination of serum microRNAs for the detection of early gastric cancer
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Project description:Gastric cancer (GC) is frequently characterized by resistance to standard chemotherapeutic regimens and poor clinical outcomes. We aimed to identify a novel therapeutic approach using drug sensitivity testing (DST) and our computational SynerySeq pipeline. DST of GC cell lines was performed with a library of 215 Federal Drug Administration (FDA) approved compounds and identified clofarabine as a potential therapeutic agent. RNA-sequencing (RNAseq) of clofarabine treated GC cells was analyzed according to our SynergySeq pipeline and identified pictilisib as a potential synergistic agent. Clonogenic survival and Annexin V assays demonstrated increased cell death with clofarabine and pictilisib combination treatment (P<0.01). The combination induced double strand breaks (DSB) as indicated by phosphorylated H2A histone family member X (γH2AX) immunofluorescence and western blot analysis (P<0.01). Pictilisib treatment inhibited the protein kinase B (AKT) cell survival pathway and promoted a pro-apoptotic phenotype as evidenced by quantitative real time polymerase chain reaction (qRT-PCR) analysis of the B-cell lymphoma 2 (BCL2) protein family members (P<0.01). Patient derived xenograft (PDX) data confirmed that the combination is more effective in abrogating tumor growth with prolonged survival than single-agent treatment (P<0.01). The novel combination of clofarabine and pictilisib in GC promotes DNA damage and inhibits key cell survival pathways to induce cell death beyond single-agent treatment.
| S-EPMC8571525 | biostudies-literature
Project description:Combination of four serum exosomal miRNAs as novel diagnostic biomarkers for early-stage gastric cancer
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Project description:Registered systemic treatment options for glioblastoma patients are limited, particularly for progressive glioblastoma, including bevacizumab in the US and some other countries as well as lomustine. The phase II REGOMA trial suggested an improvement of overall survival in progressive glioblastoma by the multi-tyrosine kinase inhibitor regorafenib. This has not been confirmed by GBM AGILE, however. Of note, regorafenib has been administered as monotherapy in progressive glioblastoma or as an addition to standard of care in newly diagnosed glioblastoma. Rational combination therapies involving regorafenib, instead of entirely discontinuing any consideration of this compound, might be therefore a reasonable strategy. We addressed this research gap and used an innovative discovery and validation approach to identify functionally-instructed combination therapies. We applied a genome-wide CRISPR-Cas9-based functional genomics experimental approach including activation and knockout screens for the discovery of potential molecular targets modifying the therapeutic efficacy of regorafenib in experimental glioma. Selected functionally instructed molecular hits were then validated regarding synergistic interaction in vitro, ex vivo and in two orthotopic mouse models in vivo.This functional genomics approach led to the discovery of several molecular targets. Based on subsequent validation experiments including a customized compound library, we identified novel combination therapies. Particularly, the combination of regorafenib with Bcl-2/Bcl-xL inhibition was among the most promising strategies.
2024-08-21 | GSE272737 | GEO
Project description:This study was conducted to identify transcriptional profiles predictive of a clinical response of metastatic gastric cancer patients to cisplatin and fluorouracil (CF) combination chemotherapy. Endoscopic biopsy samples were collected from CF-treated metastatic gastric cancer patients prior to therapy and following the development of resistance to therapy.
2011-06-01 | E-GEOD-14208 | biostudies-arrayexpress
Project description:Despite continual efforts to establish pre-operative prognostic model of gastric cancer by using clinical and pathological parameters, a staging system that reliably separates patients with early and advanced gastric cancer into homogeneous groups with respect to prognosis does not exist. With use of microarray and quantitative RT-PCR technologies, we exploited series of experiments in combination with complementary data analyses on tumor specimens from 161 gastric cancer patients. Various statistical analyses were applied to gene expression data to uncover subgroups of gastric cancer, to identify potential biomarkers associated with prognosis, and to construct molecular predictor of risk from identified prognostic biomarkers.Two subgroups of gastric cancer with strong association with prognosis were uncovered. The robustness of prognostic gene expression signature was validated in independent patient cohort with use of support vector machines prediction model. For easy translation of our finding to clinics, we develop scoring system based on expression of six genes that can predict the likelihood of recurrence after curative resection of tumors. In multivariate analysis, our novel risk score was an independent predictor of recurrence (P=0.004) in cohort of 96 patients, and its robustness was validated in two other independent cohorts. We identified novel prognostic subgroups of gastric cancer that are distinctive in gene expression patterns. Six-gene signature and risk score derived from them has been validated for predicting the likelihood of survival at diagnosis. 65 primary gastric adenocarcinoma, 6 GIST and 19 surrounding normal fresh frozen tissues were used for microarray. All the tissues were obtained after curative resection after pathologic confirm at Yonsei cancer center(Seoul, Korea). Microarray experiment and data analysis were done at Dept. of systems biology, MDACC DNA microarray (Illumina human V3)
2011-07-18 | E-GEOD-13861 | biostudies-arrayexpress