Coordinated over-expression of genes in the EGFR pathway predicts sensitivity to EGFR inhibition in pancreatic cancer
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE9599 | GEO | 2008/04/16
SECONDARY ACCESSION(S): PRJNA103445
REPOSITORIES: GEO
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2008-06-16 | E-GEOD-9599 | biostudies-arrayexpress
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2009-07-15 | GSE12113 | GEO
Project description:Tumors from pancreatic cancer specimens obtained at surgery were used for efficacy testing and biologic analysis. These tumors were s.c. explanted in xenograft models for subsequent treatment experiments. This study aimed to assess the antitumor activity of the Hsp90 inhibitor, IPI-504, in pancreatic cancer and to determine the biological effects of the agent. Experiment Overall Design: Five tumor samples were profiled in treated and untreated with IPI504. Untreated samples were profiled in duplicate. The overall design is to identify differentially expressed genes in IPI504 treated versus untreated.
2009-08-07 | E-GEOD-12113 | biostudies-arrayexpress
Project description:A major impediment to the effective treatment of patients with PDAC (Pancreatic Ductal Adenocarcinoma) is the molecular heterogeneity of the disease, which is reflected in an equally diverse pattern of clinical responses to therapy. We developed an efficient strategy in which PDAC samples from 17 consecutively patients were obtained by EUS-FNA or surgery, their cells maintained as a primary culture and tumors as breathing tumors by xenografting in immunosuppressed mice. For these patients a clinical follow up was obtained. On the breathing tumors we studied the RNA expression profile by an Affymetrix approach. We observed a significant heterogeneity in their RNA expression profile, however, the transcriptome was able to discriminate patients with long- or short-time survival which correspond to moderately- or poorly-differentiated PDAC tumors respectively. Cells allowed us the possibility to analyze their relative sensitivity to several anticancer drugs in vitro by developing a chimiogram, like an antibiogram for microorganisms, with several anticancer drugs for obtaining an individual profile of drug sensitivity and as expected, the response was patient-dependent. Interestingly, using this approach, we also found that the transcriptome analysis could predict the sensitivity to some anticancer drugs of patients with a PDAC. In conclusion, using this approach, we found that the transcriptome analysis could predict the sensitivity to some anticancer drugs and the clinical outcome of patients with a PDAC. PDAC samples from 17 consecutively patients were obtained by Endoscopic Ultrasound-Guided Fine-Needle Aspiration (EUS-FNA) or surgery. Consents of informed patients were collected, and the ethics review board of the three centers approved the study. All samples were anonymized and obtained in accordance with institutional review boards. EUS-FNA cells were maintained as a primary culture and tumors as breathing tumors by xenografting in immunosuppressed mice. We characterized 17 PDAC-derived xenografts by duplicate. Total RNA was extracted and hybridized on Human Gene 2.0 (Genechip, Affymetrix) as described previously (Hamidi et al. JCI 122: 2092-2103, 2012).
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2018-08-22 | GSE85916 | GEO
Project description:A major impediment to the effective treatment of patients with PDAC (Pancreatic Ductal Adenocarcinoma) is the molecular heterogeneity of the disease, which is reflected in an equally diverse pattern of clinical responses to therapy. We developed an efficient strategy in which PDAC samples from 17 consecutively patients were obtained by EUS-FNA or surgery, their cells maintained as a primary culture and tumors as breathing tumors by xenografting in immunosuppressed mice. For these patients a clinical follow up was obtained. On the breathing tumors we studied the RNA expression profile by an Affymetrix approach. We observed a significant heterogeneity in their RNA expression profile, however, the transcriptome was able to discriminate patients with long- or short-time survival which correspond to moderately- or poorly-differentiated PDAC tumors respectively. Cells allowed us the possibility to analyze their relative sensitivity to several anticancer drugs in vitro by developing a chimiogram, like an antibiogram for microorganisms, with several anticancer drugs for obtaining an individual profile of drug sensitivity and as expected, the response was patient-dependent. Interestingly, using this approach, we also found that the transcriptome analysis could predict the sensitivity to some anticancer drugs of patients with a PDAC. In conclusion, using this approach, we found that the transcriptome analysis could predict the sensitivity to some anticancer drugs and the clinical outcome of patients with a PDAC.
2015-04-09 | GSE55513 | GEO
Project description:Approximately 15-30% of patients with lung cancer harbor mutations in the EGFR gene. Major EGFR mutations (>90% of EGFR-mutated lung cancer) are highly sensitive to EGFR tyrosine kinase inhibitors (TKIs). Many uncommon EGFR mutations have been identified, but little is known regarding their characteristics, activation, and sensitivity to various EGFR-TKIs, including allosteric inhibitors. We encountered a case harboring an EGFR-L747P mutation, originally misdiagnosed with EGFR-del19 mutation using a routine diagnostic EGFR mutation test, which was resistant to EGFR-TKI gefitinib. Using this minor mutation and common EGFR-activating mutations, we performed the binding free energy calculations and microsecond-timescale molecular dynamic (MD) simulations, revealing that the L747P mutation considerably stabilizes the active conformation through a salt-bridge formation between K745 and E762. We further revealed why several EGFR inhibitors, including the allosteric inhibitor, were ineffective. Our computational structural analysis strategy would be beneficial for future drug development targeting the EGFR minor mutations.
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Project description:Pancreatic acinar cell carcinoma (PACC) is a rare cancer. When the tumor is metastatic, few therapeutic options are available. Precision medicine using next-generation sequencing is defined by the administration of drugs based on the tumor genetic mutations. The usage of precision medicine for finding new therapeutic options for rare cancers is an emerging field. We have reported here the case of a patient bearing a multitreated metastatic PACC. This patient underwent somatic and constitutional exome analyses. The analyses revealed in the liver metastasis an amplification of the EGFR gene. Accordingly, the patient was treated with off-label usage of panitumumab. We observed rapid response with necrosis of the liver metastasis, while no efficacy was observed in the primary tumor. An exome analysis of the primary tumor revealed amplification of HER2 and MET with EGFR amplification. Such amplifications are known as a resistance mechanism to antiEGFR therapy. Our results suggest that exome analysis may be helpful to highlight targets in rare cancers, such as PACC. EGFR amplification in this pathology should be determined and could be used as a biomarker to propose antiEGFR therapy.
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Project description:Background:Bladder cancer remains a cancer type in need of novel and alternative therapies. While multiple inhibitors of EGFR have been evaluated for efficacy in bladder cancer, the results have largely been disappointing with few patients responding to these therapies. Yet, there is a subset of patients that positively responds to EGFR inhibition with tumor shrinkage, indicating it is an effective treatment for a targeted set of bladder tumors. Objective:To derive a gene expression signature capable of predicting the response to EGFR inhibition in bladder cancer cell lines. Methods:he response to cetuximab for 68 colorectal cancer patients was used as training data to generate a gene expression signature. We applied this signature to bladder cancer cell lines and predictions were compared to the responses to seven EGFR inhibitors. Results:A novel 67-gene signature derived from colorectal cancer was able to significantly identify bladder cancer cell lines by their response to several EGFR inhibitors. Conclusions:The 67-gene signature can determine bladder cancer cell line sensitivity to EGFR inhibition. This work demonstrates a preclinical strategy to identify bladder cancer cell lines for EGFR-targeted therapy.
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