Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcriptome Analysis Predicts Clinical Outcome and Sensitivity to Anticancer Drugs of patients with a Pancreatic Adenocarcinoma

ABSTRACT: 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).

ORGANISM(S): Homo sapiens

SUBMITTER: Ezequiel Calvo

PROVIDER: E-GEOD-55513 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Transcriptomic analysis predicts survival and sensitivity to anticancer drugs of patients with a pancreatic adenocarcinoma.

Duconseil Pauline P Gilabert Marine M Gayet Odile O Loncle Celine C Moutardier Vincent V Turrini Olivier O Calvo Ezequiel E Ewald Jacques J Giovannini Marc M Gasmi Mohamed M Bories Erwan E Barthet Marc M Ouaissi Mehdi M Goncalves Anthony A Poizat Flora F Raoul Jean Luc JL Secq Veronique V Garcia Stephane S Viens Patrice P Iovanna Juan J Dusetti Nelson N

The American journal of pathology 20150309 4

A major impediment to the effective treatment of patients with pancreatic ductal adenocarcinoma (PDAC) is the molecular heterogeneity of this disease, which is reflected in an equally diverse pattern of clinical outcome and in responses to therapies. We developed an efficient strategy in which PDAC samples from 17 consecutive patients were collected by endoscopic ultrasound-guided fine-needle aspiration or surgery and were preserved as breathing tumors by xenografting and as a primary culture of ...[more]

PMID: 25765988

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Project description:Pancreatic ductal adenocarcinoma (PDAC) remains a major unsolved health problem. Most drugs that pass preclinical tests fail in these patients, emphasizing the need of appropriate preclinical models to test novel anticancer strategies. We developed four orthotopic mouse models employing primary human PDAC cells expressing Firefly and Gaussia luciferases, enabling bioluminescence monitoring of tumor growth and metastasis formation. Additional tumor characterization was performed using MR and high frequency ultrasound imaging. Genomic and immunohistochemical analysis revealed c-Met amplification and overexpression in one of four models. Analysis of c-Met inhibitors in vitro showed that crizotinib had the most potent effect. Moreover, we demonstrated synergistic effects between crizotinib and gemcitabine M-bM-^@M-^S the standard of care therapeutic in PDAC patients - in vitro and in vivo. Importantly, crizotinib reduced the cytidine deaminase activity in PDAC cells causing prolonged activity of gemcitabine due to diminished metabolic inactivation, as measured by LC-MS/MS. This might at least in part explain the observed prolonged survival of concomitantly treated mice with PDAC tumors and metastases. In conclusion, our orthotopic PDAC models enabled PDAC tumor imaging, and showed genetic, histopathological and metastatic features similar to their originator tumors. This allowed the identification of c-Met as a potential therapeutic target in PDAC, and revealed a cytidine deaminase-mediated synergistic mechanism between crizotinib and gemcitabine, a combination of drugs that warrants further investigation for the potential treatment of PDAC patients. 12 test samples in total [4 PDAC models (PDAC-1, PDAC-2, PDAC-3, PDAC-4; in three panel: primary human PDAC, primary tumor culture and mouse sample)] and reference sample (healthy control (mix/pool of healthy volunteers DNA) were analyzed as following (8 hybridizations); PDAC-1 primary human-Cy3 vs PDAC-4 cultured cells -Cy5 PDAC-1 cultured cells-Cy3 vs PDAC-4 mouse-Cy5 PDAC-1 mouse-Cy3 vs reference-Cy5 PDAC-2 primary human-Cy3 vs reference-Cy5 PDAC-2_cultured cells-Cy3 vs PDAC-2 mouse-Cy5 PDAC-3_primary human-Cy3 vs reference-Cy5 PDAC-3_cultured cells-Cy3 vs PDAC-3 mouse-Cy5 PDAC-4 primary human-Cy5 vs reference-Cy3 Normalized log2 ratio of (sample/references) data were calculated for 12 samples [*txt files on Series records]. Description of experimental/analysis design in r-program to generate 12 samples from 8 raw data files is provided in README.txt [Series supplementary file]

Dataset Information

Transcriptome Analysis Predicts Clinical Outcome and Sensitivity to Anticancer Drugs of patients with a Pancreatic Adenocarcinoma

Publications

Transcriptomic analysis predicts survival and sensitivity to anticancer drugs of patients with a pancreatic adenocarcinoma.

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