Dataset Information

Azathioprine induction of tumors with microsatellite instability: risk evaluation using a mouse model.

ABSTRACT: Mismatch-repair (MMR)-deficient cells show increased in vitro tolerance to thiopurines because they escape apoptosis resulting from MMR-dependent signaling of drug-induced DNA damage. Prolonged treatment with immunosuppressants including azathioprine (Aza), a thiopurine prodrug, has been suggested as a risk factor for the development of late onset leukemias/lymphomas displaying a microsatellite instability (MSI) phenotype, the hallmark of a defective MMR system. We performed a dose effect study in mice to investigate the development of MSI lymphomas associated with long term Aza treatment. Over two years, Aza was administered to mice that were wild type, null or heterozygous for the MMR gene Msh2. Ciclosporin A, an immunosuppressant with an MMR-independent signaling, was also administered to Msh2(wt) mice as controls. Survival, lymphoma incidence and MSI tumor phenotype were investigated. Msh2(+/-) mice were found more tolerant than Msh2(wt) mice to the cytotoxicity of Aza. In Msh2(+/-) mice, Aza induced a high incidence of MSI lymphomas in a dose-dependent manner. In Msh2(wt) mice, a substantial lifespan was only observed at the lowest Aza dose. It was associated with the development of lymphomas, one of which displayed the MSI phenotype, unlike the CsA-induced lymphomas. Our findings define Aza as a risk factor for an MSI-driven lymphomagenesis process.

SUBMITTER: Bodo S

PROVIDER: S-EPMC4694807 | biostudies-other | 2015 Sep

REPOSITORIES: biostudies-other

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Azathioprine induction of tumors with microsatellite instability: risk evaluation using a mouse model.

Bodo Sahra S Svrcek Magali M Sourrouille Isabelle I Cuillières-Dartigues Peggy P Ledent Tatiana T Dumont Sylvie S Dinard Laetitia L Lafitte Philippe P Capel Camille C Collura Ada A Buhard Olivier O Wanherdrick Kristell K Chalastanis Alexandra A Penard-Lacronique Virginie V Fabiani Bettina B Fléjou Jean-François JF Brousse Nicole N Beaugerie Laurent L Duval Alex A Muleris Martine M

Oncotarget 20150901 28

Mismatch-repair (MMR)-deficient cells show increased in vitro tolerance to thiopurines because they escape apoptosis resulting from MMR-dependent signaling of drug-induced DNA damage. Prolonged treatment with immunosuppressants including azathioprine (Aza), a thiopurine prodrug, has been suggested as a risk factor for the development of late onset leukemias/lymphomas displaying a microsatellite instability (MSI) phenotype, the hallmark of a defective MMR system. We performed a dose effect study ...[more]

PMID: 26327213

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Project description:Background & aimsMicrosatellite instability (MSI) and mismatch-repair deficiency (dMMR) in colorectal tumors are used to select treatment for patients. Deep learning can detect MSI and dMMR in tumor samples on routine histology slides faster and less expensively than molecular assays. However, clinical application of this technology requires high performance and multisite validation, which have not yet been performed.MethodsWe collected H&E-stained slides and findings from molecular analyses for MSI and dMMR from 8836 colorectal tumors (of all stages) included in the MSIDETECT consortium study, from Germany, the Netherlands, the United Kingdom, and the United States. Specimens with dMMR were identified by immunohistochemistry analyses of tissue microarrays for loss of MLH1, MSH2, MSH6, and/or PMS2. Specimens with MSI were identified by genetic analyses. We trained a deep-learning detector to identify samples with MSI from these slides; performance was assessed by cross-validation (N = 6406 specimens) and validated in an external cohort (n = 771 specimens). Prespecified endpoints were area under the receiver operating characteristic (AUROC) curve and area under the precision-recall curve (AUPRC).ResultsThe deep-learning detector identified specimens with dMMR or MSI with a mean AUROC curve of 0.92 (lower bound, 0.91; upper bound, 0.93) and an AUPRC of 0.63 (range, 0.59-0.65), or 67% specificity and 95% sensitivity, in the cross-validation development cohort. In the validation cohort, the classifier identified samples with dMMR with an AUROC of 0.95 (range, 0.92-0.96) without image preprocessing and an AUROC of 0.96 (range, 0.93-0.98) after color normalization.ConclusionsWe developed a deep-learning system that detects colorectal cancer specimens with dMMR or MSI using H&E-stained slides; it detected tissues with dMMR with an AUROC of 0.96 in a large, international validation cohort. This system might be used for high-throughput, low-cost evaluation of colorectal tissue specimens.

Dataset Information

Azathioprine induction of tumors with microsatellite instability: risk evaluation using a mouse model.

Publications

Azathioprine induction of tumors with microsatellite instability: risk evaluation using a mouse model.

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