Predictive model for risk of severe gastrointestinal toxicity following chemotherapy using patient immune genetics and type of cancer: a pilot study

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• 作者：Janet K. Coller ; Imogen A. White ; Richard M. Logan…
• 关键词：Chemotherapy ; induced gastrointestinal toxicity ; 5 ; fluorouracil ; Cytokines ; Toll ; like receptors ; Predictive genetic model
• 刊名：Supportive Care in Cancer
• 出版年：2015
• 出版时间：May 2015
• 年：2015
• 卷：23
• 期：5
• 页码：1233-1236
• 全文大小：323 KB
• 参考文献：1. Sonis, ST, Elting, LS, Keefe, D, Peterson, DE, Schubert, M, Hauer-Jensen, M, Bekele, BN, Raber-Durlacher, J, Donnelly, JP, Rubenstein, EB (2004) Perspectives on cancer therapy-induced mucosal injury: pathogenesis, measurement, epidemiology, and consequences for patients. Cancer 100: pp. 1995-2025 k" title="It opens in new window">CrossRef
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  R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
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• 刊物类别：Medicine
• 刊物主题：Medicine & Public Health
  Oncology
  Nursing
  Nursing Management and Research
  Pain Medicine
  Rehabilitation Medicine
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1433-7339

文摘

Purpose Severe chemotherapy-induced gastrointestinal toxicity (CIGT) is common and results in treatment delays, dose reductions, and potential premature treatment discontinuation. Currently, there is no diagnostic marker to predict CIGT. Proinflammatory cytokines, produced via toll-like receptor signaling, are key mediators of this toxicity. Hence, this pilot study investigated the association between immune genetic variability and severe CIGT risk. Methods Genomic DNA from 34 patients (10 with severe CIGT) who had received 5-fluoruracil-based chemotherapy regimens was analyzed for variants of IL-1B, IL-2, IL-6, IL-6R, IL-10, TNF-a, TGF-b, TLR2, TLR4, MD2, MYD88, BDNF, CRP, ICE, and OPRM1. Multivariate logistic regression created a prediction model of severe CIGT risk. Results There were no significant differences between patients with and without severe CIGT with regards to age, sex, type of cancer, or chemotherapy treatment regimens. The prediction model of severe CIGT risk included TLR2 and TNF-a genetic variability and cancer type (colorectal and gastric). This prediction model was both specific and sensitive, with a receiver operator characteristic area under the curve of 87.3?%. Conclusions This is the first report of immune genetic variability, together with cancer type, being predictive of severe CIGT risk. These outcomes are being validated in a larger patient population.