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首页> 外文期刊>BMC Medical Genomics >Integrating Factor Analysis and a Transgenic Mouse Model to Reveal a Peripheral Blood Predictor of Breast Tumors
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Integrating Factor Analysis and a Transgenic Mouse Model to Reveal a Peripheral Blood Predictor of Breast Tumors

机译：整合因子分析和转基因小鼠模型揭示乳腺肿瘤的外周血预测因子

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Background Transgenic mouse tumor models have the advantage of facilitating controlled in vivo oncogenic perturbations in a common genetic background. This provides an idealized context for generating transcriptome-based diagnostic models while minimizing the inherent noisiness of high-throughput technologies. However, the question remains whether models developed in such a setting are suitable prototypes for useful human diagnostics. We show that latent factor modeling of the peripheral blood transcriptome in a mouse model of breast cancer provides the basis for using computational methods to link a mouse model to a prototype human diagnostic based on a common underlying biological response to the presence of a tumor. Methods We used gene expression data from mouse peripheral blood cell (PBC) samples to identify significantly differentially expressed genes using supervised classification and sparse ANOVA. We employed these transcriptome data as the starting point for developing a breast tumor predictor from human peripheral blood mononuclear cells (PBMCs) by using a factor modeling approach. Results The predictor distinguished breast cancer patients from healthy individuals in a cohort of patients independent from that used to build the factors and train the model with 89% sensitivity, 100% specificity and an area under the curve (AUC) of 0.97 using Youden's J-statistic to objectively select the model's classification threshold. Both permutation testing of the model and evaluating the model strategy by swapping the training and validation sets highlight its stability. Conclusions We describe a human breast tumor predictor based on the gene expression of mouse PBCs. This strategy overcomes many of the limitations of earlier studies by using the model system to reduce noise and identify transcripts associated with the presence of a breast tumor over other potentially confounding factors. Our results serve as a proof-of-concept for using an animal model to develop a blood-based diagnostic, and it establishes an experimental framework for identifying predictors of solid tumors, not only in the context of breast cancer, but also in other types of cancer.
机译：背景技术转基因小鼠肿瘤模型的优点是在常见遗传背景下可控制体内致癌性扰动。这为生成基于转录组的诊断模型提供了理想的环境，同时最大程度地减少了高通量技术的固有噪声。但是，问题仍然在于，在这种情况下开发的模型是否适合用于有用的人类诊断的原型。我们显示乳腺癌小鼠模型中外周血转录组的潜在因子建模为使用计算方法将小鼠模型链接到基于对肿瘤存在的常见基础生物学反应的原型人类诊断提供了基础。方法我们使用监督分类和稀疏ANOVA，使用小鼠外周血细胞（PBC）样品中的基因表达数据来鉴定差异显着的基因。我们采用因子模型方法，将这些转录组数据作为从人外周血单核细胞（PBMC）开发乳腺肿瘤预测因子的起点。结果预测变量将乳腺癌患者与健康个体区分开来，与一群独立于用于构建因素的患者和使用尤登氏J-值以89％的敏感性，100％的特异性和0.97的曲线下面积（AUC）训练模型的患者无关。统计以客观地选择模型的分类阈值。通过置换训练集和验证集对模型进行的排列测试和评估模型策略都突出了其稳定性。结论我们基于小鼠PBCs的基因表达描述了人类乳腺肿瘤的预测因子。该策略通过使用模型系统来降低噪声并识别与乳腺肿瘤的存在相关的转录本，从而克服了其他潜在的混杂因素，从而克服了早期研究的许多局限性。我们的研究结果为使用动物模型开发基于血液的诊断方法提供了概念验证，它为识别实体瘤的预测因子（不仅在乳腺癌方面，而且在其他类型中）建立了实验框架癌症。

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