Use of Mouse Gene Expression Profiles to Predict Human Breast Cancer Prognosis

癌症患者的临床结局因多种因素而变化很大，其中一些基因决定了肿瘤侵袭和转移的可能性。这种易感性可以在原发肿瘤的基因表达模式中得到反映，它可能比其他临床预测因子更好地预测预后和指导治疗选择。我们开发了一个基于mRNA表达的模型，该模型可以预测人类乳腺癌患者的预后/结果，而不受微阵列平台和患者组的影响。我们的模型是利用在体内生长的小鼠浆细胞肿瘤与在体外生长的小鼠浆细胞肿瘤中差异表达的基因开发的。该预测系统使用已编制微阵列和临床数据的三个队列患者的已发表数据进行了验证。该模型将患者分成四个独立的生存组(最佳、良好、差和最差：对数检验，p=1.7x10-8)。与其他基于表达的模型相比，我们的模型显著改善了生存预测，并允许识别雌激素受体阳性组和同一病理肿瘤类别中预后不同的患者。基于来自不同物种和不同细胞类型的数据集，我们的预测值可能会使其对增殖差异不那么敏感，并赋予它广泛的适用性。

Cancer patients have highly variable clinical outcomes owing to many factors, among which are genes that determine the likelihood of invasion and metastasis. This predisposition can be reflected in the gene expression pattern of the primary tumor, which may predict outcomes and guide the choice of treatment better than other clinical predictors. We developed an mRNA expression-based model that can predict prognosis/outcomes of human breast cancer patients regardless of microarray platform and patient group. Our model was developed using genes differentially expressed in mouse plasma cell tumors growing in vivo versus those growing in vitro. The prediction system was validated using published data from three cohorts of patients for whom microarray and clinical data had been compiled. The model stratified patients into four independent survival groups (BEST, GOOD, BAD, and WORST: log-rank test p=1.7 x 10-8). Our model significantly improved the survival prediction over other expression-based models and permitted recognition of patients with different prognoses within the estrogen receptor-positive group and within a single pathological tumor class. Basing our predictor on a dataset that originated in a different species and a different cell type may have rendered it less sensitive to proliferation differences and endowed it with wide applicability.