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

癌症患者由于许多因素而具有高度可变的临床结果，其中 这些基因决定了肿瘤侵袭和转移的可能性这种倾向 可以反映在原发肿瘤的基因表达模式中，这可以预测 结果和指导治疗的选择比其他临床预测更好。我们开发 一种基于mRNA表达的模型，可以预测人类乳腺癌的预后/结果 无论微阵列平台和患者组如何。我们的模型是用 在体内生长的小鼠浆细胞肿瘤中差异表达的基因与 在试管中生长。预测系统使用来自三个队列的已发表数据进行验证 这些患者的微阵列和临床数据已经被编辑。模型分层 将患者分为四个独立的生存组（最佳、良好、不良和最差：对数秩检验 p=1.7 x 10-8）。我们的模型比其他模型显著提高了生存预测 基于表达的模型，并允许识别不同疾病的患者， 雌激素受体阳性组和单一病理肿瘤类别内。接受那些基于 起源于不同物种和不同细胞类型的数据集上的预测因子可以 使其对扩散差异不那么敏感， 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.