Heterologous Tissue Culture Expression Signature Predicts Human Breast Cancer Prognosis

BackgroundCancer 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.
Methodology/Principal FindingsWe 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 = 1.7×10−8).
ConclusionsOur 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.
SignificancePrognosis prediction for patients with breast cancer is currently based on histopathological typing and estrogen receptor positivity. Yet both assays define groups that are heterogeneous in survival. Gene expression profiling allows subdivision of these groups and recognition of patients whose tumors are very unlikely to be lethal and those with much grimmer outlooks, which can augment the predictive power of conventional tumor analysis and aid the clinician in choosing relaxed vs. aggressive therapy.

研究背景：癌症患者的临床结局存在高度异质性，受多种因素影响，其中包括决定肿瘤侵袭与转移可能性的基因。这类肿瘤易感倾向可通过原发肿瘤的基因表达模式（gene expression pattern）体现，且相较其他临床预测因子（clinical predictors），该表达模式能更精准地预测患者结局、指导治疗方案的选择。
研究方法与主要结果：我们构建了一种基于mRNA表达（mRNA expression）的预测模型，可在不受微阵列平台（microarray platform）及患者亚组限制的前提下，预测人类乳腺癌患者的预后结局。该模型以体内（in vivo）与体外（in vitro）培养的小鼠浆细胞瘤（mouse plasma cell tumors）之间的差异表达基因作为核心构建基础。我们采用已发表的三组患者队列数据对该预测系统进行验证，这些队列均已整合了微阵列数据与临床随访信息。模型将患者分层为四个独立的生存组（BEST、GOOD、BAD、WORST：对数秩检验（log-rank test）p=1.7×10^−8）。
研究结论：相较于其他基于基因表达的预测模型，本模型显著提升了生存预测效能，且可在雌激素受体阳性（estrogen receptor-positive）组及单一病理肿瘤亚型中，区分出不同预后的患者群体。本预测器以跨物种、跨细胞类型的数据集构建，这一特性可能使其对肿瘤增殖差异的敏感性更低，同时赋予其更广泛的适用范围。
研究意义：当前乳腺癌患者的预后预测主要基于组织病理分型（histopathological typing）与雌激素受体阳性状态。然而这两种检测方法所划分的群体在生存结局上仍存在显著异质性。基因表达谱（gene expression profiling）可实现对这些群体的进一步细分，识别出肿瘤极大概率不会致命的患者与预后极差的患者，从而增强传统肿瘤分析的预测能力，帮助临床医生选择温和或激进的治疗方案。