Superior breast cancer metastasis risk stratification using an epithelial-mesenchymal-amoeboid transition gene signature
Superior breast cancer metastasis risk stratification using an epithelial-mesenchymal-amoeboid transition gene signature
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DOI：
10.1186/s13058-020-01304-8
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发表时间：
2020-07-08
影响因子：
7.4
通讯作者：
Ray, Partha S.
中科院分区：
文献类型：
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作者：
Emad, Amin;Ray, Tania;Ray, Partha S.
背景已知癌细胞表现出不同程度的转移倾向，但这种异质性的分子基础尚不清楚。我们在这项研究中的目的是（i）阐明原发性肿瘤的预后亚型的基础上，上皮间质到阿米巴样转变（EMAT）连续捕捉转移倾向的异质性和（ii）更全面地定义生物学上的亚型预测乳腺癌转移和淋巴结阴性（LNN）患者的生存。方法我们构建了一种新的基于转移生物学的基因签名（EMAT），该基因签名仅来源于诱导经历上皮-间充质转化（EMT）或间充质-变形虫转化（MAT）的癌细胞，以评估其转移潜力。分析了从913例淋巴结阴性乳腺癌（LNNBC）患者的原发肿瘤中获得的全基因组基因表达数据。对患者进行基于EMAT基因特征的预后分层，以确定与不同转移倾向相关的生物学相关亚型。结果描述的EMAT亚型显示了从干细胞样程度较低到干细胞样程度较高的细胞状态以及从侵袭性较低到侵袭性较高的癌症进展模式的生物学范围。考虑EMAT亚型与标准临床参数相结合，显著提高了生存预测。EMAT亚型的预后准确性优于受体或基于PAM 50的BC内在亚型，即使在3个独立的LNNBC队列（包括一个初治患者队列）中调整治疗变量后也是如此。结论EMAT分类是一种生物学信息的方法，提供了超出传统癌症分期或PAM 50分子亚型状态所能提供的预后信息，并可能改善早期LNNBC患者的转移风险评估，否则可能被认为是低转移风险。
Background Cancer cells are known to display varying degrees of metastatic propensity, but the molecular basis underlying such heterogeneity remains unclear. Our aims in this study were to (i) elucidate prognostic subtypes in primary tumors based on an epithelial-to-mesenchymal-to-amoeboid transition (EMAT) continuum that captures the heterogeneity of metastatic propensity and (ii) to more comprehensively define biologically informed subtypes predictive of breast cancer metastasis and survival in lymph node-negative (LNN) patients. Methods We constructed a novel metastasis biology-based gene signature (EMAT) derived exclusively from cancer cells induced to undergo either epithelial-to-mesenchymal transition (EMT) or mesenchymal-to-amoeboid transition (MAT) to gauge their metastatic potential. Genome-wide gene expression data obtained from 913 primary tumors of lymph node-negative breast cancer (LNNBC) patients were analyzed. EMAT gene signature-based prognostic stratification of patients was performed to identify biologically relevant subtypes associated with distinct metastatic propensity. Results Delineated EMAT subtypes display a biologic range from less stem-like to more stem-like cell states and from less invasive to more invasive modes of cancer progression. Consideration of EMAT subtypes in combination with standard clinical parameters significantly improved survival prediction. EMAT subtypes outperformed prognosis accuracy of receptor or PAM50-based BC intrinsic subtypes even after adjusting for treatment variables in 3 independent, LNNBC cohorts including a treatment-naive patient cohort. Conclusions EMAT classification is a biologically informed method that provides prognostic information beyond that which can be provided by traditional cancer staging or PAM50 molecular subtype status and may improve metastasis risk assessment in early stage, LNNBC patients, who may otherwise be perceived to be at low metastasis risk.