七基因预测四阴性乳腺癌远处转移
淋巴结阴性、雌激素受体阴性、孕激素受体阴性、人类表皮生长因子受体HER2阴性乳腺癌与其他乳腺癌相比，即使术后接受辅助化疗，预后仍然较差，故需可靠的预后生物标志物对远处转移风险进行预测，将有助于优化个体治疗。虽然多基因检测已被用于乳腺癌，例如70基因、21基因、12基因、50基因，但是主要用于激素受体阳性乳腺癌。
2021年9月16日，瑞士《肿瘤学前沿》在线发表复旦大学附属肿瘤医院彭雯婷、林偲进、经姗姗、苏冠华、金希、狄根红、邵志敏等学者的研究报告，根据7基因特征建立了一种预测淋巴结阴性三阴性乳腺癌远处转移的预后模型。
该研究对复旦大学附属肿瘤医院202例淋巴结阴性三阴性乳腺癌患者术后原发肿瘤组织核糖核酸测序数据和临床病理数据进行分析，将该队列患者随机分为训练集（演算组）和验证集（验算组）。利用训练集，通过最小绝对值收敛和选择算子比例风险回归，对远处转移相关基因组进行收敛校正，确定与远处转移显著相关的7基因特征，随后通过多因素比例风险回归，构建临床预后模型和联合预后模型，并利用验证集进行验证。
结果，根据7基因特征，低风险组与高风险组相比，远处转移风险的差异具有统计学意义（训练集：P<0.001；验证集：P=0.039）。
根据临床特征＋7基因特征的联合预后模型，低风险组与高风险组相比，远处转移风险仍然存在差异（训练集：P<0.001；验证集：P=0.071）。
对于训练集，7基因特征与临床特征相比，预后准确性显著较高（4年无远处转移生存的真假阳性率曲线下面积值：0.879比0.699，P=0.046）。
此外，临床特征＋7基因特征的联合预后模型与临床特征相比，预后准确性显著较高（4、5年无远处转移生存的真假阳性率曲线下面积值：0.888比0.699、0.882比0.693，P=0.029、0.038）。
最终，该研究根据7基因特征、患者年龄和肿瘤大小，还构建了联合预后模型列线图。
因此，该研究结果表明，7基因特征可能改善淋巴结阴性三阴性乳腺癌患者的远处转移风险分层，高风险淋巴结阴性三阴性乳腺癌患者可能对治疗升级获益，故有必要进一步开展多中心大样本前瞻研究进行验证。
相关链接
Front Oncol. 2021 Sep 16;11:746763.
A Novel Seven Gene Signature-Based Prognostic Model to Predict Distant Metastasis of Lymph Node-Negative Triple-Negative Breast Cancer.
Peng W, Lin C, Jing S, Su G, Jin X, Di G, Shao Z.
Fudan University Shanghai Cancer Center, Shanghai, China; Shanghai Medical College, Fudan University, Shanghai, China; The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China.
BACKGROUND: The prognosis of lymph node-negative triple-negative breast cancer (TNBC) is still worse than that of other subtypes despite adjuvant chemotherapy. Reliable prognostic biomarkers are required to identify lymph node-negative TNBC patients at a high risk of distant metastasis and optimize individual treatment.
METHODS: We analyzed the RNA sequencing data of primary tumor tissue and the clinicopathological data of 202 lymph node-negative TNBC patients. The cohort was randomly divided into training and validation sets. Least absolute shrinkage and selection operator Cox regression and multivariate Cox regression were used to construct the prognostic model.
RESULTS: A clinical prognostic model, seven-gene signature, and combined model were constructed using the training set and validated using the validation set. The seven-gene signature was established based on the genomic variables associated with distant metastasis after shrinkage correction. The difference in the risk of distant metastasis between the low- and high-risk groups was statistically significant using the seven-gene signature (training set: P < 0.001; validation set: P = 0.039). The combined model showed significance in the training set (P < 0.001) and trended toward significance in the validation set (P = 0.071). The seven-gene signature showed improved prognostic accuracy relative to the clinical signature in the training data (AUC value of 4-year ROC, 0.879 vs. 0.699, P = 0.046). Moreover, the composite clinical and gene signature also showed improved prognostic accuracy relative to the clinical signature (AUC value of 4-year ROC: 0.888 vs. 0.699, P = 0.029; AUC value of 5-year ROC: 0.882 vs. 0.693, P = 0.038). A nomogram model was constructed with the seven-gene signature, patient age, and tumor size.
CONCLUSIONS: The proposed signature may improve the risk stratification of lymph node-negative TNBC patients. High-risk lymph node-negative TNBC patients may benefit from treatment escalation.
KEYWORDS: distant metastasis; modeling; prognostic biomarker; transcriptomics; triple-negative breast cancer
PMID: 34604089
PMCID: PMC8481824
DOI: 10.3389/fonc.2021.746763