乳腺癌二十一基因复发评分临床加强版
对于激素受体阳性、HER2阴性、淋巴结阴性早期乳腺癌术后患者，21基因复发评分可预测远处复发风险，并可预测术后辅助化疗能否获益，而临床病理因素仅可预后。基因组＋临床特征有可能更精准地指导术后是否需要辅助化疗。
2020年12月11日，美国临床肿瘤学会《临床肿瘤学杂志》在线发表美国爱因斯坦医学院、匹兹堡大学、哈佛大学达纳法伯癌症研究院、以色列拉宾医疗中心、特拉维夫大学的研究报告，开发并验证了21基因复发评分临床加强版及其对早期乳腺癌化疗获益的个体化预后和预测效果。
该研究首先对10004例激素受体阳性、HER2阴性、淋巴结阴性乳腺癌女性进行患者水平荟萃分析，其中包括：
内分泌治疗未化疗患者：TAILORx研究4854例、NSABP B-14研究577例
内分泌治疗＋化疗患者：TAILORx研究4573例
随后，结合21基因复发评分和临床病理因素（肿瘤分级、肿瘤大小、年龄）开发了一种新工具：复发风险临床加强版（RSClin）并利用多因素比例风险回归模型和似然比检验对3种方法（RSClin、单用复发评分、单用临床病理因素）进行比较。根据反映当前医疗实践的TAILORx研究基线风险和事件发生率，通过RSClin推算远处复发风险。根据随机化TAILORx和NSABP B-20研究的化疗个体相对效果，推算患者水平化疗绝对获益率。
最后，根据以色列克拉利特医疗服务登记数据库1098例女性观察到的远处复发风险，对RSClin推算出的远处复发风险进行外部验证。
结果，RSClin与单用复发评分或单用临床病理因素相比，对远处复发的预测准确率显著较高（似然比检验，全部P<0.001）。
外部验证表明，对于以色列克拉利特医疗服务登记数据库1098例女性，RSClin推算出的远处复发风险准确率显著较高（P<0.001），并且与观察到的10年远处复发风险非常接近（林氏一致性指数高达0.962）。对于肿瘤分级中等、肿瘤大小1.5厘米的年龄55岁女性，如果RSClin复发评分范围为11～50，那么化疗绝对获益率仅为0%～15%，可避免化疗。
因此，该研究结果表明，RSClin结合了临床病理和基因组风险，可指导淋巴结阴性乳腺癌术后辅助化疗，与单用临床病理数据或单用基因组数据相比，能够提供更个体化的预后信息。
对此，美国哥伦比亚大学欧文医学中心赫伯特·欧文综合癌症中心发表同期评论：在一起更好——临床数据＋基因组数据有助医患共同决策。
J Clin Oncol. 2020 Dec 11. Online ahead of print.
Development and Validation of a Tool Integrating the 21-Gene Recurrence Score and Clinical-Pathological Features to Individualize Prognosis and Prediction of Chemotherapy Benefit in Early Breast Cancer.
Sparano JA, Crager MR, Tang G, Gray RJ, Stemmer SM, Shak S.
Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York; Exact Sciences, Redwood City, CA; University of Pittsburgh, NRG Oncology Statistical and Data Management Center, Pittsburgh, PA; Dana Farber Cancer Institute, ECOG-ACRIN Statistical Center, Boston, MA; Davidoff Center, Rabin Medical Center, Petah Tikva, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
PURPOSE: The 21-gene recurrence score (RS) is prognostic for distant recurrence (DR) and predictive for chemotherapy benefit in early breast cancer, whereas clinical-pathological factors are only prognostic. Integration of genomic and clinical features offers the potential to guide adjuvant chemotherapy use with greater precision.
METHODS: We developed a new tool (RSClin) that integrates RS with tumor grade, tumor size, and age using a patient-specific meta-analysis including 10,004 women with hormone receptor-positive, human epidermal growth factor receptor 2-negative, and node-negative breast cancer who received endocrine therapy alone in the B-14 (n = 577) and TAILORx (n = 4,854) trials or plus chemotherapy in TAILORx (n = 4,573). Cox models for RSClin were compared with RS alone and clinical-pathological features alone using likelihood ratio tests. RSClin estimates of DR used a baseline risk with TAILORx event rates to reflect current medical practice. A patient-specific estimator of absolute chemotherapy benefit was computed using individualized relative chemotherapy effect from the randomized TAILORx and B-20 trials. External validation of risk estimation was performed by comparing RSClin estimated risk and observed risk in 1,098 women in the Clalit registry.
RESULTS: RSClin provides more prognostic information (likelihood ratio χ2) for DR than RS or clinical-pathological factors alone (both P < .001, likelihood ratio test). In external validation, the RSClin risk estimate was prognostic for DR risk in the Clalit registry (P < .001) and the estimated risk closely approximated the observed 10-year risk (Lin concordance 0.962). The absolute chemotherapy benefit estimate ranges from 0% to 15% as the RS ranges from 11 to 50 using RSClin in a 55-year-old woman with a 1.5-cm intermediate-grade tumor.
CONCLUSION: The RSClin tool integrates clinical-pathological and genomic risk to guide adjuvant chemotherapy in node-negative breast cancer and provides more individualized information than clinical-pathological or genomic data alone.
PMID: 33306425
DOI: 10.1200/JCO.20.03007
J Clin Oncol. 2020 Dec 11. Online ahead of print.
Better Together: Clinical and Genomic Data to Inform Shared Decision Making.
Crew KD, Hershman DL.
Columbia University Irving Medical Center, Herbert Irving Comprehensive Cancer Center, New York, NY.
PMID: 33306424
DOI: 10.1200/JCO.20.03234