Chen Yanhua, Cai Hao, Chen Wannan, Guan Qingzhou, He Jun, Guo Zheng, Li Jing
Fujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.
Key Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.
Front Mol Biosci. 2020 Mar 25;7:34. doi: 10.3389/fmolb.2020.00034. eCollection 2020.
For estrogen receptor (ER)-negative breast cancer patients, paclitaxel (P), doxorubicin (A) and cyclophosphamide (C) neoadjuvant chemotherapy (NAC) is the standard therapeutic regimen. Pathologic complete response (pCR) and residual disease (RD) are common surrogate measures of chemosensitivity. After NAC, most patients still have RD; of these, some partially respond to NAC, whereas others show extreme resistance and cannot benefit from NAC but only suffer complications resulting from drug toxicity. Here we developed a qualitative transcriptional signature, based on the within-sample relative expression ordering (REO) of gene pairs, to identify extremely resistant samples to PAC NAC. Using gene expression data for ER-negative breast cancer patients including 113 pCR samples and 137 RD samples from four datasets, we selected 61 gene pairs with reversal REO patterns between the two groups as the resistance signature, denoted as NR61. Samples with more than 37 signature gene pairs that had the same REO patterns within the extremely resistant group were defined as having extreme resistance; otherwise, they were considered responders. In the GSE25055 and GSE25065 dataset, the NR61 signature could correctly identify 44 (97.8%) of the 45 pCR samples and 22 (95.7%) of the 23 pCR samples as responder samples, respectively; it also identified 13 (16.9%) of 77 RD samples and 8 (21.1%) of 38 RD samples as extremely resistant samples, respectively. Survival analysis showed that the distant relapse-free survival (DRFS) time of the 14 extremely resistant cases was significantly shorter than that of the 108 responders ( < 0.01; HR = 3.84; 95% CI = 1.91-7.70) in GSE25055. Similar results were obtained in GSE25065. Moreover, in the integrated data of the two datasets with 94 responders and 21 extremely resistant samples identified from RD patients, the former had significantly longer DRFS than the latter ( < 0.01; HR = 2.22; 95% CI = 1.26-3.90). In summary, our signature could effectively identify patients who completely respond to PAC NAC, as well as cases of extreme resistance, which can assist decision-making on the clinical therapy for these patients.
对于雌激素受体（ER）阴性的乳腺癌患者，紫杉醇（P）、多柔比星（A）和环磷酰胺（C）新辅助化疗（NAC）是标准治疗方案。病理完全缓解（pCR）和残留疾病（RD）是化疗敏感性常见的替代指标。NAC治疗后，大多数患者仍有RD；其中一些患者对NAC部分缓解，而另一些患者则表现出极度耐药，无法从NAC中获益，反而仅遭受药物毒性导致的并发症。在此，我们基于基因对的样本内相对表达排序（REO）开发了一种定性转录特征，以识别对PAC NAC极度耐药的样本。利用来自四个数据集的113例pCR样本和137例RD样本的ER阴性乳腺癌患者的基因表达数据，我们选择了两组之间具有反向REO模式的61对基因作为耐药特征，记为NR61。在极度耐药组中具有超过37对具有相同REO模式的特征基因对的样本被定义为具有极度耐药性；否则，它们被视为反应者。在GSE25055和GSE25065数据集中，NR61特征分别能正确地将45例pCR样本中的44例（97.8%）和23例pCR样本中的22例（95.7%）识别为反应者样本；它还分别将77例RD样本中的13例（16.9%）和38例RD样本中的8例（21.1%）识别为极度耐药样本。生存分析表明，在GSE25055中，14例极度耐药病例的远处无复发生存（DRFS）时间显著短于108例反应者（<0.01；HR = 3.84；95%CI = 1.91 - 7.70）。在GSE25065中也获得了类似结果。此外，在从RD患者中识别出的94例反应者和21例极度耐药样本的两个数据集的整合数据中，前者的DRFS明显长于后者（<0.01；HR = 2.22；95%CI = 1.26 - 3.90）。总之，我们的特征能够有效地识别对PAC NAC完全反应的患者以及极度耐药病例，这有助于对这些患者的临床治疗做出决策。
