Mazo Claudia, Barron Stephen, Mooney Catherine, Gallagher William M
UCD School of Computer Science, University College Dublin, Dublin 4, Ireland.
CeADAR: Centre for Applied Data Analytics Research, University College Dublin, Dublin 4, Ireland.
Cancers (Basel). 2020 May 1;12(5):1133. doi: 10.3390/cancers12051133.
Determining which patients with early-stage breast cancer should receive chemotherapy is an important clinical issue. Chemotherapy has several adverse side effects, impacting on quality of life, along with significant economic consequences. There are a number of multi-gene prognostic signatures for breast cancer recurrence but there is less evidence that these prognostic signatures are predictive of therapy benefit. Biomarkers that can predict patient response to chemotherapy can help avoid ineffective over-treatment. The aim of this work was to assess if the OncoMasTR prognostic signature can predict pathological complete response (pCR) to neoadjuvant chemotherapy, and to compare its predictive value with other prognostic signatures: EndoPredict, Oncotype DX and Tumor Infiltrating Leukocytes. Gene expression datasets from ER-positive, HER2-negative breast cancer patients that had pre-treatment biopsies, received neoadjuvant chemotherapy and an assessment of pCR were obtained from the Gene Expression Omnibus repository. A total of 813 patients with 66 pCR events were included in the analysis. OncoMasTR, EndoPredict, Oncotype DX and Tumor Infiltrating Leukocytes numeric risk scores were approximated by applying the gene coefficients to the corresponding mean probe expression values. OncoMasTR, EndoPredict and Oncotype DX prognostic scores were moderately well correlated according to the Pearson's correlation coefficient. Association with pCR was estimated using logistic regression. The odds ratio for a 1 standard deviation increase in risk score, adjusted for cohort, were similar in magnitude for all four signatures. Additionally, the four signatures were significant predictors of pCR. OncoMasTR added significant predictive value to EndoPredict, Oncotype DX and Tumor Infiltrating Leukocytes signatures as determined by bivariable and trivariable analysis. In this analysis, OncoMasTR, EndoPredict, Oncotype DX, and Tumor Infiltrating Leukocytes were significantly predictive of pCR to neoadjuvant chemotherapy in ER-positive and HER2-negative breast cancer patients.
确定哪些早期乳腺癌患者应接受化疗是一个重要的临床问题。化疗有多种不良副作用，会影响生活质量，还会带来重大经济后果。有许多用于预测乳腺癌复发的多基因预后特征，但较少有证据表明这些预后特征能预测治疗获益。能够预测患者对化疗反应的生物标志物有助于避免无效的过度治疗。这项研究的目的是评估OncoMasTR预后特征能否预测新辅助化疗后的病理完全缓解（pCR），并将其预测价值与其他预后特征进行比较：EndoPredict、Oncotype DX和肿瘤浸润白细胞。从基因表达综合数据库中获取了雌激素受体（ER）阳性、人表皮生长因子受体2（HER2）阴性乳腺癌患者的基因表达数据集，这些患者进行了治疗前活检、接受了新辅助化疗并对pCR进行了评估。分析共纳入813例患者，其中有66例出现pCR事件。通过将基因系数应用于相应的平均探针表达值来估算OncoMasTR、EndoPredict、Oncotype DX和肿瘤浸润白细胞的数值风险评分。根据皮尔逊相关系数，OncoMasTR、EndoPredict和Oncotype DX预后评分的相关性中等。使用逻辑回归评估与pCR的关联。对所有四个特征而言，风险评分每增加1个标准差（经队列校正）的优势比在幅度上相似。此外，这四个特征都是pCR的显著预测指标。通过双变量和三变量分析确定，OncoMasTR为EndoPredict、Oncotype DX和肿瘤浸润白细胞特征增加了显著的预测价值。在这项分析中，OncoMasTR、EndoPredict、Oncotype DX和肿瘤浸润白细胞对ER阳性和HER2阴性乳腺癌患者新辅助化疗后的pCR具有显著预测作用。
