Department of Biostatistics and BioinformaticsH. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA.
Department of Cancer Cell BiologyTianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, People's Republic of China.
Endocr Relat Cancer. 2018 Jun;25(6):595-605. doi: 10.1530/ERC-17-0495. Epub 2018 Mar 29.
ER-negative breast cancer includes most aggressive subtypes of breast cancer such as triple negative (TN) breast cancer. Excluded from hormonal and targeted therapies effectively used for other subtypes of breast cancer, standard chemotherapy is one of the primary treatment options for these patients. However, as ER- patients have shown highly heterogeneous responses to different chemotherapies, it has been difficult to select most beneficial chemotherapy treatments for them. In this study, we have simultaneously developed single drug biomarker models for four standard chemotherapy agents: paclitaxel (T), 5-fluorouracil (F), doxorubicin (A) and cyclophosphamide (C) to predict responses and survival of ER- breast cancer patients treated with combination chemotherapies. We then flexibly combined these individual drug biomarkers for predicting patient outcomes of two independent cohorts of ER- breast cancer patients who were treated with different drug combinations of neoadjuvant chemotherapy. These individual and combined drug biomarker models significantly predicted chemotherapy response for 197 ER- patients in the Hatzis cohort (AUC = 0.637, = 0.002) and 69 ER- patients in the Hess cohort (AUC = 0.635, = 0.056). The prediction was also significant for the TN subgroup of both cohorts (AUC = 0.60, 0.72, = 0.043, 0.009). In survival analysis, our predicted responder patients showed significantly improved survival with a >17 months longer median PFS than the predicted non-responder patients for both ER- and TN subgroups (log-rank test -value = 0.018 and 0.044). This flexible prediction capability based on single drug biomarkers may allow us to even select new drug combinations most beneficial to individual patients with ER- breast cancer.
ER- 阴性乳腺癌包括大多数侵袭性乳腺癌亚型，如三阴性（TN）乳腺癌。由于这些患者被排除在其他乳腺癌亚型有效使用的激素和靶向治疗之外，标准化疗是这些患者的主要治疗选择之一。然而，由于 ER- 患者对不同的化疗反应表现出高度异质性，因此很难为他们选择最有效的化疗治疗方法。在这项研究中，我们同时为四种标准化疗药物：紫杉醇（T）、5-氟尿嘧啶（F）、阿霉素（A）和环磷酰胺（C）开发了单一药物生物标志物模型，以预测接受联合化疗的 ER- 乳腺癌患者的反应和生存。然后，我们灵活地将这些个体药物生物标志物结合起来，预测接受新辅助化疗不同药物组合治疗的两个独立 ER- 乳腺癌患者队列的患者结局。这些个体和组合药物生物标志物模型显著预测了 Hatzis 队列中的 197 名 ER- 患者（AUC = 0.637， = 0.002）和 Hess 队列中的 69 名 ER- 患者（AUC = 0.635， = 0.056）的化疗反应。对于两个队列的 TN 亚组，预测也是显著的（AUC = 0.60、0.72、 = 0.043、0.009）。在生存分析中，我们预测的应答者患者的中位 PFS 比预测的非应答者患者显著延长了 17 个月以上，这对于 ER- 和 TN 亚组的患者均适用（对数秩检验值 = 0.018 和 0.044）。这种基于单一药物生物标志物的灵活预测能力可能使我们能够为 ER- 乳腺癌患者选择最受益于个体患者的新药物组合。
