Large-scale tumor-associated collagen signatures identify high-risk breast cancer patients.
Large-scale tumor-associated collagen signatures identify high-risk breast cancer patients.
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作者：
Xi G;Guo W;Kang D;Ma J;Fu F;Qiu L;Zheng L;He J;Fang N;Chen J;Li J;Zhuo S;Liao X;Tu H;Li L;Zhang Q;Wang C;Boppart SA;Chen J
个性化医疗的概念需要适当的预后生物标志物来指导浸润性乳腺癌患者的最佳治疗。然而，各种风险预测模型的基础上，传统的临床病理因素和紧急的分子检测已经常受到限制，无论是低强度的预后或限制适用于特定类型的患者。因此，迫切需要开发一种强大的通用计算器。研究方法：我们观察到五个大规模的肿瘤相关的胶原蛋白的签名（TACS 4 -8），通过多光子显微镜获得的乳腺原发性肿瘤的浸润前沿，这与三个肿瘤相关的胶原蛋白的签名（TACS 1 -3）由基利和同事在一个较小的规模发现。高度一致的TACS 1 -8分类获得了三个独立的观察员。使用岭回归分析，我们获得了一个TACS评分为基础的组合TACS 1 -8每个患者，并建立了一个风险预测模型的基础上，TACS评分。以盲态方式，从一个临床中心收集的训练队列（n= 431）和内部验证队列（n = 300）以及从不同临床中心收集的外部验证队列（n = 264）中的995例乳腺癌患者中获得一致的回顾性预后。结果如下：在预测无病生存期方面，单独的TACS 1 -8模型与所有已报道的模型竞争良好（三个队列中AUC：0.838，[0.800-0.872]; 0.827，[0.779-0.868]; 0.807，[0.754-0.853]），并对低风险和高风险患者进行分层（HR 7.032，[4.869-10.158]; 6.846，[4.370-10.726]，4.423，[2.917-6.708]）。将这些因素与TACS评分结合成列线图模型进一步改善了预后（AUC：0.865，[0.829-0.896]; 0.861，[0.816-0.898]; 0.854，[0.805-0.894]; HR 7.882，[5.487-11.323]; 9.176，[5.683-14.816]和5.548，[3.705-8.307]）。诺模图确定了357例患者中的72例（约20%）5年无病生存率不成功，可能是术后治疗不足。结论：基于TACS 1 -8的风险预测模型大大优于背景临床模型，因此可以说服病理学家追求基于TACS的乳腺癌预后。我们的方法确定了一个显着的一部分患者易受治疗不足（高风险患者），相比之下，多基因检测，往往努力减轻过度治疗。我们的方法与使用传统（非组织微阵列）福尔马林固定石蜡包埋（FFPE）组织切片的标准组织学的兼容性可以简化随后的临床翻译。
The notion of personalized medicine demands proper prognostic biomarkers to guide the optimal therapy for an invasive breast cancer patient. However, various risk prediction models based on conventional clinicopathological factors and emergent molecular assays have been frequently limited by either a low strength of prognosis or restricted applicability to specific types of patients. Therefore, there is a critical need to develop a strong and general prognosticator. Methods: We observed five large-scale tumor-associated collagen signatures (TACS4-8) obtained by multiphoton microscopy at the invasion front of the breast primary tumor, which contrasted with the three tumor-associated collagen signatures (TACS1-3) discovered by Keely and coworkers at a smaller scale. Highly concordant TACS1-8 classifications were obtained by three independent observers. Using the ridge regression analysis, we obtained a TACS-score for each patient based on the combined TACS1-8 and established a risk prediction model based on the TACS-score. In a blind fashion, consistent retrospective prognosis was obtained from 995 breast cancer patients in both a training cohort (n= 431) and an internal validation cohort (n = 300) collected from one clinical center, and in an external validation cohort (n = 264) collected from a different clinical center. Results: TACS1-8 model alone competed favorably with all reported models in predicting disease-free survival (AUC: 0.838, [0.800-0.872]; 0.827, [0.779-0.868]; 0.807, [0.754-0.853] in the three cohorts) and stratifying low- and high-risk patients (HR 7.032, [4.869-10.158]; 6.846, [4.370-10.726], 4.423, [2.917-6.708]). The combination of these factors with the TACS-score into a nomogram model further improved the prognosis (AUC: 0.865, [0.829-0.896]; 0.861, [0.816-0.898]; 0.854, [0.805-0.894]; HR 7.882, [5.487-11.323]; 9.176, [5.683-14.816], and 5.548, [3.705-8.307]). The nomogram identified 72 of 357 (~20%) patients with unsuccessful 5-year disease-free survival that might have been undertreated postoperatively. Conclusions: The risk prediction model based on TACS1-8 considerably outperforms the contextual clinical model and may thus convince pathologists to pursue a TACS-based breast cancer prognosis. Our methodology identifies a significant portion of patients susceptible to undertreatment (high-risk patients), in contrast to the multigene assays that often strive to mitigate overtreatment. The compatibility of our methodology with standard histology using traditional (non-tissue-microarray) formalin-fixed paraffin-embedded (FFPE) tissue sections could simplify subsequent clinical translation.
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影响因子：
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作者：
Bredfeldt JS;Liu Y;Conklin MW;Keely PJ;Mackie TR;Eliceiri KW
通讯作者：
Eliceiri KW
影响因子：
4.3
作者：
Baenke F;Peck B;Miess H;Schulze A
通讯作者：
Schulze A
影响因子：
11
作者：
Campagnola, Paul J.;Dong, Chen-Yuan
通讯作者：
Dong, Chen-Yuan
影响因子：
6
作者：
Conklin, Matthew W.;Eickhoff, Jens C.;Keely, Patricia J.
通讯作者：
Keely, Patricia J.
DOI：
10.1083/jcb.201102147
发表时间：
2012-02-20
期刊：
The Journal of cell biology
影响因子：
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作者：
Lu P;Weaver VM;Werb Z
通讯作者：
Werb Z