大规模肿瘤相关胶原标签(TACS)识别高危乳腺癌患者

已有 3371 次阅读2021-3-5 15:13 |个人分类:TET学习|系统分类:医学科学| TACS

大规模的肿瘤相关胶原蛋白特征识别高危乳腺癌患者,Large-scale tumor-associated collagen signatures identify high-risk breast cancer patients
个体化医疗的概念需要合适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,基于常规临床病理因素和新兴分子分析的各种风险预测模型常常受到预后强度低或对特定类型患者适用性有限的限制。因此,迫切需要开发一个强大而通用的预测工具。
方法:观察通过多光子显微镜在乳腺原发肿瘤浸润前沿获得5个大范围肿瘤相关胶原信号(TACS4-8),并与Keely及其同事在较小范围内发现的3个肿瘤相关胶原信号(TACS1-3)进行对比。三名独立观察者获得高度一致的TACS1-8分类。使用岭回归分析,我们根据合并TACS1-8得出每个患者的TACS评分,并建立了基于TACS评分的风险预测模型。采用盲法,从一个临床中心收集的995名乳腺癌患者的训练队列(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)中获得一致的回顾性预后。
结果:TACS1-8模型在预测无病生存率(AUC:0.838,[0.800-0.872];0.827,[0.779-0.868];0.807,[0.754-0.853];三个队列中的低危和高危患者分层(HR 7.032,[4.869-10.158];6.846,[4.370-10.726];4.423,[2.917-6.708])方面与所有已报道的模型有较好的竞争性。将这些因素与TACS评分结合成诺模图模型进一步改善预后(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])。列线图显示,357例患者中有72例(约20%)5年无病生存率不成功,术后治疗不足。
结论:基于TACS1-8的风险预测模型明显优于相关的临床模型,因此可以说服病理学家追求基于TACS的乳腺癌预后。我们的方法确定了相当一部分易受治疗不足影响的患者(高危患者),这与通常致力于减轻过度治疗的多基因分析相反。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转化。

Large-scale tumor-associated collagen signatures identify high-risk breast cancer patients
Abstract
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.

Keywords:Breast cancer; disease-free survival; multiphoton imaging; tumor-associated collagen signatures.

Figure 1 :(A) Extraction and quantification of TACSs for one exemplary patient among the training, internal validation, and external validation cohorts. For one H&E section of a patient, a total of 9 regions of interest (ROIs) are located either at the invasive front (1-8) or inside the tumor (9). (B) Study flowchart to exclude patients with neoadjuvant chemotherapy or radiotherapy, unknown pathological characteristics and follow-up, or damaged and tumor-free sections. The TACS-score is calculated for each patient using the linear combination of TACS percentages weighted by their regression coefficients. (C) Illustration of the structural and organizational features of collagen in the TACSs. TACS1-3 are plotted in the tumor center for simplicity but may be present in the invasion front like TACS4-8.

Figure 2 .Images of TACS1-3 (or TACS4) at the initiation (or expansion) stage of tumor development. TACS1: curved collagen fibers wrapped around emergent tumor foci; TACS2: collagen fibers stretched due to tumor growth and aligned more parallel to tumor boundary; TACS3: collagen fibers aligned perpendicular to the tumor boundary in a radiation pattern to facilitate tumor cell migration; TACS4: reticular distribution of collagen fibers adjacent to expanding tumor that leads to a clear tumor boundary. Scale bar: 500 μm.

Figure 3:Images of TACS5-8 at the invasion stage of tumor development. TACS5: directionally distributed collagen fibers that enables unidirectional tumor cell migration without a clear tumor boundary; TACS6: chaotically aligned collagen fibers that enables multidirectional tumor cell migration without a clear tumor boundary; TACS7: densely-distributed collagen fibers at the tumor invasion front largely free of tumors cells; TACS8: sparsely-distributed collagen fibers at the tumor invasion front largely free of tumors cells. Scale bar: 500 μm.

Figure 4 :(A) Recurrence histograms of TACS-score for three cohorts. (B) Nomogram of TACS-score, molecular subtype, tumor size and nodal status derived from the training cohort. (C) Calibration curves of the nomogram to predict 5-year DFS rate for three cohorts.

原文链接:https://www.thno.org/v11p3229.htm

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. Large-scale tumor-associated collagen signatures identify high-risk breast cancer patients. Theranostics. 2021 Jan 1;11(7):3229-3243. doi: 10.7150/thno.55921. PMID: 33537084; PMCID: PMC7847696.

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