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纤维胶原蛋白的定量图像分析揭示了肺癌的新型诊断和预后生物标志物以及组织依赖性异常力学生物学。

Quantitative Image Analysis of Fibrillar Collagens Reveals Novel Diagnostic and Prognostic Biomarkers and Histotype-Dependent Aberrant Mechanobiology in Lung Cancer.

机构信息

Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute for Science and Technology (BIST), Barcelona, Spain.

Unit of Biophysics and Bioengineering, Department of Biomedicine, School of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain; Thoracic Oncology Unit, Hospital Clinic Barcelona, Barcelona, Spain.

出版信息

Mod Pathol. 2023 Jul;36(7):100155. doi: 10.1016/j.modpat.2023.100155. Epub 2023 Mar 12.

DOI:10.1016/j.modpat.2023.100155
PMID:36918057
Abstract

Fibrillar collagens are the most abundant extracellular matrix components in non-small cell lung cancer (NSCLC). However, the potential of collagen fiber descriptors as a source of clinically relevant biomarkers in NSCLC is largely unknown. Similarly, our understanding of the aberrant collagen organization and associated tumor-promoting effects is very scarce. To address these limitations, we identified a digital pathology approach that can be easily implemented in pathology units based on CT-FIRE software (version 2; https://loci.wisc.edu/software/ctfire) analysis of Picrosirius red (PSR) stains of fibrillar collagens imaged with polarized light (PL). CT-FIRE settings were pre-optimized to assess a panel of collagen fiber descriptors in PSR-PL images of tissue microarrays from surgical NSCLC patients (106 adenocarcinomas [ADC] and 89 squamous cell carcinomas [SCC]). Using this approach, we identified straightness as the single high-accuracy diagnostic collagen fiber descriptor (average area under the curve = 0.92) and fiber density as the single descriptor consistently associated with a poor prognosis in both ADC and SCC independently of the gold standard based on the TNM staging (hazard ratio, 2.69; 95% CI, 1.55-4.66; P < .001). Moreover, we found that collagen fibers were markedly straighter, longer, and more aligned in tumor samples compared to paired samples from uninvolved pulmonary tissue, particularly in ADC, which is indicative of increased tumor stiffening. Consistently, we observed an increase in a panel of stiffness-associated processes in the high collagen fiber density patient group selectively in ADC, including venous/lymphatic invasion, fibroblast activation (α-smooth muscle actin), and immune evasion (programmed death-ligand 1). Similarly, a transcriptional correlation analysis supported the potential involvement of the major YAP/TAZ pathway in ADC. Our results provide a proof-of-principle to use CT-FIRE analysis of PSR-PL images to assess new collagen fiber-based diagnostic and prognostic biomarkers in pathology units, which may improve the clinical management of patients with surgical NSCLC. Our findings also unveil an aberrant stiff microenvironment in lung ADC that may foster immune evasion and dissemination, encouraging future work to identify therapeutic opportunities.

摘要

纤维胶原是非小细胞肺癌(NSCLC)中最丰富的细胞外基质成分。然而,胶原纤维描述符作为 NSCLC 中具有临床相关性的生物标志物的潜力在很大程度上尚未被知晓。同样,我们对异常胶原组织及其相关的促肿瘤作用的了解也非常有限。为了解决这些限制,我们确定了一种数字病理学方法,该方法可以基于 CT-FIRE 软件(版本 2;https://loci.wisc.edu/software/ctfire)对偏光下纤维胶原的 picrosirius 红(PSR)染色进行分析,在病理单位中轻松实施。CT-FIRE 设置是预先优化的,以评估组织微阵列中手术 NSCLC 患者(106 例腺癌 [ADC]和 89 例鳞状细胞癌 [SCC])PSR-PL 图像中胶原纤维描述符的面板。使用这种方法,我们确定了直线度作为单个高精度诊断胶原纤维描述符(平均曲线下面积= 0.92),并且纤维密度是与 ADC 和 SCC 中 TNM 分期为基础的较差预后均一致相关的唯一描述符(危险比,2.69;95%CI,1.55-4.66;P <.001)。此外,我们发现与来自未受影响的肺组织的配对样本相比,肿瘤样本中的胶原纤维明显更直、更长且更对齐,特别是在 ADC 中,这表明肿瘤硬度增加。一致地,我们观察到在高胶原纤维密度患者组中,与僵硬相关的一系列过程增加,特别是在 ADC 中,包括静脉/淋巴管侵犯、成纤维细胞激活(α-平滑肌肌动蛋白)和免疫逃逸(程序性死亡配体 1)。同样,转录相关性分析支持主要的 YAP/TAZ 途径在 ADC 中的潜在参与。我们的结果提供了一个原理证明,即在病理单位中使用 PSR-PL 图像的 CT-FIRE 分析来评估新的基于胶原纤维的诊断和预后生物标志物,这可能会改善手术 NSCLC 患者的临床管理。我们的发现还揭示了肺 ADC 中异常僵硬的微环境,这可能促进免疫逃逸和扩散,鼓励未来的工作以确定治疗机会。

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