Bredfeldt Jeremy S, Liu Yuming, Conklin Matthew W, Keely Patricia J, Mackie Thomas R, Eliceiri Kevin W
Laboratory for Optical and Computational Instrumentation, Madison, WI 53715, USA ; Morgridge Institute for Research, Madison, WI 53715, USA.
Laboratory for Optical and Computational Instrumentation, Madison, WI 53715, USA.
J Pathol Inform. 2014 Aug 28;5(1):28. doi: 10.4103/2153-3539.139707. eCollection 2014.
Mortality in cancer patients is directly attributable to the ability of cancer cells to metastasize to distant sites from the primary tumor. This migration of tumor cells begins with a remodeling of the local tumor microenvironment, including changes to the extracellular matrix and the recruitment of stromal cells, both of which facilitate invasion of tumor cells into the bloodstream. In breast cancer, it has been proposed that the alignment of collagen fibers surrounding tumor epithelial cells can serve as a quantitative image-based biomarker for survival of invasive ductal carcinoma patients. Specific types of collagen alignment have been identified for their prognostic value and now these tumor associated collagen signatures (TACS) are central to several clinical specimen imaging trials. Here, we implement the semi-automated acquisition and analysis of this TACS candidate biomarker and demonstrate a protocol that will allow consistent scoring to be performed throughout large patient cohorts.
Using large field of view high resolution microscopy techniques, image processing and supervised learning methods, we are able to quantify and score features of collagen fiber alignment with respect to adjacent tumor-stromal boundaries.
Our semi-automated technique produced scores that have statistically significant correlation with scores generated by a panel of three human observers. In addition, our system generated classification scores that accurately predicted survival in a cohort of 196 breast cancer patients. Feature rank analysis reveals that TACS positive fibers are more well-aligned with each other, are of generally lower density, and terminate within or near groups of epithelial cells at larger angles of interaction.
These results demonstrate the utility of a supervised learning protocol for streamlining the analysis of collagen alignment with respect to tumor stromal boundaries.
癌症患者的死亡直接归因于癌细胞从原发肿瘤转移至远处部位的能力。肿瘤细胞的这种迁移始于局部肿瘤微环境的重塑，包括细胞外基质的变化和基质细胞的募集，这两者都有助于肿瘤细胞侵入血流。在乳腺癌中，有人提出肿瘤上皮细胞周围胶原纤维的排列可作为浸润性导管癌患者生存的基于图像的定量生物标志物。已确定特定类型的胶原排列具有预后价值，现在这些肿瘤相关胶原特征（TACS）是多项临床标本成像试验的核心。在此，我们实施了这种TACS候选生物标志物的半自动采集和分析，并展示了一种能够在大型患者队列中进行一致评分的方案。
使用大视野高分辨率显微镜技术、图像处理和监督学习方法，我们能够量化和评分胶原纤维相对于相邻肿瘤 - 基质边界的排列特征。
我们的半自动技术产生的分数与由三名人类观察者组成的小组生成的分数具有统计学上的显著相关性。此外，我们的系统生成的分类分数准确预测了196名乳腺癌患者队列中的生存情况。特征排名分析表明，TACS阳性纤维彼此排列更整齐，密度通常较低，并以较大的相互作用角度终止于上皮细胞群内或附近。
这些结果证明了一种监督学习方案在简化胶原相对于肿瘤基质边界排列分析方面的实用性。
