CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题:Quantifying Spatial Heterogeneity of Tumor-Infiltrating Lymphocytes to Predict Survival of Individual Cancer Patients.
Quantifying Spatial Heterogeneity of Tumor-Infiltrating Lymphocytes to Predict Survival of Individual Cancer Patients.
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TIL(肿瘤浸润淋巴细胞)(TILs)在癌症区域的HE染色组织病理学图像上被识别,是对癌症适应性免疫反应的指标,并在个性化癌症免疫治疗中发挥重要作用。近期研究表明,TILs的空间组织可能对疾病特异性生存和复发具有预后价值。然而,在TILs空间结构分析中提出并测试的方法数量有限。在本研究中,我们从可重复性、独特性和对患者生存的影响方面,评估了14种不同的空间度量,包括为其他组学数据开发的度量,在来自23种癌症类型的10,532张TIL图谱上的表现。对于每种空间度量,测试了16种不同的预后因素定义场景。我们发现,当TIL图谱以二值图像或连续TIL概率分数存储时,生存预测没有差异。当空间度量被离散化为低和高类别时,观察到与生存的相关性更高。具有最高癌症预后能力的三种度量是空间自相关、GLCM M1和接近中心性。大多数测试的度量可以进一步调整以提高预测性能。
Tumor-infiltrating lymphocytes (TILs), identified on HE-stained histopathological images in the cancer area, are indicators of the adaptive immune response against cancers and play a major role in personalized cancer immunotherapy. Recent works indicate that the spatial organization of TILs may be prognostic of disease-specific survival and recurrence.
However, there are a limited number of methods that were proposed and tested in analyses of the spatial structure of TILs. In this work, we evaluated 14 different spatial measures, including the one developed for other omics data, on 10,532 TIL maps from 23 cancer types in terms of reproducibility, uniqueness, and impact on patient survival. For each spatial measure, 16 different scenarios for the definition of prognostic factor were tested.
We found no difference in survival prediction when TIL maps were stored as binary images or continuous TIL probability scores. When spatial measures were discretized into a low and high category, a higher correlation with survival was observed. Three measures with the highest cancer prognosis capability were spatial autocorrelation, GLCM M1, and closeness centrality. Most of the tested measures could be further tuned to increase prediction performance.
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