Development and validation of a vascularity-based architectural classification for clear cell renal cell carcinoma: correlation with conventional pathological prognostic factors, gene expression patterns, and clinical outcomes
Development and validation of a vascularity-based architectural classification for clear cell renal cell carcinoma: correlation with conventional pathological prognostic factors, gene expression patterns, and clinical outcomes
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DOI：
10.1038/s41379-021-00982-9
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发表时间：
2021-11-30
期刊：
影响因子：
7.5
通讯作者：
Tsuta, Koji
中科院分区：
文献类型：
--
作者：
Ohe, Chisato;Yoshida, Takashi;Tsuta, Koji
肾透明细胞癌（ccRCC）的结构分级系统的预后意义最近已被证明。本研究旨在使用436例接受摘除手术的局部ccRCC患者队列建立基于血管的结构分类，并将结果与常规病理因素，基因表达和预后相关。首先，我们评估了苏木精和伊红染色切片上最高级别区域的结构模式，然后分别评估了我们的血管分布替代评分。我们根据血管网络评分将九种结构模式分为三类。定义了“基于血管的结构分类”：第1类：以血管网络丰富为特征，包括紧凑/小巢状、大囊/微囊和管状/腺泡模式;第2类：以广泛间隔的血管网络为特征，包括肺泡/大巢状、厚小梁/岛状、乳头状/假乳头状模式;第3类：特征为散在的血管分布，无血管网，包括实性片状、横纹肌样和肉瘤样模式。不良的病理预后因素如TNM分期、WHO/ISUP分级和坏死与3类显著相关，其次是2类（所有p < 0.001）。我们使用癌症基因组图谱（TCGA）队列（n = 162）成功验证了分类，并且从TCGA获得的RNA测序数据显示，与类别2和3相比，血管生成基因签名在类别1中显著富集，而与类别1和2相比，免疫基因签名在类别3中显著富集。在单变量分析中，基于血管的结构分类在预测无复发生存的病理预后因素中显示出最佳准确性（c指数= 0.786）。我们的模型整合了WHO/ISUP分级、坏死、TNM分期和基于血管的结构分类，其预测准确性高于传统风险模型（c指数= 0.871 vs. 0.755-0.843）。我们的研究结果表明，血管为基础的建筑分类是解剖学上有用的，并可能有助于分层患者适当的管理基于他们的术后复发的可能性。
The prognostic significance of an architectural grading system for clear cell renal cell carcinoma (ccRCC) has recently been demonstrated. The present study aimed to establish a vascularity-based architectural classification using the cohort of 436 patients with localized ccRCC who underwent extirpative surgery and correlated the findings with conventional pathologic factors, gene expression, and prognosis. First, we assessed architectural patterns in the highest-grade area on hematoxylin and eosin-stained slides, then separately evaluated our surrogate score for vascularity. We grouped nine architectural patterns into three categories based on the vascular network score. "Vascularity-based architectural classification" was defined: category 1: characterized by enrichment of the vascular network, including compact/small nested, macrocyst/microcystic, and tubular/acinar patterns; category 2: characterized by a widely spaced-out vascular network, including alveolar/large nested, thick trabecular/insular, papillary/pseudopapillary patterns; category 3: characterized by scattered vascularity without a vascular network, including solid sheets, rhabdoid and sarcomatoid patterns. Adverse pathological prognostic factors such as TNM stage, WHO/ISUP grade, and necrosis were significantly associated with category 3, followed by category 2 (all p < 0.001). We successfully validated the classification using The Cancer Genome Atlas (TCGA) cohort (n = 162), and RNA-sequencing data available from TCGA showed that the angiogenesis gene signature was significantly enriched in category 1 compared to categories 2 and 3, whereas the immune gene signature was significantly enriched in category 3 compared to categories 1 and 2. In univariate analysis, vascularity-based architectural classification showed the best accuracy in pathological prognostic factors for predicting recurrence-free survival (c-index = 0.786). The predictive accuracy of our model which integrated WHO/ISUP grade, necrosis, TNM stage, and vascularity-based architectural classification was greater than conventional risk models (c-index = 0.871 vs. 0.755-0.843). Our findings suggest that the vascularity-based architectural classification is prognostically useful and may help stratify patients appropriately for management based on their likelihood of post-surgical recurrence.