Bhat Prahlad, Tamboli Pheroze, Sircar Kanishka, Kannan Kasthuri
Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, 2130 W Holcombe Blvd., Houston, TX 77030, USA.
Department of Pathology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA.
Cancers (Basel). 2025 Jan 14;17(2):249. doi: 10.3390/cancers17020249.
Predicting the behavior of clear cell renal cell carcinoma (ccRCC) is challenging using standard-of-care histopathologic examination. Indeed, pathologic RCC tumor grading, based on nuclear morphology, performs poorly in predicting outcomes of patients with International Society of Urological Pathology/World Health Organization grade 2 and 3 tumors, which account for most ccRCCs. We applied spatial point process modeling of H&E-stained images of patients with grade 2 and grade 3 ccRCCs ( = 72) to find optimum separation into two groups. One group was associated with greater spatial randomness and clinical metastasis ( < 0.01). Notably, spatial analysis outperformed standard pathologic grading in predicting clinical metastasis. Moreover, cell-to-cell interaction distances in the metastasis-associated group were significantly greater than those in the other patient group and were also greater than expected by the random distribution of cells. Differential gene expression between the two spatially defined groups of patients revealed a matrisome signature, consistent with the extracellular matrix's crucial role in tumor invasion. The top differentially expressed genes (with a fold change > 3) stratified a larger, multi-institutional cohort of 352 ccRCC patients from The Cancer Genome Atlas into groups with significant differences in survival and TNM disease stage. Our results suggest that the spatial distribution of ccRCC tumor cells can be extracted from H&E-stained images and that it is associated with metastasis and with extracellular matrix genes that are presumably driving these tumors' aggressive behavior.
使用标准的组织病理学检查来预测透明细胞肾细胞癌(ccRCC)的行为具有挑战性。事实上,基于核形态学的病理RCC肿瘤分级在预测国际泌尿病理学会/世界卫生组织2级和3级肿瘤患者的预后方面表现不佳,而这些肿瘤占大多数ccRCC。我们对72例2级和3级ccRCC患者的苏木精和伊红(H&E)染色图像应用空间点过程建模,以找到最佳的分为两组的方法。一组与更大的空间随机性和临床转移相关(P<0.01)。值得注意的是,在预测临床转移方面,空间分析优于标准病理分级。此外,转移相关组中的细胞间相互作用距离明显大于另一患者组中的距离,也大于细胞随机分布所预期的距离。两组空间定义患者之间的差异基因表达揭示了一种基质特征,这与细胞外基质在肿瘤侵袭中的关键作用一致。差异表达最显著的基因(倍数变化>3)将来自癌症基因组图谱的352例ccRCC患者的更大规模多机构队列分层为在生存和TNM疾病分期上有显著差异的组。我们的结果表明,可以从H&E染色图像中提取ccRCC肿瘤细胞的空间分布,并且它与转移以及可能驱动这些肿瘤侵袭行为的细胞外基质基因相关。