Bin Satter Khaled, Ramsey Zach, Tran Paul M H, Hopkins Diane, Bearden Gregory, Richardson Katherine P, Terris Martha K, Savage Natasha M, Kavuri Sravan K, Purohit Sharad
Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, 1120, 15th St., Augusta, GA 30912, USA.
Department of Pathology, Medical College of Georgia, Augusta University, 1120 15th St., Augusta, GA 30912, USA.
Cancers (Basel). 2022 Jul 1;14(13):3242. doi: 10.3390/cancers14133242.
Malignant chromophobe renal cancer (chRCC) and benign oncocytoma (RO) are two renal tumor types difficult to differentiate using histology and immunohistochemistry-based methods because of their similarity in appearance. We previously developed a transcriptomics-based classification pipeline with "Chromophobe-Oncocytoma Gene Signature" (COGS) on a single-molecule counting platform. Renal cancer patients ( = 32, chRCC = 17, RO = 15) were recruited from Augusta University Medical Center (AUMC). Formalin-fixed paraffin-embedded (FFPE) blocks from their excised tumors were collected. We created a custom single-molecule counting code set for COGS to assay RNA from FFPE blocks. Utilizing hematoxylin-eosin stain, pathologists were able to correctly classify these tumor types (91.8%). Our unsupervised learning with UMAP (Uniform manifold approximation and projection, accuracy = 0.97) and hierarchical clustering (accuracy = 1.0) identified two clusters congruent with their histology. We next developed and compared four supervised models (random forest, support vector machine, generalized linear model with L2 regularization, and supervised UMAP). Supervised UMAP has shown to classify all the cases correctly (sensitivity = 1, specificity = 1, accuracy = 1) followed by random forest models (sensitivity = 0.84, specificity = 1, accuracy = 1). This pipeline can be used as a clinical tool by pathologists to differentiate chRCC from RO.
恶性嫌色细胞肾细胞癌（chRCC）和良性嗜酸性细胞瘤（RO）是两种肾肿瘤类型，由于它们外观相似，使用基于组织学和免疫组织化学的方法难以区分。我们之前在单分子计数平台上开发了一种基于转录组学的分类流程，即“嗜色细胞 - 嗜酸性细胞瘤基因特征”（COGS）。从奥古斯塔大学医学中心（AUMC）招募了肾癌患者（n = 32，chRCC = 17，RO = 15）。收集了他们切除肿瘤的福尔马林固定石蜡包埋（FFPE）块。我们为COGS创建了一个定制的单分子计数码集，以检测来自FFPE块的RNA。利用苏木精 - 伊红染色，病理学家能够正确分类这些肿瘤类型（91.8%）。我们使用UMAP（均匀流形近似和投影，准确率 = 0.97）和层次聚类（准确率 = 1.0）进行无监督学习，识别出两个与其组织学一致的聚类。接下来，我们开发并比较了四种监督模型（随机森林、支持向量机、具有L2正则化的广义线性模型和监督UMAP）。监督UMAP已显示能够正确分类所有病例（敏感性 = 1，特异性 = 1，准确率 = 1），其次是随机森林模型（敏感性 = 0.84，特异性 = 1，准确率 = 1）。该流程可作为病理学家区分chRCC和RO的临床工具。
