Pathology differentiation of renal cancer types is challenging due to tissue similarities or overlapping histological features of various tumor (sub)types. As assessment is often manually conducted outcomes can be prone to human error and therefore require high-level expertise and experience. Mass spectrometry can provide detailed histo-molecular information on tissue and is becoming increasingly popular in clinical settings. Spatially resolving technologies such as mass spectrometry imaging and quantitative microproteomics profiling in combination with machine learning approaches provide promising tools for automated tumor classification of clinical tissue sections. In this proof of concept study we used MALDI-MS imaging (MSI) and rapid LC-MS/MS-based microproteomics technologies (15 min/sample) to analyze formalin-fixed paraffin embedded (FFPE) tissue sections and classify renal oncocytoma (RO, n=11), clear cell renal cell carcinoma (ccRCC, n=12) and chromophobe renal cell carcinoma (ChRCC, n=5). Both methods were able to distinguish ccRCC, RO and ChRCC in cross-validation experiments. MSI correctly classified 87% of the patients whereas the rapid LC-MS/MS-based microproteomics approach correctly classified 100% of the patients. This strategy involving MSI and rapid proteome profiling by LC-MS/MS reveals molecular features of tumor sections and enables cancer subtype classification. Mass spectrometry provides a promising complementary approach to current pathological technologies for precise digitized diagnosis of diseases.

中文翻译：

---

质谱成像和福尔马林固定石蜡包埋的肿瘤组织切片的快速蛋白质组分析对肾癌亚型的组织分子分化

由于组织相似性或各种肿瘤（亚型）类型的组织学特征重叠，肾癌类型的病理区分具有挑战性。由于评估通常是手动进行的，因此结果容易出现人为错误，因此需要高水平的专业知识和经验。质谱可以在组织上提供详细的组织分子信息，并且在临床环境中越来越受欢迎。诸如质谱成像和定量微蛋白质组分析等空间分辨技术与机器学习方法相结合，为临床组织切片的自动肿瘤分类提供了有希望的工具。在此概念验证研究中，我们使用了MALDI-MS成像（MSI）和基于LC-MS / MS的快速蛋白质组学技术（每分钟15分钟）来分析福尔马林固定石蜡包埋（FFPE）组织切片并对肾上皮细胞瘤（RO）进行分类，n = 11），透明细胞肾细胞癌（ccRCC，n = 12）和发色肾细胞癌（ChRCC，n = 5）。两种方法都可以在交叉验证实验中区分ccRCC，RO和ChRCC。MSI正确分类了87％的患者，而基于LC-MS / MS的快速微蛋白质组学方法正确分类了100％的患者。这种涉及MSI和通过LC-MS / MS进行快速蛋白质组分析的策略揭示了肿瘤切片的分子特征，并使癌症亚型分类成为可能。