Table_1_In situ Metabolic Profiling of Ovarian Cancer Tumor Xenografts: A Digital Pathology Approach.docx

Metabolic profiling of cancer is a rising interest in the field of biomarker development. One bottleneck of its clinical exploitation, however, is the lack of simple and quantitative techniques that enable to capture the key metabolic traits of tumor from archival samples. In fact, liquid chromatography associated with mass spectrometry is the gold-standard technique for the study of tumor metabolism because it has high levels of accuracy and precision. However, it requires freshly frozen samples, which are difficult to collect in large multi-centric clinical studies. For this reason, we propose here to investigate a set of established metabolism-associated protein markers by exploiting immunohistochemistry coupled with digital pathology. As case study, we quantified expression of MCT1, MCT4, GLS, PHGDH, FAS, and ACC in 17 patient-derived ovarian cancer xenografts and correlated it with survival. Among these markers, the glycolysis-associated marker MCT4 was negatively associated with survival of mice. The algorithm enabling a quantitative analysis of these metabolism-associated markers is an innovative research tool that can be exported to large sets of clinical samples and can remove the variability of individual interpretation of immunohistochemistry results.

癌症代谢谱分析（metabolic profiling）在生物标志物开发领域正日益受到学界关注。然而，其临床开发应用的一大瓶颈在于缺乏能够从存档样本中获取肿瘤关键代谢特征的简便定量技术。事实上，液相色谱-质谱联用技术（liquid chromatography associated with mass spectrometry）是肿瘤代谢研究的金标准技术，因其具备极高的准确度与精密度。但该技术需要新鲜冷冻样本，而在大规模多中心临床研究中，此类样本的采集颇具难度。鉴于此，本研究拟通过免疫组织化学（immunohistochemistry）联合数字病理技术，对一系列已验证的代谢相关蛋白标志物展开研究。作为案例研究，本研究对17株患者来源的卵巢癌异种移植瘤模型中MCT1、MCT4、GLS、PHGDH、FAS及ACC的表达量进行了定量分析，并将其与小鼠生存情况进行关联分析。在上述标志物中，糖酵解相关标志物MCT4与小鼠生存情况呈负相关。本研究中用于这些代谢相关标志物定量分析的算法是一款创新性研究工具，可推广应用于大规模临床样本集，同时能够消除免疫组化结果人工判读带来的个体差异。