Metabolomics reveals intratumor heterogeneity - Implications for precision medicine.
Metabolomics reveals intratumor heterogeneity - Implications for precision medicine.
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DOI：
10.1016/j.ebiom.2017.04.030
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发表时间：
2017-05
期刊：
影响因子：
11.1
通讯作者：
Locasale JW
中科院分区：
文献类型：
--
作者：
Liu X;Locasale JW
癌细胞已经改变了新陈代谢，开发针对癌症新陈代谢的治疗方法的持续努力显示出希望(DeBerardinis和Chandel 2016)。然而，这些疗法显示出不同的疗效，这种差异的来源是否与肿瘤之间的代谢差异有关，或者可能与单个肿瘤内的代谢差异有关，尚不清楚。在这一期的EBioMedicine中，Okegawa等人。使用代谢组学不仅可以证明人类肾脏肿瘤之间的差异，而且还可以识别同一肿瘤区域内的代谢差异(Okegawa等人。2017年)。将这些结果纳入研究治疗的策略可能会导致根据他们的新陈代谢进行更精确的患者分层。肿瘤间基因表达的异质性已被广泛认识(Hu等人。2013年)。例如，基因表达特征现在被常规用于临床指导治疗决定，就像早期乳腺癌手术切除后是否使用化疗的情况一样(Cardoso等人。2016)。与基因表达所取得的成就相比，基于新陈代谢的预测模型尚未得到广泛应用。这是因为在临床上使用代谢组学方法治疗癌症仍然存在许多挑战。除了肿瘤细胞的遗传构成外，肿瘤的新陈代谢还在很大程度上受到环境因素的影响(Vander Heiden和DeBerardinis 2017)，例如肿瘤起源的位置、血管的位置、可受饮食、肝功能和肠道微生物组成影响的血浆中营养物质的可获得性，以及与基质细胞(如免疫隔间和内皮细胞)的相互作用。因此，肿瘤组织内和肿瘤组织间变异来源的复杂性限制了翻译的适用性。在目前的技术下，代谢组学数据可以从人体样本中获得。例如，通过对138对匹配的人类肾脏肿瘤和正常组织进行代谢物分析，最近的一项研究能够根据肾癌的代谢特征对肾癌进行分组，并将特定的亚组与低存活率联系起来(Hakimi等人)。2016)。此外，使用同位素示踪的非侵入性方法已经被用来测量患者体内通过代谢途径的营养流动。例如，Fan等人。在肺癌患者手术前进行了13C葡萄糖输注(Fan等人)。2009年)。他们发现，与周围的正常肺相比，人类肺癌中的丙酮酸羧基酶(PC)活性增加。Hensley等人。通过临床成像和术中13C-葡萄糖输注，进一步证明人类肿瘤不仅表现出与邻近良性肺组织相比的肿瘤代谢改变，而且还表现出显著的肿瘤间和肿瘤内代谢的异质性(Hensley等人)。2016)。卵巢癌的其他代谢组学研究已经证明，从手术切除获得的人类肿瘤的代谢物图谱中可能包含药物反应的特征(Liu等人。2016)。现在Okegawa等人。不仅在人类肾脏肿瘤中表现出不同的代谢特征，而且在同一肿瘤的不同区域也是如此。通过测量人类肾脏肿瘤内空间分离位置的代谢物并执行无监督聚类，他们首先确定了两个具有MC2的簇(MC1和MC2)，其特征与丙酮酸代谢增加相关。他们通过对肿瘤切片的稳定同位素示踪进一步证实了他们的结果。有趣的是，这些差异与…的遗传变异无关
Cancer cells have altered metabolism and ongoing efforts in developing therapies that target cancer metabolism are showing promise (DeBerardinis and Chandel 2016). However, these therapies show variable efficacy and whether the source of this variation is linked to the metabolic differences across or possibly within individual tumors is unknown. In this issue of EBioMedicine, Okegawa et al. use metabolomics to not only demonstrate differences across human kidney tumors, but they also identify metabolic differences within regions of the same tumor (Okegawa et al. 2017). The incorporation of these results into the strategy of investigational therapies may lead to more precise patient stratifications according to their metabolism. The intertumor heterogeneity of gene expression has been widely appreciated (Hu et al. 2013). For example, gene expression signatures are now routinely used clinically to guide treatment decisions as is the case of whether to use chemotherapy after surgical resection in early stage breast cancer (Cardoso et al. 2016). In contrast to what has been achieved with gene expression, predictive models based on metabolism have not been widely used. This is because many challenges remain for using metabolomics approaches clinically in cancer. In addition to the genetic makeup of the tumor cells, tumor metabolism can be largely affected by environmental factors (Vander Heiden and DeBerardinis 2017), such as the location of the tumor origin, positioning of the vasculature, nutrient availability in the plasma that can be affected by diet, liver function and gut microbial composition, and interactions with stromal cells such as the immune compartment and endothelial cells. Thus, the complexity of the sources of variation within and across tumor tissues has limited translational applicability. With current technology, metabolomics data can be acquired from human samples. For instance, from performing metabolite profiling on138 matched human kidney tumors and normal tissue pairs, a recent study was able to group kidney cancers according to their metabolic profiles and associate particular sub-groups with poor-survival (Hakimi et al. 2016). Furthermore, noninvasive approaches using isotope tracing have been employed to measure the flow of nutrients through metabolic pathways in patients. For example, Fan et al. performed a 13C glucose infusion in human lung cancer patients prior to surgery (Fan et al. 2009). They identified increased pyruvate carboxylase (PC) activity in human lung tumors as compared to the surrounding normal lung. Hensley et al. further demonstrated that human tumors exhibit not only altered tumor metabolism compared to what is observed in adjacent benign lung, but also significant intertumor and intratumor heterogeneity in tumor metabolism by employing clinical imaging and intraoperative 13C–glucose infusions in human lung tumors (Hensley et al. 2016). Other metabolomics studies in ovarian cancer have demonstrated that signatures of drug response may be contained in the metabolite profiles of human tumors obtained from surgical resection (Liu et al. 2016). Now Okegawa et al. not only characterized distinct metabolic profiles across human kidney tumors but also in different regions of the same tumor. By measuring metabolites in spatially-separated sites within human kidney tumors and performing unsupervised clustering, they first identified two clusters (MC1 and MC2) with MC2 exhibiting features associated with increased pyruvate metabolism. They further confirmed their results via stable isotope tracing in tumor slices. Interestingly, these differences were not associated with genetic variation …