434 Investigating the metabolic-inflammatory mechanisms of cachexia symptoms in head and neck cancer patient plasma via multiomics integration of the metabolome, lipidome, and inflammation cytokines

Ronald C. Eldridge, Nabil F. Saba, Andrew Miller, E. Wommack, Jennifer Felger, Deborah W. Bruner, C. Xiao
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Abstract

OBJECTIVES/GOALS: Cachexia is the involuntary and irreversible loss of muscle and fat and is a major cause of morbidity and mortality in head and neck cancer (HNC). It remains a poorly understood disease diagnosed by weight loss and a confluence of symptoms. We explored the metabolic and inflammatory mechanisms of cachexia symptoms via an multiomics network algorithm. METHODS/STUDY POPULATION: Prior to chemoradiotherapy, HNC subjects completed questionnaires and donated blood for untargeted (metabolites) and targeted (lipids and cytokines) assays. Metabolites and lipids were measured by liquid chromatography mass spectrometry. Cytokines were measured by multiplex assays. We plotted a multiomics network graph by estimating partial least squares correlations amongst metabolites, lipids, cytokines, and common cachexia symptoms—max percent weight loss over 1 year, baseline BMI, fatigue, performance, albumin, hemoglobin, and white blood cell count. To interpret the network, an algorithm identified highly correlated clusters of metabolites-lipids-cytokines-symptoms representing possible biological relatedness, which were functionally annotated via metabolic enrichment analysis. RESULTS/ANTICIPATED RESULTS: In 123 subjects (59 years of age, 72% male, 84% white, avg weight loss of 13%), we analyzed 186 metabolites, 54 lipids, 7 cytokines and 7 cachexia symptoms. We required a correlation >0.25 and P-value <.05 to be included in the network graph, resulting in 323 connections and 3 identified clusters. Max weight loss and baseline BMI were in a cluster enriched by unsaturated fatty acid biosynthesis (P<.0001) and arachidonic acid (P=.01) metabolic pathways but not linked to inflammation cytokines. The five other cachexia symptoms were in a cluster with 4 cytokines (C-reactive protein, interleukin 6, IL10, IL1, Tumor necrosis factor receptor 2) and enriched by aminoacyl tRNA (P<.01) and valine biosynthesis (P=.02). We observed no meaningful differences when we stratified the analysis by human papillomavirus. DISCUSSION/SIGNIFICANCE: Cachexia symptoms in head and neck cancer may be linked to specific metabolic dysregulation—weight loss and BMI were linked to fatty acids; fatigue, anemia and others were linked to amino acids and inflammation. This information may allow for the recognition of a cachexic-metabolic subtype or provide novel targets for metabolic intervention.
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434 通过代谢组、脂质组和炎症细胞因子的多组学整合研究头颈癌患者血浆中恶病质症状的代谢-炎症机制
目的/目标:恶病质是指肌肉和脂肪不自主、不可逆的流失,是头颈癌(HNC)发病和死亡的主要原因。人们对这种疾病仍然知之甚少,它是通过体重减轻和一系列症状诊断出来的。我们通过多组学网络算法探索了恶病质症状的代谢和炎症机制。方法/研究对象:在化疗放疗前,HNC 受试者填写问卷并献血,以进行非靶向(代谢物)和靶向(脂质和细胞因子)检测。代谢物和脂质通过液相色谱质谱法测定。细胞因子通过多重检测法进行测定。我们通过估算代谢物、脂质、细胞因子与常见恶病质症状(1 年内体重减轻最大百分比、基线体重指数、疲劳、表现、白蛋白、血红蛋白和白细胞计数)之间的偏最小二乘法相关性,绘制了多组学网络图。为了解释该网络,一种算法确定了代谢物-血脂-细胞因子-症状的高度相关群组,这些群组代表了可能的生物学相关性,并通过代谢富集分析对其进行了功能注释。结果/预期结果:在 123 名受试者(59 岁,72% 为男性,84% 为白人,平均体重减轻 13%)中,我们分析了 186 种代谢物、54 种脂质、7 种细胞因子和 7 种恶病质症状。我们要求相关性大于 0.25 且 P 值小于 0.05 才能将其纳入网络图中,结果发现了 323 个连接和 3 个已识别的群集。最大体重减轻和基线体重指数位于不饱和脂肪酸生物合成(P<.0001)和花生四烯酸(P=.01)代谢途径富集的聚类中,但与炎症细胞因子无关。其他 5 种恶病质症状与 4 种细胞因子(C 反应蛋白、白细胞介素 6、IL10、IL1、肿瘤坏死因子受体 2)组成一个群集,并通过氨基酰 tRNA(P<.01)和缬氨酸生物合成(P=.02)富集。在按人类乳头瘤病毒进行分层分析时,我们没有观察到有意义的差异。讨论/意义:头颈癌患者的头痛症状可能与特定的代谢失调有关--体重减轻和体重指数与脂肪酸有关;疲劳、贫血等症状与氨基酸和炎症有关。这些信息可能有助于识别恶病质代谢亚型,或为代谢干预提供新的目标。
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