Validating a prognostic plasma metabolomic biomarker to improve precision medicine in head and neck cancer patients

在过去的十年里，头颈部癌症(Hnc)的精确医学研究受到了极大的关注。 在单个生物标志物-人乳头瘤病毒(HPV)上，针对低风险患者进行治疗非强化治疗 (即减少治疗以提高生活质量，而不牺牲生存)。去强化至关重要，因为标准 治疗包括毒性剂量的放化疗，会导致不良事件和使人虚弱的长期副作用 对大多数患者的影响。然而，2019年，两项III期临床试验证实，HPV不足以 瞄准最有希望的去强化疗法。这次失败为检验新的预测提供了一个及时的机会 在现有的41项临床试验中，针对去强化治疗的标记物，但缺乏有前途的生物标记物阻碍了 进步。HNC是一种以广泛的代谢异质性为特征的疾病，它正在成为一种丰富的 新的预后生物标志物，但尚未得到充分的研究。例如，体内的许多体细胞突变 与预后相关的HNC(如TP53和PIK3CA)是关键代谢的上游调节因子 糖酵解、脂类、氨基酸和细胞信号的过程。最近的研究表明，华宝 效应，一种非典型的代谢状态，在这种状态下，肿瘤的糖酵解上调，但绕过线粒体氧化 磷酸化(OXPHOS)，是HNC细胞系之间的关键区别特征，与预后有关。 最近，2020年的五项不同研究发现，针对新陈代谢调节的基因表达探针 基因创造了可以预测HNC患者生存的信号。证据导致了一个结论-- 新陈代谢与HNC的发生、发展和生存密切相关。因此，代谢组学 可能是发现对HNC精确医学的未来至关重要的翻译生物标记物的最佳方法。 然而，调查和验证HNC代谢生物标记物所需的数据，特别是在临床上 环境，是严重缺乏的。我的研究项目旨在填补这一空白。在我的KL2研究中，包括209 HNC患者的血浆代谢组学，我使用机器学习算法来识别一个预处理 代谢组型包括糖酵解、氧磷脂、脂肪生物合成和氨基酸代谢产物。这个 内型将患者分为与总存活率(危险)相关的两个截然不同的组之一 比值为2.39，95%可信区间为1.19~4.78)。最重要的是，内型的预后能力独立于 相关临床因素，如HPV、吸烟、年龄、性别、种族、肿瘤部位和分期，提示它可能 在其他人失败的地方，成功地成为HNC生物标记物。此R03的目标是验证我的KL2调查结果 一个独立的队列(目标1)，并研究血液中代谢内型与新陈代谢的关系 和肿瘤中的代谢特征(目标2)。这项研究的积极发现将帮助我翻译这一点 NRG Oncology正在进行的临床试验数据的生物标记物，NRG Oncology是一个多地点癌症临床试验联盟，以进行测试 前处理代谢性血生物标志物能否识别对去甲肾上腺素治疗有反应的HNC患者 加强治疗，使他们免受标准放化疗的毒副作用。

Over the past decade, precision medicine research in head and neck cancer (HNC) has been singularly focused on a single biomarker, human papillomavirus (HPV), to target low-risk patients for treatment de-intensification (i.e., reduce therapy to improve quality of life without sacrificing survival). De-intensification is vital since standard treatment involves toxic doses of chemoradiation that causes adverse events and debilitating long-term side effects in most patients. In 2019, however, two phase-III clinical trials confirmed that HPV was insufficient to target the most promising de-intensified therapy. This failure presents a timely opportunity to test new prognostic markers to target de-intensified therapy in the 41 existing clinical trials, but a lack of promising biomarkers hinders progress. HNC is a disease marked by extensive metabolic heterogeneity that is emerging as a rich source of novel prognostic biomarkers but has yet to be fully investigated. For instance, many of the somatic mutations in HNC that are associated with prognosis (e.g., TP53 and PIK3CA) are upstream regulators of key metabolic processes in glycolysis, lipids, amino acids, and cell signaling. Recent studies have showed that the Warburg effect, the atypical metabolic state in which a tumor upregulates glycolysis but bypasses mitochondrial oxidative phosphorylation (OXPHOS), is a key discriminating characteristic between HNC cell lines linked to prognosis. Most recently, five different studies in 2020 found that gene expression probes targeting metabolic regulating genes created signatures that can predict HNC patient survival. The evidence leads to one conclusion— metabolism is intimately involved in the development, progression, and survival of HNC. Thus, metabolomics may be the best approach to discover the translational biomarkers vital to the future of HNC precision medicine. However, the data required to investigate and validate an HNC metabolomic biomarker, particularly in a clinical setting, is severely lacking. My program of research aims to fill that gap. In my KL2 research, which includes 209 HNC patients with blood plasma metabolomics, I used machine learning algorithms to identify a pretreatment metabolomic endotype comprised of glycolysis, OXPHOS, lipid biosynthesis, and amino acid metabolites. The endotypes categorized patients into one of two distinct groups that were associated with overall survival (hazard ratio = 2.39, 95% CI: 1.19-4.78). Most importantly, the prognostic ability of the endotype was independent of relevant clinical factors such as HPV, smoking, age, sex, race, tumor site and stage, suggesting that it may succeed as an HNC biomarker where others have failed. The goal of this R03 is to validate my KL2 findings with an independent cohort (Aim 1) and investigate how the metabolomic endotype in blood is related to metabolism and metabolic characteristics in the tumor (Aim 2). Positive findings from this study will help me translate this biomarker to ongoing clinical trials data within NRG Oncology, a multisite cancer clinical trials consortium, to test whether the pretreatment metabolomic blood biomarker can identify HNC patients who will respond to de- intensified therapy and spare them the toxic effects of standard chemoradiotherapy.