Leveraging Hyperpolarized MRI for Precision Oncology Approaches in Head and Neck Cancer

利用超极化 MRI 实现头颈癌的精准肿瘤学方法

• 批准号: 10530043
• 负责人: James A Bankson
• 金额: $ 67.51万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10530043
• 关键词: Address; Affect; Algorithms; Animal Model; Biochemical; Biological Assay; Biology; Biopsy; Cancer Etiology; Carbon; Cessation of life; Characteristics; Chemicals; Cisplatin; Clinical Trials; Coenzymes; DNA; DNA Damage; Data; Data Analyses; Development; Dose; Effectiveness; Evaluation; Exposure to; Failure; Feedback; Genotoxic Stress; Glycolysis; Goals; Grant; Head and Neck Cancer; Head and Neck Squamous Cell Carcinoma; Head and neck structure; Heterogeneity; Human; Human Papillomavirus; Image; In Vitro; Individual; Knowledge; Lactate Dehydrogenase; Link; Magnetic Resonance Imaging; Measurable; Measurement; Measures; Metabolic; Metabolic Pathway; Mission; Modeling; Mus; Mutagens; NADP; Oxidation-Reduction; Patient Recruitments; Patients; Pharmaceutical Preparations; Pre-Clinical Model; Public Health; Publishing; Pyruvate; Radiation therapy; Reaction; Recycling; Regimen; Research; Resistance; Resistance development; Resolution; Selection Criteria; Sensitivity and Specificity; Series; Shunt Device; Solid Neoplasm; Suggestion; Testing; Therapeutic; Time; Toxic effect; Translational Research; Treatment Effectiveness; Treatment Failure; Treatment Protocols; Tumor Burden; Tumor Volume; United States; United States National Institutes of Health; Work; Xenograft procedure; base; cell injury; cell killing; chemotherapeutic agent; chemotherapy; clinical application; clinically relevant; design; effective therapy; first-in-human; imaging biomarker; imaging modality; in vivo; individual patient; innovation; insight; interest; metabolic imaging; metabolic phenotype; metabolomics; minimally invasive; neoplastic cell; non-invasive imaging; novel; oral lesion; patient derived xenograft model; personalized management; precision oncology; preclinical trial; predicting response; predictive modeling; prospective; quantitative imaging; randomized trial; response; standard of care; success; targeted agent; transcriptomics; treatment optimization; treatment response; tumor; tumor growth; tumor metabolism

PROJECT SUMMARY/ABSTRACT Head and neck squamous cell carcinoma (HNSCC) remains a leading cause of cancer deaths worldwide. Genotoxic agents, including radiation therapy (RT) and cisplatin (CDDP), are treatments that damage cellular DNA. RT and CDDP are the current standard of care in multiple solid tumors, including HNSCC. CDDP is the most commonly used chemotherapeutic agent in HNSCC proving superior to novel targeted agents in recent large randomized trials. Despite this, high rates of treatment failure persist in patients who develop resistance following this toxic chemotherapy. Treatment failure is uniformly fatal. However, no robust predictors of acquired cisplatin resistance or tumor response exist. Given this critical unmet need, we have focused our efforts on the assessment of tumor response using minimally invasive quantitative imaging (hyperpolarized magnetic resonance imaging; HP-MRI) while patients are undergoing therapy. We showed that CDDP and other genotoxic agents trigger measurable fluctuations in tumor cell metabolism detectable through HP-MRI with [1-13C]-pyruvate in real time (confirmed by conventional biochemical assays). Genotoxic stress suppresses the apparent rate of pyruvate conversion into lactate (kPL) via lactate dehydrogenase (LDH) in a manner that correlates with anti- tumor effectiveness. We therefore hypothesize that changes in kPL provide unique insight into metabolic changes induced by cisplatin that can be used to optimize response to therapy in HNSCC. In Aim 1, we will characterize baseline HP-MRI parameters such as kPL across the spectrum of HNSCC subtypes and validate the relationship between CDDP and associated shifts in carbon flux. We will also identify metabolomic differences in HNSCC models that affect baseline values of metabolic imaging biomarkers and modulate apparent changes induced by cisplatin. In Aim 2, we will integrate the dose-response data from Aim 1 to develop a predictive model of response to CDDP based on metabolic imaging parameters. We will use a simple algorithm to adjust therapeutic dose based on HP-MRI data in animal models of HNSCC to maximize tumor growth delay, and test whether thresholds suggestive of strong response can be used to select the more effective treatment regimen when multiple regimens are tested in parallel. In Aim 3, we will conduct a first-in- human evaluation of changes in HP-MRI to detect shifts in carbon flux following CDDP in HNSCC patients. We will correlate changes in metabolic imaging parameters with the baseline metabolic phenotype of tumors as determined from metabolomic analysis and direct measurements of tumor LDH. Successful completion of this study will establish HP-MRI as a non-invasive imaging modality able to predict response to treatment, which will be a noteworthy first step towards a precision oncology approach that we have been seeking for nearly half a century. Thus, the proposed research is relevant to the part of the NIH’s mission that pertains to developing and applying fundamental knowledge that will help to reduce the burdens of human illness and addresses directly the recently published “Notice of Special Interest: Precision Imaging of Oral Lesions” (NOT-DE-21-010).

项目总结/摘要 头颈部鳞状细胞癌（HNSCC）仍然是全球癌症死亡的主要原因。 遗传毒性药物，包括放射治疗（RT）和顺铂（CDDP），是损害细胞增殖的治疗方法。 DNA. RT和CDDP是目前多种实体瘤（包括HNSCC）的标准治疗。CDDP是 HNSCC中最常用的化疗药物，证明其上级优于最近的新型靶向药物 大型随机试验。尽管如此，在产生耐药性的患者中， 在这种有毒的化疗之后。治疗失败一律是致命的。然而，没有强有力的预测 存在获得性顺铂耐药性或肿瘤反应。考虑到这一尚未满足的关键需求， 使用微创定量成像（超极化磁共振成像）评估肿瘤反应 共振成像; HP-MRI）。我们发现CDDP和其他遗传毒性物质 通过HP-MRI用[1- 13 C]-丙酮酸盐可检测的药物触发肿瘤细胞代谢的可测量波动 以真实的时间（通过常规生物化学测定证实）。遗传毒性应激抑制了 丙酮酸通过乳酸脱氢酶（LDH）转化为乳酸（kPL），与抗- 肿瘤有效性因此，我们假设kPL的变化提供了对代谢的独特见解。 顺铂诱导的变化，可用于优化HNSCC的治疗反应。 在目标1中，我们将描述基线HP-MRI参数，如HNSCC谱中的kPL 亚型和验证CDDP和相关的碳通量变化之间的关系。我们还将确定 HNSCC模型中影响代谢成像生物标志物基线值的代谢组学差异， 调节顺铂诱导的明显变化。在目标2中，我们将整合目标1的剂量-反应数据 建立基于代谢成像参数的CDDP反应预测模型。我们将使用一个 基于HNSCC动物模型中HP-MRI数据调整治疗剂量的简单算法， 肿瘤生长延迟，并测试是否可以使用提示强烈反应的阈值来选择更多的 当多个方案并行测试时，有效的治疗方案。在目标3中，我们将进行首次- 人类评估HP-MRI的变化，以检测HNSCC患者中CDDP后碳通量的变化。我们 将代谢成像参数的变化与肿瘤的基线代谢表型相关联， 通过代谢组学分析和肿瘤LDH的直接测量确定。成功完成本 研究将建立HP-MRI作为能够预测对治疗反应的非侵入性成像模式， 这将是迈向精确肿瘤学方法的值得注意的第一步， 半个世纪。因此，拟议的研究与NIH的使命的一部分有关， 应用基础知识，这将有助于减少人类疾病的负担，并直接解决 最近出版的“特别关注通知：口腔病变的精确成像”（NOT-DE-21-010）。