Breath Volatile Metabolites for the Diagnosis of Emerging Invasive Mold Infection

描述（由申请人提供）：本重新提交的K23提案概述了Sophia Koo的五年发展计划，以实现作为以患者为导向的临床医生-科学家的独立性，在侵袭性霉菌感染（IMI）的新型诊断模式的开发和评估方面具有特殊专长。本提案中概述的策略将使顾博士能够建立在她护理有IMI风险的患者的临床背景和她的临床研究的基础上，这些研究定义了目前可用的这些感染诊断模式的局限性 利用布里格姆妇女医院和丹娜-法伯癌症研究所独特的临床和研究优势，查尔斯斯塔克德雷珀实验室的生物医学工程和生物信息学能力，以及哈佛公共卫生学院、东北大学和马萨诸塞州理工学院的流行病学、生物统计学和分析化学培训机会。她将由哈佛医学院医学副教授Lindsey Baden博士和哈佛公共卫生学院流行病学教授Earl弗朗西斯Cook博士共同指导。Baden博士是真菌诊断方面的专家，Cook博士是研究设计和统计学习/数据挖掘方面的专家，包括分析高维生物标志物数据。古博士还将与丹尼尔·库里茨克斯博士、拉斐尔·多林博士、弗朗西斯科·马蒂博士、约翰·完美博士和约翰·温加德博士密切合作，他们将为拟议研究计划的执行提供职业指导和科学建议，而她的合作者保罗·卡罗斯博士、特伦斯·里斯比博士和詹姆斯·科莫利博士将为她的研究计划贡献他们在分析化学和呼吸分析方面的专业知识。该研究计划将采用分析化学和统计监督学习方法来定义体外和患者呼吸中新兴侵入性霉菌物种的挥发性有机化合物（VOC）代谢物模式，为新兴IMI患者的非侵入性即时呼吸测试奠定基础。具体而言，该方案提出（1） 最常引起人类IMI的毛霉目、镰刀菌属和赛多孢子菌属物种的VOC代谢物组的详细定义，以及（2）在患有新发IMI的患者、具有提示新发IMI的临床和放射学发现的患者以及具有这些霉菌的呼吸道定植的患者的呼吸中的VOC代谢物的前瞻性收集，通过推导和验证VOC特征支持向量机分类算法，以区分患有新兴IMI的患者与患有其他真菌感染或非真菌肺部过程的患者。目前对这些感染的诊断方法是不充分的，往往是不可接受的病态，这种新的，非侵入性的诊断方法的发展应该允许这些感染的早期诊断比目前可能的，在不断增长的人群中的前瞻性监测的风险，新兴IMI，更合理的抗真菌治疗的目标，并改善临床结果。

DESCRIPTION (provided by applicant): This resubmission K23 proposal outlines a five-year development plan for Sophia Koo to achieve independence as a patient-oriented clinician-scientist with particular expertise in the development and assessment of novel diagnostic modalities for invasive mold infections (IMI). The strategy outlined in this proposal will enable Dr. Koo to build upon her clinical background in caring for patients at risk for IMI and her clinicl research defining the limitations of currently available diagnostic modalities for these infections by taking advantage of the unique clinical and research strengths of Brigham and Women's Hospital and the Dana-Farber Cancer Institute, biomedical engineering and bioinformatics capabilities of the Charles Stark Draper Laboratory, and epidemiology, biostatistics, and analytical chemistry training opportunities at the Harvard School of Public Health, Northeastern University, and Massachusetts Institute of Technology. She will be co-mentored by Dr. Lindsey Baden, Associate Professor of Medicine at Harvard Medical School, and Dr. Earl Francis Cook, Professor of Epidemiology at the Harvard School of Public Health. Dr. Baden is an expert in fungal diagnostics, and Dr. Cook is an expert in study design and statistical learning/data mining, including analysis of highly dimensional biomarker data. Dr. Koo will also work closely with Drs. Daniel Kuritzkes, Raphael Dolin, Francisco Marty, John Perfect, and John Wingard, who will provide career guidance and scientific advice on the execution of the proposed research plan, and her collaborators Drs. Paul Vouros, Terence Risby, and James Comolli, who will contribute their expertise in analytical chemistry and breath analysis to her research plan. The research program will employ analytical chemistry and statistical supervised learning approaches to define volatile organic compound (VOC) metabolite patterns of emerging invasive mold species in vitro and in patient breath to lay the groundwork for a noninvasive, point-of-care breath test for patients with emerging IMI. Specifically, this program proposes (1) a detailed definition of the VOC metabolome of the Mucorales, Fusarium, and Scedosporium species that most commonly cause human IMI, and (2) the prospective collection of VOC metabolites in the breath of patients with emerging IMI, patients with clinical and radiographic findings suggestive of an emerging IMI, and patients with respiratory tract colonization by these molds, with derivation and validation of VOC feature support vector machine classification algorithms to discriminate patients with emerging IMI from patients with other fungal infections or non-fungal pulmonary processes. Current diagnostic modalities for these infections are inadequate and often unacceptably morbid, and development of this novel, noninvasive diagnostic approach should allow earlier diagnosis of these infections than currently possible, prospective surveillance in the growing population of patients at risk for emerging IMI, more rational targeting of antifungal therapy, and improved clinical outcomes.