There is a current need in the clinical sciences for new technologies to rapidly diagnose cancers based on the detection of dysregulated molecular signatures. Abnormal expression of small metabolites involved in key steps of glucose transport, glutaminolysis, aerobic glycolysis and Krebs cycle, and larger metabolites such as fatty acids and complex lipids has been observed in various types of cancers. Moreover, abnormal metabolic patterns have been associated with specific genes linked to cancer prognosis. The MYC oncogene, for example, which is amplified in 55% of human hepatocellular carcinomas (HCC), is known to play a key role in the regulation of metabolic pathways and has also been associated with poor prognosis. Hence, the ability to rapidly and easily measure metabolites could provide a powerful approach for the clinical diagnosis and prognosis of cancers. New ambient ionization mass spectrometry imaging (MSI) techniques can perform direct analysis of tissue samples for in situ, near real time assessment of their molecular signatures. The goal of this proposal is to develop an ambient ionization MSI technique, desorption electrospray ionization (DESI-MSI), in conjunction with biostatistical tools to measure, define and validate metabolic signatures that are diagnostic and prognostic of human solid cancers, and to test this technology as a clinical tool for intrasurgical diagnosis of cancers. Application of DESI-MSI to analyze human cancerous tissue is a recent line of research developed in the last 6 years, in which I played a leading role during my PhD with Prof. R. Graham Cooks at Purdue University, and that I continue to develop in my postdoctoral research with Prof. Richard N. Zare at Stanford University. DESI-MSI allows hundreds of metabolites to be measured, imaged and accurately identified from an unmodified tissue sample in less than a second per pixel, in the open air, ambient environment. Although powerful, the DESI-MSI experiment is fairly simple: a spray of charged droplets extract metabolites from a sample surface, and are captured by a mass spectrometer for chemical analysis and identification. I believe this technology has the potential to transform the way cancer is diagnosed and treated in the clinical setting.
The specific aims of my K99/R00 proposal are: 1. Develop DESI-MSI and refined statistical tools to identify and validate metabolic signatures diagnostic of a solid tumor, human HCC, 2. Investigate if certain metabolic patterns are related with a specific gene, the MYC oncogene, using the refined transgenic mouse models of MYC-induced HCC, 3. Evaluate DESI-MSI as a clinical tool for intrasurgical diagnosis and prognosis of HCC and other solid human tumors. While the initial aims of this proposal are focused on HCC, the developed methods will be applicable to study other human solid cancer, as it will be pursued in my independent phase, and thus have broad significance in human cancer diagnosis, prognosis and treatment. I have strong expertise in analytical chemistry and mass spectrometry, and a track-record of success in developing novel mass spectrometry tools for biological sample analysis. I have published 36 peer-reviewed manuscripts and have been honored to receive few awards for my research achievements, including the Nobel Laureate Signature Award of the American Chemical Society in recognition as 2012's best doctoral dissertation amongst all branches of Chemistry in the USA. However, while my prior research and training experiences in MS and translational research have enabled me to conduct the MS and clinical portions of collaborative research projects, through my interdisciplinary interactions as a postdoctoral researcher at Stanford University I recognized that my chemical training is not sufficient to conduct significant biomedical research as an independent researcher. Prior to engaging in a career as an independent investigator in cancer/biomedical research, I would greatly benefit from training in high-dimensional statistics methods to properly analyze and interpret mass spectral data of clinical samples, and in basic molecular biology methods for understanding cancer biology processes. Stanford University provides a spectacular environment to pursue the interdisciplinary project I propose and to receive training from the most outstanding researchers in these areas. As part of my career development, my mentorship and training will be provided by Prof. Richard N. Zare (Department of Chemistry), an innovator in methods of chemical analysis, Prof. Robert Tibshirani (Departments of Health Research & Policy, and Statistics), famous for the development of biostatistical methods for high-dimensional data analysis, and Prof. Dean Felsher (Department of Medicine, Division of Oncology), a pioneer in the development of MYC-induced transgenic mouse models of cancers. The training will be achieved through experimental work and also formal course work. My long-term career goal as an independent researcher is to develop novel MS technology for clinical diagnosis and prognosis of human cancers. As an independent researcher, I will apply my expertise in MS, and the training in biostatistics and molecular biology that I will receive through the K99 period to develop new, automated MS tools for clinical and intrasurgical diagnosis and prognosis of various human cancers, and to translate this technology to the clinics. I have a particular interest in using MS for assessing cancer margins during surgical resection, procedure for which new and rapid diagnostic methods are greatly needed.
Aims 1 and 2 will be performed during the K99 mentored phase, and aim 3 will be pilot for HCC and much further explored in the R00 independent phase for other solid human cancers. The K99/R00 award will support my development into an independent investigator who develops relevant novel mass spectrometry tool in combination with biostatistical methods for clinical diagnosis and prognosis of human cancers.
New clinical technologies for the rapid and accurate detection of human cancers are in high demand. I will develop a novel chemical technology in conjunction with biostatistical tools for in situ, real time detection of abnormal metabolic patterns that are diagnostic and prognostic of human solid cancers, for clinical and intrasurgical use. This new method has the potential to be applicable to various cancer types and hence have a broad relevance to the study, diagnosis and treatment of human cancers.
