Immunotherapy of Cancer and the Role of Mass Spectrometry; An Overview and New Results Donald Hunt University of Virginia
Professor Donald F. Hunt joined the faculty at the University of Virginia as an assistant professor in September, 1968 and currently holds the rank of University Professor with appointments in both Chemistry and Pathology. He is the recipient of numerous awards including: Virginia?s Outstanding Scientist, the Pehr Edman Award, the Distinguished Contribution Award from the American Society for Mass Spectrometry, the Christian B. Anfinsen Award from the Protein Society, the Chemical Instrumentation Award and the Frank F. Field and Joe L. Franklin Award from the American Chemical Society, the Thomson Medal from the International Mass Spectrometry Society, Distinguished Accomplishment Awards from the Human Proteome Organization (HUPO) and ABRF, the Distinguished Scientist Award at the University of Virginia, and the School of Medicine Dean’s Award for Excellence in Team Science. He is a member of the School of Medicine’s MilliPub Club (5 papers with more than 1,000 citations) and was elected as a member of the American Academy of Arts and Sciences in 2014. He won the American Chemical Society Award in Analytical Chemistry for 2017. Professor Hunt is a co-inventor on more than 30 patents and patent applications and has more than 400 scholarly publications to his credit.
This lecture will describe how the immune system works to keep us free of cancer most of the time and how cancer cells can eventually learn to escape the immune system. Also described will be a number of scientific breakthroughs that have occurred in the last 6 years that clearly suggest that the immune system can be re-educated and upregulated to cure even late stage cancer. Science magazine labeled cancer immunotherapy as the breakthrough event of the year in 2013 and tremendous progress has been made since then. James Allison won the 2015 Lasker Award and shared the 2018 Nobel Prize in Medicine with Tasuku Honjo for their work to reactivate the immune system to fight cancer. This lecture will try capture some this excitement and also pinpoint some of the contributions that the Hunt group has made to the field over the past 25 yrs.
>> Tuesday 14:15 in Rm 2-4
Analysis of Exosomes from Patient Serum Samples : Proteomic, Metabolomic and Lipidomic Analyses David Lubman University of Michigan Medical Center
David M. Lubman is currently the Maude T. Lane Professor of Surgical Immunology and Professor of Surgery at the University of Michigan Medical Center and also Professor of Chemistry. He received his A.B. from Cornell University in 1975 and his Ph.D. in physical chemistry from Stanford University in 1979 under the direction of Professor Richard N. Zare. His present research interests involve developing new proteomic methods for searching for protein markers of cancer. These include 2-D liquid mapping and mass spectrometry of tumor cells and serum and also the development of protein microarray technology. Most recent studies involve the study of cancer stem cells, early detection of cancer, circulating tumor cells and exosomes, and changes in glycosylation during stages of cancer progression. Various collaborations with clinical researchers include studies in ovarian, pancreatic, colon, HCC, GBM and breast cancer. The Lubman laboratory has produced 57 Ph.D theses and 15 MScs and also has had over 30 postdoctorals, medical fellows and visiting scholars from around the world through the lab. We have been funded over the past 35 years by various federal agencies including NIH, NCI, NSF, NASA and DOD. Dr. Lubman has served on the editorial boards of 20 journals and recently was a standing member of the EBIT study section. He is a fellow of the American Association for the Advancement of Science, a member of the Association for the Advancement of Cancer Research, the American Chemical Society and the American Society for Mass Spectrometry.
We have isolated exosomes from the serum of ten patients with locally advanced pancreatic cancer at serial time points over a course of therapy. We have explored several different methods to purify exosomes in patient serum where it is essential to remove serum proteins including albumin for proteomics studies. Exosomes were identified by using quantitative mass spectrometry. We compared the exosomal proteome of patients at different time points during treatment to healthy controls and identified 8 proteins that show global treatment-specific changes. More recently we have studied changes in the serum exosome metabolome during therapeutic treatment where specific metabolites and lipids may be markers of treatment. The differential content of exosomes during a course of therapy suggests that exosomes may provide novel insights into the development of treatment resistance and metastasis.
>> Thursday 15:30 in Rm 3-4
Tools and Infrastructure to Enable Crowdsourcing the Molecular Analysis of Microbiome as Applied to Precision Care Pieter Dorrestein University of California, San Diego
Our work aims to develop new mass spectrometry based methods to understand the chemistry of microbes, our microbiome and their ecological niche. In short, we develop tools that translate the chemical language between cells. This research requires the understanding of (microbial) genomics, proteomics, imaging mass spectrometry, genome mining, enzymology, small molecules structure elucidation, bioactivity screening, antibiotic resistance and an understanding of small molecule structure elucidation methods. The collaborative mass spectrometry innovation center that he directs is well equipped and now has twelve mass spectrometers, that are used in the studies to investigate capture cellular chatter (e.g. metabolic exchange), metabolomics, metabolism and to develop methods to characterize natural products. These tools are used to defining the spatial distribution of natural products in 2D, 3D and in some cases real-time. Areas of recent research directions are capturing mass spectrometry knowledge to understand the microbiome, non invasive drug metabolism monitoring, informatics of metabolomics, microbe-microbe, microbe-immune cells, microbe-host, stem cell-cancer cell interactions and diseased vs. non-disease model organisms and the development of strategies for mass spectrometry based genome mining and to detect and structurally characterize metabolites through crowd source annotation of molecular information on the Global Natural Products Social Molecular Networking at http://gnps.ucsd.edu through the NIH supported center for computational mass spectrometry that is co-developed with Nuno Bandeira.
The general community is encouraged to visit the same primary care physician. This is because they “know” their patients. When the physician observes changes or upon request of the patient, a physician may request tests to be performed such as measuring small molecules. Cholesterol, lipids, thyroid hormones, and serotonin levels are representative examples. If these molecules are out of the norm, or dramatically change from previously measured levels, an intervention may be prescribed. In recent years OMICS technologies, including informatic capabilities, have advanced and led to the increasing recognition that a person is a walking ecosystem not only comprised of human cells but also bacteria, fungi and other eukaryotes and phages, collectively called the microbiome. That microbiome defines the health and disease status of the host as well as how a patient responds to interventions. Monitoring the changes due to the microbiome is difficult but, in principle, if we can perform untargeted OMICS analysis of patient samples, it can be expected that such monitoring can be used by physicians to understand their patients. This presentation describes how research grade untargeted MS and microbiome sequencing based OMICS technologies can be used to understand disease and the effect of treatments. This is achieved through building crowd sourced community knowledge capture, analysis, and data visualization infrastructures and applying them via close interactions with physicians. The ability to provide large scale data analysis and visualization capabilities at clinicalyl relevant time scales represents the tip of the iceberg of what future technologies may bring. We will showcase these capabilities through highlighting the early versions of these capabilities that has processed 100,000s of samples. Examples will be shown associated with inflammatory diseases, liver disease, metabolism, cardiac disease, infectious disease, sleep deprivation, malnutrition but also several single patient case studies, including a single patient phage therapy treatment.
MALDI Imaging MS: Transforming Drug Discovery and Development Through Molecular Images of Biological Systems Steve Castellino GlaxoSmithKline
The application of MALDI IMS in drug discovery and development has evolved from providing “snap shots” of drug and drug metabolite tissue distributions to addressing the more fundamental questions pertaining to quantitative disposition, validation of delivery strategies, and understanding the complex mechanistic relationships between molecular initiation events of pharmacologically active compounds and the cascade of biological consequences. Cumulative IMS experimental data of disease states and pharmacology models have been transformational in building and expanding our knowledge on drug delivery, disposition, pharmacology and underlying mechanisms of disease. This knowledge is critical in translating results from in vitro and in vivo models into safe and efficacious medicines to meet the medical needs of a diverse population. This presentation will include examples from early and late stage medicinal development where MALDI IMS has had a significant impact on our understanding of pharmacology and mechanisms of disease states.
Towards Automated Quality Assessment and Analysis of Targeted Mass Spectrometry Data using Machine Learning Shadi Eshghi Genentech
Shadi Eshghi is a scientist in the biomarker development department at Genentech. Her work focuses on development of bioinformatics tools and techniques to facilitate analysis and interpretation of mass spectrometry, flow cytometry and mass cytometry data. Prior to joining Genentech, Shadi worked in Dr. Hui Zhang’s lab at Johns Hopkins University on novel computational and experimental methods for exploring the glycome and glycoproteome using mass spectrometry. Shadi obtained a Ph.D. in biomedical engineering from Johns Hopkins University and a B.Sc. in electrical engineering and is a 2016 Siebel Scholar.
Targeted mass spectrometry has enabled highly multiplexed and sensitive quantitation of lipids, metabolites and protein biomarkers for clinical research and development. The increased rate of data production along with the need for improving data processing efficiency and reproducibility has created growing demand for automated analytical tools in the field. We have developed TargetedMSQC, an open source R package that takes advantage of machine learning for semi-automated quality assessment of chromatographic peaks in targeted MS data. The workflow of TargetedMSQC and its application for analysis and quality control of large datasets including clinical trial biomarker data will be discussed.
>> Wednesday 13:00 in Track 1 (Pueblo) : Session 3
Application of N-Glycan MALDI MS Imaging to Identify Cancer Biomarker Signatures in FFPE Tissues and Biofluids Richard Drake Medical University of South Carolina
Alterations in cell surface glycosylation during tumorigenesis are well documented, and most current FDA approved cancer biomarkers are glycoproteins or glycan antigens. A MALDI mass spectrometry imaging method to spatially profile N-linked glycans in formalin-fixed paraffin-embedded (FFPE) tissue sections and tissue microarrays (TMAs) has been applied to several thousand patient samples of liver, breast and prostate cancer tissues. Analysis was done using MALDI-FTICR MS and a new rapid MALDI-TOF MS TissueTyper. The workflow has also been adapted to rapidly evaluate biofluids and cells. Structural classes of N-glycans that distinguish tumor from non-tumor for each cancer type will be described. The goals are to develop the approaches as prognostic assays for disease stratification at the time of diagnosis.
Maximizing the Performance of MALDI-TOF MS-based Microbial Characterization:
Challenges, Strategies, and Successes Beyond Species-level and Pure Culture Applications Todd Sandrin Arizona State University
As a faculty member in the School of Mathematical and Natural Sciences, I teach a variety of courses ranging from introductory biology to more advanced courses in microbiology and cell biology. Research in my lab employs unique, often multidisciplinary approaches to mysteries in the natural sciences deemed intractable by strategies relying on approaches from a single discipline. Current research projects use tools in proteomics and functional genomics to: 1) leverage beneficial aspects of microorganisms (e.g., biodegradation/bioremediation of harmful chemical pollutants) and 2) develop technologies to mitigate negative effects of microorganisms (e.g., disease, microbial contamination of water). Most recently, my research group has been particularly active and has placed considerable focus on the development and optimization of rapid, mass spectrometry-enabled approaches to characterize microorganisms at the strain level.
Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) has emerged over the past decade as a rapid and accurate alternative to traditional methods of microbial characterization in the clinical laboratory. Countless successes have been reported with species-level identification of diverse microorganisms of clinical and environmental importance, but challenges have been described in the literature when applying the method at taxonomic levels beyond the species level (e.g., at the strain level). Furthermore, application of MALDI-TOF MS has largely been restricted to pure cultures of bacteria. Our group has developed and optimized strategies to enhance performance of MALDI-TOF MS-based approaches at the strain level and with polymicrobial mixtures. Collectively, our results suggest that maximizing performance beyond the species level and with polymicrobial mixtures requires rigorous optimization and standardization of culture conditions, sample preparation methods, data acquisition modes, and data analysis approaches.
The Skyline Software Ecosystem: Efforts to Achieve Transparency, Interoperability, and Robustness of Quantitative LC-MS Assays Michael MacCoss University of Washington
Skyline is a freely-available and open source Windows client application for building quantitative LC-MS methods and the analysis of the resulting mass spectrometry data. We have applied state-of-the-art technologies for creating methods, iteratively refining assays, and the visualization of quantitative mass spectrometry experiments across all major instrument vendors. Originally Skyline was developed specifically for targeted proteomics but has expanded to support method development and data analysis for all molecular species. There have been over 90,000 new installations of Skyline since it was first publicly released at ASMS 2009, with 1000 installations on average each month over the past 6 months, and consistently >8000 software boot-ups per week for the last year. Additionally, Skyline supports an external tool framework with 13 tools available currently (some with >10,000 installations themselves). The server side project Panorama (http://panoramaweb.org), supports managing Skyline documents (both results and assay validation), sharing data within a group or to the entire community, and real-time collection and tracking of instrument system suitability data. Recent capability like quantitative signal calibration, assay figures of merit, and audit logging have been developed in close collaboration and with iterative feedback with members of the MSACL community. I will present a perspective on the history of the project, provide examples of how these tools have been used in assay development, and provide a vision for the future.
Translation of Lipidomic Technologies Towards Quantification of Blood Lipids Marcus Wenk National University of Singapore
Markus Wenk has been interested in membrane lipids, their structure and function since his undergraduate years at the Biozentrum of the University of Basel. At Yale he introduced and established novel techniques for analysis of phospholipid metabolism. His work resulted in scientific publications which have major impact on conceptual advancements in the field of lipid metabolism. He established SLING, the Singapore lipidomics incubator, an interdisciplinary program dedicated to innovation, education and partnership in lipidomics research. Markus Wenk is Provost’s Chair, Professor and Head of the Department of Biochemistry at the National University of Singapore (NUS).
Singapore Lipidomics Incubator - https://sling.sg/
Department of Biochemistry at NUS - http://bch.nus.edu.sg/
Progress in Lipid Research - https://www.journals.elsevier.com/progress-in-lipid-research/
The main elements of lipidomic technologies are now available and ready for adoption in larger scale studies. However, the translation of laboratory-style methods for lipid measurements – based on mass spectrometry – towards robust, quantitative tests that deliver comparable results across different analytical sites and with appropriate turn-around times will require considerable extra efforts. Here I will introduce our model for engagement which we have been pursuing with the Singapore Lipidomics Incubator (SLING) at the National University of Singapore (NUS). Examples, in the context of natural variation of blood lipids, will be given for translation (i) of such technologies towards individualized lipidomic tracking and (ii) for better mechanistic understanding of lipid function.