Harnessing Metabolomics for Biomarker Discovery in Colon Cancer: - PowerPoint PPT Presentation

Harnessing Metabolomics for Biomarker Discovery in Colon Cancer: Opportunities and Challenges Daniel Raftery UW Medicine Northwest Metabolomics Research Center

Acknowledgements Collaborators Current and Past Members Min Zhang, Purdue Rob Pepin Nagana Gowda Jianjiang Zhu Dabao Zhang, Purdue Danijel Djukovic Chen Chen, Purdue Xinyu Zhang Lisa Bettcher Haiwei Gu Bruce Clurman, FH Natalie Nguyen Leela Paudel Gabi Chiorean, SCCA Hayley Purcell Jiyong Dong, Xiamen Lingli Deng Vadim Pascua Ping Zhang Fausto Carnevale-Neto Wentao Zhu Dan Du Renke Zhang Dongfang Wang Qiang Fei https://nwwashington.edu

Metabolomics in Context Genes Stimuli Genomics Advanced Proteins Analytical Techniques Proteomics Biological Systems O O OH H 2 N OH H OH O H OH O HO HO OH H NH OH H H Metabolites Metabolic Profiling Genotype + Environment --> Phenotype

Understanding Metabolomics Systems Biology  Analysis of small molecules in bio-systems ~20,000 aq & 200,000+ lipids Endogenous metabolites Exogenous metabolites Discovery of Biomarkers  Applications in Metabolomics Disease Diagnostics BioMarkers Sick Companion (Drug) Diagnostics Toxicology Food and Nutrition Regulation Drug Discovery Personalized Medicine Healthy Systems Biology Research Time G. A. N. Gowda, S. Zhang, H. Gu, V. Asiago, N. Shanaiah and D. Raftery, "Metabolomics-Based Methods for Early Disease Diagnostics: A Review," Expert Rev. Mol. Diagnos. , 8: 617-33 (2008)

Brief History  2000 BC Chinese/Greek apocryphal story of ants and urine  1800-1900: Identification of various metabolites  1930 – 50’s Metabolite pathways identified  1950 -1960’s: MS and NMR development  1960’s: First “metabolomics” studies  1970’s: LC and chemometrics development  1980’s: LC-MS and high field NMR development  1998-99: Metabonomics and metabolomics coined  2000’s: Development of statistical methods and databases  Field is expanding rapidly (>1000 papers/year)

Metabolism Metabolism is:  Complex  Interconnected  Influenced by genetics & environment (food, stresses including illness)  Affects upstream biology (gene expression, epigenetics, protein function)

Metabolic Maps

Metabolomics Methods and Applications Human Population Animal Models Cell Lines Analysis of complex biological samples/systems: 1000’s of small molecules Identifying Drug Targets Early Disease Detection & SysBio Translation 1 Oxaloacetate Citrate 0.8 Statistical Modeling Sensitivity Isocitrate Malate TCA 0.6 Biospecimens CYCLE 0.4 2-Oxo-glutarate Fumarate 0.2 Succinate Succinyl-CoA 0 0 0.2 0.4 0.6 0.8 1 Mechanistic Studies: 1-Specificity Unknown Identification Tracing Altered Pathways Metabolite Detection Biomarkers MS or NMR Gowda & Raftery J. Magn. Reson. 2015

Cancer Related Metabolomic Findings Sarcosine found as a strong tissue marker of PC aggressiveness. New findings link genetic defect with metabolic up- regulation of metabolite linked with brain cancer.

Lipid Panel for Alzheimer’s Disease Prediction 10 lipids found to predict AD with 90% accuracy

Biomarker Translation Process Gowda and Raftery, Current Metabolomics, 2013

Metabolomics Related Companies

The Metabolome and Its Measure Metabolome = small molecules <1500 Da Human metabolome: 20,000 aqueous + 200,000 lipid metabolites Dark Metabolome Global Profiling >2000 aq. metabolites Targeted Profiling GOT- 20-300 aq. metabolites MS Quantitative 10-70 aq. metabolites Quantitative Lipidomics: Flux ~1200 lipids, 13 classes

Assays at NW-MRC SOPs Assay Metabolites Targeted Aqueous Assay >300 aqueous metabolites from 60 pathways Global profiling ~2000 MS features, ~400 metabolites Quantitative lipid profiling Up to 1100 lipids from 13 classes Flux analysis TCA, glycolysis, amino acids, fatty acids, etc. Bile acid analysis 55 bile acids Tryptophan pathway analysis 25 Trp metabolites Carnitine analysis ~40 carnitines Cardiolipin assay ~20 cardiolipins Oxylipin assay ~100 signaling lipids Co-enzyme analysis 7 co-enzymes In situ monitoring Time resolved kinetics … …

Major Metabolomics Tools NMR LC-MS GC-MS organic acids amino acids aldehydes amines amino acids ketones fatty acids organic acids other volatiles nucleosides some amines fatty acids lipids glucose amino acids carbohydrates lipid classes steroids Etc. Detected molecules: Detected molecules: Detected molecules: 30-100 ~2000 (500 ID’d) ~300 (150 ID’d)

Targeted LC-MS Analysis Positive >350 metabolite identities verified by standards • Covers 60 KEGG pathways • Hydrophilic interaction chromatography (HILIC) column • Two columns in parallel for high throughput analysis • All metabolites verified with standards • Multiple-reaction-monitoring (MRM) mode • Throughput: 30 study samples per day • CV ~6-8% • Negative

Quantitative Lipidomics New quantitative platform targets over 1100 lipids from 13 lipid classes Measures 700-900 lipids in blood with absolute concentrations: triacylglycerols (TAG) diacylglycerols (DAG) cholesterol esters (CE) free fatty acids (FFA), phosphatidylcholines (PC) phosphatidylethanolamines (PE) lysophosphatidylcholines (LPC) lysophosphatidylethanolamines (LPE) sphingomyelins (SM) ceramides (CER, DCER, LCER, HCER) Performance: Reproducibility: ~5% within batch Accuracy: ~10%

Global Profiling 6 x10 -ESI TIC Scan Frag=120.0V 10.d How do you 9.5 9 Total Ion Current 8.5 characterize 8 7.5 these data? 7 6.5 6 5.5 5 1) #features 4.5 4 2) #compounds 3.5 3 3) Average CV 2.5 2 1.5 4) Number of 1 0.5 metabolite IDs 0 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5 12 12.5 Counts vs. Acquisition Time (min) 5 x10 Cpd 2278: 12.652: -ESI EIC(488.8730) Scan Frag=120.0V 10.d 4.2 4 3.8 3.6 3.4 Human Urine 3.2 3 ESI+ LC-QTOF-MS 2.8 2.6 m/Z 60-1000 2.4 2.2 Agilent 6520 2 1.8 >2200 compounds 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5 12 12.5 Counts vs. Acquisition Time (min)

Known Metabolome Major Challenge:

Tools for Unknown Identification Molecular Networking ClassyFire from UC Davis from UC San Diego Global mass spectral molecular Metabolic annotation of ten urine samples analyzed network of urine samples acquired in by HILIC-(-)-ESI-MS/MS (DDA), according to “direct ESI positive mode. Molecular network parent level” of the ClassyFire ranking system. colored by putative chemical “superclass level” retrieved through the MolNetEnhancer workflow and ClassyFire.

Data Quality Metrics for MS Global Profiling Despite successes and wide usage, global profiling has had  trouble with reproducibility and data quality There are some new efforts to develop standard reference  materials (SRMs) at NIST for example. Xinyu Zhang But better measures of data quality are needed.  Jiyang Dong Towards that end we’re working on a set of 5 Data Quality  Metrics (5 Easy Metrics). Experiment:  50 replicates of a pooled human serum sample  protein precipitated  Run on 2 Agilent QTOF instruments 6520, 6545  ESI+ only  Processed using Progenesis QI  Profile and Centroid data acquired  Feature/compound defined as having a minimum of 2 ions  Goal is to help define a set of consensus measures 

5 Easy Metrics of Data Quality Missing Values TIC Reproducibility Metabolites Detected Compounds detected with two-or-more ions m/Z identified by HMDB 100 8.0x10 8 5000 B% Compound Numbers 80 4000 8 6.0x10 TIC (A.U.) 60 3000 B% 8 4.0x10 40 2000 8 2.0x10 20 1000 ESI (+) 0 0 0.0 0 5 10 15 6545(P) 6545(C) 6520(P) 6520(C) Retention Time (min) CV vs Intensity ICC vs Intensity Useful for comparing and optimizing analyses as well as documenting/publishing

Typical Metabolomics Data Analysis Workflow Global Profiling Data 5 x10 Cpd 2278: 12.652: -ESI EIC(488.8730) Scan Frag=120.0V 10.d 4.2 4 1,000,000 data points 3.8 3.6 3.4 3.2 3 2.8 2.6 2.4 2.2 2 Instrument manufacturer 1.8 1.6 1.4 1.2 or 3 rd party software 1 0.8 0.6 0.4 0.2 0 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5 12 12.5 Counts vs. Acquisition Time (min) MS or NMR data 2,000 features Library of compound spectra 300 Identified metabolites Statistical methods: Feature selection 10-50 statistically different metabolites Control 0.6 Statistical methods: 0.4 GCGCMS PC1 Model building and testing 0.2 Cancer 0.0 -0.2 8000 Statistical model for 6000 -0.4 NMR PC1 4000 2000 -0.6 0 validation 2000 -2000 0 -4000 -2000 NMR PC2 -4000 -6000 -6000

Supervised Multivariate Statistics: PLS-DA PLS-DA is used to fit a model between the spectral data and the class information: Y = b 0 + b 1 X 1 + b 2 X 2 + ... + b p X p Class variables: “Control = 0” and “Case = 1” b i : re regre ression coefficients for r vari riables X 1 thr hroug ugh h X p Training Set of Samples Disease Training set samples Control Test Set of Samples Disease Statistical model Control