Clinical POLARIS:

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Clinical metabolomics @ POLARIS: Exploring the potential of small molecule biomarkers in

1 Clinical POLARIS: Exploring the potential of small molecule biomarkers in disease diagnostics Ho Ying Swan Group Leader, Metabolomics Bioprocessing Technology Institute

2 Introduction to Metabolomics genes: Genomics Genotype mrna: Transcriptomics proteins: Proteomics metabolites: Metabolomics Phenotype Adapted from Dunn et al., Metabolic profiling, U. of Manchester. (2006)

3 Introduction to Metabolomics Metabolite biomarkers as diagnostic tools Blood glucose levels: Test for diabetes Amino acid derivatives, organic acids, acylcarnitines: Presence of inborn errors of metabolism Article from The Straits Times, 9 Nov 2007

Metabolomics technology development @ POLARIS Vision: To develop a comprehensive workflow for the robust and reproducible measurement

profiling platform for biofluid samples Development of customised sample preparation and LC MS methods for individual metabolite

4 Metabolomics technology POLARIS Vision: To develop a comprehensive workflow for the robust and reproducible measurement of potential metabolite biomarkers in patient biofluid samples related to specific diseases Approach: Development of global metabolite profiling platform for biofluid samples Development of customised sample preparation and LC MS methods for individual metabolite groups BTI: UHPLC-HRMS system High resolution (global screening) Large dynamic range (quantification for customized LC-MS assays)

5 Development of global metabolite profiling platform Global method development for biofluid samples Main consideration: To minimise sample volume required for analysis 1. Sample preparation Optimisation Two phase liquid of metabolite extraction extraction (methanol, conditions water, chloroform) (Model biofluid: Serum) Simultaneous extraction of polar and lipid metabolites Key factors: Solvent type and composition 2. Sample analysis (LC MS) Optimisation HRMS system of with LC MS UHPLC analysis technology conditions Key factors: High resolution LC flow rate with & gradient, better separation MS parameters 50% reduction in sample volume required Limit of detection: moles Small scale (proof of concept) study to evaluate global profiling method Global metabolite profiling of lung pleural effusions (Dr Tony Lim, Dr Daniel Tan)

6 Background: Small scale study of lung pleural effusions Lung cancer: Leading cause of cancer death worldwide 1 with poor prognosis 2 (16.6% 5 yr survival rate) Lung pleural effusions: Estimated in 7 30% of all lung cancer cases 3 Current lung pleural effusions assessed by cytology test accuracy highly dependent on presence of sufficient cells in sample Scope for identifying additional candidate metabolite markers independent of cell availability in samples increase accuracy of diagnostic test Workflow: Small scale study comprising of benign (B) (5) and malignant (M) (5) pleural effusion samples B M Metabolomics platform Assessment of key metabolic differences MVA (PCA) Student s t test 1 WHO Cancer Fact Sheet for Eleftheriou et al., J Thorac Dis, 4:AB28, SEER Cancer Statistics Review (CSR)

Unsupervised multivariate analysis (MVA): PCA PCA plot of benign and malignant pleural effusion samples PCA: Used to visualise similarities and differences between samples QC and replicate sample

7 Unsupervised multivariate analysis (MVA): PCA PCA plot of benign and malignant pleural effusion samples PCA: Used to visualise similarities and differences between samples QC and replicate sample runs tightly clustered Confirmed good quality LC MS data obtained Distinction between B and M samples visible Implies significant differences exist in metabolite profiles of B and M samples Scope for further analysis to identify key differential metabolites > 100 components found to be elevated in M samples (p value < 0.05) Proceed to larger scale study to identify potential differentiators for B and M

8 Global metabolite profiling study of lung pleural effusions Objective: To identify potential differentiators between benign and malignant pleural effusions Involved total of 40 benign (inflammation) and malignant (lung adenocarcinoma) samples Distinction between B and M samples still visible QC and sample replicates tightly clustered good quality LC MS data Trends consistent with small scale study Next step: To ID individual components significantly different between B and M Use of supervised MVA: OPLS DA

largest influence on segregation of samples VIP plot of most influential components Influence of

9 Supervised MVA: OPLS DA OPLS DA model generated based on class information of samples (ie. B or M) Maximises separation of B and M samples Allows for ID of individual components with largest influence on segregation of samples VIP plot of most influential components Influence of each component represented by VIP value Uni variate statistical analysis also applied to supplement VIP values

10 Shortlist criteria for differential metabolites Shortlist criteria 1,2 for potential differentiators between B and M samples VIP > 1 p value < 0.05 (Student s t test) Avg ratio between B and M > 1.5 More than 50 components found to be significantly elevated in M samples Examples of shortlisted potential differentiators: p value = 2.0e 11 M/B = 3.8 p value = 5.2e 14 M/B = 2.0 Fatty acid 1 Phosphatidylcholine (PC) 1 1 Chong et al. (2005) Chemometr. Intell. Lab. 78: Robinette et al. (2009) Anal. Chem. 81: 6581.

11 Identification of differential metabolites 3 key lipid classes identified among shortlisted metabolites Includes compounds previously associated with different cancers: Metabolite species Fatty acids Acylglyerols Glycerophospholipids Observations from literature Elevation of specific fatty acid species in blood associated with prostate and breast cancers Elevation of triglycerols in blood associated with colon, kidney, thyroid and skin cancers Elevation of specific species of PC in colon and thyroid cancer tissues Linked to fatty acid synthase overexpression for cellular redox regulation 1 Alternative energy source through fatty acid oxidation 2 Linked to stimulation of cancer cell growth 3 1 Zaidi et al. (2013) Prog. Lipid Res. 52: Carracedo et al. (2013) Nat. Rev. Cancer. 13: Kurabe et al. (2013) Cancer Sci. 104: 1295.

12 Conclusions from full scale study More than 50 potential metabolite differentiators between B and M pleural effusions shortlisted based on full scale study 3 key lipid classes identified Fatty acids, acylglycerols, glycerophospholipids Associated with previously reported metabolic phenomenon in cancer cells Next step: To validate shortlisted metabolites with additional set of samples

Summary of metabolomics workflow Small scale study ( 10 20 samples) Exploratory stage To investigate whether potential differences

between conditions under study Validation I study ( 40 60 samples) 1 st validation stage To confirm validity of shortlisted

13 Summary of metabolomics workflow Small scale study ( samples) Exploratory stage To investigate whether potential differences exist between conditions under study Full scale study ( samples) Global screening stage To identify potential differentiators between conditions under study Validation I study ( samples) 1 st validation stage To confirm validity of shortlisted components derived from global screening stage Development of customised assays for candidate metabolite markers Large-scale validation studies

Ling Tan Gek San Pancreatic cancer Dr Damien Tan Dr Mac Ho Ong Zi

14 Acknowledgements POLARIS programme leads Lung cancer Prof Lam Kong Peng Dr Patrick Tan Dr Tony Lim Dr Chris Wong Dr Daniel Tan Lim Kah Ling Tan Gek San Pancreatic cancer Dr Damien Tan Dr Mac Ho Ong Zi Ling team Dr Teo Chin Chye Eddy Tan Nurhidayah Binte Basri

BIOCRATES Life Sciences AG

BIOCRATES Life Sciences AG The Deep Phenotyping Company European Business Development Conference BIO-Deutschland, 23.-24.09.2013 Dr. Wulf Fischer-Knuppertz CEO 1 The Company Targeted Metabolomics Founded