A BETTER APPROACH THAN EYEBALLING? OBJECTIVE ASSESSMENT OF CHRONIC WOUNDS WITH BIOMARKERS - LEE Sze Han
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A BETTER APPROACH THAN EYEBALLING? OBJECTIVE ASSESSMENT OF CHRONIC WOUNDS WITH BIOMARKERS LEE Sze Han Research Fellow Skin Research Institute of Singapore 5th March 2021 2:00pm - 2:30pm SG Time
Deciphering the Enigma of Chronic Wound Healing Clinical problems: 1. Management of chronic wounds is challenging due to poorly-defined features of healing 2. Clinical translation of prognostic tools is hampered by lack of established and validated biomarkers Objectives: 1. To differentiate healing and non-healing wounds, by Multiple wound etiologies (diabetic using OMICS tools to examine proteins, small foot ulcers, pressure ulcers, etc) on a patient’s foot1 molecules, and microbes in wound fluid 2. Validate biomarkers in independent cohort to ascertain diagnostic performance 2 1. Nunan, Robert, Keith G. Harding, and Paul Martin. Disease models & mechanisms 7.11 (2014): 1205-1213.
Wound Care Innovation for the Tropics (WCIT) – A Singapore Initiative Asian-centric wound study • Singapore lies in the tropics, where nature of wound likely differs from non-tropical regions • Collection of sequential wound clinical samples with matched clinical data across all 3 integrated healthcare clusters in Singapore • Key pillars under WCIT • Enabling technologies Economics • Tools, Devices, & Therapies Interlinked Epidemiolog • Clinical application Independent y Education 3
Sub-programme under WCIT Multi-OMICS biomarkers of chronic wounds Wound parameters (e.g. area) Biological markers (e.g. proteins, metabolites) Abundance Abundance of X of X Time (weeks) Time (weeks) Abundance 85 Smaller set of biomarkers of X without compromising accuracy 80 %Accuracy 75 70 Time (weeks) 65 0 2 4 6 8 10 No. of biomarkers in panel Discovery/training set • Tan Tock Seng Hospital (projected n= 250) • National University Hospital • St Luke’s Hospital Validation set • Khoo Teck Puat Hospital 4 (projected n= 200) • Singapore General Hospital
Study design Comprehensive Longitudinal sampling for data collection temporal analysis Multiple etiologies Wound size (DFU, VLU, etc) photo Client Service Protein 1 EQ-5D (QALY) Receipt Inventory Protein 2 . Billing Paper strip . Information (for metabolites) . Protein n Swab, Levine Swab, Z-stroke (for proteins) (for microbes) Blood (1-time, Others (e.g. dressing, Week 12 drug usage) Week 1 Week 2 optional) Follow up at Week 24 . . . DFU: Diabetic foot ulcers, VLU: Venous leg ulcers, 5 EQ-5D: standardized instrument for generic health, QALY: quality-adjusted life years
Wound area measurement Conventional • Wound area traced and verified by clinicians Conventional area calculation with Wound Mapping Grid. Illustration from 3M™ • Phenotypically classified into healing or non-healing based Wk 1 2 4 6 8 10 12 on area reduction trajectory Digital area calculation with ImageJ currently used in the study % area change from max (%) % area change from max (%) 100 100 50 50 0 0 0 5 10 15 0 5 10 15 Time (week) Time (week) Healing Non-healing Silhouette® wound imaging employed at St Luke’s Hospital 6
Multi-OMICS biomarkers of chronic wounds PROTEOMICS Screen for protein biomarkers prognostic of healing LC-Q-OrbitrapMS (Dr Radoslaw Sobota, IMCB) • DIA (SWATH®) analysis of ~3000 proteins Query proteomics against Correlation of protein and max (%) 100 microbial databases to assess metabolite networks provide 34 from area 35 microbial functional mechanistic insight into perturbed changewound 36 phenotypes 50 pathways 37 in chronic wounds Percentage 42 49 % area 52 0 0 5 10 15 Time (week) Non-healing and healing wounds Illumina sequencing showing different healing rates LC-TQ-MS (NovogeneAIT, Singapore) (inhouse, SRIS) • WGS analysis of • Targeted panel of ~500 microbes from wound Elucidate the crosstalk metabolites related to skin fluid between host and microbial and wounds communities METAGENOMICS METABOLOMICS Discover role of wound Understand wound biology through microbes small molecules/metabolites 7 SWATH: Sequential Window Acquisition of All Theoretical Mass Spectra , WGS: Whole-genome sequencing
Multi-OMICS biomarkers of chronic wounds PROTEOMICS max (%) 100 34 from area 35 changewound 36 50 37 Percentage 42 49 % area 52 0 0 5 10 15 Time (week) Non-healing and healing wounds showing different healing rates METAGENOMICS METABOLOMICS 8
Acquisition and Analysis Strategy Holistic profiling of the proteome • Protein > Peptides > Fragments Site-specific Reproducible digestion fragmentation • Our methodology yielded >3000 proteins in our study (DDA and DIA workflows) • Abundances of proteins between samples were then compared Healing Protein variance Data analysis with focus on temporal trends Healing (in both healing or non-healing patients) • Difference is hypothesized to be between Non- healing Non- healing earlier and later timepoints LC-MS: Liquid Chromatography - Mass spectrometry DDA: Data-dependent acquisition, DIA: Data-independent acquisition Collection period over 12 weeks 9 Illustration adapted from https://www.creative-proteomics.com/services/digestion-in-gel-or-in-solution-2.htm
Preliminary proteomics (n=30 subjects, 233 time-points) showed different profiles between healing and non-healing VLU wounds, and yielded potential predictive biomarkers Rmcorr1 statistics showed 143 longitudinally Classification model identified 9 predictive significant proteins between healing and non- biomarkers of healing, with high selectivity healing wounds towards non-healing wounds (SG patent filed) Protein A (regulates several aspects of the innate immune system) Healing Non-healing 10.0 AUROC= 0.887 10.0 9.5 P02743.SAMP_HUMAN 9.5 P02743.SAMP_HUMAN Accuracy vs Time from Accuracy vs No. of first presentation biomarkers 9.0 Smaller set of biomarkers 85 without compromising accuracy 9.0 80 %Accuracy 8.5 75 8.5 8.0 70 2 4 6 8 10 12 2 4 6 8 10 12 65 Time (weeks) Time (weeks) 0 2 4 6 8 10 10 1. Bakdash, Jonathan Z., and Laura R. Marusich. "Repeated measures correlation." No. of biomarkers in panel Frontiers in psychology 8 (2017): 456.
Preliminary proteomics findings are relevant to wound healing Biological relevance Therapeutic relevance Pathway analysis revealed perturbation of Top ranked proteins by our workflow and their wound healing processes (e.g. Neutrophil role in wound healing are supported by literature degranulation, Innate Immune System, Antimicrobial peptides) Protein A (detrimental to healing) – acute phase reactant protein – shown to inhibit dermal wound healing – a Protein X-binding hydrogel was able to speed up healing of partial thickness wound in pigs Protein B (beneficial to healing) – topical application of Protein Y promoted wound closure in a diabetic mouse model – selective inhibition with anti-Protein Y monoclonal antibody disrupted normal wound closure Protein C (beneficial to healing) – antimicrobial peptide with anti-protease activity – promotes wound healing – incorporated into biomaterials for treatment of chronic tissue ulcers 11 Protein identities and references redacted for confidentiality
Multi-OMICS biomarkers of chronic wounds PROTEOMICS max (%) 100 34 from area 35 changewound 36 50 37 Percentage 42 49 % area 52 0 0 5 10 15 Time (week) Non-healing and healing wounds showing different healing rates METAGENOMICS METABOLOMICS 12
Acquisition and Analysis Strategy Targeted profiling of the metabolome • Metabolite > Fragments • Metabolite identities were confirmed with pure commercial standards. • Current panel contains 160 metabolites; will be expanded to 500 by the end of the year • Abundance of metabolites between samples Healing were then compared Omic variance Healing Data analysis with focus on temporal trends (in both healing or non-healing patients) Non- Non- healing healing • Difference is hypothesized to be between earlier and later timepoints Collection period over 12 weeks 13 Illustration adapted from https://www.x-mol.com/paper/5739859
Preliminary metabolomics (n=19 subjects, 121 time-points) revealed analytical robustness and dominance of temporal trends PCA score plot of Healing and Non-Healing WF with pooled QC Analytical robustness • Clustering of quality control samples (pooled samples) show robustness of method across the 2-week analysis period Supervised analysis is able to separate PLS-DA score plot of Healing WF first and last 3 visits healing and non-healing subjects • Metabolites driving this separation was obtained for further evaluation 14 PLS-DA: Partial least squares-discriminant analysis
Temporal metabolite changes correlated to time in healing groups Metabolite A (nucleoside postulated to be involved in healing) Rmcorr1 captures the common correlation between metabolite and Healing Non-healing 1.0 time 0.6 0.8 • Common correlation can be 0.6 Uridine Uridine 0.4 compared between healing and non- 0.4 0.2 healing groups for each metabolite 0.2 0.0 0.0 • Allows large set of metabolites to be 2 4 6 8 10 12 2 4 6 8 10 12 Time (weeks) Time (weeks) “screened” for temporal changes A panel of 21 metabolites were temporally different in healing patients NH NH H H 15 1. Bakdash, Jonathan Z., and Laura R. Marusich. "Repeated measures correlation." Frontiers in psychology 8 (2017): 456. Metabolites’ identities and references redacted for confidentiality
Next steps: Pan-omics integration Greater prediction accuracy More accurate prediction from Receiver operating characteristic curves. Higher multi-OMICS integration area denotes higher sensitivity and selectivity. Data for • Different OMICS contribute varying illustration1 degree of marginal improvement • AI-driven analytics may further reduce noise and refine the model Metabolomics Greater biological understanding from Proteomics Greater biological multi-OMICS integration understanding Circos plot showing • Correlations (e.g. Circos plot) and correlations across omics. pathway analysis to yield clearer Data for illustration2 picture of the system under study Metagenomics 16 1. Zhou, Wenyu, et al. "Longitudinal multi-omics of host–microbe dynamics in prediabetes." Nature 569.7758 (2019): 663. 2. Yang, Zi-Yi, et al. "MSPL: Multimodal Self-Paced Learning for Multi-Omics Feature Selection and Data Integration." IEEE Access 7 (2019): 170513-170524.
Objective wound assessment as summative efforts of various components WOUND MEASUREMENT Monitor area via conventional or innovative techniques MULTI-OMICS CLINICAL DOCUMENTATION Understand wound biology Keep track of signs of infection, through small biological wound appearances, risk molecules, and generating factors/comorbidities predictor biomarkers Flanagan Components of Wound Assessment (2003) 17
Acknowledgements 18
THANK YOU www.a-star.edu.sg LEE Sze Han Research Fellow Skin Research Institute of Singapore lee_sze_han@sris.a-star.edu.sg
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