Dr Michelle Hill The University of Queensland Diamantina Institute “Turning scientific discoveries into better treatments...”
The University of Queensland Diamantina Institute (UQDI) was established on 1st January 2007 as the sixth research institute of The University of Queensland. The location of UQDI on the Princess Alexandra Hospital campus is key to the institute’s mission, “to translate scientific discoveries into better treatments”. UQDI is now part of Translational Research Institute (TRI) , a state- of-the-art facility housing four research institutes to promote better collaborative innovation. TRI houses 650+ researchers and is the first of it’s kind in Australia, allowing biopharmaceuticals and treatments to be discovered, produced, clinically tested and manufactured in the one location.
Research at UQDI Immune-related diseases Cancer • Rheumatoid arthritis • Skin cancers • Type 1 diabetes • Head & Neck cancer • Infection & immunity • Cancer vaccine & immunotherapy Genomics & Proteomics technology • Susceptibility genes, disease etiology • Biomarkers
Omics in translational biomarker research
Biomarker • Measurable attribute that can be used to indicate or predict physiological status – Blood pressure – Imaging – Metabolite/chemicals (e.g. blood glucose) – Genomic (mutation or expression level) – Protein (e.g. PSA)
Proteomic Proteomic Metabolomic Genomic Metabolomic Imaging Imaging Genomic Pharmacogenomic Simon 2011 EMBO Mol Med 3, 429 Prognostic Theragnostic Predictive
Approved In vitro diagnostics (IVD) Annual PubMed records for ‘diagnostic biomarkers’ (Moschos 2012 Bioanalysis)
In vitro diagnostic (IVD) “reagents, instruments, and Classification based on: systems intended for use in the • Intended use diagnosis of disease or other – what the test measures (biomarkers) conditions, including a • Indications for use determination of the state of – why a patient would be tested health, in order to cure, mitigate, treat or prevent disease...” Approval requires: • Preclinical evaluation (FDA guideline, Title 21 of Code of – demonstrates accurate and Federal Regulations) reproducible measurements • Clinical performance – shows that the device provides the expected results in a defined patient population for intended use Mansfield et al. 2005 J Mol Diag
Bridges over the valley of death: From biomarker to IVD • Clearly defined clinical intended use • Sufficient preliminary evidence from multiple cohorts • Select/develop suitable clinical assays • Design appropriate clinical trial for regulatory approval Multidisciplinary & multi-centre: Clinical – sample collection with controlled standard procedures Technology – establish standard measurement conditions Informatics & Statistics – consistent rigorous analysis Team decision making – when to drop biomarkers Vidal et al. 2012 Clin Proteomics
National Cancer Institute Early Detection Research Network (EDRN) 2008- present delivery of clinically useful biomarkers: 2003-2005 300+ passed phase 2 1998-2000 establish partnerships, (300+ did not) inception & 1450+ publications collaborative projects, inauguration 28+ patents, 14+ licences bioinformatics tools 2001-2003 2006-2008 biomarker development Prospective Specimen paradigm, Collection Retrospective Blinded Evaluation standardize collection and (PRoBE) design for banking of non-invasive phase 2 and 3 biomarker biosamples with validation trials comprehensive clinical data Adapted from Srivastava 2013 Clin Chem
EDRN five phase biomarker development paradigm Number of targets • Preclinical discovery : Distinction between normal and cancer Phase 1 # • Preclinical verification: Reproducibility of markers # • Development of suitable clinical assay: Portability of assay format Phase 2 Number of samples • Preclinical validation: Evaluation of sensitivity & specificity for clinical indication Phase 3 • Clinical evaluation: Estimation of false positive and false negative rates Phase 4 • Disease Control: Evaluation of overall benefits & risks of the test Phase 5
National Cancer Institute Early Detection Research Network (EDRN) 2008- present delivery of clinically useful biomarkers: 2003-2005 300+ passed phase 2 1998-2000 establish partnerships, (300+ did not) inception & 1450+ publications collaborative projects, inauguration 28+ patents, 14+ licences bioinformatics tools 2001-2003 2006-2008 biomarker development Prospective Specimen paradigm, Collection Retrospective Blinded Evaluation standardize collection and (PRoBE) design for banking of non-invasive phase 2 and 3 biomarker biosamples with validation trials comprehensive clinical data Adapted from Srivastava 2013 Clin Chem
EDRN five phase biomarker development paradigm Omics discovery $$$$ Number of targets • Preclinical discovery : Distinction between normal and cancer Phase 1 # • Preclinical verification: Reproducibility of markers # Phase • Development of suitable clinical assay: Portability of assay format 2 Number of samples • Preclinical validation: Evaluation of sensitivity & specificity for clinical Phase indication 3 • Clinical evaluation: Estimation of false positive and false negative Phase rates 4 • Disease Control: Evaluation of overall benefits & risks of the test Phase 5 Clinical assay $
Which Omics for biomarker discovery? Less than ¼ of new molecular in vitro diagnostics approved by US FDA since 1995 use nucleic acid biomarkers Moschos 2012 Bioanalysis 4, 2499
Moschos 2012 Bioanalysis 4, 2499
Companion diagnostic Assay capable of predicting drug dose, efficacy or safety risk of a drug
In vitro diagnostic multivariate index assay (IVDMIA) An IVD that measures 2 or more independent variables in parallel, and a scoring algorithm Manjili et al. 2012 Future Onc 8, 703
Why proteins? • Simple, specific and sensitive assay with antibodies • Proteome but not genome rapidly modulated by disease/treatment : disease detection vs disease risk • Can be actively released or shed from cells Less than ¼ of new molecular in vitro • Body fluid detection desirable over tissues because: diagnostics approved - less invasive, allows repeated sampling by US FDA since 1995 use nucleic acid - reduced sampling error (tumour heterogeneity) biomarkers - ability to detect microenvironmental changes (increase specificity or sensitivity) Moschos 2012 Bioanalysis 4, 2499
Comparative proteomics in blood is challenging! Anderson & Anderson 2002 MCP
Strategies for body fluid protein biomarker discovery • Depletion of abundant proteins • Target sub-proteome – Glycoproteins – Exosome/microvesicles (small circulating vesicles, also contain DNA and RNA)
Glycosylation changes of circulating proteins as biomarker • Glycosylation changes implicated in cancer pathogenesis - Change in glycosylation machinery in the cancer cell - Neo-expression in stromal cells, which has a different profile of glycosyltransferases • Lectins as affinity reagent which binds to specific glycan structures: readily adaptable for clinical assay • Glycosylation changes more specific than changes in protein, e.g. AFP-L3 test for fucosylated form of a -fetoprotein Lectin Abbrev. Ligand moiety Related cancers Liver, lung, breast, colon, Aleuria aurantia lectin AAL Core fucosylation pancreatic, esophageal etc. Helix pomatia agglutinin HPA GalNAc Breast cancer Elderberry lectin SNA α2 -6-linked sialic acid Pancreatic cancer
Lectin-magnetic bead array-coupled mass spectrometry (LeMBA-MS) for glyco-biomarker discovery
GlycoSelect database biomarker selection pipeline for LeMBA-MS Data entry/storage Analysis Lectin-protein pairs 1. Patient selection 2. Normalize to internal standard 3. Sample outlier detection 4. Identify on/off changes using group difference tool 5. Ranking of quantitative changes using sPLS-DA (sparse Partial Least Squares regression- David Chen, Kim-Anh Le Cao Discriminant Analysis, Le Cao et al. 2011 BMC Bioinformatics)
Phase 1 discovery for oesophageal adenocarcinoma (EAC) Goal: Obtain a list of differentially glycosylated serum proteins in oesophageal adenocarcinoma (EAC) by comparing with matched samples from the pre-cancer condition Barrett’s oesophagus (BE) and controls. Discovery cohort Multidisciplinary team PhD student ( Alok Shah) Epidemiologist & biological samples (David Whiteman, Australian Cancer Study, PROBE-NET) Oncology surgeon (Andrew Barbour) Informatics (David Chen) Biostatistics (Kim-Anh Le Cao) Nanotechnology for diagnostic device (Matt Trau)
Phase 1 discovery LeMBA-MS/MS outcomes
Number of targets • Preclinical discovery : Distinction between normal and cancer Phase 1 # • Preclinical verification: Reproducibility of markers # Phase • Development of suitable clinical assay: Portability of assay format 2 Number of samples • Preclinical validation: Evaluation of sensitivity & specificity for clinical Phase indication 3 • Clinical evaluation: Estimation of false positive and false negative Phase rates 4 • Disease Control: Evaluation of overall benefits & risks of the test Phase 5
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