Clinical Applications: Alternative Approaches to Cancer Proteomics

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1 Clinical Applications: Alternative Approaches to Cancer Proteomics Michelle M Hill 1,2* 1 The University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, Australia 2 QIMR Berghofer Medical Research Institute, Brisbane, Australia

2 o Laser-capture microdissection coupled to LC-MS/MS o Early detection with surrogate blood markers targeting subproteomes

3 Multidisciplinary Diagnosis of Amyloidosis A. Detailed clinical evaluation - Clinical symptoms - Associated diseases - Family history B. Current pathology assay 1. Congo Red 2. Immunohistochemistry Princess Alexandra Hospital & Pathology Queensland UQ Diamantina Institute, Translational Research Institute C. Proteomics assay LCM-MS/MS

4 Amyloidosis Group of 20+ diseases characterised by protein aggregates (amyloid fibrils) Treatment depends on underlying cause, identifying the amyloid forming protein Diagnosis AL Amyloid forming protein Immunoglobulin light chain Cause Myeloma or MGUS (Monoclonal gammopathy of undetermined significance) Treatment Chemotherapy, bone marrow transplant AA Serum amyloid A Chronic inflammation and arthritis Treat inflammation and arthritis AB2M Beta 2 microglobulin Kidney failure Kidney transplantation

5 Current Pathology Assays 1. Congo Red To confirm amyloid deposits in tissue Congo red molecules have high affinity for amyloid fibrils Apple green birefringence under polarised light 2. Immunohistochemistry Each amyloidosis subtype need a specific antibody for diagnosis Need >20 different antibody to test each case Associated with high levels of misdiagnosis Antibodies recognises protein structures Antibodies do not bind misfolded protein well Serum Amyloid A deposit in Kidney

6 Proteomics assay LCM-MS/MS Developed by Mayo Clinic (United States) 1. Laser Capture Microdissection (LCM) Extract amyloid plaques from tissue 2. Tandem Mass Spectrometry (MS/MS) Direct identification of amyloid plaque proteins

7 Congo Red stain Laser Capture Microdissection LC-MS/MS Relatively pure protein sample Trypsin digest, processing LC-MS/MS Protein identification Interpretation 4 hours 24 hours 1 hour 1 hour

8 Results ( ) 138 cases referred to Princess Alexandra Amyloidosis Centre 35 different organ sites (heart = 24, kidney=17, colon=8, small bowel =7) LCM-MS/MS attempted on 131 cases - 7 had no tissue left in block Immunohistochemistry attempted on 88 cases 92% success rate (121/131) - 15 needed repeated LCM-MS/MS, no amyloid forming protein identified in 3 cases 44% success rate (39/88) - 6% false identification (5/88) compared to LC-MS/MS results Clinical diagnosis of amyloid subtype altered by proteomics in 24% (31) of cases

9 Future work o Reduce processing time - Automation - MALDI-imaging? o Interpretation and reporting o Accreditation & implementation to policy

10 Strategies for body fluid protein biomarker discovery Depletion of abundant proteins Target sub-proteome based on tumour biology Glycoproteins Exosome/microvesicles

11 Glycosylation in cancer

12 Pinho & Reis 2015 Nature Rev Cancer 15:540

13 Thaysen-Anderson 2016 Mol Cell Proteomics. 15(6):

14 Zhang et al 2014 Clin Proteomics 11:18 aborator

15 Introduction Lectins as affinity agents for glycoprotein enrichment Naturally occurring glycan binding proteins used extensively in glycobiology. E.g. lectin histochemistry, lectin blotting, lectin-affinity chromatography-ms Lectin Abbrev. Ligand moiety Related cancers Aleuria aurantia lectin AAL Core fucosylation Liver, lung, breast, colon, pancreatic, esophageal etc. Helix pomatia agglutinin HPA GalNAc Breast cancer Elderberry lectin SNA α2-6-linked sialic acid Pancreatic cancer Maackia amurensis agglutinin MAA α 2-3-linked sialic acid Prostate cancer

16 Using lectin binding to monitor differential glycosylation Glycosylation changes can more specific than changes in protein, e.g. AFP-L3% test measures fraction of fucosylated a-fetoprotein Wako Diagnostics μtaswako i30 Automated Platform microfluidic-based clinical immunoanalyzer

17 Immunoassay Lectin Immunoassay Nanoparticle lectin Immunoassay

18 Screen 16 lectins against CA125 from (1) ovarian cancer cell line, (2) placenta (3) liver cirrhosis ascites Identify best signal:noise ratio MGL, binds terminal a, b-galnac Test performance in sera from (1) epithelial ovarian cancer, (2) benign endometriosis, (3) healthy control

19 Discrimination of epithelial ovarian cancer from benign endometriosis CA125 CA125-MGL

20 Lectin magnetic bead array-coupled mass spectrometry biomarker pipeline LC-MS/MS of nonglycosylated peptides - Shotgun approach for discovery - Multiple reaction monitoring (MRM) assay for validation (Shah et al Mol Cell Proteomics 14: , Choi et al Electrophoresis 32: ) Single-step serum glycoprotein isolation using a panel of 20 individual lectin-coated magnetic beads Optimal balance of salts and detergents to avoid co-isolation of protein complexes Liquid handler assisted automation to offer high throughput screening On-bead trypsin digest with nano LC-MS/MS and lectin exclusion list to provide nanomolar sensitivity Bioinformatic and biostatistical analysis methods

21 Esophageal adenocarcinoma (EAC) Incidence rapidly rising over last 40 years, overtaking esophageal squamous cancer. Linked to reflux disease and Barrett s esophagus (BE) Current diagnosis by endoscopy with biopsy. Most patients are diagnosed at late incurable stages of EAC. Screening program unsuccessful due to low BE to EAC conversion rate and high cost of endoscopy with biopsy. Spechler et al. 2014

22 Glycosylation changes occur during EAC development Healthy, BE, dysplastic and EAC tissue expresses different glycan structures hence show differential lectin binding Serum glycan profile significantly differ between patients and healthy controls Differential abundance of serum glycopeptides between disease free, high grade dysplasia and EAC (Song et al., J Proteome Res 2014) Alteration in glycoforms of serum glycoproteins (Poorkhalkali et al., Anat Embryol 1999; Neumann et al., Digestion 2008; Bird-Lieberman et al., Nature Medicine 2012; Bird-Lieberman et al., Gastroenterology 2012) (Mechref et al., J Proteome Res 2009; Mitra et al., Anal Chem 2012; Gaye et al., J Proteome Res 2012;) (Shah et al., Mol Cell Prot 2015)

23 Number of targets National Cancer Institute Early Detection Research Network five phase biomarker development paradigm 1 Preclinical discovery Shotgun proteomics 2 Preclinical verification # # Targeted proteomics Preclinical validation Clinical evaluation Disease Control Number of samples Targeted proteomics or Immunoassay Immunoassay

24 Biomarker discovery 29 samples ( 9 Healthy,10 BE, and 10 EAC) Australian Cancer Study, Study of Digestive Health 20 lectins List of candidates for validation

25 Outlier detection in GlycoSelector

26 246 candidates Biomarker discovery N=29 (9 Healthy, 10 BE, 10 EAC) Nano-HPLC-QTOF 20 lectins Biomarker verification N=60 (20 Healthy, 20 BE, 20 EAC) HPLC-QQQ 6 lectins

27 Linearity & Reproducibility of MRM-MS assay 41 protein candidates and 6 lectins (AAL, EPHA, JAC, NPL, PSA and WGA) were selected for validation 2-5 unique peptides per protein (149 peptides) 3 transitions per peptide (450 transitions) Linearity Reproducibility

28 Linearity of LeMBA pull-down

29 Biomarker Qualification 41 protein x 6 lectin = 246 markers measured by LeMBA-MRM. 45 markers show Kruskal-Wallis p value < 0.05 in validation cohort (18.3%) 26 markers also show Area under Receiver Operating Characteristic Curve (AUROC) >0.7 (10.6%)

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31 Complement system expression in oesophageal adenocarcinoma

32 Independent validation study (manuscript in preparation) LeMBA-MRM using 4 selected lectins. Mass spec runs over 3 months. Multimarker panel for surveillance PROBE-NET cohort (n=242) Ochsner cohort (n=49) AUROC Sensitivity Specificity Sensitivity Specificity 8-marker panel % 69.5% 61.1% 64.7% 10- marker panel % 85.3% 61.1% 70.6%

33 Summary & Future Direction o Top candidates validate in larger, independent cohorts o Personalised normal ranges based on 9 progressor longitudinal samples from PROBE-NET o Developed multimarker panel for surveillance Longitudinal samples of progressors Multiplex lectin immunoassay

34 Acknowledgements Peter Mollee (PAH), Samuel Boros, Patricia Renaut (Pathology Qld) Kim-Anh Le Cao (biostatistics) PROBE-NET: David Whiteman (QIMRB), Wayne Philips (Peter Mac), Andrew Barbour (UQ), Reg Lord (St Vincents) Virendra Joshi (Ochsner Health, USA)