Regular or Decaf? Options for Quantitative Biomarker Discovery Mary F Lopez Director, BRIMS MSACL San Diego, Feb 8, 2011

A Tale of Two Discoveries: Unbiased Targeted Metabolic Pathway Targeted, multiplexed SRM Rank by ROC, Ratio Quantitative Differential analysis Clinical samples Unbiased, quantitative LC-MS/MS Global differential analysis List of biomarkers Rank by ROC, Ratio Clinical samples

Collaboration with Dr. MingMing Ning, Mass General Hospital and Harvard University Questions we asked: 1. What proteins may be involved in PFO related strokes? 2. What proteins may differentiate ischemic from hemorrhagic strokes? Discovery and verification of cardiovascular and stroke biomarkers in blood

Unbiased Discovery of PFO related stroke biomarkers using a 2-Pass workflow Unbiased

Atrial septum The prevalence of PFOs in the general population is around 25%, but it is doubled in cryptogenic (unknown cause) stroke patients. These patients are often young and “healthy”. If there is a clot traveling into the right side of the heart, it can cross the PFO, enter the left atrium, and travel out of the heart and to the brain causing a stroke. This suggests a causal relationship between PFO and cryptogenic stroke. Supported by NIH/NINDS (Dr Tom Jacobs), MGH Cardio- Neurology Division evaluates patients with PFO related stroke and the therapeutic efficacy of surgical PFO closure and other stroke treatment. Venous blood samples from stroke patients are taken before (upon admission) and at12 month follow up after PFO closure. Biomarkers for PFO-related stroke could be clinically useful. Number of patients Sample typePatient 5PFO pre OPStroke 8Patient matched PFO post OPStroke PFO and Stroke When the atrial septum does not close properly, it is called a patent foramen ovale or PFO.

Design and Optimization Robust, commercially available nanoflow LC Commercially available columns Focus on stable spray Focus on high reproducibility of peak intensities, CV<8% Pass 1: Quantification Chromatographic alignment Uncompromised full scan measurements Each sample is measured once – no need for replicates Internal peptide standards (normalization) Triplicate runs of peptide standards every 12 runs (instrument QC) “Top10” data dependent acquisition Stringent Precursor ion selection criteria Pass 2: Identification Targeted fragmentation by Inclusion list Relaxed Precursor ion selection criteria Not all samples measured – subset as determined from SIEVE analysis Internal peptide standards Marker stratification using multi marker and single ROC AUC (SIEVE 1.3) Export to Ingenuity pathway analysis methods Inclusion list Unbiased Discovery using LC-MS/MS requires a new approach to make it quantitative

Strategy for label-free LC-MS/MS differential expression analysis

LC setup for Two-Pass Workflow Controlled trapping flow rates ensure consistent sample retention and salt removal. Rapid column equilibration allows for enhanced duty cycle. Hydrophobicity differences from trapping column to resolving column allow for effective refocusing. Larger resolving column allows for higher capacity, and rapid application of gradient to the column(flow rates to 1.0uL/min) Waste tubing HV in(from source) 5cm trap column 25cm resolving column From pump/ autosampler To Orbitrap Velos

Data Quality – Spray Stability March 30 April 4 Spray stability is the largest factor in reproducible measurements. * Get it on BRIMS *

Data Quality – Peak Shape

Data Reproducibility - CV Aligned data

Monitor Lab Environment BRIMS Lab Weather Temperature, Humidity, Power Temperature, Humidity, Power

Method for Assessing Systematic Errors without Sample Technical Replicates Systematic errors are assessed from triplicate acquisitions of standard sample. Internal standards are spiked in all samples. Systematic errors are assessed from triplicate acquisitions of standard sample. Internal standards are spiked in all samples. Blank Standard s Calibration Top 10 Fragmen ta-tion Sample Full Scan Blank run Standards calibration Column regeneration – top 10 Patient samples – full scan only Pass 1 Acquisition cycle

The Two- Pass workflow increases sensitivity by effectively fractionating samples in silico Typical MS acquisition parameters are not geared for quantification. Data dependent acquisition triggers MS2 based on intensity so most low abundance biomarkers are not identified in complex mixtures with large dynamic range ie blood. Classical “shotgun” approaches focus on physical sample fractionation strategies such as depletion and cation exchange coupled with data dependent acquisition. Physical fractionation such as depletion and cation exchange results in loss of albumin binding proteins and multiple runs for each sample. These approaches are very labor intensive, time consuming and typically do not allow for rigorous quantification and statistical power because fewer samples are analyzed due to time and instrument constraints. The Two-Pass Workflow using Inclusion Lists optimizes parameters for full scan quantification and MS2 triggering separately. This results in: Higher sensitivity and getting deeper into the proteome, ie more ID’s Precise and reproducible quantification Flexibility in creating the inclusion list based upon desired attributes such as differential expression, PTM’s or other parameters. Reducing the number of replicates needed since LC reproducibility and %CV’s are so low (ca 8%) Increases the biological sampling power (can run more samples in a shorter time). Decreases the circular biomarker identification syndrome, ie we identified Albumin AGAIN.

Quantitative Statistics for the Two-Pass Workflow unique peptides Data dependent “Top 10” Inclusion list 1 Inclusion list 2 Inclusion list 3 Data Dependent “Top 10” vs Inclusion list Ion Score vs Concentration of Spiked Standard Peptide in Plasma

Experiment for the PFO stroke study Sample groups were identified in SIEVE at the beginning of the analysis Number of patients Sample typePatient 5PFO pre OPStroke 8Patient matched PFO post OPStroke

Data demonstrated high reproducibility and robustness of measurements Reconstructed ion chromatogram of an example frame (not differentially expressed) Whisker plot of expression ratios for all 13 peptides identified for protein gi Gray area represents 90% confidence interval for expected protein ratio 3575 unique peptides and 263 proteins were identified in the study with high confidence 128 were differentially expressed (determined by ratio)

ROC* analysis: How can we quickly rank the potential “usefulness” of putative biomarkers for clinical research? Why? Expression ratio and Pvalue may not necessarily be specific to the pathology. How can we query the data and test the classification power of the target analytes? Create ROC curves by plotting false positives vs true positives while adjusting the criteria threshold. The area under the curve, AUC is a measurement of classification power. Use AUC to select optimal candidates and discard suboptimal candidates. AUC values range from 0.5 to 1.0. An AUC of 1.0 indicates a specificity and sensitivity of 100%. We have developed a multi marker ROC algorithm that calculates errors *Receiver Operating Characteristic (a classification model) Specificity Sensitivity

ROC Station* algorithm calculates AUC Single and multiple markers Calculates errors Simulates data * Get it on BRIMS

Top 21 single proteins with highest ROC AUC for PFO Stroke Study * Ratio = PRE OP/POST OP

Top 2 ROC AUC candidates, selected literature references Clin Chim Acta Apr;402(1-2): Inter-alpha-trypsin inhibitor heavy chain 4 is a novel marker of acute ischemic stroke. Kashyap RSKashyap RS, Nayak AR, Deshpande PS, Kabra D, Purohit HJ, Taori GM, Daginawala HF.Nayak ARDeshpande PSKabra DPurohit HJTaori GMDaginawala HF Biochemistry Research Laboratory, Central India Institute of Medical Sciences, 88/2 Bajaj Nagar Nagpur-10, India. Stroke Jul;38(7): Epub 2007 May 24. Prothrombotic mutations as risk factors for cryptogenic ischemic cerebrovascular events in young subjects with patent foramen ovale. Botto NBotto N, Spadoni I, Giusti S, Ait-Ali L, Sicari R, Andreassi MG.Spadoni IGiusti SAit-Ali LSicari RAndreassi MG CNR Institute of Clinical Physiology, G. Pasquinucci Hospital, Massa, Italy.

Biological context? Ingenuity Pathways Analysis (IPA) Top networkLipid Metabolism Top physiological system development and function Neurological Disease Top diseaseHematological system Top Canonical pathways Acute phase signaling Coagulation system Complement system Intrinsic Prothrombin Pathway Extrinsic Prothrombin Pathway The entire PFO stroke dataset was uploaded and analyzed with IPA

Targeted Targeted discovery of stroke biomarkers using a multiplexed assay for a panel of apolipoproteins

Number of patients Blood Collection timesSample type 53Upon admissionIschemic Stroke 26Upon admissionHemorrhagic stroke Apolipoproteins and stroke  The relative levels of various apolipoproteins can be important biomarkers for heart disease, stroke, Alzheimer’s, diabetes and metabolic syndrome.  Typically, these proteins are individually measured in blood by immunoassay.  The availability of a multiplexed assay that could simultaneously and quantitatively measure a panel of apolipoproteins would be an extremely useful clinical research tool.  We decided to interrogate clinical samples to see if apolipoproteins could be used to classify different types of strokes. Clinical Samples

Ischemic vs hemorrhagic stroke About 80 percent of strokes are ischemic, caused by a blockage of the vessels that supply blood to the brain. More than 400,000 people in the United States every year are affected. About 20 percent of all strokes are hemorrhagic; this type of stroke involves the rupture of a blood vessel in or around the brain. TPA is the only treatment for ischemic stroke. It can only be given within 6 hrs of the event. If TPA is given to a hemorrhagic stroke patient, death can result. An assay that could accurately differentiate ischemic from hemorrhagic stroke quickly would be clinically useful. Diagnosis for acute stroke is currently by: Neurological exam CAT scan MRI Lumbar pucture

SRM assay development is automated and efficient List of Targeted Proteins Discovery data: Protein Discoverer SIEVE Peptide Atlas NIST GPM Recombinant Protein Heavy-Labeled Peptides QC Standards Exhaustive List: - Peptides - Transitions Identify and Verify: - Best Peptides - Best Transitions Refine Transition List Optimize LC Gradient Verify the LC-SRM Assay with Recombinant Digests Analyze Biological Samples Pinpoint Pinpoint Algorithmic prediction

Single day development of a multiplexed assay for a panel of apolipoproteins Import protein sequences and prior LC-MS/MS discovery data library for 10 Apolipoproteins 1 Choose optimal “proteotypic” peptides: ie, Highest intensity and unique. Narrow list down to one peptide per protein 2 3 Choose at least 5 fragment transitions per peptide. This ensures accurate identification of peptides. Create method and run sample triplicates.

ROC analysis of apolipoprotein levels in hemorrhagic vs ischemic stroke patients: Single marker AUC Top AUC for single marker Apo CIII0.85 +/ Apolipoprotein Panel Apo AI Apo AII Apo AIV Apo B Apo CI Apo CII Apo CIII Apo D Apo E Apo H

ROC analysis of apolipoprotein levels in hemorrhagic vs ischemic stroke patients: Multi marker AUC Top AUC for multi markers Apo CIII and Apo AI1.0 +/- 0 Apo CIII and Apo CI1.0 +/ Apolipoprotein Panel Apo AI Apo AII Apo AIV Apo B Apo CI Apo CII Apo CIII Apo D Apo E Apo H

? Protein heterogeneity and isoforms complicate biomarker discovery ? ? ?

Parathyroid hormone (84 aa)is an example of a protein that in vivo has several clinically relevant variants Relative Intensity Renal failure samples Therefore, protein biomarker discovery must include biomarker characterization in a variety of bona fide clinical samples

Summary Unbiased Discovery workflows must include quantification and a significant sample N to encompass biological variability. Targeted discovery using SRM can be a shortcut if relevant metabolic pathways are known. Characterization of protein biomarker heterogeneity and isoforms are a necessary part of discovery workflows. Targeted Unbiased

Acknowledgements Mary Lopez- Director David Sarracino- Manager, Biomarker Workflows Bryan Krastins- Biomarker Scientist Amol Prakash- Bioinformatic Scientist Michael Athanas- Software Consultant Jennifer Sutton- Manager, Biomarker Research BRIMS TEAM Thermo Fisher Scott Peterman Amy Zumwalt Andreas Huhmer Bernard Delanghe IBI, ASU Biodesign Institute Randall Nelson Dobrin Nedelkov Paul Oran Chad Borges Mass General Hospital, Harvard U. MingMing Ning Ferdinando S Buonanno Eng H Lo Mayo Clinic Ravinder Singh David Barnidge