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Figures
Figure 1. Types of biomarkers and their utility during different time points along the development and progression of a disease. A biomarker can be developed to target one of many different critical decision points during the natural history of a disease. The same biomarker may function for different purposes.
Figure 2. Biomarker and diagnostic assay development pathways. Critical steps in the discovery, clinical assay development and validation, clinical utility determination, and commercial development phases of biomarker development are shown. The discovery phase needs high-quality, well-characterized samples that may be human or from animal models. Once a promising lead is found, the presence of the biomarker should be confirmed in different samples. The next stage is to develop a clinically useful assay (often in serum or urine) and validate if it can detect established disease. The clinical utility of the biomarker is established in a retrospective longitudinal study and a prospective study and finally to determine whether the biomarker screening strategy can reduce the burden of disease. The final stage, often not appreciated, is the commercial development of the assay by industry.
Figure 3. Strategies for biomarker development. Four typical but conceptually different biomarker development schemes are shown. (A) Biomarker for simple disease found in diseased tissue by subtractive method, then clinical assay developed (reformulated) to detect protein product in serum. Clinical assay validated initially on few samples and then on an independent larger set. (B) Biomarker for complicated disease with subgroups or near neighbors found in diseased tissue using multiple-group microarray or proteomic method. Clinical assay developed and validated as in case 1. (C) Biomarker for simple disease detected in serum by subtraction method, then assay reformulated to measure biomarker in serum. (D) Biomarkers for simple or complex disease found using surface-enhanced laser desorption ionization approach and initially validated on same sample set. D, disease; N, normal; D-N, disease minus normal; DDD/NNN, simultaneous measurement of several diseased and normal samples; A, B, closely related diseases that must be differentiated from disease D; DDD/NNN/AAA/BBB, simultaneous measurement of samples from disease, normal, and two closely related diseases.
Figure 4. The potential power of multivariate analysis. (A) Two individual biomarkers that cannot discriminate between disease and normal. (B) Simple addition of the two biomarkers allows easy segregation of normal from disease.
Tables
Table 1. Definitions
From reference 10. Biomarker. A biomarker is a biological characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic response to therapeutic intervention. Surrogate end-point marker. A biomarker that is used in substitution for other clinical end points, such as survival. Tissue marker. A biomarker that is detected in tissue. Typically these are immunohistochemical stains but, more recently, mRNA or protein. Discovery phase. The initial investigations to identify potential biomarkers worthy of further study. Table 2. Role of biomarkers
Research/preclinical phase end point marker in animal studies proof of concept testing screening tool for leads rank compounds in portfolio pharmacodynamic evaluation toxicity profile Clinical phase early detection differential diagnosis identify subpopulations for clinical study type/location of injury mechanism of disease, mechanism of action predict severity and prognosis, regression, etc. Surrogate end point drug effect, dose ranging studies focused hypothesis may shorten and decrease size of trial speed agents through testing and approval process Commercial phase test to aid drug dosing Table 3. Phases of biomarker development
Adapted from reference 7. 1a. Initial preclinical discovery discovery biomarker on tissue or serum samples 1b. Confirmation of preclinical discovery Validate biomarker on same type of samples Promising direction identified and prioritized 2. Clinical assay development and validation set up clinical assay and test on existing samples clinical assay detects established disease 3. Retrospective longitudinal test biomarker in completed clinical trial detects disease early before it becomes clinically obvious “screen positive” rule is determined; evaluate sensitivity/specificity 4. Prospective screening Use biomarker to screen population extent and characteristics of disease detected by test false referral rate identified 5. Disease control impact of screening on reducing the burden of disease Table 4. Advantages and disadvantages of platformsa
Platform Advantages for Biomarker Discovery Disadvantages for Biomarker Discovery a 2-D DIGE, two-dimensional difference gel electrophoresis; TOF, time of flight mass spectrometry; SELDI, surface-enhanced laser desorption ionization; ICAT, isotope-coded affinity tags; SNP, single nucleotide polymorphism. b Proteins from normal and diseased samples are labelled with different flurescent dyes and then separated by two dimensional electrophoresis. Size of peptide (mass to charge ratio) is calculated based on the length of time for the peptide to travel through a vacuum. c Proteins from a sample(s) bind to a chip if the coating of the chip allows an adequate protein-surface affinity. For example, hydrophobic proteins bind to a hydrophobic chip surface. Then the proteins are identified by a TOF mass spectrometer. d Complex peptide mixtures are separated by chromatography (e.g., reverse phase, cation exchange), then the chromatography fractions are analysed by TOF mass spectrometry. When two TOF mass spectrometers are used in “series,” this is referred to as MS/MS. This allows actual peptide sequencing. e Proteins from two different sources (e.g., disease versus normal) can be labelled with “light” and “heavy” tags. After LC/MS/MS, the relative abundances of different peptides in the two samples can be calculated. Measurement of mRNA expression (e.g., differential display, SAGE, microarray) Able to screen large number of “genes” RNA levels may not directly relate to protein levels Commercially available Provide no information about posttranslational protein modifications Difficult to handle large volume of data 2-D DIGEb Assay of the actual biomarker not mRNA Poor technique for difficult-to-solubilize proteins (e.g., membrane proteins), low-abundance proteins, and low-molecular-weight proteins Allows identification of previously unknown biomarkers Can quantify amplitude of change in biomarker Well established technique Not high throughput, i.e., labor intensive SELDIc Well suited to generating a pattern of peptide peaks corresponding to a disease biomarker Difficult to identify proteins Difficult to measure protein abundance High throughput, less labor intensive, and cheaper than 2-D electrophoresis Specimen handling can have large impact on quality Can focus on certain subsets of proteins LC/MS/MSd Higher throughput than 2D DIGE Need to use ICATe to measure biomarker abundance Can identify protein by amino acid sequencing Increased yield of membrane proteins and low-abundance proteins Tissue microarray High-throughput validation and prioritization of tissue biomarkers (Pepe stage 1b) Immunohistochemistry: need antibody; cannot detect “unknown” proteins. Obtain protein location by immunohistochemistry In situ hybridization—detects mRNA only Quantitation issues Specimen quality issues SNP detection May produce unexpected new leads about pathogenesis of and biomarkers for disease Only gives information about an individual’s risk of disease, not presence of disease per se Provides no information about expression of protein Table 5. Acute renal failure serum and urinary biomarkers under developmenta
Biomarker Question Discovery Method Discovery Species, Source Preclinical Validation Clinical Validation Pepe Discovery Stage Reference a CKD, chronic kidney disease; MALDI, matrix-assisted laser desorption ionization. Urinary Kim-1 Detection Kim-1 antibody Rodent Kidney Rodent Urine Human urine 2 66, 67 Urinary Cyr61 Detection mRNA then Western blot Rodent Kidney Rodent urine 1b 32 Urinary lipocalin Detection Microarray Kidney, Rodents Rodent urine 1b 57 Urinary NHE3 membranes Detection Immunoblot Human Urine — Human urine 2 18 Urinary actin Detection Physiology Human Urine — Human urine 2 68 Urinary α-GST Detection Physiology Rat Urine 69 Urinary cystatin C and α1-microglobulin Outcome CKD analogy Human Urine — Human urine 2 70 Urinary cytokines (IL-6, IL-8, IL-18) Detection Physiology Rodent Urine Rodent Urine Human urine 2 68, 71 Urinary mass spec protein profile Detection SELDI Human urine Human urine 2 72 Serum cystin C Detection Analogy from CKD Human Serum — Human serum 3 73 Serum TNF-α receptor levels Prognosis Physiology Human Serum — Human placebo arm of CT 3 74 Plasma S100B Detection Analogy from brain injury Rat plasma 1b 75 Plasma fumarylacetoacetate hydrolase Detection 2-D/MALDI Rodent plasma 1b 11
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