Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
  • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
  • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
  • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
  • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
  • G01N33/6848—Methods of protein analysis involving mass spectrometry
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
  • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
  • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
  • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
  • G01N30/02—Column chromatography
  • G01N30/62—Detectors specially adapted therefor
  • G01N30/72—Mass spectrometers
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
  • G01N30/02—Column chromatography
  • G01N30/62—Detectors specially adapted therefor
  • G01N30/74—Optical detectors
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
  • G01N30/02—Column chromatography
  • G01N30/62—Detectors specially adapted therefor
  • G01N30/72—Mass spectrometers
  • G01N30/7233—Mass spectrometers interfaced to liquid or supercritical fluid chromatograph

Definitions

• the present invention relates to an assay for biomarkers.
• the invention describes a multiplex assay capable of automatically screening for the presence of biomarkers in samples by mass spectrometry.
• biomarkers Various biological markers, known as biomarkers, have been identified and studied through the application of biochemistry and molecular biology to medical and toxicological states. Biomarkers can be discovered in both tissues and biofluids, where blood is the most common biofluid used in biomarker studies.
• Biomarkers may have a predictive power, and as such may be used to predict or detect the presence, level, type or stage of particular conditions or diseases (including the presence or level of particular microorganisms or toxins), the susceptibility (including genetic susceptibility) to particular conditions or diseases, or the response to particular treatments (including drug treatments). It is thought that biomarkers will play an increasingly important role in the future of drug discovery and development, by improving the efficiency of research and development programs. Biomarkers can be used as diagnostic agents, monitors of disease progression, monitors of treatment and predictors of clinical outcome. For example, various biomarker research projects are attempting to identify markers of specific cancers and of specific cardiovascular and immunological diseases.
• Intact proteins can be assayed in a number of ways utilizing both gel-based as well as liquid phase separation technologies.
• Two-dimensional gel electrophoresis is used with solubilised protein mixtures where the proteins are separated based upon charge and size.
• the proteins are resolved such that both isomeric forms, as well as post-translational modifications, are resolved.
• Quantitation of the proteins is made by staining techniques, where both pre- and post staining techniques can be applied. Metabolic labelling also allows the linear range to be extended up to 5 orders of magnitude, offering sensitivities within the femtomolar range.
• Protein identification is performed from excised gel spots. The proteins are digested after chemical degradation and modification.
• Multidimensional HPLC High Performance Liquid Chromatography
• HPLC High Performance Liquid Chromatography
• HPLC is flexible for many experimental approaches and various stationary and mobile phases can be selected for their suitability in resolving specific protein or peptide classes of interest and for compatibility with each other and with downstream mass spectrometric methods of detection and identification.
• High Performance Liquid Chromatography is currently the best methodology for solute separations which also allows for automated operation with a high degree of reproducibility. On-line configurations of these types of multi-mechanism separation platforms are commonly applied within proteomics studies.
• MS Mass spectrometry
• MS mass spectrometry
• MALDI matrix-assisted laser desorption ionization
• ESI electrospray ionization
• TOF time-of- flight
• peptides are co-crystallized with the matrix, and pulsed with lasers. This treatment vaporizes and ionizes the peptides. The molecular weights (masses) of the charged peptides are then determined in a TOF analyzer. In this device, an electric field accelerates the charged molecules toward a detector, and the differences in the length of time it takes ionized peptides to reach the detector (their time-of-flight) reveal the molecular weights of the peptides; smaller peptides reach the detector more quickly. This method generates mass profiles of the peptide mixtures - that is, profiles of the molecular weights and amounts of peptides in the mixture. These profiles can then be used to identify known proteins from protein sequence databases.
• LC/MS/MS liquid chromatography
• the eluting peptides from the LC-column are introduced into the ion source of the mass spectrometer.
• a voltage is applied to a very fine needle.
• the needle then sprays droplets into a mass spectrometric analyzer where the droplets evaporate and peptide ions are released corresponding to a variety of charge states that are fragmented and from where the sequence can be determined.
• LC/MS/MS researchers use microcapilliary LC devices to initially separate peptides.
• MS Mass spectrometry
• Proteins are bio-macro molecules that are difficult to separate by liquid phase chromatographic separation techniques, due to the unfavorable mass transfer within the particles of the chromatographic column material, the stationary phase.
• proteins can be rendered into smaller unit (peptide or polypeptide) form by breaking the peptide bond joining two adjacent amino acids. This can be accomplished by enzymatic cleavage by proteases, proteins that are capable of interacting and dissolving peptide bonds on other proteins. Trypsin is the most commonly used protease, used in protein expression analysis studies. After the enzymatic degradation, a resulting complex mixture of peptides will be separated and fractionated by capillary chromatography.
• All peptides that are the sum of the digested proteins in the sample will be unresolved at this stage.
• the peptides that have been generated from the corresponding protein will not be separated as one unit in the chromatographic fractionation step, but rather will be separated together with the resulting peptides from all other proteins in the sample.
• the high resolved and separated eluting peptides from the capillary will be fractioned most commonly based upon charge and hydrophobicity.
• the separated peptides are introduced on-line from the chromatographic part of the platform into the mass spectrometer, thereby circumventing possible contaminations.
• the peptides are then mass determined (m/z), in order to capture all the peptides present in that given time window.
• a number of peptide masses are selected for sequencing (MS/MS), based upon their abundance in the given time window.
• This is performed by a new ion sampling interface by an electrospray ionization ion trap mass spectrometer system.
• the interface uses linear quadrupoles as ion guides and ion traps to enhance the performance of the trap. Trapping ions in the linear quadrupoles is demonstrated to improve the duty cycle of the system. Dipolar excitation of ions trapped in a linear quadrupole is used to eject unwanted ions.
• Electrospray is a gentle source that can ionize important analytes such as peptides, and proteins. Highly charged ions produced in ESI can extend the range of mass analyzers. Trap mass spectrometers have favorable capabilities such as flexible tandem MS capability (MS n ). In this ionization process, the precursor ion is activated by acceleration into a mass-selective linear ion trap under conditions whereby some of the fragment ions formed are unstable within the trap.
• the fragmentation involves activation of a precursor ion via collisions with a target gas and may produce charged and neutral fragments.
• the nature of the fragment ions, as well as their intensities, is often indicative of the structure of the precursor ion and thus can yield useful information for the identification of unknown analytes, as well as providing a useful screening technique for different classes of analytes.
• Activation via multiple collisions both prolongs the activation time and enables higher energies to be deposited into precursor ions.
• Higher collision gas pressures also imply higher collision relaxation rates.
• the invention provides an assay for biomarkers in a biological sample which is automated and accurate.
• the assay relies on mass spectrometry to identify biomarkers, and is referred to herein as the mass spectrometry biomarker assay (MSBA).
• MSBA mass spectrometry biomarker assay
• the invention provides a method for dete ⁇ nining the presence of one or more polypeptide biomarkers in, preferably, a human test sample, which may including non-human test samples, which is typically confined in a volume of a biofluid containing naturally occurring proteins and peptides contained within an amount of tissue, blood, or other clinically obtained speciments.
• the method preferably comprises the following steps: (a) subjecting the sample to a mass spectrometric (MS) analysis and recording retention time index and corresponding mass for each signal detected;
• MS mass spectrometric
• the method of the invention uses the master data set in the test sample screening phase.
• the method filters and screens mass and sequence identities of data sets that are based on each of the unique properties of charge, mass, sequence spectra associated with certain identified protein sequences in the master data set.
• the invention provides a method for determining the presence of one or more polypeptide biomarkers in a sample, comprising the steps of:
• the method of the invention allows users to analyse, simultaneously, hundreds or thousands of biomarkers in a sample.
• the method relies on a database of biomarkers, which have been shown to be associated with a disease, which comprises mass and spectral data for each of the biomarkers and allows the said biomarkers to be indentified precisely by the MSBA software in a given sample.
• a database of biomarkers which have been shown to be associated with a disease, which comprises mass and spectral data for each of the biomarkers and allows the said biomarkers to be indentified precisely by the MSBA software in a given sample.
• the sample can be subjected to MS analysis without prior separation procedures.
• the sample is preferably analysed by direct infusion using static nano- electrospray principles, flow injection analysis or flow injection with sample enrichment.
• the sample is processed prior to MS analysis, preferably to separate sample components prior to loading them into the MS.
• the sample processing comprises sample separation by single- or multi-phase high-pressure liquid chromatography (HPLC).
• HPLC high-pressure liquid chromatography
• the MS system itself is preferably electrospray ionisation (ESI) MS, matrix-assisted laser desorption ionisation - time of flight (MALDI-TOF) MS or surface enhanced laser desorption ionisation - time of flight (SELDI-TOF) MS.
• ESI electrospray ionisation
• MALDI-TOF matrix-assisted laser desorption ionisation - time of flight
• SELDI-TOF surface enhanced laser desorption ionisation - time of flight
• the method according to the invention is advantageously automated and performed under computer control. Identification of biomarkers in a sample is made by comparison with reference data for said biomarkers; preferably, reference mass and MS spectral data for a plurality of biomarkers are stored on a computer.
• Reference MS spectra for a defined biomarker are preferably averaged spectra obtained from actual and measured data obtained by a clustering calculation.
• the method of the invention may be implemented in two ways; using internal standards to provide a reference for quantitating signal intensity, and without such standards.
• one or more internal standards are added to the sample prior to analysis by MS.
• the internal standards are labelled.
• the absolute signal intensity for each biomarker signal is scored by measuring the biomarker signal intensity and comparing it to the signal intensity of one or more known internal standards.
• the sample is processed without the addition of internal standards.
• the relative signal intensity is scored by measuring the ratio between the individual biomarker signal intensities in a patient and the reference signal intensity for a patient group.
• a biomarker can be described as "a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention".
• a biomarker is any identifiable and measurable indicator associated with a particular condition or disease where there is a correlation between the presence or level of the biomarker and some aspect of the condition or disease (including the presence of, the level or changing level of, the type of, the stage of, the susceptibility to the condition or disease, or the responsiveness to a drug used for treating the condition or disease). The correlation may be qualitative, quantitative, or both qualitative and quantitative.
• a biomarker is a compound, compound fragment or group of compounds. Such compounds may be any compounds found in or produced by an organism, including proteins (and peptides), nucleic acids and other compounds.
• the sample may be any biological substance of interest, but is advantageously a biological tissue and preferably a biological fluid such as blood or plasma.
• the method of the invention relies upon correlation of observed MS signals with reference masses and MS spectra of known biomarkers.
• the reference data is preferably stored on a computer server, which allows the entire procedure to be carried out under computer control.
• Signals are con-elated to reference standards by comparison, for example using computational functions as described herein.
• signals are characterised as "positive” or “negative” according to whether a threshold level of similarity is achieved; signals which are negative and do not achieve the threshold level of similarity are discarded in the MSBA process, whilst those signals which are positive are matched with biomarkers and result in a diagnosis of the presence of said biomarkers in a biological sample.
• Signal intensity is measured with reference to known control standards added to the biological sample, or to by comparison with a reference intensity calculated across a patient group, depending on the implementation of the MSBA assay.
• the MSBA scoring of the biomarker signals is calculated by the ratio between the signal of biomarker present in the sample and the internal standard added to the sample. AU biomarkers in a multiplex assay will be analysed the same way resulting in a final MSBA scoring factor.
• a new method is provided, composed of multiple linked steps, for detecting and quantifying protein sequence biomarkers with a multiplex read-out where the expression levels of, but not restricted to, 2-100 biomarkers can be mapped in one single MSBA read-out.
• the MSBA system is built on a liquid phase platform that can handle single line diagnostic mapping, or a multiple flow configuration with simultaneous parallel processing of samples, thereby increasing the capacity and throughput of the system.
• the detection mode of the MSBA method is the accurate mass identification and sequence determination and subsequent quantitation by mass spectrometry.
• This methodology may be applied to any type of biological sample that is in, or can be transformed into, a liquid form.
• the MSBA methodology can also process samples from any type of cellular, or biotechnology processes where for instance kinetic profiles over time are measured. This analysis over time is performed by subsequent sample introduction into the MSBA platform automatically over time.
• the entire analysis capability of the MSBA diagnostic profiling is entirely computer control including the mass signal evaluation, the sequence analysis, the multiplex quantitation by weighing discrimination and finally the MSBA SCORE diagnosis. All of the intermediate steps within the MSBA cycle run on this platform are evaluated by dedicated algorithms that make accurate decision making from the massive amount of data generated in each cycle of MSBA analysis from any given biological sample.
• the MSBA method results in the identification of specific peptides, as well as biochemically modified variants thereof, present as separate entities or present within complex mixtures of proteins and peptides.
• Each peptide may be defined by a specific sequence of amino acids, that can be selectively identified by either its precise mass, or its unique immuno-affinity binding properties to a given immunological reagent.
• the method allows the identification of statistically significant protein identities and modified versions thereof.
• These internal standards can then be made as cold amino acid sequences, or as isotope labeled amino acid sequences.
• the standards have identical sequences to the selected biomarkers, with the possible exception of the labelling.
• the method combines several key steps which results in the specific processing, separation, isolation, and identification of unique protein sequences present in a biological material sample.
• the method may be applied to human clinical samples.
• the method may also be applied to samples derived from non-human animals.
• statistically significant similarities may be detected and registered as unique protein sequencs identities or multiple-peptide identities. Determining statistically significant similarities involves using publicly available protein and gene sequence data bases as well as algorithms developed specifically to meet the demands of the MSBA methodology.
• the integration of process steps for biomarker identification is advantageous. The integrated process relies on the following principles: 1) high quality biomedical clinical material, 2) reproducible and high speed sample processing with subsequent liquid phase separations, 3) accurate quantitative and qualitative determination of a multiplex set of biomarkers and 4) algorithms that will control the data generation and calculate and allow the isolation of the biomaiicers in the multiplex protein sequence group, one by one.
• Figure 1 shows a schematic illustration of the MSB A principle.
• FIG. 2 illustrates in more detail the data handling procedures involved in MSBA.
• Figure 3 shows a mass spectrum from a blood sample from a lung disease patient. Multiple biomarkers are identified in the sample.
• Figure 4 shows an example of biomarker annotation made form the multiplex assay, presented by the MS spectrum where the biomarker was recognised by the MSBA software, and the follow up MS/MS spectrum that represents the resulting CVLFPYGGCQGNGNK biomarker.
• Figure 5 shows an example of evaluating the predictability of an MSBA model with 11 biomarker signals on sample data of 19 patients, as described in Example 2. 10 cases and 9 controls were used as if they were blinded samples. The MSBA score for each subject was calculated using Eq.5. In this example, subjects whose MSBA score was equal or greater than 1 were diagnosed as cases (red circles). Otherwise the subject was considered to be a control (blue circles). The prediction accuracy was 100%.
• Figure 6 shows the auto-discrimination results using an MSBA model with 10 signals on sample data of 96 patients, as described in Example 3. Each dot represents a patient, and vertical axis represents the discriminant score (z), calculated using Eq. 6. If this score was >0, it was interpreted as a case of the disease (red circles). Otherwise the subject was considered to be a control (blue circles). The prediction accuracy was 83.3%.
• biomarker is "a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention".
• biomarker refers to a polypeptide which can be use to monitor the presence or the progress of a disease, consistent with the above FDA definition.
• Biomarkers can be used as diagnostic agents, monitors of disease progression, monitors of treatment and predictors of clinical outcome. For example, various biomarker research projects are attempting to identify markers of specific cancers and of specific cardiovascular and immunological diseases.
• Some of these disease-associated proteins may be identified as novel drug targets and some may be useful as biomarkers of disease progression. Such biomarkers may be used to improve clinical development of a new drug or to develop new diagnostics for the particular disease.
• Disease-associated proteins are known in the art, and their use as biomarkers for the disease is established. Such biomarkers can be monitored by means of the present invention. Novel disease-associated proteins, however, may be identified. Detection of disease-associated proteins may be achieved, for example, by the following method. Protein samples are taken from single patients or groups of patients. These samples may be cells, tissues, or biological fluids that are processed to extract and enrich protein and/or peptide constituents. Typically the process entails partitioning into solution phase but may also include the establishment of protein and/or peptide components attached to solid matrixes. After separation and analysis (proteomics, peptidonomics), protein expression fingerprints are produced for both diseased and healthy subjects by qualitative and quantitative measurement.
• These fingerprints may be used as unique identifiers to distinguish individuals and/or establish and/or track certain natural or disease processes.
• These prototype fingerprints are established for each individual sample/subject and are recorded as numerical values in a computer database. The fingerprints are then analysed using bioinformatic tools to identify and select the proteins or peptides that are present in the prototype fingerprints and whose expression may or may not be differentially present in the samples derived from the healthy and diseased subject samples. These proteins/peptides are then further characterised and detailed profiles are produced which identify the characteristic physical properties of the proteins or peptides. Either a singular proteins/peptide or groups of proteins/peptides may be determined to be significantly associated with certain natural or diseased processes.
• Mass spectrometry is the method of choice for the analysis of proteins and peptides.
• Modern biomarker discovery research employs two major mass spectrometry principles: MALDI-TOF (matrix assisted laser desoiption ionisation time of flight) mass spectrometry where the proteins are analysed in a crystalline state, and ESI (electrospray ionisation ) mass spectrometry where the proteins are analysed in liquid state.
• MALDI-TOF matrix assisted laser desoiption ionisation time of flight
• ESI electrospray ionisation
• the surface-enhanced laser desorption/ionization (SELDI)-TOF-MS technology uses chromatographic surfaces coupled to the assay target plate. The protein-bound material on the plates is then directly analyzed by MALDI-MS. SELDI assays peptides and proteins predominantly in the low molecular mass range. This technology is applicable to the major, to medium-abundant peptides and proteins where a suitable upfront purification scheme is not integrated. The SELDI technology leads primarily to a pattern from where sequencing can be performed using MALDI-TOF-TOF identification of peptides.
• Multi-mechanism separation platforms enable high resolution peptide separation configured on-line with electrospray ionization mass spectrometry, or off-line with ionization principles such as matrix assisted laser desorption ionization mass spectrometry. See, for example, Aebersold,R. & Goodlett,D.R. Chem. Rev. 2001, 101, 269-295; Mann, et al., Amu. Rev. Biochem, 2001. 70, 437-473; Wolters,et al. Anal. Chem. 73, 5683-5690 (2001); and Washbum,et al., Nat. Biotechnol. 19, 242-247 (2001).
• MS Mass spectrometry
• MS Mass spectrometry
• a pre-determined amount of peptide standard is added to the sample. This addition will be made both before and after, or, either before or after the digestion of the samples.
• the standards used will be the actual biomarker sequences synthesized as isotope labelled sequences, or without isotope labelling, and spiked with the samples.
• HPLC High Performance Liquid Chromatography
• the protein or peptide mixture is passed through a succession of chromatographic stationary phases or dimensions which gives a higher resolving power.
• HPLC is adaptable for many experimental approaches and various stationary and mobile phases can be selected for their suitability in resolving specific protein or peptide classes of interest and for compatibility with each other and with downstream mass spectrometric methods of detection and identification.
• HPLC is used to separate clinical samples that have been digested by a proteolytic enzyme where the corresponding enzyme products, the peptide mixtures, are generated. Sample preparation procedures are applied to protein samples such as blood, tissue, or any other type of biofluid.
• HPLC chromatographic stationary phases or dimensions which gives a high resolving power.
• HPLC is flexible for many experimental approaches; in the setting of the present invention an optimization is made that specifically eliminates the high abundance fraction of proteins expressed in human blood samples, whereby enrichment is made of proteins in the medium-, and low abundance region.
• the separation of peptides and proteins is based on the peptide sequence, the functional groups of the peptide sequence, as well as the physical properties.
• sample preparation Prior to exposing samples to MSBA, a sample handling and preparation step is required in most cases.
• the aim of introducing this step prior to the MSBA methodology is to eliminate interfering agents and matrix components, thereby facilitating improved overall detectability resulting an increase in annotation, as well as overall sensitivity.
• sample preparation can be dispensed with, particularly if the biomarker is in higher abundance and the sample of low complexity. Those skilled in the art will be able to determine whether a preparation step is essential.
• the MSBA platform can be operated in a number of different ways, predominantly determined by the nature of the sample and its complexity.
• the biomarker protein sequences are determined qualitatively and quantitatively in the patient sample by multiplex analysis. Both Labelled and Unlabelled MSBA principles can be applied, employing configurations of the MSBA assay according to two possible principles: the internal standard addition principle and the no internal standard principle.
• sample preparation After sample preparation, the sample is injected into the MSBA platform. Next, the following operations are undertaken;
• the biomarker MS-signals need to be identified within the sample.
• a predefined list of Biomarker list masses +/- 1 Dalton that correlates with the retention time index and corresponding mass of the respective biomarker is screened for in the biofluid sample.
• the relative retention time indexes obtained in most MSBA assays is defined in minutes and has a variability of about +/- 2%, altough this figure may vary.
• the biomarker candidate mass is identified as that of a biomarker having a matching MS spectrum within the reference list, within +/- 1 Da, the information therein is saved on the MSBA-server. In case thst the mass is incorrect, the MSBA screening makes no spectral file savings to the server.
• mass identity in the MS-spectra is identified, mass identification and sequence identity analysis is initiated.
• the pattern matching step within the MSBA software will identify a certain similarity measure, for example the cosine correlation. Using the similarity measure, the correct protein sequence is confirmed. This confirmation is made by spectral matching. The spectral matching is performed by comparison of the sample spectra and the reference spectra in the MSBA database. For a positive identity at this stage a cosine correlation factor of 0.8 or higher is required in order to confirm the accurate protein sequence.
• a certain similarity measure for example the cosine correlation.
• the reference spectral comparison and evaluation is performed in the following way.
• the MS/MS spectrum is represented as a list of doublets (m,v) where m represents mass- to-charge ratio, and v means the ion signal intensity value.
• m mass- to-charge ratio
• v the ion signal intensity value.
• the Cosine correlation (S) of 2 different MS/MS spectra (- ⁇ 1 , ⁇ 2 ) can be calculated as a cosine correlation according to eq 1;
• the two MS/MS spectra vectors must have the same binning, i.e., if the binning of m of one vector is 500-501, 501-502, ... 1999-2000, then another spectrum must be binned in the same manner. Consequently, the length of two vectors must be the same.
• the MS/MS portion of the measured signals is extracted and compared with the MS/MS reference spectrum of the sample by using for example the cosine correlation described above.
• the measured signals are judged to be the derived from the putative biomarker in the reference set.
• the following section describes how to construct reference spectra that are obtained as a group specific spectrum from many individual patients. For each candidate biomarker, once such biomarker is established, several MS/MS spectra should be collected to construct a reference MS/MS spectrum map. This is an averaged spectrum from actual and measured data sets and is obtained by a clustering calculation.
• the mass spectrometer for example the Finnigan LTQ
• the mass spectrometer once a positive biomarker mass has been identified, will stay on that mass target in order to make repeated scanning of the biomarker ion signal.
• the number of scans will be dependent on the score match generated for each particular protein sequence, but will be aligned to the positive identity of the biomarker.
• the scanning window will be determined automatically by the MSBA software.
• the criterion for a positive correlation should be higher or equal to 0.8 in a cosine correlation similarity measure.
• the next succeeding step will be to make a statistically significant identity of the protein sequence by utilizing commercial search engines such as MASCOT or SEQUEST or any other search engine with the protein data bases, to confirm that it is the correct Biomarker identity.
• commercial search engines such as MASCOT or SEQUEST or any other search engine with the protein data bases, to confirm that it is the correct Biomarker identity.
• the MSBA system will only store and archive those signals and data files that are within the mass and sequence area of the biomarkers. All other data generated from the assay are not transferred to the MSBA database.
• Calculation of the multiplex biomarker assay read-out The calculation of the multiplex biomarker assay read-out is performed by the application of the MSBA algorithm which consists of a discrimination function that will calculate the diagnostic MSBA score.
• a discrimination function is defined as a function of X] ? •••? X n , where x,- represents n absolute or relative signal intensity of the z ' :th biomarker.
• the output of a discrimination function must be either positive or negative value according to the diagnosis result. For example, if the diagnosis is positive, the output value of the discrimination function must be positive, and vice versa.
• n is the number of multiple biomarkers used for the diagnosis
• xi is the absolute or relative signal intensity of the /:th biomarker
• x total is the total signal intensity of the MS measurement.
• a vector ⁇ Cl ⁇ , - ⁇ , Cl n , a ⁇ j is a weight vector that determines the direction of the normal vector of a separating hyper-plane that divides the n-dimensional signal intensity space into two: diagnosis positive and diagnosis negative.
• An example of the procedure to determine the weight vector is described afterward, however various kind of algorithms e.g. Support Vector Machine, Artificial Neural Network, and others can be used to determine the weight vector.
• the function/, and/, is an arbitrary function that give a measure of either similarity or distance between a set of measured biomarkers in a patient to be diagnosed and sets of reference biomarkers signals in the MSBA server.
• f p denotes the similarity or difference function from the diagnosis positive references
• f n denotes that from diagnosis negative ones.
• ⁇ and ⁇ are coefficients that can be used to unequally weight the diagnosis-positive and the diagnosis-negative metrics.
• Xi is the i:th biomarker signal intensity of the reference set.
• Another example is a standard error of the predicted value in the regression:
• n is the number of biomarker signals
• x,- is the measured z:th signal intensity of a patient sample
• _v / is the predicted value from each x,- by using a linear regression line that was calculated by the least square fitting between the measured ⁇ : / 'S and the reference signals.
• the MSBA assay platform builds on a:
• Disposable nanospray needles are used, where each nano electrospray needle will only be exposed to one biological sample, thereby circumventing sample overload and memory effects.
• the sample volume chosen within the plug is directly related to the signal intensity of the respective biomarker protein sequence. It is also possible for low abundant biomarkers to use large (several ml) sample injection volumes thereby reaching a saturation (steady-state) of the ion signal efficiency of the mass spectrometer.
• sample enrichment mode (iii) we are able to generate signal amplification factors ranging but not restricted to 2-500. Additionally, this approach will improve on the detectability of biomarkers expressed at low levels, but also on the accuracy of the protein sequence annotation.
• liquid chromatography (LC) integrated biomarker identification relies upon the high resolving power of LC that can be operated in the single column mode (see Figure x) or in the multi-column mode utilizing column switching where the samples are analyzed in a sequential mode, thereby improving the sample throughput.
• LC liquid chromatography
• a model will be constructed from a training data set (train) , and then will be utilized to generate a prediction (pred) for a given test data set (test).
• Data sets train and test are data frames containing plural number of data points, which consist of an object Diag containing diagnosis results (categorical value: either "Positive” or "Negative". The values are empty for the case of pred), and a vector containing signal intensity for each biomarker and total signal intensity.
• the MSBA programming will rely on data generated from the protein sequence screening performed on the two patient groups from where the biomarkers have been generated.
• model$SV selected support vectors are: model$SV ## and weight vectors are: modelScoefs pred ⁇ - predict( model, test )
• the following examples are illustrations from a lung cancer study that was performed by LC-MS protein profiling in human blood samples. Two patient groups were analysed, the CASE and the CONTROL cancer group with differential protein expression differences analysed.
• the Multiplex biomarker summary plot presents the multiplex expression data of the patient biomarkers within the Lung cancer study.
• the 10-multiplex biomarker diagnostic read-out generated from the MSBA methodology illustrates each and every biomarker separately (see Figure 3).
• the quantitative difference i.e. the fold change difference that is already known and stored within the MSBA database (see Figure 1) is used together with the qualitative differences to assay the biomarkers.
• Biomarker data generated from the lung cancer study correctly identifies all of these patients as positive from the diagnostic multiplex MSBA read-out.
• Figure 4 shows an example of biomarker annotation made form the multiplex assay, presented by the MS spectrum where the biomarker was recognised by the MSBA software, and the follow up MS/MS spectrum (see Figure 4) that represents the resulting CVLFPYGGCQGNGNK biomarker.
• the MSBA matching using the reference biomarker spectra in the MSBA-database applying cosine correlation, shows the cosine correlation factor to be equal, or higher than 0.8.
• Table 1 presents the details of the MSBA-data generation, where pre-defined masses of the regulated biomarkers are analyzed.
• Another example described as follows is derived from a lung disease CASE-CONTROL study that was performed by LC-MS protein profiling in human blood samples. Two patient groups were analyzed, the CASE and the CONTROL lung disease group with differential protein expression analysis.
• the following example was also derived from the lung disease CASE-CONTROL study performed by LC-MS protein profiling in human blood samples with two patient groups (CASE and CONTROL), hi this example, another set of multiplex biomarkers was used to construct an MSBA model, with different patient dataset of much larger size.

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