BRPI0711967A2 - mass spectrometry biomarker assay - Google Patents

Abstract

MASS SPECTROMETRY BIOMARKER TEST. The present invention relates to a method for determining the presence of one or more polypeptide biomarkers in a sample, comprising the steps of: (a) subjecting the sample to mass spectrometric analysis (MS) and recording the time index and the corresponding mass for each detected signal; (b) correlate the mass corresponding to each signal to a reference database of the biomarker masses to form a correlation between each signal and a reference biomarker, and discard those signals whose masses do not correlate to a mass of the reference biomarker ; (c) store those signals whose masses correlate with a reference biomarker; (d) confirm the correlation between each stored signal and a reference biomarker by combining the MS spectrum of each signal with the MS spectrum of the reference biomarker in the database using a similarity measure, to define a set of signal positive correlation; (d) measure the intensity of each positive correlation signal and the classification of its absolute intensity or its relative signal intensity using a discrimination function; (e) apply a limit to the classification values obtained from the discrimination function to determine the presence or absence of the biomarker.

Description

Report of the Invention Patent for "MASS SPECTROMETRY BIOMARKER TEST"

The present invention relates to an assay for biomarkers. In particular, the invention describes a multiplex assay capable of automatically tracing the presence of biomarkers in mass spectrometry samples.

Several biological markers, known as biomarkers, have been identified and studied through the application of biochemistry and molecular biology to clinical and toxicological conditions. Biomarkers can be discovered in both tissue and biofluids, where blood is the most common biofluid used in biomarker studies.

Biomarkers may have 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), susceptibility (including genetic susceptibility) to particular conditions or diseases, or response to particular treatments (including drug treatments). Biomarkers are believed to play an increasingly important role in the future of drug discovery and development, improving the efficiency of research and development programs. Biomarkers can be used as diagnostic agents, disease progress monitors, treatment monitors, and clinical outcome pregnorizers. For example, several biomarker research projects are trying to identify markers of specific cancers and specific cardiovascular and immune diseases.

Intact proteins can be tested in various ways using gel-based technologies as well as liquid phase separation. Two-dimensional gel electrophoresis is used with solubilized protein mixtures where proteins are separated based on charge and size. Proteins are transformed in such a way that both isomeric forms as well as post-translational modifications are transformed. Protein quantification is done by staining techniques, in which both pre- and post-staining techniques can be applied. Metabolic marking also allows the linear range to be extended up to 5 orders of magnitude, offering sensitivities within the fentomolar range. Protein identification is performed from excised gel points. Proteins are fragmented after degradation and chemical modification. The resulting peptide mixtures are extracted from isolated gel sample and subsequently identified by mass spectrometry.

Multidimensional High Performance Liquid Chromatography (HPLC) can be used as a good alternative to separate proteins or peptides. The protein or peptide mixture is passed through a succession of chromatographic stationary phases or dimensions giving a higher transformation power. HPLC is flexible for many experimental approaches, and various stationary and mobile phases can be selected for their suitability for transforming specific protein or peptide classes of interest, and for compatibility with each other and with the spectrometry methods of downstream mass of detection and identification. High Performance Liquid Chromatography is currently the best methodology for solute separations that also allow automated operation with a high degree of reproducibility. The inline configurations of these types of multi-mechanism separation platforms are commonly applied within proteomic studies.

Mass spectrometry (MS) is also an essential element of the proteomic field. In fact, MS is the main tool used to study and characterize purified proteins in this field. The interface link in proteomics and MS, displaying hundreds or thousands of proteins, is made by gel technology where high resolution can be achieved on a single gel. Researchers are successfully utilizing the power of MS to put aside the two-dimensional gels that originally gave proteomics their momentum.

The application and development of mass spectrometry (MS) to identify separated proteins or peptides through liquid phase separation techniques and / or gel based separation techniques has led to a significant technological advance in protein expression analysis. and peptide. There are two main methods for characterizing protein and peptide mass spectrometry: matrix assisted laser desorption ionization (MALDI) and electrospray ionization (ESI).

Using various approaches, MALDI and ESI ion sources can be combined with time-to-flight (TOF) or other types of mass spectrometric analyzers to determine the mass or sequence of peptides.

In MALDI, the peptides are co-crystallized with the matrix, and pulsed with lasers. This treatment vaporizes and ionizes the peptides. The molecular weights (masses) of the loaded peptides are then determined in a TOF analyzer. In this device, an electric field accelerates charged molecules toward a detector, and the differences in the length of time that ionized peptides spend to reach the detector (their flight time) reveal the molecular weights of the detectors. peptides; Smaller peptides reach the detector faster. This method generates mass profiles of peptide mixtures - that is, profiles of the weights and molecular quantities of peptides in the mixture. These profiles can then be used to identify known proteins from protein sequence databases.

By interfacing ESI-MS with liquid chromatography (LC / MS / MS), peptides that are purified from the LC- column are introduced into the ion source of the mass spectrometer. A voltage is applied to a very thin needle. The needle then atomizes droplets into the mass spectrometric analyzer, where the droplets evaporate and the peptide ions are released corresponding to a variety of charge states that are fragmented and from which the sequence can be determined. In LC / MS / MS, researchers use microcapillary LC devices to initially separate peptides.

Mass spectrometry (MS) is a valuable analytical technique because it measures an intrinsic property of a biomolecule, its mass, with very high sensitivity. MS can therefore be used to measure a wide variety of molecule types (proteins, peptides, or any other biomolecules) and a wide range of sample type / biological materials. Correct sample preparation is known to be crucial for MS signal generation and spectrum resolution and sensitivity. Sample preparation is thus a crucial area for the feasibility and sensitivity of the analysis.

Proteins are biomacromolecules that are difficult to separate by liquid phase chromatographic separation techniques due to unfavorable mass transfer within the particles of the chromatographic column material, the stationary phase. However, proteins can turn into smaller unit forms (peptide or polypeptide) by breaking the peptide bond that joins two adjacent amino acids. This can be accomplished by enzymatic cleavage by proteases, proteins that are able to interact and dissolve peptide bonds into other proteins. Trypsin is the most commonly used protease used in protein expression analysis studies. Following enzymatic degradation, a resulting complex mixture of peptides will be separated and fractionated by capillary chromatography. All peptides that are the sum of the failed proteins in the sample will not be transformed at this stage. Peptides that have been generated from the corresponding protein will not be separated as a unit in the chromatographic fractionation step, but will instead be separated together with the resulting peptides from all other proteins in the sample. The high separated and transformed capillary clearance peptides will be fractionated most commonly based on charge and hydrophobicity. The separated peptides are introduced inline from the chromatographic part of the platform into the mass spectrometer, thus avoiding possible contamination. Peptides are then determined by mass (m / z) to capture all peptides present in a given time window. Then several peptide masses are selected to sequence (MS / MS) based on their abundance in a given time window. This is accomplished through a new ion sampling interface by ionizing an electrospray ion separation ion mass spectrometer system. The interface uses linear quadrupoles such as ion guides and ion separators to increase filter performance. Separation ions in linear quadrupoles have been shown to improve the obligatory system cycle. Dipolar excitation of separated ions in a linear quadrupole is used to eject undesirable ions.

After the first appearance of a successful instrumentation in 1990, electrospray ionization (ESI) separated ion mass spectrometry became a widely used tool for research analysis. Electrospray is a mild source that can ionize important analytes such as peptides, and proteins. Highly charged ions produced in ESI can extend the range of mass analyzers. Separation mass spectrometers have favorable capabilities such as flexible series MS capability (MS n ....). In this ionization process, the precursor ion is activated by acceleration in a mass selective linear ion separator, under conditions whereby some of the fragments ions formed are unstable within the separator. After a time delay, the ion separator stability parameters are changed to allow the capture of fragments that were previously unstable. The result is a product ion spectrum that originates from the precursor ions with a modified internal energy distribution. It is possible to follow the evolution of the internal energy distribution of the precursor for many milliseconds after admitting the precursor ions in a linear ion separation. The ion spectrum of the time-consuming fragmentation product typically exhibits reduced sequential fragmentation products leading to the more easily interpreted spectra. Several experimental parameters important for time fragmentation have been identified and discussed. The technique has applications for both small precursor ions and multiplied charged peptides.

Serial mass spectrometry (MS / MS) is at the heart of the latest mass spectrometric investigations of complex mixtures. Fragmentation involves activation of a precursor ion through collisions with a target gas and can produce charged and neutral fragments. The nature of fragment ions, as well as their intensities, is often indicative of the precursor ion structure and thus can yield useful information for identifying unknown analytes, as well as providing a useful tracking technique for different classes. of analyzed. Activation through multiple collisions prolongs time activation and allows higher energies to be deposited on the precursor ions. Higher collision gas pressures also imply higher relaxation rates.

While the combination of protein separation by D2 gel electrophoresis and mass spectrometric analysis has been found to be useful for biomarker analysis, multi-duplex systems capable of analyzing various biomarkers are currently at the experimental stage.

Many diseases have been presented as being associated with a complex pattern of biomarkers, which may be diagnostic for the disease or indicative of a patient's response to drug treatment. These patterns often involve multiple biomarkers, requiring multiple simultaneous analyzes. There is thus a need for a system capable of testing several biomarkers simultaneously. Ideally, the system could be automated.

Summary of the Invention

The invention provides an assay for biomarkers in a biological sample that is automated and accurate. The assay relies on mass spectrometry to identify biomarkers, and is referred to hereinafter as the mass spectrometry biomarker (MSBA) assay.

The invention provides a method for determining the presence of one or more polypeptide biomarkers preferably in a human test sample, which may include non-human test samples, which is typically confined to a volume of a protein-containing biofluid. - naturally occurring within a quantity of clinically obtained tissue, blood, or other specimens.

The method preferably comprises the following steps:

(a) subject the sample to mass spectrometric analysis (MS) and record the retention time and mass index corresponding to each detected signal;

(b) correlate the mass corresponding to each signal to a reference database maintaining a master set of biomarker masses of a known disease or biological change to form a correlation between each test sample signal and a biomarker. the master set of biomarkers within the reference database, and discard those test signals whose masses do not correlate with a reference biomarker mass in the master data set;

(c) store these signals from test samples whose masses are correlated with a reference biomarker in the master data;

(d) confirm the correlation between each stored signal and a reference biomarker by equating the MS spectrum of each signal with the MS spectrum of the reference biomarker in the database, using a similarity measure to positively define a signal set. correlated;

(d) measuring the intensity of each positively correlated stored test signal and classifying it by its absolute signal intensity, or relative signal intensity, using a discretion function;

(e) apply a limit to note the values obtained from the discrimination function to determine the presence or absence of the biomarker.

Preferably, the method of the invention uses the master data set in the test sample tracking phase.

Advantageously, the method filters and streams the mass and sequence identities of datasets that are based on each of the unique load, mass, sequence spectra properties associated with certain protein sequences identified in the data set. - dos-masters.

In a first aspect, therefore, the invention provides a method for determining the presence of one or more polypeptide biomarkers in a sample, comprising the steps of:

(a) subject the sample to mass spectrometric analysis (MS) and record the retention time index and corresponding mass for each detected signal;

(b) correlating the mass corresponding to each signal to a biomarker mass reference database to form a correlation between each signal and a reference biomarker, and discarding those signals whose masses do not correlate to a biomark mass - reference holder;

(c) store those signals whose masses correlate with the reference biomarker;

(d) confirm the correlation between each stored signal and a reference biomarker by combining the MS spectrum of each signal with the reference biomarker MS spectrum in the database, using a similarity measure to positively define a signal set. correlated;

(d) measure the intensity of each signal by positively correlating the test signal and classifying its absolute signal strength or its relative signal strength using a discrimination function;

(e) apply a principle to note the values obtained from the discrimination functions to determine the presence or absence of the biomarker.

The method of the invention allows users to simultaneously analyze hundreds or thousands of biomarkers in a sample. The method has a database of biomarkers, which have been shown to be associated with the disease, which comprises mass and spectral data for each of the biomarkers and allows said biomarkers to be precisely identified by the MSBA software at a given time. - show. Investigating the peptides present in a sample and eliminating undesirable sequences based on the retention time index, which correlates with the arrival time of the peptide in the MS1 detector, over 30,000 sequences can be analyzed in minutes, and certain biomarkers can be analyzed. identified with high confidence. The method is automated, the operating productivity high and operable by relatively inexperienced technicians.

The sample may be subjected to MS analysis without prior separation procedures. In such an embodiment, the sample is preferably analyzed by direct infusion using principles of static nanoelectrospray, flow injection analysis and sample enrichment flow injection.

Advantageously, the sample is processed prior to MS analysis, preferably to separate the sample components before loading them into the MS. For example, sample processing comprises sample separation by single phase or multi-phase high pressure liquid chromatography (HPLC).

The MS system itself is preferably electrospray (ESI) MS ionization, matrix assisted laser desorption ionization - (MALDI-TOF) MS flight time or increased surface laser desorption ionization - flight time of (SELDI-TOF) MS.

The method according to the invention is advantageously automated and performed under computer control. The identification of biomarkers in a sample is made by comparison with the reference data for said biomarkers; preferably reference mass and MS spectral data for a plurality of biomarkers are stored in a computer.

Reference MS spectra for a defined biomarker are preferably proportional spectra obtained from actual and measured data obtained by an aggregation calculation.

The method of the invention may be implemented in two ways; using internal standards to provide a reference for quantified signal strength, and without such standards. Thus, in one embodiment, one or more internal standards are added to the sample prior to MS analysis. Preferably, the internal standards are labeled.

In such an implementation of the invention, the absolute signal strength for each biomarker signal is classified by measuring the signal strength of the biomarker and comparing it to the signal strength of one or more known internal standards.

In an alternative implementation, the sample is processed without the addition of internal standards. In such an embodiment, the relative signal strength is classified by measuring the relationship between the individual biomarker signal strengths in a patient and the reference signal strength for a group of patients.

The biomarker can be described as "a trait that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacological 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 level of change of, the type of, stage of, susceptibility to the condition or disease, or responsiveness to a drug used to treat the condition or disease). The correlation may be qualitative, quantitative, or both qualitative and quantitative. Typically, a biomarker is a compound, compound fragment or group of compounds. Such compounds may be any compounds found 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 on the correlation of observed MS signals with known reference masses and MS spectra of biomarkers. Reference data is preferably stored on a computer server, which allows the entire procedure to be performed under computer control.

Signals are correlated to reference standards by comparison, for example using computational functions as described hereinafter. Preferably, the signals are characterized as "positive" or "negative" according to whether a similarity threshold level is reached; signals that are negative and do not reach the similarity threshold level are discarded in the MSBA process, while signals that are positive are combined with biomarkers and result in a diagnosis of the presence of said biomarkers in a biological sample.

Signal intensity is measured by reference to known control standards added to the biological sample, or by comparison with a reference intensity calculated by a group of patients, depending on the implementation of the MSBA assay.

In case of implementation with standards, the MSBA classification of biomarker signals is calculated by the relationship between the biomarker signal present in the sample and the internal standard added to the sample. All biomarkers in a multiple assay will be analyzed in the same manner resulting in a final MSBA classification factor.

In order to have absolute quantification integrated with the MSBA methodology, the use of internal calibrator standards is preferred. Such standards are, for example, labeled isotope, reading the assay highly accurate in terms of protein sequence as well as advantageous in terms of absolute quantitation. Integrated calibration sequences within MSBA screening will allow measurement of absolute protein biomarker levels in the blood, or in any other clinical specimen.

A new multi-step linked method is provided to detect and quantify protein sequence biomarkers with a multi-duplex reader in which expression levels of, but not restricted to, 2-100 biomarkers can be mapped. in a single read from MSBA memory. The MSBA system is developed on a liquid phase platform that can handle single-line diagnostic mapping, or a multiple flow configuration with simultaneous parallel sample processing, thereby increasing system capacity and operating productivity. The detection mode of the MSB method is the accurate mass identification and sequence determination, and subsequent quantification by mass spectrometry.

This methodology can 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 of any type of cell or biotechnology process where, for example, the kinetic profiles are measured over time. This overtime analysis is performed by subsequent introduction of the sample into the MSBA platform automatically over time. The entire analysis capability of profiling MSBA is fully computer controlled including mass signal evaluation, sequence analysis, multi-duplex quantification by weighting discrimination and ultimately MSBA SCORE diagnostics. (MSBA CLASSIFICATION). All intermediate steps within the MSBA cycle that follow this platform are evaluated by dedicated algorithms that make accurate decision making from the massive amount of data generated in each MSBA analysis cycle of 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 can be defined by a specific amino acid sequence, which can be selectively identified or by its precise mass, or its unique immunoaffinity binding properties for a given immunological reagent.

The method allows the identification of statistically significant protein identities and modified versions of them. Furthermore, it is possible to measure the relative amounts of each biomarker in the sample even without the use of internal standards. Alternatively, absolute quantifications can be made of each biomarker separately in any given biological sample by the use of internal standards, where these internal standards (eg, n = 1-20) are the protein sequences of the biomarkers. These internal patterns can then be made as cold amino acid sequences, or as isotope-labeled amino acid sequences. The patterns have sequences identical to the selected biomarkers, with the possible exception of marking.

The method combines several key steps that result in specific processing, separation, isolation, and identification of unique protein sequences present in a sample of biological material. The method can be applied to clinical specimens from humans. The method may also be applied to samples derived from non-human animals.

A multistep method has been provided to identify the identity of unique protein sequences presented for example as atomic mass units of entities from a biological sample, which has been shown to have a quantitative change in a given multi-duplex biomarker group, the size of which may vary for example from 2-100 of a given sample.

We further provide a method for determining or confirming that biomarkers in any specific biological sample have the multiple quantitative deviation of a set of protein sequence biomarkers that is predetermined in a clinical, cellular, or cellular sample. any other kind. This quantitative change is finally calculated by the MSBA algorithms to generate an MSBA SCORE that will read the diagnosis.

Additionally, statistically significant similarities can be detected and recorded as single protein sequence identities, or multiple peptide identities. Statistically determining significant similarities involves using publicly available protein and gene sequence databases, 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 has the following principles: 1) high quality biomedical clinical material, 2) reproducible, high-speed sample processing with subsequent liquid phase separations, 3) accurate quantitative and qualitative determination of a set multiple biomarkers and 4) algorithms that will control data generation and calculate and allow the isolation of biomarkers into a multiple protein sequence group one by one. Brief Description of the Figures

Figure 1 shows a schematic illustration of the principle of MSBA.

Figure 2 illustrates in more detail the data handling procedures involved in MSBA.

Figure 3 shows a mass spectrum of a blood sample from a patient with lung disease. Multiple biomarkers are identified in the sample.

Figure 4 shows an example of biomarker comment made from the multiple assay, presented by the MS spectrum in which the biomarker was recognized by the MSBA software, and the MS / MS spectrum tracking representing the C-VLFPYGGCQGNGNK biomarker. resulting.

Figure 5 shows an example of evaluating the predictability of an MSBA model with 11 biomarker signals in the 19 patient sample data as described in Example 2. 10 cases and 9 controls were used as blind samples. The MSBA classification for each subject was calculated using Eq.5. In this example, subjects whose MSBA score was 1 or higher were diagnosed as cases (red circles). In other respects, the individual was considered to be a control (blue circles). The accuracy of the prognosis was 100%.

Figure 6 shows the self-discrimination results using a 10-sign MSBA model over 96 patient sample data as described in Example 3. Each dot represents a patient, and the vertical axis represents the discriminatory classification (z). , calculated using Eq. 6. If this classification had been> 0, it would be interpreted as a case of the disease (red circles). In other respects, the individual was considered to be a control (blue circles). The forecast accuracy was 83.3%.

Detailed Description of the Invention

Biomarkers

The FDA definition of biomarker is "a feature that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention."

As used hereinafter, the term "biomarker" refers to a polypeptide that can be used to monitor the presence or progression of a disease consistent with the definition of the FDA above.

Biomarkers can be used as diagnostic agents, disease progress monitors, treatment monitors, and clinical outcome predictors. For example, several biomarker research projects are trying to identify markers of specific cancers, and specific cardiovascular and immune diseases.

Some of these disease-associated proteins may be identified as new drug targets, and some may be useful as biomarkers of disease progress. Such biomarkers may be used to improve the clinical development of a new drug or to develop new diagnoses for the particular disease.

Disease-associated proteins are known in the art, and their use as biomarkers for disease is established. Such biomarkers may be monitored by the present invention. New disease-associated proteins, however, can be identified. Detection of disease-associated proteins may be performed, for example, by the following method. Protein samples are taken from single patients or from patient groups. Such samples may be cells, tissues, or biological fluids that are processed to extract and enrich the protein and / or peptide constituents. Typically the process entails division in the solution phase, but may also include the establishment of protein and / or solid matrix-bound components. After separation and analysis (proteomics, peptidonomics), expression fingerprints are produced for both sick and healthy individuals by qualitative and quantitative measurement. These fingerprints can be used as unique identifiers to distinguish individuals and / or establish and / or track certain natural or disease processes. These fingerprint prototypes are established for each individual sample / individual and are recorded as numeric values in a computer database. Fingerprints are then analyzed using bioinformatics tools to identify and select the proteins or peptides that are present in the prototype fingerprints, and whose expression may or may not be present differently in samples derived from healthy and healthy subjects. sick These proteins / peptides are further characterized and detailed profiles are produced which identify the characteristic physical properties of the proteins or peptides. Whether it is a single protein / peptide or protein / peptide groups, can be determined to be significantly associated with certain natural or unhealthy processes.

Mass spectrometry

Mass spectrometry is the method of choice for protein and peptide analyzes. Research with the modern discoverable biomarker employs two main principles of mass spectrometry: MALDI-TOF (matrix assisted laser desorption ionization flight time) mass spectrometry in which proteins are analyzed in a crystalline state, and ESI (electrospray ionization) mass spectrometry in which proteins are analyzed in a liquid state. In addition, an MALDI augmented surface chip application, called augmented surface laser desorption ionization (SELDI), has been widely used in biomarker discovery studies. See, for example, Petricoin et al., Lancet, 16, 572-577, 2002; Alexe et al., Proteomics. 2004, 4766-4783; and Liotta et al., Endocr Cancer Report. 2004 Dec; 11 (4): 585-7. Enhanced Surface Laser Desorption / Ionization (SELDI) -TOF-MS technology uses chromatographic surfaces coupled to the test slide. The protein-bound material on the slides is then directly analyzed by MALDI-MS. SELDI test peptides and proteins predominantly in the low molecular weight range. This technology is applicable to peptides and proteins of higher to medium abundance where an appropriate advance purification scheme is not integrated.

SELDI technology mainly leads to the standard from which sequence can be performed using peptide MALDI-TOF-TOF identification.

Multi-engine sorting platforms enable high resolution peptide separation configured in-line with electrospray ionization mass spectrometry, or "off-line" with ionization principles such as assisted laser desorption ionization mass spectrometry by the matrix. 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).

Mass spectrometry (MS) is also an essential element in the proteomic field. In fact, MS is the main tool used to study and characterize protein structure and sequence within this field. See Aebersold, R. & Mann, M. Mass-based proteomics spectrometry. Nature 422, 198-207 (2003); Steen, H. and M. Mann (2004). Nat Rev Mol Cell Biol 5 (9): 699-711; and Olsen, J. V. and M. Mann (2004) Proc Natl Acad Sci US 101 (37): 13417-22.

Researchers are successfully utilizing the power of MS to put aside the two-dimensional gels that originally gave proteomics their momentum. Using ESI and liquid chromatography (LC) / MS / MS, a voltage is applied to a very fine needle that contains a peptide mixture, generating peptide sequences, purified from the LC- column. The needle then sprays droplets into a mass spectrometric analyzer in which the droplets evaporate and the peptide ions are released. In LC / MS / MS, researchers use microcapillary LC devices to initially separate peptides.

Mass spectrometry (MS) is a valuable analytical technique because it measures an intrinsic property of a biomolecule, its mass, with very high sensitivity. MS can thus be used to measure a wide range of molecule types (proteins, peptides, or any other biomolecules), a wide range of samples of biological types / materials.

Correct sample preparation can influence signal generation and spectrum resolution and MS sensitivity. High resolution separation systems such as single dimensional high pressure liquid chromatography (LC) and multidimensional liquid chromatography (LC / LC) can interface directly with mass spectrometry. This interface enables rapid automated acquisition and collection of large sets of data representing both quantification as well as sequence information within the generated mass spectrum. This shotgun protein technology is known as MudPIT (Multidimensional Protein Identification Technology). See Eng et al., J. Am Soc Mass Spectrom 1994, 5: 976-989; Link et al., Nat Biotechnol. 1999 Jul; 17 (7): 676-82; Washburn et al., Nat Biotechnol. 2001 Mar; 19 (3): 242-7; Lin et al., American Genomic / Proteomic Technology, 2001 1 (1): 38-46; and Tabb et al., J. Proteome Res. 2002 1: 21-26.

In the approach to shotgun proteomics, protein-generated peptides digesting enzymes such as trypsin and other endo-, and exopeptidases / proteinases are analyzed instead of intact proteins. This fragmentation offers definite advantages due to the fact that even very large proteins with varying physical and chemical characteristics such as very hydrophobic or very basic proteins can be analyzed. Such protein classes may instead be difficult to manipulate. These proteins will give rise to mixtures resulting from peptides of sufficient size and number that allow for accurate comment and identification of the protein. However, since several peptides are generated from each respective protein, the complexity of the mixture to be analyzed increases. Consequently, considerable instrument of time and computing power are required for the "shotgun" approach. However, the richness of protein expression information is extensive, and generated in a fully automated nesting with simultaneous real-time protein identification.

In order to be able to manipulate different patient samples with varying degrees of disease, different methods can be applied to help and align the chromatograms and mass spectra resulting from liquid chromatography. This normalization can be accomplished through a software approach whereby total signal generation from the entire experiment is used and compared to that of the various patient samples analyzed. The mean values and common properties of all signals will be aligned to allow for different quantifications.

In a second approach, a predetermined amount of standard peptide is added to the sample. This addition will be done before and after, or before and after digestion of samples. The patterns used will be the current biomarker sequences synthesized as isotope tagged sequences, or without isotope labeling, and spiked with the samples.

The use of labeling technologies within the field of Proteomics for quantitative clinical protein regulation studies is highly common. Various marking techniques have been developed and applied using a variety of binding chemicals. Among the most commonly used brands within the field of proteomics are the ICAT and ITRAQ labels. See Parker et al., Mol. Cell. Proteomics, 625-659, 3, 2004; Ross et al., Cell. Proteomics, 3, 1153-1169, 2004; and DeSouza et al., J. Proteome Res., 2005, 4, 377-386.

Sample Separation

Single- or multidimensional High Performance Liquid Chromatography (HPLC) will be used with the preferred alternative to separate proteins or peptides. The protein or peptide mixture is passed through a sequence of chromatographic stationary phases or dimensions which gives a higher resolution power. HPLC is adaptable for many experimental approaches, and various stationary and mobile phases can be selected for their applicability in resolving specific protein or peptide classes of interest, and for their compatibility with spectrometric methods. downstream detection and identification. HPLC is used to separate clinical samples that have been digested by a proteolytic enzyme in which 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. See, for example, Schulte et al., Expert Rev. Diagn., 5 (2), 2005, 145-157; Chertov et al., Expert Rev. Diagn., 5 (2), 2005, 139-145; Adkins et al., Mol. Cell. Proteomics, 1 (12), 2002 947-955; Pieper et al., Proteomics. 2003 Jul; 3 (7): 1345-64; and Anderson, N.L. & Anderson, N.G. Mol. Cell Proteom.2002 1, 845-867.

The corresponding peptide mixture is passed through a succession of chromatographic stationary phases or dimensions giving a high resolution power. HPLC is flexible for many experimental approaches; In tuning the present invention an optimization is made that specifically eliminates the high abundance fraction of proteins expressed in human blood samples, thereby 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.

MSBA Principles of Operation

Prior to exposing samples to MSBA, a sample handling and preparation step is required in most cases. The purpose of introducing this step prior to the MSBA methodology is to eliminate interference agents and matrix components, thereby facilitating better overall detectability that results in increased comment as well as overall sensitivity. However, in certain embodiments, sample preparation may be dispensed with, particularly if the biomarker is larger in abundance and the sample is of low complexity. Those skilled in the art will be able to determine if a preparation step is essential.

The MSBA platform can be operated in several different ways, predominantly determined by the nature of the sample and its complexity.

Biomarker protein sequences are qualitatively and quantitatively determined in the patient sample by multiple analysis. Both Marked and Unmarked MSBA principles can be applied by employing MSBA assay configurations according to two possible principles: the internal standard addition principle and the non-internal standard principle.

General Methodology

After sample preparation, it is injected into the MSBA platform. Then the following operations are undertaken;

IA stage

First, the biomarker of MS signals needs to be identified within the sample.

A predefined list of +/- 1 Dalton Biomarker list masses that correlate with the retention time index and corresponding mass of the respective biomarker is screened in the biofluid sample. The relative retention time index obtained in most MSBA assays is set in minutes and has a variability of about +/- 2%, although this figure may vary.

These steps are performed by real-time mass spectral matching to the MS-BA reference spectrum repository, as illustrated in Figure 1.

The masses in the MS-spectra are then compared with the Biomarker reference list masses to about +/- 1 Dalton. Stage IB

When the biomarker candidate mass is identified as that of a biomarker that has an MS spectrum matching within the reference list, within +/- 1 Da1 the information about it is saved on the MSBA- server. In the event that the mass is incorrect, MSBA tracing does not save spectral files to the server.

These operations are performed by a file share and an interprocess communication mechanism (such as client-server communication).

Step 2A

When 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 measure of similarity, for example cosine correlation. Using the similarity measure, the correct protein sequence is confirmed. This confirmation is made by spectral combination. Spectral matching is performed by comparing 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 greater is required in order to confirm the accurate protein sequence.

Equivalent operating productivity values for alternative similarity measures will be apparent to those skilled in the art.

The reference spectral comparison and evaluation is performed as follows.

The MS / MS spectrum is represented as a list of doublets (m, v) where m represents the mass-to-charge ratio, and ν means the value of the ion signal strength. By m starting the nib interval, (mi, = current bin width) the MS / MS spectrum can also be expressed by a vector ν = [-y,.,., - y}, where its length (') Is equal to the number of bins, and the value of each element is the sum of all signals within each bin. This is a representation profile of an MS / MS spectrum.

The cosine correlation (S) of 2 different MS / MS spectra (Vt 'V2) Pocle will be calculated as a cosine correlation according to

with eq 1;

<formula> formula see original document page 24 </formula>

The value of S ranges from 0 to 1.0. A value of 0 means that two vectors are completely independent; In the case of a vector of S with the value = 1, this means that the direction of the vectors is the same.

Note that the two vectors of the MS / MS spectra must have the same start, that is, if the start of m of a vector is 500-501, 501-502, ... 1999-2000, then the other spectrum must be binned in the same way. Consequently, the length of the two vectors must be the same.

In order to judge whether the measured MS signals are the correct biomarker or not, the MS / MS portion of the measured signals is extracted and compared to the sample MS / MS reference spectrum using for example correlation. cosine described above.

The cosine correlation value S is equal to or greater than a predefined threshold value, for example 0.8, then the measured signals are judged to be derived from the reputed 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 a biomarker is established, several MS / MS spectra must be collected to construct a reference MS / MS spectrum map. This is a spectrum produced on average from actual and measured data sets and is obtained by a clustered calculation. An example of constructing such a reference spectrum is as follows:

(1) Collection of multiple MS / MS spectra for the targeted biomarker. These MS / MS spectra should be confirmed to be derived from the target biomarker by MASCOT, SEQUEST or other programs with a given confidence level.

(2) The investigation of the similarity of each MS / MS spectrum collected with the similarity mentioned above is investigated. This is accomplished by calculating the cluster using the similarity measure. The cluster calculation is performed to a point where the similarity measure decreases to the predefined threshold value. Following these cluster calculations, a summary list of all ions of the remaining protein sequence within the MS / MS spectra is generated. The next step is to remove the ions from the remaining sequence in the cluster calculation summary list.

(3) Using the MS / MS spectrum of the summary established and qualified from the cluster, it is possible to calculate the arithmetic mean for each element of the spectrum vector. The evaluated vector can now be used as the MS / MS reference spectrum.

(4) During the cluster process, it is possible to achieve a result that generates more than one referral from the patient group.

a) In this case, there is more than one cluster containing a difference in the MS / MS spectral profile map. The set of criteria in these situations is that these groups need to have reliable biomarker identification. It is then possible to generate and make use of more than one reference spectrum for a target. Such cases appear if there are MS / MS fragment ions that are different in the groups but correlated to the same commented protein.

(b) It is also possible to arrive at situations with cluster analysis data where there is a difference in the biomarker profile map. This would mean that several groups of individuals can be established from the cohort of patients, for example different phenotypes. In such cases, the comparative multiple pattern will be a specific phenotype. However, there will also be possibilities for the biomarker to overlap between phenotypes.

These confirmation algorithms will be applied as used in real time within the high operational productivity tracking operations of the MSBA platform exemplified below.

Step 2B

The mass spectrometer (e.g. Finnigan LTQ), once a positive biomarker mass has been identified, will be on that mass target in order to make repeated traces of the biomarker ion signal. The number of scans will depend on the combination of classifications generated for each particular protein sequence, but will be aligned with the positive identity of the biomarker. The tracking window will be determined automatically by the MSBA software.

The criterion for a positive correlation should be greater than or equal to 0.8 in a cosine correlation similarity measure.

The next step will be to make a statistically significant protein sequence identity using commercial research tools such as MASCOT or SEQUEST or any other protein database instrument 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 is not transferred to the MSBA database.

Step 3

Calculation of multiple biomarker assay reading

The calculation of the multiple biomarker assay reading is performed by applying the MSBA algorithm consisting of a discrimination function that will calculate the classification of the MSBA diagnosis.

A discrimination function is defined as a function of X1, ..., Xn, where xi represents an absolute or relative signal intensity of η of the biomarker / ': th. The power of a discrimination function must be a positive or negative value according to the result of the diagnosis. For example, if the diagnosis is positive, the power value of the discrimination function must be positive and vice versa.

For example, in Eq 2, the discrimination function used is outlined:

<formula> formula see original document page 27 </formula>

where, η is the number of multiple biomarkers used for diagnosis, Xi is the absolute or relative signal strength of the biomarker i: th biomarker, and Xtotai is the total signal strength of the MS measurement.

There is also a weight factor included in the MSBA software algorithms.

A vector {α1, ..., αη, αθ] is a weight vector that determines the direction of the normal vector of a separating hyperplane that divides the n-dimensional signal intensity space into two: positive and negative diagnostics. . An example of the procedure for determining the weight vector is described later, however, various types of algorithms, for example, Support Vector Machine, Artificial Neural Network, and others may be used to determine the weight vector.

Another example of a discrimination function is included in the MSBA algorithms and are defined as follows:

<formula> formula see original document page 27 </formula>

The fp and fn function is an arbitrary function that gives a measure of similarity or distance between a set of measured biomarkers in a patient to be diagnosed and reference biomarker signal sets on the MSBA server. fp denotes the function of similarity or difference of positive diagnostic references, and fn denotes those of negative diagnoses.

α and β are coefficients that can be used to unequally weigh positive-diagnostic and negative-diagnostic metrics. If the function fp and fn give a measure of similarity, then a patient's sample will be diagnosed as positive when equation 3 generates a positive value. If the functions give distance measurements, the positive value of eqn3 means the negative diagnosis.

An example of such a function is Euclidean distance, where y is the i: th biomarker signal strength in a patient to be diagnosed,

<formula> formula see original document page 28 </formula>

and X is the i: th biomarker signal strength of the reference set.

Another example is a standard error of the predicted regression value:

<formula> formula see original document page 28 </formula>

where η is ο number of biomarker signals, x, is the signal intensity of z: th measured from a patient sample, and y is the predicted value of each χ, using a linear regression line that was calculated by smaller square adapted between the measured x, 's and the reference signals.

The entire software scheme, including the algorithms that control each specific step within the process, is outlined in Figure 2.

Step 4

Biomarker Annotation and Quantification The MSBA assay platform is based on one: A) separation principle

B) principle of non-separation

In the case of the non-separation principle we are able to make biomarker comments and quantifications by: (a) Direct MS analysis

i) direct infusion of biological sample using static nanoelectrospray principles. Disposable nanospray needles are used, where each nanoelectrospray needle will only be exposed to one biological sample, thereby circumventing sample overload and memory effects.

(ii) flow injection analysis mode in which the sample is injected as a buffer. The sample volume chosen within the plug is directly related to the signal strength of the respective biomarker sequence. It is also possible for low abundance biomarkers to use large (several ml) sample injection volumes, thereby achieving a saturation (steady state) of the mass spectrometer ion signal efficiency.

(iii) flow injection with sample enrichment by MS- analysis using a chromatographic solid phase extraction enrichment column. This step allows simultaneous total cleaning by eliminating matrix components within the sample and enriching the trace biomarkers. The advantage of this approach is that it is possible to analyze biomarkers from highly complex sample sources, for example tissue extracts.

Additionally, in sample enrichment mode (iii), we are able to generate signal amplification factors ranging but not restricted to 2-500. Additionally, this approach will improve the detectability of biomarkers expressed at low levels, but also the accuracy of protein sequence annotation.

B) In the separation analysis, liquid chromatography (LC) integrated with biomarker identification has the high LC resolution power that can be operated in a single column mode (see Figure x), or in a multicolumn design using column switching where samples are analyzed in a sequential mode, thereby improving sample operational productivity.

MSBA Programming

Here is an example of core part code to calculate a vector weight (or a model) and to predict diagnosis using the Support Vector Machine algorithm. The code is written in R- language. A model will be constructed from the train data set, and then used to generate a prediction for a given test data set. Train and test datasets are data structures containing plural numbers of data points, consisting of a Diag object containing diagnostic results (categorical value: Positive or Negative. Values are (for pred), and a vector containing signal strength for each biomarker and a total signal strength. MSBA programming will rely on data generated from the protein sequence performed in the two patient groups from which the biomarkers were generated.

Programming within the MSBA software is carried out as follows;

library (el071) model <- svm (Diag data = train) ## The above equation can calculate the model, but it can be very simple to reflect eqn 2.

## selected support vectors are: model $ SV ## and weight vectors are: modeIScoefs pred <- predict (model, test)

Examples

The following examples are illustrations of a lung cancer study that was performed by profiling the protein by LC-MS in human blood samples. Two patient groups were analyzed, the CASO and CONTROL cancer groups with differential protein expression differences analyzed.

Example 1

Experimental Details

Approximately 6 ml of each patient's blood was collected into a test tube containing heparin sodium salt and mixed well 2-3 times. It was then centrifuged at 2,000 χ g for 10 min at 4 ° C. Three ml of plasma were obtained from a supernatant. The sample was stored frozen at -80 ° C. The proteins were then extracted from plasma and subjected to tryptic digestion after albumin and IgG depletion from abundant human plasma. Aliquots of the fractionated plasma sample were then analyzed by LC-MS, and sequenced by MS / MS, as previously described [W006100446].

The MSBA Chart

The multi-duplex biomarker summary graph shows the data on the multi-duplex expression of patient biomarkers within the lung cancer study.

The readout of the diagnosis of the multi-duplex biomarker 10 generated from the MSBA methodology illustrates each and all biomarkers separately (see Figure 3). The quantitative difference, that is, the difference in timeshift that is already known and stored within the MSBA database (see Figure 1) is used along with the qualitative differences to evaluate the biomarkers. Biomarker data generated from the lung cancer study correctly identify all of these patients as positive from the diagnostic multi-duplex MSBA reading.

It was found that the classification of all these 10 patients is in the range of 0.80 to 1.0.

Figure 4 shows an example of biomarker annotation made from the multiplex assay, presented by the MS spectrum where the biomarker was recognized by the MSBA1 software and the MS / MS spectrum tracking (see Figure 4) representing the resulting CVLFPYGGCQGNGNK biomarker. The combination of MSBA, using the reference biomarker spectra in the MSBA database applying cosine correlation, shows that the cosine correlation factor is equal to or greater than 0.8. .

Table 1 presents the details of MS-BA data generation where the predefined masses of the regulated biomarkers are analyzed. Table 1

<table> table see original document page 32 </column> </row> <table> <table> table see original document page 33 </column> </row> <table>

Example 2

Another example described below is derived from a CASE - CONTROL study lung disease that was performed by profiling the protein by LC - MS in human blood samples. Two patient groups were analyzed, the CASO lung disease group and the CONTROL lung disease group with differential protein expression analysis. Experimental Details

Plasma sample collection and preparation procedures were the same as in the above example.

MSBA Model Building and Evaluation

Sample data from 46 patients consisting of 10 cases and 36 controls were used to construct the MSBA models. Five different MSBA models were constructed containing 14, 8, 26, 8, and 11 signals, respectively. After the combination of the 5 models, it was found that the final (5th) model of MSBA had dominant discriminating ability. Thus, only the 5th model was used in the next step. The LC-MS (retention time (min) / MS value (m / z)) information for the 11 final model signals was as follows: 11.6 / 485.2, 12.5 / 608.1, 18.3 / 547.0, 20.2 / 681.3, 21.1 / 575.1, 21.3 / 531.5, 25.5 / 561.6, 23.1 / 514.5, 32.5 / 682.2, 44.0 / 985.2, 48.7 / 945.8.

In order to evaluate the model's predictive capacity, we tried to predict CASO / CONTROL using 10 cases and 9 controls as if they were blind samples. According to the MSBA diagnostic procedure described above (also illustrated in figure 2), the multiplex biomarker signals 11 were identified for each test sample, and quantification of each peak signal was performed. From the amount of all the multiplex signals 11, the MSBA rating for each individual was calculated using the formula described above (Eq. 5). In this example, individuals whose MSBA score was equal to or greater than 1 were diagnosed as cases (Table 2, MSBA diagnosis). Consequently, all samples could be correctly predicted, ie the discriminatory capacity was 100% (see figure 5).

Example 3

The following example was also derived from the CASO-CONTROL lung disease study performed by profiling the protein by LC-MS in human blood samples with two patient groups (CASE and CONTROL. In this example, another set of Multiplex biomarkers were used to build an MSBA model, with a different data set of an even larger size.

Experimental Details

Plasma sample collection and preparation procedures were the same as the example described above.

MSBA Model Building and Evaluation

Sample data from 96 patients consisting of 21 cases and 75 controls were used as a training data set. As the number of samples increased, sample variability did not increase. Thus, the Smirnov test was first applied to remove outlier points. Consequently, 5 samples containing divergence points were also removed from the analysis set. Using the t-test result, an initial MSBA model containing 100 candidate biomarker signals was constructed.

Next, recursively applying discriminant analysis to remove the minimum contributed signal from the discrimination model, we finally obtained a 10-signal MSBA model. See table 3 for a list of 10 multi-duplex marker signals.

In this example, to calculate the discriminant classification (z), another classification function was used as the following equation:

<formula> formula see original document page 35 </formula>

(χ,: signal strength, a, & C: coefficients described in Table 3)

If the rating value is positive, it is interpreted as CASE, if not CONTROL.

In order to evaluate the model's prediction capacity, we tried to predict CASE / CONTROL using the same data set (21 cases + 75 controls, 96 in total). According to the MSBA diagnostic procedure described above (also illustrated in figure 2), the multi-duplex biomarker signals 10 were identified for each sample, and quantification of each peak signal was performed. From the amount of all multiplex signals 10, the MSBA rating for each individual was calculated using the formula described above (Eq.6). In this example, individuals whose MSBA score was equal to or greater than 0 were diagnosed as cases. Table 4 and Figure 6 show the results of self-discrimination. In figure 6, each point represents a patient, and the vertical axis represents the discriminant classification (z). In this example, the sensitivity was 85.7%, the specificity was 82.7%, and the prediction accuracy was 83.3%.

Table 2

<table> table see original document page 36 </column> </row> <table>

Table 3

<table> table see original document page 36 </column> </row> <table> <table> table see original document page 37 </column> </row> <table>

Table 4

<table> table see original document page 37 </column> </row> <table> <table> table see original document page 38 </column> </row> <table> <table> table see original document page 39 < / column> </row> <table> <table> table see original document page 40 </column> </row> <table>

Claims (17)

1. A method for determining the presence of one or more polypeptide bi-markers in a sample, comprising the steps of: (a) subjecting the sample to mass spectrometric analysis (MS) and recording the retention time index and the corresponding mass for each signal detected; (b) correlating the mass corresponding to each signal to a biomarker mass reference database to form a correlation between each signal and a reference biomarker, and discarding those signals whose masses do not correlate to a biomark mass - reference holder; (c) storing those signals whose masses correlate to a reference biomarker; (d) confirm the correlation between each stored signal and a reference biomarker by combining the MS spectrum of each signal with the reference biomarker MS spectrum in the database using a similarity measure to define a set of signaling signals. positive correlation; (d) measure the intensity of each positive correlation signal and classify its absolute signal strength or relative signal strength using a discrimination function; (e) apply a limit to the classification values obtained from the discrimination function to determine the presence or absence of the biomarker. The method according to claim 1, wherein the test sample is subjected to MS analysis without prior separation procedures. A method according to claim 2, wherein the test sample is analyzed by direct infusion using the principles of static nanoelectrospray, flow injection analysis or sample enrichment flow injection. The method of claim 1, wherein the test sample is processed prior to MS analysis. The method of claim 4, wherein the sample processing comprises separating the sample by single-phase or single-phase high pressure liquid chromatography (HPLC). A method according to any preceding claim, wherein the MS is electrospray ionization (ESI) MS, matrix assisted laser desorption ionization MS - flight time (MALDI-TOF) or desorption ionization MS. surface-enhanced laser - flight time (SELDI-TOF). A method according to any preceding claim, wherein the reference mass and MS spectral data for a plurality of biomarkers are stored in paper or electronic form. A method according to any preceding claim, wherein the reference MS spectra for a defined biomarker are the average of the current spectra and measured data obtained by a cluster calculation. A method according to any preceding claim, wherein one or more internal standards of the reference peptides are added to the sample prior to MS analysis. The method of claim 9, wherein the internal standards are labeled with a molecular tag. The method according to claim 9, wherein the internal standards are marked and included in the main data set. A method according to claim 11, wherein the absolute signal strength is classified by measuring the signal strength of the biomarker and comparing it with the signal strength of one or more known internal standards. A method according to any one of claims 1 to 9, wherein the sample is processed without the addition of internal standards. A method according to claim 13, wherein the relative signal strength is classified by measuring the ratio between the individual biomarker signal strengths in a patient and the reference signal strength for a patient group. . The method of claim 13, which is completely automated. The method according to claim 1, wherein the discriminatory function for calculating the MS signal strength rating optionally includes the use of any clinical variables such as clinical examination and / or phenotyping results. clinical observation and / or medical recordings. 17. Diagnostic method for determining the presence of a disease comprising comparing the protein sequence biomarkers of a test sample with reference biomarkers, wherein the reference biomarkers comprise the peptides identified in Table 1.