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 Quality assurance/quality control for high throughput bioassay processUSPTO Application #: 20080103063Title: Quality assurance/quality control for high throughput bioassay process Abstract: The present invention relates to a method of quality assurance/quality control for high-throughput bioassay processes. The method includes generating a bioassay process model, and then comparing spectral data based on a combination of a biochip and a test serum to the bioassay process model to determine if the test sample and the bioassay process are producing acceptable data. Alternatively, the method may include comparing spectral data based on a combination of serum and diluents used in an electrospray process to the bioassay process model. If the bioassay process and test sample fall within the model, then the spectrum produced may be further analyzed. (end of abstract)
Agent: Cooley Godward Kronish LLP Attn: Patent Group - Washington, DC, US Inventors: Ben A. Hitt, Peter J. Levine, Emanuel F. Petricoin USPTO Applicaton #: 20080103063 - Class: 506024000 (USPTO) The Patent Description & Claims data below is from USPTO Patent Application 20080103063. Brief Patent Description - Full Patent Description - Patent Application Claims
CROSS-REFERENCE TO RELATED APPLICATIONS [0001] This application claims priority under 35 U.S.C. .sctn. 119(e) to U.S. Provisional Patent Application No. 60/389,831, entitled "Quality Assurance/Quality Control for SELDI-TOF Mass Spectra," filed on Jul. 29, 2002, the contents of which are hereby incorporated by reference in its entirety. BACKGROUND OF THE INVENTION [0003] The present invention relates generally to the field of bioinformatics. More specifically, the present invention relates to a method of quality assurance/quality control ("QA/QC") for bioinformatic systems. [0004] Methods of analyzing biological samples are generally known. In a typical analysis, mass spectroscopy is performed on the biological sample to determine its overall biochemical make-up. Based on the mass spectra obtained from the mass spectroscopy, various diagnostics may be run. [0005] When biological samples are analyzed, it is desirable to run more than one trial on the biological sample, thereby improving the accuracy of the diagnostic. Analysis of biological samples may be performed by using various high-throughput mass spectrometry related bioassay processes. A process can include using matrix assisted laser desorption ionization time-of-flight (MALDI-TOF) or electrospray techniques (i.e., generation of droplets by applying a high voltage to a stream of liquid). When performing multiple mass spectral analyses on the same sample, however, the spectra obtained can vary. This variation may be due to the mass spectrometer itself, from inconsistencies in the sample, heterogeneity in the patient population, or in sample handling and processing. A process that employed a protein chip or surface enhanced type of mass spectrometry (SELDI-TOF) indicated that various chips yielded spectra that were inconsistent with one another. Similar effects were observed with respect to spectra obtained using electrospray techniques. This inconsistency can lead to inaccurate results when running a diagnostic. SUMMARY OF THE INVENTION [0006] The present invention provides a QA/QC method for filtering out inconsistencies across high-throughput bioassay processes, particularly across different biochips and different diluents or concentrations of diluents used in electrospray techniques. [0007] The present invention uses the Knowledge Discovery Engine ("KDE") to identify hidden patterns across a wide variety of serum samples and biochips to generate a control model and agnostic to the underlying disease processes in question. Electrospray, MALDI-TOF (Matrix Assisted Laser Desorption/Ionization-Time of Flight) mass spectra, or SELDI-TOF (Surface Enhanced Laser Desorption/Ionization-Time of Flight) mass spectra can be analyzed in this manner, for example. Alternatively, the invention may use the KDE to identify hidden patterns across a variety of serum to diluent concentrations to generate a control model. In yet another embodiment, the KDE may be used to identify hidden patterns across a variety of diluents and sera samples to generate a control model. [0008] The KDE is disclosed in U.S. patent application Ser. No. 09/883,196, now U.S. Application Publication No. 2002/0046198A1, entitled "Heuristic Methods of Classification," filed Jun. 19, 2001 ("Heuristic Methods"), and U.S. patent application Ser. No. 09/906,661, now U.S. Application Publication No. 2003/0004402A1, entitled "A Process for Discriminating Between Biological States Based on Hidden Patterns from Biological Data," filed Jul. 18, 2001 ("Hidden Patterns"); the contents of both applications are hereby incorporated by reference in their entirety. Software running the KDE is available from Correlogic Systems, Inc., under the name Proteome Quest.TM.. [0009] After the KDE is used to generate a control model, a test serum may be compared to the control model to determine if the spectra produced by the high-throughput bioassay process and the serum are acceptable. [0010] The KDE will search for hidden or subtle patterns of molecular expression that are, in and of themselves, "diagnostic." The level of the identified molecular products is termed per se diagnostic, because the level of the product is diagnostic without any further consideration of the level of any other molecular products in the sample. [0011] In the data cluster analysis utilizing the KDE, the diagnostic significance of the level of any particular marker, e.g., a protein or transcript, is a function of the levels of the other elements that are used to calculate a sample vector. Such products are referred to as "contextual diagnostic products." The KDE's learning algorithm discovers wholly new classification patterns without knowing any prior information about the identity or relationships of the data pattern, i.e., without prior input that a specified diagnostic molecular product is indicative of a particular classification. [0012] If the spectrum produced by the biochip and the serum map to the control model, then the data obtained from mass spectrometry of the serum and biochip may be used for further analysis. If the spectrum produced by the biochip and the serum fail to map to the control model, the data is deemed uncertified, and new data must be acquired. Alternatively, if a spectrum produced by a serum sample and a diluent map to the control model, then the spectrum obtained from an electrospray process may be used for further analysis. By using this method, inconsistencies across bioassay processes may be avoided, thereby improving the reliability of data obtained using the bioassay process. Other advantages may also be realized from the methods disclosed herein, as would be obvious to the ordinarily skilled artisan. BRIEF DESCRIPTION OF THE DRAWINGS [0013] FIG. 1 is a flow chart illustrating a method of obtaining a control model. [0014] FIG. 2 depicts a table having various serum/biochip combinations that may be used to obtain the control model. [0015] FIG. 3 illustrates a method of comparing the test serum to the control model. [0016] FIG. 4 is a depiction of mapping of exemplary tolerances in three-dimensional space according to one aspect of the present invention. [0017] FIG. 5 is a table illustrating results obtained from the KDE using two different types of biochips and 256 sera. [0018] FIG. 6 is a flow chart of an alternative embodiment of the present invention for use with an electrospray process. DETAILED DESCRIPTION [0019] Generally, the invention includes a method of obtaining a control model for use in a bioinformatics system and a method for comparing a test sample against the model for the purpose of QA/QC. [0020] A method of obtaining a control model according to one aspect of the present invention is illustrated in FIG. 1. To ensure a highly articulate model, a variety of serum samples are selected at step 100. The selection should include selecting serum from as diverse a group of individuals as possible. The more diverse the selected sera, the more articulate the control model will be. For example, sera may be taken from healthy males, healthy females, males afflicted with various diseases, females afflicted with various diseases, persons of different ages, and persons of different races. Continue reading... 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