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  • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
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  • 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
  • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
  • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
  • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
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• • G—PHYSICS
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  • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
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  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H70/00—ICT specially adapted for the handling or processing of medical references
  • G16H70/60—ICT specially adapted for the handling or processing of medical references relating to pathologies
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
  • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression

Definitions

• BIOMARKERS Inventor: F. Randall Grimes (Scottsdalc, Aruona)
• the invention relates generally to methods and apparatus for identifying disease status in a patieni, and more particularly to identifying disease .status in a patient according to levels of one or more biomarkcrs.
• Biomarkcrs are used in medicine to help diagnose or determine the presence, absence, status and/or stage of particular diseases
• ⁇ iagnostically useful biomarkers have been identified using measured levels of a single biomarker obtained from a statistically significant number of disease-negative and disease-positive subjects in a population and establishing a mean and a standard deviation tor the disease negative and positive stales. Tf the measured biomarker concentrations for the disease-positive and -negative states were found to have widely separated Gaussian or nearly Gaussian distributions, the biomarker was considered useful for predicting instances of the disease.
• biomarkers Human biochemistry is a complex system In which many components serve multiple functions and are themselves regulated by a variety of other components. ⁇ s such, it is common to find biomarkers thai display non-Gaussian distributions, which include values that lie substantially apart (at the fur high end and/or far low end of the distribution) from the bulk of the values, and may span several orders of magnitude.
• Methods and apparatus for identifying disease status include analyzing the levels of one or more biomarkers.
• the methods and apparatus may use biomarker data for a condition-positive cohort and a condition-negative cohort and automatically select multiple relevant biomarkers from the plurality of biomarkers.
• the system may automatically generate a statistical model for determining the disease status according to differences between the biomarker data for lhe relevant biomarkers of the respective cohorts.
• the methods and apparatus may also facilitate ascertaining the disease status of an individual by producing a composite score for u ⁇ individual patient and comparing the patient's composite score to one or more thresholds for identifying potential disease status.
• Figure 1 is a block diagram of a computer system.
• J I'igure 2 is a flow chart of a process for identifying disease status.
• Figure 3 is a flow chart of ti process fnr controlling a range of values.
• FIG. 5 is a flow chart of a process for classifying data according to cut points.
• Figure 6 is a plot of cumulative frequencies of disease-positive and disease- negative biomarker concentrations.
• FIG. 7 is a flow chart of a process for establishing a disease status model.
• Figure 8 is a flow chart of a process for identifying disease status in an individual.
• FIG. 9 is a plot of cumulative frequencies of breast cancer positive and breast cancer negative concentrations versus PS ⁇ concentration.
• Figure 10 illustrates data scoring model tor selecting one or more cut points.
• the present invention is described partly in terms of functional components and various processing steps. Such functional components and processing steps may be realized by any number of components, operations and techniques configured to perform the specified functions and achieve the various results.
• the present invention may employ various biological samples, Womarkers, elements, materials, computers, data sources, storage systems and media, information gathering techniques and processes, data processing criteria, statistical analyses, regression analyses and the like, which may carry out a variety of functions.
• the invention is described in the medical diagnosis context, the present invention may be practiced in conjunction with any number of applications, environments and data analyses; the systems described arc merely exemplary applications for the invention.
• an exemplary biomarker analysis system 100 may be implemented in conjunction with a computer system 1 10, for example a conventional computer system comprising a. processor 1 12 and a random access memory 1 14, such as a remotely-accessible application server, network server, personal computer or workstation.
• the computer system 1 10 also suitably includes additional memory devices or information storage systems, such as a mass storage system I 16 and a user interface J 18, for example a conventional monitor, keyboard and tracking device.
• the computer system 1 10 may, however, comprise any suitable computer system and associated equipment and may be configured in any suitable manner.
• the computer system 1 10 comprises a stand-alone system.
• the computer system 1 10 is part of a network of computers including a server 120 and a database 122.
• the database stores information that may be made accessible to multiple users 124 A-C, such as different users connected to the server 120.
• the server 120 comprises ⁇ remotely-accessible server, such as an application server that may be accessed via a network, such as a local area network or the Internet. [00023J "L he software required for receiving, processing, and analyzing biomarker information may be implemented in a single device or implemented in a plurality of devices.
• the software may be accessible via a network such that storage and processing of information takes place remotely with respect to users 124A-C.
• the biomarker analysis system 100 according to various aspects of the present invention and its various elements provide functions and operations to facilitate biomarker analysis, such as data gathering, processing, analysis, reporting and/or diagnosis.
• the present biomarker analysis system I ⁇ 0 maintains information relating to biomarkers and facilitates the analysis and/or diagnosis.
• the computer system 1 10 executes Lhe computer program, which may receive, store, search, analyze, and report information relating to biomarkers.
• the computer program may comprise multiple modules performing various functions or operations, such as a processing module for processing raw data and generating supplemental data and an analysis module for analyzing raw data and supplemental data to generate a disease status model and/or diagnosis information.
• the procedures performed by the biomarker analysis system 100 may comprise any suitable processes to facilitate biomarker analysis and/or diagnosis.
• the biomarker analysis system 100 is configured to establish a disease status model and/or determine disease status in a patient. Determining or identifying disease status may comprise generating any useful information regarding the condition of the patient relative to the disease, such as performing a diagnosis, providing information helpful to a diagnosis, assessing the stage or progress of a disease, identifying a condition that may indicate a susceptibility to the disease, identity whether further tests may be recommended, or otherwise assess the disease status, likelihood of disease, or other health aspect of the patient.
• the biomarker analysis system 100 receives raw biomarker data and subject data (210) relating to one or more individuals providing the biological samples from which the biomarker data is drawn.
• the biomarker analysis system 100 processes the raw data and subject data to generate supplemental data (212), and analyzes the raw data, subject data, and/or supplemental data (214) to establish a disease state model and/or a patient diagnosis (216).
• biomarker analysis system 1 00 may also provide various additional modules and/or individual functions.
• the biomarker analysis system 100 may also include a reporting function, for example to provide information relating to the processing and analysis functions.
• the biomarker analysis system 100 may also provide various administrative and management functions, such as controlling access and performing other administrative functions.
• the biomarker analysis system 100 suitably generates a disease status model and/or provides a diagnosis for a patient based on raw biomarker data and/or additional subject data relating to the subjects in the cohorts.
• the biomarker data may be acquired from any suitable biological samples containing measurable amounts of the biomarkers.
• biomarker data are obtained and processed to establish a disease status model that incorporates data from a. plurality of biomarkers, such as data from members of disease-negative and disease-positive cohorts or other condition-positive and/or -negative groups.
• the biological samples are suitahly obtained from a statistically significant number of disease-positive and -negative subjects.
• Disease-positive and -negative cohorts may contain a sufficient number of subjects to ensure that the data obtained are substantially characteristic of the disease-negative and disease-positive states, such as statistically representative groups.
• each cohort may have at least 30 subjects in each cohort.
• Bach cohort may be characterized by several sub-cohorts, reflecting, for example, that the disease can exist in disease-positive individuals at various stages, or other demographic, behavioral, or other factors that may affect the biomarker levels in either disease-positive or -negative individuals.
• the biomarker analysis system 100 may utilize any single or combination of biological materials from which the levels of potential biomarkers may be reproducibly determined.
• levels of all measured biomarkers are obtained from as few sample sources as possible, such as from a single, readily obtained sample.
• sample sources may include, but are not limited to, whole blood, serum, plasma, urine, saliva, mucous, aspirates (including nipple aspirates) or tissues (including breast tissue or other tissue sample).
• Biomarker levels may vary from source-to-sourcc and disease- indicating levels may be found only in a particular sample source. Consequently, the same sample sources are suitably used both for creating disease status models and evaluating patients. If a disease status model is constructed from biomarker levels measured in whole blood, then the test sample from a patient may also be whole blood. Where samples are processed before testing, all samples may be treated in ⁇ like manner and randomly collected and processed.
• the biomarker analysis system 100 may analyze any appropriate quantity or characteristic.
• a biomarker may comprise any disease-mediated physical trait that can be quantified, and in one embodiment, may comprise a distinctive biochemical indicator of a biological process or event.
• Many biom ⁇ rkers are available for use, and the blomarker analysis system 100 provides an analytical framework for modeling and evaluating blomarker level data.
• Raw biomarker levels in the samples may be measured using any of a variety of methods, and a plurality of measuring tools may be used io acquire biomarker level data.
• suitable measuring tools may include, but arc not limited to, any suitable format of enzyme- 1 inked immunosorbent assay (ELTSA), radioimmunoassay (RlA), Mow cytometry, mass spectrometry or the like.
• ELTSA enzyme- 1 inked immunosorbent assay
• RlA radioimmunoassay
• Mow cytometry mass spectrometry or the like.
• biomarker levels may vary from method to method and from procedure to procedure, the biomarker analysis system 100 of the present embodiment uses consistent methods and procedures for creating disease status models as well as for evaluating putienls. For example, if a disease status model is constructed from biomarker levels measured using a specified ELlSA protocol, then the test sample from a patient should be measured using the same ELISA protocol.
• the blomarker data such as the raw biomarker levels and any other relevant data, arc provided to the biomarker analysis system 100 for processing.
• One or more markers may be analyzed by the biomarker analysis system 100.
• the biomarker analysis system 100 may process the biomarker data to incorporate multiple markers, minimize potential impact of non-Gaussian distributions, and account for biodiversity. Tn the present embodiment, the biomarker analysis system 100 analyzes multiple biomarlcers, assigns boundary values for the biomarker levels, generates normalized data bused on the raw data and potentially relevant biomarker-af ⁇ ecting factors, compares biomarkers to cut points, and/or reduces the range of raw and/or adjusted data values.
• the biomarker analysis system 100 may also adjust the data for disease-specific risk factors and analyze the data to generate the disease status model.
• the biomarker analysis system 100 may analyze multiple biomarkers to establish a disease status model and generate a diagnosis. Given the complex interaction of human biochemistry, multiple markers may have a relationship with the presence or absence of the disease state. Further, a single biomarker may not be associated exclusively with only one disease. While a single biomarker may provide useful information, diagnostic reliability may be improved by including a plurality of biomarkers, for example the most informative biomarkers.
• the biomarker analysis system 100 suitably integrates these multiple, less than ideal, but still statistically significant and informative biomarkers.
• the biomarker analysis system 100 may assess whether a given biomarker is informative, such as according to a classification of not informative, informative, or highly informative, and whether it is productive to include the marker in the disease status model. For example, various biovnarkers are associated with breast cancer and, when modeling characteristic biomarker levels and evaluating breast cancer in subjects, such markers may be highly relevant.
• up-regulated (elevated) and/or down-regul ⁇ ted (suppressed) levels in serum of prostate-specific antigen (PS ⁇ ), tumor necrosis factor alpha (TNF-u), intcrlcukln-6 (1L-6), interleukin-8 (IL-8), vascular endothelial growth factor (VEOF), and/or riboflavin carrier protein (RCP) are associated with breast cancer.
• PS ⁇ prostate-specific antigen
• TNF-u tumor necrosis factor alpha
• IL-8 interleukin-8
• VEOF vascular endothelial growth factor
• RCP riboflavin carrier protein
• Biomarker analysis system 100 may process the data to accommodate effects of nun-Gaussian distributions. Unlike Gaussian distributions, non-Gaussian distributions may be skewed to the left or to the right with respect to a data mean.
• Non- ⁇ a ⁇ ssian distributions can be mathematically transformed to Gaussian distributions using logarithmic transformation.
• Non-Gaussian data can be subjected to sub-group averaging, data segmenting, using differential distributions, or using non-parametric statistics. [00035] To integrate a plurality of biomarkers and control any adverse impact of non-
• the biomarker analysis system may pre- process the biomarker data to generate additional data to facilitate the analysis.
• the biomarker analysis system 100 may impose various constraints upon, make adjustments to ⁇ nd/or calculate additional data from the raw biomarker level data to generate supplemental data comprising a set of variables in addition to the raw data that may be processed, for example using logistic regression to generate a linear model or other appropriate Statistical analysis lhal describes the relationship of the biomarkers to the disease state.
• the biomarker analysis system 100 may be configured to process the raw biomarker data to reduce negative effects of non-Gaussian distributions.
• the biomarker analysis system 100 may reduce the influence of non-normal biomarker levels in biomarkers with non-Gaussian distributions, such as by assigning maximum and/or minimum allowable values or caps for each such bioinarker.
• the caps may be assigned according to u ⁇ y suitable criteria, such as to encompass between about 66% and about 99.7% oCihe susured levels and exclude extraordinarily high values.
• the maximum and/or minimum allowable values for each candidate biom ⁇ rker may be established by first determining an intermediate value (310), such as the mean or median value, of that biomarker in the disease-negative cohort, and determining (he standard deviation of n selected quantity of the measured biomarker levels (312), such as approximately 30% - 45% of the data points on either side of the median value when the data is plotted on a histogram, such that the central 60% to 90% of the measured data points are accounted for in determining the standard deviation.
• an intermediate value such as the mean or median value
• ⁇ maximum allowable value may be determined (314) according to the intermediate value and the standard deviation of the selected biomarker data, for example by adding to the median value to a multiple of the standard deviation, such as no more than four times the standard deviation, and more typically, an amount between one and a half and three times the standard deviation.
• the biomarker analysis system 100 uses the median, instead of the mean, as the basis for determining the allowed maximum to more accurately reflect the majority of the values while reducing the impact of one or a few very high outlying, non-Gaussian values.
• Maximum values may also be calculated using data from any suitable set of data and any suitable technique or algorithm, such as data from ⁇ disease-positive cohort or from a mixture of disease-positive and di$ease-negative subjects. Maximum values may be calculated for each of the relevant biomarkers.
• the maximum values for the applicable biomarkers are calculated by adding the median value of the biomarker for all subjects without breast cancer to two-and-a-half times the standard deviation of the marker for all subjects without breast cancer.
• suitable median values for PSA, TT, -6, TNF -ot. IL-8, and VEGH" may be within ranges of 0.01 -10, 0.5-25, 0.1 -10, 5-150, and 100-5,000 pic ⁇ grams per milliliter (pg/ml) respectively, such as .53, .34, 2.51 , 52.12, and 329.98 pg/ml, respectively.
• Maximum values may he assigned for each of the biomarkers PSA, 1L-6, TNF- ⁇ , II . -8, and VEGF, for example within the ranges of 5-200, 10-300, 0.5-50, 100-2,000, and 500- 10,000 pg/ml, respectively, such as 122.15, 12.52, 48.01, 350.89, and 821.15 pg/ml, respectively.
• different maximum values may be calculated for the PSA, 1L-6, RCP, TMF-u, IL-8, and VEGF biomarkers, or for the RCP, TNF- ⁇ , IL-8, and VEGF biomarkers alone.
• these figures are determined using ELISA measurements for healthy women. The values may change as more data is added, variations in the EL1S ⁇ procedure and/or test kits, reliance on data for disease-positive women, or use of non-ELlS ⁇ techniques.
• the resulting maximum allowable value may then be compared to the individual measured biomarker levels (316). If a particular subject's measured level is above the maximum value, a modification designator or flag, such as an integral value of 1 or 0 or other appropriate designator, may be associated with the subject's biomarker data, such as recorded in a particular field in his or her supplemental data set; if the biomarker level is below the maximum, an integral value of 0 is recorded in his or her supplemental data set (3 18).
• the designator criteria may be applied consistently between generating a disease status model and scoring an individual patient's biomarker levels to ease disease status model interpretation.
• the designators may also comprise more than just two discrete levels.
• biomarker values exceed the maximum allowable value for that biomarker, (he raw bi ⁇ mtirkcr values may be replaced with the maximum allowable value for that biomarker (320).
• the adjusted data having capped values and additional designators may be part of the supplemental data, so that the raw data is preserved and the adjusted data wilh capped values and additional designators become part of the supplemental data set.
• the additional designator denotes that lhc measured values were unusually high, which may be informative about the disease status, while the replacement with the cap value limits the influence of the extremely high values. WiLhoul such caps, the extremely high values may "pull" the linear model to fit data that is the exception, not the norm .
• Hag is set in the subject's supplemental data to indicate that the RCP biomarker exceeded the limit and the raw biomarker level may be replaced with the maximum allowable value. Conversely, if the TNF- ⁇ biomarker level is within the range of accepted values, the original biomarker level is retained and lhc corresponding flag in the subject's supplemental data remains unset.
• the biomarker analysis system 100 may also be configured to generate and analy/e normalized data, for example based on the raw biomarker data and/or the capped supplemental data.
• Normalized data comprises the original data adjusted to account for variations observed in the measured values that may be attributed to one or more statistically significant biomarker level-affecting factors. For example, genetic, behavioral, age, medications, or other factors can increase or decrease the observed levels of specific biomarkers in an individual, independent of the presence or absence of a disease state.
• potential factors that may substantially affect the levels of biomarkers indicative of breast cancer include: age; menopausal status; whether a hysterectomy has been performed; the usage of various hormones such birth control, estrogen replacement therapy. Tamoxifen or Raloxifene, and fertility drugs; the number or full-term pregnancies; the total number of months engaged in breast-feeding; prior breast biopsies; prior breast surgeries; a family history of breast cancer; height; weight; ethnicity; dietary habits; medicinal usage, including the use of NSAlOs; presence of other diseases; alcohol consumption; level of physical activity; and tobacco use.
• Any suitable source or system may be used to identify factors that may affect a given biomarker, such as literature and research.
• any suitable processes or techniques may be used to determine whether particular factors are applicable and to whul degree. For example, upon collecting the biological samples, members of the cohorts can be queried through subject questionnaires, additional clinical tests, or other suitable processes and mechanisms about various factors that can possibly affect the levels of their markers.
• the subject data containing this information relating to the subjects themselves may he provided to the biomarker analysis system 100 with the raw biomarker data, for example in the form of discrete and/or continuous variables.
• biomarker analysis system 100 may analyze the raw data and additional factors to identify such factors with a statistically significant affect.
• the biomarker analysis system 100 may also automatically select multiple relevant hiomarkers from the plurality of biomarkers.
• the biomarker analysis system 100 performs regression analyses or other appropriate statistical analyses using each biomarker as a dependent variable and the factors that potentially affect its level as independent variables (410).
• the biomarker analysis system 100 may, however, use any appropriate analysis to identify potential relationships between the factors and variations in the biomarker data.
• biomarker analysis system 100 may also be configured to compensate for
• raw data may be transformed using the inverse of a linear equation describing the relationship between the biomarker level and the factor or factors found to be significant.
• the selected p-value to determine statistical significance for biomarkers specific to detecting breast cancer may be selected at .05.
• the relationship the observed biomarker levels and the subject's age and gender could be described by the equation:
• Mi and M 2 are the coefficients as determined by the linear regression
• (Age) is a continuous variable that was found to be a statistically significant dclcrminate ⁇ f Y
• (Male) is a discrete variable that was found to be a statistically significant determinate of Y, where 1 equals male and 0 equals female
• B is an intercept (412).
• a normalized or adjusted value Y' for the potential Alzheimer's disease biomarker Y may be calculated according to the inverse equation (414):
• Normalized data may be generated applying the inverse equation to the raw data and/or the supplemental data and added to the supplemental data. By removing variation due to known causes, a greater percentage of the remaining variation may be ascribed to the presence or absence of a disease state, thus clarifying a marker's relationship to the disease state that might otherwise be obscured. When statistically significant factors are identified as affecting the level of one or more potential biomarkers, both raw data and normalized data may he used in subsequent analyses. Analysis of normalized values may elucidate relationships Lhat would otherwise be obscured, while raw data may provide greater ease of test administration and delivery.
• the biomarker analysis system IUU may further process the raw and/or supplemental data in any suitable manner, such as to reduce the influence of non-Gaussian distributions.
• the biomarker analysis system 100 may select one or more biomarker cut points and compare the raw and/or supplemental biomarker data to at lcasi one designated biomarker cut point.
• Biomarker cut points may be selected according to tiny suitable criteria, such as according to known levels corresponding to disease or based on the raw and/or normalized biomarker data.
• the biomarker analysis system 100 may compare cumulative frequency distributions ol " lhc condition-positive and -negative biomarker data for a particular biomarker and select one or more cut points for the biomarker according to a maximum difference between the condition-positive cumulative frequency distribution and the condition-negative cumulative frequency distribution for the selected biomarker.
• the biomarker analysis system 100 designates at least one cut point for each biomarker.
• the biomarker analysis system 100 may initially generate cumulative frequency distributions for the raw and/or supplemental data for both the disease-positive cohort 630 and the disease-negative cohort 620 for each relevant biomarker (510), such as for each individual biomarker PS ⁇ , TL-6, RCP, TNF- ⁇ , IL-8, and VEOF.
• the biomarker analysis system 100 may select one or more cut points (512), for example at a level where the difference between the cumulative frequency distribution of measured values in the disease-positive cohort and in the disease- negative cohort exceeds a predetermined value.
• the predetermined value may be any suitable threshold, such as where the cumulative frequency difference exceeds 10%, with higher values indicating greater difference between the positive and negative cohorts.
• the present biomarker analysis system 100 may seek levels at which the difference between the positive and negative cohorts is greatest to establish cut points 640. A greater difference in the cumulative frequencies of the disease-positive and -negative states indicates a propensity to belong to either the disease-positive or disease-negative cohort. Conversely, potential markers that display less than a 10% difference in cumulative frequency at any point are less likely to be informative to a useful extent and may optionally be dropped from further analysis.
• a cut point 640 may be selected even where the differences in cumulative frequency are low, particularly where the cut point may be deemed to be particularly informative, such as in lhc ease where there are no disease-positive or disease-negative values beyond a certain biomarker level.
• cut-points for the biomarker PS ⁇ may be selected for values that are at a local maximum with an absolute difference exceeding 10% using a cumulative frequency plot 900. Tn this embodiment, a first cut point 910 is selected at 1.25, a second cut point 920 is selected at 2.5, and a third cut point 930 is selected at 4.5.
• the differences in the cumulative frequency between disease-positive cohort plot 940 and disease-negative cohort plot 950 ai each of the three cut points are 24%, 22%, and 12% respectively.
• the third cut point 930 may be suitably .selected despite the relatively low difference in cumulative frequency since the lack of disease-negative values beyond a PSA concentration ol ' 4.5 indicates a point that is particularly informative to the distribution.
• the raw and/or normalized bi ⁇ markcr data may he compared to the cut points (514) and the biomarker analysis system 100 may record a value indicating the result of the comparison ⁇ $ a cut point designator (516).
• the cut point designator may comprise any suitable value or indicator, such as the difference between the value and the cut point or other value.
• un integral value of 1 is recorded as the cut point designator and stored in the supplemental data; if the level is below the cut point, an integral value of 0 is recorded.
• the integral values could likewise indicate whether the biomarker levels are below the more than one cut-point, or exceed a cut point for some of a patient's biomarkers and not exceeding a cut point for others. Conversion of a continuous variable into a discrete variable indicates a propensity to belong to either a disease-positive or -negative cohort. All values on a particular side of a cut point may receive equal weight, regardless of how high or low they may be, which tends to eliminate the inliuence of non-Gaussian distributions. [00056]
• the biomarker analysis system 100 may also be configured to reduce the range of values in data, for example where the range of measured or normalized level values for a biomarker is extremely wide.
• the range of values may be narrowed and the number of extremely high values reduced, while maintaining a meaningful distinction between values at the low and high ends of the range.
• the biomarker analysis system 100 may adjust the range of values in any suitable manner, for example by raising the measured values to fractional powers to obtain a set of reduced values for the biomarker.
• the biomarker analysis system 100 may select any suitable exponent values Io maintain meaningful distinctions in the data. MeaningtUI distinctions can be lost if the range is narrowed too much by choosing a fractional power that is too small.
• the biomarker analysis system may adjust the measured value fur each biomarker, such as the PS ⁇ , 1L-6, RCP, TNi ' - ⁇ , 1L-8, and VIi(Jl " biomarkers, in each cohort member by raising each value to a fractional power.
• Multiple different fractional powers such as exponential values ranging from V* to 1/1 ⁇ , such as 2/3 and 1/2, can be included in the analysis for each biomarker.
• Each reduced value may be Included in the supplemental data associated with the relevant biomarker's data set.
• the biomarker analysis system 100 may analyze the results, such as in the course of performing later regression analysis, to identity the fractional power value(s) that best accommodates the dala, for example by removing those sets of values that lack statistical significance. Exponentially raising measured or normalized level values by fractional values reduces the ilatiTs range, allows linear models to better fit non-linear data, and provides a continuum of scoring where differing weights can be applied as high or low values. Tn an embodiment configured to detect breast cancer, for example, suitable fractional powers for the PSA, 11. -6, RCP, TNF- ⁇ , IL-8, and VEGF biomarkers may include 1/10, 1/5, 1/3, 1/2, and 2/3 for each of the relevant biomarkers.
• the biomarker analysis system 100 may generate the disease status model on the raw data, the normalized data, any other supplemental data, and/or any additional disease risk factors that may have an impact or influence on specific risk for development of a disease.
• many factors can affect the measured concentration of one or more biomarkers, including, but not limited to, a patient's demographic characteristics, family history, and medical history. These factors all increase the potential markers* observed variabilities and standard deviations, masking or obscuring the relationship to the disease state.
• the biomarker analysis system 100 may analyze and/or process disease risk factors that can affect a subject's risk, as well as biomarker factors that can affect biomarker levels di fferently as described above.
• the biomarker analysis system 100 may, for example, account for disease risk factors in the overall analysis of the data in conjunction with analyzing the marker specific scores. Considering risk factors accounts for differences in prevalence and essentially shifts the overall score to reflect the prevalence.
• disease risk factors may be included among Lhc identified variables in detenu in ing the relationship between Lh- variables and disease status.
• the additional disease risk factors may be selected according to any suitable criteria and/or from any suitable source.
• biomarkcr analysis system 100 may record the subjects' disease risk facior data with the subjects' biomarkcr (actor data as additional continuous or discrete variables.
• the biomarker analysis system 100 suitably analyzes the data to identify relationships between the disease state and various raw data, supplemental data, and/or subject data.
• the relationship may be identified according to any suitable analysis and criteria.
• the biomarker analysis system 100 may establish an equation, such as a linear equation, that describes a relationship between the identified variables and disease status.
• the biomarker analysis system 100 may apply any suitable analysis, such us one or more conventional regression analyses (e.g., linear regression, logistic regression, and/or Poisson regression) using the disease status as the dependent variable and one or more elements of the raw data and the supplemental data as the independent variables, or employ Other analytical approaches, such as a generalized linear model approach, logit upprouoh, discriminant function analysis, analysis of covariance, matrix algebra and calculus, and/or receiver operating characteristic approach.
• the biomarker analysis system I QO automatically generates a statistical model tor determining disease status according to differences between the biomarker data for the relevant biomarkers of the respective cohorts.
• the present biomarker analysis system 100 may assess the relevance of a biomarker to a particular disease or condition according to any suitable technique or process.
• tne biomarker analysis system 100 performs statistical analyses of the biomarker data, such as statistical significance analyses.
• the biomarker analysis system 100 may automatically generate a disease status model that eliminates non- informative and some less informative biomarkers, for example by disregarding all potential biomarkers that yield p-values less than a predetermined value upon statistical analysis against the disease status.
• the biomarker analysis system 100 may determine the relative contribution or strength of the remaining individual biomarkers, for example by the coefficients that the model applies to the markers or by the product of the coefficient of each marker and its range of values.
• the analysis may reduce the number of cut points and fractional exponent values used, in many cases to a single cut point and/or fractional exponent. Some of the factors are likely to relate io duplicate Information, so the biomarker analysis system 100 may select the factor that is most useful, such as the factor having the lowest p-valuc.
• the biomarker analysis system 100 may perform an iterative analysis cither starting with a single variable and adding variables one at a time, or .starting with all variables and removing variables one at a time, until all variables are determined to be statistically significant, such as by having p-values lower than a predetermined level (e.g., without limitation, p ⁇ 0.1 , p ⁇ 0.05, or p ⁇ .025) (710).
• the iterative analysis may be configured to identify and remove biomarker data that is less informative regarding disease status than other data. For example, independent variables that demonstrate a p-value less than a predetermined value arc retained in the model, while those with p-values higher than the predetermined value are discarded (712).
• the biomarker analysis system 100 may analyze multiple variations of additions u ⁇ d subtractions of variables to acquire an optimal solution (714), for example to maximize the model's adjusted R squared or the Uayesian information criterion and avoid sub-optimizing the model.
• the resultant scoring model may take the form of the following equation:
• [00064J y triiXi + m 2 x 2 + m 3 x,, + m 4 d ⁇ + m s d2+ m s d,, + b
• y is a continuous variable representing disease status
• .n are continuous variables, such as raw biomarker levels measured in biological samples and/or normalized or capped values which have been identified as statistically significant, such as raw and supplemental data for the RCP, TNF- ⁇ , IL-8, and
• mi m n are coefficients associated with each identified variable
• b is the y-intercept of the equation.
• the biomarker analysis system 100 establishes the resulting equation as the disease status model (716).
• the biomarker analysis system 100 may establish multiple disease status models as candidates for further evaluation.
• the biomarker analysis system 100 may generate composite scores for various subjects in the relevant cohorts by multiplying values for the variables in the disease status model by the coefficient determined during modeling and adding the products along with the intercept value (718).
• the disease status model may comprise, however, any suitable model or relationship for predicting disease status according to the raw data, supplemental data, and/or subject data.
• the biomarker analysis system 100 may utilize the results of the analysis of relationships between the disease state and various raw data, supplemental data, and/or subject data to establish diagnosis criteria for determining disease status using data identified as informative.
• the biomarker analysis system 100 may establish lhe diagnosis criteria according to any appropriate process and/or techniques. For example, the biomarker analysis system 100 may identify and/or quantify differences between informative data (and/or eombinalio ⁇ s of informative data) for the disease-positive cohort, and corresponding informative data (and/or combinations of informative data) for the disease-negative cohort.
• the biomarker analysis system 100 compares the composite scores for the respective cohorts to identify one or more cut points in the composite that may indicate a disease-positive or -negative status, for example, the biomarker analysis system 100 may select and/or retrieve one or more diagnosis cut points and compare the composite scores for the respective cohorts to the diagnosis cut points (722).
• the diagnosis cut points may be selected according to any suitable criteria, such as according Io differences in median ⁇ nd/or cumulative frequency of the composite scores for the respective cohorts. Alternatively, the cut points may be regular intervals across the range of composite scores.
• the biomarker analysis system 100 may compare the composite score Tor each member of a cohort to one or more cut points and record ⁇ value indicating the result of the comparison as a composite score cut point designator (724).
• the composite score cut point designator may comprise any suitable value or indicator, such as the difference between the value and the cut point or other value. In one embodiment, If a composite score is above the cut point, an integral value of 1 is recorded os the composite score cut point designator; if the level is below the cut point, an integral value of 0 is recorded. The integral values could likewise indicate whether the composite scores are below more than one cut point.
• each cohort subject's eumposiie score is suitably evaluated at different cut-points which span the data's range. Al each cut point, values that are equal to or less than the cut point may be considered disease-negative and values above the cut point may be considered disease-positive point, or vice versa according to the nature of the relationship between the data and the disease.
• the biomarker analysis system 100 may compare the composite score cut point designator for each cut point candidate to each cohort member's true diagnostic state (726), and quantify the test's performance at each cut-point (728), for example as defined by sensitivity, specificity, true positive fraction, true negative Traction, false positive fraction, false negative traction, and so on.
• the biomarker analysis system 100 may select one or more cut points for future evaluations of data such that sensitivity is maxtmi/.cd, specificity is maximized, or the overall lest performance is maximized as a compromise between maximum sensitivity and specificity.
• an appropriate cut point may be se lee ted by using a data scoring model 1000.
• the data scoring model 1000 includes a table 1020 that indicates lest accuracy for specificity and sensitivity at various cut points. Using the data provided in the table 1020, the biomarker analysis system 100 may select a cut point 1010 to provide an optimum balance between sensitivity and specificity, such as at .55 in the present exemplary embodiment.
• the biomarker analysis system 100 may also be configured to verily validity of the disease status model.
• the biomarker analysis system 100 may receive blind data from disease-negative u ⁇ d disease-positive individuals.
• the blind data may be analyzed Io arrive at diagnoses that may he compared to actual diagnoses to confirm that the disease stale model distinguishes disease-negative and disease-positive solely on the basis of the values of measured and determined variables. If several models are viable, the model that has the highest agreement with the clinical diagnosis may be selected for further evaluation of subjects.
• the biomarker analysis system 100 may analyze biological sample data and/or subject data to apply the disease status model as an indicator of disease status of individual patients.
• the relevant biomarker levels may be measured and provided to the biomarker analysis system 100, along with relevant subject data.
• the biomarker analysis system 100 may process the biomarker data and subject data, for example to adjust the biomarker levels in view of any relevant biomarker factors.
• the biomarker analysis system 100 may not utilize various variables, such as. one or more integral values associated with a biomnrkcr specific cut-point, reduced values, integral values denoting extraordinary values, and raw or normalized data. Data that is noi needed for the particular disease status model may be discarded.
• the biomarkcr analysis system 100 may use and/or generate only relevant biomarkers and variables, which are those that demonstrate statistical significance ⁇ nd/or are used in the disease status model, to evaluate individual patients.
• the biomarker analysis system 100 may discard data for the PSA and IL-6 biomarkers and proceed with analysis of the RCP, TNF- ⁇ , IL-8, and VEGF biomarkers.
• the biomarkcr analysis system 100 may perform any suitable processing of lhc raw biomarkcr data and olhcr patient information.
• the biomarker analysis system 100 may establish for each of the patient's relevant biomarker levels a designator, such as an integral value, that indicates whether the level for each biomarker exceeds the relevant biomarker-specific maximum allowable value designated in the disease status model (810).
• the biomarker analysis system 100 may also associate the. corresponding designators with the patient's supplemental data set, indicating that the raw value exceeded the relevant limit.
• the biomarker analysis system 100 may generate normalized data for the patient according to the normalization criteria established in generating the disease status model and the subject data tor the patient, such as the patient's age, smoking habits, and the like (812).
• the normalized data may be added to the supplemental data for the patient.
• 'Hie biomarker analysis system 100 may also compare the patient's raw data and/or supplemental data to the biomarker cut points and generate cut point designators for each relevant biomarkcr cut point and the corresponding data (814).
• the biomarker analysis system 100 may further establish reduced data values for the each of the patient's relevant measured biomarker levels, for example by raising the relevant data to the fractional powers used by the disease status model, and associating alt such reduced data values with the patient's data set (816).
• the biomarker analysis system 100 may evaluate the raw biomarker data and any other relevant data in conjunction with the disease status model. For example, the biomarker analysis system 100 may calculate a composite score for the patient using the patient's biomarker data and other data and the disease status model (818). The biomarkcr analysis system 100 may compare the composite score to the scoring model cut points (820). Scores above the cut point suggest that the disease status of the subject is positive, while scores below the cut point indicate that the subject is negative. The biomarker analysis system 100 may also compare the composite score to boundary definitions for indeterminate zone that may be constructed around the cut-point where no determination can be made. The indeterminate zone may account, for example, for both a patient's biological variability (the typical day to day variations in the biomarkers of interest) and the evaluation methods error.

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