Method for improving disease diagnosis using measured analytes

Abstract

Methods for improving clinical diagnostic tests are provided, along with associated diagnostic techniques.

1 . A method for treating cancer, comprising the steps of:
 a) receiving concentrations of three or more predetermined analytes in a blind sample from a subject;   b) selecting one or more meta-variables associated with the subject, which vary in a population associated with the subject for members of the population who are known either to have or not have the disease;   c) transforming the concentrations of the analytes as a function of one or more population distribution characteristics and the one or more meta-variables to compute a pseudo-concentration that represents each analyte, wherein the pseudo-concentration is computed as a unitless, clinically predictive measure of each analyte;   d) scoring the pseudo-concentrations against a training set model of pseudo-concentrations determined for members of the population who are known either to have or not have the disease, wherein the score is determined by determining a distance from each pseudo-concentration to the nearest measured data points of the training set model; and   e) diagnosing the disease by determining whether the score of the pseudo-concentrations indicates that the subject has the disease, wherein false negative and false positive performance of the training set model is at least better than 75%, and wherein one or more anti-cancer drugs capable of treating the indicated disease are administered based on the diagnosis.   
     
     
         2 . The method of  claim 1 , wherein the meta-variable is selected from the groups consisting of: pre, peri and post menopausal status, pubescence, body mass, geographic location of the source of the sample, body fat percent, age, race or racial mix or ethnicity, species or era of time. 
     
     
         3 . The method of  claim 1 , wherein at least one of the three or more predetermined analytes is a low abundance protein. 
     
     
         4 . The method of  claim 1 , wherein the received concentrations are correlated to one or more population distribution characteristics. 
     
     
         5 . The method of  claim 1 , wherein the blind sample is taken from blood, tissue, urine, or plasma. 
     
     
         6 . The method of  claim 1 , wherein the training set of samples includes an equal number of disease samples and not-disease samples. 
     
     
         7 . The method of  claim 1 , wherein the at least three predetermined analytes are selected from the group comprising disease biomarkers or cytokines that indicate secretions of the immune system to fight the disease or the disease itself actions on the body. 
     
     
         8 . The method of  claim 1 , wherein the pseudo-concentrations are calculated as a logarithmic function of analyte concentrations. 
     
     
         9 . The method of  claim 1 , wherein the training set model is adjusted to weight the influence of one or more of the analytes relative to the other analytes. 
     
     
         10 . The method of  claim 1 , wherein the three or more predetermined analytes are chosen from among immune system inflammatory markers, tumor angiogenesis markers, cell apoptosis markers, vascularization proteins, and tissue markers for cancer specific diseases. 
     
     
         11 . A system for treating a cancer comprising a computer that:
 a) receives concentrations of three or more predetermined analytes in a blind sample from a subject;   b) selects one or more meta-variables associated with the subject, which vary in a population associated with the subject for members of the population who are known either to have or not have the disease;   c) transforms the concentrations of the analytes as a function of one or more population distribution characteristics and the one or more meta-variables to compute a pseudo-concentration that represents each analyte, wherein the pseudo-concentration is computed as a unitless, clinically predictive measure of each analyte;   d) scores the pseudo-concentrations against a training set model of pseudo-concentrations determined for members of the population who are known either to have or not have the disease, wherein the score is determined by determining a distance from each pseudo-concentration to the nearest measured data points of the training set model; and   e) diagnoses the disease by determining whether the score of the pseudo-concentrations indicates that the subject has the disease, wherein false negative and false positive performance of the training set model is at least better than 75%, and wherein one or more anti-cancer drugs capable of treating the indicated disease are administered based on the diagnosis.   
     
     
         12 . The system of  claim 11 , wherein the meta-variable is selected from the groups consisting of: pre, peri and post menopausal status, pubescence, body mass, geographic location of the source of the sample, body fat percent, age, race or racial mix or ethnicity, species or era of time. 
     
     
         13 . The system of  claim 11 , wherein at least one of the three or more predetermined analytes is a low abundance protein. 
     
     
         14 . The system of  claim 11 , wherein the received concentrations are correlated to one or more population distribution characteristics. 
     
     
         15 . The system of  claim 11 , wherein the blind sample is taken from blood, tissue, urine, or plasma. 
     
     
         16 . The system of  claim 11 , wherein the training set of samples includes an equal number of disease samples and not-disease samples. 
     
     
         17 . The system of  claim 11 , wherein the at least three predetermined analytes are selected from the group comprising disease biomarkers or cytokines that indicate secretions of the immune system to fight the disease or the disease itself actions on the body. 
     
     
         18 . The system of  claim 11 , wherein the pseudo-concentrations are calculated as a logarithmic function of analyte concentrations. 
     
     
         19 . The system of  claim 11 , wherein the training set model is adjusted to weight the influence of one or more of the analytes relative to the other analytes. 
     
     
         20 . The system of  claim 11 , wherein the three or more predetermined analytes are chosen from among immune system inflammatory markers, tumor angiogenesis markers, cell apoptosis markers, vascularization proteins, and tissue markers for cancer specific diseases.