Metabolomic Signatures for Predicting, Diagnosing, and Prognosing Various Diseases Including Cancer

Abstract

A system and method for using new biomarkers to assess individual diseases is provided. In one embodiment of the present invention, absolute quantification of annotated metabolites by mass spectrometry is used to identify certain biomarkers and derivatives thereof (i.e., signatures), which are then used to screen for, diagnose, predict, prognose, and treat various diseases, including, but not limited to, breast cancer, ovarian cancer, colorectal cancer, pancreatic cancer, and acute graft-versus-host disease.

What is claimed is: 
     
         1 . A method for assessing a human patient for ovarian cancer, comprising:
 using a technology selected from chromatography, spectroscopy, and spectrometry to quantify a plurality of metabolites included in a blood sample obtained from said human patient, including at least Tiglylcarnitine and Glutaconylcarnitine;   normalizing data values obtained in the quantification of at least said Tiglylcarnitine and said Glutaconylcarnitine, as quantified using said technology;   comparing at least a result of an equation comprising at least a first ratio of said Tiglylcarnitine to said Glutaconylcarnitine, as normalized, to at least one predetermined value to both diagnose said human patient for said ovarian cancer and determine a prognosis for said human patient;   wherein said diagnosis includes whether said human patient has ovarian cancer and said prognosis includes a risk factor associated with said ovarian cancer, said risk factor being a score on a linear scale that indicates at least one of a survival rate for said human patient and a response to treatment for ovarian cancer.   
     
     
         2 . The method of  claim 1 , further comprising the steps of quantifying and normalizing Dodecanedioylcarnitine, wherein said equation further comprises at least a second ratio of said Dodecanedioylcarnitine, as quantified and normalized, to said first ratio. 
     
     
         3 . The method of  claim 1 , further comprising the steps of quantifying and normalizing Octadecenoylcarnitine and Aspartate, where said equation further comprises at least a second ratio of said Octadecenoylcarnitine to said Aspartate, as quantified and normalized. 
     
     
         4 . The method of  claim 3 , wherein said equation further comprises at least a third ratio comprising at least said second ratio to said first ratio. 
     
     
         5 . The method of  claim 1 , further comprising the steps of quantifying and normalizing Ornithine, Arginine, and Tryptophan, and the step of comparing at least a second result of a second equation comprising said Ornithine, said Arginine, and said Tryptophan, as quantified and normalized to at least one other predetermined value to at least determine said prognosis for said human patient. 
     
     
         6 . The method of  claim 1 , further comprising the steps of quantifying and normalizing Ornithine, Aspartate, Octadecenoylcarnitine, and comparing at least a second result of a second equation comprising said Ornithine, said Aspartate, and said Octadecenoylcarnitine to at least one other predetermined value to at least determine said prognosis for said human patient. 
     
     
         7 . The method of  claim 1 , wherein said step of normalizing at least said Tiglylcarnitine and said Glutaconylcarnitine further comprises using at least a log-transformation to normalize at least said Tiglylcarnitine and said Glutaconylcarnitine. 
     
     
         8 . The method of  claim 1 , wherein said risk factor comprises at least a survival rate of said human patient from said ovarian cancer. 
     
     
         9 . The method of  claim 1 , wherein said risk factor comprises at least a relapse rate of said ovarian cancer. 
     
     
         10 . The method of  claim 1 , wherein said step of comparing is further used to determine a degree of said ovarian cancer, said determined degree being one of non-invasive, invasive, metastatic, and lethal. 
     
     
         11 . The method of  claim 1 , wherein said response to treatment for ovarian cancer comprises determining a viability of at least one treatment for said ovarian cancer. 
     
     
         12 . A system for assessing a human patient for ovarian cancer, comprising:
 a computing system comprising at least one memory device for storing machine readable instructions adapted to perform the steps of:
 receive a plurality of quantified metabolites from a sample provided by said human patient, including at least Tiglylcarnitine and Glutaconylcarnitine; 
 normalize said plurality of quantified metabolites; 
 compare at least a result of an equation comprising at least a first ratio of said Tiglylcarnitine to said Glutaconylcarnitine, as normalized, to at least one predetermined value to determine at least one level of similarity therebetween; and 
 use said at least one level of similarity to determine a diagnosis and a prognosis for said human patient regarding said ovarian cancer; 
   wherein said diagnosis includes at least whether said human patient has ovarian cancer and said prognosis includes at least a risk factor associated with said ovarian cancer, said risk factor being a score on a linear scale that indicates at least one of a survival rate for said human patient and a response to treatment for ovarian cancer.   
     
     
         13 . The system of  claim 12 , wherein said quantified metabolites further include Dodecanedioylcarnitine, and said equation further comprises at least a second ratio of said Dodecanedioylcarnitine to said first ratio. 
     
     
         14 . The system of  claim 12 , wherein said quantified metabolites further include Octadecenoylcarnitine and Aspartate, and said equation further comprises at least a second ratio of said Octadecenoylcarnitine to said Aspartate. 
     
     
         15 . The system of  claim 14 , wherein said equation further comprises at least a third ratio comprising at least said second ratio to said first ratio. 
     
     
         16 . The system of  claim 12 , wherein said quantified metabolites further include Ornithine, Arginine, and Tryptophan, and said machine readable instructions are further adapted to compare at least a second result of a second equation comprising at least said Ornithine, said Arginine, and said Tryptophan to at least one other predetermined value to determine a level of similarity therebetween, said level of similarity being used at least to determine said prognosis for said human patient. 
     
     
         17 . The system of  claim 12 , wherein said quantified metabolites further include Ornithine, Aspartate, and Octadecenoylcarnitine, and said machine readable instructions are further adapted to compare at least a second result of a second equation comprising at least said Ornithine, said Aspartate, and said Octadecenoylcarnitine to at least one other predetermined value to determine a level of similarity therebetween, said level of similarity being used to at least determine said prognosis for said human patient. 
     
     
         18 . The system of  claim 12 , wherein said machine readable instructions are further adapted to use a log-transformation to normalize said quantified metabolites. 
     
     
         19 . The system of  claim 12 , wherein said risk factor comprises at least a survival rate of said human patient from said ovarian cancer. 
     
     
         20 . The system of  claim 12 , wherein said machine readable instructions are further adapted to use said level of similarity therebetween to determine a viability of at least one treatment for said ovarian cancer.