Patent Description:
MYC is a member of a family of regulator genes and proto-oncogenes that code for transcription factors. As such, MYC leads to the increased expression of many genes, some of which are involved in metabolic reprogramming and cell proliferation, contributing to the formation of cancer. In fact, it is largely accepted that in order to meet cancer biochemical requirements tumor metabolism become addicted to local MYC oncogene activation. However, studies performed by the inventors suggest that in situ tumor gene activation should be seen as a confined replication of a previously existent systemic inborn-like condition already detectable in cancer-free participants at elevated risk of cancer development.

The latter is the effect of variable elevated levels of insulin resistance over patients exhibiting phenotypic mild deficiencies known as Fatty Acids Oxidation Defects (FAOD) exhibiting energy production deficiencies due to β-oxidation impairments followed by hypoglycemia due to insufficiencies in gluconeogenesis pathways.

Indeed, in these patients, high insulin levels systemically activate MYC proto-oncogene inducing glutaminolysis, glycolysis, Δ9-stearoyl-CoA desaturase (SCD) activity and inhibition of liver gluconeogenesis. When added to prominent blood levels of very-long chain acylcarnitines, lactate, fumarate and succinate, the final phenotypic scenario is highly suggestive of peroxisome and/or mitochondrial β-oxidation dysfunctions.

As an example, in studies conducted by the inventors, the phenotypic quantification of this MYC-induced "ambiance" was able to accurately discriminate between breast cancer patients from controls at AUC=<NUM> (<NUM>% CI:<NUM>-<NUM>), Sensitivity=<NUM>%, Specificity=<NUM>%, PPV=<NUM>%, NPV=<NUM>%, Average Accuracy=<NUM> (<NUM>-fold cross validations) and Predictive Accuracy Statistics p<<NUM>. 2e-<NUM> (<NUM> permutations) irrespective to disease stage, histology, intrinsic subtype classification, BMI, menopausal status, age, and patient's continental geographic localization (South American or European Continent).

As a proof-of-principle of the direct connections to stemness and cancer, the phenotypic metabolic deviations identified in the studies were highly correlated to human embryo metabolism and exhibited elevated predictive capabilities of chemotherapy response and outcomes of survival. The validation process of these findings, besides confirmation in independent cohorts, were also present, to a considerable extend, in other malignancies of glandular origin.

This research provides biochemical support to the hypothesis of cancer as a physical epiphenomenon of a preexisting MYC-induced systemic condition. In addition to ratifying local malignant lipidogenesis, glutaminolysis and glycolysis as major drivers in cancer, this study is one of the first to provide largely validated biochemical support to the hypothesis of cancer as a physical epiphenomenon of a systemic, preexistent, stemmness-like MYC-related condition, that according to results of the studies, closely resemble specific inborn errors of metabolism.

In doing so, the inventors relied on targeted quantitative metabolomics, which is the absolute quantitative measurement, by liquid chromatography followed by tanden mass spectrometry (LC-MS/MS), of low molecular weight compounds covering key biochemically active metabolites belonging to the whole range of pathways related to biosynthesis, signaling and catabolism of (i) structural and non-structural lipids, (ii) amino acids, (iii) biogenic amines, and (iv) components of intermediary metabolisms.

Considered as the gold standard of quantification, the very recent popularity of clinical mass spectrometry can be attributed to the high specificity, accuracy and reliability due to the direct analysis of ions that constitute that specific analyte, without the risk of cross reactivity as described for direct antibody assay detection.

The capability to analyze large arrays of annotated metabolites extracts biochemical information reflecting true functional end-points of overt biological events while genomics, transcriptomics and proteomics technologies, though highly valuable, merely indicate the potential cause for phenotypic response, and therefore cannot necessarily predict drug effects, toxicological response or disease states at the phenotype level unless functional validation is added. Metabolomics bridges this information gap by depicting functional information, since metabolite differences in biological fluids and tissues provide the closest link to the various phenotypic responses.

Needless to say, such changes in the biochemical phenotype are of direct interest to pharmaceutical, biotech and health industries once appropriate technology allows the cost-efficient mining and integration of this information. In general, phenotype is not necessarily predicted by genotype. The gap between genotype and phenotype is spanned by many biochemical reactions each with individual dependencies to various influences, including drugs, nutrition and environmental factors.

In this chain of biomolecules from the genes to phenotype, metabolites are the quantifiable molecules with the closest link to phenotype. Studies conducted by the inventors show that many phenotypic and genotypic states, such as a toxic response to a drug or disease prevalence are predicted by differences in the concentrations of functionally relevant metabolites within biological fluids and tissue.

Thus, in light of the foregoing, it would be advantageous to develop a system and method that uses targeted metabolomics, or absolute quantification of annotated metabolites by mass spectrometry, to identify certain biomarkers and derivatives thereof, such as ratios, etc. (i.e., "signatures") that can be used to screen for, diagnose, predict, prognose, and treat various diseases.

<CIT> teaches that certain metabolites can be used to diagnose ovarian cancer.

There remains a need for new biomarkers that allow accurate diagnosis and prognosis of ovarian cancer.

The present invention provides 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 Tiglycarnitine and Glutaconylcarnitine; normalizing at least said Tiglycarnitine 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 at least whether said human patient has ovarian cancer and said prognosis includes at least a risk factor associated with said ovarian cancer.

The present invention further provides 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 Tiglycarnitine 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.

The inventors used absolute quantification of annotated metabolites by mass spectrometry to identify certain biomarkers and derivatives thereof (i.e., "signatures"), which can be used to screen for, diagnose, predict, and prognose ovarian cancer.

In one embodiment of the present invention, targeted metabolomic analysis of plasma and/or tissue samples are performed. Absolute quantification (µmol/L) of blood metabolites is achieved by targeted quantitative profiling of certain (e.g., up to <NUM>) annotated metabolites by electrospray ionization (ESI) tandem mass spectrometry (MS/MS).

In one embodiment of the present invention, a targeted profiling scheme is used to quantitatively screen for fully annotated metabolites using multiple reaction monitoring, neutral loss and precursor ion scans. Quantification of metabolite concentrations is performed, resulting in at least one file that includes (i) sample identification, (ii) metabolite names (e.g., up to <NUM>), and (iii) concentrations (e.g., µmol/L of plasma).

For metabolomic data analysis, log-transformation is then applied to all quantified metabolites to normalize the concentration distributions and provided to software for comparing (e.g., mapping, plotting, etc.) to previously known "signatures. " In one embodiment, signature identification may involve uploading the data into MetaboAnalyst <NUM> (a web-based analytical pipeline) and ROCCET (a Receiver Operating Characteristic Curve Explorer & Tester) for the generation of uni and multivariate ROC (Receiver Operating Characteristic) curves obtained through SVM (Support Vector Machine), PLS_DA (Partial Least Squares-Discriminant Analysis), and Random Forests as well as Logistic Regression Models.

In certain embodiments of the present invention, there are up to <NUM> annotated metabolites that are quantified for comparision, including <NUM> acylcanitines (ACs), <NUM> amino acids (AAs), <NUM> biogenic amines (BA), sum of hexoses (Hex), <NUM> phosphatidylcholines (PCs), <NUM> lyso-phosphatidylcholines (LPCs) and <NUM> sphingomyelins (SMs). Glycerophospholipids were further differentiated with respect to the presence of ester (a) and ether (e) bonds in the glycerol moiety, where two letters denote that two glycerol positions are bound to a fatty acid residue (aa=diacyl, ae=acyl-alkyl), while a single letter indicates the presence of a single fatty acid residue (a=acyl or e=alkyl). Samples may also be analyzed for energy metabolism metabolites, including lactate, pyruvate/oxaloacetate, alpha ketoglutarate, fumarate and succinate.

In addition to individual metabolite quantification, groups of metabolites related to specific functions were assembled as ratios based on previous observation that the proportions between metabolite concentrations can strengthen the association signal and at the same time provide new information about possible metabolic pathways. As discussed below, these ratios are (at least in certain embodiments) extremely important aspects of a disease's "signature," and can, in and of themselves, indicate the presence or likelihood of a particular disease, the patient's prognosis, and available treatments.

In other embodiment, other groupings were also found to be important, including groups of amino acids (AA) that are computed by summing the levels of AAs belonging to certain families or chemical structures depending on their functions, such as the sum of: <NUM>) essential amino acids (essential AA); <NUM>) non-essential amino acids (non-essential AA); <NUM>) glucogenic (Ala+Gly+Ser) amino acids (Gluc AA); <NUM>) branched-chain (Leu+Ile+Val) amino acids (BCAA); <NUM>) Aromatic (His+Tyr+Trp+Phe) amino acids (Arom AA); <NUM>) glutaminolytic derivatives (Ala+Asp+Glu); and <NUM>) the sum of total amino acids.

Groups of acylcarnitines (AC), important to evaluate mitochondrial function, may also be computed by summing total acylcarnitines (AC), C2+C3, C16+C18, C16+C18:<NUM> and C16-OH+C18:<NUM>-OH). Groups of lipids, important to evaluate lipid metabolism, may also be analyzed by summing: <NUM>) total lysophosphatidylcholines (total LPC); <NUM>) total acyl-acyl; and <NUM>) total acyl-alkyl phosphatidylcholines (total PC aa and total PC ae, respectively); <NUM>) total sphingomielins (total SM); and <NUM>) sum of total (LPC + PC aa +PC ae +SM) lipids (structural lipids).

Proportions among sums of saturated, monounsaturated and polyunsaturated structural lipids may also be assembled as proxies to estimate elongases and desaturases activities towards ether lipids: <NUM>) Desaturase <NUM> [(PC ae C36:<NUM> + PC ae C38:<NUM> + PC ae C42:<NUM>) / (PC ae C42:<NUM>)], Desaturase <NUM> [(PC ae C44:<NUM> + PC ae C44:<NUM> + PC ae C42:<NUM> + PC ae C40:<NUM> + PC ae C40:<NUM> + PC ae C38:<NUM> + PC ae C38:<NUM> + PC ae C36:<NUM>) / (PC ae C36:<NUM> + PC ae C38:<NUM> + PC ae C42:<NUM>)].

Clinical indicators of liver metabolism and function may also be obtained by applying either the classical (leucine+isoleucine+valine/(tyrosine+phenylalanine) or variations (Val/Phe, Xleu/Phe) of the Fischer quotient. Clinical indicators of isovaleric acidemia, tyrosinemia, urea cycle deficiency and disorders of β-oxidation may also be calculated by adopting the ratios of valerylcarnitine to butyrylcarnitine (C5/C4), tyrosine to serine (Tyr/Ser), glycine to alanine and glutamine (Gly/Ala, Gly/Gln) and lactate to pyruvate (Lac/Pyr), respectively. Proxies for enzyme function related to the diagnosis of very long-chain acyl-CoA dehydrogenase (VLCAD) and type <NUM> carnitine-palmitoyl transferase (CPT-<NUM>) deficiencies may also be achieved by assembling the ratios (C16+C18:<NUM>/C2), (C14:<NUM>/C4), (C14:<NUM>-OH/C9), (C14/C9), (C14:<NUM>/C9) and to the elongation of very-long-chain-fatty acids (ELOVL2) (PC aa C40:<NUM>/PC aa C42:<NUM>). Levels of methionine sulfoxide (Met-SO) alone or in combination to unmodified methionine (Met-SO/Met) as well as symmetric (SDMA), asymmetric (ADMA) and total dimethylation of argine residues (Total DMA) may then be quantified to gain access to ROS-mediated protein modifications as well as to systemic arginine methylation status, respectively.

Knowing that liver inhibition of gluconeogenesis is a bona fide insulin-MYC-dependent biochemical reaction, a shift from normal to lower values in the ratio of glucose to glucogenic amino acids (Glucose/Ser, Glucose/Gly and Glucose/Ala) after insulin administration, may be adopted as a measurement of insulin-MYC-related activity.

The same procedure may then be applied to other MYC-responsive enzymes as follows: arginine methyltransferases (ADMA, ADMA/Arg, SDMA, SDMA/Arg and total DMA, total DMA/Arg), ornithine decarboxylase (Glu, Glu/Orn, Pro, Pro/Orn, Orn, Orn/Arg, Putrescine, Putrescine/Orn, Spermidine, Spermidine/Putrescine, Spermine and Spermine/Spermidine), alanine aminotransferase (Ala), (Ala/Glu), aspartate aminotransferase (Asp) and (Asp/Glu), glutaminase (Glu), (Gln/Glu), [(Glu+Asp+Ala)/Gln], [(Gln/Glu)/Asp], (Glu/Glucose)/(Ala/Glu) and [(Glu/Gln)/Glucose]/(Ala/Glu).

The latter two ratios are related to the "glutamate pulling effect," which is defined as the hypoglycemia-induced up-regulation in the deaminated, rather than transaminated, production of glutamate through insulin-MYC-dependent glutamate dehydrogenase (GDH) stimulation of glutaminolysis with consequent increased amounts of net keto acids to anaplerosis.

The ratios of (Ser/C2, Ser/Gln, Ser/Thr) and of (PC aa C42:<NUM>/PC ae C32:<NUM>, PC aa C32:<NUM>/PC ae C34:<NUM>) as proxies for glycolysis-related phosphoglycerate dehydrogenase (PHGDH) and glucokinase regulator (GCKR) activities may also be considered. The later inhibits glucokinase activity in liver and pancreas and the former catalyses the rate limiting step of serine biosynthesis.

In parallel, and assuming the ratio values of glutamine to glutamate (Gln/Glu) and to aspartate [(Gln/Glu)/Asp], as proxys for glutaminolytic activity, the ratios [(Ser/C2)/(Gln/Glu)], [(Ser/C2)/(Gln/Glu)/Asp], [(PC aa C32:<NUM>/PC ae C34:<NUM>)/(Gln/Glu)] and [(PC aa C32:<NUM>/PC ae C34:<NUM>)/(Gln/Glu)/Asp] may be assembled as theoretical equations to gain access to the balance between glycolysis and glutaminolysis.

It should be appreciated that with respect to the foregoing metabolites and sets thereof (e.g., summation, ratio, etc.), certain ones may be critical to analyzing a particular disease, whereas others may be less important. Thus, provided below are metabolites and/or sets thereof that are critical (i.e., most important) to individual signatures. For the sake of brevity, critical aspects of individual signatures for individual disease will be covered in the appropriate sections below.

A more complete understanding of a system and method for using new biomarkers to assess individual diseases will be afforded to those skilled in the art, as well as a realization of additional advantages and objects thereof, by a consideration of the following detailed description of the preferred embodiment. Reference will be made to the appended sheets of drawings, which will first be described briefly.

The present invention derives from the inventors' use of targeted metabolomics, or absolute quantification of annotated metabolites by mass spectrometry, to identify never described biomarkers and/or derivatives thereof (e.g., ratios, etc.) (i.e., "signatures") suitable for assessing various diseases, including, but not limited to breast cancer, ovarian cancer, colorectal cancer, pancreatic cancer, and acute graft-versus-host disease, to name a few. The claimed invention however relates to ovarian cancer. Other diseases and cancers are disclosed but not claimed.

It should be appreciated that while a first disease (e.g., breast cancer) may have a first signature, and a second disease (e.g., ovarian cancer) may have a second, different signature, the method used in identifying each signature is very similar, and in certain instances identical. Thus, while different diseases have been discussed in different sections below, for the sake of brevity, details concerning how a signature is identified and subsequently used to assess a particular disease are equally applicable to other signatures and other diseases unless stated otherwise. For example, details concerning absolute quantification of annotated metabolites by mass spectrometry provided in the breast cancer section applies equally to the ovarian cancer section, as do other details, unless stated otherwise.

It should also be appreciated that a disease may have more than one signature or portions thereof. For example, a first signature may be used for diagnoses, a second signature (or portion of the first signature) may be used for prognoses, etc. It should also be appreciated that while a disease may have more than one signature, there may be individual aspects (e.g., individual metabolites or derivatives thereof) that are common to several signatures, and can therefore provide, in and of themselves, information on diagnosis, prognosis, treatment, etc. Specifics concerning signatures will be discussed in the corresponding sections below.

The present invention provides a method and a system for assessing a human patient for ovarian cancer. Those skilled in the art will understand that the methods disclosed herein can be used to identify signatures for, and assess, diseases other than ovarian cancer, which are not part of the claimed subject-matter. The present invention is not limited to use of mass spectrometry, or any particular type of mass spectrometry (e.g., electrospray ionization (ESI) tandom mass spectrometry (MS/MS), etc.), and includes other methods for quantifying metabolites, such as chromatography or spectrometry. That being said, the inventors have found that there are benefits to using mass spectrometry, and in particular ESI MS/MS, and the data analysis described herein (e.g., log-transformation, ROC curves, etc.). As such, the methods described in detail herein are preferred embodiments, and should be treated as such.

Prior to discussing the inventions, including individual signatures, the methods used to identify the same, and assess various diseases, certain definitions of term or phrases used herein will first be provided.

By employing the biomarkers (or specific sets thereof) and the methods according to the present invention it has become possible to assess a disease (e.g., ovarian cancer, colorectal cancer, etc.) with improved accuracy and reliability. It has surprisingly been achieved in the present invention to provide biomarkers or biomarker sets by measuring the amount and/or ratios of certain metabolites in samples, such as blood samples, of subjects that make it possible to screen and diagnose diseases (e.g., ovary cancer, etc.) in an improved manner and at an early stage of the disease.

In general, a biomarker is a valuable tool due to the possibility to distinguish two or more biological states from one another, working as an indicator of a normal biological process, a pathogenic process or as a reaction to a pharmaceutical intervention.

A metabolite is a low molecular compound (<1kDa), smaller than most proteins, DNA and other macromolecules. Small changes in activity of proteins result in big changes in the biochemical reactions and their metabolites (=metabolic biomarker, looking at the body's metabolism), whose concentrations, fluxes and transport mechanisms are sensitive to diseases and drug intervention.

This enables getting an individual profile of physiological and pathophysiological substances, reflecting both genetics and environmental factors like nutrition, physical activity, gut microbial and medication. Thus, a metabolic biomarker gives more comprehensive information than for example a protein or hormone, which are biomarkers, but not metabolic biomarkers.

In view thereof, the term "metabolic biomarker" or short "biomarker" as used herein is defined to be a compound suitable as an indicator of the presence and state of a disease (e.g., cancer) as well as its subtype (e.g., subtype of tumor), being a metabolite or metabolic compound occurring during metabolic processes in the mammalian body.

The terms "biomarker" and "metabolic biomarker" are in general used synonymously in the context of the present invention and typically refer to the amount of a metabolite or to the ratio of two or more metabolites. Hence, the term metabolic biomarker or biomarker is intended to also comprise ratios (or other mathematical relationships) between two or more metabolites.

The term "amount" typically refers to the concentration of a metabolite in a sample, such as blood sample, and is usually given in micromol/L, but may also be measured in other units typically used in the art, such as g/L, mg/dL, etc. The term "sum" typically means the sum of molar amount of all metabolites as specified in the respective embodiment.

The term "ratio" typically means the ratio of amounts of metabolites as specified in the respective embodiment. The metabolic biomarker (set) measured according to the present invention may comprise the classes of metabolites (i.e. analytes) of amino acids and biogenic amines, acylcarnitines, hexoses, sphingolipids and glycerophospholipids, as listed in <FIG>.

Biogenic amines in <FIG> are understood as a group of naturally occurring biologically active compounds derived by enzymatic decarboxylation of the natural amino acids. A biogenic substance is a substance provided by life processes, and the biogenic amines contain an amine group.

It has surprisingly been found that measuring a set of biomarkers comprising these classes of metabolites, i.e. measuring the amount and/or ratios of certain indicative metabolites, allows for screening and diagnosing various diseases (e.g., ovary cancer, etc.) in an improved manner and at an early stage and allows for assessing biochemical reflection of tumor activity, allowing for the prediction of a therapeutic response as well as for sub classification among a disease's behavior.

While a modified "signature" can be used, if one metabolite or one class of metabolites as specified for the respective biomarker combination is omitted or if the number thereof is decreased the assessment of the disease becomes less sensitive and less reliable.

This particularly applies for the early stages of the disease being not reliably detectable according to known methods using known biomarkers at all. In fact, the measurement of the metabolites contained in the respective sets of biomarkers at the same time allows a more accurate and more reliable assessment of a disease, typically with (A) a sensitivity of greater than <NUM>%, preferably greater than <NUM>%, and more preferably greater than <NUM>%, (B) a specificity of greater than <NUM>%, preferably greater than <NUM>%, and more preferably greater than <NUM>%, (C) a positive predictive value (PPV) of greater than <NUM>%, preferably greater than <NUM>%, and more preferably greater than <NUM>%, and (D) a negative predictive value (NPV) of greater than <NUM>%, preferably greater than <NUM>%, and more preferably greater than <NUM>%. Obviously, biomarkers (or sets thereof) that can reach or achieve <NUM>% (or near <NUM>%) sensitivity, specificity, PPV, and/or NPV is desired.

The meanings of the terms "sensitivity", "specificity", "positive predictive value" and "negative predictive value" are typically known in the art and are defined in the context of the present invention according to the "Predictive Value Theory", as established by the University of Iowa, USA. In this theory, the diagnostic value of a procedure is defined by its sensitivity, specificity, predictive value and efficiency. Description of the formulae are summarized below.

Sensitivity of a test is the percentage of all patients with disease present who have a positive test. (TP/(TP+FN)) x <NUM> = Sensitivity (%) where TP= Test Positive; FN= False Negative.

Specificity of a test is the percentage of all patients without disease who have a negative test. (TN/(FP+TN)) x <NUM> = Specificity (%) where TN= Test Negative; FP= False Positive.

Predictive value of a test is a measure (%) of the times that the value (positive or negative) is the true value, i.e. the percent of all positive tests that are true positives is the Positive Predictive Value ((TP/(TP+FP)) x <NUM> = Predictive Value of a Positive Result (%); ((TN/(FN+TN)) x <NUM> = Predictive Value Negative Result (%)).

Likelihood Ratios: The performance of biomarkers can further be assessed by determining the Positive and Negative Likelihood Ratios (LR) used herein during Statistical Univariate Analysis (see <FIG>).

Multivariate Data Analysis: Training cases were used for marker discovery and to identify any clinical variable that might be associated with a disease (e.g., ovarian cancer, colorectal cancer, etc.) by logistic regression analysis. Quantification of metabolite concentrations and quality control assessment was performed with the MetIDQ® software package (BIOCRATES Life Sciences AG, Innsbruck, Austria). Internal standards serve as the reference for the metabolite concentration calculations. An xls file was then exported, which contained sample names, metabolite names and metabolite concentration with the unit of µmol/L of in plasma.

Data was then uploaded into the web-based analytical pipeline MetaboAnalyst <NUM> (www. metaboanalyst. ca) and normalized using MetaboAnalyst's normalization protocols (Xia et al <NUM>) for uni and multivariate analysis, high dimensional feature selection, clustering and supervised classification, functional enrichment as well as metabolic pathway analysis.

Data was also imported to ROCCET (ROC Curve Explorer & Tester) available at http://www. ca/ROCCET/ for the generation of uni and multivariate Receiver Operating Characteristic (ROC) curves obtained through Support Vector Machine (SVM), Partial Least Squares-Discriminant Analysis (PLS-DA) and Random Forests.

Curves were generated by Monte-Carlo cross validation (MCCV) using balanced subsampling where two thirds (<NUM>/<NUM>) of the samples were used to evaluate the feature importance. Significant features were then used to build classification models, which were validated on the <NUM>/<NUM> of the samples that were left out. The same procedure was repeated multiple times to calculate the performance and confidence interval of each model.

Up and down regulation: An up-regulation means an increase in the concentration of a metabolite, e.g. an increase in the rate of at which this biochemical reaction occurs due to for example a change in enzymatic activity. For a down-regulation, it's the other way around.

T-test: The t-test is a statistical hypothesis test and the one used is the one integrated in the MarkerView software and is applied to every variable in the table and determines if the mean for each group is significantly different given the standard deviation and the number of samples, e.g. to find out if there is a real difference between the means (averages) of two different groups.

P-value: The p-value is the probability of obtaining a result at least as extreme as the one that was actually observed, assuming that the null hypothesis (the hypothesis of no change or effect) is true. The p-value is always positive and the smaller the value the lower the probability that it is a change occurrence. A p-value of <NUM> or less rejects the null hypothesis at the <NUM>% level, which means that only <NUM>% of the time the change is a chance occurrence. This is the level set in the tables provided herein.

Log-fold change: Log-fold change is defined as the difference between the average log transformed concentrations in each condition. This is a way of describing how much higher or lower the value is in one group compared to another. For example, a log-fold change of <NUM> is "equivalent" to an exp (. <NUM>)=<NUM> fold change increase compared to the control (healthier group). Further, a log-fold change of -<NUM> is "equivalent" to a exp(-. <NUM>)=<NUM>=(<NUM>/<NUM>) fold change increase compared to the control or decrease fold change of <NUM> to the disease. See <FIG> for ideal tumor marker according to Sokoll and Chan.

Signatures for particular diseases, including the identification thereof and use of the same for assessing (e.g., screening, diagnosing, prognosing, treating, etc.) particular diseases, will now be discussed.

Studies were performed to identified signatures that could be used to assess diseases, such as ovarian cancer, colorectal cancer, pancreatic cancer, and acute graft-versus-host disease, to name a few.

In the present invention, the disease at issue is ovarian cancer, and may be at a particular stage (e.g., stages I, II, III or IV). Definition of the medical stages of cancer is defined by the American Joint Committee on Cancer (AJCC) of the United States National Cancer Institute at the National Institutes of Health. The staging system provides a strategy for grouping patients with respect to prognosis. Therapeutic decisions are formulated in part according to staging categories but primarily according to tumor size, lymph node and distant metastasis status.

The biological sample is obtained from a human patient, preferably from a woman. The biological sample is blood. The blood sample typically is full blood, serum or plasma, wherein blood plasma is preferred. Dried samples collected in paper filter are also accepted. Thus, the methods according to the invention typically are in vitro methods.

For the measurement of the metabolite concentrations in the biological sample a quantitative analytical method such as chromatography, spectroscopy, or mass spectrometry is employed, where chromatography may comprise GC, LC, HPLC, and UPLC, spectroscopy may comprise UV/Vis, IR, and NMR, and mass analyzers/spectrometry may comprise ESI-QqQ, ESI-QqTOF, MAL¬DI-QqQ, MAL¬DI-QqTOF, and MAL¬DI-TOF-TOF. More preferably, mass analyzers/spectrometry comprises Quadrupole Mass Analyzers, Ion Trap Mass Analyzers, TOF (Time of Flight) Mass Analyzer, Orbitrap mass analyser, Magnetic Sector Mass Analyzer, Electrostatic Sector Mass Analyzer, Ion Cyclotron Resonance (ICR) and combinations of mass analyzers, including single quadrupole (Q) and triple quadrupole (QqQ), QqTOF, TOF-TOF, Q-Orbitrap. The inventors have discovered that use of FIA- and HPLC-tandem mass spectrometry is preferred and has certain benefits.

Abbreviations that are used herein are as follows: GC= Gas Chromatography, CE= Capillary electrophoresis, LC= Liquid Chromatography, HPLC= High Preasure Liquid Chromatography, UHPLC= Ultra High Preasure Liquid Chromatography, UV-Vis= Ultraviolet-Visible, IR= Infrared, NIR= Near Infrared, NMR=Nuclear Magnetic Ressonance, ESI=Electron Spray Ionization, MALDI= Matrix-assisted laser desorption/ionization, TOF= Time-of-Flight, APCI= Atmospheric pressure chemical ionization, QqQ= Triple quadrupole configuration also known as Q1q2Q3 (Q1 and Q3 quadrupoles are mass filters and q2 is a no mass-resolving quadrupole).

For measuring the metabolite amounts targeted metabolomics is used to quantify the metabolites in the biological sample including the analyte classes of amino acids, biogenic amines, acylcarnitines, hexoses, sphingolipids and glycerophospholipids. The quantification is done using in the presence of isotopically labeled internal standards and determined by the methods as described above. A list of analytes including their abbreviations (BC codes) being suitable as metabolites to be named according to the invention is indicated in <FIG>.

In order to reach the highest capability to detect a disease using metabolomics, the present invention identified its discriminant biochemical features and ratios not only by comparing sick patients (i.e., ones having a particular disease, such as ovarian cancer) to healthy controls but also to a larger group of participants with other malignant and benign conditions. Samples were prospectively collected and analyzed by the same, fee-for-service, standardized, targeted quantitative mass spectrometry technique at the same centralized and independent company (Biocrates, Austria).

A group of plasma samples of woman having certain cancers at various stages (i.e., stage I, II and III) with no previous treatment were included, the cancer patients (n = <NUM>) were composed by: i) breast cancer volunteers from Brazil and Europe (n = <NUM>) in addition to ii) lung (n=<NUM>), iii) head and neck (n = <NUM>), iv) liver (n = <NUM>), v) hematological malignancies (n = <NUM>), and vi) colon cancer patients (n = <NUM>) together to respective normal (n = <NUM>) and tumor tissues (n = <NUM>).

The remaining <NUM> samples were included as control groups, out of which: <NUM> controls (<NUM> women and <NUM> men) were from the São Paulo Population-based Health Investigation Project (ISA <NUM>) that due to its population characteristics, allowed us to analyzed them according their frequency of metabolic syndrome distributed according the <NUM> progressive stages following the recommendation of the Joint Interim Statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity.

Controls also included <NUM> women at elevated risks of breast cancer development, <NUM> participants with histologically proven non-invasive in situ carcinoma, <NUM> women at low risk of breast cancer development, <NUM> with polycystic ovary syndrome, <NUM> HIV-infected individuals prior of treatment, <NUM> women with rheumatoid arthritis, <NUM> autoimmune hemolytic disorders, <NUM> participants with cirrhosis, <NUM> with hyper and <NUM> with hypothyroidism.

Targeted (ESI-MS/MS) Quantitative Metabolomics/Lipidomics profiling, was performed in an independent validation set with plasma samples from woman with various cancers as well as a number of controls, on two independent, fee-for-service basis using quantitative metabolomics platform at Biocrates Life Sciences AG, Innsbruck, Austria and Quest Diagnostics Nichols Institute San Juan Capistrano, CA, USA.

Briefly, a targeted profiling scheme was used to quantitatively screen for known small molecule metabolites using multiple reaction monitoring, neutral loss and precursor ion scans. Quantification of the metabolites of the biological sample is achieved by reference to appropriate internal standards and the method has been proven to be in conformance with <NUM> C. , Part <NUM>, which implies proof of reproducibility within a given error range. Concentrations of all analyzed metabolites were reported in µM.

In total, <NUM> different metabolites were detected being <NUM> acylcanitines, <NUM> proteinogenic aminoacids, ornithine and citrulline, <NUM> biogenic amines, sum of Hexoses, <NUM> phosphatidylcholines, <NUM> lyso-phosphatidylcholines and <NUM> sphingomyelins.

Glycerophospholipids are further differentiated with respect to the presence of ester (a) and ether (e) bonds in the glycerol moiety, where two letters (aa=diacyl, ae=acyl-alkyl, ee=dialkyl) denote that two glycerol positions are bound to a fatty acid residue, while a single letter (a=acyl or e=alkyl) indicates the presence of a single fatty acid residue.

Lipid side chain composition is abbreviated as Cx:y, where x denotes the number of carbons in the side chain and y the number of double bonds, e.g. "PC ae C38:<NUM>" denotes a plasmalogen/plasmenogen phosphatidylcholine with <NUM> carbons in the two fatty acid side chains and a single double bond in one of them.

Training cases were used for marker discovery and to identify any clinical variable that might be associated with a particular disease by logistic regression analysis. Quantification of metabolite concentrations and quality control assessment was performed with the MetIDQ® software package (BIOCRATES Life Sciences AG, Innsbruck, Austria). Internal standards serve as the reference for the metabolite concentration calculations. An xls file was then exported, which contained sample names, metabolite names and metabolite concentration with the unit of µmol/L of in plasma.

Data was then uploaded into the web-based analytical pipeline MetaboAnalyst <NUM> (www. metaboanalyst. ca) and normalized using MetaboAnalyst's normalization protocols (Xia et al <NUM>) for uni and multivariate analysis (see above discussion concerning normalization), high dimensional feature selection, clustering and supervised classification, functional enrichment as well as metabolic pathway analysis.

Ovary cancer today is recognized as a type of malignancy originated, in the majority of times, from its surrounding tissues, particularly the fimbria, the very external end of the fallopian tube. The American Cancer Society estimates that in the United States, for <NUM>, there are about <NUM>,<NUM> new cases, out of which, more than <NUM>% (<NUM>,<NUM>) of women will die from this disease. Ovarian cancer, therefore, is accounting for more deaths than any other cancer of the female reproductive system. This cancer mainly develops in <NUM> years or older women and it is more common in white than African-American women.

Ovarian cancer is difficult to detect, especially in the early stages. This is partly due to the fact that the ovaries - two small, almond-shaped organs on either side of the uterus - are deep within the abdominal cavity.

Fewer than one-half of women diagnosed with ovarian cancer survive longer than <NUM> years, and although the <NUM>-year survival of patients with localized ovarian cancer is greater than <NUM>%, only <NUM>% of all women are diagnosed with localized disease.

Currently, no organization recommends screening average-risk women for ovarian cancer. Nevertheless, screening and diagnostic methods for ovarian cancer include pelvic examination, cancer antigen <NUM> (CA <NUM>) as a tumor marker, transvaginal ultrasound (TVU), and potentially multimarker panels and bioinformatic analysis of proteomic patterns.

However, the performance of these tests for screening when used alone or in combination has been poor. The sensitivity and specificity of pelvic examination for the detection of asymptomatic ovarian cancer are poor and do not support physical examination as a screening method. CA <NUM> has limited sensitivity and specificity, as does TVU when used independently or in combination.

In <NUM>, the Prostate Lung Colorectal and Ovarian (PLCO) initiative concluded, with regards of ovarian cancer screening, that there was adequate evidence that annual screening with CA <NUM> and TVU does not reduce ovarian cancer mortality, and that, there was adequate evidence that screening for ovarian cancer can lead to important harms, mainly surgical interventions in women without ovarian cancer.

Therefore, an urgent need exists in the art for new and highly sensitive screening procedures, preferably less demanding without the need of several specialized equipment and personnel or resources.

In view of the above-mentioned problems existing in the art, the object underlying the present invention is the provision of new biomarkers for assessing ovary cancer, which allows for screening of ovary cancer in an early stage of disease progression with high accuracy and reliability.

Optimally, the marker should be easily detectable in a biological sample such as in blood and its level should be consistently related to the stage of ovary cancer. Moreover, it is an object of the present invention to provide for a method for assessing ovary cancer in a biological sample, which allows for fast, convenient and high throughput performance.

In order to solve the objects underlying the present invention the inventors based their investigations on metabolomics as it could give insight in the biochemical changes occurring in the course of ovary cancer development and offer several novel and potentially better biomarkers.

The invention is an early-diagnosis-tool that identifies patients with ovarian cancer in its earliest stages, when intervention offers the highest possibility of cure. The invention provides prognostic information and serves as a predictive test for clinical response and survival.

The inventors found that a more comprehensive picture of all metabolomics pathways and mechanisms involved in ovary cancer is given when using a panel of metabolites that are altered in parallel of cancer rather than employing the screening techniques performed in the art, such as ultrasound.

Therefore, the present invention provides for never described biomarkers (i.e. a new biomarker set) suitable for assessing ovary cancer, including early and more advanced stages of disease and also provides biomarker sets that clearly discriminate, at baseline, patients with elevated risk of relapse after initial treatment.

Moreover, the present invention also provides for a method for assessing ovary cancer in a mammalian subject that was achieved and developed taking into consideration comprehensive and extensive comparisons not only with several other malignancies but also with several metabolic benign conditions and, therefore, can be considered as the closest stage of an ideal tumor marker.

In particular, the application of targeted quantitative mass spectrometry (MS/MS) to the blood of ovarian cancer patients led to the creation of a metabolic signature that provides clinically validated diagnostic and prognostic information for women with ovarian cancer and those at risk for the disease.

Targeted, quantitative MS/MS provides annotated blood concentrations of metabolites that are essential for the accurate determination of clinically relevant metabolic signatures. Individual metabolite concentrations and qualitative, non-targeted, measures do not provide the necessary rigor that is required for the accurate identification of cancer-related metabolic perturbations.

In a first embodiment, the biomarkers and biomarker sets of the present invention are used for screening of subjects, such as human patients, potentially suffering from ovary cancer and diagnosis of ovary cancer in these subjects.

It has surprisingly been found in the present invention that the biomarkers and biomarker sets as described herein are particularly useful for fast, easy and high throughput screening of a large number of subjects, such as human patients, and for diagnosis of ovary cancer from blood samples of these subjects with improved accuracy of results.

Although accuracy and reliability of screening and/or diagnosis, as determined by the parameters of one or more of specificity, sensitivity, PPV and NPV, by using the above-specified biomarker combination is already greatly improved compared with the prior art techniques, such as ultrasound, the accuracy and reliability can be further improved by using one or more, preferably two or more, further preferably three or more additional metabolites.

Hence, in a preferred embodiment the biomarker set further comprises one or more additional amino acid, such as those included in <FIG>. The additional amino acids are preferably selected from glucogenic/ketogenic amino acids such as glycine, cysteine, alanine, arginine, proline, aspartate, asparagine, methionine, isoleucine, leucine, lysine, threonine phenylalanine, tyrosine and tryptophan, most preferably asparagine and aspartate.

Moreover, the lipid is preferably selected from sphingolipids and glycerolipids, such as glycerophospholipids, e.g. one or more of the lipids included in <FIG>.

Further preferably, the lipid is derived from arachidonic acid, preferably arachidonic acid derived lipids containing <NUM> or more carbon atoms, and most preferably is selected from arachidonic polyunsaturated phosphatidylcholine acyl-alkyl or acyl-acyl, arachidonic mono-unsaturated phosphatidylcholine acyl-alkyl or acyl-acyl and arachidonic saturated phosphatidylcholine acyl-alkyl or acyl-acyl.

In a further preferred embodiment, the combination of metabolites further comprises one or more of lipids described in <FIG> and one or more acylcarnitines as well as carnitine (C0) described in <FIG>.

As the method of this embodiment can be performed from blood samples, the method greatly increases the subject's compliance compared to prior art screening techniques, such as ultrasound. In particular, the method greatly increases reliability and sensitivity of the screening results, in particular reduces the number of false positive and false negative results, and is less time consuming, and thus can be performed with a high number of patients.

This can be seen, for example, in <FIG>, showing that the signatures developed for assessing ovarian cancer (i.e., one embodiment of the present invention) have a sensitivity of <NUM>%, a specificity of <NUM>%, and a negative predictive value of <NUM>%. In particular, <FIG> shows a multivariate ROC curve analysis for ovary cancer patients (n-<NUM>) compared to healthy participants as well as other malignant and non-malignant conditions (n=<NUM>). <FIG> depicts the performance of the identified metabolites and ratios for ovary cancer patients. The near <NUM>% negative predictive value (<NUM>%) makes the present test highly indicative as a powerful screening tool.

<FIG> shows an Ortho-PLSDA Score's plot of ovary cancer patients (n=<NUM>) compared to healthy participants as well as other malignant and non-malignant conditions (n=<NUM>). By processing (e.g., isolating, quantifying, normalizing, etc.) each sample (e.g., blood sample), and then plotting the initial results (e.g., using an Ortho-PLSDA Score's plot) based on at least one ovarian cancer signature (as identified by the inventors), each patient clearly falls within (a) the control group or (b) the ovarian cancer group.

Moreover, portions of the signature provide details on each patient's prognosis. This can be seen, for example, in <FIG>, where various equations (identified at the top of each chart) provide survival rate (prognosis) information for each patient. Thus, not only have the inventors identified signatures that can be used to diagnosis ovarian cancer, but also to prognose ovarian cancer. It should be appreciated that while the charts provided in <FIG> and <FIG> illustrate (a) diagnosis for ovarian cancer and (b) survival rates, the present invention is not so limited, and the ovarian signatures (or portions thereof) can be used to provide other assessments for ovarian cancer, including screening for, diagnosing, prognosing, treating the same as discussed in greater detail in the results section below.

A preferred signature (or portions thereof) for assessing ovarian cancer is provided in <FIG>, including a core ovarian cancer equation, metabolite enhancers, ratio enhancers, and core equations with enhancers. As can be seen in <FIG>, the core ovarian cancer equation is (C5:<NUM>/C5:<NUM>-DC), or a ratio of Tiglylcarnitine to Glutaconylcarnitine (see <FIG>). The inventors have discovered that this ratio of individual metabolites, after quantification, normalization, etc., are critical in assessing a patient for ovarian cancer. Other key portions include [Orn/(AspdC18: <NUM>)] (where "d" is divided by, i.e., Asp/C18:<NUM>)), [(Orn/Arg)Trp], [C12-DC/(C5:<NUM>/C5:<NUM>-DC)], and [(C18:<NUM>/Asp)/(C5:<NUM>/C5:<NUM>-DC)], which can be used to not only diagnose, but prognose for ovarian cancer.

While not a limitation of the present invention, targeted metabolomic analysis of plasma and tissue samples may be performed using the Biocrates Absolute-IDQ P180 (BIOCRATES, Life Science AG, Innsbruck, Austria). This validated targeted assay allows for simultaneous detection and quantification of metabolites in plasma and tissue samples in a high-throughput manner.

As discussed in the patients and methodology section, absolute quantification (µmol/L) of blood metabolites may be achieved by targeted quantitative profiling of <NUM> annotated metabolites by electrospray ionization (ESI) tandem mass spectrometry (MS/MS) in a plurality of biological samples. The process described in that section is equality applicable here, where a targeted profiling scheme is used to quantitatively screen for fully annotated metabolites, an xls file is generated, which includes sample identification and <NUM> metabolite names and concentrations with the unit of µmol/L of plasma, and log-transformation is applied to all quantified metabolites to normalize the concentration distributions and processed. ROC curves are then generated, and significant feature are used to build classification models.

In total, <NUM> annotated metabolites were quantified using the p180 kit (BIOCRATES Life Sciences AG, Innsbruck, Austria), including the ones described in the breast cancer - patients and methodology section. As well, groups of metabolites related to specific functions were assembled as ratios, and other mathematical relationships were observed (as discussed above) (e.g., summing of levels of amino acids, summing total acylcarnitines, proportions among sums of saturated, monounsaturated and polyunsaturated structural lipids, etc.). See discussion above in the patient and methodology section.

With respect to ovarian cancer, samples were injected into a Shimadzu Prominence LC system coupled to an AB-Sciex <NUM> Triple TOF mass spectrometer instrument with an acquisition scan rate of <NUM> spectra/sec and stable mass accuracy of ~<NUM> ppm. Flow Injection Analysis (FIA) was performed using isocratic elution with Methanol/Water (<NUM>/<NUM>) with <NUM> of ammonium formate. Flow rate and injection volumes were <NUM>/min and <NUM>µL respectively.

No ion source or declustering potential (<NUM> V and -<NUM> V) optimization was performed. The following ionization parameters were applied: CUR= <NUM> psi, GS1= <NUM> psi, GS2= <NUM> psi, Temp = 250oC, IS= <NUM> V (- 4000V). MS scan ranging from m/z <NUM> to <NUM> with accumulation time of <NUM> and product ion scan from m/z <NUM> to <NUM> and accumulation time of <NUM> are the adopted parameters during survey and dependent scans respectively.

Specific parameters defining the presence of ovarian cancer using targeted quantitative MS/MS are provided in <FIG>. Specific metabolic ratios defining presence of ovarian cancer using targeted quantitative MS/MS are provided in <FIG>.

When the metabolic profiles of patients with different tumors (lung, colon, liver, leukemias, lymphomas and squamous cells carcinoma of head and neck) were examined, the results demonstrated enhanced glutamine consumption, particularly in patients harboring tumors of glandular ancestries. Extending these studies to include patients with polycystic ovary syndrome (PCOS), cirrhosis, high-risk of breast cancer and stage <NUM> metabolic syndrome revealed that these cancer-free participants manifested glutaminolytic profiles that were very similar to those found in adenocarcinoma patients.

The ratio (Glu/Hexoses) was assembled, following the in vitro demonstration of the "glutamate pulling effect," where glucose starvation in malignant cells culture leads to elevations in glutamate through a MYC-coordinated reaction. This effect was clearly identified in the blood of patients harboring adenocarcinomas, those at higher risk of breast cancer and individuals with PCOS. Noteworthy, neither of the control groups composed of population-based normal controls or patients with non-glandular tumors (leukemias, lymphomas, multiple myelomas and squamous cell carcinomas) revealed marked changes in this ratio particularly squamous cell carcinomas that revealed similar levels to controls. Increases in the "glutamate pulling effect" have been described under conditions of metabolic stress induced by glucose deprivation.

In agreement, the inventors found a significant (p=<NUM>, FDR=<NUM>) inverse correlation between patient blood hexoses concentrations and the values of our breast cancer equation {[PC aa <NUM>:<NUM>/[(Val/Phe)/Taurine]/C10:<NUM>}. In line with the premise that glandular cancers are promoted under conditions of relative hypoglycemia, measured as the "glutamate pulling effect," their results suggest that the isolated determination of blood glucose levels may not be as informative as the measurement of hexose levels in relation to other metabolic intermediates including: i) the mitochondrial carnitine palmitoyltransferase II (CPT-<NUM>) deficiency ratio (C16/C3), ii) the peroxisomal impairment biomarkers lysoPC a C26:<NUM>, lysoPC a C26:<NUM> and lysoPC a C28:<NUM>, or iii) its relation to glutaminolysis [Phe/(Gln/Glu)/Asp].

Importantly, both CPT-<NUM> and peroxisomal deficiencies, well known inborn errors of metabolism, are associated with hypoglycemia in afflicted patients. If a state of relative hypoglycemia were to occur in ovary cancer as the result of inborn-like errors of metabolism then hyperinsulinemia associated with chronic hypoglycemia would constitute a powerful metabolic stressor capable of systemically up-regulating glycolysis and glutaminolysis, even in the absence of cancer.

In sum, carcinogenesis is a complex, polygenic process that draws upon numerous altered cellular functions leading ultimately, over decades, to a state of irreversible malignant transformation. Molecular signatures as static measures cannot capture the dynamic nature of biological processes as they fail to encompass the complexity, redundancy and promiscuity of these events.

Malignant transformation demands that cells successfully traverse metabolic, structural and immune evasive strategies. This methodology uses a multi-dimensional invention to define malignant transformation as a metabolic signature.

This invention uses targeted quantitative MS/MS, to define unique and previously unknown relationships between bio-energetic, biosynthetic and immune phenotypes in patients with ovarian cancer. This signature defines the ovarian cancer phenotype and is applied to diagnose and provide prognostic information for patients with ovarian cancer and those at risk for the development of ovarian cancer.

Disclosed but not claimed are methods for diagnosis and/or prognosis of diseases and/or cancers other than ovarian cancer. For example, to evaluate the possible differences and likenesses among breast cancer and tumors of distinctive histology and locations, the identified blood signatures were compared to blood samples from treatment-naive lung (n=<NUM>), plasma from prostate cancer patients (n=<NUM>), head and neck (n=<NUM>), liver (n=<NUM>) and colon cancer patients (n=<NUM>), the latter with respective normal (n=<NUM>) and tumor tissues (n=<NUM>) as well as hematological malignancies (n=<NUM>). Normal (n=<NUM>) and tumor tissues (n=<NUM>) from patients harboring colon cancer, were also used to validate, at tissue level, the metabolic communalities identified in blood and shared by breast and colon cancer. Important, each individual type of cancer, besides comparison to different malignancies, were also compared to the group of <NUM> controls described above containing <NUM> different benign metabolic conditions. After multivariate ROC curve analysis, the ratios shown in <FIG> and B emerged as the representation of the metabolic scenario depicting the highest specificity to each individual tumor.

Also disclosed but not claimed is a method for diagnosis and/or prognosis of colon cancer. This can be seen, for example, in <FIG>, showing that the signatures developed for assessing colon cancer have a sensitivity of <NUM>%, a specificity of <NUM>%, and a negative predictive value of <NUM>%. In particular, <FIG> shows a multivariate ROC curve analysis for colon cancer patients (n-<NUM>) compared to healthy participants as well as other malignant and non-malignant conditions (n=<NUM>). <FIG> depicts the performance of the identified metabolites and ratios for colon cancer patients. The near <NUM>% negative predictive value (<NUM>%) makes the present test highly indicative as a powerful screening tool.

<FIG> shows an Ortho-PLSDA Score's plot of colon cancer patients (n=<NUM>) compared to healthy participants as well as other malignant and non-malignant conditions (n=<NUM>). By processing (e.g., isolating, quantifying, normalizing, etc.) each sample (e.g., blood sample), and then plotting the initial results (e.g., using an Ortho-PLSDA Score's plot) based on at least one colon cancer signature (as identified by the inventors), each patient clearly falls within (a) the control group or (b) the colon cancer group.

Moreover, portions of the signature provide details on each patient's prognosis. This can be seen, for example, in <FIG>, where various equations (identified at the top of each chart) provide survival rate (prognosis) information for each patient. Thus, not only have the inventors identified signatures that can be used to diagnosis colon cancer, but also to prognose colon cancer. It should be appreciated that while the charts provided in <FIG> and <FIG> illustrate (a) diagnosis for colon cancer and (b) survival rates, the colon signatures (or portions thereof) can be used to provide other assessments for colon cancer, including screening for, diagnosing, prognosing, treating the same as discussed in greater detail in the results section below.

A preferred signature (or portions thereof) for assessing colon cancer is provided in <FIG>, including a core ovarian cancer equation, metabolite enhancers, and core equations with enhancers. As can be seen in <FIG>, the core ovarian cancer equation is (C16:<NUM>/PC aa C34:<NUM>), or a ratio of Hexadecenoylcarnitine to Phosphatidylcholine with diacyl residue sum (see <FIG> and <FIG>). The inventors have discovered that this ratio of individual metabolites, after quantification, normalization, etc., are critical in assessing a patient for colon cancer. Other key portions include {SM C20:<NUM>/[(C16:<NUM>/PC aa C34:<NUM>)/C5:<NUM>-DC]}, {SM OH C16:<NUM>/[(C16:<NUM>/PC aa C34:<NUM>)/C5:<NUM>-DC]}, and {SM OH C14:<NUM>/[(C16:<NUM>/PC aa C34:<NUM>)/C5:<NUM>-DC]}, which can be used to not only diagnose, but prognose for colon cancer.

Also disclosed but not claimed is a method for diagnosis and/or prognosis of pancreatic cancer. This can be seen, for example, in <FIG> and <FIG>, showing that the signatures developed for assessing pancreatic cancer have a sensitivity of <NUM>%, a specificity of <NUM>%, and a negative predictive value of <NUM>%. In particular, <FIG> shows a multivariate ROC curve analysis for pancreatic cancer patients (n-<NUM>) compared to healthy participants as well as other malignant and non-malignant conditions (n=<NUM>). <FIG> depicts the performance of the identified metabolites and ratios for pancreatic cancer patients. The <NUM>% negative predictive value makes the present test highly indicative as a powerful screening tool.

<FIG> shows an Ortho-PLSDA Score's plot of pancreatic cancer patients (n=<NUM>) compared to healthy participants as well as other malignant and non-malignant conditions (n=<NUM>). By processing (e.g., isolating, quantifying, normalizing, etc.) each sample (e.g., blood sample), and then plotting the initial results (e.g., using an Ortho-PLSDA Score's plot) based on at least one pancreatic cancer signature (as identified by the inventors), each patient clearly falls within (a) the control group or (b) the pancreatic cancer group.

Moreover, portions of the signature provide details on each patient's prognosis. This can be seen, for example, in <FIG>, where various equations (identified at the top of each chart) provide survival rate (prognosis) information for each patient. For example, <FIG> distinguishes short survival terms (e.g., <NUM> months) and longer survival terms (e.g., <NUM> months). <FIG> also distinguishes between short and long survival terms, but further validates that these findings were able to prove that the metabolic equation is fully functional in survival prediction even in malignancies of different origins, such as Multiple Myeloma (M. ), Leukemias, Lymphomas and Myelodisplasias (where ISS <NUM>, <NUM>, and <NUM> = Int'l Scaling System, Hem = Hematological Malignancies, and Panc = Pancreas Cancer). Thus, not only have the inventors identified signatures that can be used to diagnosis pancreatic cancer, but also to prognose pancreatic cancer.

A preferred signature (or portions thereof) for assessing pancreatic cancer is provided in <FIG>, including a core pancreatic cancer equation, metabolite enhancers, and core equations with enhancers. As can be seen in <FIG>, two core pancreatic cancer equations are (<NUM>) (C3:<NUM>/C12-DC), or a ratio of Propenoylcarnitine to Dodecanedioylcarnitine, and (<NUM>) (C6:<NUM>/C12-DC), or a ratio of Hexenoylcarnitine to Dodecanedioylcarnitine. The inventors have discovered that this ratio of individual metabolites, after quantification, normalization, etc., are critical in assessing a patient for pancreatic cancer. Other key portions include (C:<NUM>-DC/lysoPC a C17:<NUM>) and (C12-DC/lysoPC a C17:<NUM>), which can be used to not only diagnose, but prognose for pancreatic cancer.

The invention may involve a patient visiting a doctor, clinician, technician, nurse, etc., where blood or a different sample is collected. The sample would then be provided to a laboratory for analysis, as discussed above (e.g., mass spectrometry, log-transformation, comparisons, etc.). A kit can be used to obtain the sample, where the kit is made available to the patient via a medical facility, a drug store, the Internet, etc. The kit may include one or more wells and one or more inserts impregnated with at least one internal standard. The kit can be used to gather the sample from a patient, where the sample is then provided to a laboratory for analysis.

For example, as shown in <FIG>, peripheral blood may collected into EDTA-anticoagulant tubes. Plasma is isolated by centrifugation. Plasma samples may then be submitted to a p180 AbsoluteIDQ kit for extraction and processing. In one embodiment, prepared samples will then undergo liquid chromatography (LC) followed by Flow Injection Analysis (FIA) by tandem Mass Spectrometry (MS/MS) (i.e., metabolite extraction). The extracted data is then processed using computer software. For example, the data acquired may then be normalized (e.g., via log-transformation) and stored in a database that includes at least (i) patient identification, (ii) metabolite name, and (iii) quantification. If this data is on known individuals (individuals with known conditions), then it can be analyzed to determine signatures that can be used to assess a particular disease. If, however, the data is on a patient whose condition is unknown, then it can be compared to known signatures (e.g., stored in memory) to screen for, diagnose, prognose, and treat the patient.

It should be appreciated that the present invention is not limited to normalizing a quantified metabolite. In other words, other processes discussed herein and/or generally known to those skilled in the art may be performed either before or after normalization. It should also be appreciated that while certain processes can be performed manually, most (if not all) should preferably be performed using software, where initial results (data post mass spectrometry, post normalization), are stored in memory, presented on a display (e.g., computer monitor, etc.) and/or printed. The initial results can then be compared to known "signatures" for different diseases, where similarities and differences are used to screen for, diagnose, prognose, treat, etc. a particular disease. It should be appreciated that the sample may be assessed for a particular disease, or for multiple diseases, depending on the patient's sex, age, etc. Thus, the software could be used to assess a particular disease or assess at least one disease from a plurality of diseases.

It should further be appreciated that the "comparing" step can be performed by (i) software, (ii) a human, or (iii) both. For example, with respect to the prior, a computer program could be used to compare sample results to known signatures and to use differences and/or similarities thereof to assess at least one disease, and provide diagnosis, prognosis, and/or treatment for the same. Alternatively, in the second embodiment, a technician could be used to compares sample results to known signatures (or aspects thereof) and make a diagnosis, prognosis, and/or treatment decision based on perceived similarities and/or differences. Finally, with respect to the latter, a computer program could be used to plot (e.g., on a computer display) sample results alongside known signatures (e.g., signatures of healthy patients, signatures of unhealthy patients, life expectancies, etc.). A technician could then view the same and make at least one diagnosis, prognosis, treatment recommendation, etc. based on similarities and/or differences in the plotted information.

Bottom line, it is the differences and/or similarities between known signatures that allows a disease to be assessed, whether that assessment is automated (e.g., performed by a computer), performed manually (e.g., done by a human), or a combination of the two.

Results (e.g., assessments) are then provided to the patient directly (e.g., via mail, an electronic communication, etc.) or via the patient's doctor, and can include screening information, diagnosis information, prognosis information, and treatment information.

In particular, the invention can be used to distinguish a sample that is cancerous from one that is normal. Once ovarian cancer is identified, the invention can be used to define the cancer, by degree, the relative malignancy of the cancer. This can be done using terminology (e.g., non-invasive (e.g., in situ), invasive, metastatic, and lethal), at least one scale (e.g., <NUM>-<NUM>, <NUM>-<NUM>, A-F, etc.), where one end of the scale is low grade (e.g., non-invasive) and the other end is high grade (lethal), or other visual forms (e.g., color coded, 2D or 3D model, etc.).

The invention can also be used to provide a prognosis. For example, in ovarian cancer, once the ovarian signature is identified, the invention can be used to provide gradations within the signature (or signatures), subcategorizing the patient into one that is likely to survive (e.g., greater than <NUM> years, <NUM> years, <NUM> years, etc.), likely to relapse (e.g., within <NUM> years, <NUM> years, <NUM> years, etc.), or likely to die (e.g., within <NUM> years, <NUM> years, <NUM> years, etc.). Again, prognosis could be provided using terminology (e.g., low risk, medium risk, high risk, etc.), at least one scale, or other visual forms.

Not only can the present invention be used to determine life expectancy and remission rate, it can also be used to determine treatment, or viability of treatment (another form of prognosis). This could be a likelihood to respond to therapy (e.g., hormonal, radiation, chemotherapy, etc.), which again could be provided using terminology, at least one scale, or other visual forms.

Thus, by way of example, the present invention may be used to determine (i) a high likelihood that a patient harbors a cancer (diagnosis), (ii) a high likelihood that the cancer is ovarian (diagnosis), (iii) likely drug resistant (prognosis), (iv) high risk of relapse (prognosis), and (v) high risk of death within <NUM>-<NUM> years (prognosis). Clearly this is exemplary, and other diseases (e.g., breast, colon, ovarian, etc.), sub-categorizations (e.g., indolent, aggressive, very aggressive, etc.), prognosis (e.g., reoccurrence within <NUM> years, <NUM> years, <NUM> years, etc.), and treatments (e.g., resistant to hormonal therapy, chemotherapy, radiation therapy, etc.) can be identified (predicted) using the present invention.

The invention can also be used to screen for ovarian cancer. Medical screening is the systematic application of a test or inquiry to identify individuals at sufficient risk of a specific disorder to benefit from further investigation or direct preventative action (these individuals not having sought medical attention on account of symptoms of that disorder). The present invention uses metabolic signatures to screen for ovarian cancer in populations who are considered at risk, e.g., women in their <NUM> or <NUM> with a family history, or other risk factors.

As shown in <FIG>, once a sample has been received and processed (e.g., processed using techniques like the one used to identify the signatures in the first place, such as mass spectrometry (to quantify metabolites), log-transformation (or other mathematical manipulation to normalize the data), etc.), the initial results (e.g., metabolites and/or sets thereof) can then be compared to signatures (or portions thereof) that have been identified (by the inventors) as useful in assessing at least one disease. The signatures may be stored in memory, and the initial data (i.e., processed sample) may be compared to at least one signature either manually (e.g., by viewing the sample, or initial results thereof, against known signatures), automatically (e.g., using a computer program to discern differences and/or similarities between the sample, or initial results thereof, and known signatures), or both (e.g., a program determines at least one diagnosis/prognosis and a technician reviews the data to validate the same). Based on the results (i.e., comparison results), at least one diagnosis and/or prognosis, which may or may not include treatment, is identified and provided to the patient.

Claim 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 Tiglycarnitine and Glutaconylcarnitine;
normalizing at least said Tiglycarnitine 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 at least whether said human patient has ovarian cancer and said prognosis includes at least a risk factor associated with said ovarian cancer.