WO2018136085A1

WO2018136085A1 - Methods and compositions for providing an early stage ovarian cancer assessment with metabolites

Info

Publication number: WO2018136085A1
Application number: PCT/US2017/014406
Authority: WO
Inventors: Bruce Xuefeng Ling; Limin Chen; Shiying Hao; Qianyang HUANG; Jingzhi YANG; Jin You; Jaehong Kim; Yun Ding; Zhen Li; James Schilling; Zhiji LIU; Dasong HUA
Original assignee: Mprobe Inc.
Priority date: 2017-01-20
Filing date: 2017-01-20
Publication date: 2018-07-26

Abstract

Ovarian cancer markers, ovarian cancer marker panels, and methods for obtaining an ovarian cancer marker level representation for a sample are provided. These composition and methods find use in a number of applications, including, for example, diagnosing ovarian cancer, prognosing ovarian cancer, monitoring a subject with ovarian cancer, and determining a treatment for ovarian cancer, in addition, systems, devices, and kits thereof that find use in practicing the subject methods are provided.

Description

METHODS AND COMPOSITIONS FOR PROVIDING AN EARLY STAGE OVARIAN CANCER ASSESSMENT WITH METABOLITES

Field of the invention

The present disclosure generally relates to small molecule metabolic biomarkers. In particular, the present disclosure relates to a panel of metabolite species that is useful for the assessment of subjects having ovarian cancer, including methods for identifying such metabolic biomarkers within biological samples. This invention pertains to providing an early stage ovarian cancer assessment with metabolites.

Background of the invention

Ovarian cancer (OV) continues to be one of the most common malignancies in the world, and is the deadliest gynecological cancer. Standard treatment for advanced OV involves cytoreductive surgery followed by platinum-based chemotherapy.

Recurrent OV usually develops chemotherapy resistance and invariably is fatal.

The current screening modalities of bimanual examination, CA-125 and transvaginal ultrasonography together allow us to detect only 30-45% of women with early-stage OV. If ovarian cancer is found (and treated) before the cancer has spread outside the ovary (stages IA and IB), the 5-year relative survival rate is 92%. However, only 15% of all ovarian cancers are found at this early stage. The majority of patients tend to present in advanced stages. There is no reliable screening tool for early detection of ovarian cancer either in the general populations or in at-risk patient populations. To improve survival rates, efforts should be devoted to the discovery of novel biomarkers to monitor disease progression and to develop personalized therapy for OV patients.

A promising approach is metabolomics, a fast growing area in system biology that uses mass spectrometry (MS) and promises the identification of sensitive

metabolite biomarkers associated with disease, drug treatment, toxicity and

environmental effects. Metabolites are the downstream products of genes, transcripts and protein functions in biological systems. They are especially sensitive to

perturbations in a number of metabolic pathways and varied pathological conditions. This invention use MS to analyze the small molecule metabolites, and use these metabolites for OV assessment. Summary of the invention

The present disclosure relates to a panel of metabolite species that is useful for the identification of subjects having ovarian cancer, including methods for identifying such metabolic biomarkers within biological samples.

in one aspect, the disclosure includes a method comprising measuring the concentration of one to fifty-one metabolite species in a sample of a serum from a subject having ovarian cancer, wherein the metabolite species is a component of a panel of a plurality of metabolite species, wherein a change in the concentration of the metabolite species is useful for the identification of subjects having ovarian cancer. In certain embodiments the concentration of the metabolite species is normalized. In preferred embodiments, the method includes the step of comparing the measured concentration of the metabolite species to a predetermined value calculated using a model based on concentrations of a plurality of the metabolic species that are

components of the panel.

in certain embodiments, the panel of metabolite species comprises one to fifty- one compounds selected from the group consisting of C18:10H-Carnitine, C18:1- Carnitine, C80H-Carnitine, C14:1 -Carnitine, C6-Carnitine, C10:1 -Carnitine, C8- Carnitine, C10-Carnitine, C40H-Carnitine, C3DC-Carnitine, C2-Carnitine, C6DC- Carnitine, C3-Carnitine, CO-Carnitine, C10:2-Carnitine, C50H-Carnitine, C5:1 -Carnitine, d18:1-18:0 Ceramide, d18:1-16:0 Ceramide, d18:1-24:1 Ceramide, d18:1-18:1

Ceramide, d18:1-22:0 Ceramide, d18:1-20:0 Ceramide, d20:3 Fatty acid, d20:4 Fatty acid, d18:3 Fatty acid, d20:5 Fatty acid, d22:6 Fatty acid, d22:0 Fatty acid,

Progesterone, Cortisol, Cholesterol, 17-Hydroxyprogesterone, Glycine, Citrilline, Arginine, Proline, Alanine, 5-Oxoproline, Methionine, Phenylalanine, Ornithine,

Glutamate, Tyrosine, Valine, Leucine, Aspartate, Succinylacetone, Triiodothyronine, Urea, and Total Deoxycholic acid.

Also disclosed is a kit for the analysis of a sample of a biofluid of a subject, comprising aliquots of standards of each compound of a panel of metabolite species; an aliquot of an internal standard; and an aliquot of a control biofluid. Typically, the control biofluid is serum from a control source that is conspecific with the subject. In some embodiments, the internal standard consists of C18-Carnitine-d3, C14-Carnitine-d9, C5-Carnitine-d9, C8-Carnitine-d3, C4-Carnitine-d3, C3-Carnitine-d3, C2-Carnitine-d3, C0- Carnitine-d9, C5OH-Carnitine-d3, d18:1-18:0 Ceramide-d7, d18:1-16:0 Ceramide-cfr, d 18: 1-24:0 Ceramide-d7, d20:4 Fatty acid-d8, Progesterone-d9, Cholesterol-d7, Glycine- ¹³C-¹⁵N, Citrulline-d2, Arginine-¹³C-d4, Proline-¹³C5-¹⁵N, Alanine-d4, Proline-¹³C5-¹⁵N, Methionine-d3, Phenylalanine-¹³C6, Ornithine-d2, Glutamate-d3 , Tyrosine-¹³C6, Valine-d8, Leucine-d3, Aspartate-d3, Succinylacetone-¹³C5, Triiodothyronine-¹³C6, Urea-¹⁵N2, and Chenodeoxycholic acid-d9. Typically, the kit includes instructions for use.

Brief description of the drawings

The invention will be best understood from the following detailed description when read in conjunction with the accompanying drawings. The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. It is emphasized that, according to common practice, the various features of the drawings are not to-scale. On the contrary, the dimensions of the various features are arbitrarily expanded or reduced for clarity. Included in the drawings are the following figures.

Figure 1. Outline of the study

Figure 2. Scatterplot of calculated probabilities of ovarian cancer with targeted metabolomics profile. The model was trained with Random Forest algorithm, 60/68 case/control (67/76 in total) were selected out randomly to train the model. Figure 3. ROC curves for models of ovarian cancer assessment with targeted metabolomics profile evaluated on early stage patients versus normal

subjects. Average true positive rate was calculated with 500 10-fold CV fits of the model.

Figure 4. Unsupervised hierarchical clustering analysis with heat map showing the abundance pattern of targeted metabolomics profile of early stage OV patients versus normal subjects. Figure 5. Unsupervised hierarchical clustering analysis with heat map showing the abundance pattern of all metabolomics profile of early stage OV patients versus normal subjects.

Detailed description of the invention

Methods, compositions and reagents are provided for diagnosing and prognosing ovarian cancer. The methods and compositions find use in a number of applications, including, for example, diagnosing early stage ovarian cancer, and monitoring an individual with ovarian cancer. A report may be provided to the patient of the

assessment. In addition, systems, devices and kits thereof that find use in practicing the subject methods are provided. These and other objects, advantages, and features of the invention will become apparent to those persons skilled in the art upon reading the details of the compositions and methods as more fully described below.

Before the present methods and compositions are described, it is to be

understood that this invention is not limited to particular method or composition described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims.

Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limits of that range is also specifically disclosed. Each smaller range between any stated value or intervening value in a stated range and any other stated or intervening value in that stated range is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included or excluded in the range, and each range where either, neither or both limits are included in the smaller ranges is also encompassed within the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or test of the present invention, some potential and preferred methods and materials are now described. All publications mentioned herein are incorporated herein by reference to disclose and describe the methods and/or materials in connection with which the publications are cited, it is understood that the present disclosure supersedes any disclosure of an incorporated publication to the extent there is a contradiction.

As will be apparent to those of skill in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the present invention. Any recited method can be carried out in the order of events recited or in any other order that is logically possible.

it must be noted that as used herein and in the appended claims, the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a cell" includes a plurality of such cells and reference to "the peptide" includes reference to one or more peptides and equivalents thereof, e.g. polypeptides, known to those skilled in the art, and so forth.

The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the dates of publication provided may be different from the actual publication dates that may need to be independently confirmed.

As summarized above, aspects of the subject invention include compositions, methods, systems and kits that find use in providing an ovarian cancer assessment, e.g. diagnosing, prognosing, monitoring, and/or treating ovarian cancer in a subject. In describing the subject invention, compositions useful for providing an ovarian cancer assessment will be described first, followed by methods, systems and kits for their use. Ovarian cancer markers and panels

in some aspects of the invention, ovarian cancer biomarkers are provided. By a "biomarker" or "marker" it is meant a molecular entity whose representation in a sample is associated with a disease phenotype. By "ovarian cancer" it is meant any cancerous growth arising from the ovary, for example, a surface epithelial-stromal tumor

(adenocarcinoma, including, e.g., papillary serous cystadenocarcinoma, endometrioid tumor, serous cystadenocarcinoma, papillary, mucinous cystadenocarcinoma , clear-cell ovarian tumor, Mucinous adenocarcinoma, cystadenocarcinoma, and others), a carcinoma (e.g. , sex cord-stromal tumors, other carcinomas), a germ cell tumor (e.g. teratoma, Dysgerminoma, and others), Mullerian tumor, epidermoid tumor (squamous cell carcinomas), Brenner tumor, and the like, as known in the art or as described herein. Thus, by an ovarian cancer "biomarker" or "ovarian cancer marker" it is meant a molecular entity whose representation in a sample is associated with an ovarian cancer phenotype, e.g., the presence of ovarian cancer, the stage of ovarian cancer, a prognosis associated with the ovarian cancer, the predictability of the ovarian cancer being responsive to a therapy, etc. In other words, the marker may be said to be differentially represented in a sample having an ovarian cancer phenotype.

Ovarian cancer biomarkers include metabolites that are differentially represented in an ovarian cancer phenotype. As demonstrated in the examples of the present disclosure, the inventors have identified the 51 metabolites, C18:1OH-Carnitine, C18:1-Carnitine, C8OH-Carnitine, C14:1-Carnitine, C6-Carnitine, C10:1-Carnitine, C8-Carnitine, C10- Carnitine, C4OH-Carnitine, C3DC-Carnitine, C2-Carnitine, C6DC-Carnitine, C3- Carnitine, CO-Carnitine, C10:2-Carnitine, C5OH-Carnitine, C5:1 -Carnitine, d18: 1-18:0 Ceramide, d18:1-16:0 Ceramide, d18:1-24:1 Ceramide, d18:1-18:1 Ceramide, d18:1- 22:0 Ceramide, d18:1 -20:0 Ceramide, d20:3 Fatty acid, d20:4 Fatty acid, d18:3 Fatty acid, d20:5 Fatty acid, d22:6 Fatty acid, d22:0 Fatty acid, Progesterone, Cortisol, Cholesterol, 17-Hydroxyprogesterone, Glycine, Citrilline, Arginine, Proline, Alanine, 5- Oxoproline, Methionine, Phenylalanine, Ornithine, Glutamate, Tyrosine, Valine, Leucine, Aspartate, Succinylacetone, Triiodothyronine, Urea, and Total Deoxycholic acid, that are represented at elevated/lowered levels in blood samples of subtypes of ovarian cancers, and thus, that find use as biomarkers in providing an ovarian cancer assessment, e.g. diagnosing an ovarian cancer, prognosing an ovarian cancer, determining a treatment for a subject affected with ovarian cancer, monitoring a subject with ovarian cancer, and the like.

Methods

The subject ovarian cancer biomarkers find use in making an ovarian cancer assessment for a patient, or "subject". By an "ovarian cancer assessment", it is generally meant an estimation of a subject's susceptibility to ovarian cancer, a

determination as to whether a subject is presently affected by ovarian cancer, a prognosis of a subject affected by ovarian cancer (e.g., identification of ovarian cancer states, stages of the ovarian cancer, prediction of responsiveness to a therapy and/or intervention, e.g. sensitivity or resistance a chemotherapy, radiation, or surgery, likelihood that a patient will die from the ovarian cancer, etc.), and the use of

therametrics (e.g., monitoring a subject's condition to provide information as to the effect or efficacy of therapy on the ovarian cancer). Thus, for example, the subject ovarian cancer biomarkers and biomarker panels may be used to diagnose ovarian cancer, to provide a prognosis to a patient having ovarian cancer, to provide a prediction of the responsiveness of a patient with ovarian cancer to a medical therapy, to monitor a patient having ovarian cancer, to treat a patient having ovarian cancer, etc. In practicing the subject methods, an ovarian cancer biomarker signature for a patient is obtained. By an "ovarian cancer biomarker signature" or more simply, "ovarian cancer signature", it is meant a representation of the measured level/activity of an ovarian cancer biomarker or biomarker panel of interest. A biomarker signature typically comprises the quantitative data on the biomarker levels/activity of these one or more biomarkers of interest.

Examples of biomarker signatures include collections of measured small molecular metabolites levels. As used herein, the term "biomarker signature" means metabolites signature. Examples of biomarker signatures include biomarker profiles and biomarker scores. By a "biomarker profile" it is meant the normalized representation of one or more biomarkers of interest, i.e. a panel of biomarkers of interest, in a patient sample. By a "biomarker score" it is meant a single metric value that represents the sum of the weighted representations of one or more biomarkers of interest, more usually two or more biomarkers of interest, i.e. a panel of biomarkers of interest, in a patient sample. Biomarker profiles and scores are discussed in greater detail below.

For example, in some embodiments, the subject methods may be used to obtain an ovarian cancer signature. That is, the subject methods may be used to obtain a representation of the metabolite, e.g., C18:10H-Carnitine, C18:1 -Carnitine, C80H- Carnitine, C14:1-Carnitine, C6-Carnitine, C10:1-Carnitine, C8-Carnitine, C10-Carnitine, C40H-Carnitine, C3DC-Carnitine, C2-Carnitine, C6DC-Carnitine, C3-Carnitine, CO- Carnitine, C10:2-Carnitine, C50H-Carnitine, C5:1 -Carnitine, d18:1-18:0 Ceramide, d18:1-16:0 Ceramide, d18:1-24:1 Ceramide, d18:1-18:1 Ceramide, d18:1-22:0

Ceramide, d 18: 1-20:0 Ceramide, d20:3 Fatty acid, d20:4 Fatty acid, d18:3 Fatty acid, d20:5 Fatty acid, d22:6 Fatty acid, d22:0 Fatty acid, Progesterone, Cortisol, Cholesterol, 17-Hydroxyprogesterone, Glycine, Citrilline, Arginine, Proline, Alanine, 5-Oxoproline, Methionine, Phenylalanine, Ornithine, Glutamate, Tyrosine, Valine, Leucine, Aspartate, Succinylacetone, Triiodothyronine, Urea, and Total Deoxycholic acid, that are up- or down-regulated (i.e., expressed at a higher or lower level, exhibits a higher or lower level of activity, etc.), in patients with ovarian cancer.

To obtain an ovarian cancer signature, the metabolite level of the one or more ovarian cancer biomarkers of interest is detected in a patient sample. That is, the representation of one or more ovarian cancer biomarkers, e.g., C18:1OH-Carnitine, C18:1-Carnitine, C8OH-Carnitine, C14:1-Carnitine, C6-Carnitine, C10:1 -Carnitine, C8- Carnitine, C10-Carnitine, C4OH-Carnitine, C3DC-Carnitine, C2-Carnitine, C6DC- Carnitine, C3-Carnitine, CO-Carnitine, C10:2-Carnitine, C5OH-Carnitine, C5:1 -Carnitine, d18:1-18:0 Ceramide, d18:1-16:0 Ceramide, d18:1-24:1 Ceramide, d18:1-18:1

Glutamate, Tyrosine, Valine, Leucine, Aspartate, Succinylacetone, Triiodothyronine, Urea, and Total Deoxycholic acid, and in some instances other ovarian cancer biomarkers in the art, e.g. a panel of biomarkers, is determined for a patient sample. The term "sample" with respect to a patient encompasses blood and other liquid samples of biological origin, solid tissue samples such as a biopsy specimen or tissue cultures or cells derived or isolated therefrom and the progeny thereof. The definition also includes samples that have been manipulated in any way after their procurement, such as by treatment with reagents; washed; or enrichment for certain cell populations. The definition also includes samples that have been enriched for particular types of molecules, e.g., nucleic acids, polypeptides, etc. The term "biological sample" encompasses a clinical sample, and also includes tissue obtained by surgical resection, tissue obtained by biopsy, cells in culture, cell supernatants, cell lysates, tissue samples, organs, bone marrow, blood, plasma, serum, and the like. The term "blood sample" encompasses a blood sample (e.g., peripheral blood sample) and any derivative thereof (e.g., fractionated blood, plasma, serum, etc.).

in performing the subject methods, the biomarker level is typically assessed in a body fluid sample (e.g., a sample of blood, e.g., whole blood, fractionated blood, plasma, serum, etc.) that is obtained from an individual. The sample that is collected may be freshly assayed or it may be stored and assayed at a later time, if the latter, the sample may be stored by any convenient means that will preserve the sample so that gene expression may be assayed at a later date. For example, the sample may freshly cryopreserved, that is, cryopreserved without impregnation with fixative, e.g. at 4^CC, at - 20°C, at -60°C, at -80°C, or under liquid nitrogen. Alternatively, the sample may be fixed and preserved, e.g. at room temperature, at 4°C, at -20°C, at -60^CC, at -80°C, or under liquid nitrogen, using any of a number of fixatives known in the art, e.g. alcohol, methanol, acetone, formalin, paraformaldehyde, etc.

The resultant data provides information regarding activity for each of the ovarian cancer biomarkers that have been measured, wherein the information is in terms of whether or not the biomarker is present (e.g. expressed and/or active) and, typically, at what level, and wherein the data may be both qualitative and quantitative.

Once the representation of the one or more biomarkers has been determined, the measurement(s) may be analyzed in any of a number of ways to obtain a biomarker signature.

For example, the representation of the one or more ovarian cancer biomarkers may be analyzed individually to develop a biomarker profile. As used herein, a "biomarker profile" is the normalized representation of one or more biomarkers in a patient sample, for example, the normalized level of serological metabolite

concentrations in a patient sample, the normalized activity of a biomarker in the sample, etc. A profile may be generated by any of a number of methods known in the art. Other methods of calculating a biomarker signature will be readily known to the ordinarily skilled artisan.

As another example, the measurement of an ovarian cancer biomarker or biomarker panel may be analyzed collectively to arrive at an ovarian cancer biomarker score, and the ovarian cancer biomarker signature is therefore a single score. By "biomarker assessment score" it is meant a single metric value that represents the sum of the weighted representations of each of the biomarkers of interest, more usually two or more biomarkers of interest, in a biomarker panel. As such, in some embodiments, the subject method comprises detecting the amount of markers of an ovarian cancer biomarker panel in the sample, and calculating an ovarian cancer biomarker score based on the weighted levels of the biomarkers. In certain embodiments, the biomarker score is based on the weighted levels of the biomarkers. In certain embodiments, the biomarker score may be a "metabolite biomarker score", or simply "metabolite score", i.e. it comprises the weighted expression level(s) of the one or more biomarkers, e.g. each biomarker in a panel of biomarkers.

An ovarian cancer biomarker score for a patient sample may be calculated by any of a number of methods and algorithms known in the art for calculating biomarker scores. For example, weighted marker levels, e.g. iog2 transformed and normalized marker levels that have been weighted by, e.g., multiplying each normalized marker level to a weighting factor, may be totaled and in some cases averaged to arrive at a single value representative of the panel of biomarkers analyzed.

in some instances, the weighting factor, or simply "weight" for each marker in a panel may be a reflection of the change in analyte level in the sample. For example, the analyte level of each biomarker may be log2 transformed and weighted either as 1 (for those markers that are increased in level in a subgroup of ovarian cancers of interest, etc.) or -1 (for those markers that are decreased in level in a subgroup of ovarian cancers of interest, etc.), and the ratio between the sum of increased markers as compared to decreased markers determined to arrive at an ovarian cancer biomarker signature, in other instances, the weights may be reflective of the importance of each marker to the specificity, sensitivity and/or accuracy of the marker panel in making the diagnostic, prognostic, or monitoring assessment. Such weights may be determined by any convenient statistical machine learning methodology, e.g. Principle Component Analysis (PCA), linear regression, support vector machines (SVMs), and/or random forests of the dataset from which the sample was obtained may be used, in some instances, weights for each marker are defined by the dataset from which the patient sample was obtained. In other instances, weights for each marker may be defined based on a reference dataset, or "training dataset". Methods of analysis may be readily performed by one of ordinary skill in the art by employing a computer-based system, e.g. using any hardware, software and data storage medium as is known in the art, and employing any algorithms convenient for such analysis. For example, data mining algorithms can be applied through "cloud computing", smartphone based or client- server based platforms, and the like.

Thus, in some instances, an ovarian cancer biomarker signature may be expressed as a series of values that are each reflective of the level of a different biomarker (e.g., as a biomarker profile, i.e. the normalized expression values for multiple biomarkers), while in other instances, the ovarian cancer biomarker signature may be expressed as a single value (e.g., an ovarian cancer biomarker score).

in some instances, the subject methods of obtaining or providing an ovarian cancer biomarker signature for a subject further comprise providing the ovarian cancer biomarker signature as a report. Thus, in some instances, the subject methods may further include a step of generating or outputting a report providing the results of an ovarian cancer biomarker evaluation in the sample, which report can be provided in the form of an electronic medium (e.g., an electronic display on a computer monitor), or in the form of a tangible medium (e.g., a report printed on paper or other tangible medium). Any form of report may be provided, e.g. as known in the art or as described in greater detail below.

The ovarian cancer signature that is so obtained may be employed to make an ovarian cancer assessment. Typically, in making the subject ovarian cancer assessment, the ovarian cancer signature is employed by comparing it to a reference or control, and using the results of that comparison (a "comparison result") to make the ovarian cancer assessment, e.g. diagnosis, prognosis, prediction of responsiveness to treatment, etc. The terms "reference" or "control", e.t. "reference signature" or "control signature", "reference profile" or "control profile", and "reference score" or "control score" as used herein mean a standardized biomarker signature, e.g. biomarker profile or biomarker score, that may be used to interpret the ovarian cancer biomarker signature of a given patient and assign a diagnostic, prognostic, and/or responsiveness class thereto. The reference or control is typically an ovarian cancer biomarker signature that is obtained from a sample (e.g., a body fluid, e.g. blood) with a known association with a particular phenotype, Typically, the comparison between the ovarian cancer signature and reference will determine whether the ovarian cancer signature correlates more closely with the positive reference or the negative reference, and the correlation employed to make the assessment. By "correlates closely", it is meant is within about 40% of the reference, e.g. 40%, 35%, or 30%, in some embodiments within 25%, 20%, or 15%, sometimes within 10%, 8%, 5%, or less.

in certain embodiments, the obtained ovarian cancer signature for a subject is compared to a single reference/control biomarker signature to obtain information regarding the phenotype. in other embodiments, the obtained biomarker signature for the subject is compared to two or more different reference/control biomarker signatures to obtain more in-depth information regarding the phenotype of the assayed tissue. For example, a biomarker profile, or a biomarker score to obtain confirmed information regarding whether the tissue has the phenotype of interest. As another example, a biomarker profile or score may be compared to multiple biomarker profiles or scores, each correlating with a particular diagnosis, prognosis or therapeutic responsiveness.

Reports

in some embodiments, providing an ovarian cancer signature or providing an ovarian cancer assessment, e.g., a diagnosis of ovarian cancer, a prognosis for a patient with ovarian cancer, a prediction of responsiveness of a patient with ovarian cancer to a cancer therapy, includes generating a written report that includes that ovarian cancer signature and/or the ovarian cancer assessment e.g. , a "diagnosis assessment", a "prognosis assessment", a suggestion of possible treatment regimens (a "treatment assessment") and the like. Thus, the subject methods may further include a step of generating or outputting a report providing the results of an analysis of an ovarian cancer biomarker or biomarker panel, a diagnosis assessment, a prognosis assessment, or a treatment assessment, which report can be provided in the form of an electronic medium (e.g., an electronic display on a computer monitor), or in the form of a tangible medium (e.g., a report printed on paper or other tangible medium).

A "report," as described herein, is an electronic or tangible document which includes report elements that provide information of interest relating to a diagnosis assessment, a prognosis assessment, a treatment assessment, a monitoring

assessment, etc. and its results, A subject report can be completely or partially electronically generated. A subject report includes at least an ovarian cancer

assessment, e.g., a diagnosis as to whether a subject has a high likelihood of having an ovarian cancer. A subject report can further include one or more of: 1 ) information regarding the testing facility; 2) service provider information; 3) patient data; 4) sample data; 5) an assessment report, which can include various information: a) reference values employed, and b) test data, where test data can include: i) the biomarker levels of one or more ovarian cancer biomarkers, and/or ii) the biomarker signatures for one or more ovarian cancer biomarkers; 6) other features.

The report may include information about the testing facility, which information is relevant to the hospital, clinic, or laboratory in which sample gathering and/or data generation was conducted. This information can include one or more details relating to, for example, the name and location of the testing facility, the identity of the lab technician who conducted and/or analyzed, the location where the sample and/or result data is stored, the lot number of the reagents (e.g., kit, etc.) used in the assay, and the like. Report fields with this information can generally be populated using information provided by the user.

The report may include information about the service provider, which may be located outside the healthcare facility at which the user is located, or within the healthcare facility. Examples of such information can include the name and location of the service provider, the name of the reviewer, and where necessary or desired the name of the individual who conducted sample gathering and/or data generation. Report fields with this information can generally be populated using data entered by the user, which can be selected from among pre-scripted selections (e.g., using a drop-down menu). Other service provider information in the report can include contact information for technical information about the result and/or about the interpretive report.

The report may include a patient data section, including patient medical history as well as administrative patient data such as information to identify the patient (e.g., name, patient date of birth (DOB), gender, mailing and/or residence address, medical record number (MRN), room and/or bed number in a healthcare facility), insurance information, and the like), the name of the patient's physician or other health

professional who ordered the monitoring assessment and, if different from the ordering physician, the name of a staff physician who is responsible for the patient's care (e.g., primary care physician).

The report may include a sample data section, which may provide information about the biological sample analyzed in the monitoring assessment, such as the source of biological sample obtained from the patient (e.g. blood, saliva, or type of tissue, etc.), how the sample was handled (e.g. storage temperature, preparatory protocols) and the date and time collected. Report fields with this information can generally be populated using data entered by the user, some of which may be provided as pre-scripted selections (e.g., using a drop-down menu).

It will also be readily appreciated that the reports can include additional elements or modified elements. For example, where electronic, the report can contain hyperlinks which point to internal or external databases which provide more detailed information about selected elements of the report. For example, the patient data element of the report can include a hyperlink to an electronic patient record, or a site for accessing such a patient record, which patient record is maintained in a confidential database. This latter embodiment may be of interest in an in-hospital system or in-clinic setting. When in electronic format, the report is recorded on a suitable physical medium, such as a computer readable medium, e.g., in a computer memory, zip drive, CD, DVD, etc. it will be readily appreciated that the report can include all or some of the elements above, with the proviso that the report generally includes at least the elements sufficient to provide the analysis requested by the user (e.g. a diagnosis, a prognosis).

Reagents, systems and kits

Also provided are reagents, devices and kits thereof for practicing one or more of the above-described methods. The subject reagents, devices and kits thereof may vary greatly. Reagents and devices of interest include those mentioned above with respect to the methods of assaying metabolites levels, where such reagents may include stable isotope labeled internal standards for detecting C18:10H-Carnitine, C18:1 -Carnitine, C80H-Carnitine, C14:1 -Carnitine, C6-Carnitine, C10:1 -Carnitine, C8-Carnitine, C10- Carnitine, C40H-Carnitine, C3DC-Carnitine, C2-Carnitine, C6DC-Carnitine, C3- Carnitine, CO-Carnitine, C10:2-Carnitine, C50H-Carnitine, C5:1-Carnitine, d18:1-18:0 Ceramide, d18:1-16:0 Ceramide, d18:1-24:1 Ceramide, d18:1-18:1 Ceramide, d18:1- 22:0 Ceramide, d18:1 -20:0 Ceramide, d20:3 Fatty acid, d20:4 Fatty acid, d18:3 Fatty acid, d20:5 Fatty acid, d22:6 Fatty acid, d22:0 Fatty acid, Progesterone, Cortisol, Cholesterol, 17-Hydroxyprogesterone, Glycine, Citrilline, Arginine, Proline, Alanine, 5- Oxoproline, Methionine, Phenylalanine, Ornithine, Glutamate, Tyrosine, Valine, Leucine, Aspartate, Succinylacetone, Triiodothyronine, Urea, and Total Deoxycholic acid.

The subject kits may also comprise one or more biomarker signature references, e.g. a reference for an ovarian cancer signature, for use in employing the biomarker signature obtained from a patient sample. For example, the reference may be a sample of a known phenotype, e.g. an unaffected individual, or an affected individual, e.g. from a particular risk group that can be assayed alongside the patient sample, or the reference may be a report of disease diagnosis, disease prognosis, or responsiveness to therapy that is known to correlate with one or more of the subject ovarian cancer biomarker signatures.

In addition to the above components, the subject kits may further include instructions for practicing the subject methods. These instructions may be present in the subject kits in a variety of forms, one or more of which may be present in the kit. One form in which these instructions may be present is as printed information on a suitable medium or substrate, e.g., a piece or pieces of paper on which the information is printed, in the packaging of the kit, in a package insert, etc. Yet another means would be a computer readable medium, e.g., diskette, CD, DVD, etc., on which the information has been recorded. Yet another means that may be present is a website address which may be used via the internet to access the information at a removed site. Any convenient means may be present in the kits.

Examples

Examplel

Materials and methods

The outline for the study and methods is summarized in Figure 1. Each steps are described in greater details below.

Sample collection

76 Normal serum samples were purchased from ProMedEx. For the 67 ovarian cancer serum samples, 14 were purchased from Cureline and 53 were purchased from ProteoGenex. The clinical subtype information of these samples are list in Table 1.

internal standard preparation

Dilute to 200 mL with 70%MeOH in a 200-mL volumetric flask to obtain concentrations of different analytes (Table 2). Vortex vigorously and stored at 4 °C prior to use.

Table 2. Concentrations of different internal standard analytes

Sample preparation Preparation of serum sample: Serum sample was taken from -80 °C freezer and thawed on ice.10 μΙ_ of each serum sample was transferred into a new tube, and 90 μΙ_ extraction buffer was added for extraction. The samples were vortexed vigorously for 1 min and subjected to high-speed centrifuge at 12,000 g for 5 min under room

temperature. The supernatant from each sample was collected for analysis

Preparation of Quality Control Samples (Serum): 1 mL of serum samples from 3-5 normal individuals were pooled and vortexed for 1 min, then centrifuge at 3,000 g for 1 min. The pooled serum sample was divided into 10-uL aliquots and stored at -80 °C before use. 6 of 10-uL aliquots from pooled serum were unambiguously processed as internal quality controls for 90 unknown samples in a 96-well plate during the sample prep.

MS/MS detection

1. Liquid Chromatography

Pump: Thermo Scientific™ Dionex™ UltiMate™ HPG-3200 RS

Autosampler: UltiMate WPS-3000 TRS

Mobile phase 50:50:1 acetonitrile/water/formic acid

LC flow gradient (Table 3)

Table 3. LC flow gradient parameter characteristics

2. Mass spectrometer

Machine: TSQ Quantiva triple quadrupole mass spectrometer. The mass spectrometer conditions were as follows:

ionization: Heated electrospray ionization (HESi)

Spray voltage: Positive, 3500 V

Sheath gas: 40 Arb

Aux gas: 10 Arb

Sweep gas: 1 Arb

ion transfer tube temperature: 350 °C

Vaporizer temperature: 250 °C

Data acquisition mode: Selected-reaction monitoring (SRM)

Cycle time: 1 s

Q1 resolution (FWHM): 0.7

Q3 resolution (FWHM): 0.7

CiD gas: 1.5 mTorr

Source fragmentation: 0 V

Chrome filter: 3 s

Data analysis and statistics

The targeted metabolomics profile was firstly normalized to z-score across all the samples. The z-score of the metabolomics profiles for the samples randomized to the statistical training cohort (n=128) were then analyzed by Random Forest analysis using the R package 'randomForest' (http://www.r-project.org/). All subjects in the training cohort were subsequently assigned to one of two possible subgroups (normal and early stage). With the trained model applied to both training cohort and testing cohort (n=15), the probability of having ovarian cancer for each sample can be calculated. Receiver- operator characteristic (ROC) analysis was conducted to evaluate the ability of the targeted metabolomics profile in differentiating the subjects in the testing cohort with cancer from those normal samples. This process was repeated by 500 times using bootstrapping algorithm to get extensive evaluation of the model.

Unsupervised hierarchical clustering analysis was performed to visually depict the association between the disease status and the abundance patterns of these metabolomics profile. This analysis was used to demonstrate the effectiveness of this metabolomics profile in differentiating early stage ovarian cancer and normal class distinction.

Example 2

Results

Data collection

67 Ovarian cancer serum samples and 76 normal controls were purchased from Cureline, ProMedEx and ProteoGenex tissue banks. To compare the 51 metabolites between ovarian cancer and normal samples, 10 μΙ of each serum samples were extracted and analyzed by flow injection MS/MS on a TSQ Quantiva (Thermo) triple quadrupole mass spectrometer. Tandem MS data were processed using a meta- calculation software iRC PRO (2Next srl, Prato, Italy). Serum concentration for each analyte was calculated in μΜ unit and used for further analysis.

Statistical results for fifty-one metabolomics analytes

A standard t-test was performed for metabolomics profile between early stage ovarian cancer and normal samples. P-values and odds ratios for each metabolomics analyte were shown in Table4.

Table 4

Performance of metaboiomics profile-based prognostic algorithm

The Random Forest based model risk stratified all subjects in training and testing cohorts into two levels of risk for progression as discussed above (normal, early stage). 51 targeted metaboiomics profiles were used as the model input. The risk scores of ovarian cancer were calculated by the model (Figure 2). We use 0.5 as the cutoff threshold.

The c statistic of the model tested on the testing cohort was 0.9954 (Figure 3).

Unsupervised hierarchical clustering with metaboiomics profiles

Unsupervised hierarchical clustering analysis was applied to the targeted metaboiomics profiles to visually depict the association of the disease status with the abundance patterns of these metaboiomics profiles (Figure 4). This analysis

demonstrated two major clusters reflecting normal samples and early stage OV samples. The error rate of the unsupervised clustering is 3.9 %, which reinforcing the effectiveness of this targeted metaboiomics profiles for ovarian cancer assessment. Unsupervised hierarchical clustering analysis for all metaboiomics profiles to visually depict the association of the disease status is also included as a reference (Figure 5).

Claims

Claims What is claimed is:

1. A method comprising:

measuring the concentration of one to fifty-one metabolite species in a sample of a biofluid from a subject to be tested for ovarian cancer, wherein the one to fifty-one metabolite species is a component of a panel of a plurality of metabolite species, wherein a change in the concentration of the metabolite species is a characteristic that is associated with ovarian cancer.

2. The method of claim 1 wherein the concentrations of the metabolite species are normalized.

3. The method of claim 1 , further comprising the step of: comparing the measured concentration of the one to fifty-one metabolite species to a predetermined value calculated using a model based on concentrations of a plurality of the metabolite species that are components of the panel.

4. The method of claim 1 , wherein the panel comprises one to fifty-one metabolite species selected from the group consisting of C18:10H-Carnitine, C18:1 -Carnitine, C80H-Carnitine, C14:1-Carnitine, C6-Carnitine, C10:1-Carnitine, C8-Carnitine, C10- Carnitine, C40H-Carnitine, C3DC-Carnitine, C2-Carnitine, C6DC-Carnitine, C3- Carnitine, CO-Carnitine, C10:2-Carnitine, C50H-Carnitine, C5:1 -Carnitine, d18:1-18:0 Ceramide, d18:1-16:0 Ceramide, d18:1-24:1 Ceramide, d18:1-18:1 Ceramide, d18:1- 22:0 Ceramide, d18:1 -20:0 Ceramide, d20:3 Fatty acid, d20:4 Fatty acid, d18:3 Fatty acid, d20:5 Fatty acid, d22:6 Fatty acid, d22:0 Fatty acid, Progesterone, Cortisol, Cholesterol, 17-Hydroxyprogesterone, Glycine, Citrilline, Arginine, Proline, Alanine, 5- Oxoproline, Methionine, Phenylalanine, Ornithine, Glutamate, Tyrosine, Valine, Leucine, Aspartate, Succinylacetone, Triiodothyronine, Urea, and Total Deoxycholic acid.

5. The method of claim 1 wherein the panel comprises metabolite species that have been identified by liquid chromatography-mass spectrometry (LC-MS).

6. The method of claim 1 , wherein the biofluid is selected from the group consisting of blood, plasma, serum, sweat, saliva, sputum, and urine.

7. The method of claim 1 , wherein the biofluid is serum.

8. A panel of metabolite species, the metabolite species are selected from a group consisting of C18-Carnitine-d3, C14-Carnitine-c/9, C5-Camitine-o¾, C8-Carnitine-d3, C4- Carnitine-d3, C3-Carnitine-d3, C2-Carnitine-d3, C0-Carnitine-d9, C50H-Carnitine-d3, d18:1-18:0 Ceramide-d7, d18:1-16:0 Ceramide-d7, d18:1-24:0 Ceramide-d7, d20:4 Fatty acid-ds, Progesterone-d9, Cholesterol-d7, Glycine-¹³C-¹⁵N, Citrulline-cfe, Arginine-¹³C-d4, Proline-¹³C5-¹⁵N, Alanine-d4, Proline-¹³C5-¹⁵N, Methionine-d3, Phenylalanine-¹³C6, Ornithines, Glutamate-d3, Tyrosine-¹³C6, Valine-de, Leucine-d3, Aspartates,

Succinylacetone-¹³C5, Triiodothyronine-¹³C6, Urea-¹⁵N2, and Chenodeoxycholic acid-a¾.

9. The panel of claim 8, wherein the panel is provided in a diagnostic cassette.

10. The diagnostic cassette of claim 9, further comprising reagents for the detection of the metabolite species of the panel.

11. A kit for the analysis of a sample of a biofluid of a subject, comprising:

a. aliquots of standards of each compound of a panel of metabolite species;

b. an aliquot of an internal standard; and

c. an aliquot of a control biofluid.

12. The kit of claim 11 , wherein the control biofluid is serum from a control source that is conspecific with the subject.

13. The kit of claim 11 , wherein the panel consists of C18-Carnitine-d3, C14-Carnitine- d9, C5-Carnitine-cfe, C8-Carnitine-d3, C4-Carnitine-d3, C3-Carnitine-d3, C2-Carnitine-d3, CO-Carnitine-cfe, C50H-Carnitine-d3, d18:1-18:0 Ceramide-d7, d18:1-16:0 Ceramide-d7, d 18:1 -24:0 Ceramide-c/7, d20:4 Fatty acid-cfe, Progesterone-d9, Cholesterol-d7, Glycine- ¹³C-¹⁵N, Citrulline-d2, Arginine-¹³C-d4, Proline-¹³C5-¹⁵N, Alanines, Proline-¹³C5-¹⁵N, Methionine-d3, Phenylalanine-¹³C6, Ornithine-d2, Glutamate-d3, Tyrosine-¹³C6, Valine-ds, Leucine-d3, Aspartate-d3, Succinylacetone-¹³C5, Triiodothyronine-¹³C6, Urea-¹⁵N2, and Chenodeoxycholic acid-d9.

14. The kit of claim 11 , further comprising instructions for use.