Methods for the detection and treatment of ovarian cancer

Abstract

A novel 7-marker metabolite panel (7MetP) is described that comprises or consists of diacetyl spermine, diacetyl spermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetylmannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid.

Description

Technical Field

[0001] This application claims the benefit of priority of U.S. Provisional Patent Application No. 63 / 369,027, filed on July 21, 2022, the disclosure of which is hereby incorporated by reference in its entirety.

[0002] This invention was made with government support under grant numbers CA200462 and CA217685 awarded by the National Institutes of Health. The government has certain rights in this invention.

[0003] Disclosed herein are methods and related kits for detecting ovarian cancer. Also provided are methods for treating patients who are susceptible to, or suspected of being susceptible to, ovarian cancer.

Background Art

[0004] Ovarian cysts and pelvic tumors have been found to occur in approximately 17% of women who undergo transvaginal ultrasound (TVS). However, most such tumors are benign, and only a small fraction of them are diagnosed as ovarian cancer. Currently, neither TVS nor cancer antigen 125 (CA125), either alone or in combination, provides sufficient sensitivity and specificity to distinguish benign ovarian cysts from malignant ovarian cysts. The high false positive rate leads to unnecessary surgical procedures associated with a significant morbidity, along with increased patient anxiety.

[0005] To estimate the probability that a woman with a pelvic tumor has a malignant tumor and to determine whether to refer the patient to a general gynecologist if the tumor is likely to be benign or to a gynecologic oncology specialist if it is likely to be malignant, two risk assessment algorithms have been developed: the Risk of Ovarian Malignancy Algorithm (ROMA) and the Ovarian Cancer Risk Assessment Algorithm (OVERA). Gynecologic oncology specialists have received specialized training in incising lymph nodes, excising the ovary, and, if extensive lesions are found, excising as much cancer as possible from the surface of the intestine. The OVERA and ROMA algorithms provide high sensitivity but are limited by the aforementioned high false positive rate because their specificity is not optimal. A test that provides high sensitivity and specificity for identifying individuals at high risk of having a malignant ovarian cyst may better inform clinical decisions and improve patient outcomes.

Summary of the Invention

Problems to be Solved by the Invention

[0006] Therefore, there is a need for a method or test to assist in the detection of ovarian cancer. Impairment of cell metabolism is a characteristic of cancer. Some evidence indicates that cellular and systemic metabolic adaptations occur from the early stages of carcinogenesis, suggesting that metabolites may serve as biomarkers for cancer. A novel 7-marker metabolite panel, including or consisting of diacetyl spermine, diacetyl spermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid, has been discovered by a deep learning approach to the serum metabolic profile, which differentiates early-stage ovarian cancer from benign diseases. In combination with the ROMA algorithm that utilizes the biomarkers human epididymis protein 4 (HE4) and mucin 16 (CA125), this model has demonstrated superior ovarian cancer risk prediction in women with ovarian cysts compared to ROMA alone.

Means for Solving the Problems

[0007] The present specification provides a method for treating ovarian cancer in a patient having high levels of diacetyl spermine (DAS), diacetyl spermidine (DiAcSpd), N-(3-acetamidopropyl)pyrrolidin-2-one (N3AP), N-acetylneuraminic acid (NANA), N-acetyl-mannosamine (NAcMan), N-acetyl-lactosamine (NAcLac), and hydroxyisobutyric acid (HBA), and optionally high levels of human epididymis protein 4 (HE4) and mucin 16 (CA125), which method classifies the patient as a person having ovarian cancer by virtue of said high levels, and which comprises administering to the patient a therapeutically effective amount of an ovarian cancer therapeutic agent.

[0008] The present specification also provides a method for treating ovarian cancer, which method comprises the following: a) identifying a patient having high levels of diacetyl spermine, diacetyl spermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid, and optionally high levels of human epididymis protein 4 (HE4) and mucin 16 (CA125), which identification classifies the patient as a person having ovarian cancer by virtue of said high levels; and b) administering to said patient a therapeutically effective amount of an ovarian cancer therapeutic agent. comprises.

[0009] Furthermore, the present specification provides a method for differentiating ovarian cancer from benign pelvic masses (BPM) in a subject, which method comprises, in a biological sample obtained from said subject, the following: a) measuring the levels of diacetyl spermine, diacetyl spermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid in the biological sample, and optionally the levels of human epididymis protein 4 (HE4) and mucin 16 (CA125); and b) classifying the subject as a person having either ovarian cancer or BPM based on the measured level comprising.

[0010] Also provided herein is a method for determining the risk that a subject has ovarian cancer, which in a biological sample obtained from the subject comprises: a) measuring the levels of diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid in the biological sample, and optionally the levels of human epididymis protein 4 (HE4) and mucin 16 (CA125); and b) classifying the subject as a person at risk of having ovarian cancer or not at risk of having ovarian cancer based on the measured levels comprising.

[0011] Furthermore provided herein is a method for creating a risk profile that a subject has ovarian cancer, which in a biological sample obtained from the subject comprises: a) measuring the levels of diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid in the biological sample, and optionally the levels of human epididymis protein 4 (HE4) and mucin 16 (CA125); and b) classifying the subject as a person at risk of having ovarian cancer or not at risk of having ovarian cancer based on the measured levels comprising.

[0012] Also provided herein is a method for calculating a biomarker score or a risk score of having ovarian cancer for a patient, which in a biological sample obtained from the subject comprises: a) Measuring the levels of diacetyl spermine, diacetyl spermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetylmannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid in a biological sample, and optionally, the levels of human epididymis protein 4 (HE4) and mucin 16 (CA125); b) Calculating a biomarker score or a risk score using the measured level values in a deep learning model (DLM) comprising.

[0013] Furthermore, the present specification also provides a method for risk stratification of a patient at risk of having ovarian cancer, which comprises, in a biological sample obtained from a subject: a) Measuring the levels of diacetyl spermine, diacetyl spermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetylmannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid in a biological sample, and optionally, the levels of human epididymis protein 4 (HE4) and mucin 16 (CA125); b) Determining a risk score of the patient by a processor circuit, wherein the risk score is determined via a scoring function derived from metabolite profiles of biological samples collected from a plurality of individuals monitored for ovarian cancer comprising.

Brief Description of the Drawings

[0014]

Figure 1

Figure 2

Figure 3

BEST MODE FOR CARRYING OUT THE INVENTION

[0015] This specification provides a method for treating ovarian cancer in patients having high levels of diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetylmannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid, and optionally high levels of human epididymis protein 4 (HE4) and mucin 16 (CA125), which method classifies a patient as having ovarian cancer by virtue of said high levels, and which comprises administering to the patient a therapeutically effective amount of an ovarian cancer therapeutic agent.

[0016] This specification also provides a method for treating ovarian cancer, which method comprises the following: a) identifying a patient having high levels of diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetylmannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid, and optionally high levels of human epididymis protein 4 (HE4) and mucin 16 (CA125), which identification classifies the patient as having ovarian cancer by virtue of said high levels; and b) administering to said patient a therapeutically effective amount of an ovarian cancer therapeutic agent and which method comprises the following.

[0017] This specification further provides a method for differentiating ovarian cancer from benign pelvic masses (BPM) in a subject, which method comprises, in a biological sample obtained from said subject, the following: a) measuring the levels of diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetylmannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid in the biological sample, and optionally the levels of human epididymis protein 4 (HE4) and mucin 16 (CA125); and b) classifying the subject as a person having either ovarian cancer or BPM based on the measured level comprises.

[0018] Also provided herein is a method for determining the risk that a subject has ovarian cancer, which comprises, in a biological sample obtained from the subject: a) measuring the levels of diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid in the biological sample, and optionally, the levels of human epididymis protein 4 (HE4) and mucin 16 (CA125); and b) classifying the subject as a person at risk of having ovarian cancer or a person not at risk of having ovarian cancer based on the measured levels comprises.

[0019] Furthermore, provided herein is a method for creating a risk profile for a subject having ovarian cancer, which comprises, in a biological sample obtained from the subject: a) measuring the levels of diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid in the biological sample, and optionally, the levels of human epididymis protein 4 (HE4) and mucin 16 (CA125); and b) classifying the subject as a person at risk of having ovarian cancer or a person not at risk of having ovarian cancer based on the measured levels comprises.

[0020] Also provided herein is a method for calculating a biomarker score or a risk score for ovarian cancer possession in a patient, which comprises, in a biological sample obtained from the subject: a) Measuring the levels of diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid in a biological sample, and optionally, the levels of human epididymis protein 4 (HE4) and mucin 16 (CA125); b) Calculating a biomarker score or a risk score using the measured level values in a deep learning model (DLM). comprises.

[0021] Furthermore, the present specification also provides a method for risk stratification of a patient at risk of having ovarian cancer, which is in a biological sample obtained from a subject, the following: a) Measuring the levels of diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid in a biological sample, and optionally, the levels of human epididymis protein 4 (HE4) and mucin 16 (CA125); b) Determining a risk score of the patient by a processor circuit, wherein the risk score is determined via a scoring function derived from metabolite profiles of biological samples collected from a plurality of individuals monitored for ovarian cancer. comprises.

[0022] In some embodiments, the DLM includes an artificial neural network having 1 to 3 hidden layers and 1 to 3 nodes in each layer.

[0023] In some embodiments, the DLM includes an artificial neural network having 3 hidden layers and 3 nodes in each layer.

[0024] In some embodiments, the method further includes measuring the levels of human epididymis protein 4 (HE4) and mucin 16 (CA125), or identifying patients having high levels of human epididymis protein 4 (HE4) and mucin 16 (CA125).

[0025] In some embodiments, the levels of HE4 and CA125 are determined by immunoassay.

[0026] In some embodiments, using the levels of HE4 and CA125, a prediction index (PI) for premenopausal women is calculated by the equation: PI = -12.0 + 2.38 * ln[HE4] + 0.0626 * ln[CA125]

[0027] In some embodiments, using the levels of HE4 and CA125, a prediction index (PI) for postmenopausal women is calculated by the equation: PI = -8.09 + 1.04 * ln[HE4] + 0.732 * ln[CA125]

[0028] In some embodiments, using the prediction index (PI), an ovarian malignancy risk (ROMA) score is calculated by the equation:

Number

[0029] In some embodiments, a logistic regression is used with the ROMA score and biomarker score to calculate a combined model score.

[0030] In some embodiments, the ovarian cancer is in an early stage (e.g., stage I or II).

[0031] In some embodiments, the ovarian cancer is advanced (e.g., stage III or IV).

[0032] ​​​In some embodiments, the levels of diacetyl spermine, diacetyl spermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid, and optionally, HE4 and CA125, are high compared to a standard patient or standard group that does not have ovarian cancer.

[0033] In some embodiments, the levels of diacetyl spermine, diacetyl spermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid, and optionally, HE4 and CA125, are high compared to a standard patient or standard group having a benign pelvic mass (BPM).

[0034] In some embodiments, the subject presents with a pelvic mass.

[0035] In some embodiments, each of diacetyl spermine, diacetyl spermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid, and optionally, HE4 and CA125, generates a detectable signal.

[0036] In some embodiments, the detectable signal is detectable by spectroscopy.

[0037] In some embodiments, the spectroscopy is selected from ultraviolet-visible spectroscopy, mass spectrometry, nuclear magnetic resonance (NMR) spectroscopy, proton NMR spectroscopy, NMR spectroscopic analysis, gas chromatography, gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), correlation spectroscopy (COSY), nuclear Overhauser effect spectroscopy (NOESY), rotating frame nuclear Overhauser effect spectroscopy (ROESY), time-of-flight LC-MS (LC-TOF-MS), liquid chromatography-tandem mass spectrometry (LC-MS / MS), and capillary electrophoresis-mass spectrometry.

[0038] In some embodiments, the spectroscopy is mass spectrometry.

[0039] In some embodiments, the mass spectrometry is LC-TOF-MS.

[0040] In some embodiments, the treatment is selected from surgery, chemotherapy, immunotherapy, radiotherapy, targeted therapy, or combinations thereof.

[0041] In some embodiments, a biomarker score or risk profile is calculated using the measured levels based on values of sensitivity and specificity corresponding to the risk of a subject having ovarian cancer.

[0042] In some embodiments, the values of sensitivity and specificity are not substantially different from the curve of FIG. 1.

[0043] In some embodiments, the values of sensitivity and specificity differ by less than 10%.

[0044] In some embodiments, the values of sensitivity and specificity differ by less than 5%.

[0045] In some embodiments, the values of sensitivity and specificity differ by less than 1%.

[0046] In some embodiments, the cut-off value includes an AUC (95% CI) of at least 0.76.

[0047] In some embodiments, the method further includes assigning a patient to an appropriate risk group based on the calculated risk score.

[0048] In some embodiments, there are at least two risk groups.

[0049] In some embodiments, the AUC of the method is greater than the AUC of an algorithm incorporating different biomarkers, multiple biomarkers, panels, assays, or combinations thereof.

[0050] In some embodiments, the AUC is greater than 0.76.

[0051] In some embodiments, the AUC is between 0.76 and 0.95.

[0052] In some embodiments, the AUC is approximately 0.88.

[0053] In some embodiments, the AUC is approximately 0.86.

[0054] In some embodiments, the AUC is between 0.82 and 0.93.

[0055] In some embodiments, the AUC is approximately 0.87.

[0056] In some embodiments, the positive predictive value (PPV) of the method is greater than the AUC of an algorithm incorporating different biomarkers, multiple biomarkers, panels, assays, or combinations thereof.

[0057] In some embodiments, the PPV is greater than 0.67.

[0058] In some embodiments, the PPV is between 0.67 and 0.87.

[0059] In some embodiments, the PPV is approximately 0.79.

[0060] In some embodiments, the algorithm is the Risk of Ovarian Malignancy Algorithm (ROMA).

[0061] In some embodiments, the biomarkers are HE4 and CA125 alone.

[0062] In some embodiments, the cut-off points of each method are used for classification.

[0063] In some embodiments, various biomarkers, panels, assays, or algorithms are analyzed by the same statistical method.

[0064] In some embodiments, the levels of diacetyl spermine, diacetyl spermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid are measured against one or more predetermined thresholds.

[0065] In some embodiments, if the above values exceed one or more thresholds, the patient is classified as a person at risk of having ovarian cancer.

[0066] In some embodiments, if the above values are below one or more thresholds, the patient is classified as a person not at risk of having ovarian cancer.

[0067] In some embodiments, if the above values are below one or more thresholds, the patient is classified as a person having BPM.

[0068] In some embodiments, the patient is then instructed to undergo further ovarian cancer screening or treatment.

[0069] In some embodiments, the screening is selected from endoscopic ultrasonography, magnetic resonance imaging (MRI), and computed tomography (CT) scan.

[0070] In some embodiments, the screening is performed annually.

[0071] In some embodiments, the screening is performed semi-annually.

[0072] In some embodiments, the above and the methods disclosed herein may optionally also include a combination of metabolites selected from the metabolites disclosed in Table 4 below having a p-value < 0.05 (at least) when considering either all patient controls or early patient controls.

[0073] Definition As used herein, the following terms have the meanings set forth below.

[0074] When a range of values is disclosed and the notation "from n1... to n2" or "n1... ~ n2" is used, where n1 and n2 are numerical values, unless otherwise specified, this notation is intended to include the numerical values themselves and the range between them. This range may be integers or continuous values between them and includes the end values. By way of example, the range "from 2 to 6 carbon atoms" is intended to include 2, 3, 4, 5, and 6 carbon atoms since the carbon number is in integer units. As a comparative example, "1 to 3 μM (micromolar)" is intended to include 1 μM, 3 μM, and all (e.g., 1.255 μM, 2.1 μM, 2.9999 μM, etc.) of any significant figures between them.

[0075] The term "about" as used herein is intended to modify the numerical value it modifies and indicates that such a value can vary within a certain range. In the absence of a specific range such as the error range shown in a data chart or table or the standard deviation with respect to the average value, the term "about" should be understood to mean the larger of the range that includes the explicitly stated value, the range that can be included by rounding up or down to that numerical value taking into account significant figures, and the range that includes plus or minus 20% of the explicitly stated value.

[0076] As used herein, "ovarian cancer" refers to the malignant growth of cells formed in the ovary. Ovarian cancer is most commonly of epithelial origin (about 90% of ovarian cancers) and can be classified into various types including serous ovarian cancer and non-serous ovarian cancer. Serous ovarian cancer is the most common type of epithelial ovarian cancer, accounting for about 40% of all ovarian cancers, while non-serous ovarian cancer includes, but is not limited to, endometrioid carcinoma, mucinous carcinoma, and clear cell carcinoma. In some embodiments, ovarian cancer can have different severities (represented by stages I to IV). In some embodiments, ovarian cancer can be early (e.g., stage I or II) or advanced (e.g., stage III or IV).

[0077] When a group is defined as "null", it means that the group is absent.

[0078] As used herein, the terms "subject" or "patient" refer to a mammal, preferably a human, for whom classification as ovarian cancer positive or ovarian cancer negative is desired and for whom further treatment can be provided.

[0079] As used herein, "standard patient", "standard subject", or "standard group" refers to a group of patients or subjects for whom test samples from patients or subjects suspected of having or at risk of having ovarian cancer can be compared. In some embodiments, such comparison can be used to determine whether a patient has ovarian cancer. The standard patient or group can serve as a control for testing or diagnosis purposes. As described herein, the standard patient or group can be a sample obtained from a single patient or can represent a group of samples, e.g., a pooled group of samples.

[0080] As used herein, "healthy" refers to an individual in whom no evidence of ovarian cancer is found, i.e., an individual who does not have ovarian cancer. Such an individual may be classified as "ovarian cancer negative", or having healthy ovaries, or having normal and unimpaired ovarian function. A healthy patient or subject does not exhibit symptoms of ovarian cancer or other ovarian diseases, but may have a benign pelvic tumor, i.e., a combination of adenomas and cysts. In some embodiments, a healthy patient or subject can be used as a standard patient for comparison with a sample that is affected or suspected of being affected for the purpose of determining ovarian cancer in a patient or group of patients.

[0081] As used herein, the term "treatment" or "treating" means, in the case of a subject or patient suffering from, for either prophylaxis (prevention) or cure of debility or a disease or condition or event, or to reduce the degree or likelihood of occurrence or recurrence thereof, the administration of a medicament to the subject or the performance of a medical procedure. In the context of the present disclosure, this term may also mean the administration of a pharmacological substance or formulation, or the performance of non-pharmacological methods including, but not limited to, radiation therapy and surgery. Pharmacological substances used herein may include, but are not limited to, anti-cancer agents including chemotherapeutic agents, polyamine inhibitors, hormone therapy, and targeted therapy. Examples of chemotherapeutic drugs for ovarian cancer include paclitaxel (e.g., albumin-bound paclitaxel or nab-paclitaxel, trade name Abraxane®), altretamine (Hexalen®), capecitabine (Xeloda®), cyclophosphamide (Cytoxan®), etoposide (VP-16), gemcitabine (Gemzar®), ifosfamide (Ifex®), irinotecan (CPT-11, Camptosar®), liposomal irinotecan (Onivyde®), liposomal doxorubicin (Doxil®), melphalan, pemetrexed (Alimta®), topotecan, and vinorelbine (Navelbine®); and combination regimens of chemotherapy such as cisplatin + paclitaxel, TIP (paclitaxel / taxol, ifosfamide, and cisplatin / platinol), VeIP (vinblastine, ifosfamide, and cisplatin / platinol), VIP (etoposide / VP-16, ifosfamide, and cisplatin / platinol), VAC (vincristine, dactinomycin, and cyclophosphamide), and PEB (cisplatin / platinol, etoposide, and bleomycin).Examples of polyamine inhibitors that have been studied or are being studied in clinical trials for cancer treatment include, but are not limited to, efruxifermin (Vaniqa®) and AMXT-1501 dicaprate. Examples of hormonal therapies for ovarian cancer include luteinizing hormone-releasing hormone (LHRH) agonists (e.g., goserelin (Zoladex®) and leuprolide (Lupron®)), tamoxifen, and aromatase inhibitors (e.g., letrozole (Femara®), anastrozole (Arimidex®), and exemestane (Aromasin®)). Examples of targeted therapies for ovarian cancer include angiogenesis inhibitors such as bevacizumab (Avastin), and (poly(ADP)-ribose polymerase) (PARP) inhibitors such as olaparib (Lynparza), rucaparib (Rubraca), and niraparib (Zejula). The terms “pharmacological substance” and “anticancer agent treatment” can include substances used in immunotherapies such as checkpoint inhibitors. Treatment can include multiple pharmacological substances or, without limitation, multiple treatment modalities including surgery and chemotherapy.

[0082] As used herein, “amount” or “level” refers to a typical quantifiable measurement of a biomarker described herein, which allows for comparison of the biomarker between samples and / or to a control sample. In some embodiments, the amount or level is quantifiable and refers to the level of a particular biomarker in a biological sample (such as blood, serum, urine, etc.) determined by laboratory methods or assays such as immunoassays (e.g., antibodies), mass spectrometry, or liquid chromatography. In some embodiments, the biomarker can be present in high or low amounts in the sample. Comparison of biomarkers can be based on direct measurement of the levels of the biomarkers described herein (e.g., by protein quantification or gene expression analysis) or, for example, on measurement of reporter molecules, biomarker-receptor complexes, biomarker-relay-receptor complexes, etc.

[0083] As used herein, the term "ELISA" refers to enzyme-linked immunosorbent assay. This assay generally involves contacting a fluorescently labeled protein sample with an antibody that has specific affinity for those proteins. Detection of these proteins can be accomplished by various means including, but not limited to, laser fluorometry.

[0084] As used herein, the term "regression" refers to a statistical method that can assign a median value of the underlying characteristics of a sample based on an observable characteristic (or series of observable characteristics) of the sample. In some embodiments, the characteristics are not directly observable. For example, the regression methods used herein can relate the qualitative or quantitative results of a particular biomarker assay, or a series of biomarker assays, for a particular subject to the probability that the subject is positive for ovarian cancer.

[0085] As used herein, the term "logistic regression" refers to a regression method in which the assignment of a prediction from the model can have one of several allowed discrete values. For example, the logistic regression model used herein can assign a prediction of either positive or negative for ovarian cancer for a particular subject.

[0086] As used herein, the term "biomarker score" refers to a numerical score for a particular subject, which is calculated by inputting the particular biomarker levels of the subject into a statistical technique.

[0087] As used herein, the term "composite score" refers to the sum of the normalized values for a given marker measured in a sample from a patient. In one embodiment, the normalized values are reported as biomarker scores, which are then summed to provide a composite score for each subject tested. When used in connection with a risk classification table and correlated with stratification grouping based on the range of composite scores in the risk classification table, the "composite score" is used to determine the "risk score" for each subject tested, where in this case, the multiplier indicating an increased likelihood of having cancer for stratification grouping becomes the "risk score".

[0088] As used herein, the term "risk score" refers to a single numerical value indicating the risk of ovarian cancer in an asymptomatic human patient as compared to the known prevalence of ovarian cancer in a disease cohort. In certain embodiments, the composite score is calculated for a human subject and correlated with a multiplier indicating the risk of ovarian cancer, where in this case, the composite score is correlated based on the range of composite scores for each stratification grouping in the risk classification table. In this way, the composite score is converted to a risk score based on the multiplier indicating an increased likelihood of having cancer for the grouping that best fits the composite score.

[0089] As used herein, the term "cutoff" or "cutoff point" refers to a mathematical value associated with a particular statistical technique that can be used to assign an ovarian cancer positive or ovarian cancer negative classification to a subject based on the subject's biomarker score.

[0090] As used herein, if a numerical value above or below the cutoff value is "characteristic of ovarian cancer", it means that the subject from whom the numerical value was obtained by analysis of the sample has ovarian cancer or is at risk of ovarian cancer.

[0091] As used herein, "use" of a marker for ovarian cancer diagnosis refers to quantifying the level or amount of one or more markers in a biological sample as described herein. Quantification can be performed using methods or techniques known in the art or described herein. In some embodiments, the markers may be used as a panel or combined together for statistical comparison with other samples.

[0092] In some embodiments, the amount or level of DAS, NANA, NAcMan, NAcLac, DiAcSpmd, N3AP, and HBA, or the amount or level of DAS, NANA, NAcMan, NAcLac, DiAcSpmd, N3AP, HBA, HE4, and CA125, is compared to a cut-off value that includes an AUC (95% CI) of about 0.48 to about 0.88, such as about 0.48, about 0.49, about 0.50, about 0.51, about 0.52, about 0.52, about 0.53, about 0.54, about 0.55, about 0.56, about 0.57, about 0.58, about 0.59, about 0.60, about 0.61, about 0.62, about 0.63, about 0.64, about 0.65, about 0.66, about 0.67, about 0.68, about 0.69, about 0.70, about 0.71, about 0.72, about 0.73, about 0.74, about 0.75, about 0.76, about 0.77, about 0.78, about 0.79, about 0.80, about 0.81, about 0.82, about 0.83, about 0.84, about 0.85, about 0.86, about 0.87, about 0.88. In some embodiments, when the markers DAS, NANA, NAcMan, NAcLac, DiAcSpmd, N3AP, and HBA are used together as a panel, or when the markers DAS, NANA, NAcMan, NAcLac, DiAcSpmd, N3AP, HBA, HE4, and CA125 are used together as a panel, the AUC (95% CI) can be 0.66 or greater, including, for example, about 0.66, about 0.67, about 0.68, about 0.69, about 0.70, about 0.71, about 0.72, about 0.73, about 0.74, about 0.75, about 0.76, about 0.77, about 0.78, about 0.79, about 0.80, about 0.81, about 0.82, about 0.83, about 0.84, about 0.85, about 0.86, about 0.87, about 0.88, about 0.89, about 0.90, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, about 0.99, etc.

[0093] In some embodiments, when analyzing marker DAS, NANA, NAcMan, NAcLac, DiAcSpmd, N3AP, and HBA together as a panel for diagnosing ovarian cancer using fixed coefficients, or when analyzing marker DAS, NANA, NAcMan, NAcLac, DiAcSpmd, N3AP, HBA, HE4, and CA125 together as a panel for diagnosing ovarian cancer using fixed coefficients, as a result, an AUC (95% confidence interval) of from about 0.82 to about 0.93, such as about 0.82, about 0.83, about 0.84, about 0.85, about 0.86, about 0.87, about 0.88, about 0.89, about 0.90, about 0.91, about 0.92, about 0.93, etc., can be obtained for distinguishing ovarian cancer patients from individuals with benign diseases. In some embodiments, when analyzing these marker panels using fixed coefficients, an AUC (95% CI) of 0.88 can be obtained for distinguishing ovarian cancer patients from individuals with benign diseases. In some embodiments, when analyzing any of the marker panels described herein for diagnosing ovarian cancer using fixed coefficients, an AUC (95% CI) of from about 0.76 to about 0.95, such as about 0.76, about 0.77, about 0.78, about 0.79, about 0.80, about 0.81, about 0.82, about 0.83, about 0.84, about 0.85, about 0.86, about 0.87, about 0.88, about 0.89, about 0.90, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, etc., can be obtained for distinguishing early-stage ovarian cancer. In some embodiments, when analyzing these marker panels using fixed coefficients, an AUC (95% CI) of 0.86 can be obtained for distinguishing early-stage ovarian cancer.

[0094] In some embodiments, the cut-off value of DAS includes an AUC (95% CI) of at least 0.76, such as about 0.76, about 0.77, about 0.78, about 0.79, about 0.80, about 0.81, about 0.82, about 0.83, about 0.84, about 0.85, about 0.86, about 0.87, about 0.88, about 0.89, about 0.90, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, about 0.99, etc.

[0095] In some embodiments, the cut-off value of NANA includes an AUC (95% CI) of at least 0.58, such as about 0.58, about 0.59, about 0.60, about 0.61, about 0.62, about 0.63, about 0.64, about 0.65, about 0.66, about 0.67, about 0.68, about 0.69, about 0.70, about 0.71, about 0.72, about 0.73, about 0.74, about 0.75, about 0.76, about 0.77, about 0.78, about 0.79, about 0.80, about 0.81, about 0.82, about 0.83, about 0.84, about 0.85, about 0.86, about 0.87, about 0.88, about 0.89, about 0.90, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, about 0.99, etc.

[0096] In some embodiments, the cut-off value of NAcMan includes an AUC (95% CI) of at least 0.50, such as about 0.50, about 0.51, about 0.52, about 0.53, about 0.54, about 0.55, about 0.56, about 0.57, about 0.58, about 0.59, about 0.60, about 0.61, about 0.62, about 0.63, about 0.64, about 0.65, about 0.66, about 0.67, about 0.68, about 0.69, about 0.70, about 0.71, about 0.72, about 0.73, about 0.74, about 0.75, about 0.76, about 0.77, about 0.78, about 0.79, about 0.80, about 0.81, about 0.82, about 0.83, about 0.84, about 0.85, about 0.86, about 0.87, about 0.88, about 0.89, about 0.90, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, about 0.99, etc.

[0097] In some embodiments, the cut-off value of NAcLac includes an AUC (95% CI) of at least 0.48, such as about 0.48, about 0.49, about 0.50, about 0.51, about 0.52, about 0.53, about 0.54, about 0.55, about 0.56, about 0.57, about 0.58, about 0.59, about 0.60, about 0.61, about 0.62, about 0.63, about 0.64, about 0.65, about 0.66, about 0.67, about 0.68, about 0.69, about 0.70, about 0.71, about 0.72, about 0.73, about 0.74, about 0.75, about 0.76, about 0.77, about 0.78, about 0.79, about 0.80, about 0.81, about 0.82, about 0.83, about 0.84, about 0.85, about 0.86, about 0.87, about 0.88, about 0.89, about 0.90, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, about 0.99, etc.

[0098] In some embodiments, the cut-off value of DiAcSpmd includes an AUC (95% CI) of at least 0.60, such as about 0.60, about 0.61, about 0.62, about 0.63, about 0.64, about 0.65, about 0.66, about 0.67, about 0.68, about 0.69, about 0.70, about 0.71, about 0.72, about 0.73, about 0.74, about 0.75, about 0.76, about 0.77, about 0.78, about 0.79, about 0.80, about 0.81, about 0.82, about 0.83, about 0.84, about 0.85, about 0.86, about 0.87, about 0.88, about 0.89, about 0.90, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, about 0.99, etc.

[0099] In some embodiments, the cut-off value of N3AP includes an AUC (95% CI) of at least 0.49, such as about 0.49, about 0.50, about 0.51, about 0.52, about 0.53, about 0.54, about 0.55, about 0.56, about 0.57, about 0.58, about 0.59, about 0.60, about 0.61, about 0.62, about 0.63, about 0.64, about 0.65, about 0.66, about 0.67, about 0.68, about 0.69, about 0.70, about 0.71, about 0.72, about 0.73, about 0.74, about 0.75, about 0.76, about 0.77, about 0.78, about 0.79, about 0.80, about 0.81, about 0.82, about 0.83, about 0.84, about 0.85, about 0.86, about 0.87, about 0.88, about 0.89, about 0.90, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, about 0.99, etc.

[0100] In some embodiments, the cut-off value of HBA includes an AUC (95% CI) of at least 0.64, such as about 0.64, about 0.65, about 0.66, about 0.67, about 0.68, about 0.69, about 0.70, about 0.71, about 0.72, about 0.73, about 0.74, about 0.75, about 0.76, about 0.77, about 0.78, about 0.79, about 0.80, about 0.81, about 0.82, about 0.83, about 0.84, about 0.85, about 0.86, about 0.87, about 0.88, about 0.89, about 0.90, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, about 0.99, etc.

[0101] As used herein, a subject at "risk of ovarian cancer" is a subject who has not yet shown obvious symptoms of ovarian cancer but produces a biomarker at a level indicating that the subject has ovarian cancer or is likely to develop it in the near future. Subjects with ovarian cancer or suspected of having ovarian cancer can receive treatment for that cancer or suspected cancer.

[0102] As used herein, the term "classification" refers to assigning a subject as having or not having a risk of ovarian cancer based on the results of biomarker scores obtained for the subject.

[0103] As used herein, the term "Wilcoxon rank sum test" is also known as the Mann-Whitney U test, the Mann-Whitney-Wilcoxon test, or the Wilcoxon-Mann-Whitney test, and refers to a specific statistical method used for comparing two populations. For example, in this specification, this test can be used to associate observable characteristics, particularly biomarker levels, with the absence or risk of ovarian cancer in subjects of a specific population.

[0104] As used herein, the term "positive hit rate" refers to the proportion of truly positive results among the positive results obtained by a specific method.

[0105] As used herein, the term "negative hit rate" refers to the proportion of truly negative results among the negative results obtained by a specific method.

[0106] As used herein, the term "sensitivity" in relation to various biochemical assays refers to the ability of the assay to correctly identify individuals with a disease (i.e., the true positive rate). By comparison, as used herein, the term "specificity" in relation to various biochemical assays refers to the ability of the assay to correctly identify individuals without a disease (i.e., the true negative rate). Sensitivity and specificity are statistical measures of the performance of a binary classification test (i.e., a classification function). Sensitivity quantifies the avoidance of false negatives, and specificity quantifies the avoidance of false positives.

[0107] As used herein, "fixed coefficient" or "fixed model coefficient" refers to a statistical method of standardizing coefficients to enable comparison of the relative importance of each coefficient in a regression model. In some embodiments, obtaining the composite score of a developed combination rule using the same β coefficients from a logistic regression model, and finally using this composite score to make a clinical decision based on a decision threshold.

[0108] As used herein, "sample" refers to a test substance that is assayed for the presence, level or concentration of a biomarker described herein. A sample can be any substance suitable according to the present disclosure and includes, but is not limited to, blood, serum, plasma, or a portion thereof.

[0109] As used herein, "metabolite" refers to a small molecule that is an intermediate and / or product of cellular metabolism. Metabolites can perform various functions within cells, such as, for example, structural, signaling, stimulatory and / or inhibitory effects on enzymes. In some embodiments, metabolites can be non-protein, plasma-derived metabolite markers such as, but not limited to, DAS, NANA, NAcMan, NAcLac, DiAcSpmd, N3AP, and HBA. In some embodiments, useful metabolites described herein may be "polyamines", i.e., organic compounds having three or more amino groups. In some embodiments, the polyamines described herein are plasma polyamines. In some embodiments, polyamines useful for the present panel and methods include, but are not limited to, DAS, NANA, NAcMan, NAcLac, DiAcSpmd, N3AP, and HBA. These polyamines may be combined with other markers, such as CA125 or HE4, for enhanced detection of ovarian cancer as described herein.

[0110] As used herein, the term "7-marker metabolite panel" or "7MetP" refers to a panel of seven biomarkers including DAS, NANA, NAcMan, NAcLac, DiAcSpmd, N3AP, and HBA that is useful for detecting ovarian cancer in patients suspected of having ovarian cancer. In some embodiments, the 7-marker metabolite panel may be evaluated in combination with additional markers such as plasma polyamines to enhance the detection of ovarian cancer in biological samples from patients suspected of having ovarian cancer. Useful plasma polyamines include, but are not limited to, N3AP, AcSpmd, DiAcSpmd, and / or DAS. Other polyamines are also known in the art and can be included if the clinician determines it to be appropriate.

[0111] As used herein, the term "ROC" refers to Receiver Operating Characteristic, and is a graphical plot used herein to evaluate the performance of a particular diagnostic method at various cut-off points. An ROC plot can be constructed from the ratios of true positives and false positives at various cut-off points.

[0112] As used herein, the term "AUC" refers to the area under the curve of an ROC plot. The AUC can be used to estimate the predictive power of a particular diagnostic test. Generally, a larger AUC corresponds to higher predictive power and a lower frequency of prediction errors. The possible values of the AUC range from 0.5 to 1.0, with the latter value indicating the characteristics of a prediction method without errors.

[0113] As used herein, the term "p-value" or "p" refers, in the context of the Wilcoxon rank sum test, to the probability that the distributions of biomarker scores for ovarian cancer positive and ovarian cancer negative subjects are the same. Generally, the closer the p-value is to zero, the higher the predictive power of a particular statistical method in classifying subjects.

[0114] As used herein, the term "CI" refers to a confidence interval, i.e., an interval within which a particular value is predicted to exist with a particular degree of confidence. As used herein, the term "95% CI" refers to an interval within which a particular value can be predicted to exist with 95% confidence.

[0115] As used herein, the term "disease progression" or "early disease progression" is defined as an upward modification of the Gleason score and / or an increase in tumor volume in surveillance biopsies within 18 months after the start of active surveillance.

[0116] The expression "therapeutically effective" is intended to limit the amount of active ingredient used in the treatment of a disease or disorder or with respect to the achievement of a clinical evaluation item.

[0117] List of Abbreviations AUC = Area Under the Curve; DAS = N1,N12-diacetylspermine; DiAcSpmd = N1,N8-diacetylspermidine; HBA = hydroxyisobutyric acid; HILIC = hydrophilic interaction liquid chromatography; HPLC = high performance liquid chromatography; N3AP = N-(3-acetamidopropyl)pyrrolidin-2-one; NANA = N-acetylneuraminic acid; NAcMan = N-acetyl-mannosamine; NAcLac = N-acetyl-lactosamine; OvCa = ovarian cancer; ROC = Receiver Operating Characteristic; SEM = Standard Error of the Mean; TCGA = The Cancer Genome Atlas; UPLC = ultra performance liquid chromatography; UPLC / MS = ultra performance liquid chromatography / mass spectrometry.

Example

[0118] The following examples are included to illustrate embodiments of the present disclosure. The following examples are presented solely as illustrations and to assist those skilled in the art in using the present disclosure. These examples are not intended to limit the scope of the present disclosure in any other way. It should be understood by those skilled in the art that, in light of the present disclosure, many modifications can be made to the specific embodiments disclosed, and still obtain the same or similar results without departing from the spirit and scope of the present disclosure.

[0119] Example 1: Specimen Set Blood samples were collected preoperatively with informed consent from patients who had received permission for surgery based on tumors found by ultrasound examination, high CA125, or positive biopsies at the University of Texas M.D. Anderson Cancer Center (MDACC) and the Fred Hutchinson Cancer Research Center (FHCRC, IRB 4563) according to an IRB / ethics committee-approved protocol (LAB04-0687). All patients were fasting at the time of blood collection. Samples were processed on the same day, generally within 4 hours of blood collection, according to standardized procedures, aliquoted to minimize the effects of freeze-thaw cycles, and stored at -80°C until use. The sample sets consisted of plasma from 59 stage I-II patients, 160 stage III-IV invasive epithelial ovarian cancer patients, and 190 patients with benign pelvic tumors. Biopsy samples were examined by board-certified pathologists for the diagnosis of cancer or benign pelvic conditions. Detailed patient and tumor characteristics are shown in Table 1. Information regarding histological subtypes of ovarian cancer and benign etiologies is shown in Table 2. All participants provided consent for the use of samples in an ethically approved secondary study.

[0120]

Table 1

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Table 2

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Table 3

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Table 11

[0131] Example 2: Metabolome Analysis Primary metabolites and biogenic amines Serum metabolites were extracted from pre - aliquoted EDTA plasma (10 μL) using 30 μL of LCMS - grade methanol (ThermoFisher) in 96 - well microplates (Eppendorf). The plates were heat - sealed, vortexed at 750 rpm for 5 minutes, and then centrifuged at 2000×g for 10 minutes at room temperature. The supernatant (10 μL) was carefully transferred to a 96 - well plate, leaving the precipitated proteins behind. The supernatant was further diluted with 10 μL of 100 mM ammonium formate, pH 3. For hydrophilic interaction liquid chromatography (HILIC) analysis, the samples were diluted with 60 μL of LCMS - grade acetonitrile (ThermoFisher), and for C18 analysis, the samples were diluted with 60 μL of water (GenPure ultrapure water system, ThermoFisher). Each sample solution was transferred to a 384 - well microplate (Eppendorf) for LCMS analysis.

[0132] Untargeted analysis of primary metabolites and biogenic amines Untargeted metabolomics analysis was performed on a Waters Acquity™ UPLC system with a 2D column regeneration configuration (class I and class H) connected to an Xevo G2 - XS quadrupole time - of - flight (qTOF) mass spectrometer. Chromatographic separation was performed at 45 °C using HILIC (Acquity™ UPLC BEH amide, 100 Å, 1.7 μm, 2.1×100 mm, Waters Corporation, Milford, U.S.A.) and C18 (Acquity™ UPLC HSS T3, 100 Å, 1.8 μm, 2.1×100 mm, Waters Corporation, Milford, U.S.A.) columns.

[0133] The fourth solvent system mobile phase was (A) 0.1% formic acid in water, (B) 0.1% formic acid in acetonitrile, and (D) 100 mM ammonium formate, pH 3. Samples were separated on a HILIC column using the following gradient profile: an initial gradient of 95% B and 5% D was linearly increased to 70% A, 25% B, 5% D over 5 minutes at a flow rate of 0.4 mL / min, followed by an isocratic gradient of 100% A for 1 minute at a flow rate of 0.4 mL / min. For C18 separation, the chromatography gradient was carried out as follows: starting conditions were 100% A, and after a linear increase to the final conditions of 5% A, 95% B, an isocratic gradient of 95% B, 5% D was run for 1 minute.

[0134] A binary pump was used for column regeneration and equilibration. The solvent system mobile phases were (A1) 100 mM ammonium formate, pH 3, (A2) 0.1% formic acid in 2-propanol, (B1) 0.1% formic acid in acetonitrile. The HILIC column was stripped for 5 minutes with 90% A2 and then equilibrated for 2 minutes at a flow rate of 0.3 mL / min with 100% B1. Reverse phase C18 column regeneration was carried out for 2 minutes with 95% A1, 5% B1, followed by column equilibration for 5 minutes with 5% A1, 95% B1.

[0135] Acquisition of Mass Spectrometry Data For primary metabolites, mass spectrometry data was acquired in the range of 50 to 1200 Da, and for complex lipids in the range of 100 to 2000 Da, using the "sensitivity" mode in both positive and negative electrospray ionization modes. For electrospray acquisition, the capillary voltage was set to 1.5 kV (positive), 3.0 kV (negative), the sample cone voltage to 30 V, the source temperature to 120 °C, the cone gas flow rate to 50 L / h, and the desolvation gas flow rate to 800 L / h. The scan time was 0.5 seconds in continuous mode. Leucine enkephalin; 556.2771 Da (positive) and 554.2615 Da (negative) were used for lockspray correction, and the scan was performed in 0.5 minutes. The injection volume of each sample was 3 μL unless otherwise specified. Acquisition was performed using instrument automatic gain control to optimize the sensitivity of the instrument over the sample acquisition time.

[0136] Data processing The data were processed using Progenesis QI (Nonlinear, Waters). Peak picking and retention time adjustment of LC-MS and MSe data were performed using Progenesis QI software (Nonlinear, Waters). Data processing and peak annotation were performed using an in-house automated pipeline. Annotation was determined by matching the exact mass and retention time using a customized library created from authentic standards and matching the experimental tandem mass spectrometry data to the theoretical fragmentation of NIST MSMS, LipidBlast, or HMDB v3; for complex lipids, retention time patterns characteristic of lipid subclasses were also considered. To correct for injection order drift, each feature was normalized using data from repeated injections of quality control samples collected every 10 injections throughout the run sequence. The measured data were smoothed by locally weighted scatterplot smoothing (LOESS) signal correction (QC-RLSC) as previously described. Values were reported as the ratio to the median of past quality control reference samples run in all analytical batches for a given analyte.

[0137] Assays for CA125 and HE4 Serum CA125 and HE4 concentrations were measured using the Architect CA125II assay (Abbott Diagnostics, Abbott Park) and the HE4 EIA assay (Fujirebio Diagnostics, Malvern, PA). To calculate the ROMA score, a prediction index (PI) was calculated using one of the following equations according to the serum HE4 and CA125 II concentrations and the patient's menopausal status. 1. Premenopausal: Prediction index (PI) = -12.0 + 2.38 * ln[HE4] + 0.0626 * ln[CA125] 2. Postmenopausal: Prediction index (PI) = -8.09 + 1.04 * ln[HE4] + 0.732 * ln[CA125]

[0138] The following formula was used to calculate the Risk of Ovarian Malignancy Algorithm (ROMA) score using the Prediction Index (PI) for each patient.

Number

[0139] Example 3: Statistical Analysis The overall general workflow of this study is shown in Figure 2. Selection of metabolites and model building were performed using metabolic profiles generated from serum samples at the FHCRC. To rank the appropriate variables to include in the model, the method reported by Gedeon was used. This method removes irrelevant and noisy variables by analyzing the relative weights of each variable across the entire data matrix. The importance score is calculated by dividing the absolute value of the weight of the input connected to the output by the sum of the absolute values of all weights from that input. When applied to a deep learning model, this approach recursively extends backward through the layers by removing the influence of the neuron on the connected node, multiplying the derived weights by the influence of a given node on the target output, and summing all connected nodes.

Number

[0140] Here, P jk represents the average contribution of node j in a given layer to node k in the next layer. w is the weight for the connection, and nh is the number of nodes in the next layer.

[0141] The contribution of the input neuron to the output is as follows.

Number

[0142] Using this approach, 20 iterations with slight modifications to the hyperparameters were introduced, and the relative variable importance scores were recalculated for each metabolite. Metabolites that consistently yielded a relative variable importance score > 0.7 (corresponding to metabolites in the top 30 percentile of importance scores) across all 20 iterations were selected to develop an algorithm for differentiating early OvCa from benign diseases. Seven models, including algorithms of deep learning, random forest, ensemble learning, and gradient boosting methods incorporating seven metabolites, were evaluated for differentiating early OvCa from benign diseases. Five-fold cross-validation was used to evaluate the performance of the models. To further evaluate the stability of the models, perturbations (e.g., random selection and replacement) were introduced into the training set and the performance was re-evaluated.

[0143] To model the 7-marker metabolite panel (7MetP) based on AUC, a deep learning model (DLM) with three hidden layers and three nodes in each layer was selected, where 7MetP used fixed parameters validated for the detection of OvCa in the MDACC cohort.

[0144] To evaluate the contributions of 7MetP and ROMA, first, logistic regression was applied with 7MetP and ROMA as two individual predictors (Table 3). For ROMA, the percentage risk was used as described above. Initial modeling was performed using early OvCa patients from FHCRC and individuals with BPM, and model validation was performed in the MDACC cohort.

[0145] To directly compare the performance of the combined model of 7MetP + ROMA with ROMA, fixed risk thresholds of 11.4% for premenopausal women and 29.9% for postmenopausal women were used to calculate the positive predictive value (PPV), negative predictive value (NPV), and estimates of sensitivity and specificity.

[0146] The composite score from the logistic regression model was converted to risk by exp(composite score) / (1 + exp(composite score)).

[0147] The identification of the models was evaluated based on the receiver operating characteristic (ROC) curves and the estimated values of sensitivity and specificity. The 95% confidence intervals (CIs) of the AUC were estimated using the Delong method. The P-values of specificity and the P-values of sensitivity were estimated by calculating the 2.5 and 97.5 percentiles of 1,000 bootstrap resamples of the delta values. All modeling was performed using the h2o package and the R statistical program.

[0148]

Table 12

[0149] Example 4: Cancer-related metabolite database Untargeted metabolomics was performed using a training set consisting of sera from 101 OvCa patients (39 early-stage and 62 late-stage) at the Fred Hutchinson Cancer Research Center (FHCRC) and 134 subjects with BPM (Table 1). A total of 475 uniquely annotated metabolites were quantified (Table 4). To rank the metabolites, relative importance scores were calculated using the Gedeon method, and metabolites were selected based on always showing importance scores exceeding 0.7. By this approach, seven metabolites, each with prior evidence of their association with cancer, were selected for model construction: diacetyl spermine (DAS), diacetyl spermidine (DiAcSpmd), N-(3-acetamidopropyl)pyrrolidin-2-one (N3AP), N-acetylneuraminic acid (NANA), N-acetyl-mannosamine (NAcMan), N-acetyl-lactosamine (NAcLac), and hydroxyisobutyric acid (HBA). The performance of the individual classifiers of these metabolites for distinguishing OvCa patients from individuals with BPM ranged from 0.55 to 0.82 (Table 5; Figure 3).

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Table 48

[0186] Example 5: Model construction and testing An optimal combination rule incorporating seven metabolites for differentiating early OvCa from benign diseases was developed. To construct the model, seven different machine learning algorithms were tested. Among these, a deep learning model (DLM) with three hidden layers and three nodes in each layer achieved the highest prediction performance. Therefore, this was used to establish a seven-marker metabolite panel (7MetP), which yielded an AUC of 0.75 (95% CI: 0.66 - 0.85) for differentiating early OvCa patients from benign diseases (Tables 6 - 8). When OvCa patients were stratified into serous and non-serous, 7MetP had AUCs of 0.85 (95% CI: 0.79 - 0.91) and 0.80 (95% CI: 0.71 - 0.89), respectively (Table 9).

[0187] The validation of 7MetP using fixed parameters was performed on an independent test set from the MD Anderson Cancer Center (MDACC), which consisted of 118 OvCa patients (20 early-stage and 98 late-stage) and 56 individuals with BPM. 7MetP showed an AUC of 0.88 (95% CI: 0.82 - 0.93) for differentiating all OvCa patients from individuals with BPM (Table 7), and in the case of early OvCa, an AUC of 0.86 (95% CI: 0.76 - 0.95) (Figure 1; Table 7).

[0188]

Table 49

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[0191]

Table 52

[0192] Contribution of metabolite panel using the ROMA algorithm Next, it was evaluated whether 7MetP could improve the predictive performance of the ROMA algorithm. Using the model scores obtained from 7MetP and the ROMA algorithm, a logistic regression model for distinguishing early OvCa from BPM was created in the training set, and its performance was evaluated in the test set. For the combination of 7MetP+ROMA, the AUC obtained for early OvCa in the test set was 0.93 (95% CI: 0.86-1.00), whereas for ROMA alone, the AUC was 0.91 (95% CI: 0.84-0.98) (likelihood ratio test p: 0.03). Compared with ROMA, the combination of 7MetP+ROMA resulted in an improvement in PPV by 21.0% (one-sided p<.001) and in specificity by 14.0% (one-sided p<.001) for early OvCa (Table 10). Considering all OvCa patients, the combined model of 7MetP+ROMA yielded an AUC of 0.97 (95% CI: 0.94-0.99) in the test set (Table 11).

[0193]

Table 53

[0194]

Table 54

[0195] Performance of metabolite panel alone and in combination with ROMA in the combined training and test sets. The predictive performance of 7MetP alone and in combination with ROMA was further evaluated in the entire sample set (n = 219 OvCa patients (59 early and 160 advanced, and 190 BPM)). 7MetP had an AUC of 0.85 (95% CI: 0.81 - 0.88) for distinguishing all OvCa patients from individuals with BPM, and an AUC of 0.81 (95% CI: 0.76 - 0.86) for distinguishing early OvCa patients (Table 7). In the 7MetP + ROMA combination model, an AUC of 0.87 (95% CI: 0.85 - 0.93) was obtained for early OvCa, which was significantly improved compared to ROMA alone (AUC: 0.84 (95% CI: 0.81 - 0.90); likelihood ratio test p-value: <0.001) (Tables 12 and 13). Importantly, compared to ROMA alone, the 7MetP + ROMA model yielded significantly (one-sided P <.001) higher PPV (0.68 vs. 0.52) and specificity (0.89 vs. 0.78) for early OvCa (Table 5).

[0196]

Table 55

[0197]

Table 56

[0198] All references, patents, or applications (U.S. or foreign) cited in this application are hereby incorporated by reference into this specification as if fully set forth herein. In case of conflict, the materials disclosed in this specification shall prevail literally.

[0199] From the above description, those skilled in the art can easily identify the essential features of the present invention and, without departing from the spirit and scope thereof, can make various changes and modifications to the present invention to adapt it to various uses and conditions.

Claims

**Claim 1** A method for treating ovarian cancer in a patient having high levels of diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetylmannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid, and optionally high levels of human epididymis protein 4 (HE4) and mucin 16 (CA125), wherein said high levels classify said patient as having ovarian cancer, and administering to said patient a therapeutically effective amount of an ovarian cancer therapeutic agent. **Claim 2** The following: a) identifying a patient having high levels of diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetylmannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid, and optionally high levels of human epididymis protein 4 (HE4) and mucin 16 (CA125), wherein said high levels classify said patient as having ovarian cancer; and b) administering to said patient a therapeutically effective amount of an ovarian cancer therapeutic agent A method for treating ovarian cancer comprising. **Claim 3** A method for differentiating ovarian cancer from benign pelvic masses (BPM) in a subject, comprising, in a biological sample obtained from said subject: a) measuring the levels of diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetylmannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid in said biological sample, and optionally the levels of human epididymis protein 4 (HE4) and mucin 16 (CA125); and b) classifying said subject as having either ovarian cancer or BPM based on said measured levels A method comprising. **Claim 4** A method for determining the risk that a subject has ovarian cancer, comprising, in a biological sample obtained from said subject: a) measuring the levels of diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid in the biological sample, and optionally, the levels of human epididymis protein 4 (HE4) and mucin 16 (CA125); b) classifying the subject as a person at risk of having ovarian cancer or a person not at risk of having ovarian cancer based on the measured levels A method comprising the steps of:

5. A method for creating a risk profile for a subject of having ovarian cancer, in a biological sample obtained from the subject, the following steps: a) measuring the levels of diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid in the biological sample, and optionally, the levels of human epididymis protein 4 (HE4) and mucin 16 (CA125); b) classifying the subject as a person at risk of having ovarian cancer or a person not at risk of having ovarian cancer based on the measured levels A method comprising the steps of:

6. A method for stratifying the risk of a patient at risk of having ovarian cancer, in a biological sample obtained from the patient, the following steps: a) measuring the levels of diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid in the biological sample, and optionally, the levels of human epididymis protein 4 (HE4) and mucin 16 (CA125); b) determining the risk score of the patient by a processor circuit, the risk score being determined via a scoring function derived from metabolite profiles of biological samples taken from a plurality of individuals monitored for ovarian cancer A method comprising the steps of:

7. A method for calculating a biomarker score or a risk score of ovarian cancer possession for a patient, in a biological sample obtained from the patient, the following steps: Step of measuring the levels of diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetylmannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid in the biological sample, and optionally, the levels of human epididymis protein 4 (HE4) and mucin 16 (CA125); Step of calculating the biomarker score or risk score using the measured level values in the deep learning model (DLM) A method comprising the steps of.

8. The method according to claim 7, wherein the DLM includes an artificial neural network having three hidden layers and three nodes in each layer.

9. The method according to any one of claims 1 to 7, further comprising measuring the levels of human epididymis protein 4 (HE4) and mucin 16 (CA125), or identifying patients having high levels of human epididymis protein 4 (HE4) and mucin 16 (CA125).

10. The method according to any one of claims 1 to 9, wherein the levels of HE4 and CA125 are determined by immunoassay.

11. Using the levels of HE4 and CA125, a prediction index (PI) for premenopausal women is calculated by the equation: PI = -12.0 + 2.38 * ln[HE4] + 0.0626 * ln[CA125] The method according to claim 10.

12. Using the levels of HE4 and CA125, a prediction index (PI) for postmenopausal women is calculated by the equation: PI = -8.09 + 1.04 * ln[HE4] + 0.732 * ln[CA125] The method according to claim 10.

13. Using the prediction index (PI), an ovarian malignancy risk (ROMA) score is calculated by the equation: 【Number 1】 The method according to claim 11 or 12.

14. The method according to claim 13, wherein a comprehensive model score is calculated using logistic regression with the ROMA score and the biomarker score.

15. The method according to any one of claims 1 to 14, wherein the ovarian cancer is in an early stage (e.g., stage I or II).

16. The method according to any one of claims 1 to 14, wherein the ovarian cancer is advanced (e.g., stage III or IV).

17. The method according to any one of claims 1 to 7, wherein the levels of diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetylmannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid, and optionally HE4 and CA125, are high as compared to a standard patient or a standard group who does not have ovarian cancer.

18. The method according to any one of claims 1 to 7, wherein the levels of diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetylmannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid, and optionally HE4 and CA125, are high as compared to a standard patient or a standard group having a benign pelvic tumor (BPM).

19. The method according to any one of claims 1 to 7, wherein the subject exhibits a pelvic tumor.

20. The method according to any one of claims 1 to 19, wherein each of diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetylmannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid, and optionally HE4 and CA125, generates a detectable signal.

21. The method according to claim 20, wherein the detectable signal is detectable by spectroscopy.

22. The method according to claim 21, wherein the spectroscopy is selected from ultraviolet-visible spectroscopy, mass spectrometry, nuclear magnetic resonance (NMR) spectroscopy, proton NMR spectroscopy, nuclear magnetic resonance (NMR) spectroscopy analysis, gas chromatography, gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), correlation spectroscopy (COSY), nuclear Overhauser effect spectroscopy (NOESY), rotating frame nuclear Overhauser effect spectroscopy (ROESY), time-of-flight LC-MS (LC-TOF-MS), liquid chromatography-tandem mass spectrometry (LC-MS / MS), and capillary electrophoresis-mass spectrometry.

23. The method according to claim 22, wherein the spectroscopy is mass spectrometry.

24. The method according to claim 23, wherein the mass spectrometry is LC-TOF-MS.

25. The method according to any one of claims 1, 2, 15, or 16, wherein the treatment is selected from surgery, chemotherapy, immunotherapy, radiotherapy, targeted therapy, or a combination thereof.

26. The method according to any one of claims 1 to 7, further comprising calculating a biomarker score or risk profile based on values of sensitivity and specificity corresponding to the risk of the subject having ovarian cancer, using the measured levels.

27. The method according to claim 26, wherein the values of sensitivity and specificity are not substantially different from the curve of FIG.

1.

28. The method according to claim 27, wherein the values of sensitivity and specificity differ by less than 10%.

29. The method according to claim 28, wherein the values of sensitivity and specificity differ by less than 5%.

30. The method according to claim 29, wherein the values of sensitivity and specificity differ by less than 1%.

31. The method according to any one of claims 1 to 7, wherein the cut-off value includes an AUC (95% CI) of at least 0.

76.

32. The method according to any one of claims 1 to 31, further comprising assigning the patient to an appropriate risk group based on the calculated risk score.

33. The method according to claim 32, wherein there are at least two risk groups.

34. The method according to any one of claims 1 to 7, wherein the AUC of the method is greater than the AUC of an algorithm incorporating different biomarkers, multiple biomarkers, panels, assays, or combinations thereof.

35. The method according to claim 34, wherein the AUC is greater than 0.

76.

36. The method according to claim 35, wherein the AUC is from 0.76 to 0.

95.

37. The method according to claim 36, wherein the AUC is about 0.

88.

38. The method according to claim 36, wherein the AUC is about 0.

86.

39. The method according to claim 35, wherein the AUC is from 0.82 to 0.

93.

40. The method according to claim 39, wherein the AUC is about 0.

87.

41. The method according to any one of claims 1 to 7, wherein the positive predictive value (PPV) of the method is greater than the PPV of an algorithm incorporating different biomarkers, multiple biomarkers, panels, assays, or combinations thereof.

42. The method according to claim 41, wherein the PPV is greater than 0.

67.

43. The method according to claim 42, wherein the PPV is 0.67 to 0.

87.

44. The method according to claim 43, wherein the PPV is about 0.

79.

45. The method according to any one of claims 34 to 44, wherein the algorithm is the ovarian malignancy risk (ROMA).

46. The method according to any one of claims 34 to 44, wherein the biomarker is HE4 and CA125 alone.

47. The method according to claim 45 or 46, wherein the cut-off point of each of the methods is used for classification.

48. The method according to claim 45 or 46, which is analyzed by the same statistical method.

49. The method according to any one of claims 1 to 48, wherein the levels of diacetyl spermine, diacetyl spermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminic acid, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid are measured against one or more predetermined thresholds.

50. The method according to claim 49, wherein when the value exceeds the one or more thresholds, the patient is classified as a person at risk of having ovarian cancer.

51. The method according to claim 50, wherein when the value is below one or more thresholds, the patient is classified as a person having no risk of having ovarian cancer.

52. The method according to claim 50, wherein when the value is below one or more thresholds, the patient is classified as a person having BPM.

53. The method according to claim 51, wherein the patient is then instructed to undergo further ovarian cancer screening or treatment.

54. The method according to claim 53, wherein the screening is selected from endoscopic ultrasonography, magnetic resonance imaging (MRI), and computed tomography (CT) scan.

55. The method according to claim 54, wherein the screening is performed annually.

56. The method according to claim 54, wherein the screening is performed every six months.

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