Methods and compositions for treating, diagnosing and prognosing ovarian cancer

Abstract

The present disclosure relates to methods, compositions and kits for treating ovarian cancer and for evaluating subjects. Aspects of the present disclosure relate to methods for evaluating a patient, the method comprising measuring the expression level of one or more of the listed miRNAs in a biological sample from the patient: miR-182-5p, miR-183-5p, miR-202-3p, miR-205-5p, miR-508-3p, miR-509-3-5p, miR-513b-5p or miR-513c-5p.

Description

[0001] describe

[0002] This application claims the benefit of priority to U.S. Provisional Patent Application Serial No. 62 / 642,294, filed on March 13, 2018, which is incorporated herein by reference in its entirety. Background of the Invention

[0003] 1. Field of the Invention

[0004] The present invention relates generally to the fields of molecular biology and oncology. More specifically, it relates to methods and compositions including biomarkers and cancer prediction, diagnosis and treatment.

[0005] 2. Description of related technologies

[0006] Ovarian cancer (OC) is the most lethal gynecological malignancy in women in the United States and worldwide [1]. Approximately 75% of patients are diagnosed at an advanced stage of the disease, when the tumor has spread to the abdomen, resulting in a 5-year survival rate of only 10-30% [2]. Although the survival rate for stage I is 90% [1], the lack of effective screening strategies and early detection markers has prevented these patients from being diagnosed early [3]. Routine pelvic examinations and transvaginal ultrasounds are not very effective population screening tools due to their low sensitivity [4]. Serum CA125 protein levels have been routinely used in clinical practice for three decades, but CA125 has very poor sensitivity and limited specificity in detecting early cancer [5, 6]. Unfortunately, three large randomized clinical trials testing CA 125 along with other markers, PLCO[7] (prostate, lung, colorectal, and ovarian cancer), UKCTOCS[8] (UK Collaborative Trial of Ovarian Cancer Screening), and the recently concluded USPST F[9] (United States Preventive Services Task Force), as well as ultrasound, did not provide any clues for the use of CA 125 as a screening tool, nor did the trial show any overall survival benefit.

[0007] Although advances in surgical treatment and chemotherapeutic regimens have modestly improved survival in OC, most women are diagnosed at very advanced stages of the disease, which are unlikely to be curable.

[32] Therefore, there is an urgent need to identify clinically translatable, highly accurate, non-invasive, and cost-effective biomarkers that can aid in early detection and population screening of high-risk individuals, which could also improve survival outcomes in OC. SUMMARY OF THE INVENTION

[0008] The present disclosure relates to methods, compositions and kits for treating ovarian cancer and for evaluating subjects. Aspects of the present disclosure relate to methods for evaluating a patient, comprising measuring the expression level of one or more of the listed miRNAs in a biological sample from the patient: miR-182-5p, miR-183-5p, miR-202-3p, miR-205-5p, miR-508-3p, miR-509-3-5p, miR-513b-5p or miR-513c-5p.

[0009] Additional aspects relate to methods of treating a patient having ovarian cancer, comprising administering chemotherapy and / or radiation and / or performing surgery to remove all or part of one or both ovaries after measuring the expression level of at least one of the following biomarkers listed in a biological sample from the patient: miR-182-5p, miR-183-5p, miR-202-3p, miR-205-5p, miR-508-3p, miR-509-3-5p, miR-513b-5p, or miR-513c-5p.

[0010] Additional aspects relate to methods for diagnosing a patient with ovarian cancer, comprising: a) measuring the expression level of at least one of the listed biomarkers: miR-182-5p, miR-183-5p, miR-202-3p, miR-205-5p, miR-508-3p, miR-509-3-5p, miR-513b-5p or miR-513c-5p in a biological sample from the patient; b) comparing the measured expression level to a control level or control sample, wherein the control level or control sample represents an expression level that is negative for ovarian cancer or positive for ovarian cancer; c) diagnosing the patient with ovarian cancer if at least one measured expression level is elevated compared to a representative expression level in normal ovarian cells, or determining that the patient does not have ovarian cancer based on the measured expression level.

[0011] Additional aspects relate to methods for diagnosing a patient with early-stage ovarian cancer, comprising: a) measuring the expression levels of at least the following listed biomarkers in a biological sample from the patient: miR-182-5p, miR-183-5p, miR-202-3p, miR-205-5p, miR-508-3p, miR-509-3-5p, miR-513b-5p, and miR-513c-5p; b) comparing the measured expression levels to a control level or a control sample, wherein the control level or control sample represents an expression level that is negative for ovarian cancer or positive for ovarian cancer; c) diagnosing the patient with ovarian cancer if the measured expression levels of at least eight of the listed biomarkers are elevated compared to representative expression levels in normal ovarian cells, or determining that the patient does not have ovarian cancer based on the measured expression levels.

[0012] Additional aspects relate to kits comprising 1, 2, 3, 4, 5, 6, 7 or 8 probes or primer sets for determining the expression level of miR-182-5p, miR-183-5p, miR-202-3p, miR-205-5p, miR-508-3p, miR-509-3-5p, miR-513b-5p and / or miR-513c-5p.

[0013] In some embodiments, at least the listed biomarker miR-182-5p is measured. In some embodiments, at least the listed biomarker miR-183-5p is measured. In some embodiments, at least the listed biomarker miR-202-3p is measured. In some embodiments, at least the listed biomarker miR-205-5p is measured. In some embodiments, at least the listed biomarker miR-508-3p is measured. In some embodiments, at least the listed biomarker miR-509-3-5p is measured. In some embodiments, at least the listed biomarker miR-513b-5p is measured. In some embodiments, at least the listed biomarker miR-513c-5p is measured. In some embodiments, the expression levels of at least two listed biomarkers are measured. In some embodiments, the expression levels of at least three listed biomarkers are measured. In some embodiments, the expression levels of at least four listed biomarkers are measured. In some embodiments, the expression levels of at least five listed biomarkers are measured. In some embodiments, the expression levels of at least six of the listed biomarkers are measured. In some embodiments, the expression levels of at least seven of the listed biomarkers are measured. In some embodiments, the expression levels of all eight of the listed biomarkers are measured.

[0014] In some embodiments, the method comprises or further comprises measuring Figure 7 In some embodiments, the measurement is Figure 7 In some embodiments, measuring Figure 7 In some embodiments, measuring Figure 7 In some embodiments, at least one of miR-182-5p, miR-183-5p, miR-202-3p, miR-205-5p, miR-508-3p, miR-509-3-5p, miR-513b-5p, or miR-513c-5p is excluded from measurement. In some embodiments, at least two of miR-182-5p, miR-183-5p, miR-202-3p, miR-205-5p, miR-508-3p, miR-509-3-5p, miR-513b-5p, or miR-513c-5p are excluded from measurement. In some embodiments, at least three of miR-182-5p, miR-183-5p, miR-202-3p, miR-205-5p, miR-508-3p, miR-509-3-5p, miR-513b-5p, or miR-513c-5p are excluded from measurement.

[0015] In some embodiments, the method further comprises comparing the expression level(s) with the control sample(s) or the control expression level(s). In some embodiments, the control sample(s) have expression levels representing normal ovarian cells. In some embodiments, the expression levels of the control sample(s) represent expression levels in biological samples from patients who do not suffer from ovarian cancer. In some embodiments, the control expression levels are expression levels in biological samples from patients who do not suffer from ovarian cancer. In some embodiments, the control expression levels represent expression levels in negative samples of ovarian cancer. In some embodiments, the control sample(s) have expression levels representing positive samples of ovarian cancer. In some embodiments, the control sample(s) have expression levels representing ovarian cancer cells. In some embodiments, the expression levels of the control sample(s) represent expression levels in biological samples from patients or multiple patients with ovarian cancer. In some embodiments, the control expression levels are expression levels in biological samples from patients with ovarian cancer.

[0016] In some embodiments, at least one measured expression level of the listed biomarkers in the biological sample from the patient is reduced compared to the expression level in ovarian cancer cells or is within the expression range representing normal ovarian cells. In some embodiments, at least two measured expression levels of the listed biomarkers in the biological sample from the patient are reduced compared to the expression level in ovarian cancer cells or are within the expression range representing normal ovarian cells. In some embodiments, at least three measured expression levels of the listed biomarkers in the biological sample from the patient are reduced compared to the expression level in ovarian cancer cells or are within the expression range representing normal ovarian cells. In some embodiments, at least four measured expression levels of the listed biomarkers in the biological sample from the patient are reduced compared to the expression level in ovarian cancer cells or are within the expression range representing normal ovarian cells. In some embodiments, at least five measured expression levels of the listed biomarkers in the biological sample from the patient are reduced compared to the expression level in ovarian cancer cells or are within the expression range representing normal ovarian cells. In some embodiments, at least six measured expression levels of the listed biomarkers in the biological sample from the patient are reduced compared to the expression level in ovarian cancer cells or are within the expression range representing normal ovarian cells. In some embodiments, the expression levels of at least seven of the listed biomarkers measured in the biological sample from the patient are reduced compared to the expression levels in ovarian cancer cells or are within the expression range representative of normal ovarian cells. In some embodiments, the expression levels of all eight of the listed biomarkers measured in the biological sample from the patient are reduced compared to the expression levels in ovarian cancer cells or are within the expression range representative of normal ovarian cells.

[0017] In some embodiments, at least one measured expression level of the listed biomarkers in the biological sample from the patient is elevated compared to the expression level in normal ovarian cells or is within the expression range representative of ovarian cancer cells. In some embodiments, at least two measured expression levels of the listed biomarkers in the biological sample from the patient are elevated compared to the expression level in normal ovarian cells or are within the expression range representative of ovarian cancer cells. In some embodiments, at least three measured expression levels of the listed biomarkers in the biological sample from the patient are elevated compared to the expression level in normal ovarian cells or are within the expression range representative of ovarian cancer cells. In some embodiments, at least four measured expression levels of the listed biomarkers in the biological sample from the patient are elevated compared to the expression level in normal ovarian cells or are within the expression range representative of ovarian cancer cells. In some embodiments, at least five measured expression levels of the listed biomarkers in the biological sample from the patient are elevated compared to the expression level in normal ovarian cells or are within the expression range representative of ovarian cancer cells. In some embodiments, at least six measured expression levels of the listed biomarkers in the biological sample from the patient are elevated compared to the expression level in normal ovarian cells or are within the expression range representative of ovarian cancer cells. In some embodiments, at least seven of the measured expression levels of the listed biomarkers in the biological sample from the patient are elevated compared to the expression levels in normal ovarian cells or are within the expression range representative of ovarian cancer cells. In some embodiments, all eight of the measured expression levels of the listed biomarkers in the biological sample from the patient are elevated compared to the expression levels in normal ovarian cells or are within the expression range representative of ovarian cancer cells.

[0018] In some embodiments, the at least two biomarkers include miR-182-5p and miR-183-5p. In some embodiments, the at least two biomarkers include miR-182-5p and miR-202-3p. In some embodiments, the at least two biomarkers include miR-182-5p and miR-205-5p. In some embodiments, the at least two biomarkers include miR-182-5p and miR-508-3p. In some embodiments, the at least two biomarkers include miR-182-5p and miR-509-3-5p. In some embodiments, the at least two biomarkers include miR-182-5p and miR-513b-5p. In some embodiments, the at least two biomarkers include miR-182-5p and miR-513c-5p. In some embodiments, the at least two biomarkers include miR-183-5p and miR-202-3p. In some embodiments, the at least two biomarkers include miR-183-5p and miR-205-5p. In some embodiments, the at least two biomarkers include miR-183-5p and miR-508-3p. In some embodiments, the at least two biomarkers include miR-183-5p and miR-509-3-5p. In some embodiments, the at least two biomarkers include miR-183-5p and miR-513b-5p. In some embodiments, the at least two biomarkers include miR-183-5p and miR-513c-5p. In some embodiments, the at least two biomarkers include miR-202-3p and miR-205-5p. In some embodiments, the at least two biomarkers include miR-202-3p and miR-508-3p. In some embodiments, the at least two biomarkers include miR-202-3p and miR-509-3-5p. In some embodiments, the at least two biomarkers include miR-202-3p and miR-513b-5p. In some embodiments, the at least two biomarkers include miR-202-3p and miR-513c-5p. In some embodiments, the at least two biomarkers include miR-205-5p and miR-508-3p. In some embodiments, the at least two biomarkers include miR-205-5p and miR-509-3-5p. In some embodiments, the at least two biomarkers include miR-205-5p and miR-513b-5p. In some embodiments, the at least two biomarkers comprise miR-205-5p and miR-513c-5p.In some embodiments, the at least two biomarkers include miR-508-3p and miR-509-3-5p. In some embodiments, the at least two biomarkers include miR-508-3p and miR-513b-5p. In some embodiments, the at least two biomarkers include miR-508-3p and miR-513c-5p. In some embodiments, the at least two biomarkers include miR-509-3-5p and miR-513b-5p. In some embodiments, the at least two biomarkers include miR-509-3-5p and miR-513c-5p. In some embodiments, the at least two biomarkers include miR-513b-5p and miR-513c-5p. In some embodiments, the at least two biomarkers include miR-513b-5p and miR-513c-5p.

[0019] In some embodiments, the at least three biomarkers include miR-182-5p, miR-183-5p, and miR-202-3p. In some embodiments, the at least three biomarkers include miR-182-5p, miR-183-5p, and miR-205-5p. In some embodiments, the at least three biomarkers include miR-182-5p, miR-183-5p, and miR-508-3p. In some embodiments, the at least three biomarkers include miR-182-5p, miR-183-5p, and miR-509-3-5p. In some embodiments, the at least three biomarkers include miR-182-5p, miR-183-5p, and miR-513b-5p. In some embodiments, the at least three biomarkers include miR-182-5p, miR-183-5p, and miR-513c-5p. In some embodiments, the at least three biomarkers include miR-182-5p, miR-183-5p, and miR-202-3p. In some embodiments, the at least three biomarkers include miR-182-5p, miR-202-3p, and miR-205-5p. In some embodiments, the at least three biomarkers include miR-182-5p, miR-202-3p, and miR-508-3p. In some embodiments, the at least three biomarkers include miR-182-5p, miR-202-3p, and miR-509-3-5p. In some embodiments, the at least three biomarkers include miR-182-5p, miR-202-3p, and miR-513b-5p. In some embodiments, the at least three biomarkers include miR-182-5p, miR-202-3p, and miR-513c-5p. In some embodiments, the at least three biomarkers include miR-182-5p, miR-205-5p, and miR-508-3p. In some embodiments, the at least three biomarkers include miR-182-5p, miR-205-5p, and miR-509-3-5p. In some embodiments, the at least three biomarkers include miR-182-5p, miR-205-5p, and miR-513b-5p. In some embodiments, the at least three biomarkers include miR-182-5p, miR-205-5p, and miR-513c-5p. In some embodiments, the at least three biomarkers include miR-182-5p, miR-508-3p, and miR-509-3-5p.In some embodiments, the at least three biomarkers include miR-182-5p, miR-508-3p, and miR-513b-5p. In some embodiments, the at least three biomarkers include miR-182-5p, miR-508-3p, and miR-513c-5p. In some embodiments, the at least three biomarkers include miR-182-5p, miR-509-3-5p, and miR-513b-5p. In some embodiments, the at least three biomarkers include miR-182-5p, miR-509-3-5p, and miR-513c-5p. In some embodiments, the at least three biomarkers include miR-182-5p, miR-513b-5p, and miR-513c-5p. In some embodiments, the at least three biomarkers include miR-183-5p, miR-202-3p, and miR-205-5p. In some embodiments, the at least three biomarkers include miR-183-5p, miR-202-3p, and miR-508-3p. In some embodiments, the at least three biomarkers include miR-183-5p, miR-202-3p, and miR-509-3-5p. In some embodiments, the at least three biomarkers include miR-183-5p, miR-202-3p, and miR-513b-5p. In some embodiments, the at least three biomarkers include miR-183-5p, miR-202-3p, and miR-513c-5p. In some embodiments, the at least three biomarkers include miR-183-5p, miR-205-5p, and miR-508-3p. In some embodiments, the at least three biomarkers include miR-183-5p, miR-205-5p, and miR-509-3-5p. In some embodiments, the at least three biomarkers include miR-183-5p, miR-205-5p, and miR-513b-5p. In some embodiments, the at least three biomarkers include miR-183-5p, miR-205-5p, and miR-513c-5p. In some embodiments, the at least three biomarkers include miR-183-5p, miR-508-3p, and miR-509-3-5p. In some embodiments, the at least three biomarkers include miR-183-5p, miR-508-3p, and miR-513b-5p. In some embodiments, the at least three biomarkers include miR-183-5p, miR-508-3p, and miR-513c-5p.In some embodiments, the at least three biomarkers include miR-183-5p, miR-509-3-5p and miR-513b-5p. In some embodiments, the at least three biomarkers include miR-183-5p, miR-509-3-5p and miR-513c-5p. In some embodiments, the at least three biomarkers include miR-183-5p, miR-513b-5p and miR-513c-5p. In some embodiments, the at least three biomarkers include miR-202-3p, miR-205-5p and miR-508-3p. In some embodiments, the at least three biomarkers include miR-202-3p, miR-205-5p and miR-509-3-5p. In some embodiments, the at least three biomarkers include miR-202-3p, miR-205-5p, and miR-513b-5p. In some embodiments, the at least three biomarkers include miR-202-3p, miR-205-5p, and miR-513c-5p. In some embodiments, the at least three biomarkers include miR-202-3p, miR-508-3p, and miR-509-3-5p. In some embodiments, the at least three biomarkers include miR-202-3p, miR-508-3p, and miR-513b-5p. In some embodiments, the at least three biomarkers include miR-202-3p, miR-508-3p, and miR-513c-5p. In some embodiments, the at least three biomarkers include miR-202-3p, miR-509-3-5p, and miR-513b-5p. In some embodiments, the at least three biomarkers include miR-202-3p, miR-509-3-5p, and miR-513c-5p. In some embodiments, the at least three biomarkers include miR-202-3p, miR-513b-5p, and miR-513c-5p. In some embodiments, the at least three biomarkers include miR-205-5p, miR-508-3p, and miR-509-3-5p. In some embodiments, the at least three biomarkers include miR-205-5p, miR-508-3p, and miR-513b-5p. In some embodiments, the at least three biomarkers include miR-205-5p, miR-508-3p, and miR-513c-5p. In some embodiments, the at least three biomarkers include miR-205-5p, miR-509-3-5p, and miR-513b-5p.In some embodiments, the at least three biomarkers include miR-205-5p, miR-509-3-5p, and miR-513c-5p. In some embodiments, the at least three biomarkers include miR-205-5p, miR-513b-5p, and miR-513c-5p. In some embodiments, the at least three biomarkers include miR-508-3p, miR-509-3-5p, and miR-513b-5p. In some embodiments, the at least three biomarkers include miR-508-3p, miR-509-3-5p, and miR-513c-5p. In some embodiments, the at least three biomarkers include miR-508-3p, miR-513b-5p, and miR-513c-5p. In some embodiments, the at least three biomarkers include miR-509-3-5p, miR-509-3-5p, and miR-513c-5p. In some embodiments, the at least three biomarkers include miR-509-3-5p, miR-513b-5p, and miR-513c-5p.

[0020] In some embodiments, the biological sample is a tissue sample, a tumor sample, a lymph node sample, blood, serum, saliva or a squamous cell sample. In some embodiments, the biological sample is a serum sample. In some embodiments, the biological sample comprises nucleic acid. In some embodiments, the biological sample comprises a fractionated sample of nucleic acid. In some embodiments, the biological sample is a fractionated sample enriched for nucleic acid. In some embodiments, the biological sample is a sample comprising ovarian cells.

[0021] In some embodiments, the patient has symptoms of ovarian cancer. In some embodiments, the patient has no symptoms of ovarian cancer. In some embodiments, the patient has undergone an early screening program for ovarian cancer. In some embodiments, the patient has not undergone an early screening program for ovarian cancer.

[0022] In some embodiments, the method further comprises diagnosing the patient with ovarian cancer based on one or more measured expression levels of the listed biomarkers in the patient's biological sample. In some embodiments, the patient is diagnosed with stage I or stage II cancer based on the expression level of at least one of the listed biomarkers.

[0023] In some embodiments, the method further comprises treating the patient's ovarian cancer after measuring the expression level of one or more listed biomarkers. In some embodiments, the patient is treated after measuring an increase in the expression level of at least one listed biomarker compared to normal ovarian cells. In some embodiments, the patient is treated after measuring an increase in the expression level of at least two listed biomarkers compared to normal ovarian cells. In some embodiments, the patient is treated after measuring an increase in the expression level of at least three listed biomarkers compared to normal ovarian cells. In some embodiments, the patient is treated after measuring an increase in the expression level of at least four listed biomarkers compared to normal ovarian cells. In some embodiments, the patient is treated after measuring an increase in the expression level of at least five listed biomarkers compared to normal ovarian cells. In some embodiments, the patient is treated after measuring an increase in the expression level of at least six listed biomarkers compared to normal ovarian cells. In some embodiments, the patient is treated after measuring an increase in the expression level of at least seven listed biomarkers compared to normal ovarian cells. In some embodiments, the patient is treated after elevated expression levels of all eight listed biomarkers are measured compared to normal ovarian cells.

[0024] In some embodiments, the patient has been diagnosed with ovarian cancer based on the expression level of one or more listed biomarkers. In some embodiments, the patient has been diagnosed with ovarian cancer based on the expression levels of all eight listed biomarkers compared to the expression levels representing normal ovarian cells. In some embodiments, the pathology of one or more ovarian cells is analyzed. In some embodiments, the patient is diagnosed with ovarian cancer after measuring an increase in the expression level of at least one listed biomarker compared to normal ovarian cells. In some embodiments, the patient is diagnosed with ovarian cancer after measuring an increase in the expression level of at least two listed biomarkers compared to normal ovarian cells. In some embodiments, the patient is diagnosed with ovarian cancer after measuring an increase in the expression level of at least three listed biomarkers compared to normal ovarian cells. In some embodiments, the patient is diagnosed with ovarian cancer after measuring an increase in the expression level of at least four listed biomarkers compared to normal ovarian cells. In some embodiments, the patient is diagnosed with ovarian cancer after measuring an increase in the expression level of at least five listed biomarkers compared to normal ovarian cells. In some embodiments, the patient is diagnosed with ovarian cancer upon measuring elevated expression levels of at least six of the listed biomarkers compared to normal ovarian cells. In some embodiments, the patient is diagnosed with ovarian cancer upon measuring elevated expression levels of at least seven of the listed biomarkers compared to normal ovarian cells. In some embodiments, the patient is diagnosed with ovarian cancer upon measuring elevated expression levels of all eight of the listed biomarkers compared to normal ovarian cells.

[0025] In some embodiments, the control sample(s) have expression levels representative of ovarian cancer negative samples. In some embodiments, the control expression level(s) represent expression levels in ovarian cancer negative samples. In some embodiments, the control sample(s) have expression levels representative of ovarian cancer positive samples. In some embodiments, the control expression level(s) represent expression levels in ovarian cancer positive samples.

[0026] In some embodiments, the control sample(s) have expression levels representative of biological samples from ovarian cancer negative patients. In some embodiments, the control expression levels represent expression levels in biological samples from ovarian cancer negative patients. In some embodiments, the control sample(s) have expression levels representative of biological samples from ovarian cancer positive patients. In some embodiments, the control expression levels represent expression levels in biological samples from ovarian cancer positive patients.

[0027] In some embodiments, the ovarian cancer is stage I or stage II. In some embodiments, the pathology of the patient's ovarian cells has been analyzed. In some embodiments, the biomarkers are human biomarkers. In some embodiments, the biomarkers are mammalian biomarkers. In some embodiments, the biomarkers comprise mature and / or processed miRNAs.

[0028] In some embodiments, the kit further comprises one or more agents for detecting one or more controls. In some embodiments, the kit further comprises a reagent for isolating nucleic acids from a biological sample. In some embodiments, the reagent is used to isolate nucleic acids from a serum sample. In some embodiments, the reagent is used to isolate nucleic acids from a sample described herein.

[0029] The term subject or patient can refer to an animal (e.g., a mammal), including, but not limited to, a human, a non-human primate, a rodent, a dog, or a pig. The methods of obtaining provided herein include biopsy methods, such as fine needle aspiration, core needle biopsy, vacuum-assisted biopsy, incisional biopsy, excisional biopsy, punch biopsy, shave biopsy, or skin biopsy.

[0030] In certain embodiments, the sample is obtained from a biopsy of an ovary, a polyp, a uterus, a mucosa, or a submucosa. In some embodiments, the sample is from a polyp removed by surgery. In other embodiments, the sample can be obtained from any tissue provided herein, including but not limited to gallbladder, skin, heart, lung, breast, pancreas, liver, muscle, kidney, smooth muscle, ovary, bladder, intestine, brain, prostate, or thyroid tissue.

[0031] Alternatively, the sample can include, but is not limited to, blood, serum, sweat, hair follicles, cheek tissue, tears, menstruation, urine, feces or saliva. In a specific embodiment, the sample can be a tissue sample, a whole blood sample, a urine sample, a saliva sample, a serum sample, a plasma sample or a feces sample.

[0032] In some aspects, the sample is obtained from cystic fluid or a fluid derived from a tumor or a vegetation. In other embodiments, the cyst, tumor or vegetation is located in the reproductive system. In some aspects of the current method, any medical professional, such as a doctor, nurse or medical technician, can obtain a biological sample for testing. In other aspects of the current method, a patient or subject can obtain a biological sample for testing without the help of a medical professional, such as obtaining a whole blood sample, a urine sample, a fecal sample, a cheek sample or a saliva sample.

[0033] In other embodiments, the sample can be a fresh, frozen or preserved sample or fine needle aspirate. In a specific embodiment, the sample is a formalin-fixed, paraffin-embedded (FFPE) sample. By placing the sample obtained in a suitable medium, excipient, solution or container, short-term or long-term storage is possible. In some cases, storage may require that the sample be stored in a refrigerated or frozen environment. Before being stored in a frozen environment, the sample can be quickly frozen. In some cases, the frozen sample can be contacted with a suitable cryopreservation medium or compound. The example of a cryopreservation medium or compound includes but is not limited to: glycerol, ethylene glycol, sucrose or glucose.

[0034] Some embodiments also relate to isolating nucleic acids, such as ribonucleic acids or RNA, from biological samples or patient samples. Other steps may or may not include amplifying nucleic acids in the sample and / or hybridizing one or more probes to amplified or unamplified nucleic acids. The method may also include analyzing nucleic acids in the sample. In certain embodiments, a microarray may be used to measure or analyze the level of biomarker expression in a sample. The method may also include recording biomarker expression levels in a tangible medium or reporting expression levels to a patient, a health care payer, a physician, an insurance agent, or an electronic system.

[0035] The weighting factors or differences between or among expression levels or differences between or among weighted comparisons can be at least or at most about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0, 11.5, 12.0, 12.5, 13.0, 13.5, 14.0, 14.5, 15.0, 15.5, 16.0, 16.5, 17.0, 17.5, 18.0, 18.5, 19.0, 19.5, 20.0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74 ,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,105,110,115,120,125,130,135,140,145,150,155,160,165,170,175,180,185,190,195,200,205,210,215,220,225,230,235,240,245,250,255,260,265,27 0, 275, 280, 285, 290, 295, 300, 305, 310, 315, 320, 325, 330, 335, 340, 345, 350, 355, 360, 365, 370, 375, 380, 385, 390, 395, 400, 410, 420, 425, 430, 440, 441, 450, 460, 470, 475, 480, 490, 500, 510, 520, 525, 530, 540, 550, 560, 570, 575, 580, 590, 600, 610, 620, 625, 630, 640, 650, 660, 670, 675, 680, 690, 700, 710, 720, 725, 730, 740, 750, 760, 770, 775, 780, 790, 800, 810, 820, 825, 830, 840, 850, 860, 870, 875, 880, 890, 900, 910, 920, 925, 930, 940, 950, 960, 970, 975, 980, 990, 1000 times or fold (or any range derivable therein).

[0036] In some embodiments, the determination of the diagnosis, prediction, or calculation of the risk score is performed by applying a classification algorithm based on the expression values ​​of the biomarkers, wherein the p-value for differential expression is about, between about, or at most about 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.011, 0.012, 0.013, 0.014, 0.015, 0.016, 0.017, 0.018, 0.019, 0.020, 0.021, 0.022, 0.023, 0.024, 0.025, 0.026, 0.027, 0.028, 0.030, 0.031, 0.032, 0.033, 0.034, 0.035, 0.036, 0.037, 0.038, 0.039, 0.040, 0.041, 0.042, 0.043, 0.044, 0.045, 0.046, 0.047, 0.048, 0.049, 0.050, 0.051, 0.052, 0.053, 0.054, 0.055 0.022、0.023、0.024、0.025、0.026、0.027、0.028、0.029、0.03、0.031、0.032、0.033、0.034、0.035、0.036、0.037、0.038、0.039、0.040、0.041、0.042、0.043、0.044、0.045、0.046、0.047、0.048、0.049、0.050、0 .051, 0.052, 0.053, 0.054, 0.055, 0.056, 0.057, 0.058, 0.059, 0.060, 0.061, 0.062, 0.063, 0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.070, 0.071, 0.072, 0.073, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079 , 0.080, 0.081, 0.082, 0.083, 0.084, 0.085, 0.086, 0.087, 0.088, 0.089, 0.090, 0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097, 0.098, 0.099, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 or more (or any range derivable therein). In certain embodiments, the predictive score is calculated using one or more statistically significantly differentially expressed biomarkers (alone or as differential pairs).

[0037] Any of the methods described herein can be implemented on a tangible computer-readable medium comprising computer-readable code that, when executed by a computer, causes the computer to perform one or more operations. In some embodiments, there is a tangible computer-readable medium comprising computer-readable code that, when executed by a computer, causes the computer to perform the following operations, including: a) receiving information corresponding to the expression level of a biomarker in a sample from a patient; and b) using the information corresponding to the expression level in the sample, determining a differential value of the expression level compared to a control or reference expression level of the gene.

[0038] In other aspects, the tangible computer-readable medium also includes computer-readable code, which, when executed by the computer, causes the computer to perform one or more additional operations, including making a recommendation, the recommendation comprising: wherein the patient in step a) is during or after a first treatment for cancer, if the patient's expression level is not increased, the patient is given the same treatment as the first treatment; if the patient's expression level is increased, the patient is given a treatment different from the first treatment.

[0039] In some embodiments, receiving information includes receiving information corresponding to the expression level from a tangible storage device from a tangible data storage device. In other embodiments, the medium also includes computer readable code that, when executed by a computer, causes the computer to perform one or more additional operations, including: sending information corresponding to the difference value to a tangible data storage device, calculating a predictive score for the patient, treating the patient with a conventional therapy if the patient does not have an expression level, and / or treating the patient with an alternative esophageal therapy if the patient has an elevated expression level.

[0040] The tangible computer readable medium also includes computer readable code, which, when executed by the computer, causes the computer to perform one or more additional operations, including calculating a predictive score for the patient. The operations may also include making a recommendation, the recommendation including: administering treatment to the patient determined to have a reduced expression level.

[0041] As used herein, the terms "or" and "and / or" are used to describe multiple components that are combined with or mutually exclusive of each other. For example, "x, y, and / or z" can refer to "x" alone, "y" alone, "z" alone, "x, y, and z", "(x and y) or z", "x or (y and z)", or "x or y or z". It is specifically contemplated that x, y, or z can be specifically excluded from the embodiments.

[0042] Throughout this application, the term "about" is used according to its plain and ordinary meaning in the art of cell biology to indicate that a value includes the standard deviation of error for the device or method being employed to determine the value.

[0043] The term "comprising," which is synonymous with "including," "containing," or "characterized by," is inclusive or open-ended, and does not exclude other unrecited elements or method steps. The phrase "consisting of excludes any element, step, or ingredient not specified. The phrase "consisting essentially of limits the scope of the described subject matter to the specified materials or steps, as well as those that do not materially affect the basic and novel characteristics thereof. It is contemplated that embodiments described in the context of the term "comprising" may also be implemented in the context of the terms "consisting of" or "consisting essentially of."

[0044] It is particularly contemplated that any limitation discussed with respect to one embodiment of the invention may apply to any other embodiment of the invention. In addition, any composition of the invention may be used in any method of the invention, and any method of the invention may be used to prepare or utilize any composition of the invention. Aspects of the embodiments set forth in the Examples are also embodiments that may be implemented elsewhere in the context of embodiments discussed in various Examples or elsewhere in this application, such as the Summary of the Invention, Detailed Description of Embodiments, Claims, and Figure Legends. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The following drawings constitute part of this specification and are included to further illustrate certain aspects of the present invention. The present invention may be better understood by reference to one or more of these drawings in combination with the detailed description of specific embodiments presented herein.

[0046] Figure 1A-C. Discovery and independent tissue validation of OCaMIR. A) Differentially expressed miRNAs between stage I ovarian cancer and normal, with adjusted p-value less than 0.05 and a fold change of 2 in absolute logarithm from small RNA sequencing. Of the 32 miRNAs, 24 were downregulated (black) and 8 were upregulated (red) in ovarian cancer; B) Risk score from 8-miRNA signature (OCaMIR) and accuracy of analysis for determining stage I ovarian cancer in the discovery phase; C) Independent tissue validation ROC curves of OCaMIR in TCGA (stage I and all stages) and GSE65819 (all stages) cohorts.

[0047] Figure 2A-EIndependent training and validation of OCaMIR analysis sera in four retrospective cohorts and one prospective cohort. ROC curve and risk score plot of OCaMIR. A) Serum retrospective training cohort; GSE106817 miRNA microarray, B) Serum retrospective validation cohort 1; GSE31568 miRNA microarray, C) Serum retrospective validation cohort 2; GSE113486 miRNA microarray, D) Serum retrospective validation cohort 3; Czech Republic RT-PCR, and E) Serum prospective validation cohort; West China RT-PCR.

[0048] Figure 3A-D Evaluation of the performance of OCaMIR analysis in detecting stage I ovarian cancer in serum and its specificity for ovarian cancer detection. ROC curves and staged risk score plots of OCaMIR. A) Serum retrospective training cohort; GSE106817 miRNA microarray, B) Serum retrospective validation cohort 3; Czech Republic RT-PCR, C) Serum prospective validation cohort; West China RT-PCR, and D) ROC curves of OCaMIR analysis in multiple diseases including ovarian cancer.

[0049] Figure 4 .Individual miRNA expression levels between cancer and healthy in all serum groups.

[0050] Figure 5 .Individual miRNA expression levels between cancer and healthy in all serum groups.

[0051] Figure 6 .Workflow for biomarker discovery.

[0052] Figure 7 . Training cohort (n=31, 9 normal, 22 stage I); 32 miRNAs differentially expressed between tumor and normal samples (abs(log2FC)>=2 and adjusted P value<=0.05). 8 miRNAs were highly expressed in tumors compared with normal samples (log2FC>=2 and adjusted P value<=0.05). Risk score=1.71+0.25*MIR202-0.03*MIR508+0.56*MIR509-3+0.005*MIR205+0.05*MIR182-0.49*MIR513C+0.23*MIR183-0.05*MIR513B.

[0053] Figure 8 .In the cohort, the receiver operating characteristic (ROC) and precision-recall (PRC) of the 8-miRNA signature were 0.99 and 0.998.

[0054] Fig. 9Heatmap and risk score curve of the identified 8-miRNA signature. In the cohort, tumors had both high risk scores and miRNA signatures.

[0055] Fig.10 .Phase II: In silico validation of the diagnostic value of the 8-miRNA signature.

[0056] Fig.11 .In TCGA (stage I and normal), the receiver operating characteristic (ROC) and precision-recall (PRC) of the 8-miRNA signature were 0.96 and 0.974.

[0057] Fig.12 Heatmap and risk score curve of the identified 8-miRNA signature. In TCGA (stage I and normal), tumors had both high risk scores and miRNA signatures.

[0058] Fig.13 In TCGA (stage I, stage II, and normal), the receiver operating characteristic (ROC) and precision-recall (PRC) of the 8-miRNA signature were 0.96 and 0.974.

[0059] Fig.14 Heatmap and risk score curve of the identified 8-miRNA signature. In TCGA (stage I, stage II, and normal), tumors had both high risk scores and miRNA signatures.

[0060] Fig.15 In TCGA (all), the receiver operating characteristic (ROC) and precision-recall (PRC) of the 8-miRNA signature were 0.89 and 0.998.

[0061] Fig.16 Heatmap and risk score curves of the identified 8-miRNA signature. In TCGA (all), tumors had both high risk scores and miRNA signatures.

[0062] Fig.17 In GSE65819 (primary and normal), the receiver operating characteristic (ROC) and precision-recall (PRC) of the 8-miRNA signature were 0.83 and 0.98.

[0063] Fig.18 Heatmap and risk score curve of the identified 8-miRNA signature. In GSE65819 (primary and normal), tumors had both high risk scores and miRNA signatures.

[0064] Fig.19In GSE65819 (ascites and normal), the receiver operating characteristic (ROC) and precision-recall (PRC) of the 8-miRNA signature were 0.81 and 0.94.

[0065] Fig. 20 Heatmap and risk score curve of the identified 8-miRNA signature. In GSE65819 (ascites and normal), tumors had both high risk scores and miRNA signatures.

[0066] Fig.21 . Independent serum validation cohort GSE106817 (n=3079, normal 2759, OV 320, all female samples) serum.

[0067] Fig. 22 Independent serum validation cohort GSE106817 (n=3079, 2759 normal, 320 OV, all female samples) serum In this large public serum cohort, all 8 miRNAs were significantly overexpressed.

[0068] Fig.23 . Serological validation of the cohort (n=91, 54 normal, 37 stage I). Description of Illustrative Embodiments

[0069] Certain aspects of the present invention provide a test that can help physicians select the best treatment for their patients from several alternative treatment options. A major clinical challenge in cancer treatment is to determine the subset of patients who will benefit from treatment options in both metastatic and adjuvant settings. Over the past decade, the number of anticancer drugs and multiple drug combinations has increased greatly, however, treatment continues to be applied empirically using trial and error. Methods and compositions for diagnosing patients are provided herein to determine the best treatment options for cancer patients. By using reliable statistical methods, the inventors have identified a group of eight miRNAs that are highly accurate in determining early ovarian cancer. In addition, by developing a classifier based on multivariate logistic regression, the identified tissue miRNA markers can be independently validated in a variety of retrospective tissue and serum cohorts from different ethnic groups.

[0070] I. Definitions

[0071] The terms "substantially the same", "not significantly different" or "within the range" refer to an expression level that is not significantly different from the object to which it is compared. Alternatively or simultaneously, the term substantially the same refers to an expression level that is less than 2, 1.5 or 1.25 times different from the expression level compared thereto, or an expression difference that is less than 20, 15, 10 or 5%.

[0072] "Subject" or "patient" refers to any single subject in need of treatment, including humans, cows, dogs, guinea pigs, rabbits, chickens, etc. Also intended to be included as a subject is any subject participating in a clinical research trial who does not show any clinical symptoms of disease, or any subject participating in an epidemiological study, or any subject used as a control.

[0073] As used herein, the term "primer" or "probe" is intended to encompass any nucleic acid capable of initiating the synthesis of a nascent nucleic acid in a template-dependent process. Typically, a primer is an oligonucleotide of ten to twenty and / or thirty base pairs in length, but longer sequences may be used. Primers may be provided in double-stranded and / or single-stranded form, but the single-stranded form is preferred. A probe may also refer to a nucleic acid capable of hybridizing to a nucleic acid of a target gene or fragment thereof by base complementation.

[0074] As used herein, "increased expression" or "elevated expression" or "reduced expression" refers to the expression level of a biomarker in a subject sample compared to a reference level representing the same biomarker or a different biomarker. In certain aspects, the reference level may be a reference expression level of a non-cancerous tissue from the same subject. Alternatively, the reference level may be a reference expression level from a different subject or subject group. For example, the reference expression level may be an expression level obtained from a sample (e.g., a tissue, a body fluid, or a cell sample) of a subject or subject group that has never had cancer, or an expression level obtained from a non-cancerous tissue of a subject or subject group that has cancer. The reference level may be a single value or may be a range of values. The reference expression level may be determined using any method known to those of ordinary skill in the art. In some embodiments, the reference level is the average expression level determined from a group of subjects with or without cancer. The reference level may also be graphically depicted as an area on a chart. In certain embodiments, the reference level is a normalized level, while in other embodiments, the reference level may be an unstable level relative to the tissue or biological sample being tested.

[0075] As used herein, the terms "determine" or "evaluate" may refer to measuring, determining an amount, or quantifying (qualitatively or quantitatively).

[0076] II. MiRNA

[0077] Certain aspects are based in part on the systematic discovery and validation of (multiple) miRNA biomarkers for ovarian cancer. In certain embodiments, microRNAs (abbreviated as miRNAs) can be used in methods and compositions for determining predictions, for diagnosing subjects, for determining the response of a particular patient to a particular cancer treatment, and for treating individuals with esophageal cancer.

[0078] MiRNA can be a naturally occurring small non-coding RNA, the length of which is about 17 to about 25 nucleotide bases (nt) in its biologically active form. MiRNA regulates gene expression after transcription by inhibiting translation of target mRNA. It is believed that miRNA plays the role of a negative regulator, i.e., a large amount of specific miRNA will be associated with lower levels of target gene expression.

[0079] There are three forms of miRNA in vivo, namely primary miRNA (pri-miRNA), precursor miRNA (pre-miRNA) and mature miRNA.Primary miRNA (pri-miRNA) is expressed as a transcript of a stem-loop structure of about hundreds of bases to more than 1kb.Pri-miRNA transcripts are cleaved in the nucleus by an RNase II endonuclease called Drosha, which cuts both strands of the stem near the base of the stem-loop. Drosha cuts the RNA duplex with staggered cuts, leaving a 5' phosphate and a 2nt overhang at the 3' end.

[0080] The cleavage product, the precursor miRNA (pre-miRNA), can be about 60 to about 110 nt long and has a hairpin structure formed in a folded-back manner. Pre-miRNA is transported from the nucleus to the cytoplasm by Ran-GTP and exportin-5. Pre-miRNA is further processed in the cytoplasm by another RNase II endonuclease called Dicer. Dicer recognizes the 5' phosphate and 3' overhangs and cuts off the loop at the stem-loop junction to form a miRNA duplex. The miRNA duplex binds to the RNA-induced silencing complex (RISC), in which the antisense strand is preferentially degraded, while the sense strand mature miRNA guides RISC to its target site. Mature miRNA is the biologically active form of miRNA and is about 17 to 25 nt in length.

[0081] MicroRNAs act by base pairing (perfectly or imperfectly) with specific sequences in the messenger (mRNA) of their target genes. miRNAs degrade or inhibit translation of mRNA, resulting in post-transcriptional downregulation, inhibition, or silencing of target gene expression. In animals, miRNAs do not necessarily have perfect homology to their target sites, and partial homology can lead to translational inhibition, whereas in plants, miRNAs often show complete homology to their target sites, so degradation of the messenger (mRNA) prevails.

[0082] MicroRNAs are widely distributed in the genome, dominate gene regulation, and actively participate in many physiological and pathological processes. For example, it has been found that the regulation of certain miRNAs can control cell proliferation, differentiation, and apoptosis; and abnormal miRNA profiles are associated with tumorigenesis. In addition, it is proposed that viral infection causes an increase in miRNAs that target and silence "primitive cell survival" genes, while miRNAs that inhibit genes related to apoptosis (programmed cell death) decrease, thereby shifting the balance toward obtaining apoptotic signal transmission.

[0083] III. Ovarian Cancer Staging and Treatment

[0084] Methods and compositions for treating ovarian cancer using specific applications of biomarkers can be provided. Based on the biomarker profile, different treatments can be prescribed or recommended for different cancer patients and patient groups.

[0085] A. Cancer Staging

[0086] The type of cell in which the cancer begins determines the type of ovarian cancer that is present. Types of ovarian cancer include: epithelial tumors, which begin in the thin layer of tissue that covers the outside of the ovary; stromal tumors, which begin in the ovarian tissue that contains cells that produce hormones; and germ cell tumors, which begin in the cells that produce eggs. In certain embodiments, the cancer comprises an epithelial tumor. In some embodiments, the cancer comprises a stromal tumor. In some embodiments, the cancer comprises a germ cell tumor.

[0087] Three factors can be used to classify or stage a tumor. (T) relates to the size of the tumor and is classified by determining the spread of the tumor to the fallopian tubes and / or nearby pelvic organs such as the uterus or bladder. (N) relates to the spread of the tumor to the lymph nodes. N is determined by determining the spread of the tumor to the lymph nodes in the pelvis or around the aorta. (M) relates to whether the tumor has metastasized.

[0088] The staging system in the table below uses pathological staging (also called surgical staging). This can be determined by examining tissue removed during surgery. This is also called surgical staging. Sometimes, if surgery isn't possible right away, the cancer is staged clinically. This is based on the results of a physical exam, biopsy, and imaging tests done before surgery.

[0089]

[0090]

[0091]

[0092]

[0093] "Cancer" referred to in the methods described herein may include or exclude any of the above stages or TNM categories. For example, cancer may be or may exclude stage I, IA, IB, IC, II, IIA, IIB, IIIA1, IIIA2, IIIB, IIIC, IVA, and / or IVB. The patient may be a patient who has and / or has been determined to have stage I, IA, IB, IC, II, IIA, IIB, IIIA1, IIIA2, IIIB, IIIC, IVA, and / or IVB cancer. In addition, the cancer can be classified or further classified as T1, N0 and / or M0; T1a, N0 and / or M0; T1b, N0 and / or M0; T1c, N0 and / or M0; T2, N0 and / or M0; T2a, N0 and / or M0; T2b, N0 and / or M0; T1, T2 and / or N1; M0, T3a, N0 and / or N1; M0, T3b, N0 and / or N1; M0, T3c, N0, N1 and / or M0; any T, any N and / or M1a; any T, any N and / or M1b.

[0094] B. Treatment

[0095] Treatment of ovarian cancer usually involves a combination of surgery and chemotherapy. Treatments for ovarian cancer include those described below.

[0096] 1. Surgery

[0097] Surgery to remove ovarian cancer includes: 1) Surgery to remove one ovary. For very early-stage cancer that has not spread beyond one ovary, surgery may include removing the affected ovary and its fallopian tube. 2) Surgery to remove both ovaries. If cancer is present in both ovaries, but there are no other signs of cancer, the surgeon may remove both ovaries and both fallopian tubes. 3) Surgery to remove both ovaries and the uterus. If the cancer is more extensive, or if you don't want to preserve your fertility, the surgeon will remove the ovaries, fallopian tubes, uterus, nearby lymph nodes, and a layer of fatty abdominal tissue (omentum).

[0098] 2. Chemotherapy

[0099] Chemotherapy can be used in a neoadjuvant or adjuvant setting. Chemotherapeutic drugs are typically injected into a vein, taken orally, or given intraperitoneally. In some embodiments, chemotherapy is given to a patient. Exemplary chemotherapy regimens include carboplatin and paclitaxel or a combination thereof. Other exemplary chemotherapy regimens include cisplatin and docetaxel and combinations thereof. Other chemotherapy treatment regimens that can be used in embodiments of the present disclosure include cisplatin, etoposide, bleomycin, and combinations thereof. In some embodiments, a combination of carboplatin and etoposide is used.

[0100] Suitable types of chemotherapeutic agents include: (a) alkylating agents, such as nitrogen mustards (e.g., dichloromethane, cyclophosphamide, ifosfamide, melphalan, chlorambucil), ethyleneimine and methylmelamines (e.g., hexamethylmelamine, thiotepa), alkyl sulfonates (e.g., busulfan), nitrosoureas (e.g., carmustine, lomustine, chlorozotocin, streptozotocin) and triazines (e.g., dacarbazine), (b) antimetabolites, such as folate analogs (e.g., methotrexate), pyrimidine analogs (e.g., 5-fluorouracil, floxuridine, cytarabine, azauridine) and purine analogs and related substances (e.g., 6-mercaptoethanol). (e.g., vinca alkaloids, 6-thioguanine, pentostatin), (c) natural products, such as vinca alkaloids (e.g., vinblastine, vincristine), epipodophyllotoxins (e.g., etoposide, teniposide), antibiotics (e.g., actinomycin, daunorubicin, doxorubicin, bleomycin, plicamycin and mitoxantrone), enzymes (e.g., L-asparaginase) and biological response modifiers (e.g., interferon-α), and (d) other agents, such as platinum coordination complexes (e.g., cisplatin, carboplatin), substituted ureas (e.g., hydroxyurea), methylhydrazine derivatives (e.g., methylbenzylhydrazine) and adrenocortical inhibitors (e.g., paclitaxel and mitotane). In some embodiments, cisplatin is a particularly suitable chemotherapeutic agent.

[0101] Other suitable chemotherapeutic agents include anti-microtubule agents such as paclitaxel ("Taxol") and doxorubicin hydrochloride ("doxorubicin"). The combination of the Egr-1 promoter / TNFα construct delivered via an adenoviral vector and doxorubicin was determined to be effective in overcoming resistance to chemotherapy and / or TNF-α, suggesting that combination therapy of the construct with doxorubicin can overcome resistance to both doxorubicin and TNF-α.

[0102] Doxorubicin is poorly absorbed and is preferably administered intravenously. In certain embodiments, suitable intravenous doses for adults include about 60 mg / m 2 Up to about 75 mg / m 2 , about 21 days apart, or about 25 mg / m2 per day for 2 or 3 consecutive days 2 About 30 mg / m 2 , repeated after an interval of about 3 to 4 weeks, or about 20 mg / m 2 , once a week. In elderly patients, when previous myelosuppression has occurred due to previous chemotherapy or neoplastic bone marrow infiltration, or when the drug is used in combination with other myelosuppressive drugs, the lowest dose should be used.

[0103] Nitrogen mustards are another suitable chemotherapeutic agent for use in the methods of the present disclosure. Nitrogen mustards may include, but are not limited to, dichloromethyl diethylamine (HN2), cyclophosphamide and / or ifosfamide, melphalan (L-phenylalanine mustard), and chlorambucil. Cyclophosphamide Available from Mead Johnson, and Can be obtained from Adria, is another suitable chemotherapeutic agent. Suitable oral dosages for adults include, for example, about 1 mg / kg / day to about 5 mg / kg / day, and intravenous dosages include, for example, initially about 40 mg / kg to about 50 mg / kg in a time period of about 2 days to about 5 days, or about 10 mg / kg to about 15 mg / kg every 7 days to about 10 days, or about 3 mg / kg to about 5 mg / kg twice per week, or about 1.5 mg / kg / day to about 3 mg / kg / day. Due to bad gastrointestinal effects, intravenous routes are preferred. The drug can also be given by muscle sometimes, given by osmosis or given in the body cavity.

[0104] Other suitable chemotherapeutic agents include pyrimidine analogs such as cytarabine (cytosine arabinoside), 5-fluorouracil (fluorouracil; 5-FU) and floxuridine (fluorodeoxyuridine; FudR). 5-FU can be administered to a subject at any dose between about 7.5 and about 1000 mg / m2. In addition, the 5-FU administration schedule can be various time periods, such as up to six weeks, or as determined by a person of ordinary skill in the art to which the present disclosure belongs.

[0105] 3. Targeted therapy

[0106] Targeted therapy uses drugs that target specific defects present in cancer cells. Targeted therapy drugs are usually reserved for treating ovarian cancer that has come back after initial treatment or for cancer that has resisted other treatments. Doctors can test cancer cells to determine which targeted therapy is most likely to have an effect on the cancer. Exemplary targeted therapies include bevacizumab. Other treatments include olaparib (Lynparza), rucaparib (Rubraca), and niraparib (Zejula).

[0107] 4. Radiation therapy

[0108] In some embodiments, the additional treatment or previous treatment includes radiation, such as ionizing radiation. As used herein, "ionizing radiation" refers to radiation that includes particles or photons that have sufficient energy or can generate sufficient energy through nuclear interactions to produce ionization (gain or loss of electrons). Exemplary and preferred ionizing radiation is x-radiation. Means for delivering x-radiation to target tissues or cells are well known in the art.

[0109] In some embodiments, the amount of ionizing radiation is greater than 20 Gy and is administered in one dose. In some embodiments, the amount of ionizing radiation is 18 Gy and is administered in three doses. In some embodiments, the amount of ionizing radiation is at least, at most, or just 2, 4, 6, 8, 10, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 18, 19, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 40 Gy (or any range derived therefrom). In some embodiments, ionizing radiation is administered in at least, at most, or just 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 doses (or any range derived therefrom). When administered in more than one dose, the doses may be separated by about 1, 4, 8, 12 or 24 hours, or 1, 2, 3, 4, 5, 6, 7 or 8 days, or 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14 or 16 weeks, or any range derivable therein.

[0110] In some embodiments, the amount of IR can be provided as a total dose of IR, which is then administered in fractionated doses. For example, in some embodiments, the total dose is 50 Gy, administered in 10 fractions of 5 Gy each. In some embodiments, the total dose is 50-90 Gy, administered in 20-60 fractions of 2-3 Gy each. In some embodiments, the total dose of IR is at least, up to, or about 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75 , 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 125, 130, 135, 140 or 150 (or any range derivable therein). In some embodiments, the total dose is administered in divided doses of at least, at most, or exactly 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 15, 20, 25, 30, 35, 40, 45, or 50 Gy (or any range derivable therein). In some embodiments, at least, at most, or exactly 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, , 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100 divided doses (or any range derivable therein). In some embodiments, at least, at most, or exactly 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 (or any range derivable therein) divided doses are administered daily.In some embodiments, at least, at most, or exactly 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 (or any range derivable therein) divided doses are administered per week.

[0111] 5. Other treatments

[0112] Additional therapeutic agents include hormone therapy, leuprolide, goserelin (Zoladex), tamoxifen, or aromatase inhibitors.

[0113] 6. Immunotherapy

[0114] Immunotherapy designed to strengthen the body's natural defense against cancer can also be used. Generally, immunotherapeutics rely on the use of immune effector cells and molecules to target and destroy cancer cells. Immune effectors can be, for example, antibodies that are specific to certain markers on the surface of tumor cells. Individual antibodies can serve as effectors for treatment, or other cells can be recruited to actually achieve cell killing. Antibodies can also be conjugated to drugs or toxins (chemotherapeutic agents, radionuclides, ricin A chains, cholera toxin, pertussis toxin, etc.), and only serve as targeting agents. Alternatively, the effector can be a lymphocyte carrying a surface molecule that interacts directly or indirectly with a tumor cell target. Various effector cells include cytotoxic T cells and NK cells. Immunotherapeutic methods are further described below:

[0115] a. Checkpoint inhibitors and combination therapy

[0116] Embodiments of the present disclosure may include administration of immune checkpoint inhibitors, which are further described below.

[0117] (i) PD-1, PDL1, and PDL2 inhibitors

[0118] PD-1 can play a role in the tumor microenvironment where T cells encounter infection or tumor. Activated T cells upregulate PD-1 and continue to express PD-1 in peripheral tissues. Cytokines such as IFN-γ induce the expression of PDL1 on epithelial cells and tumor cells. PDL2 is expressed on macrophages and dendritic cells. The main role of PD-1 is to limit the activity of peripheral effector T cells during immune response and prevent excessive damage to tissues. The inhibitors of the present disclosure can block one or more functions of PD-1 and / or PDL1 activity.

[0119] Alternative names for "PD-1" include CD279 and SLEB2. Alternative names for "PDL1" include B7-H1, B7-4, CD274, and B7-H. Alternative names for "PDL2" include B7-DC, Btdc, and CD273. In some embodiments, PD-1, PDL1, and PDL2 are human PD-1, PDL1, and PDL2.

[0120] In some embodiments, a PD-1 inhibitor is a molecule that inhibits the binding of PD-1 to its ligand binding partner. In a specific aspect, the PD-1 ligand binding partner is PDL1 and / or PDL2. In another embodiment, a PDL1 inhibitor is a molecule that inhibits the binding of PDL1 to its binding partner. In a specific aspect, the PDL1 binding partner is PD-1 and / or B7-1. In another embodiment, a PDL2 inhibitor is a molecule that inhibits the binding of PDL2 to its binding partner. In a specific aspect, the PDL2 binding partner is PD-1. The inhibitor can be an antibody, an antigen binding fragment thereof, an immunoadhesin, a fusion protein, or an oligopeptide. Exemplary antibodies are described in U.S. Patent Nos. 8,735,553, 8,354,509, and 8,008,449, which are incorporated herein by reference. Other PD-1 inhibitors for use in the methods and compositions provided herein are known in the art, for example, as described in U.S. Patent Application Nos. US2014 / 0294898, US2014 / 022021, and US2011 / 0008369, all of which are incorporated herein by reference.

[0121] In some embodiments, the PD-1 inhibitor is an anti-PD-1 antibody (e.g., a human antibody, a humanized antibody, or a chimeric antibody). In some embodiments, the anti-PD-1 antibody is selected from nivolumab, pembrolizumab, and pilizumab. In some embodiments, the PD-1 inhibitor is an immunoadhesin (e.g., an immunoadhesin comprising an extracellular or PD-1 binding portion of PDL1 or PDL2 fused to a constant region (e.g., an Fc region of an immunoglobulin sequence). In some embodiments, the PDL1 inhibitor comprises AMP-224. Nivolumab, also known as MDX-1106-04, MDX-1106, ONO-4538, BMS-936558, and is an anti-PD-1 antibody described in WO2006 / 121168. Pembrolizumab, also known as MK-3475, Merck3475, Rambozumab, and SCH-900475, are anti-PD-1 antibodies described in WO2009 / 114335. Pilivizumab, also known as CT-011, hBAT or hBAT-1, is an anti-PD-1 antibody described in WO2009 / 101611. AMP-224, also known as B7-DCIg, is a PDL2-Fc fusion soluble receptor described in WO2010 / 027827 and WO2011 / 066342. Additional PD-1 inhibitors include MEDI0680, also known as AMP-514 and REGN2810.

[0122] In some embodiments, the immune checkpoint inhibitor is a PDL1 inhibitor, such as durvalumab, also known as MEDI4736, atezolizumab, also known as MPDL3280A, avelumab, also known as MSB00010118C, MDX-1105, BMS-936559 or a combination thereof. In some aspects, the immune checkpoint inhibitor is a PDL2 inhibitor, such as rHIgM12B7.

[0123] In some embodiments, the inhibitor comprises the heavy and light chain CDRs or VRs of nivolumab, pembrolizumab or pilizumab. Thus, in one embodiment, the inhibitor comprises the CDR1, CDR2 and CDR3 domains of the VH region of nivolumab, pembrolizumab or pilizumab, and the CDR1, CDR2 and CDR3 domains of the VL region of nivolumab, pembrolizumab or pilizumab. In another embodiment, the antibody competes for binding to the same epitope as the above-mentioned antibodies on PD-1, PDL1 or PDL2 and / or binds to the same epitope as the above-mentioned antibodies on PD-1, PDL1 or PDL2. In another embodiment, the antibody has at least about 70, 75, 80, 85, 90, 95, 97 or 99% (or any range that can be derived therein) of variable region amino acid sequence identity with the above-mentioned antibodies.

[0124] (i) CTLA-4, B7-1, and B7-2

[0125] Another immune checkpoint that can be targeted in the methods provided herein is cytotoxic T lymphocyte-associated protein 4 (CTLA-4), also known as CD152. The Genbank accession number of the complete cDNA sequence of human CTLA-4 is L15006. CTLA-4 is present on the surface of T cells and acts as a "close" switch when bound to B7-1 (CD80) or B7-2 (CD86) on the surface of antigen-presenting cells. CTLA4 is a member of the immunoglobulin superfamily, which is expressed on the surface of helper T cells and transmits inhibitory signals to T cells. CTLA4 is similar to T cell co-stimulatory protein CD28, and both molecules bind to B7-1 and B7-2 on antigen-presenting cells. CTLA-4 transmits inhibitory signals to T cells, while CD28 transmits stimulatory signals. Intracellular CTLA-4 is also present in regulatory T cells and may be important for their function. T cell activation by T cell receptors and CD28 leads to increased expression of CTLA-4 (inhibitory receptor for B7 molecules). The inhibitors of the present disclosure can block one or more functions of CTLA-4, the activity of B7-1 and / or B7-2. In some embodiments, the inhibitor blocks the interaction between CTLA-4 and B7-1. In some embodiments, the inhibitor blocks the interaction between CTLA-4 and B7-2.

[0126] In some embodiments, the immune checkpoint inhibitor is an anti-CTLA-4 antibody (e.g., a human antibody, a humanized antibody, or a chimeric antibody), an antigen-binding fragment thereof, an immunoadhesin, a fusion protein, or an oligopeptide.

[0127] Anti-human CTLA-4 antibodies (or VH and / or VL domains derived therefrom) suitable for use in the methods of the present invention can be produced using methods well known in the art. Alternatively, anti-CTLA-4 antibodies known in the art can be used. For example, anti-CTLA-4 antibodies disclosed in the following can be used in the methods disclosed herein: US 8,119,129, WO 01 / 14424, WO 98 / 42752; WO 00 / 37504 (CP675,206, also known as tremelimumab; formerly known as ticilimumab), U.S. Pat. No. 6,207,156; and Hurwitz et al., 1998. The teachings of each of the aforementioned publications are incorporated herein by reference. Antibodies that compete with any of these antibodies known in the art that bind to CTLA-4 can also be used. Humanized CTLA-4 antibodies are described, for example, in International Patent Application Nos. WO2001 / 014424, WO2000 / 037504, and U.S. Patent No. 8,017,114; all of which are incorporated herein by reference.

[0128] Another anti-CTLA-4 antibody used as a checkpoint inhibitor in the methods and compositions of the present disclosure is ipilimumab (also known as 10D1, MDX-010, MDX-101, and ) or antigen-binding fragments and variants thereof (see, e.g., WO0 1 / 14424).

[0129] In some embodiments, the inhibitor comprises the heavy and light chain CDRs or VRs of tsimab or ipilimumab. Therefore, in one embodiment, the inhibitor comprises the CDR1, CDR2 and CDR3 domains of the VH region of tsimab or ipilimumab, and the CDR1, CDR2 and CDR3 domains of the VL region of tsimab or ipilimumab. In another embodiment, the antibody competes for binding to the same epitope as the above-mentioned antibodies on PD-1, B7-1 or B7-2 and / or binds to the same epitope as the above-mentioned antibodies on PD-1, B7-1 or B7-2. In another embodiment, the antibody has at least about 70, 75, 80, 85, 90, 95, 97 or 99% (or any range that can be derived therein) of variable region amino acid sequence identity with the above-mentioned antibodies.

[0130] b. Inhibition of co-stimulatory molecules

[0131] In some embodiments, immunotherapy comprises inhibitors of costimulatory molecules. In some embodiments, the inhibitors include inhibitors of B7-1 (CD80), B7-2 (CD86), CD28, ICOS, OX40 (TNFRSF4), 4-1BB (CD137; TNFRSF9), CD40L (CD40LG), GITR (TNFRSF18) and combinations thereof. Inhibitors include inhibitory antibodies, polypeptides, compounds and nucleic acids.

[0132] c. Dendritic cell therapy

[0133] Dendritic cell therapy stimulates an anti-tumor response by having dendritic cells present tumor antigens to lymphocytes, thereby activating lymphocytes and causing them to kill other cells presenting the antigens. Dendritic cells are antigen presenting cells (APCs) in the mammalian immune system. In cancer treatment, they help target cancer antigens. An example of a dendritic cell-based cellular cancer therapy is sipuleucel-T.

[0134] One way to induce dendritic cells to present tumor antigens is through vaccination with autologous tumor lysates or short peptides (small parts of proteins that correspond to protein antigens on cancer cells). These peptides are often used in combination with adjuvants (highly immunogenic substances) to increase immune and anti-tumor responses. Other adjuvants include proteins or other chemicals that attract and / or activate dendritic cells, such as granulocyte macrophage colony-stimulating factor (GM-CSF).

[0135] Dendritic cells can also be activated in vivo by causing tumor cells to express GM-CSF. This can be achieved by genetically engineering tumor cells to produce GM-CSF or by infecting tumor cells with an oncolytic virus that expresses GM-CSF.

[0136] Another strategy is to remove dendritic cells from the patient's blood and activate them in vitro. Dendritic cells are activated in the presence of tumor antigens, which can be single tumor-specific peptides / proteins or tumor cell lysates (a solution of broken tumor cells). These cells (along with an optional adjuvant) are injected and an immune response is elicited.

[0137] Dendritic cell therapy involves the use of antibodies that bind to receptors on the surface of dendritic cells. Antigens can be added to the antibodies and can induce dendritic cells to mature and provide immunity to the tumor. Dendritic cell receptors such as TLR3, TLR7, TLR8 or CD40 have been used as antibody targets.

[0138] d.CAR-T cell therapy

[0139] Chimeric antigen receptors (CARs, also called chimeric immunoreceptors, chimeric T-cell receptors, or artificial T-cell receptors) are engineered receptors that combine new specificities with immune cells to target cancer cells. Typically, these receptors graft the specificity of a monoclonal antibody onto a T cell. The reason the receptors are called chimeric receptors is because they fuse parts from different sources. CAR-T cell therapy refers to the use of such transformed cells for cancer treatment.

[0140] The basic principle of CAR-T cell design involves recombinant receptors that combine antigen binding and T cell activation functions. The general premise of CAR-T cells is to artificially generate T cells that target markers present on cancer cells. Scientists can remove T cells from a person, genetically modify them, and then put them back into the patient to enable them to attack cancer cells. Once a T cell is modified into a CAR-T cell, it can act as a "living drug." CAR-T cells establish a link between an extracellular ligand recognition domain and an intracellular signaling molecule, thereby activating the T cell. The extracellular ligand recognition domain is usually a single-chain variable fragment (scFv). An important aspect of the safety of CAR-T cell therapy is how to ensure that only cancer tumor cells are targeted, and not normal cells. The specificity of the CAR-T cell is determined by the choice of the targeted molecule.

[0141] Exemplary CAR-T therapies include Tisagenlecleucel (Kymriah) and Axicabtageneciloleucel (Yescarta). In some embodiments, the CAR-T therapy targets CD19.

[0142] e. Cytokine therapy

[0143] Cytokines are proteins produced by many types of cells present within tumors. They can modulate the immune response. Tumors often use them to allow them to grow and reduce the immune response. These immunomodulatory effects make them useful as drugs to elicit an immune response. Two commonly used cytokines are interferons and interleukins.

[0144] Interferons are produced by the immune system. They are usually involved in antiviral responses but can also be used to treat cancer. They are divided into three categories: type I (IFNα and IFNβ), type II (IFNγ), and type III (IFNλ).

[0145] Interleukins have a variety of immune system effects. IL-2 is an exemplary interleukin cytokine therapy.

[0146] f. Adoptive T cell therapy

[0147] Adoptive T-cell therapy is a form of passive immunization performed by infusing T cells (adoptive cell transfer). They are present in the blood and tissues and are usually activated when they detect a foreign pathogen. Specifically, T cells become activated when their surface receptors encounter cells that display parts of foreign proteins on their surface antigens. These can be infected cells or antigen-presenting cells (APCs). They are present in both normal tissues and tumor tissues, where they are called tumor-infiltrating lymphocytes (TILs). They are activated by the presence of APCs such as dendritic cells that present tumor antigens. Although these cells can attack tumors, the environment within the tumor is highly immunosuppressive, preventing immune-mediated tumor death.

[60]

[0148] A variety of methods have been developed to generate and obtain tumor-targeted T cells. T cells specific for tumor antigens can be removed from tumor samples (TILs) or filtered from the blood. They are then activated and cultured ex vivo and the resulting products are re-infused. Activation can be performed by gene therapy or by exposing T cells to tumor antigens.

[0149] In some embodiments, the treatments described herein are excluded from the methods and / or compositions of the present disclosure. In addition, embodiments of the present disclosure include patients who have previously received treatments described herein, are currently receiving treatments described herein, or have not yet received treatments described herein. In some embodiments, the patient is a patient who has been determined to be resistant to treatments described herein. In some embodiments, the patient is a patient who has been determined to be sensitive to treatments described herein.

[0150] C. Monitoring

[0151] The method of the present disclosure may include preoperative or postoperative testing and examination to monitor the ovaries. Monitoring methods may include pelvic examination, imaging examinations such as ultrasound or CT scans, or abdominal and pelvic and blood tests to check organ function and overall health. In addition, ovarian cancer markers such as CA125 may be tested in the method of the present disclosure. It is also contemplated that the method of the present disclosure may include one or more monitoring techniques. Certain embodiments exclude the CA125 level of the test patient.

[0152] D. ROC analysis

[0153] In statistics, a receiver operating characteristic (ROC) or ROC curve is a graph that illustrates the performance of a binary classification system as the discrimination threshold of the binary classification system changes. The curve is created by plotting the true positive rate relative to the false positive rate at various threshold settings. (The true positive rate is also called sensitivity in biomedical informatics, or recall in machine learning. The false positive rate is also called the false alarm rate and can be calculated as 1-specificity). Therefore, the ROC curve is a function of sensitivity as a false alarm rate. In general, if the probability distribution for detection and false alarms is known, the ROC curve can be generated by plotting the cumulative distribution function of the probability of detection on the y-axis (the area under the probability distribution from negative infinity to positive infinity) relative to the cumulative distribution function of the probability of false alarms on the x-axis.

[0154] ROC analysis provides a tool to select the best possible model and discard suboptimal models independently of cost context or class distribution (and prior to concretization).ROC analysis relates in a direct and natural way to cost / benefit analysis of diagnostic decisions.

[0155] The ROC curve was first developed by electrical engineers and radar engineers during World War II to detect hostile targets on the battlefield and was soon introduced into the field of psychology to explain the perceptual detection of stimuli. Since then, ROC analysis has been used for decades in medicine, radiology, biostatistics, and other fields, and is increasingly used in machine learning and data mining research.

[0156] ROC is also called a relative operating characteristic curve because it is a comparison of two operating characteristics (TPR and FPR) as the standard changes. ROC analysis curves are known in the art and are described in Metz CE (1978) Basic Principles of ROC analysis. Seminars in Nuclear Medicine 8: 283-298; Youden WJ (1950) An index for rating diagnostic tests. Cancer 3: 32-35; Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clinical Chemistry 39: 561-577; and Greiner M, Pfeiffer D, Smith RD (2000) Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Preventive Veterinary Medicine 45: 23-41, which are hereby incorporated by reference in their entirety. ROC analysis can be used to create cutoff values ​​for predictive and / or diagnostic purposes.

[0157] IV. Sample Preparation

[0158] In some aspects, the method involves obtaining a sample from a subject. The acquisition method provided herein may include a biopsy method, such as fine needle aspiration, core needle biopsy, vacuum-assisted biopsy, incisional biopsy, excisional biopsy, punch biopsy, scrape biopsy or skin biopsy. In certain embodiments, a sample is obtained from a biopsy of esophageal tissue by any of the aforementioned biopsy methods. In other embodiments, the sample can be obtained from any tissue provided herein, and the tissue includes but is not limited to non-cancerous or cancerous tissue and non-cancerous or cancerous tissue from the following: serum, gallbladder, mucosa, skin, heart, lung, breast, pancreas, blood, liver, muscle, kidney, smooth muscle, bladder, colon, intestine, brain, prostate, esophagus or thyroid tissue. Alternatively, the sample can be obtained from any other source, including but not limited to blood, sweat, hair follicles, cheek tissue, tears, menstruation, feces or saliva. In some aspects of the current method, any medical professional, such as a doctor, nurse or medical technician, can obtain a biological sample for testing. Further, a biological sample can be obtained without the help of a medical professional.

[0159] The sample may include, but is not limited to, tissue, cells, or biological material from cells or derived from cells of a subject. The biological sample may be a heterogeneous or homogeneous population of cells or tissue. The biological sample may be obtained using any method known in the art that can provide a sample suitable for the analytical methods described herein. The sample may be obtained by non-invasive methods, including but not limited to: scraping the skin or cervix, swabbing the cheek, collecting saliva, collecting urine, collecting feces, collecting menstruation, tears, or semen.

[0160] Sample can be obtained by methods known in the art. In certain embodiments, sample is obtained by biopsy. In other embodiments, sample is obtained by wiping, endoscopy, scraping, phlebotomy or any other method known in the art. In some cases, the components of the kit of the present method can be used to obtain, store or transport samples. In some cases, multiple samples, such as multiple esophageal samples, can be obtained by methods described herein for diagnosis. In other cases, multiple samples, such as one or more samples from a type of tissue (such as esophagus) and one or more samples from another sample (such as serum) can be obtained by the method for diagnosis. In some cases, multiple samples can be obtained at the same or different times, such as one or more samples from a type of tissue (such as, esophagus) and one or more samples from another sample (such as, serum). Samples obtained at different times can be stored and / or analyzed by different methods. For example, samples can be obtained and analyzed by conventional staining methods or any other cytological analysis methods.

[0161] In some embodiments, biological samples can be obtained by doctors, nurses or other medical professionals such as medical technicians, endocrinologists, cytologists, blood drawing technicians, radiologists or thoracic surgeons. Medical professionals can specify the appropriate test or analysis to be performed on the sample. In some aspects, the company drawing the molecular spectrum can consult which analysis or test is the most suitable for the specified. In other aspects of the current method, the patient or subject can obtain the biological sample for testing without the assistance of a medical professional, such as obtaining a whole blood sample, a urine sample, a fecal sample, a cheek sample or a saliva sample.

[0162] In other cases, the sample is obtained by an invasive procedure including, but not limited to, biopsy, needle aspiration, endoscopy, or phlebotomy. Needle aspiration methods may further include fine needle aspiration, core needle biopsy, vacuum-assisted biopsy, or large core biopsy. In some embodiments, multiple samples may be obtained by the methods herein to ensure a sufficient amount of biological material.

[0163] The general method for obtaining biological samples is also known in the art. Publications such as Ramzy, Ibrahim Clinical Cytopathology and Aspiration Biopsy 2001 describe the general method for biopsy and cytological methods, which are incorporated herein by reference in their entirety. In one embodiment, the sample is a fine needle aspirate of esophagus or suspected esophageal tumor or vegetation. In some cases, the fine needle aspiration sampling process can be guided by using ultrasound, X-ray or other imaging equipment.

[0164] In some embodiments of the present method, the company that draws the molecular profile can obtain the biological sample directly from the subject, medical professional, third party, or a kit provided by the company that draws the molecular profile or a third party. In some cases, the biological sample can be obtained by the company that draws the molecular profile after the subject, medical professional, or third party obtains the biological sample and sends it to the company that draws the molecular profile. In some cases, the company that draws the molecular profile can provide suitable containers and excipients for storing and transporting the biological sample to the company that draws the molecular profile.

[0165] In some embodiments of the methods described herein, medical professionals do not need to participate in initial diagnosis or sample collection. Individuals can alternatively obtain samples by using over-the-counter (OTC) test kits. The OTC test kit may include a device for obtaining the sample described herein, a device for storing the sample for inspection, and instructions for the correct use of the test kit. In some cases, the price of the test kit includes the drawing of molecular spectra services. In other cases, the drawing of molecular spectra services requires a separate charge. The sample used by the enterprise suitable for drawing molecular spectra can be any material containing tissues, cells, nucleic acids, genes, gene fragments, expression products, gene expression products, or gene expression product fragments of the individual to be tested. A method for determining sample suitability and / or suitability is provided.

[0166] In some embodiments, the subject can be entrusted to a specialist, such as an oncologist, surgeon, or endocrinologist. The specialist can also obtain the biological sample for testing, or entrust the individual to a testing center or laboratory to submit the biological sample. In some cases, a medical professional can entrust the subject to a testing center or laboratory to submit the biological sample. In other cases, the subject can provide the sample. In some cases, a company that draws a molecular profile can obtain the sample.

[0167] V. Evaluation of biomarker levels

[0168] In some aspects, a meta-analysis of expression or activity can be performed. In statistics, a meta-analysis combines the results of several studies for a set of related research hypotheses. This is usually done by determining a universal measure of effect size, which is modeled using a meta-regression form. Typically, three types of models can be distinguished in the literature of meta-analysis: simple regression, fixed-effect meta-regression, and random-effect meta-regression. When the study characteristics are controlled, the overall mean obtained can be regarded as a meta-effect size, which is a more effective estimate of the true effect size compared to those obtained from a single study conducted under a given single set of assumptions and conditions. In this context, the meta-gene expression value should be understood as the median of the normalized expression of a biomarker gene or activity. The normalization of biomarker gene expression is preferably achieved by dividing the expression level of a single marker gene to be normalized by the corresponding single median expression of the marker gene, wherein the median expression is preferably calculated by multiple measurements of the corresponding gene in a sufficiently large group of test individuals. The test group preferably includes at least 3, 10, 100, 200, 1000 or more individuals, including all values ​​and ranges thereof. Dataset-specific biases can be eliminated or minimized, allowing multiple datasets to be combined for meta-analysis (see Sims et al., BMC Medical Genomics (1:42), 1-14, 2008, incorporated herein by reference in its entirety).

[0169] The calculation of metagene expression values ​​is performed by (i) determining gene expression values ​​for at least two, preferably more genes, (ii) "normalizing" the gene expression value of each individual gene by dividing the expression value by a coefficient that approximates the median expression value of each gene in a representative breast cancer cohort, and (iii) calculating the median of the group of normalized gene expression values.

[0170] If the expression level of a gene in a cell type is at least about 2-fold, 5-fold, 10-fold, 100-fold, 1000-fold, or 10000-fold (or any range derivable therein) higher than the expression level in a reference cell type or a mixture of reference cell types, then it is understood that the gene is specifically expressed in a particular cell type. Reference cell types include noncancerous tissue cells or heterogeneous cancer populations.

[0171] In some algorithms, first determine a suitable threshold level for the marker gene. Suitable threshold levels can be determined by measuring the marker gene expression in multiple individuals from the test group. The median expression of the marker gene in the multiple expression measurements is considered to be a suitable threshold.

[0172] Comparison of multiple marker genes with threshold levels can be performed as follows: 1. Compare a single marker gene to its corresponding threshold level. 2. Determine the number of marker genes whose expression levels are above their corresponding threshold level. 3. If the marker gene expression is above its corresponding threshold level, then the expression level of the marker gene is considered "above the threshold level."

[0173] Some embodiments include determining that the measured expression level is above a predetermined amount of a reference expression level, below a predetermined amount of a reference expression level, increased relative to a predetermined amount of a reference expression level, decreased relative to a predetermined amount of a reference expression level, equal to a predetermined amount of a reference expression level, or within a predetermined amount of a reference expression level. In some embodiments, the higher, lower, increased, or decreased expression level is at least 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 50, 100, 150, 200, 250, 500, or 1000 times (or any range derivable therein) or at least 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, or 900% or any range derivable therein as compared to the reference level. These values ​​can represent predetermined threshold levels, and some embodiments include determining whether the measured expression level is a predetermined amount higher or lower than a reference level. In some embodiments, the expression level can be defined as "low" or "high", which means that the patient expresses a certain gene or miRNA at a level relative to a reference level or at a level with a range of reference levels determined by multiple samples that meet specific criteria. Levels or ranges of levels in multiple control samples are such examples. In some embodiments, the specific level or predetermined threshold is at, below or above 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 98, 99, 100%, or any range derivable therein. In addition, a threshold level can be derived from a group of individuals meeting a particular criterion.The number in a group may be at least or at most 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 500, 510, 520, 530, 540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670, 680, 690, 701 410,420,430,440,441,450,460,470,480,490,500,510,520,530,540,550,560,570,580,590,600,700,800,900,1000,1100,1200,1300,1400,1500,1600,1700,1800,1900,2000 or more (or any range therein that can be derived). If the expression level measured is within a certain amount of the reference expression level, then it can be considered that the expression level of the measurement is equal to the reference expression level, and the amount can be a predetermined amount. For example, when using a classifier to determine the molecular subtype of transfer, this may be the case. The predetermined amount may be 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45 or 50% of the reference level or any range derivable therein.

[0174] For any comparison of gene or miRNA expression levels to an average expression level or reference expression level, the comparison will be on a gene-by-gene and miRNA-by-miRNA basis. For example, if the expression levels of gene A, gene B, and miRNA X in a patient's cancerous sample are measured, then their comparison to the average expression level in the cancerous samples of a patient cohort will involve: comparing the expression level of gene A in the patient's cancerous sample to the average expression level of gene A in the cancerous samples of the patient cohort, comparing the expression level of gene B in the patient's sample to the average expression level of gene B in the samples of the patient cohort, and comparing the expression level of miRNA X in the patient's metastasis to the average expression level of miRNA X in the cancerous samples of the patient cohort. Similarly, comparisons involving determining whether the expression level measured in the patient's sample is within a predetermined amount of the average expression level or reference expression level are performed on a gene-by-gene and miRNA-by-miRNA basis.

[0175] VI. Nucleic Acid Analysis

[0176] Aspects of the method include analyzing nucleic acids to determine expression levels. Arrays can be used to detect differences between two samples. Applications of particular consideration include determining and / or quantifying differences between miRNA from normal samples and miRNA from abnormal samples, differences between cancerous conditions and non-cancerous conditions, or differences between two differently processed samples. In addition, miRNA can be compared between samples that are considered susceptible to a particular disease or condition and samples that are considered not susceptible to the disease or condition or resistant to it. Abnormal samples are samples that exhibit (multiple) phenotypic characteristics of a disease or condition, or are considered to be abnormal samples with respect to the disease or condition. It can be compared with normal cells with respect to the disease or condition. Phenotypic characteristics include symptoms of a certain disease or condition or susceptibility to it, and the composition of the disease or condition may be or may not be genetic, or caused by hyperproliferation or neoplastic cells or multiple hyperproliferation or neoplastic cells.

[0177] Arrays include solid supports, to which nucleic acid probes are attached. Arrays typically include multiple different nucleic acid probes, which are coupled to substrate surfaces at different known locations. These arrays, also referred to as "microarrays" or commonly known as "chips", have been generally described in the art, for example, No. 5,143,854, No. 5,445,934, No. 5,744,305, No. 5,677,195, No. 6,040,193, No. 5,424,186 U.S. Patents and Fodor et al., 1991, each of which is incorporated by reference as a whole for all purposes. The technology of synthesizing these arrays using mechanical synthesis methods is described in, for example, No. 5,384,261 U.S. Patents, which is incorporated by reference as a whole for all purposes. Although planar array surfaces have been used in some aspects, the array can be manufactured on almost any shape of surface or even multiple surfaces. The arrays can be nucleic acids on beads, gels, polymer surfaces, fibers such as fiber optics, glass, or any other suitable substrate, see U.S. Pat. Nos. 5,770,358, 5,789,162, 5,708,153, 6,040,193, and 5,800,992, which are incorporated herein in their entirety for all purposes.

[0178] In addition to the use of arrays and microarrays, it is contemplated that a number of differential analyses can be employed to analyze miRNA, its activity and its effects. Such analyses include, but are not limited to, nucleic acid amplification, polymerase chain reaction, quantitative PCR, RT-PCR, in situ hybridization, Northern hybridization, hybridization protection analysis (HPA) (GenProbe), branched DNA (bDNA) analysis (Chiron), rolling circle amplification (RCA), single molecule hybridization detection (US Genomics), Invader analysis (ThirdWave Technologies) and / or bridge ligation analysis (Genaco).

[0179] VII. Administration of Therapeutic Compositions

[0180] Treatment provided herein may include administering a combination of therapeutic agents, such as a first cancer treatment and a second cancer treatment. Treatment may be administered in any suitable manner known in the art. For example, the first and second cancer treatments may be administered sequentially (at different times) or simultaneously (at the same time). In some embodiments, the first and second cancer treatments are administered in separate compositions. In some embodiments, the first and second cancer treatments are in the same composition.

[0181] Embodiments of the present disclosure relate to compositions and methods comprising therapeutic compositions. Different treatments can be given in one composition or in more than one composition (e.g., 2 compositions, 3 compositions, or 4 compositions). Various combinations of agents can be used, for example, the first cancer treatment is "A" and the second cancer treatment is "B":

[0182] A / B / AB / A / BB / B / AA / A / BA / B / BB / A / AA / B / B / BB / A / B / BB / B / B / AB / B / A / BA / A / B / BA / B / A / BA / B / B / AB / B / A / AB / A / B / AB / A / A / BA / A / A / BB / A / A / AA / B / A / AA / A / B / A

[0183] The therapeutic agents of the present disclosure may be administered by the same route of administration or by different routes of administration. In some embodiments, cancer treatment is administered intravenously, intramuscularly, subcutaneously, topically, orally, transdermally, intraperitoneally, intraorbitally, by implantation, by inhalation, intrathecally, intraventricularly, or intranasally. In some embodiments, antibiotics are administered intravenously, intramuscularly, subcutaneously, topically, orally, transdermally, intraperitoneally, intraorbitally, by implantation, by inhalation, intrathecally, intraventricularly, or intranasally. Appropriate dosages may be determined based on the type of disease to be treated, the severity and course of the disease, the individual's clinical condition, the individual's clinical history and response to treatment, and the judgment of the attending physician.

[0184] Treatment may include various "unit doses". A unit dose is defined as a therapeutic composition containing a predetermined amount. The amount administered, as well as the specific route and formulation, are within the discretion of those skilled in the clinical art. A unit dose need not be administered as a single injection, but may include continuous infusion over a set period of time. In some embodiments, a unit dose includes a single administrable dose.

[0185] The amount given according to the number of treatments and the unit dose depends on the desired therapeutic effect. An effective dose is understood to mean the amount required to achieve a specific effect. In practice in certain embodiments, it is expected that a dose within the range of 10 mg / kg to 200 mg / kg may affect the protective ability of these agents. Thus, contemplated dosages include dosages of about 0.1, 0.5, 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, and 200, 300, 400, 500, 1000 μg / kg, mg / kg, μg / day, or mg / day, or any range available therein. In addition, such dosages may be given multiple times in a day and / or over several days, weeks, or months.

[0186] In certain embodiments, the effective dose of the pharmaceutical composition is a dose that can provide a blood level of about 1 μM to 150 μM. In another embodiment, the effective dose provides a blood level of about 4 μM to 100 μM; or about 1 μM to 100 μM; or about 1 μM to 50 μM; or about 1 μM to 40 μM; or about 1 μM to 30 μM; or about 1 μM to 20 μM; or about 1 μM to 10 μM; or about 10 μM to 150 μM; or about 10 μM to 100 μM; or about 10 μM to 50 μM; or about 25 μM to 150 μM; or about 25 μM to 100 μM; or about 25 μM to 50 μM; or about 50 μM to 150 μM; or about 50 μM to 100 μM (or any range available therein). In other embodiments, the dose can provide a blood level of the agent resulting from administration of the therapeutic agent to a subject of about, at least about, or at most about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75 6, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100 μM or any range available therein. In certain embodiments, the therapeutic agent administered to the subject is metabolized in vivo to a metabolized therapeutic agent, in which case the blood level can refer to the amount of the agent. Alternatively, in the case where the therapeutic agent is not metabolized by the subject, the blood levels discussed herein can refer to the unmetabolized therapeutic agent.

[0187] The precise amount of the therapeutic composition also depends on the judgment of the practitioner and is unique to each individual. Factors affecting dosage include the patient's physical and clinical state, the route of administration, the intended therapeutic goal (relief of symptoms vs. cure), and the potency, stability, and toxicity of the specific therapeutic substance or other treatment that the subject may be receiving.

[0188] Those skilled in the art will understand and appreciate that dosage units of μg / kg or mg / kg body weight can be converted and expressed in similar concentration units of μg / ml or mM (blood levels), for example 4 μM to 100 μM. It should also be understood that uptake is species and organ / tissue dependent. Applicable conversion factors and physiological assumptions related to uptake and concentration measurements are well known and will allow those skilled in the art to convert one concentration measurement to another and make reasonable comparisons and inferences about the dosages, efficacies and results described herein.

[0189] VIII. Kit

[0190] Some aspects of the present invention also relate to a kit comprising a composition of the present invention or a composition for implementing the method of the present invention. In some embodiments, the kit can be used to evaluate one or more miRNA molecules. In certain embodiments, the kit contains, contains at least or contains at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 100, 500, 1,000 or more probes, synthetic molecules or inhibitors, or any value or range and combination that can be derived therefrom. In some embodiments, there is a kit for evaluating the activity of biomarkers in cells.

[0191] The kit may comprise components that may be individually packaged or placed in a container, such as a tube, bottle, vial, syringe or other suitable container means.

[0192] Individual components may also be provided in a concentrated form in the kit; in some embodiments, the components are provided individually at the same concentration as the other components in solution. The concentration of the components may be provided at 1x, 2x, 5x, 10x or 20x or greater.

[0193] Included as part of the present disclosure are kits for using the probes, synthetic nucleic acids, non-synthetic nucleic acids and / or inhibitors of the present disclosure for predictive or diagnostic applications. Specifically contemplated are any such molecules corresponding to any biomarker identified herein.

[0194] In certain aspects, certain kit embodiments include negative and / or positive control nucleic acids, probes, and inhibitors. Control molecules can be used to verify transfection efficiency and / or control transfection-induced changes in cells.

[0195] It is contemplated that any method or composition described herein may be implemented with respect to any other method or composition described herein, and that the different embodiments may be combined.The claims originally filed are intended to cover multiple claims that refer to any filed claim or combination of filed claims.

[0196] It is contemplated that any embodiments of the invention directed to a particular biomarker also encompass embodiments directed to a biomarker whose sequence is at least 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99% identical to the mature sequence of a specified miRNA.

[0197] Embodiments of the present disclosure include a kit for analyzing a pathology sample by evaluating the biomarker profile of the sample, the kit comprising two or more biomarker probes in a suitable container device, wherein the biomarker probe detects one or more biomarkers determined herein. The kit may also include a reagent for labeling the nucleic acid in the sample. The kit may also include a labeling agent, the labeling agent including at least one of an amine-modified nucleotide, a poly (A) polymerase, and a poly (A) polymerase buffer. The labeling agent may include an amine-reactive dye. Example

[0198] The following examples are included to illustrate preferred embodiments of the present invention. It will be appreciated by those skilled in the art that the techniques disclosed in the following examples represent techniques that the inventors have found to work well in the practice of the present invention and therefore can be considered to constitute preferred modes for its practice. However, it will be appreciated by those skilled in the art based on this disclosure that many changes may be made in the disclosed specific embodiments without departing from the spirit and scope of the present invention and still similar or similar results may be obtained.

[0199] Example 1 OCaMIR non-invasive early diagnosis test for ovarian cancer: multi-cohort retrospective and prospective analysis

[0200] Ovarian cancer (OC) remains a deadly disease with the highest mortality rate among other gynecological cancers due to the lack of effective screening tools and early detection biomarkers. So far, no attempt has been made to discover biomarkers using early-stage OC patients. MicroRNAs (miRNAs) are recognized as an important tool for developing non-invasive biomarkers in various cancers, including ovarian cancer.

[0201] The inventors have performed small RNA sequencing on fresh frozen primary tissue samples collected from stage I OC (n=31) and identified a set of eight miRNAs (OCaMIRs) for early detection of OC using limma. Subsequently, OCaMIRs were independently validated in 543 tissue samples from TCGA (The Cancer Genome Atlas) and in n=87 from the GSE65819 tissue cohort. In addition, by adopting a training model based on multivariate logistic regression, the inventors independently validated OCaMIRs in four retrospective serum cohorts (GSE106817, n=640; GSE31568, n=85; GSE113486, n=140; Czech Republic cohort, n=182) and one prospective serum cohort (N=131, West China cohort). OCaMIR analysis of the Czech Republic and West China cohorts was performed using Taqman-based RT-PCR analysis. In addition, the specificity of OCaMIR was tested by analyzing other cancer / disease serum samples from the GSE31568 cohort of n=384. The diagnostic accuracy of OCaMIR was determined by plotting ROC curves with 1000 cross-validations. The cost-effectiveness analysis of OCaMIR high-risk population screening was performed using a Markov model.

[0202] OCaMIR had an AUC of 0.88 for detecting stage I OC in the tissue discovery cohort, 0.90 for stage I in TCGA, 0.99 for all stages, and 0.98 in the GSE65819 independent tissue validation cohort. In the retrospective serum validation cohort, the AUCs for training and validation of OCaMIR based on logistic regression were 0.86 (GSE106817), 0.84 (GSE31568), 0.87 (GSE113486), and 0.91 (Czech Republic cohort). Excitingly, the prospective validation of OCaMIR had an AUC of 0.92 in the West China cohort. In addition, OCaMIR outperformed CA125 and was highly specific for OC detection. Large-scale screening using the inventors' miRNA signature is cost-effective relative to the current practice of not screening [ICER = CNY 5139.4 / QALY].

[0203] In multiple independent cohorts across different ethnic backgrounds, OCaMIR demonstrated excellent accuracy in detecting OC at both tissue and serum levels, providing an excellent tool for early detection and population screening of OC. Exciting prospective validation results using RT-PCR-based assays in independent laboratories highlight the clinical applicability of this assay. OCaMIR's performance in detecting stage I OC, superiority over CA125, high OC specificity, and cost-effectiveness for screening set this assay apart from previously established biomarkers.

[0204] A. Patients and Methods

[0205] 1. Clinical samples

[0206] For the initial tissue discovery phase, the inventors have obtained a total of 22 freshly frozen stage I high-grade serous epithelial ovarian cancer samples and 9 adjacent normal fallopian tube tissues from the Asterand biobank. Tumors were staged according to FIGO (The International Federation of Gynecology and Obstetrics) criteria, and all pathological data were reviewed by pathologists. All tissue samples included had greater than 70% tumor cellularity. For independent tissue validation, the inventors used TCGA and GSE65819

[26] public cohorts, whose level 3 or pre-processed miRNA expression values ​​and clinical data were downloaded from Broad GDAC Firehose and Gene Expression Omnibus (GEO), respectively. For independent serum validation, the inventors have used three previously published serum cohorts and two international independent external retrospective and prospective validation cohorts. Public cohorts include GSE106817

[13] , GSE113486

[27] , and GSE31568

[28] , whose pre-processed miRNA expression values ​​and clinical data were downloaded from GEO. Retrospective serum cohorts were collected from the Department of Comprehensive Cancer Care, Masaryk Memorial Cancer Institute, Faculty of Medicine, Masaryk University, Brno, Czech Republic, and sent to us on dry ice for RT-PCR-based validation. All samples were collected before treatment and at the time of initial diagnosis of OC. In addition to clinical and epidemiological data, the inventors also received CA125 levels tested at the Masaryk Memorial Cancer Institute. Healthy samples from the Masaryk Memorial Cancer Institute were collected from the screening population and confirmed to be free of underlying disease or cancer. Prospective serum samples were collected from patients with new diagnoses and before surgery according to standard operating procedures, and miRNA expression was analyzed by RT-PCR at West China Hospital. Written informed consent was obtained from each patient, and the institutional review boards of all participating centers approved the study. Table 1 lists the clinical data of all patient cohorts used in this study. In this study, the inventors analyzed a total of 661 tissue samples and 1142 serum samples. Our study followed the Transparent Reporting of Multivariate Prediction Models for Individual Prediction or Diagnosis (TRIPOD) guidelines

[29] , and this document and detailed information are provided as Supplementary Methods file 1.

[0207] 2. Tissue RNA Isolation and Small RNA Sequencing

[0208] Total RNA was isolated from freshly frozen OC tissue using the AllPrep DNA / RNA / miRNA universal kit according to the manufacturer's recommendations (Qiagen, USA). In brief, NGS library construction of miRNA from tissue was performed using a modified protocol for the Truseq small RNA kit (Illumina), with a total RNA input of up to 200 ng. The quality of each library was assessed using a high-sensitivity DNA kit (Agilent). The size of the library (about 148 nt) was selected individually by gel electrophoresis using a Pippin HT instrument (Sage Science). The efficiency of size selection was assessed using a high-sensitivity DNA kit (Agilent). The libraries were merged equimolarly, and the combined libraries were quantified by qPCR using a universal KAPA library quantification kit (KAPA Biosystems), and then sequenced on an Illumina High Seq 2500 with an average of 10 million reads per sample using a single-end 35-base read length. For preprocessing, the 3′ adapter of the Illumina small RNA sequence was removed by Cutadapt software. After trimming, all sequences contained high quality scores and peaks centered at 22 nt, representing microRNAs. Clean reads were aligned to hg38 and annotated with “microRNA.subset.of.GENCODE.V24.gtf”. Mapping of reads was done and counts of miRNAs were obtained using the STAR alignment tool. Differential miRNA expression analysis was performed using limma

[30] (Linear Models for Microarrays and RNA-Seq Data Package). Raw sequencing data can be accessed as fastq files through the Gene Expression Omnibus (GEO) database (www.ncbi.nlm.nih.gov / geo accession number GSEXXXX).

[0209] 3. Serum miRNA Isolation and qRT-PCR for Independent Retrospective and Prospective Validation

[0210] RNA enriched for small non-coding RNAs was extracted from 200ul serum samples using the miRNeasy serum kit (Qiagen, Valencia, CA) according to the manufacturer's instructions. To normalize differences between samples during RNA isolation, synthetic C. elegans miRNA (cel-miR-39, Qiagen) was added to each 200ul denatured serum. The expression of miRNAs was quantified using the QuantStudioTM 7 Flex Real-Time PCR System (Applied Biosystems) by TaqMan miRNA Real-Time qRT-PCR Analysis (Applied Biosystems, Foster City, CA). The following primers were used for "OCaMIR" qRT-PCR analysis: hsa-miR-202-3p, hsa-miR-205-5p, hsa-miR-miR-508-3p, hsa-miR-509-3-5p, hsa-miR-513c-5p, hsa-miR-183-5p, hsa-miR-182-5p, and hsa-miR-513b-5p (Catalog No.: 4427975, Thermo Fisher Scientific, Waltham, MA). Several endogenous controls were tested, including U6, RNU44, hsa-miR-16-5p, hsa-miR-103a-3p (Catalog No.: 4427975, Thermo Fisher Scientific) for data normalization. Most of these endogenous controls were well expressed, and there was no difference between healthy controls and cancer samples. The inventors used U6 for data normalization. The expression of each miRNA was calculated using the 2-ΔCT method. A similar approach was also used in a prospective setting at West China Hospital.

[0211] 4. Statistical analysis and model building

[0212] Statistical analysis was performed using R 3.2.4, IBM SPSS version 23, and GraphPad Prism version 6.0. The BH (Benjamini-Hochberg) method was used to correct for multiple hypothesis testing where applicable, and all statistical tests were two-sided. Power calculations for the discovery phase were performed using the “RNASeqPower” package in R

[31] , resulting in a power greater than 0.90. To identify differentially expressed miRNAs between cancer and normal tissues from small RNA sequencing data, the inventors used limma (Bioconductor package in R 3.2.4)

[30] . An absolute log 2 fold change of less than 2 and a BH-corrected adjusted p-value of less than 0.05 were used to determine miRNA targets and subsequent model building. A multivariate logistic regression model consisting of 8 upregulated miRNAs from the discovery cohort was established, and the coefficients from the tissue cohort were applied to TCGA and GSE65819 for independent tissue validation. As expected, the dynamic expression range of miRNAs differed between tissue RNA sequencing and serum, therefore, the inventors developed a serum-based logistic regression training model for the 8 miRNA panels in GSE106817, and the coefficients and logistic regression model derived from this training cohort were applied to all public and retrospective serum validation cohorts in the Czech Republic. The same regression model was also applied to the prospective serum validation cohort. The serum multivariate logistic regression model was as follows: logit(P)=-1.3374+(0.0887*MIR-202-3P)+(0.1622*miR-508-3p)+(0.3776*MIR-509-3-5P)+(-0.0381*MIR-205-5P)+(0.1101*MIR-182-5P)+(0.1038*MIR-513C-5P)+(-0.0690*MIR-183-5P)+(0.2317*MIR-513B-5P). To determine the accuracy of OCaMIR analysis, the predicted probability value (PPV) (also called risk score) derived from the 8-miRNA logistic regression model was used to plot a receiver operating characteristic (ROC) curve with a 95% confidence interval (CI). All ROC curves presented in the inventors' results were cross-validated 1000 times. Based on the ROC curves, the inventors calculated the sensitivity, specificity, positive predictive value, and negative predictive value of OCaMIR analysis in all groups. OCaMIR analysis was directly compared with CA125 using ROC curves.

[0213] 5. Cost-Benefit Analysis

[0214] Cost-effectiveness analysis (CEA) of the hypothetical cohort was performed by Markov modeling to compare the total costs and health benefits of current clinical practice of no screening with large-scale screening using the inventors' miRNA signature. Markov modeling of CEA was performed by the R package "heemod". Details of the clinical assumptions of the hypothetical cohort can be found in Supplementary Methods File 1, Supplementary Tables 3 and 4.

[0215] B. Results

[0216] 1. Discovery of OCaMIR signatures from whole-genome small RNA sequencing data

[0217] The study design involving all three phases is described in Supplementary Fig. 1: 1) OCaMIR tissue discovery and independent validation; 2) OCaMIR serum training and independent retrospective validation; and 3) OCaMIR prospective serum validation. In the discovery phase, small RNA sequencing allowed us to identify 32 differentially regulated miRNAs with adjusted p-values ​​less than 0.05 and an absolute log-fold change of 2 between stage I OC and normal tissues (Fig. 1A). Among them, the inventors prioritized eight miRNAs upregulated in cancer: hsa-miR-182-5p, hsa-miR-183-5p, hsa-miR-202-3p, hsa-miR-205-5p, hsa-miR-miR-508-3p, hsa-miR-509-3-5p, hsa-miR-513b-5p and hsa-miR-513c-5p (referred to as "OCaMIR") to evaluate their diagnostic accuracy in stage I OC. The logistic regression model derived from the combination of eight miRNAs and the resulting risk score for the discovery cohort are shown in Figure 1B. In Figure 1B, in the tissue discovery cohort, OCaMIR had an AUC of 0.92 without cross-validation and an AUC of 0.88 for detecting stage I OC after 1000 cross-validations. The logistic regression model from the discovery cohort was applied to the TCGA and GSE65819 tissue cohorts to evaluate OCaMIR in an independent tissue cohort. Consistent with the discovery cohort, OCaMIR had an AUC of 0.90 for detecting stage I OC in the TCGA cohort, while the AUCs for detecting all stages in TCGA and GSE65819 were 0.99 and 0.98, respectively, after 1000 cross-validations. Figure 1C ). These results confirm the ability of OCaMIR to detect OC at the tissue level.

[0218] 2. Independent retrospective validation of OCaMIR analysis in serum samples across an international multicenter cohort

[0219] To evaluate the potential of OCaMIR as a non-invasive diagnostic assay, the inventors then used qRT-PCR to analyze multiple publicly available serum miRNA expression cohorts as well as an external retrospective serum validation cohort. In all serum validation cohorts, expression of individual miRNAs was upregulated in cancer compared to healthy normal subjects ( Figure 4 ). Table 2 provides the AUCs of individual miRNAs for all serum cohorts. Subsequently, to develop the 8-miRNA OCaMIR signature, the inventors used the GSE106817 miRNA microarray cohort consisting of OC serum (n=320) / healthy serum (n=320) samples and trained a logistic regression model using all eight miRNAs. The risk score plot (OCaMIR risk score) shown in Figure 2A clearly shows the differences between healthy serum samples and cancer serum samples. Excitingly, in the GSE106817 serum training cohort, the AUC of OCaMIR for detecting OC was 0.86 after 1000 cross-validations (Figure 2A). In addition, OCaMIR obtained 84% sensitivity, 74% specificity, 77% positive predictive value (PPV) and 83% negative predictive value (NPV) in the GSE106817 serum training cohort (Supplementary Table 1).

[0220] The logistic regression model for OCaMIR analysis developed by the GSE106817 serum training group was subsequently applied to three independent retrospective serum groups. 1) GSE31568 miRNA microarray data consisting of OC serum (n = 15) / healthy serum (n = 70), 2) GSE113486 miRNA microarray group consisting of OC serum (n = 40) / healthy serum (n = 100), and 3) expression data based on RT-PCR in the Czech Republic, OC serum (n = 96) / healthy serum (n = 96). The OCaMIR risk score graphs shown in Figures 2B, C, and D clearly show the differences between healthy and cancer serum samples in all serum validation groups. In all these groups, the AUC of OCaMIR for detecting ovarian cancer (including all stages) after 1000 cross-validations was 0.84 (GSE31568), 0.87 (GSE113486), and 0.91 (Czech Republic) (Figures 2B, C, and D, respectively). OCaMIR sensitivity, specificity, PPV, and NPV for all serum validation cohorts are provided in Supplementary Table 1. These results highlight the suitability of our markers for non-invasive testing in serum.

[0221] 3. OCaMIR analysis in serum is highly accurate for detecting stage 1 OC, superior to CA125, and highly specific for OC detection

[0222] In the inventors' two retrospective serum cohorts, the inventors obtained many stage I OCs, and their staging analysis showed that, consistent with the tissue results, OCaMIR was highly accurate in detecting stage I OCs, with AUCs of 0.88 and 0.86 for GSE106817 and Czech Republic serum cohorts, respectively (Fig. 3A and B). The sensitivity, specificity, PPV, and NPV of OCaMIR for stage I OC detection are provided in Supplementary Table 2. The inventors' Czech Republic serum validation cohort has CA125 levels for both healthy and cancer samples, which enables us to compare the performance of OCaMIR with CA125 in OC detection. As shown earlier in Fig. 2D, OCaMIR has an AUC of 0.91 for all stages and an AUC of 0.86 for stage I cancer (Fig. 3B). Compared to and inferior to OCaMIR, CA125 has an AUC of 0.82 and 0.73, respectively, for detecting all stages and stage I OCs (Fig. 3B). In addition, to evaluate the specificity of OCaMIR for OC detection, the inventors have utilized the GSE31568 microarray expression cohort, in which miRNA expression data are available for various diseases including OC. As shown in Figure 3D, OCaMIR has an AUC of 0.85 in OC, while the detection accuracy of other diseases including acute myocardial infarction, chronic obstructive pulmonary disease, lung cancer, melanoma, multiple sclerosis, pancreatic cancer, pancreatitis, periodontitis, prostate cancer, sarcoidosis, gastric cancer, and Wilms tumor ranges from 0.16 to 0.73. This confirms that OCaMIR analysis is superior to the conventional marker CA125 and is highly specific for OC detection.

[0223] 4. OCaMIR analysis achieved excellent accuracy in prospective serum validation and is an economical option for non-invasive population screening

[0224] In view of the accuracy of OCaMIR for OC detection in the retrospective serum cohort, the inventors conducted an independent international prospective validation work at West China Hospital (WCH). qRT-PCR analysis was also performed at WCH to analyze all 8 miRNAs using the standard protocol used by the inventors in the retrospective validation. The logistic regression model developed by the inventors in their retrospective stage was applied to the prospective setting, and the resulting risk score of the prospective cohort is depicted in Figure 2E. Similarly, OCaMIR has a high accuracy in detecting OC patients, with an AUC of 0.92, and the sensitivity obtained in this prospective cohort is 86%, the specificity is 92%, the positive predictive value is 91% (PPV), and the negative predictive value is 89% (NPV) (Supplementary Table 1). In the prospective cohort, the AUC for stage I OC was 0.81 (Figure 3C). Consistent with the retrospective serum validation data from the Czech Republic, OCaMIR is superior to CA125 in detecting stage I and all stages of the West China prospective cohort (Figure 3C). Using the sensitivity and specificity obtained in the prospective validation cohort, we performed a cost-effectiveness analysis (CEA) to evaluate the cost-effectiveness of large-scale screening using our miRNA signature in a clinical setting in China. The target high-risk group was Chinese women aged 55 to 80 years. Markov model-based CEA predicted that large-scale screening using our miRNA signature would be cost-effective relative to current no-screening practice [ICER = CNY 5139.4 / QALY], Supplementary Tables 3 and 4. These results highlight the effectiveness of OCaMIR analysis in a whole-laboratory and prospective setting, making it a viable option for high-risk population-based screening.

[0225] C. Discussion

[0226] Although advances in surgical and chemotherapeutic regimens have modestly improved survival rates for OC, most women are diagnosed at very advanced stages, when cure is unlikely

[32] . Therefore, there is an urgent need to identify clinically translatable, highly accurate, non-invasive, and cost-effective biomarkers that can aid early detection and population screening of high-risk groups, which could also improve survival outcomes for OC. To achieve this, we have performed one of the small RNA sequencing discoveries using stage I OC tissue and identified an eight-miRNA signature (OCaMIR) that is highly accurate in detecting stage I OC. We have subsequently validated OCaMIR in an international multicenter independent retrospective tissue, serum, and prospective serum cohort covering different platforms and ethnicities that can be easily transferred to clinical care. Excitingly, OCaMIR outperformed the currently widely used OC tumor marker CA125 for the detection of both early and advanced OC [6]. Interestingly, a cost estimation analysis based on a Markov model showed that large-scale screening of high-risk groups aged 55 to 80 years for OC using OCaMIR is cost-effective relative to the current no screening practice.

[0227] Due to the stability of miRNAs in serum and plasma, these markers have attracted great interest in the scientific community for the identification of non-invasive early detection biomarkers in various cancers [19, 33, 34] including ovarian cancer [12-14, 17, 35-40]. Although several studies have identified miRNAs in OC, there are still certain limitations, so the inventors conducted this comprehensive study and addressed the following issues of earlier studies - 1) to identify early detection markers, which are crucial for analyzing stage I OC at the discovery stage, and our study is the first study to utilize early OC tissue for small RNA sequencing, 2) secondly, the identified tissue-based markers have been independently validated in TCGA as well as GSE tissue cohorts consisting of patients from multiple ethnicities, providing further evidence for the utility of these markers in different patient populations, 3) thirdly, the inventors studied tissue-derived markers in multiple independent serum-based OC cohorts covering various ethnicities. markers, which further emphasizes cancer specificity and is consistent with tissue data, OCaMIR is again applicable to different patient populations, 4) our retrospective serum validation cohort consists of several different methodological platforms, including microarray-, sequencing-, and qRT-PCR-based methods, which shows that the miRNA markers identified by the inventors are consistent across platforms, 5) our study is the first study to evaluate the identified markers in a prospective setting by performing standard operating procedures in an independent laboratory, which shows that the OCaMIR analysis can be easily applied to routine clinical laboratories after additional studies, 6) Finally, the inventors evaluated the specificity of the OCaMIR analysis by analyzing a variety of other cancer and disease samples. In addition, the inventors compared the performance of the OCaMIR analysis with the current gold standard CA125 in both retrospective and prospective settings.

[0228] Although screening high-risk groups for OC could improve outcomes for this deadly disease, unfortunately, previous large-scale prospective screening studies of OC in postmenopausal, average-risk women, including the PLCO[7], UKCTOCS[8], and the recently concluded USPSTF[9] (United States Preventive Services Task Force), failed to provide any statistically significant difference in ovarian cancer mortality associated with screening. These three studies investigated the potential of CA125 and / or transvaginal ultrasound and compared the results with an unscreened population. In these trials, the cumulative false-positive rate ranged from 9.8% to 44%[9], which could lead to unnecessary surgical procedures and complications. Therefore, there is an urgent need to identify highly accurate, non-invasive, cost-effective, and easily translatable biomarkers to aid screening, at least in high-risk groups. The OCaMIR assay developed by the inventors in this study showed great potential across different patient populations and sample sources, which is very exciting and provides great prospects for future research.

[0229] In conclusion, by performing comprehensive small RNA sequencing analysis, the inventors identified a clinically translatable liquid biopsy-based early diagnostic analysis "OCaMIR", and furthermore, the inventors' analysis was validated in multiple ethnicities and platforms in both retrospective and prospective settings using logistic regression models, providing a generalizable rationale for early detection of ovarian cancer and possible population screening based on further studies. Excitingly, all eight miRNAs identified by the inventors using a genome-wide unbiased approach were previously reported to be associated with OC progression, metastasis, and chemoresistance, which will significantly increase the strength of the OCaMIR analysis compared to earlier studies.

[0230]

[0231]

[0232]

[0233]

[0234] Supplementary Table 2. OCaMIR analysis accuracy statistics in the serum training and validation cohorts for stage I OC diagnosis

[0235]

[0236] Supplementary Table 3. Results of cost-effectiveness analysis of non-invasive screening in Chinese women

[0237]

[0238]

[0239] Base Case Values ​​in Cost-Benefit Modeling

[0240]

[0241]

[0242] Phase I: Genome-wide discovery of an 8-miRNA signature for early detection of HGSOV.

[0243]

[0244]

[0245] According to the present disclosure, all methods disclosed and claimed herein can be performed and executed without undue experimentation. Although the compositions and methods of the present invention have been described according to preferred embodiments, it is obvious to those skilled in the art that the methods described herein and the steps or step sequences of the methods described herein can be changed without departing from the concept, spirit and scope of the present invention. More specifically, it is obvious that certain agents that are both chemically and physiologically related can replace the agents described herein, and the same or similar results will be obtained. It is obvious to those skilled in the art that all these similar substitutions and modifications are considered to fall within the spirit, scope and concept of the present invention as defined by the appended claims. For all purposes, all references and publications cited throughout the present disclosure are incorporated herein by reference.

[0246] References

[0247] The following references and publications cited throughout the specification are hereby expressly incorporated herein, to the extent that they provide exemplary procedural or other details supplementary to those set forth herein.

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Claims

1. Use of a probe or primer set in the preparation of a kit for diagnosing a patient with ovarian cancer, wherein the probe or primer set is used to measure the expression level of the listed miRNAs in a biological sample from the patient: miR-182-5p, miR-183-5p, miR-202-3p, miR-205-5p, miR-508-3p, miR-509-3-5p, miR-513b-5p and miR-513c-5p.

2. The use of claim 1, wherein the kit comprises one or more control samples.

3. The use of claim 2, wherein the one or more control samples have expression levels representative of normal ovarian cells.

4. The use of claim 2, wherein the one or more control samples have expression levels representative of expression levels in ovarian cancer negative samples.

5. The use of claim 2, wherein the one or more control samples have expression levels representative of ovarian cancer positive samples.

6. The use of claim 2, wherein the one or more control samples have expression levels representative of ovarian cancer cells.

7. The use of any one of claims 1 to 6, wherein the measured expression level of at least one of the listed biomarkers in the biological sample from the patient is reduced compared to the expression level in ovarian cancer cells or is within the expression range representative of normal ovarian cells.

8. The use of claim 7, wherein the at least two measured expression levels of the listed biomarkers in the biological sample from the patient are reduced compared to the expression levels in ovarian cancer cells or are within the expression range representative of normal ovarian cells.

9. The use of claim 8, wherein the at least three measured expression levels of the listed biomarkers in the biological sample from the patient are reduced compared to the expression levels in ovarian cancer cells or are within the expression range representative of normal ovarian cells.

10. The use of claim 9, wherein the measured expression levels of at least four of the listed biomarkers in the biological sample from the patient are reduced compared to the expression levels in ovarian cancer cells or are within the expression range representative of normal ovarian cells.

11. The use of claim 10, wherein the measured expression levels of at least five of the listed biomarkers in the biological sample from the patient are reduced compared to the expression levels in ovarian cancer cells or are within the expression range representative of normal ovarian cells.

12. The use of claim 11, wherein the measured expression levels of at least six of the listed biomarkers in the biological sample from the patient are reduced compared to the expression levels in ovarian cancer cells or are within the expression range representative of normal ovarian cells.

13. The use of claim 12, wherein the measured expression levels of at least seven of the listed biomarkers in the biological sample from the patient are reduced compared to the expression levels in ovarian cancer cells or are within the expression range representative of normal ovarian cells.

14. The use of claim 13, wherein the measured expression levels of all eight of the listed biomarkers in the biological sample from the patient are reduced compared to the expression levels in ovarian cancer cells or are within the expression range representative of normal ovarian cells.

15. The use of any one of claims 1-6, wherein the measured expression level of at least one of the listed biomarkers in the biological sample from the patient is elevated compared to the expression level in normal ovarian cells or is within the expression range representative of ovarian cancer cells.

16. The use of claim 15, wherein the at least two measured expression levels of the listed biomarkers in the biological sample from the patient are elevated compared to the expression levels in normal ovarian cells or are within the expression range representative of ovarian cancer cells.

17. The use of claim 16, wherein the at least three measured expression levels of the listed biomarkers in the biological sample from the patient are elevated compared to the expression levels in normal ovarian cells or are within the expression range representative of ovarian cancer cells.

18. The use of claim 17, wherein the measured expression levels of at least four of the listed biomarkers in the biological sample from the patient are elevated compared to the expression levels in normal ovarian cells or are within the expression range representative of ovarian cancer cells.

19. The use of claim 18, wherein the measured expression levels of at least five of the listed biomarkers in the biological sample from the patient are elevated compared to the expression levels in normal ovarian cells or are within the expression range representative of ovarian cancer cells.

20. The use of claim 19, wherein the measured expression levels of at least six of the listed biomarkers in the biological sample from the patient are elevated compared to the expression levels in normal ovarian cells or are within the expression range representative of ovarian cancer cells.

21. The use of claim 20, wherein the measured expression levels of at least seven of the listed biomarkers in the biological sample from the patient are elevated compared to the expression levels in normal ovarian cells or are within the expression range representative of ovarian cancer cells.

22. The use of claim 21, wherein the expression levels of all eight of the listed biomarkers measured in the biological sample from the patient are elevated compared to the expression levels in normal ovarian cells or are within the expression range representative of ovarian cancer cells.

23. The use of claim 22, wherein the biological sample is a tissue sample, a tumor sample, a lymph node sample, blood, saliva or a squamous cell sample.

24. The use of claim 23, wherein the biological sample is a sample comprising ovarian cells.

25. A kit for diagnosing a patient suffering from ovarian cancer, comprising: a) a probe or primer set for measuring the expression level of the listed biomarkers in a biological sample from a patient: miR-182-5p, miR-183-5p, miR-202-3p, miR-205-5p, miR-508-3p, miR-509-3-5p, miR-513b-5p, and miR-513c-5p; b) one or more control samples, wherein the one or more control samples represent expression levels that are negative for ovarian cancer or positive for ovarian cancer; Wherein the patient is diagnosed with ovarian cancer if the measured expression levels of all eight listed biomarkers are elevated compared to representative expression levels in normal ovarian cells, or the patient is determined not to have ovarian cancer based on the measured expression levels.

26. The kit of claim 25, wherein the one or more control samples have expression levels representative of samples negative for ovarian cancer.

27. The kit of claim 25, wherein the one or more control samples have expression levels representative of ovarian cancer-positive samples.

28. The kit of claim 25, wherein the biological sample is a tissue sample, a tumor sample, a lymph node sample, blood, or saliva.

29. The kit of claim 25, wherein the biological sample is an ovarian sample.

30. The kit of claim 25, wherein the ovarian cancer is stage I or stage II.

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