Biomarkers for the diagnosis of diseases or disorders of the female reproductive tract

Abstract

The present invention relates to a method for diagnosing, predicting, predicting susceptibility to treatment, and / or classifying the onset, progression, and / or outcome of diseases or disorders of the female reproductive tract, particularly in the context of endometriosis, wherein the method determines biomarkers in Table 1, such as CCL5 and / or NEAT1. The present invention further relates to a pharmaceutical product for use in patients stratified according to the method of the present invention, and a composition comprising reagents for detecting biomarkers in Table 1 for the diagnosis of diseases or disorders of the female reproductive tract.

Description

Technical Field

[0001] The present invention relates to methods for diagnosing the onset, progression and / or outcome of a disease, methods for prediction, methods for predicting sensitivity to treatment, and / or methods for classification in the context of a disease or disorder of the female genital tract, wherein the biomarkers in Table 1 are determined. The present invention further relates to pharmaceutical products for use in patients stratified according to the method of the present invention, and compositions comprising reagents for the detection of the biomarkers in Table 1 for the diagnosis of diseases or disorders of the female genital tract.

Background Art

[0002] Diseases or disorders of the female genital tract can occur as a result of a disease in one of the reproductive organs. These disorders often manifest as menstrual changes, pelvic pain, or infertility during the reproductive period. Diseases or disorders of the female genital tract include, but are not limited to, endometrial cancer, ovarian cancer, adenomyosis, and endometriosis.

[0003] Ovarian cancer is a group of diseases that occur in the ovaries, or related areas of the fallopian tubes and peritoneum. Ovarian cancer is often diagnosed at a late stage (stages III and IV, metastatic) of the disease due to its asymptomatic onset. Early detection of ovarian cancer means a good response to treatment. Ovarian cancer is relatively rare (female morbidity is 0.0146), but mutations in BRCA1 and BRCA2, as well as Lynch syndrome, are high risk factors for ovarian cancer. Ovarian cancer is divided into several subtypes: invasive epithelial, stromal, germ cell tumors, fallopian tube tumors. The 5-year survival rate depends on the subtype, which ranges from about 95% for localized tumors, 50 - 94% for regional tumors, and 30 - 70% for distant settings.

[0004] The diagnosis of ovarian cancer includes several imaging techniques such as MRI and CT scans, as well as blood tests.

[0005] Endometriosis is an estrogen-dependent disease characterized by the growth of endometrial tissue outside the uterus. These "lesions" can be found throughout the abdominal cavity, thus dividing the disease into three main subtypes: superficial peritoneal (SUP), ovarian (OMA), and deep endometriosis (DIE) (Chapron, C., et al., Hum Reprod, 2011.26(8):p.2028-35 (Non-patent Literature 1)). DIE is considered the most severe form of the disease and is defined by lesions that invade the underlying tissue more than 5 cm. Endometriosis is very prevalent, occurring in up to 10% of older women of reproductive age, but it is also extremely heterogeneous, as a wide range of symptoms and medical complications can manifest. This can lead to severe pelvic pain, low fertility, and pregnancy complications (Giudide, LC, Clinical practice. Endometriosis. N Engl J Med, 2010. 362(25): p.2389-98 (Non-patent Literature 2)), and is associated with an increased risk of developing ovarian cancer with age (Wentzensen, N., et al., J Clin Oncol, 2016. 34(24): p.2888-98 (Non-patent Literature 3)).

[0006] The etiology of endometriosis remains unknown. The most commonly accepted theory is Sampson's retrograde menstruation theory, first proposed in 1927 (Sampson, JA, Am J Pathol, 1927.3(2):p.93-110 43 (Non-Patent Literature 4)). Under this theory, viable endometrial epithelial and stromal cells are circulated retrogradely through the fallopian tubes into the abdominal cavity during menstruation. These cells are able to evade immune surveillance and eventually bind to and proliferate in the mesothelial cell lining. However, since only a fraction of women who experience retrograde menstruation develop the disease, other abnormal biological factors must be involved.

[0007] Currently, there are no non-invasive diagnostic tests, and direct observation via laparoscopy is the gold standard for diagnosis. If endometriosis is detected during laparoscopy, the lesions are removed, and a portion of this tissue is examined to confirm the diagnosis. In addition, ascites, which provides information about the lesion microenvironment, is regularly removed. Both disease progression and associated symptoms are periodically stimulated by the influence of menstrual hormones. While lesion growth can be controlled by hormonal regulation, unfortunately, this is an insufficient option for women who wish to maintain their fertility. Furthermore, resistance to hormone therapy has recently been observed. The prevalence and need for surgery place an abnormal burden on the healthcare system and economic productivity.

[0008] Therefore, improved methods are needed to examine and / or predict disease or disorder of female reproductive tract-related parameters such as diagnosis, disease onset, disease progression, disease outcome, and / or sensitivity to treatment. [Prior art documents] [Non-patent literature]

[0009] [Non-Patent Document 1] Chapron, C., et al., Hum Reprod, 2011.26(8):p.2028-35 [Non-Patent Document 2] Giudide,LC,Clinical practice.Endometriosis.N Engl J Med,2010.362(25):p.2389-98 [Non-Patent Document 3] Wentzensen, N., et al., J Clin Oncol, 2016.34(24):p.2888-98 [Non-Patent Document 4] Sampson, JA, Am J Pathol, 1927.3(2):p.93-110 43 [Overview of the Initiative]

[0010] The above technical problems are solved by embodiments disclosed herein and defined in the claims.

[0011] Therefore, the present invention relates, in particular, to the following embodiments. 1.i) A step of determining the RNA levels of at least two biomarkers in an endometrial tissue sample from a female subject, wherein the biomarkers are a) CCL5 and / or NEAT1; and / or b) Additional biomarkers (multiple selections allowed) from Table 1 The steps of determining, which include or consist of, ii) A step of determining the status of endometriosis in the endometrial tissue sample based on the RNA levels of at least two of the biomarkers in i) A method for determining the state of endometriosis in endometrial tissue samples from women, including the method described above.

[0012] 2.a)i) A change in one or more biomarkers selected from Table 2 compared to the reference value indicates the pathogenesis of endometriosis; and / or ii) Changes in NEAT1 and / or further biomarkers (multiple may be selected from Table 3) compared to reference values ​​indicate a non-endometriotic condition, and b) The above reference value indicates a healthy state, The method according to Embodiment 1.

[0013] 3.i) A step of determining the frequency of cells expressing at least two biomarker RNAs in multiple cells of an endometrial sample from a woman, wherein the biomarkers are a) CCL5 and / or NEAT1; and / or b) Additional biomarkers (multiple selections allowed) from Table 1 The steps of determining, which include or consist of, ii) A step of determining the endometriosis state based on the frequency determined in i) A method for determining an endometrial condition based on a plurality of endometrial cells, including

[0014] 4.a) i) A change in the frequency of cells expressing one or more biomarkers selected from Table 2, compared to a reference frequency, indicates the pathological condition of endometriosis; and / or ii) A change in the frequency of cells expressing NEAT1 and / or one or more additional biomarkers (if any) selected from Table 3, compared to a reference frequency, indicates the pathological condition of non-endometriosis, and b) The reference value indicates a healthy state The method according to Embodiment 3.

[0015] 5.i) The following: a) Immune cells selected from the group consisting of B cells, T cells, dendritic cells, and macrophages; b) Epithelial cells selected from the group consisting of basal cells, ciliated cells, and non-ciliated cells; c) Endothelial cells; and d) Smooth muscle cells Cells selected from the group consisting of, and / or ii) Cells having at least one cell lineage marker, preferably from the following groups: a) CD14, CD16, CD45, CD15, CD11b; b) EpCAM and KRT18; and c) COL18A1, COL4A2, COL4A1, VIM, or CALD1 Using at least one cell lineage marker selected from The method according to any one of Embodiments 1 to 4, including at least one step of preselecting

[0016] 6. The method according to any one of Embodiments 1 to 5, wherein at least 3, 4, 5, 6, 7, 8, or 9 biomarkers are determined.

[0017] 7. Further comprising determining or obtaining at least one non-molecular marker, preferably, the non-molecular marker comprises a marker selected from the group consisting of age, weight, BMI, pregnancy, number of deliveries, race, fertility status, past laparoscopy, past drug use, and other gynecological disorders, the method according to any one of embodiments 1 to 6.

[0018] 8. The method according to any one of embodiments 1 to 7, wherein the method is at least partially implemented on a computer, and the RNA level is determined by obtaining data indicating the RNA level.

[0019] 9. The method according to any one of embodiments 1 to 8, wherein the sample is a proliferative phase sample, or the cell is a cell obtained during the proliferative phase.

[0020] 10. An increase in one or more biomarkers selected from Table 4 indicates the pathological condition of endometriosis, and / or an increase in one or more biomarkers selected from Table 5 indicates the pathological condition of non-endometriosis, the method according to embodiment 9.

[0021] 11. i) Determining the endometriosis status according to embodiments 1 to 8; ii) Determining or obtaining the status of the menstrual cycle of the subject at the time when the endometrial cells or the endometrial sample was obtained; iii) Determining the validity of the endometriosis status based on the status of the menstrual cycle, preferably, the validity is considered to be higher when the status of the menstrual cycle is in the proliferative phase than when the status of the menstrual cycle is in a different menstrual cycle status, the step of determining. A method for determining the validity of an endometriosis status, comprising.

[0022] 12. The method according to embodiment 11, wherein the step of determining the status of the menstrual cycle comprises determining the RNA level of at least one menstrual cycle biomarker.

[0023] 13. The method according to Embodiment 12, wherein the menstrual cycle markers include the markers in Table 8.

[0024] 14. A method for predicting the outcome of endometriosis, the onset of the disease, and / or the progression of the disease in women who have endometriosis or are at risk of developing endometriosis, a) A step of determining the endometriosis state according to the method described in any one of Embodiments 1 to 10; b) The step of comparing the endometriosis condition determined in a) with a predictive criterion pattern; c) A step in which, based on the comparison in step b), predicts the outcome of the disease in the female subject, the onset of the disease, and / or the progression of the disease. The method, including the method described above.

[0025] 15.1) Determine the increase in the frequency of cells expressing the biomarker(s) listed in Table 2 compared to the aforementioned prediction criterion pattern; and / or 2) Determine the increase in the level of the biomarker(s) listed in Table 2 compared to the aforementioned reference pattern. The method according to Embodiment 13, which shows a high probability of disease onset and / or disease progression, and an exacerbation of the disease outcome.

[0026] 16.1) Determine the increase in the frequency of cells expressing the biomarker(s) listed in Table 3 compared to the aforementioned prediction criterion pattern; and / or 2) Determine the increase in the NEAT1 level and / or the increase in the levels of additional biomarkers (multiple) listed in Table 3, compared to the aforementioned reference pattern. The method according to Embodiment 14 or 15, which shows an improved disease outcome, but with a low probability of developing the disease and / or a low probability of disease progression.

[0027] 17. A method for predicting susceptibility to endometriosis treatment in women who have endometriosis or are at risk of developing endometriosis, a) A step of determining the endometriosis state according to the method described in any one of Embodiments 1 to 10; b) A step of comparing the endometriotic condition with a susceptibility criterion pattern; c) A step of predicting the sensitivity of the female subject to treatment for endometriosis based on the comparison in step b) The method, including the method described above.

[0028] 18. The method according to Embodiment 17, wherein the treatment for endometriosis is a treatment selected from the group consisting of analgesics, hormone therapy, infertility treatment, and surgery.

[0029] 19. The method according to any one of embodiments 14 to 18, wherein the susceptibility criterion pattern or the prediction criterion pattern is obtained from a reference subject, and at least one of the reference subjects has been diagnosed with endometriosis.

[0030] 20. Obtaining the sensitivity criterion pattern or the prediction criterion pattern from the reference subject is the method according to Embodiment 19, comprising machine learning techniques, preferably convolutional neural networks and / or logistic regression.

[0031] 21. A method for classifying women who have endometriosis or are at risk of developing endometriosis into different classes, ai) A step of determining the endometriosis state according to the method described in any one of Embodiments 1 to 10; ii) A step of predicting the outcome of a disease in a woman, the onset of the disease, and / or the progression of the disease, according to the method of any one of Embodiments 14-16, 19, or 20; and / or iii) A step of predicting the susceptibility of a female subject to treatment for endometriosis according to the method described in any one of Embodiments 17 to 20, and A step of classifying the female subjects according to the frequency determined in bi), the signature determined in ii), the prediction in iii), and / or the prediction in iv). The method, including the method described above.

[0032] 22. The method according to Embodiment 21, wherein at least one class indicates the stage and / or severity of the endometriosis.

[0033] 23. A composition comprising a reagent for detecting a biomarker for the diagnosis of endometriosis, wherein the biomarker comprises or consists of at least two markers from Table 1.

[0034] 24. A pharmaceutical product comprising a compound for endometriosis for use in the treatment of women who are expected to be susceptible to treatment of endometriosis by the method described in any one of Embodiments 17 to 20.

[0035] 25. The pharmaceutical product according to Embodiment 24, wherein the compound for endometriosis is selected from the group consisting of ibuprofen, naproxen, oxycodone, desogestrel, dienogestrel, levonorgestrel, clomiphene citrate, gonadotropins, metformin, letrozole, and bromocriptine.

[0036] 26. The method according to any one of Embodiments 1 to 22, the composition according to Embodiment 23, or the pharmaceutical product according to Embodiment 24 or 25, wherein the endometriosis is selected from the group consisting of peritoneal endometriosis, endometrioma, deep endometriosis, tubal endometriosis, and abdominal wall endometriosis.

[0037] 27. The method according to any one of Embodiments 1 to 22, 26, the composition according to Embodiment 23 or 26, or the pharmaceutical product according to any one of Embodiments 24, 25 or 26, wherein the endometriosis is rASRM stage II, III, or IV.

[0038] 28. A computer program product comprising instructions for performing the method described in any one of embodiments 8 to 22, 26, or 27, wherein the method is computer-implemented in the computer program product. [Modes for carrying out the invention]

[0039] Accordingly, in the first embodiment, the present invention relates to a method for determining the status of endometriosis in an endometrial tissue sample from a female subject, comprising the steps of: i) determining the RNA levels of at least two biomarkers in the endometrial tissue sample from a female subject, wherein the biomarkers are selected from Table 1; and ii) determining the status of endometriosis in the endometrial tissue sample based on the RNA levels of the at least two biomarkers in i).

[0040] As used herein, the term “endometriosis status” refers to a measure or pattern that indicates whether a subject has endometriosis based on its endometrial RNA expression pattern. Endometriosis status may further indicate what type of endometriosis the sample exhibits. In some embodiments, endometriosis status may further indicate treatment sensitivity, disease progression, and / or disease outcome compared to their respective criteria.

[0041] The term "RNA level" as used herein may be any RNA level exhibiting the biomarkers described herein, and preferably may be the mRNA level of each of the biomarkers described herein.

[0042] The term “endometrial tissue sample” refers to any sample obtained from the endometrium. The sample may be any endometrial-derived sample containing a sufficient amount of RNA for analysis. Typically, the instrument is passed into the uterus through the cervix to collect the tissue sample. In some embodiments, the endometrial tissue sample described herein is endometrial biopsy material. In other embodiments, the endometrial tissue sample described herein is obtained via a swab. In other embodiments, the endometrial tissue sample described herein is obtained from menstrual blood.

[0043] In certain embodiments, the present invention relates to a method for determining the frequency of disease or disorder of female reproductive tract signature cells in a plurality of cells, comprising: i) determining the level of expression of at least two biomarkers selected from Table 1 in the plurality of cells; and ii) determining the frequency of disease or disorder of the female reproductive tract signature cells in the plurality of cells based on the expression of at least two biomarkers selected from Table 1.

[0044] The term “disease or disorder of the female reproductive tract” as used herein means any disease or disorder of the ovaries, fallopian tubes, uterus, cervix, and / or vagina, and / or any disease or disorder resulting therefrom. In some embodiments, the disease or disorder of the female reproductive tract as described herein is a non-transmissible disease or disorder of the female reproductive tract. In some embodiments, the disease or disorder of the female reproductive tract as described herein is a non-transmissible disease or disorder of the female reproductive tract, with symptoms including pelvic pain and / or low fertility. In some embodiments, the disease or disorder of the female reproductive tract as described herein is endometriosis, ovarian cancer, and / or adenomyosis. In some embodiments, the disease or disorder of the female reproductive tract as described herein is endometriosis, ovarian cancer, and adenomyosis and / or endometrial cancer.

[0045] As used herein, the term “biomarker” refers to a molecule that is part of a cell and / or produced by a cell and that functions as an indicator of disease. In many cases, a biomarker is a gene variant or gene product, such as RNA or polypeptide.

[0046] As used herein, the phrase "determine the level of a biomarker" refers to the acquisition of information indicating the level of a biomarker from nucleic acid detection techniques, peptide or protein detection techniques, and / or data sources.

[0047] As used herein, the term “subject” refers to mammals such as mice, guinea pigs, rats, dogs, or humans. It is understood that the preferred subject is human.

[0048] As used herein, the term “female subject” refers to a subject having a uterus. In some embodiments, the female subject described herein is a premenopausal female subject. In some embodiments, the female subject described herein is over 18 years of age.

[0049] The term “disease or disorder of female reproductive tract signature cells,” as used herein, refers to cells exhibiting disease or disorder of the female reproductive tract in a subject. That is, if a specific relative frequency of disease or disorder of female reproductive tract signature cells is determined in multiple cells obtained from a subject, the subject is diagnosed with disease or disorder of the female reproductive tract. Furthermore, by determining the relative frequency of disease or disorder of female reproductive tract signature cells in samples from the same subject at two or more time points, it becomes possible to monitor the progression of disease or disorder of the female reproductive tract in that subject.

[0050] The present invention relates to a method for detecting diseases or disorders of the female reproductive tract with high specificity and sensitivity. Other approaches to detecting diseases or disorders of the female reproductive tract, such as laparoscopy or other biomarkers, are invasive, have reduced sensitivity, are not scalable, and / or are not suitable for early detection. On the other hand, the method of the present invention makes it possible to measure the level of biomarkers at the single-cell level. That is, for each cell in a plurality of cells, the levels of two or more, three or more, or four or more biomarkers are determined, and based on the levels of these biomarkers, it is determined for each cell whether or not it is an indicator of a disease or disorder of the female reproductive tract. The frequency of these indicator cells in the plurality of cells can then be used to determine whether the subject from which the plurality of cells were obtained, the donor, suffers from a particular medical condition, and / or to determine the progression of a particular medical condition in the subject from which the plurality of cells were obtained.

[0051] Therefore, the present invention is at least in part based on the finding that combinations of biomarkers are particularly useful for detecting cells that indicate disease or disorder of the female reproductive tract.

[0052] In certain embodiments, the present invention relates to a method for determining the frequency of disease or impairment of female reproductive tract signature cells in a plurality of cells, the method comprising the steps of: i) determining the level of expression of at least two biomarkers selected from Table 1 in the plurality of cells; and ii) determining the frequency of disease or impairment of the female reproductive tract signature cells in the plurality of cells based on the expression of the at least two biomarkers selected from Table 1, wherein an increase in one or more biomarkers selected from Table 2 indicates disease or impairment of the female reproductive tract signature cells, and / or an increase in one or more biomarkers selected from Table 3 indicates non-disease or impairment of the female reproductive tract signature cells.

[0053] In certain embodiments, the present invention relates to a method of the present invention, wherein a)i) a change in one or more biomarkers selected from Table 2 compared to a reference value indicates a pathological condition of endometriosis; and / or ii) a change in biomarker(s) selected from Table 3 indicates a pathological condition of non-endometriosis; and b) the reference value indicates a healthy state.

[0054] In certain embodiments, the present invention relates to a method for determining the frequency of disease or disorder of female reproductive tract signature cells in a plurality of cells, comprising the steps of: i) determining the levels of expression of at least two biomarkers selected from Table 1 in the plurality of cells; and ii) determining the frequency of disease or disorder of the female reproductive tract signature cells in the plurality of cells based on the expression of at least two biomarkers selected from Table 1, wherein at least one biomarker is selected from Table 6.

[0055] The inventors found that the biomarkers in Table 6 are differentially expressed in all cycle phases. Therefore, by selecting markers from this table, it becomes possible to detect cells indicating disease or impairment of the female reproductive tract in a cycle phase-independent manner.

[0056] In certain embodiments, the present invention relates to a method for determining a disease or disorder of a female reproductive tract factor signature in a sample of a female subject, comprising: i) determining the expression levels of at least two biomarkers selected from Table 1 in a sample of a female subject; and ii) determining a disease or disorder of the female reproductive tract factor signature in the sample based on the expression of at least two biomarkers selected from Table 1.

[0057] In certain embodiments, the present invention relates to a method for determining an endometriosis state based on a plurality of endometrial cells, comprising the steps of: i) determining the frequency of cells expressing at least two biomarker RNAs in a plurality of cells of an endometrial sample from a female subject, wherein the biomarkers are selected from Table 1; and ii) determining the endometriosis state based on the frequencies determined in i).

[0058] In certain embodiments, the present invention relates to a method of the present invention, wherein a)i) a change in the frequency of cells expressing one or more biomarkers selected from Table 2, compared to a reference frequency, indicates a pathological condition of endometriosis; and / or ii) a change in the frequency of cells expressing one or more biomarkers selected from Table 3, compared to a reference frequency, indicates a pathological condition of non-endometriosis; and b) the reference value indicates a healthy state.

[0059] The term “disease or disorder of female reproductive tract factor signature” refers, as used herein, to the levels and / or ratios of biomarkers that indicate a disease or disorder of the female reproductive tract. Thus, a disease or disorder of female reproductive tract factor signature may include data indicating bulk RNA, protein levels, and / or RNA or protein levels. Therefore, samples may be processed and live cells are not required. This allows for rapid, standardized, and robust analysis of the samples.

[0060] As used herein, the term “sample” refers to any sample that a person skilled in the art would recognize may contain a biomarker. In some embodiments, the sample described herein is a tissue sample, a wash sample, or a body fluid sample. In some embodiments, the sample described herein is a FACS-sorted tissue sample. In some embodiments, the sample described herein is an endometrial tissue sample.

[0061] The inventors have found that biomarkers represented by signatures in a sample enable the diagnosis of diseases or disorders of the female reproductive tract with high specificity and sensitivity.

[0062] In certain embodiments, the present invention relates to a method for determining a disease or disorder of a female reproductive tract factor signature in a sample of a female subject, the method comprising the steps of: i) determining the level of expression of at least two biomarkers selected from Table 1 in a sample of a female subject; and ii) determining a disease or disorder of the female reproductive tract factor signature in the sample based on the expression of the at least two biomarkers selected from Table 1, wherein an increase in one or more biomarkers selected from Table 2 indicates a disease or disorder of the female reproductive tract factor signature, and / or an increase in one or more biomarkers selected from Table 3 does not indicate a disease or disorder of the female reproductive tract factor signature.

[0063] In certain embodiments, the present invention relates to a method for determining a disease or disorder of a female reproductive tract factor signature in a sample of a female subject, comprising the steps of: i) determining the expression levels of at least two biomarkers selected from Table 1 in a sample of a female subject; and ii) determining a disease or disorder of the female reproductive tract factor signature in the sample based on the expression of at least two biomarkers selected from Table 1, wherein at least one biomarker is selected from Table 6.

[0064] In certain embodiments, the present invention relates to a method of the present invention comprising at least one step of pre-selecting cells from the group consisting of i) a) immune cells selected from the group consisting of B cells, T cells, dendritic cells, and macrophages; b) epithelial cells selected from the group consisting of basal cells, ciliated cells, and non-ciliated cells; c) endothelial cells, and d) smooth muscle cells, and / or ii) cells having at least one cell lineage marker.

[0065] In certain embodiments, the present invention relates to a method of the present invention comprising at least one step of pre-selecting cells using at least one cell lineage marker selected from the group consisting of: a) CD14, CD16, CD45, CD15, CD11b; b) EpCAM and KRT18; and c) COL18A1, COL4A2, COL4A1, VIM, or CALD1.

[0066] In certain embodiments, the present invention relates to a method comprising at least one step of pre-selecting cells using at least one cell lineage marker CD14, CD16, CD45, CD15, and / or CD11b.

[0067] In certain embodiments, the present invention relates to a method comprising at least one step of pre-selecting cells using at least one cell lineage marker EpCAM and / or KRT18.

[0068] In certain embodiments, the present invention relates to a method comprising at least one step of pre-selecting cells using at least one cell lineage marker COL18A1, COL4A2, COL4A1, VIM and / or CALD1.

[0069] In certain embodiments, the present invention relates to a method of the present invention in which the sample is a growth phase sample.

[0070] In certain embodiments, the present invention relates to a method of the present invention in which the cells are cells obtained during the proliferation phase.

[0071] As used herein, the term “proliferative phase sample” refers to a sample obtained during the proliferative phase of the menstrual cycle in a female subject. In some embodiments, the proliferative phase is the first half of the menstrual cycle. In some embodiments, the proliferative phase is the pre-ovulation phase. Ovulation may be determined by any method known in the art, for example, based on the number of days from the start of the menstrual cycle, based on changes in vaginal secretions, based on changes in progesterone levels, and / or based on body temperature.

[0072] The inventors have found that, for certain menstrual cycle phases, the means and methods described herein are particularly sensitive to and / or specific to diseases or disorders of the female reproductive tract.

[0073] In certain embodiments, the present invention relates to a method of the present invention, wherein the sample is a proliferative stage sample, and an increase in one or more biomarkers selected herein from Table 4 indicates disease or impairment of female reproductive tract signature cells, and / or an increase in one or more biomarkers selected herein from Table 5 indicates non-disease or impairment of female reproductive tract signature cells.

[0074] In certain embodiments, the present invention relates to a method of the present invention wherein an increase in one or more biomarkers selected from Table 4 indicates a pathological condition of endometriosis, and / or an increase in one or more biomarkers selected from Table 5 indicates a pathological condition of non-endometriosis.

[0075] In a particular embodiment, the present invention relates to a method of the present invention, wherein the sample is a growth phase sample and at least one biomarker is selected from Table 7.

[0076] In certain embodiments, the present invention relates to a method for determining the validity of an endometriotic state, comprising: i) determining an endometriotic state in accordance with the present invention; ii) determining or obtaining the state of the menstrual cycle of the subject at the time the endometrial cells or the endometrial sample are obtained; and iii) determining the validity of the endometriotic state based on the state of the menstrual cycle, preferably the determination step such that the validity is considered higher when the state of the menstrual cycle is the proliferative phase than when the state of the menstrual cycle is in a different menstrual cycle state.

[0077] In certain embodiments, the present invention relates to a method for determining the state of a menstrual cycle, comprising determining the RNA level of at least one menstrual cycle biomarker.

[0078] In certain embodiments, the present invention relates to a method of the present invention in which the menstrual cycle markers include the markers listed in Table 8.

[0079] The inventors have found that certain markers are particularly useful information for certain menstrual cycle phases, and therefore the means and methods described herein may be particularly sensitive to and / or specific to diseases or disorders of the female reproductive tract.

[0080] In certain embodiments, the present invention relates to a method of the present invention, comprising at least one step of pre-selecting cells having at least one cell lineage marker, preferably using at least one cell lineage marker selected from the following groups: a) ITGAM (encoding CD11b), ITGB2 (encoding CD18), CD44, FCGR3A (CD16), FCGR2A (CD32), S100A8 or S100A9; b) DRC3, RSPH3, ARMC2, LRRC23, C16orf46, ZNF487, or BBOF1; and c) COL18A1, COL4A2, COL4A1, VIM, or CALD1.

[0081] The inventors have found that certain cell types or cell states contain information relevant to the diagnosis of diseases or disorders of the female reproductive tract. Therefore, the selection of these cell types and / or cell states may improve the sensitivity and / or specificity of the method(s) of the present invention.

[0082] Within the scope of the present invention, the levels of any number of biomarkers can be determined. Sensitivity and specificity are expected to increase with the number of biomarkers used in the method of the present invention. At the same time, the number of biomarkers that can be used in the method of the present invention may be limited by experimental methods for determining the levels of the biomarkers and the availability of suitable binders.

[0083] In certain embodiments, the present invention relates to a method for determining at least 3, 4, 5, 6, 7, 8, or 9 biomarkers.

[0084] The inventors have found that measuring JUP in combination with additional biomarkers can enhance the sensitivity and / or specificity of the method described herein.

[0085] The set of biomarkers described herein may be adapted to obtain an adapted panel for use in the method of the present invention, and to maintain high sensitivity and specificity, the adapted panel consisting of the same or fewer number of biomarkers by a method comprising the following steps: i. The step of obtaining an alternative panel by adding one or more biomarkers to the set of biomarkers described herein; ii. A step of weighting the biomarkers of the alternative panel (e.g., learned by CellCnn) by testing the alternative panel against a set of samples having known classifications for diseases or disorders of the female reproductive tract-related parameters, iii. To obtain a provisional adaptation panel, the step of excluding one or more biomarkers whose absolute weight is less than the average weight of the biomarkers in the alternative panel; iv. A step of verifying the specificity and selectivity using a validation dataset and identifying the adapted panel.

[0086] In step (i) of the method for obtaining an adapted panel, the biomarker(s) added to the panel may be any biomarker, but preferably, they are biomarker(s) selected from the group listed in Table 1. In some embodiments of the present invention, one of the biomarker(s) added to the panel of the present invention is known to have a similar biological function and / or be characteristic of the same cell type as one of the biomarkers in the panel of the present invention. In some embodiments of the present invention, the biomarker(s) added to the panel may be selected for a variety of reasons, including but not limited to economic reasons, reagent availability, and compatibility with the measuring instrument.

[0087] In step (ii) of the method for obtaining a fitted panel, weighting may be performed using CellCnn as described in the Examples, or using any suitable weighting method known to those skilled in the art. A certain number of biomarkers in the complete substitute panel and / or substitute panel may be tested to obtain information for weighting the biomarkers. For example, a substitute panel minus one control may be used to obtain information for weighting (as described, e.g., by Tung, James W et al. Clinics in Laboratory Medicine vol.27,3(2007):453-68).

[0088] In some embodiments of the present invention, in step (iii) of the method for obtaining an adapted panel, the biomarker with the lowest weight is excluded.

[0089] In step (iv) of the method for obtaining a fitted panel, the specificity and selectivity of the provisional fitted panel may be verified as described in the examples. Provisional fitted panels having specificity and selectivity below a certain threshold are excluded.

[0090] In certain embodiments, the present invention relates to a method of the present invention in which the determination of expression levels includes nucleic acid detection techniques.

[0091] Nucleic acid detection techniques are well known in the art (see, for example, Kolpashchikov, DM, & Gerasimova, YV (Eds.), 2013. Nucleic Acid Detection: Methods and Protocols. Humana Press.). In some embodiments, the nucleic acid detection techniques described herein are at least one method selected from the group consisting of qPCR, ddPCR, isothermal amplification techniques, assays using visual or electrical signals for point-of-care diagnosis, fluorescence in situ hybridization, and signal amplification techniques.

[0092] Therefore, the biomarkers described herein can be detected at the DNA or RNA level, preferably at the mRNA level.

[0093] In certain embodiments, the present invention relates to a method of the present invention in which the level(s) of the biomarker(s) includes the protein level(s).

[0094] Protein levels can be determined by any method known in the art. In some embodiments, the protein levels described herein are determined by an antibody-based assay. That is, any assay that involves the use of an antibody and is suitable for determining the expression level of a biomarker may be used in the present invention. Preferably, an antibody that directly binds to the biomarker is used. Within the scope of the present invention, the antibody is preferably labeled to facilitate the detection and / or quantification of the biomarker. For example, the antibody can be labeled with a fluorophore to enable the detection and / or quantification of the biomarker in a flow cytometry-based assay, or it can be labeled with a metal isotope to enable the detection and / or quantification of the biomarker in a mass cytometry-based assay. In some embodiments, the present invention relates to a method according to the present invention in which the antibody-based assay is an antibody-based flow cytometry or mass cytometry assay. In some embodiments, the protein levels described herein are determined by an ELISA, preferably a multiplex ELISA.

[0095] In certain embodiments, the present invention relates to a method of the present invention in which a plurality of cells are primary cells, or the sample is a primary sample.

[0096] As used herein, the term “primary sample” refers to any sample that has not been cultured for cell proliferation. Nevertheless, primary samples described herein may be stored, maintained, or processed.

[0097] The inventors have found that the method described herein does not require cell culture-mediated cell proliferation because it is specific and / or highly sensitive. This makes the method more efficient than previous methods.

[0098] In certain embodiments, the present invention relates to a method for determining levels in endometrial samples, menstrual blood samples, vaginal smear samples and / or cervical smear samples.

[0099] In certain embodiments, the present invention relates to a method for determining a level in a blood sample, such as a plasma or serum sample.

[0100] The inventors have found that the method of the present invention is particularly sensitive, specific, and / or minimally invasive when using certain types of samples.

[0101] In certain embodiments, the present invention relates to a method as described herein, further comprising determining at least one non-molecular marker, preferably the non-molecular marker being selected from the group consisting of age, weight, BMI, pregnancy, number of births, race, fertility status, past laparoscopy, past drug use, and other gynecological disorders.

[0102] In certain embodiments, the present invention relates to a method of the present invention, further comprising determining or obtaining at least one non-molecular marker, preferably the non-molecular marker being selected from the group consisting of age, weight, BMI, pregnancy, number of births, race, fertility status, past laparoscopy, past drug use, and other gynecological disorders.

[0103] As used herein, the term “other gynecological disorders” refers to any gynecological disorder other than diseases or disorders of the female reproductive tract diagnosed, predicted, and / or classified according to the methods of the present invention. In some embodiments, the other gynecological disorders described herein are gynecological disorders other than endometriosis and ovarian cancer. In some embodiments, the other gynecological disorders described herein are gynecological disorders other than endometriosis. In some embodiments, the other gynecological disorders described herein are gynecological disorders other than ovarian cancer.

[0104] The inventors have found that non-molecular markers can improve the sensitivity and / or specificity of the method of the present invention.

[0105] In certain embodiments, the present invention relates to a method of the present invention, which is at least partially implemented on a computer, and in which an expression level is determined by obtaining data indicating the expression level.

[0106] The inventors have found that the method of the present invention can be used for sample databases and / or data. This makes it possible to scalable and / or separate the sample acquisition procedure from the interpretation of the sample.

[0107] In certain embodiments, the present invention relates to a method for predicting the onset, progression, and / or outcome of a disease in a woman who has or is at risk of having a disease or disorder of the female reproductive tract, the method comprising: a)i) determining the frequency of disease or disorder of female reproductive tract signature cells in a sample of the woman according to the method of the present invention; and / or ii) determining the disease or disorder of the female reproductive tract factor signature in the sample of the woman according to the method of the present invention; b) comparing the frequency determined in a)i) and / or the factor signature determined in a)ii) with a predictive criterion pattern; and c) predicting the onset, progression, and / or outcome of the disease in the woman based on the comparison in step b).

[0108] In certain embodiments, the present invention relates to a method for predicting disease outcomes, disease onset and / or disease progression in women who have or are at risk of having endometriosis, the method comprising: a) determining an endometriotic state according to the method of the present invention; b) comparing the endometriotic state determined in a) with a predictive criterion pattern; and c) predicting the disease outcomes, disease onset and / or disease progression in the women based on the comparison in step b).

[0109] When used herein, the phrase "risk of having a disease or disorder of the female reproductive tract" means having a risk factor for a disease or disorder of the female reproductive tract and / or at least one symptom of a disease or disorder of the female reproductive tract.

[0110] As used herein, the term “reference pattern” may be used for comparison and preferably refers to a predetermined pattern or data point obtained from a reference object. The reference pattern includes at least one data point, such as a data point that can be used as a threshold. In some embodiments, the reference pattern is a machine learning model.

[0111] In certain embodiments, the present invention relates to a method of the present invention that 1) shows an increased frequency of disease or disorder in female reproductive tract signature cells expressing the biomarkers in Table 2 compared to a reference pattern; and / or 2) shows that an increased level of the biomarkers in Table 2 in the female reproductive tract factor signature in the disease or disorder compared to a reference pattern indicates a higher likelihood of developing the disease, a higher likelihood of disease progression, and / or a worsening of the disease outcome.

[0112] In certain embodiments, the present invention relates to a method of the present invention that 1) determines an increase in the frequency of cells expressing one or more biomarkers in Table 2 compared to a predictive reference pattern; and / or 2) determines an increase in the level of one or more biomarkers in Table 2 compared to a reference pattern, which indicates a higher likelihood of disease onset and / or disease progression, or a worsening of the disease outcome.

[0113] In certain embodiments, the present invention relates to a method of the present invention that 1) an increased frequency of disease or disorder in female reproductive tract signature cells expressing the biomarkers in Table 3 compared to a reference pattern; and / or 2) an increased level of the biomarkers in Table 3 in the female reproductive tract factor signature in the disease or disorder compared to a reference pattern, indicating a lower likelihood of disease onset, a lower likelihood of disease progression, and / or an improved disease outcome.

[0114] In certain embodiments, the present invention relates to a method of the present invention that 1) determines an increase in the frequency of cells expressing one or more biomarkers in Table 3 compared to a predictive reference pattern; and / or 2) determines an increase in the level of one or more biomarkers in Table 3 compared to a reference pattern, which indicates an improved disease outcome, such as a lower likelihood of developing and / or progressing to the disease.

[0115] In certain embodiments, the present invention relates to a method for diagnosing a female subject having a disease or disorder of the female reproductive tract, comprising: a)i) determining the frequency of disease or disorder of female reproductive tract signature cells in a sample of the female subject according to the method of the present invention; and / or ii) determining the disease or disorder of the female reproductive tract factor signature in a sample of the female subject according to the method of the present invention; b) comparing the frequency determined in a)i) and / or the factor signature determined in a)ii) with a diagnostic criterion pattern; and c) diagnosing the female subject having a disease or disorder of the female reproductive tract based on the comparison in step b).

[0116] In certain embodiments, the present invention relates to a method for predicting susceptibility to treatment for endometriosis in women who have or are at risk of having endometriosis, the method comprising: a) determining an endometriosis state according to the method of the present invention; b) comparing the endometriosis state with a susceptibility criterion pattern; and c) predicting the susceptibility to treatment for endometriosis in the women based on the comparison in step b).

[0117] In certain embodiments, the present invention relates to a method for monitoring a female subject for disease or disorder of the female reproductive tract, comprising: a) at a first time point, i) determining the frequency of disease or disorder of female reproductive tract signature cells in a sample of the female subject according to the method of the present invention, and / or ii) determining disease or disorder of female reproductive tract factor signatures in a sample of the female subject according to the method of the present invention; b) at a second time point, i) determining the frequency of disease or disorder of female reproductive tract signature cells in a sample of the female subject according to the method of the present invention, and / or ii) determining disease or disorder of female reproductive tract factor signatures in a sample of the female subject according to the method of the present invention; c) comparing the frequencies determined in a)i) and / or b)i) and / or a)ii) and the factor signatures determined in a)ii) and / or b)ii) with a monitoring reference pattern, wherein the first time point is combined with the second time point; and d) monitoring the female subject based on the comparison in step b).

[0118] In some embodiments, the present invention relates to a monitoring method or diagnostic method(s) described herein, which is used as a screening method for detecting the onset of disease in healthy women.

[0119] In certain embodiments, the present invention relates to a method for predicting susceptibility to treatment for a disease or disorder of the female reproductive tract in female subjects who have or are at risk of having a disease or disorder of the female reproductive tract, the method comprising: a)i) determining the frequency of disease or disorder of female reproductive tract signature cells in a sample of the female subject according to the method of the present invention; and / or ii) determining the disease or disorder of the female reproductive tract factor signature in a sample of the female subject according to the method of the present invention; b) comparing the frequency determined in a)i) and / or the signature determined in a)ii) with a susceptibility criterion pattern; and c) predicting the susceptibility of the female subject to treatment for a disease or disorder of the female reproductive tract based on the comparison in step b).

[0120] In certain embodiments, the present invention relates to a method for predicting susceptibility to treatment for a female reproductive tract disease or disorder in a female subject who has or is at risk of having a female reproductive tract disease or disorder, comprising: a)i) determining the frequency of disease or disorder of female reproductive tract signature cells in a sample of the female subject according to the method of the present invention; and / or ii) determining the disease or disorder of the female reproductive tract factor signature in a sample of the female subject according to the method of the present invention; b) comparing the frequency determined in a)i) and / or the signature determined in a)ii) with a susceptibility criterion pattern; and c) predicting the susceptibility of the female subject to treatment for a female reproductive tract disease or disorder based on the comparison in step b), wherein a frequency of disease or disorder of female reproductive tract signature cells and / or disease or disorder of female reproductive tract factor signature exceeding a susceptibility criterion pattern indicates that the subject is susceptible to treatment.

[0121] In some embodiments, the sensitivity to the treatment described herein is the disease outcome and / or disease progression after the treatment. For example, the method of the present invention may be used to predict disease recurrence after laparoscopic removal of endometriotic lesions.

[0122] In certain embodiments, the present invention relates to a method of the present invention in which treatment for a disease or disorder of the female reproductive tract is an anticancer treatment. In some embodiments, the anticancer treatment described herein is at least one compound selected from the group consisting of carboplatin, avastin, paclitaxel, doxil, methotrexate, lymphalza, adriamycin, gesal, olaparib, doxorubicin, alkeran, paraplatin, zejula, bevacizumab, cisplatin, doxorubicin, gemcitabine, rubraca, cosmegen, hicamtin, topotecan, cyclophosphamide, melphalan, toposal, and etopophos.

[0123] In certain embodiments, the present invention relates to a method of the present invention, wherein the treatment of a disease or disorder of the female reproductive tract is an endometriosis treatment selected from the group consisting of hormone therapy, physiotherapy, surgery, multimodal pain therapy (drugs (e.g., tarzine, oxynorm), TENS machines), individual nutritional counseling (e.g., reducing meat intake), and complementary medicines.

[0124] In certain embodiments, the present invention relates to a method of the present invention, wherein treatment for a disease or disorder of the female reproductive tract is a treatment selected from the group consisting of analgesics, hormone therapy, fertility treatment and surgery.

[0125] In certain embodiments, the present invention relates to a method of the present invention, wherein the treatment of endometriosis is a treatment selected from the group consisting of analgesics, hormone therapy, infertility treatment, and surgery.

[0126] As used herein, the term “analgesic” refers to any analgesic used to treat the symptoms of endometriosis (see, for example, Ruhland, B., et al., 2011, Minerva Ginecol 63:1-2). In some embodiments, the analgesic described herein is an analgesic selected from the group of ibuprofen, naproxen, and oxycodone.

[0127] The term “hormone therapy,” as used herein, refers to any hormonal therapy used to treat the symptoms of endometriosis (see, for example, Ruhland, B., et al., 2011, Minerva Ginecol 63:1-2). In some embodiments, the hormonal therapy described herein is a progesterone treatment, preferably a gestager selected from the group of desogestrel, dinogest, and levonorgestrel. The hormonal therapy described herein may be administered orally, implanted, by injection, percutaneously, or using an intrauterine contraceptive device.

[0128] As used herein, the term “fertility treatment” refers to any fertility treatment used to treat infertility or attenuation in connection with endometriosis (see, for example, Becker, CM, Gattrell, WT, Gude, K., & Singh, SS, 2017, Fertility and Sterility, 108(1), 125-136). In some embodiments, the fertility treatment described herein is in vitro fertilization. In some embodiments, the fertility treatment described herein is a treatment selected from the group consisting of clomiphene citrate, gonadotropins, metformin, letrozole, bromocriptine, follitropin alfa, intrauterine contraception (IUI), and in vitro fertilization (IVF).

[0129] As used herein, the term “surgery” refers to any surgical procedure used to treat the symptoms of endometriosis (see, for example, Leonardi, M., et al. 2020, Journal of minimally invasive gynecology, 27(2), 390-407). In some embodiments, the surgery described herein is a form of surgery selected from the group of conservative surgery, complex surgery, radical surgery, and laparoscopy.

[0130] The choice of treatment is particularly important in endometriosis because it can have irreversible effects on disease progression and / or fertility.

[0131] Accordingly, the present invention is at least in part based on the finding that accurate prediction of susceptibility to treatment is possible by the methods described herein.

[0132] In certain embodiments, the present invention relates to a method of the present invention, wherein a susceptibility criterion pattern or predictive criterion pattern is obtained from a reference group, and at least one of the reference group members has been diagnosed with a disease or disorder of the female reproductive tract.

[0133] In certain embodiments, the present invention relates to a method of the present invention, wherein a susceptibility criterion pattern or predictive criterion pattern is obtained from a reference group, and at least one of the reference group members has been diagnosed with endometriosis.

[0134] The inventors have found that data from subjects suffering from diseases or disorders of the female reproductive tract can be used as a standard.

[0135] Therefore, the present invention is at least in part based on the finding that data from affected subjects are particularly useful for establishing reference patterns in the methods described herein.

[0136] In certain embodiments, the present invention relates to a method of the present invention, which includes machine learning techniques for obtaining a susceptibility criterion pattern or a predictive criterion pattern from a reference object.

[0137] As used herein, the term “machine learning technique” refers to a computer implementation technique that enables automated learning and / or improvement from experience (e.g., training data and / or acquired data) without requiring explicit programming of what has been learned and / or improved. In some embodiments, the machine learning technique includes at least one technique selected from the group consisting of logistic regression, CART, bagging, random forest, gradient boosting, linear discriminant analysis, Gaussian process classifier, Gaussian NB, linear, lasso, ridge, ElasticNet, partial least squares, KNN, DecisionTree, SVR, AdaBoost, GradientBoost, neural networks, and ExtraTrees.

[0138] The inventors have found that machine learning techniques provide an efficient and / or unbiased method for identifying patterns that predict disease and treatment-related parameters.

[0139] In certain embodiments, the present invention relates to a method of the present invention, which includes a convolutional neural network and / or logistic regression for obtaining a susceptibility criterion pattern or a predictive criterion pattern from a reference object.

[0140] In certain embodiments, the present invention relates to a method of the present invention, which involves obtaining a susceptibility criterion pattern or a predictive criterion pattern from a reference object using machine learning techniques, preferably convolutional neural networks and / or logistic regression.

[0141] The CellCnn convolutional neural network has been previously described (Arvaniti, E., Classen, M., 2017, Nat Commun 8, 14825; Bodenmiller et al., Nat Biotechnol, 2012, 30(9), 858-867; Amir et al., Nat Biotechnol, 2013, 31(5), 545-552; Levine et al., Cell, 2015, 162(1), 184-197; Horowitz et al., Sci Transl Med, 2013, 5(208), 208ra145) and is publicly available (https: / / github.com / eiriniar / CellCnn). Furthermore, the examples illustrate how the CellCnn convolutional neural network can be used in relation to the present invention.

[0142] In certain embodiments, the present invention relates to a method for classifying female subjects who have or are at risk of having a disease or disorder of the female reproductive tract, the method comprising: ai) identifying the frequency of disease or disorder of the female reproductive tract signature cells in a sample of the female subject according to the method of the present invention; ii) determining disease or disorder of the female reproductive tract factor signature in a sample of the female subject according to the method of the present invention; iii) predicting the onset, progression, and / or outcome of the disease in the female subject according to the method of the present invention; and / or iv) predicting the female subject's susceptibility to treatment for disease or disorder of the female reproductive tract according to the method of the present invention; and classifying the female subject according to the frequency determined in bi), the factor signature determined in ii), the prediction in iii), and / or the prediction in iv).

[0143] In certain embodiments, the present invention relates to a method for classifying female subjects who have or are at risk of having endometriosis, the method comprising: a)i) determining the endometriotic status according to the method of the present invention; ii) predicting the outcome of the disease, the onset of the disease, and / or the progression of the disease in the female subjects according to the method of the present invention; and / or iii) predicting the female subjects' sensitivity to treatment for endometriosis according to the method of the present invention; and b) classifying the female subjects according to the frequency determined in i), the signature determined in ii), the prediction in iii), and / or the prediction in iv).

[0144] In certain embodiments, the present invention relates to a method of the present invention in which at least one class indicates the stage and / or severity of endometriosis.

[0145] As used herein, the term “stage of endometriosis” refers to established stages of endometriosis, such as the four rASRM stages (Rock, JA, & ZOLADEX Endometriosis Study Group, 1995, Fertility and Steryl, 63(5), 1108-1110) or the ENZIAN stages P1-3, O1-3, T1-3, A1-3, B1-3, C1-3, F (location).

[0146] In certain embodiments, the present invention relates to a composition comprising a reagent for detecting a biomarker for the diagnosis of a disease or disorder of the female reproductive tract, wherein the biomarker comprises or consists of at least two markers listed in Table 1.

[0147] In certain embodiments, the present invention relates to a composition comprising a reagent for detecting a biomarker for the diagnosis of endometriosis, wherein the biomarker comprises or consists of at least two markers listed in Table 1.

[0148] In certain embodiments, the present invention relates to a pharmaceutical product comprising a compound for diseases or disorders of the female reproductive tract, for use in the treatment of a woman who is expected to be sensitive to treatment for a disease or disorder of the female reproductive tract according to the method of the present invention.

[0149] As used herein, the term "pharmaceutical product" means a preparation that is in a form that enables the biological activity of the active ingredient contained therein, and that does not contain any additional components that are unacceptably toxic to the subject to which the preparation is to be administered.

[0150] As used herein, the term “compound for disease or disorder of the female reproductive tract” means any compound known to be effective in treating disease or disorder of the female reproductive tract and / or its symptoms.

[0151] The inventors have found that using the method(s) of the present invention, it is possible to identify target populations that are particularly sensitive to specific pharmaceutical products. Therefore, the pharmaceutical products dramatically improve the risk-reward ratio in this / these target populations(s).

[0152] In certain embodiments, the present invention relates to a pharmaceutical product of the present invention, wherein the compound for disease or disorder of the female reproductive tract is an anti-cancer treatment.

[0153] In certain embodiments, the present invention relates to a pharmaceutical product of the present invention in which a compound for disease or disorder of the female reproductive tract is selected from the group consisting of ibuprofen, naproxen, oxycodone, desogestrel, dienogest, levonorgestrel, clomiphene citrate, gonadotropins, metformin, letrozole, and bromocriptine.

[0154] In certain embodiments, the present invention relates to a treatment method comprising: 1) a step of classifying and / or predicting the susceptibility of a female subject to treatment for a female reproductive tract disease or disorder, or a female subject at risk of having such a disease or disorder, according to the method of the present invention; and 2) a step of treating the female subject with a treatment for the female reproductive tract disease or disorder, wherein the selection of the treatment for the female reproductive tract disease or disorder depends on the predicted susceptibility and / or classification of susceptibility in step (1).

[0155] In certain embodiments, the present invention relates to a treatment method comprising: 1) a step of classifying and / or predicting the sensitivity to treatment for a disease or disorder of the female reproductive tract in a woman who has or is at risk of having a disease or disorder of the female reproductive tract, according to the method of the present invention; and 2) a treatment for a woman having at least one disease or disorder of the female reproductive tract selected from the group of anticancer treatment, pain medication, hormone therapy, fertility treatment and surgery, wherein the selection of the treatment for the disease or disorder of the female reproductive tract depends on the predicted sensitivity and / or classification of the sensitivity in step (1).

[0156] In certain embodiments, the present invention relates to a treatment method comprising: 1) classifying and / or predicting the susceptibility of a female subject to treatment for a female reproductive tract disease or disorder, who has or is at risk of having a female reproductive tract disease or disorder, according to the present invention; and 2) treating the female subject with a pharmaceutical product selected from the group ibuprofen, naproxen, oxycodone, desogestrel, dienogestrel, levonorgestrel, clomiphene citrate, gonadotropin, metformin, letrozole, and bromocriptine, wherein the selection of the pharmaceutical product depends on the classification of the predicted susceptibility and / or susceptibility in step (1).

[0157] In certain embodiments, the present invention relates to a method, composition, or pharmaceutical product of the present invention in which the disease or disorder of the female reproductive tract is endometriosis, ovarian cancer, and / or adenomyosis.

[0158] In certain embodiments, the present invention relates to a method, composition, or pharmaceutical product of the present invention in which the disease or disorder of the female reproductive tract is endometriosis, ovarian cancer, adenomyosis, and / or endometrial cancer.

[0159] As used herein, the term “ovarian cancer” refers to a condition characterized by the abnormal and rapid proliferation of cells in the ovarian region and / or ovarian area in question. In some embodiments, the ovarian cancer described herein is primary ovarian cancer.

[0160] As used herein, the term “adenomyosis” refers to a condition characterized by intrauterine cell proliferation, which is characterized by cell proliferation that thickens and / or enlarges the uterus.

[0161] As used herein, the term “endometrial cancer” refers to a condition characterized by abnormally rapid proliferation of cells in the tissue lining the uterus. In some embodiments, the endometrial cancer described herein is primary endometrial cancer.

[0162] The inventors have found that the means and methods of the present invention are particularly sensitive and / or specific with respect to endometriosis, ovarian cancer, adenomyosis, and / or in distinguishing such indications.

[0163] In certain embodiments, the present invention relates to a method, composition, or pharmaceutical product of the present invention, wherein the disease or disorder of the female reproductive tract is endometriosis.

[0164] As used herein, the term “endometriosis” refers to a disorder of the female reproductive system in which cells similar to those of the endometrium, the layer of tissue that normally lines the inside of the uterus, proliferate outside the uterus.

[0165] Risk factors for endometriosis include, but are not limited to, genetic risk factors (e.g., a relative diagnosed with endometriosis, and / or mutations in one or more of the WNT4, GREB1 / FN1, ID4, 7p15.2, CDKN2BAS, 10q26, VEZT, MUC16 genes / regions), and a history of symptoms of endometriosis and environmental toxins (e.g., exposure to estrogen, exposure to dioxins, or obstruction of menstrual flow).

[0166] The inventors have found that the means and methods of the present invention are particularly sensitive and / or specific with respect to endometriosis.

[0167] In certain embodiments, the present invention relates to a method, composition, or pharmaceutical product of the present invention, wherein endometriosis is selected from the group consisting of peritoneal endometriosis, endometrioma, deep endometriosis, tubal endometriosis, and abdominal wall endometriosis.

[0168] The inventors have found that the biomarkers described herein are particularly altered in certain types of endometriosis. Therefore, the present invention is at least in part based on the finding that the methods described herein are particularly sensitive and / or specific in certain types of endometriosis.

[0169] In certain embodiments, the present invention relates to a method, composition, or pharmaceutical product of the present invention for endometriosis in rASRM stage I. In certain embodiments, the present invention relates to a method, composition, or pharmaceutical product of the present invention for endometriosis in rASRM stage II. In certain embodiments, the present invention relates to a method, composition, or pharmaceutical product of the present invention for endometriosis in rASRM stage III. In certain embodiments, the present invention relates to a method, composition, or pharmaceutical product of the present invention for endometriosis in rASRM stage IV. In certain embodiments, the present invention relates to a method, composition, or pharmaceutical product of the present invention for endometriosis in rASRM stage III or IV. In certain embodiments, the present invention relates to a method, composition, or pharmaceutical product of the present invention for endometriosis in rASRM stage II or III. In certain embodiments, the present invention relates to a method, composition, or pharmaceutical product of the present invention for endometriosis in rASRM stage I or II. In certain embodiments, the present invention relates to a method, composition, or pharmaceutical product of the present invention for endometriosis in stage I, II, or III of rASRM.

[0170] In certain embodiments, the present invention relates to a method, composition, or pharmaceutical product of the present invention for endometriosis in rASRM stage II, III, or IV.

[0171] The inventors have found that the biomarkers described herein change particularly in the later stages of endometriosis.

[0172] Therefore, the present invention is at least in part based on the finding that the methods described herein are particularly sensitive and / or specific in the later stages of endometriosis.

[0173] In certain embodiments, the present invention relates to a computer program product comprising instructions for performing the method of the present invention, wherein the method is implemented in a computer.

[0174] The computer program products described herein may include computer-readable program instructions that can be downloaded from a computer-readable storage medium to their respective computing / processing devices, or via a network to an external computer or external storage device.

[0175] Computer-readable program instructions for performing the operation of the present invention may be assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code, written in any combination of one or more programming languages, including, for example, object-oriented programming languages ​​such as Smalltalk and C++, and conventional procedural programming languages ​​such as the "C" programming language or a similar programming language.

[0176] Computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer, partially on a remote computer, or fully on a remote computer or server.

[0177] In this specification, the indefinite article (a: a, one), the indefinite article (an: a, one), and the definite article (the: this, that) are used to refer to one or more grammatical objects of the article (i.e., at least one, or more than one).

[0178] "Or" should be understood as meaning one of the options, both, or any combination thereof.

[0179] "And / or" should be understood as meaning either one or both of the options.

[0180] Throughout this specification, unless otherwise required by context, the terms “comprise,” “comprises,” and “comprising” shall be understood to mean the inclusion of the described step or element or group of steps or elements, but not the exclusion of any other step or element or group of steps or elements.

[0181] The terms "include" and "comprise" are used synonymously. "Preferably" means one option from a set of options that does not exclude other options. "For example" means an example that is not limited to the example mentioned. "Consists of" means that it includes and is limited to everything that precedes the expression "consists of".

[0182] When used herein, the terms “about” or “approximately” mean within 20%, more preferably within 10%, and even more preferably within 5% of a given value or range.

[0183] Throughout this specification, any reference to “one embodiment,” “an embodiment,” “a particular embodiment,” “a related embodiment,” “a specific embodiment,” “an additional embodiment,” “several embodiments,” “a specific embodiment,” or “a further embodiment,” or any combination thereof, means that the specific features, structures, or characteristics described in relation to that embodiment are included in at least one embodiment of the present invention. Therefore, the above phrases appearing in various places throughout this specification do not necessarily all refer to the same embodiment. Furthermore, specific features, structures, or characteristics may be combined in any preferred manner in one or more embodiments. It is also understood that a positive description of a feature in one embodiment may serve as a basis for excluding a feature in a particular embodiment.

[0184] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art relating to this invention. Methods and materials similar to or equivalent to those described herein may be used in carrying out or testing the invention, but preferred methods and materials are described below. In case of any conflict, this specification shall prevail, including definitions. Furthermore, materials, methods, and examples are illustrative and not intended to limit the scope.

[0185] The general methods and techniques described herein are carried out in accordance with the conventional methods described in the various general and more specific references cited and discussed herein, unless otherwise indicated, which are well known in the art. See, for example, Sambrook et al., Molecular Cloning: A Laboratory Manual, 2nd ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, (1989); Ausubel et al., Current Protocols in Molecular Biology, Greene Publishing Associates, (1992); and Harlow and Lane, Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, (1990).

[0186] Embodiments of the present invention are illustrated and described in detail in the drawings and the preceding description, but such illustrations and descriptions should be considered illustrative or exemplary and not limiting. It will be understood that modifications and alterations can be made by those skilled in the art within the scope of the following claims and spirit. In particular, the present invention encompasses further embodiments having any combination of features from the different embodiments described above and below.

[0187] The present invention further relates to the following items: 1. A method for determining the frequency of disease or disorder of female reproductive tract signature cells in multiple cells, i) A step of determining the expression levels of at least two biomarkers selected from Table 1 in multiple cells; and ii) A step of determining the frequency of disease or impairment of the female reproductive tract signature cells in the plurality of cells based on the expression of at least two biomarkers selected from Table 1, wherein an increase in one or more biomarkers selected from Table 2 indicates disease or impairment of the female reproductive tract signature cells, and / or an increase in one or more biomarkers selected from Table 3 indicates non-disease or impairment of the female reproductive tract signature cells. The method, including the method described above.

[0188] 2. A method for determining disease or disorder of female reproductive tract factor signatures in female samples, i) In female samples, determine the expression levels of at least two biomarkers selected from Table 1; and ii) Determining the disease or disorder of the female reproductive tract factor signature in the sample based on the expression of at least two biomarkers selected from Table 1, preferably such that an increase in one or more biomarkers selected from Table 2 indicates a disease or disorder of the female reproductive tract factor signature, and / or an increase in one or more biomarkers selected from Table 3 does not indicate a disease or disorder of the female reproductive tract factor signature. The method, including the method described above.

[0189] 3. The method according to item 1 or 2, wherein the sample is a proliferative stage sample, and preferably an increase in one or more biomarkers selected from Table 4 indicates disease or impairment of female reproductive tract signature cells, and / or an increase in one or more biomarkers selected herefrom Table 5 indicates non-disease or impairment of female reproductive tract signature cells.

[0190] 4. Cells having at least one cell lineage marker are preferably from the following group: a) ITGAM (encoding CD11b), ITGB2 (encoding CD18), CD44, FCGR3A (CD16), FCGR2A (CD32), S100A8, or S100A9; b) DRC3, RSPH3, ARMC2, LRRC23, C16orf46, ZNF487, or BBOF1; and c) COL18A1, COL4A2, COL4A1, VIM or CALD1 Using at least one cell lineage marker selected from The method described in any one of items 1-3, including at least one step of preliminary selection.

[0191] 5. The method described in any one of items 1 to 4, wherein at least 3, 4, 5, 6, 7, 8, or 9 biomarkers are determined.

[0192] 6. The method according to any one of items 1 to 5, wherein determining the expression level includes nucleic acid detection technology.

[0193] 7. The method according to any one of items 1 to 6, wherein the plurality of cells are primary cells, or the sample is a primary sample.

[0194] 8. The method according to any one of items 1 to 7, wherein the level is determined in an endometrial sample, menstrual blood sample, vaginal smear sample and / or cervical smear sample.

[0195] 9. The method according to any one of items 1 to 7, wherein the level is determined in a blood sample such as plasma or serum.

[0196] 10. The method according to any one of items 1 to 9, further comprising determining at least one non-molecular marker, preferably the non-molecular marker being a marker selected from the group consisting of age, weight, BMI, pregnancy, number of births, race, fertility status, past laparoscopy, past drug use, and other gynecological disorders.

[0197] 11. The method according to any one of items 1 to 10, wherein the method is at least partially implemented on a computer, and the expression level is determined by obtaining data indicating the expression level.

[0198] 12. A method for predicting the onset, progression, and / or outcome of a disease in a woman who has a disease or disorder of the female reproductive tract, or who is at risk of having a disease or disorder of the female reproductive tract, a)i) Determining the frequency of disease or disorder of the female reproductive tract signature cells in a female subject sample according to the method described in any one of items 1, 3 to 11; and / or ii) A step of determining the disease or disorder of the female reproductive tract factor signature in a sample from a female subject, according to the method described in any one of items 2 to 11. The steps include comparing the frequency determined in b)a)i) and / or the factor signature determined in a)ii) with the predictive criterion pattern, c) A step in which, based on the comparison in step b), predicts the onset, progression, and / or outcome of the disease in the female subject. The method, including the method described above.

[0199] 13.1) Increased frequency of disease or disorder in female reproductive tract signature cells expressing the biomarkers in Table 2 compared to the reference pattern; and / or 2) Increased levels of the biomarkers in Table 2 in female reproductive tract factor signature disease or disorder compared to the reference pattern. The method described in item 12, which indicates a high probability of disease onset, a high probability of disease progression, and / or a worsening of the disease outcome.

[0200] 14.1) Increased frequency of disease or disorder in female reproductive tract signature cells expressing the biomarkers in Table 3 compared to the reference pattern; and / or 2) Increased levels of the biomarkers in Table 3 in female reproductive tract factor signature disease or disorder compared to the reference pattern. The method described in item 12 or 13, which indicates a low likelihood of developing the disease, a low likelihood of disease progression, and / or an improvement in the disease outcome.

[0201] 15. A method for predicting the susceptibility of a woman to treatment for a disease or disorder of the female reproductive tract, in a woman who has a disease or disorder of the female reproductive tract or is at risk of having such a disease or disorder of the female reproductive tract, a)i) the step of determining the frequency of disease or disorder of the female reproductive tract signature cells in a female subject sample according to the method described in any one of items 1, 3 to 11; and / or ii) A step of determining the disease or disorder of the female reproductive tract factor signature in a sample from a female subject, according to the method described in any one of items 2 to 11. The steps include comparing the frequency determined in b)a)i) and / or the signature determined in a)ii) with the susceptibility criterion pattern, c) Based on the comparison in step b), predict the susceptibility of the female subject to treatment for the female reproductive tract disease or disorder, preferably, that the frequency of the female reproductive tract signature cell disease or disorder and / or the female reproductive tract factor signature disease or disorder exceeding the susceptibility criterion pattern indicates that the subject is susceptible to treatment. The method, including the method described above.

[0202] 16. The method according to item 15, wherein the treatment for disease or disorder of the female reproductive tract is a treatment selected from the group consisting of analgesics, hormone therapy, fertility treatment, and surgery.

[0203] 17. The method described in any one of items 12-16, wherein a susceptibility criterion pattern or predictive criterion pattern is obtained from a reference group, and at least one of the reference group members has been diagnosed with a disease or disorder of the female reproductive tract.

[0204] 18. The method according to item 17, wherein obtaining the sensitivity criterion pattern or the prediction criterion pattern from a reference subject includes machine learning techniques, preferably convolutional neural networks and / or logistic regression.

[0205] 19. A method for classifying women who have or are at risk of having a disease or disorder of the female reproductive tract, a)i) A step of determining the frequency of disease or disorder of the female reproductive tract signature cells in a female subject sample according to the method described in any one of items 1, 3 to 11; ii) A step of determining the disease or disorder of the female reproductive tract factor signature in a female subject sample according to the method described in any one of items 2 to 11; iii) A step of predicting the onset, progression, and / or outcome of a disease in a woman, in accordance with the method described in any one of items 12-14, 17, or 18; and / or iv) A step of predicting the susceptibility of a female subject to treatment for a disease or disorder of the female reproductive tract in accordance with the method described in any one of items 15-18, and The step of classifying the female subjects according to the frequency determined in bi), the factor signature determined in ii), the prediction in iii), and / or the prediction in iv). The method, including the method described above.

[0206] 20. The method according to item 19, wherein at least one class indicates the stage and / or severity of the endometriosis.

[0207] 21. A composition comprising a reagent for detecting a biomarker for the diagnosis of a disease or disorder of the female reproductive tract, wherein the biomarker comprises or consists of at least two markers from Table 1.

[0208] 22. A pharmaceutical product comprising a compound for disease or disorder of the female reproductive tract, for use in treatment of a woman who is expected to be susceptible to treatment for disease or disorder of the female reproductive tract, in accordance with the methods described in items 15-18.

[0209] 23. A pharmaceutical product as described in item 22, wherein the compound for disease or disorder of the female reproductive tract is selected from the group consisting of ibuprofen, naproxen, oxycodone, desogestrel, dienogestrel, levonorgestrel, clomiphene citrate, gonadotropin, metformin, letrozole, and bromocriptine.

[0210] 24. The method according to items 1 to 20, the composition according to item 21, or the pharmaceutical product according to item 22 or 23, wherein the disease or disorder of the female reproductive tract is endometriosis, ovarian cancer and / or adenomyosis.

[0211] 25. The method according to item 24, the composition according to item 24, or the pharmaceutical product according to item 24, wherein the disease or disorder of the female reproductive tract is endometriosis.

[0212] 26. The method according to item 25, the composition according to item 25, or the pharmaceutical product according to item 25, wherein the endometriosis is selected from the group consisting of peritoneal endometriosis, endometrioma, deep endometriosis, tubal endometriosis, and abdominal wall endometriosis.

[0213] 27. The method described in item 25 or 26, the composition described in item 25 or 26, or the pharmaceutical product described in item 25 or 26, wherein the endometriosis is rASRM stage II, III, or IV.

[0214] 28. A computer program product comprising instructions for performing the method described in any one of items 11-20 or 24-27, wherein the method is computer-implemented in the computer program product. [Brief explanation of the drawing]

[0215] [Figure 1A] ROC curves for three learners in the growth phase with AUC = 1.00 (A). [Figure 1B] ROC curves for three learners in the growth phase with AUC=1.00(B). [Figure 1C] ROC curves for three learners in the growth phase with AUC=0.78(C). [Figure 2A] ROC curve for a representative learner in the entire periodic phase with AUC = 0.83(A). [Figure 2B] ROC curve for a representative learner in the entire periodic phase with AUC=0.79(B). [Figure 2C] ROC curve for a representative learner in the entire periodic phase with AUC=0.95(C). [Figure 3] Expression levels of six genes selected from differential expression analysis between endometriosis samples and non-endometriosis samples during the entire circadian cycle (A-F) or proliferative phase (G-L). [Figure 4] The top 50 GO terms and pathways of gene signatures ranked by adjusted p-value. The number of genes involved in each GO term and pathway (intersection_size) and the -log10 p-value are shown. [Figure 5] UMAP highlights the target cell type that expresses the signature of samples from the proliferative phase (A) and samples from both the proliferative and secretory phases (B). [Figure 6] The AUCs of the trained models are shown for the inventors' signature (A, median AUC = 0.78), the competitor's signature (B, median AUC = 0.56), and the total gene list derived from the competitor and the inventor, respectively (C, median AUC = 0.78). [Examples]

[0216] Aspects of the present invention are further illustrated by the following exemplary, non-limiting embodiments, which provide a better understanding of embodiments of the present invention and its many advantages. The following embodiments are included to support preferred embodiments of the present invention. It should be understood by those skilled in the art that the techniques disclosed in the following embodiments represent techniques discovered by the inventors to work well in the execution of the present invention and may therefore be considered to constitute a preferred mode for its execution. However, those skilled in the art should understand that, in light of this disclosure, many modifications are made in the particular embodiments disclosed without departing from the spirit and scope of the present invention, but similar or comparable results can still be obtained.

[0217] Example 1 clinical research design Phase I Discovery: An open-label study to discover biomarkers for the diagnosis and prognosis of endometriosis.

[0218] Study group Patients were recruited at the Frauenklinik in Bern following approval of this application. Inclusion criteria for this study included women who provided informed consent and were scheduled for laparoscopic surgery as part of a planned clinical procedure for reasons including suspected endometriosis, tubal ligation, idiopathic infertility, or other gynecological conditions. Women of all races and social backgrounds aged 18 years or older were included. Patients with other pre-existing inflammatory diseases, pregnancy, malignancies, or those undergoing emergency surgery were excluded.

[0219] Blood and / or endometrial biopsies were isolated from a total of 256 patients suspected of having endometriosis immediately before surgery.

[0220] Purpose of clinical testing Main: The primary objective of this project is to identify significant biomarker characteristics in women's tissues (endometrial biopsy, ectopic lesions, and peritoneal fluid), regardless of whether or not endometriosis is present, in order to contribute to the identification of patients with endometriosis.

[0221] secondary: A secondary objective of this project is to identify the following: i) Biological differences in tissue among women with fluctuating endometriosis-related symptoms; i) Biological differences in tissues among women that affect outcomes of endometriosis-related treatments; iii) The above biological differences observed in tissues are It serves as a basis for the development and / or evaluation of potential drugs.

[0222] Data type Clinical parameters: Fully anonymized clinical data, such as age, weight, BMI, number of pregnancies and births, race, past laparoscopy and (hormone) drug use, and other gynecological disorders.

[0223] Unicellular RNA sequencing: RNA expression profiles obtained from endometrial biopsies (Pipelle) of 42 patients.

[0224] inclusion criteria An informed consent form with signature and date. Age: Women over 18 years old who have not yet gone through menopause. A woman undergoing laparoscopic surgery. A good overall health condition as demonstrated by the medical history, physical examination, gynecological examination, and clinical test results.

[0225] This study was conducted on patients in the proliferative or secretory phase of their menstrual cycle. Patients' progesterone levels were measured before surgery, and endometrial biopsies were analyzed by pathologists to confirm the menstrual cycle phase.

[0226] The planned research will not affect further treatment steps.

[0227] Exclusion criteria Patients who are unlikely to cooperate or are legally incapacitated (including patients confined to an institution by court or public order). Any condition that may prevent a patient from complying with the research. Pregnant or breastfeeding patients. Patients who are using any anti-inflammatory medication or who have another inflammatory disease. Patients receiving hormone therapy or an IUD for less than three months prior to surgery were excluded. Exclusion of patients who are not clearly diagnosed with endometriosis histologically. Patients with a menstrual cycle exceeding 35 days at the time of surgery were excluded.

[0228] Additional selection criteria included balancing samples according to the patient's cyclical phase, diverse endometriosis stages (rASRM I-IV), endometriosis type (DIE, endometrioma, peritoneal, or a combination thereof), and pain scores.

[0229] methodology Biomarkers were identified through analysis of a patient cohort. Endometrial biopsies (pipelles: endometrial scratches) from 42 patients were processed and stored under multiple conditions. The majority of pipelles were digested into single-cell suspensions and cryopreserved for further analysis of gene expression. The remaining samples were stored in DMSO and / or fresh-frozen in liquid nitrogen for study of protein expression, isolation, and culture of endometrial cells and immune cells present in endometrial tissue.

[0230] Cell capture and cDNA library generation were performed using a chromium system (10X Genomics). The cDNA library was sequenced using the Illumina platform.

[0231] Sample quality This study included only Pippel samples with a single-cell survival rate exceeding 70% after thawing and more than 100,000 single cells.

[0232] Observation of piperus quality: After visually evaluating the piperus, they were treated by single-cell dissociation.

[0233] Patient data The collected medical data was refined into a format for integration into the inventors' internal deep learning platform, ScaiVision®, or into another suitable data analysis workflow that uses patient data as a tool to identify disease-related molecular profiles / or cellular identity biomarkers.

[0234] Data Analysis Data preprocessing Quality control to check technical or batch effects Automated cell type annotation Supervised discovery of predictive biomarkers using convolutional neural networks The goal is to identify and validate gene signatures that predict one or more of the described endpoints with a sensitivity and specificity of 80% or higher.

[0235] Project Period 24 months

[0236] statistical analysis Using the CellCnn-based machine learning algorithm ScaiNet, we identified a set of biomarkers that define signatures specifically present in samples from endometriosis patients (Arvaniti and Cloasen 2017). To create an independent validation set, 40% of the 42 samples were randomly removed before starting network training. Any biomarker profile that passed an accuracy significance threshold of >80% was considered a potential candidate. We evaluated the reproducibility of the ScaiNet algorithm for specific datasets using 3x cross-validation.

[0237] Description of the work performed Biomarker discovery was performed in a cohort of endometrial samples (pipel) collected from 256 patients immediately before planned surgery, of which 105 were subsequently diagnosed with endometriosis. Single-cell RNA sequencing was performed on 42 of the isolated pipel samples to determine the detectable levels of RNA transcripts in single cells within endometrial tissue.

[0238] This workflow consists of steps including raw read sequence quality control, transcript quantification, sample quality control at the gene and cell levels, normalization, dimensionality reduction, and sample class prediction using ScaiNet. The workflow is embedded in the workflow management engine Snakemake for automation and to ensure reproducibility (Koster, J., & Rahmann, S. (2012). Bioinformatics, 28(19)).

[0239] Raw read mapping and quantification are performed at the transcript level. Gene indexing is performed by Salmon package, cell debarcoding, duplication removal, read mapping, and transcript-level expression estimation using pseudo-alignment with Salmon alevin software.

[0240] For quality control (QC) of raw reads, the software MultiQC (Ewels, P., Magnusson, M., Lundin, S., & Kaller, M. (2016). Bioinformatics, 32(19)) is used. QC for quantitative steps is performed using the package AlevinQC (Charlotte Sooneson and Avi Srivastava (2021). https: / / github.com / csoneson / alevinQC). The Seurat (Satija, R., Farrell, JA, Gennert, D., Schier, AF, & Regev, A. (2015) Nature Biotechnology, 33(5)) and Scactor (McCarthy, DJ, Campbel, KR, Lun, ATL, & Wills, QF (2017). Bioinformatics, 33(8)) packages are used for quality control and visualization of sample-level, cell-level, and gene-level data. This includes detection and removal of outlier cells based on transcript and gene metrics, detection of potentially double-lined cells, and batch effects.

[0241] Samples that did not meet the following quality criteria were excluded: median mitochondrial expression per cell <30%, median number of genes per cell >1000, number of cells per sample >5000, median number of unique transcripts per cell >1000.

[0242] Dimensionality reduction is performed by selecting highly variable genes that cause the greatest variation within the cell population. Next, the selected features are used to train ScaiNet for sample classification.

[0243] The patient samples may be divided into two groups: one group consisting of patients diagnosed with endometriosis during surgery and pathologically, and the other consisting of patients without endometriosis. This resulted in 24 endometriosis samples and 18 non-endometriosis samples.

[0244] Approximately 40% of the samples from each group were set aside for use in model validation. The remaining 60% of the samples were used to train a series of ScaiNet neural networks to distinguish between the endometriosis group and the non-endometriosis group. Fifty to one hundred such networks were trained within the selected range of hyperparameters.

[0245] To generate a model independent of the menstrual cycle phase, the network was trained on samples from all patients (all menstrual phases, n = 35). However, assuming that the cycle phase affects the model, the network was separately trained on samples from patients in the proliferative phase (n = 18, 9 non-endometriosis and 9 endometriosis).

[0246] For ScaiNet training, 60% of the samples were used to perform three cross-validation (CV) splits. For the proliferative samples, the most performant network that achieved AUCs of 1.00, 1.00, and 0.78 in each of the three independent CV splits was selected for further analysis (Figure 1). For samples of all cycles, the most performant network from three independent CV splits that achieved AUCs of 0.83, 0.79, and 0.95 was selected for further analysis (Figure 2).

[0247] In conclusion, the network efficiently identifies endometriosis patients and is more accurate in collecting samples in the proliferative phase than at any stage of the menstrual cycle.

[0248] Example 2 ScaiNet learned filters from the most performant networks, at least one of which was positively correlated with endometriosis and one of which was negatively correlated with endometriosis (or positively correlated with non-endometriosis) (Table 1).

[0249] Gene signatures were derived from filters predicting endometriosis (Tables 2 and 4) and non-endometriosis (Tables 3 and 5) in the best-performing network, using consensus from top genes associated with endometriosis or non-endometriosis from whole-cycle phase samples or proliferating samples. Each gene was identified by weights assigned during the model generation process, used as estimates of their impact on endometriosis vs. non-endometriosis predictions. Gene signatures were cross-validated through differential expression analysis of all genes in the dataset. This analysis showed that a mean 38% of the predicted genes from ScaiNet were differentially expressed between endometriosis and non-endometriosis samples (Tables 6 and 7). However, when proliferating samples were separated from all cycle phase samples and a new differential expression analysis of all genes in the dataset was performed, the overlap between differentially expressed genes and predicted genes from ScaiNet was 17% and 78%, respectively (Figures 3A and 3B). This significant overlap in internal validation enhances the reliability of ScaiNet predictions.

[0250] Example 3 The discovered gene signatures were subjected to gene ontology (GO) analysis to determine categories of biological processes that are important or misregulated in endometriosis samples. Among the top 50 GO terms and pathways, ranked by adjusted p-values, were chemokine receptor activity and binding, as well as neutrophil and granulocyte chemotaxis and migration, highlighting the role of the myeloid compartment of the immune system in endometriosis. Furthermore, extracellular space and extracellular regions were ranked highly as cellular compartments (Figure 4).

[0251] Example 4 Using weights from filters positively or negatively correlated with endometriosis or non-endometriosis from the ScaiNet prediction network, we calculated one filter response score for each cell in the dataset, per CV split. These scores were used to identify cells that predict endometriosis in both proliferative and whole-cycle phase samples.

[0252] Interestingly, as determined / discovered by the GO analysis described above, certain subsets of myeloid cells, as well as epithelial cells (ECs) and fibroblasts, were identified to express biomarker gene signatures predicting endometriosis in both proliferative and whole-cycle samples (Figure 5A and B). Myeloid cells were characterized by the expression of the following markers: CD14, CD16, CD15, and CD11b.

[0253] EC cells are defined by the expression of the following genes: EpCAM and KRT18.

[0254] Fibroblasts showed elevated expression of the following markers: COL18A1, COL4A2, COL4A1, VIM, and CALD1.

[0255] [Table 1] TIFF2026510532000003.tif199165TIFF2026510532000004.tif198165TIFF2026510532000005.tif192165TIFF20265105320 00006.tif198165TIFF2026510532000007.tif173165TIFF2026510532000008.tif201165TIFF2026510532000009.tif192165 TIFF2026510532000010.tif201165TIFF2026510532000011.tif201165TIFF2026510532000012.tif200165TIFF20265105320 00013.tif201165TIFF2026510532000014.tif201165TIFF2026510532000015.tif194165TIFF2026510532000016.tif146165

[0256] [Table 2] TIFF2026510532000018.tif81165

[0257] Table 3 TIFF2026510532000020.tif160165

[0258] Table 4 TIFF2026510532000022.tif170165

[0259] Table 5 TIFF2026510532000024.tif139165

[0260] Table 6 TIFF2026510532000026.tif208165TIFF2026510532000027.tif201165TIFF2026510532 000028.tif201165TIFF2026510532000029.tif201165TIFF2026510532000030.tif20116 5TIFF2026510532000031.tif201165TIFF2026510532000032.tif201165TIFF2026510532 000033.tif201165TIFF2026510532000034.tif201165TIFF2026510532000035.tif61165

[0261] Table 7 TIFF2026510532000037.tif201165TIFF2026510532000038.tif201165TIFF2026510532000039.tif99165

[0262] Example 5 A comparison of the predictive values ​​of our signature in the diagnosis of endometriosis with previously published gene signatures selected from candidates (Chen-Wei Chen, et al., 2021bioRxiv 2021.01.25.428135) highlights the superiority of our method and our unbiased approach in the context of endometriosis detection (Figure 6). In fact, as indicated by the mean median AUC score of 0.78 for our model, competing signatures performed poorly on our dataset (mean median AUC = 0.56), and their combination with our model did not add value to the predictive score (mean median AUC = 0.78) (average of median AUCs of three independent CV splits).

[0263] Example 6 Planned verification Enroll an independent validation cohort of 30-40 patients, consisting of 50% with endometriosis and 50% without. Endometrial biopsies will be obtained from patients in the validation cohort between different cyclic phases and analyzed using the same single-cell RNA sequencing method as for the discovery cohort. The same preprocessing steps will be applied to the data.

[0264] The probability of endometriosis in each patient of the validation cohort is predicted using the best-performing ScaiNet network trained on the discovery cohort. Using only single-cell RNA sequencing data, the inventors aim to achieve an accuracy of AUC > 0.85.

[0265] The inventors aim to further improve prediction accuracy by integrating clinical data into the ScaiNet network.

[0266] The probability of endometriosis in patients within the validation cohort is identified using the optimized and reduced gene set shown in Table 1. The characterization and performance of each gene are carefully evaluated across several human tissue types to provide a comprehensive, specific, and highly sensitive assay for the diagnosis of endometriosis, as well as to predict or rule out other diseases or disorders of the female reproductive tract (e.g., ovarian cancer and endometrial cancer).

[0267] Using nucleotide (e.g., RNA) and protein (e.g., FACS and ELISA) assays, the inventors extend and specialize the use of their panel. They also conduct comparisons with other methods used for diseases or disorders of the female reproductive tract.

[0268] Finally, our invention will be evaluated as the gold standard method for the diagnosis and treatment of diseases or disorders of the female reproductive tract (i.e., surgical laparoscopy for endometriosis).

[0269] Example 7 The inventors have identified biomarkers that are differentially expressed during the proliferative phase of the menstrual cycle compared to the secretory phase. These markers may be used to identify menstrual cycle phases in silico, particularly the proliferative phase, based solely on RNA in the sample, or based on a combination of RNA in the sample and other data, such as body temperature, secret viscosity measurements, and / or patient background data, such as days since the last menstrual period or the length of past cycles.

[0270] [Table 8] TIFF2026510532000041.tif203165TIFF2026510532000042.tif203165TIFF2026510532000043.tif128165

Claims

1. (i) A step of determining the RNA levels of at least two biomarkers in an endometrial tissue sample from a woman, wherein the biomarkers are (a) CCL5 and / or NEAT1; and / or (b) Additional biomarkers (multiple selections allowed) from Table 1 The steps of determining, which include or consist of, (ii) A step of determining the status of endometriosis in the endometrial tissue sample based on the RNA levels of the at least two biomarkers in (i); A method for determining the state of endometriosis in endometrial tissue samples from women, including the method described above.

2. (a)(i) A change in one or more biomarkers selected from Table 2 compared to the reference value indicates the pathogenesis of endometriosis; and / or (ii) Changes in NEAT1 and / or further biomarkers (multiple may be selected from Table 3) compared to reference values ​​indicate a non-endometriotic condition, and (b) The above reference value indicates a healthy state, The method according to claim 1.

3. (i) A step of determining the frequency of cells expressing at least two biomarker RNAs in multiple cells of an endometrial sample from a woman, wherein the biomarkers are (a) CCL5 and / or NEAT1; and / or (b) Additional biomarkers (multiple selections allowed) from Table 1 The steps of determining, which include or consist of, (ii) A step of determining the endometriosis state based on the frequency determined in (i) A method for determining the state of endometriosis based on multiple endometrial cells, including those mentioned above.

4. (a)(i) A change in the frequency of cells expressing one or more biomarkers selected from Table 2, compared to a baseline frequency, indicates the pathogenesis of endometriosis; and / or (ii) A change in the frequency of cells expressing NEAT1 and / or further biomarkers (multiple) selected from Table 3, compared to the reference frequency, indicates a non-endometriotic condition, and (b) The above reference value indicates a healthy state, The method according to claim 3.

5. (i) Below: (a) Immune cells selected from the group consisting of B cells, T cells, dendritic cells, and macrophages; (b) Epithelial cells selected from the group consisting of basal cells, ciliated cells, and non-ciliated cells; (c) Endothelial cells; and (d) Smooth muscle cells Cells selected from the group consisting of, and / or (ii) Cells having at least one cell lineage marker, preferably from the following group: (a) CD14, CD16, CD45, CD15, CD11b; (b) EpCAM and KRT18; and (c) COL18A1, COL4A2, COL4A1, VIM, or CALD1 Using at least one cell lineage marker selected from The method according to any one of claims 1 to 4, comprising at least one step of pre-selection.

6. The method according to any one of claims 1 to 5, wherein at least 3, 4, 5, 6, 7, 8, or 9 biomarkers are determined.

7. The method according to any one of claims 1 to 6, further comprising determining or obtaining at least one non-molecular marker, preferably the non-molecular marker being selected from the group consisting of age, weight, BMI, pregnancy, number of births, race, fertility status, past laparoscopy, past drug use, and other gynecological disorders.

8. The method according to any one of claims 1 to 7, wherein the method is at least partially implemented on a computer, and the RNA level is determined by acquiring data indicating the RNA level.

9. The method according to any one of claims 1 to 8, wherein the sample is a sample in the growth phase, or the cells are cells obtained during the growth phase.

10. The method according to claim 9, wherein an increase in one or more biomarkers selected from Table 4 indicates a pathological condition of endometriosis, and / or an increase in one or more biomarkers selected from Table 5 indicates a pathological condition of non-endometriosis.

11. (i) the step of determining the endometriosis state in accordance with any one of claims 1 to 8; (ii) A step of determining or obtaining the state of the menstrual cycle of the subject at the time the endometrial cells or the endometrial sample are obtained; (iii) A step of determining the validity of the endometriosis state based on the state of the menstrual cycle, preferably the step of determining that the validity is considered to be higher when the state of the menstrual cycle is in the proliferative phase than when the state of the menstrual cycle is in a different menstrual cycle state. A method for determining the validity of an endometriotic condition, including the method described above.

12. The method according to claim 11, wherein the step of determining the state of the menstrual cycle includes determining the RNA level of at least one menstrual cycle biomarker.

13. The method according to claim 12, wherein the menstrual cycle markers include the markers in Table 8.

14. A method for predicting the outcome of endometriosis, the onset of the disease, and / or the progression of the disease in women who have endometriosis or are at risk of developing endometriosis, (a) the step of determining the endometriosis state according to the method described in any one of claims 1 to 10; (b) The step of comparing the endometriosis condition determined in (a) with a predictive criterion pattern; (c) A step of predicting the outcome of the disease, the onset of the disease, and / or the progression of the disease in the female subject based on the comparison in step (b). The method, including the method described above.

15. (1) To determine an increase in the frequency of cells expressing the biomarker(s) listed in Table 2 compared to the aforementioned prediction criterion pattern; and / or (2) Determine the increase in the level of the biomarker(s) in Table 2 compared to the reference pattern. The method according to claim 13, wherein the disease outcome is worse, which is likely to be more likely to develop and / or to progress.

16. (1) To determine an increase in the frequency of cells expressing the biomarker(s) listed in Table 3 compared to the aforementioned prediction criterion pattern; and / or (2) Determine the increase in the NEAT level and / or the increase in the levels of further biomarkers (multiple) listed in Table 3, compared to the aforementioned reference pattern. The method according to claim 14 or 15, which shows an improvement in the disease outcome, wherein the likelihood of developing the disease is low and / or the likelihood of disease progression is low.

17. A method for predicting susceptibility to endometriosis treatment in women who have endometriosis or are at risk of developing endometriosis, (a) the step of determining the endometriosis state according to the method described in any one of claims 1 to 10; (b) The step of comparing the endometriotic condition with a susceptibility criterion pattern; (c) A step of predicting the sensitivity of the female subject to treatment for endometriosis based on the comparison in step (b) The method, including the method described above.

18. The method according to claim 17, wherein the treatment for endometriosis is a treatment selected from the group consisting of analgesics, hormone therapy, infertility treatment, and surgery.

19. The method according to any one of claims 14 to 18, wherein the susceptibility criterion pattern or the prediction criterion pattern is obtained from reference subjects, and at least one of the reference subjects has been diagnosed with endometriosis.

20. The method according to claim 19, wherein obtaining the sensitivity criterion pattern or the prediction criterion pattern from a reference subject includes machine learning techniques, preferably convolutional neural networks and / or logistic regression.

21. A method for classifying women who have endometriosis or are at risk of developing endometriosis into different classes, b. (i) A step of determining the endometriosis condition according to the method described in any one of claims 1 to 10; (ii) A step of predicting the outcome of a disease in a woman, the onset of the disease, and / or the progression of the disease, according to the method of any one of claims 14 to 16, 19, or 20; and / or (iii) A step of predicting the susceptibility of a female subject to treatment for endometriosis according to the method of any one of claims 17 to 20; and c. A step of classifying the female subjects according to the frequency determined in (i), the signature determined in (ii), the prediction in (iii), and / or the prediction in (iv). The method, including the method described above.

22. The method according to claim 21, wherein at least one class indicates the stage and / or severity of the endometriosis.

23. A composition comprising a reagent for detecting a biomarker for the diagnosis of endometriosis, wherein the biomarker comprises or consists of at least two markers listed in Table 1.

24. A pharmaceutical product comprising a compound for endometriosis for use in the treatment of women who are expected to be susceptible to treatment of endometriosis according to the method of any one of claims 17 to 20.

25. The pharmaceutical product according to claim 24, wherein the compound for endometriosis is selected from the group consisting of ibuprofen, naproxen, oxycodone, desogestrel, dienogestrel, levonorgestrel, clomiphene citrate, gonadotropin, metformin, letrozole, and bromocriptine.

26. The method according to any one of claims 1 to 22, the composition according to claim 23, or the pharmaceutical product according to claim 24 or 25, wherein the endometriosis is selected from the group consisting of peritoneal endometriosis, endometrioma, deep endometriosis, tubal endometriosis, and abdominal wall endometriosis.

27. The method according to any one of claims 1 to 22, 26, the composition according to claim 23 or 26, or the pharmaceutical product according to any one of claims 24, 25 or 26, wherein the endometriosis is rASRM stage II, III, or IV.

28. A computer program product comprising instructions for performing the method described in any one of claims 8 to 22, 26, or 27, wherein the method is computer-implemented in the computer program product.

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