WO2019115829A1 - Method of diagnosing endometriosis - Google Patents

Abstract

The present invention relates to an in vitro method of diagnosing endometriosis in a subject comprising: providing an endometrial sample from the subject, measuring the levels of at least two biomarkers in said sample, thereby obtaining at least two scores, combining said scores in a diagnostic score, comparing said diagnostic score to a reference value of a control sample, and diagnosing the subject.

Description

METHOD OF DIAGNOSING ENDOMETRIOSIS

FIELD OF INVENTION

The present invention relates to the diagnosis of endometriosis, a disorder characterized by the presence of endometrial-like-tissue outside the uterus in pelvic or extra-pelvic locations.

BACKGROUND OF INVENTION

Endometriosis is an inflammatory disorder. This disabling gynecological disease affects 10% of reproductive aged-women, is associated with severe symptoms, mainly consisting in acute pelvic pain and infertility, and is characterized by high recurrence rates (up to 50% at two years) imposing a substantial economic burden estimated in 2015 at $77,5 billions across nine European counties and $78 billion in the USA.

The average delay between the disease onset and effective diagnosis has been estimated to 7 to 9 years but according to a better identification of symptoms and efficient knowledge and detection of the condition.

Several theories have been explored to explain endometriosis etiology, however they are not fully confirmed. Improving knowledge on endometriosis pathogenesis may help in identifying novel targets for formulating more effective therapies or identifying condition specific biomarkers. Several teams have published potential biomarkers associated with endometriosis condition, but the heterogeneity and high risk of bias of these studies did not allow to confirm the clinical validity of the studied biomarkers. None of the biomarkers evaluated in reviews were considered as statistically relevant due to insufficient or poor-quality evidence. None of these biomarkers, alone or in combination, have been so far recommended for use in clinical practice for the diagnosis of endometriosis.

Currently, only the removal of endometriotic lesions via surgical laparoscopy, under general anesthesia, and their histological analysis by a pathologist enable the diagnosis of endometriosis with certainty. In this instance, there is no non-invasive imaging modalities available in clinical practice that can be used to accurately diagnose all forms of endometriosis and laparoscopy remains the gold standard for the diagnosis of endometriosis. Consequently, there is a major need for a non-invasive method for diagnosing endometriosis.

SUMMARY

The present invention responds to this need and provides an in vitro method of diagnosing endometriosis in a subject wherein an endometrial sample from the subject is provided, the levels of at least two biomarkers within said sample are measured, thereby obtaining at least two scores that are combined in a diagnostic score, said diagnostic score is compared to a reference value, and diagnosing the subject.

In a specific aspect of the invention, the in vitro method described above, the diagnostic score is compared to a control score obtained for the same biomarkers in a control sample, the subject is diagnosed with endometriosis when the diagnostic score is greater than this control score.

In a specific aspect of the invention, the in vitro method described above, the diagnostic score is compared to a pre-determined cut-off value obtained for the same biomarkers and diagnosing the subject with endometriosis when the diagnostic score is greater than this cut-off value.

In a further aspect, the scores of the at least two biomarkers are combined using an algorithm.

In a specific aspect of the invention, the algorithm is a mathematical function more particularly a logistic regression or any other predictive modeling method known to the skilled man in the art. (J. M. T. Hendriksen et a , J. Thromb. Haemost, 11, 1 Special Issue: State of the Art, 2013). In another aspect of the invention, the combination of biomarkers comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16 biomarkers.

According to certain embodiments, the biological marker is a protein.

According to another aspect, the biomarkers are selected from the group comprising: o a human interleukin signaling marker, selected among the molecules listed in the Reactome pathway Knowledgebase“R-HSA-6785807 Interleukin-4 and Interleukin- 13 signaling” (Fabregat A. et a , Nucleic Acids Res. 2016 Jan 4;44), including but not limited to p53, BCL2 or c-MYC;

o a human intracellular signaling marker, selected among the molecules listed in the Reactome pathway Knowledgebase “R-HS A- 162582 Intracellular signaling by second messengers” (Fabregat A. et a , Nucleic Acids Res. 2016 Jan 4;44), including but not limited to c-Erb2 or CD34;

o a human nuclear signaling marker, selected among the molecules listed in the Reactome pathway Knowledgebase “R-HSA-8939211 ESR-mediated signaling” (Fabregat A. et a , Nucleic Acids Res. 2016 Jan 4;44), including but not limited to ERa, PR, c-Fos or c-Jun;

o a human transcription marker, selected among the molecules listed in the Reactome pathway Knowledgebase“R-HSA-73857.5 RNA Polymerase II transcription” (Fabregat A. et a , Nucleic Acids Res. 2016 Jan 4;44), including but not limited to AR, HOXA10, Ki67 or NFE2L3; or o a human immune system marker, selected among the molecules listed in the Reactome pathway Knowledgebase “R-HSA-6798695 Neutrophil degranulation” (Fabregat A. et a , Nucleic Acids Res. 2016 Jan 4;44), including but not limited to CD31, CD45 or CD68; In a specific aspect of the invention, the combination of biomarkers of the at least two biomarkers are chosen among p53, BCF2, c-MYC, c-Erb2, CD34, ERa, PR, c-Fos, c- Jun, AR, HOXA10, Ki67, NFE2F3, CD31, CD45 or CD68.

In another specific aspect of the invention, the combination of biomarkers consists of AR, c-Erb2, c-Fos, c-Jun, Ki67, p53, PR, ERa. The in vitro method of anyone of the preceding claims wherein the combination of biomarkers consists of AR, c-Jun, Ki67, p53, PR, ERa In another specific aspect of the invention, the combination of biomarkers consists of AR and Ki67.

In another specific aspect of the invention, the combination of biomarkers consists of ERa and CD31.

In a specific aspect of the invention, the combination of biomarkers consists of c-Jun, CD31, Ki-67 and AR.

In another specific aspect of the invention, the combination of biomarkers consists of p53, c-Fos, CD31, AR. In a specific aspect of the invention, the combination of biomarkers consists of Ki67, c-Jun, AR.

In another aspect of the invention, the measure of the levels of the biomarkers is made by any protein- specific detection method such as immunohistochemistry (IHC), immunofluorescence, flow cytometry, ELISA, protein immunoprecipitation, protein immunoelectrophoresis, Western Blot, liquid chromatography, mass spectrometry or any combination thereof.

In a specific aspect of the invention, the measure of the biomarkers is made by immunohistochemistry (IHC)

In a further aspect of the method of the invention, the subject is (i) a- symptomatic, (ii) pre- symptomatic for endometriosis or (iii) already displaying clinical symptoms of endometriosis.

In a still further aspect, the invention relates to a kit for the detection of endometriosis in a sample comprising reagents for measuring the levels of a combination of at least two biomarkers reflecting endometriosis and/or material for collecting the sample(s) and/or reaction vessel and/or primary monoclonal and/or polyclonal antibodies and/or secondary polyclonal antibodies and/or other specific reagents for the signal amplification of primary antibodies, and buffers and solutions for use and/or reference samples for each biomarker and instructions for use.

In a particular aspect, the kit comprises the reagents for the detection of biomarkers, wherein the biomarkers are those above described, and consist more particularly, of p53, BCL2, c-MYC, c-Erb2, CD34, ERa, PR, c-Fos, c-Jun, AR, HOXA10, Ki67, NFE2L3, CD31, CD45 and CD68.

In another aspect, the invention deals with the use of the kit for the implementation of a method of diagnostic of endometriosis.

In another aspect, the invention deals with the use of the combination of biomarkers as above described as diagnostic biomarker for endometriosis.

DEFINITIONS

In the present invention, the following terms have the following meanings:

“a” and“an” refer to one or to more than one {i.e., to at least one) of the grammatical object of the article. By way of example,“a biomarker” means one element or more than one biomarker.

“AUROC” stands for area under the ROC curve and is an indicator of the accuracy of a prognostic or diagnostic test. ROC curve and AUROC are well-known in the field of statistics.

- “Biomarker” refers to a molecule that is associated either quantitatively or qualitatively with a biological change. Examples of biomarkers include polypeptides, proteins or fragments of a polypeptide or protein; and polynucleotides, such as a gene product, RNA or RNA fragment; and other body metabolites. In certain embodiments, a "biomarker" means a compound that is differentially present (i.e., increased or decreased) in a biological sample from a subject or a group of subjects having a first phenotype (e.g., having endometriosis) as compared to a biological sample from a subject or group of subjects having a second phenotype (e.g., not having endometriosis). “Combination of biomarkers” refers to combinations of at least two different biomarkers. A combination means that biomarkers mutually support each other in their effects to such an extent that a new technical result is achieved. It is irrelevant whether each individual biomarker is known by itself.

- “Control sample” refers to a sample from a subject without endometriosis or from a population of subjects without endometriosis.

“Control score” refers to the combination of levels of biomarkers with or without weight measured in a control sample.

“Cut-off’ or‘‘cut-off value” [for diagnosing a subject with endometriosis] refers to reliable dividing points on measuring scales where the diagnostic score results are divided into different categories; typically, positive (indicating someone has the condition of interest), or negative (indicating someone does not have the condition of interest). As such, a value coming greater than or equal to this cut-off will be treated as positive while less than cut-off as negative. If a diagnostic score is such that low value indicates disease (positive) and high value as normal (negative) then also this method can be used to find a cut-off. Statistical methods are typically used in addition to the clinical and experimental evidences for finding reliable cut-off values for classifying subjects as positive or negative depending on the selected parameters for diagnosis (sensitivity, specificity, predictive values).

- “Diagnostic score” refers to the sum of the weighted and unweighted measure or score of the levels each of the biomarkers and any combination therof.

“Different than cut-off’ and“Different than the control score” means when referring to a diagnostic score that such score is higher by at least 5% or is lower by at least 5% when compared to the cut-off value or the score of the control sample. - “Immune system marker” refers to a human immune system marker selected among the molecules listed in the Reactome pathway Knowledgebase“R-HSA-6798695 Neutrophil degranulation” (Fabregat A. et a , Nucleic Acids Res. 2016 Jan 4;44), including but not limited to CD31, CD45 or CD68; “Interleukin signaling marker” refers to a human interleukin signaling marker selected among the molecules listed in the Reactome pathway Knowledgebase“R- HSA-6785807 Interleukin-4 and Interleukin- 13 signaling” (Fabregat A. et a , Nucleic Acids Res. 2016 Jan 4;44), including but not limited to p53, BCL2 or c-MYC.

- “Intracellular signaling marker” refers to a human intracellular signaling marker selected among the molecules listed in the Reactome pathway Knowledgebase“R- HSA- 162582 Intracellular signaling by second messengers” (Fabregat A. et a , Nucleic Acids Res. 2016 Jan 4;44), including but not limited to c-Erb2 or CD34.

"Measuring" and "determining" are used interchangeably throughout and refer to methods which include obtaining or providing a sample from a subject and/or detecting the level (or amount) of a biomarker(s) in a sample. The term "measuring" is also used interchangeably throughout with the term "detecting." In certain embodiments, the term is also used interchangeably with the term "quantifying."

“Measure of biomarkers”,“Measure of the level of the biomarkers”,“Measure of the combination of biomarkers” are used interchangeably throughout.

“Nuclear signaling marker” refers to a human nuclear signaling marker selected among the molecules listed in the Reactome pathway Knowledgebase“R-HSA- 8939211 ESR-mediated signaling” (Fabregat A. et a , Nucleic Acids Res. 2016 Jan 4;44), including but not limited to ERa, PR, c-Fos or c-Jun;

- “Reference value” refers to the measure or the score of one biomarker or a plurality of biomarkers made in a control sample or obtained by comparing a population of endometriosis subjects to a control sample. Typically, the reference value is an average or another statistical measure representing the expression level of each of one biomarker in a plurality of samples. The reference value of the invention can be a control score or a cut-off value.

“Sensitivity of a method of diagnostic” refers to the proportion of subjects with endometriosis that are correctly identified as such using a method of diagnostic.

“Significantly different” means when referring to the levels of one biomarker measured in two different groups of samples that the difference between such levels satisfies a commonly accepted level of significance: e.g., a p-value less than 0.05 and/or a q-value of less than 0.10 (as determined using, for example, either Welch's T-test or Wilcoxon's rank-sum Test, or Mann Whitney U Test or any other appropriate statistical method known to a person having ordinary skill in the art).

- “Transcription marker” refers to a human transcription marker selected among the molecules listed in the Reactome pathway Knowledgebase“R-HSA-73857.5 RNA Polymerase II transcription” (Fabregat A. et a , Nucleic Acids Res. 2016 Jan 4;44), including but not limited to AR, HOXA10, Ki67 or NFE2L3;

“Specificity of a method of diagnostic” refers to a measure of the proportion of subjects without endometriosis that are correctly identified as such using a method of diagnostic.

- “ROC” In statistics, a receiver operating characteristic (ROC), or ROC curve, is a graphical plot that illustrates the performance of a binary classifier system as its discrimination threshold is varied. The curve is created by plotting the sensitivity against the specificity (usually 1- specificity) obtained for all possible values of discrimination threshold.

DETAILED DESCRIPTION

The present invention describes an accurate and non-invasive diagnostic tool in endometriosis based on a combination of at least two biomarkers.

Peripheral blood or other biological fluids may contain pertinent information to indicate the presence of endometriosis. But according to the widely accepted theory of retrograde menstruation, the menstrual endometrium is the source of ectopic endometriotic foci. Using the direct potential source of the disease seemed interesting to investigate in our quest to identify biomarkers for endometriosis.

The human endometrium is a dynamic tissue that undergoes cyclic growth, differentiation, desquamation and regeneration, all of these processes driven by ovarian steroidal hormones (estrogen and progesterone) and cytokines. While the main function of endometrium is to provide time support to enable embryo implantation, growth and maturation, understanding the complex mechanisms controlling changes within endometrium may help to elicit gynecological disorders such as endometriosis, endometrial cancer, which may impact endometrial functions leading to infertility or miscarriage.

The method of obtaining endometrial samples by either pipelle or curette is minimally invasive. Aside this minimal discomfort for the subject, endometrial biopsy has proven to be useful to test endometrial receptivity in infertile women independently of endometriosis status. It could also be a diagnostic tool for endometriosis in an outpatient setting.

Endometriosis is a benign disease, but it shares certain characteristics with cancer tumors such as altered cell signaling and transcription regulation promoting cell migration, adhesion, invasion, matrix remodeling, proliferation, survival, angiogenesis, inflammation and abnormal immune response. The identified proteins of our biomarker combinations belong to these pathways and were differentially expressed between normal endometrium from subjects without endometriosis and eutopic endometrium from endometriosis subjects.

The present invention relates to an in vitro method of diagnosing endometriosis in a subject comprising the steps of providing an endometrial sample from the subject, measuring the levels of at least two biomarkers in said sample, thereby obtaining at least two scores, combining said scores in a diagnostic score, comparing said diagnostic score to a reference value and diagnosing the subject.

In a specific aspect of the invention, the diagnostic score is compared to a cut-off value, and the subject is diagnosed with endometriosis when the diagnostic score is different han this cut-off value.

In another aspect, the diagnostic score is compared to a control score, and the subject is diagnosed with endometriosis when the diagnostic score is different than this control score. Within the meaning of the invention, the sample is obtained from intra-uterine endometrium and the subject refers to a woman who is awaiting the receipt of, or is receiving medical care or was/is/will be the object of a medical procedure, or is screened for endometriosis, or was previously diagnosed with endometriosis, or is monitored for the development of endometriosis. In one embodiment, the subject is a female subject of any age and preferably of reproductive age, i.e. in between menarche and menopause.

The measure of the level of the protein biomarkers can be made by any protein- specific detection method such as immunohistochemistry (IHC), immunofluorescence, flow cytometry, ELISA, protein immunoprecipitation, protein immunoelectrophoresis, Western Blot, liquid chromatography, mass spectrometry or any combination thereof.

In a preferred embodiment of the invention, the detection and the measure of the biomarkers is made using immunochemistry (IHC). IHC is an important technique to both researchers and clinicians, and is used to detect the presence and locations of proteins (antigens). For clinicians, IHC using formalin-fixed paraffin embedded (FFPE) tissues or OCT (Optimal Cutting Temperature compound) frozen tissues is important in the diagnosis of several diseases, as it allows the identification of protein markers known to be associated with the condition, for exemple a differential level of expression (e.g. overexpression of Ki-67 in skin cancer vs control), or a mutated or activated state (e.g. detection of BRAF V600E mutant protein in colorectal cancers). Researchers also frequently utilize IHC to identify new potential protein biomarkers for disease identification and progression and to identify potential therapeutic targets.

When using IHC for the detection and the measure of the level of biomarkers, the resultis obtained by observing microscopy images and optionally converting image features into numerical scores. Three types of scores well known to the skilled man in the art can be used:

The H score is obtained by multiplying the intensity of the stain (0: no staining, 1: weak, 2: moderate staining, 3: intense staining) by the percentage (0-100) of the cells showing that staining intensity (Hscore ranges 0-300) [Netto et a , 2011]. H score: 3x%3+cells + 2x%2+cells + lx% l+cells

Intensity score or %max score: this scoring method takes into account the value of the staining intensity (0, 1 ,2 or 3) and/or the value of the staining intensity for which there are more stained cells. (Fedchenko N. et ah, Diagn Pathol. 2014; 9:221). - Density score: this scoring method uses the proportion of positively stained cells (0,

1, 2 or 3; 0 = no staining, 1= light staining, 2-3 = high staining). (Fedchenko N. et ah, Diagn Pathol. 2014; 9:221).

Allred score takes into consideration the proportion of positively stained cells (scored on a scale of 0-5) and staining intensity (scored on a scale of 0-3). The proportion and intensity are then summed to obtain total scores comprised between 0 and 8 (Qureshi et a , 2010)

Within a specific aspect, a combination of at least two biomarkers is used in the method the invention. According to the invention“at least 2” means 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,

12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,

37, 38, 39, 40 . . . N, where "N" is the total number of biomarkers in the particular embodiment. The term also encompasses at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 15 ,16 ,17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40 . . . N. It is understood that the recitation of biomarkers herein includes the phrase “one or more of the biomarkers and, in particular, includes the“at least 2, at least 3”” and so forth language in each recited embodiment of a biomarker panel.

Examples of biomarkers that can be used include polypeptides, proteins or fragments of a polypeptide or protein; and polynucleotides, such as a gene product, RNA or RNA fragment; and other body metabolites. The prefered biomarkers of the invention are proteins, including and not limiting to their wild-type, activated or mutated forms and all their isoforms. The biomarker of the invention refers to a molecule that is associated either quantitatively or qualitatively with endometriosis. A non-limitative list of biomarkers that can be used in the method or the kit of the invention is set below.

In a specific aspect, the biomarkers are selected from the group comprising:

o a human interleukin signaling marker, selected among the molecules listed in the Reactome pathway Knowledgebase“R-HSA-6785807 Interleukin-4 and Interleukin- 13 signaling” (Fabregat A. et a , Nucleic Acids Res. 2016 Jan 4;44), including but not limited to p53, BCL2 or c-MYC;

o a human intracellular signaling marker, selected among the molecules listed in the Reactome pathway Knowledgebase “R-HS A- 162582 Intracellular signaling by second messengers” (Fabregat A. et a , Nucleic Acids Res. 2016 Jan 4;44), including but not limited to c-Erb2 or CD34;

o a human nuclear signaling marker, selected among the molecules listed in the Reactome pathway Knowledgebase “R-HSA-8939211 ESR-mediated signaling” (Fabregat A. et a , Nucleic Acids Res. 2016 Jan 4;44), including but not limited to ERa, PR, c-Fos or c-Jun;

o a human transcription marker, selected among the molecules listed in the Reactome pathway Knowledgebase“R-HSA-73857.5 RNA Polymerase II transcription” (Fabregat A. et a , Nucleic Acids Res. 2016 Jan 4;44), including but not limited to AR, HOXA10, Ki67 or NFE2L3; or o a human immune system marker, selected among the molecules listed in the Reactome pathway Knowledgebase “R-HSA-6798695 Neutrophil degranulation” (Fabregat A. et a , Nucleic Acids Res. 2016 Jan 4;44), including but not limited to CD31, CD45 or CD68;

In a specific aspect, the biomarkers are selected from the group consisting of p53, BCF2, c-MYC, c-Erb2, CD34, ERa, PR, c-Fos, c-Jun, AR, HOXA10, Ki67, NFE2F3, CD31, CD45 or CD68.

In a specific aspect, the biomarkers are selected from the group consisting of AR, c-Erb2, c-Fos, c-Jun, Ki67, p53, PR, ERa and CD31; or consisting of AR, c-Jun, Ki67, p53, PR, ERa; or consisting of AR and Ki67 ; or consisting of ERa and CD31 ; or consisting of c- Jun, CD31, Ki-67 and AR; or consisting of p53, c-Fos, CD31, AR.

In a further aspect of the invention, the level measured (or the scores in case of IHC) of each biomarker are combined to obtain a diagnostic score or diagnostic score value said value is compared to a reference value.

The simple combination of the levels of said biomarkers differentiates an endometriosis subject from a control subject with a sensitivity of 87% and a specificity of 89%

(Figure 2).

According to further embodiment, the measures of the levels of at least two biomarkers are combined using an algorithm, more preferably a mathematical function such as a logistic regression or any other predictive modeling method known to the skilled man in the art. (J. M. T. Hendriksen et a , J. Thromb. Haemost, 11, 1 Special Issue: State of the Art, 2013) and such combination results in a diagnostic score.

The combination of the levels of said biomarkers using an algorithm reliably differentiates an endometriosis subject from a control subject with a sensitivity of 100% and a specificity of 95% (Figure 4).

The combination of the levels of said biomarkers with an algorithm optimized for sensitivity reliably rules-out endometriosis with a sensitivity of 100% and a specificity of 89% (Figure 5). The combination of the levels of said biomarkers in an algorithm optimized for sensibility reliably rules-in endometriosis with a sensitivity of 81% and a specificity of 100% (Figure 5).

According to a more specific embodiment, the subject is diagnosed with endometriosis when the diagnostic score is greater than the cut-off value.

The definition of the cut-off value depends on the method used for the detection and measure of the biomarkers.

When the diagnostic score value is significantly different than the cut off value, when that score is different by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%, by at least 75%, by at least 80%, by at least 85%, by at least 90%, by at least 95%, by at least 100%, by at least 110%, by at least 120%, by at least 130%, by at least 140%, by at least 150%, or more when compared to the cut-off value, the the subject is diagnosed with endometriosis

In a specific embodiment of the invention, when the diagnostic score in the test sample obtained is different than this cut-off value, the endometriosis subjects tested will be diagnosed with endometriosis. Conversely, when the value obtained is lower than this cut-off value, the endometriosis subjects tested will be diagnosed without endometriosis. The choice of the combination of biomarkers and the resulting score (using or not an algorithm) has an impact on the specificity of the diagnotic method and/or on the sensitivity of the method. Depending on the type of results to be obtained, the skilled man in the art can choose the biomarkers and optimize the algorithm in order to have either a highly specific (rule in) or a highly sensitive (rule out) method. He also can choose the biomarkers in order to have a replacement method.

The predetermined criteria for a clinically useful diagnostic test in endometriosis are known to the skilled man in the art (Gupta D. et a , Cochrane Database of Systematic Reviews, Issue 4. Art. No.: CD012165, 2016). The criteria for a test to replace diagnostic surgery is one with a sensitivity of 94% and a specificity of 79%. The criteria for triage tests are a sensitivity at or above 95% and specificity at or above 50%, which in case of negative results rules out the diagnosis or sensitivity at or above 50% with specificity at or above 95%, which in case of positive result rules in the diagnosis.

In a further aspect, the invention deals with a kit for the detection of endometriosis in a sample comprising reagents for measuring the levels of a combination of biomarkers reflecting endometriosis and/or material for collecting the sample(s) and/or reaction vessel and/or monoclonal and/or polyclonal antibodies against said biomarkers and/or buffers and/or control samples for each biomarker and solutions for use and instructions for use. The instructions for use include a publication, a recording, a diagram, or any other medium of expression which can be used to communicate the usefulness of the kit of the invention. The instructional material of the kit of the invention may, for example, be affixed to a container which contains the reagents for implementing the method of the invention or be shipped together with a container which contains the reagents for implementing the method of the invention. Alternatively, the instructional material may be shipped separately from the container with the intention that the instructional material be used cooperatively by the recipient.

The biomarkers to be used in the kit are both above described. The kit of the invention can be used for the implementation of a method of diagnostic of endometriosis.

The present invention also deals with the use of the combination of biomarkers as listed above as diagnostic biomarker for endometriosis.

In a further aspect of the invention, the biomarkers described above are used either alone or incombination for the treatment or prognosis of endometriosis.

Another object of the invention is at least one of the biomarkers described above for use in the treatment of endometriosis, in a subject, wherein the subject to be treated is identified as described hereinabove, and wherein the treatment is adapted to the subject as described hereinabove, depending on the severity of endometriosis in said subject and/or on the underlying cause responsible for endometriosis in said subject.

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1 represents from a) to h): Histograms showing the number of positive samples (y-axis) with no to light staining vs high staining (x-axis) obtained by IHC in endometrium biopsies from control and endometriosis subjects using visual IHC interpretation (see Example 1, Mat&Met) using antibodies againts a) ERa, b) KI67, c) PR, d) P53, e) c-Erb2, f) AR, g) FOS, h) JUN. i) to q): Dot plot showing the score (%max or Allred) (y axis) obtained by IHC in endometrium biopsies from control and endometriosis subjects using visual IHC interpretation (x-axis) (see Example 1, Mat&Met) using antibodies against i) CD34, j) c- myc, k) Bcl2, 1) NFE2L3, m) CD45, n) CD31, o) c-myc, p) HOXA10, q) CD68 r) to y): Dot plot showing the HScore (y axis) obtained by IHC in endometrium biopsies from control and endometriosis subject using automatic and quantitative IHC interpretation (x axis) (see Example 1, Mat&Met) using antibodies againt r) p-c-jun, s) pc-jun, t) AR, u), AR v) PR, w) PR, x) ER, y) ER, z) KI67, a) KI67, b) p53, g) p53.

Figure 2 is a graph showing the diagnostic performance (Sensitivity and specificity with a 95% of confidence interval) of linear combinations of biomarkers described in Figure 1 without optimization algorithm a) (dark circle) using intensity score or density score determined by visual analysis in the following combined manner:

(AR.dens_glande xl) + (AR.dens_stroma xl) + (AR.int_glande xl) + (AR.int_stroma xl) + (C.Erb.2.dens_glande xl) + (C.Erb.2.int_glande xl) +

(c.Fos.dens_glande xl) + (c.Fos.dens_stroma xl) + (c.Fos.int_glande xl) +

(c.Fos.int_stroma xl) + (KI67.dens_glande xl) + (KI67.dens_stroma xl) +

(KI67.int_glande xl) + (KI67.int_stroma xl) + (p.c.jun.dens_glande xl) +

(p.c.jun.dens_stroma xl) + (p.c.jun.int_glande xl) + (p.c.jun.int_stroma xl) + (P53.dens_glande xl) + (P53.dens_stroma xl) + (P53.int_glandexl) +

(P53.int_stroma xl) + (PR.dens_stroma xl) + (PR.int_glande xl) +

(PR.int_stroma xl) + (Re.a.dens_glande xl) + (Re.a.dens_stroma xl) + (Re.a.int_glande xl) + (Re.a.int_stroma xl) b) (grey triangles) using intensity score or density score determined by visual analysis in the following combined manner:

(AR.dens_glande xl) + (AR.dens_stroma xl) + (AR.int_glande xl) + (AR.int_stroma xl) + (BCL2.int_glande xl) + (BCL2.int_stroma xl) + (C.Erb.2.dens_glande xl) + (C.Erb.2.int_glande xl) + (c.Fos.dens_glande xl) + (c.Fos.dens_stroma xl) + (c.Fos.int_glande xl) + (c.Fos.int_stroma xl) + (C . Myc . dens_glande xl) + (C.Myc.dens_stroma xl) + (C.Myc.int_glande xl) + (C . Myc int_stroma xl) + (CD31 int_stroma x(-l)) + (CD34.dens_stroma xl)

(CD34.int_stroma xl) + (CD45.int_glande xl) + (CD45.int_stroma xl)

(CD68.dens_stroma xl) + (CD68.int_stroma xl) + (HOXAl0.int_stroma xl)

(KI67 dens_stroma xl) + (KI67.int_glande xl) + (KI67.int_stroma xl)

(NFE2L3 int_glande xl) + (NFE2L3.int_stroma xl) + (p.c.jun.dens_glande xl)

(p.c.jun.dens_stroma xl) + (p.c.jun.int_glande xl) + (p.c.jun.int_stroma xl) + (P53.int_glande xl) + (P53.int_stroma xl) + (PR.int_glande xl) + (Re.a.dens_stroma xl) + (Re.a.int_stroma xl) c) using Hscore determined by automatic and quantitative analysis in the following combined manner (dot plot of diagnostic (Hscore) with cut off value and diagnostic performance of a combination of 6 biomarkers without optimization):

(pc-jun.Hscore_glande xl) + (pc-jun.Hscore_stroma xl) + (AR.Hscore_glande xl) + (AR.Hscore_stroma xl) + (PR.Hscore_glande xl) + (PR.Hscore_stroma xl) + (ER.Hscore_glande xl) + (ER.Hscore_stroma xl) + (KI67.Hscore_glande xl) + (KI67.Hscore_stroma xl) + (p53.Hscore_glande xl) + (p53.Hscore_stroma xl)

Figure 3 is a graph showing the diagnostic performance (Sensitivity and specificity with a 95% of confidence interval) of optimized combinations of 2 biomarkers selected from those described in Figure 1 a) (dark circle) using intensity and or density score determined by visual analysis in the following combined manner:

(AR.dens_stroma x(-l)) + (AR.int_stroma xl5) + (KI67.dens_stroma xlO) b) (grey triangle) using intensity and or density score determined by visual analysis for the same 2 biomarkers than a) using another combination:

(AR.dens_stroma x(-2) + (AR.int_stroma x20) + (KI67.dens_stroma xlO) c) (dark square) using intensity score determined by visual analysis for 2 other biomarkers:

(Re.a.dens_gland x(-2)) + (Re.a.int_stroma xlO) + (CD31 int_stroma x(-20)) d) (grey circle) using intensity and or density score determined by visual analysis for the same 2 biomarkers than c) using another combination:

(Re.a.dens_gland x(-l)) + (Re.a.int_stroma xlO) + (CD3l.int_stroma c(-10))

Figure 4 represents

A) (dark circle) graph showing the diagnostic performance (Sensitivity and specificity with a 95% of confidence interval) of the linear combination of 8 bio markers selected from those described in Figure 1 with optimization algorithm to match replacement test performance:

(AR.dens_stroma x2) + (AR.int_stroma x4) + (C.Erb.2.dens_glande xl) +

(C.Erb.2.int_glande xl) + (c.Fos.dens_glande x3) + (c.Fos.dens_stroma x2) +

(c.Fos.int_stroma xl) + (CD3l.int_stroma xO) + (KI67.dens_stroma xl) + (KI67.int_glande x3) + (KI67.int_stroma x3) + (p.c.jun.dens_glande xO) +

(p.c.jun.dens_stroma xl) + (p.c.jun.int_stroma xl) + (P53.int_glande xl) +

(PR.int_glande xl) + (PR.int_stroma x5) + (Re.a.dens_glande xl) +

(Re.a.int_glande x2) +( Re.a.int_stroma x4)

(Grey triangle) Graph showing the diagnostic performance (Sensitivity and specificity with a 95% of confidence interval) of the linear combination of 4 bio markers selected from those described in Figure 1 with optimization algorithm to match replacement test performance :

(AR.dens_stroma x2) + (AR.int_stroma xlO) + (CD3l.int_stroma x(-l7)) +

(KI67.dens_stroma xlO) + (p.c.jun.dens_glande x7) B) ROC curve of the combination in a) with an area under the ROC curve of 0,8734 with a P value of 0,0001; C) Alternative representation of the performances of the combination in a) as a whisker plot. The cut-off has been setup at 65 and with P value of 0,0001. Sensitivity of 87% and specificity of 95%;

Figure 5 represents: a) (dark circle) graph showing the diagnostic performance (Sensitivity and specificity with a 95% of confidence interval) of the linear combination of 4 biomarkers selected from those described in Figure 1 with optimization algorithm to maximize sensitivity (rule out test) using intensity and or density score determined by visual analysis in the following combined manner (rule out test): (AR.int_stroma xl6) + (c.Fos.int_stroma xlO) + (CD3l.int_stroma x(-l9))

+ (P53.dens_stroma xl2) + (P53.int_stroma x(-l5)) b) (grey triangle) graph showing the diagnostic performance (Sensitivity and specificity with a 95% of confidence interval) of the linear combination of 3 biomarkers selected from those described in Figure 1 with optimization algorithm to maximize sensibility (rule in test) using intensity and or density score determined by visual analysis in the following combined manner (rule in test):

(AR.Allred_glande x(-2)) + (AR.Allred_stroma x2) + (KI67.Allred_stroma x2) + (p.c.jun.Allred_glande xl)

EXAMPLES

The present invention is further illustrated by the following examples. Example 1: Combination of 8 biomarkers without optimization algorithm Materials and Methods

Ethical Approval

Ethical approval for this project was granted by institutional review boards and the ethics Committees and a written informed consent was obtained from each subject before tissue collection as well as a detailed historical information questionnaire.

Materials

Retrospectively, from January 2013 to December 2014, women of reproductive age who were undergoing laparoscopic surgery in the Center for Endometriosis Minimally Invasive Gynecologic Surgery Saint Louis University (Missouri - USA) or in the Hospital of Fribourg (Switzerland). 32 women diagnosed with endometriosis after the laparoscopy and histopathology results were included in the study and 19 controls.

Mean age of subjects was 30,20 years (range 20 - 45). Menopaused or pregnant women were excluded. Women with metabolic, chronic, endocrine or malignant diseases were also excluded. For endometriosis group, absence of endometriosis at laparoscopy was an exclusion criterion. For control group, visual or histological confirmation of endometriosis was an exclusion criterion.

Paired eutopic and ectopic endometrial samples were collected from subjects with endometriosis at stage I-IV for immunohistochemistry (IHC) analysis. All the endometriotic implants were collected during laparoscopy and the endometrial samples were obtained by aspiration with a Pipelle curette from the corpus of the uterus. The diagnosis and histopathology results were reviewed independently by 2 pathologists. Stages of the disease were classified according to the revised American Fertility Society classification. Normal eutopic endometrial tissue for immunohistochemistry analysis were obtained from 19 women undergoing laparoscopy for tubal sterilization, or urinary incontinence, or non-fibroid ovary cyst (referred as control hereafter) without endometriosis confirmed by laparoscopic surgery. Meanwhile, peripheral whole blood serum was collected in Serum Vacutainer® tubes (Becton-Dickinson, NJ, USA) for measurement of serum hormonal levels on the same day of eutopic and or ectopic endometrium collection.

Cycle phase was determined according to Noyes’s criteria (Noyes et al., 1950). For control endometrium, 9 were in proliferative phase, 5 in secretory phase and 5 were undefined due to treatment. For endometriosis patient, amongst the 32 included for this study, 20 were in proliferative phase, 8 in secretory phase and 4 undefined due to treatment.

Antibodies The primary antibodies used were: a mouse monoclonal antibody against Re a; a mouse monoclonal antibody against KI67, a mouse monoclonal antibody against PR, a mouse monoclonal antibody against P53, a rabbit polyclonal antibody against c-erb-B2, a mouse monoclonal antibody against AR, a rabbit polyclonal antibody against c-fos, a mouse monoclonal antibody against p-c-jun, a mouse monoclonal antibody against BCL2; a mouse monoclonal antibody against CD31, a mouse polyclonal antibody against CD45, a mouse monoclonal antibody against CD68, a goat polyclonal antibody against HOXA10, a rabbit polyclonal antibody against NFE2L3, a rabbit monoclonal antibody against c-myc, a mouse monoclonal antibody against CD34. An antibody diluent was used for primary-antibodies-dilutions optimization. Immunohistochemical analysis

Prior to immunohistochemical analyses, 5pm thick serial sections were prepared from formalin-fixed, paraffin-embedded tissues block on glass slides and dried overnight at room temperature. First section was H&E stained for pathologic diagnosis.

Briefly, after deparaffination and rehydration, slides were pre-treated by heat-induced epitope retrieval (HIER). Slides were immersed in pre -heated target retrieval solution (Low or High PH depending of antibody) 20 minutes at 95°C.

After a 30 minutes cooling step, slides were washed with wash buffer. Immuno staining was performed using an automate. After inactivation of endogenous peroxidases for 5 min, slides were washed and incubated for 30 min at room temperature with primary antibodies or negative control antibody. The slides were then washed with Wash buffer. For detection, a HRP conjugated secondary antibody against the primary antibody was used. Slides were incubated 30 min at room temperature. Then, slides were washed with Wash buffer. Primary antibodies were visualized following incubation with 3,3’ diaminobenzidine (DAB). Next, the slides were counterstained with Mayer’s haematoxylin for 5 minutes at room temperature and mounted. The slides were visualized under a microscope.

Visual Immunohistochemical interpretation ( Figure 1: a) to q))

Known positive controls for each antibody were included in all batches. For negative controls, the primary antibody was omitted and / or replaced by an irrelevant IgG antibody in order to confirm the specificity of immuno staining. For qualitative analysis, samples were considered negative when no labelled cells were observed on the tissue section, and positive in all other cases.

For semi-quantitative analysis, positive cells were counted in the most characteristic areas (previously chosen after H&E staining) and the intensity of staining assessed by two independent observers. Variations between the two observers were below 5%. A positive reaction was detected with each antibody when there was distinct brown staining in the nuclei (Ki-67, ER, AR, p-c-jun, c-Fos, PR, p53) or membrane (c-erB-b2) of the cells, faint staining being regarded as negative.

Statistical analysis

The Fisher’ s exact test was performed between the immunohistochemical score of control and endometriosis subjects slides staining, using GraphPad Prism version 7 for Mac OS X, (GraphPad Software, La Jolla California, USA. www.graphpad.com). A p<0.05 was considered to indicate a statistically significant differenc ^Automatic and quantified Immunohistochemical interpretation ( Figure 1: r) to y)).

To confirm the data obtained, we chose amongst the samples used previously, 15 endometriums from endometriosis subjects and 8 endometriums from controls. New sections were cut from the corresponding FFPE blocks. Slides were processed for IHC using an automate using the same antibodies as described above. Known positive controls for each antibody were included in all batches. For negative controls, the primary antibody was omitted and / or replaced by an irrelevant IgG antibody in order to confirm the specificity of immuno staining. For qualitative analysis, samples were considered negative when no labelled cells were observed on the tissue section, and positive in all other cases after validation of the corresponding controls.

For quantitative analysis, a trainable histomorphology image analysis tool used to automatically identify stroma and glands. Then a nuclear algorithm was used to measure area and intensity of nuclear staining with DAB in individual cells. Nuclei were easily detected by counterstain, and based on their size, shape and staining nuclei were included or not in the analysis. Non-specific staining in the cytoplasm or membrane were disregarded.

A positive reaction was detected with each antibody when there was distinct brown staining in the nuclei (Ki-67, ER, AR, p-c-jun, PR, p53) of the cells, faint staining being regarded as negative.

Results were given in term of % of cells stained with an intensity scored as either 3+, 2+, 1+ or negative by the software.

Data were then used to define or calculate“Intensity of the % max of stained cells”, Hscore or Allred score. Results See Figure 1 a) Re a :

Endometriosis is an estrogen-dependent disease characterised by the growth of endometrial epithelial and stromal cells outside the uterus creating a chronic inflammatory environment that further contributes to disease progression. Estrogen works through its two distinct nuclear receptors, estrogen receptor alpha (ERa) and estrogen receptor beta (EKb). Oestrogen receptor alpha, as the predominant form of oestrogen receptors in the normal endometrium, is encoded by the oestrogen receptor 1 (ESR1) gene.

It was previously demonstrated that ER a is differentially expressed in endometriosis (Taylor and al., 2015). We assessed ER a expression in eutopic endometrium from endometriosis subject and controls using IHC.

Strong ER a staining in eutopic endometrium from endometriosis subject was found (90,63% of endometrium from endometriosis subjects). No or Light staining meant a probability of 78,57% of a control endometrium sample and the other way round in case of a high staining (78,38% of probability to be an endometrium from endometriosis subject) with a P value of 0,0003. b) KI67: Ki67 is a well-known proliferation marker for the evaluation of cell proliferation. Several team reported a dysregulation of KI67 in eutopic endometrium from endometriosis subjects (Marzioni et a , 2014; Cerbon et a , 2015). Expression of Ki67 was higher in eutopic endometrium of women affected by endometriosis than in control tissue suggesting an important enhancement of the proliferative processes in this location. Thus, an altered expression of KI67 in eutopic endometrium from endometriosis subject could be one of the possible mechanisms leading to cell survival and proliferation outside the uterus. Our results are in accordance with these finding as no or a light staining is associated with a control endometrium with a probability of 61,11%. A high staining is observed in 78,13% of the endometrium from endometriosis subject and in case of a high staining the probability for the sample to belong to an endometriosis subject is as high as 75,76% with a P value of 0,0151. c) PR:

There are two PR isoforms, PRA and PRB, which are transcribed from a single gene (Pgr) with two alternative promoters. PR-B functions as a strong activator of transcription of several PR-dependent promoters, while ligand-bound PR-A can repress transcriptional activity of PR-B and other steroid receptors.

In endometrium, levels of the progesterone receptor isoforms, PR-B and PR-A, progressively increase during the proliferative phase, peak immediately before ovulation, and diminish after ovulation, suggesting that estradiol stimulates progesterone-receptor levels (Attia et al. 2000). Bedaiwy et al. (2015), detected PR-A and PR-B isoforms in eutopic endometrium. In eutopic endometrium, levels of PR-A were significantly elevated in women with endometriosis compared with women without disease, regardless of menstrual phase.

However, the antibody used here is able to recognize the 2 isoforms. Our results show that all PR isoforms confounded, the staining was higher for endometriosis subject than for control. A high staining of the eutopic endometrium for PR meant a probability of 85,19% for it to come from an endometriosis subject with a P value of 0,0006.

For an endometrium from a control, there was a probability of 78,95% to exhibit no or low staining. d) P53: The TP53 gene is a representative tumour suppressor gene that plays an important role in the regulation of cell growth and prevention of carcinogenesis (Kern et al., 1991). The encoded protein normally induces apoptosis, cell cycle arrest or cellular senescence in response to DNA damage, resulting in the inhibition of the proliferation of damaged cells and prevention of tumorigenesis (Robles et al., 2002; Vousden and Prives, 2009).

Although endometriosis is a benign disease histologically, it seems to possess features of malignant cancer, such as local invasion and aggressive spread to distant organs (Gupta, 2004). In regards to that instance, many studies have been conducted to determine possible aetiologies and pathogenesis, and genetic factors, particularly tumour protein p53 (TP53) polymorphism, have been found to play a key role (Dastjerdi, 2013).

Using IHC we assessed the presence of P53 in the eutopic endometrium of endometriosis subject and control. We found that a high staining is associated with endometrium samples from endometriosis subject (78,79%) and no or light staining with control samples (66,67%). An endometrium from an endometriosis subject has a probability of 81,25% to show a high P53 staining with a Pvalue of 0,0023. e) c-erb-B2: HER2 or c-erb-B2 is a member of the human epidermal growth factor receptor (HER/EGFR/ERBB) family. Amplification or over-expression of this oncogene has been shown to play an important role in the development and progression of certain aggressive types of breast cancer

Recently one EGFR gene polymorphism has been associated with susceptibility to endometriosis (Hsieh, 2005), and Melega et al. reported that about 21% of endometriosis are EGFR positive, suggesting a potential role of epidermal growth factor in the growth and maintenance of endometrial ectopia (Melega, 1991).

Our results show that if there is a high staining for c-erb-B2, the endometrium sample belongs to an endometriosis subject (100%) with a p value of 0,037. If there is no or a light staining the data are inconclusive for our sample collection / population Figure 5. f) AR:

The androgen receptor (AR) is a ligand-dependent nuclear transcription factor and member of the steroid hormone nuclear receptor family it regulates eukaryotic gene expression and affect cellular proliferation and differentiation in target tissues. It mediates the actions of androgens such as testosterone and dihydrotestosterone.

In 2007, Spritzer detected AR in biopsies of pelvic endometriosis, as well as in the eutopic endometrium of endometriosis and control groups.

Taylor (2015) and Saed (2011) identified a network of transcription factors, which bind to target genes that are differentially expressed in endometriosis including known TFs such as androgen receptor (AR).

Finally, AR is expressed in normal human endometrium depending of cycle phase like PR or ER (Mertens, 2001; Ito, 2002, Cloke, 2012)

We evaluated the expression of AR in the eutopic endometrium of our population of controls and endometriosis subject using IHC and found the hereafter results: a high staining was associated in 81,48% of the cases to an eutopic endometrium from an endometriosis subject.

An eutopic endometrium from a control has a probability of 73,68% to show no or a low staining with a P value of 0,0033. g) c-fos The FOS gene family comprises four members, namely FOS, FosB proto-oncogene, AP- 1 transcription factor subunit (FOSB), FOS like 1, AP-l transcription factor subunit (FOSL1) and FOSL2.

Their encoded proteins dimerize with the Jun family members to form the group of AP- 1 proteins, and are involved in various physiological and pathological processes, such as cell proliferation, apoptosis, and differentiation and transformation. Recent studies suggest that AP-l proteins control cell life and death through their ability to regulate the expression and function of cell cycle regulators (Shaulian and Karin 2001; Crowe et al. 2000). C-fos has been reported to be related to estradiol dependent cell proliferation (Crowe et al. 2000). Reis (1999) shown that both c-fos gene and protein expression are induced in human endometrium during the proliferative phase of the human cycle.

Morsh (2009) demonstrated that c-fos gene expression is higher in endometriotic implants than in eutopic endometrium from endometriosis subjects or from a control group, suggesting that c-fos may play a role in the molecular mechanism of estrogen action in this pathology.

Meng in 2017 published abnormally high expression levels of FOS, FOSB, and FOSL1 in eutopic endometria from endometriosis subjects. Pan et al (2008) reported that FOS protein expression levels in eutopic and ectopic endometria samples from females with endometriosis were significantly higher than those in the endometria samples from healthy control subjects; however, the findings of Morsch et al (2009) were not similar.

Our results show that the expression of c-fos in eutopic endometrium greatly varies between controls and endometriosis subjects. A high staining means 86,96% of probability that the sample belongs to an endometriosis subject. On the contrary, endometriums from our control population have a probability of 84,21% to show no or a low staining with a P value of 0,0015. h) p-c-jun c-Jun is a protein that in humans is encoded by the JUN gene. c-Jun in combination with c- Fos, forms the AP-l early response transcription factor which has a wide-range action in different tissues and physiological states, such as proliferation, transformation and death (reviewed in Shaulian and Karin, 2002). c-jun protein is activated through Jun N- terminal Kinase phosphorylation. When over-expressed, c-jun was described to confer on the MCF-7 cell-line invasiveness and higher motility, as well as induction of some extracellular matrix markers such as MMPs (Smith et al., 1999). We assessed the expression of c-jun in eutopic endometrium from endometriosis subject and controls and found a significant difference between these two groups. C-Jun staining was low in 84,21% of all control samples. On the contrary, when a high staining was observed, it was associated with an endometriosis sample with a probability of 87,50% with a P value of 0,0011. i) CD34

CD34 is involved in cell-cell adhesion and migration. It is almost ubiquitously expressed in hemapoietic cells and stem cells in addition to other cells. It stains vascular endothelial cells. Usta et al., (2018) have demonstrated that CD34 immunoreactivity is significantly correlated with the persistence of non-cyclic pelvic pain and dysmenorrhea.

Signorile et al., have reported in 2009 the correlation between CD34 and Oestrogen and Progesterone receptor.

We measured the expression of CD34 in eutopic endometrium from endometriosis subjects and controls. The staining was quantified using Allred score and a significative difference between endometriosis subject and controls was observed with a pvalue of 0,0332. j) o) c-myc

C-myc is a well-known proto-oncogen involved in cell proliferation.

Vinci et al., (2016) used c-myc amongst others biomarker in IHC to identify endometriosis affected patient. Others like Pellegrini published an increase in the expression of c-myc in peritoneal endometriosis (Pellegrini et al., 2012) as well as estrogen receptor, GREB1 and cyclin Dl, estrogen regulated genes implicated in proliferation.

In our case, IHC results on eutopic endometrium from endometriosis subjects and controls show an increased expression of c-myc in endometriosis subjects with a pvalue of 0,0067 (Allred score). k) BCL2

BCL2 is a well-known apoptotic factor, which regulates cell death. It has been studied in endometriosis to evaluate the pathogenesis of tumor development from endometriosis in association with estrogen receptor and P53 (Haidarali et a , 2016 ; Nezhat et a , 2002) who concluded that alterations in BCL2 and p53 may be associated with the malignant transformation of endometriotic cysts.

We evaluated the expression of BCL2 in the eutopic endometrium of our population of controls and endometriosis subject using IHC and found the hereafter results: BCL2 is underexpressed significantly using Allred scoring in endometriosis subjects compared to controls with a pvalue of 0,0175. l) NFE2L3

NFE2L3 is described as espressed in placenta but its function is not yet known.

In 2011, painter et a , (2011) identified in a genome-wide association study a novel locus on chromosome 7pl5.2 significantly associated with risk of endometriosis which is located in an intergenic region upstream of the plausible candidate gene NFE2L3.

We assessed the expression of NFE2L3 in eutopic endometrium from endometriosis subject and controls and found a significant difference between these two groups. Using Allred score, we found a decreased expression of NFE2L3 in endometriosis subject endometrium compared to controls with a pvalue of 0,0127. m) CD45

CD45 is a leukocyte common antigen present on all differentiated hematopoietic cells except erythrocytes and plasma cells. It regulates cell growth, differentiation, mitotic cycle and oncogenic transformation.

Using eutopic endometrium from endometriosis subject and controls we evaluated the expression of CD45 using IHC and % Max as scoring method. We found a decreased expression of CD45 in endometriosis endometrium with a pvalue of 0,0127. n) CD31

Platelet endothelial cell adhesion molecule, found on the surface of platelets, lymphocytes, endothelial cells, granulocytes, osteoclasts and megakaryocytes, CD31 is involved in leukocyte transmigration, angiogenesis and integrin activation. It is linked to angiogenesis in this instance. Li et a , in 2016 evaluated the effects of progesterone on endometriosis and used IHC to evaluate CD31 expression in an mouse model, which was modified when cells were treated with progesterone. They concluded that progesterone alleviated endometriosis via inhibition of uterine cell proliferation, inflammation and angiogenesis in an immunocompetent mouse model. We assessed CD31 expression in eutopic endometrium from endometriosis subject and controls using IHC and % max as scoring method. CD31 was significantly less expressed in endometriosis subjects with a pvalue lower than 0,0001. p) HOXA10

HOXA10 is a transcription factor that regulates gene expression, morphogenesis and differentiation. Is it associated with fertility, embryo viability and regulation of hematopoietic lineage. It has been published that HOXA10 gene expression is aberrant (down regulated) in infertile women with endometriosis (Wang et ah, 2018).

Brown et a , (2006) assessed it expression in ectopic endometrial tissue and found a decreased expression in ectopic endometrium from endometriosis patient. We here reported using IHC and % max scoring method that HOXA10 is more expressed in endometrium from endometriosis subjects than in control with a pvalue of 0,0013. q) CD68

CD68 is known as a marker for detection of all types of macrophages (Ml and M2). In 2016 Scheerer investigated the immune cell infiltrate and used IHC test for detection of endometriosis-associated immune cell infiltrates (EMalCI) with CD68 in association with others markers. They found EMalCI were observed in all the types of endometriosis. Takebayashi in 2014 worked on subpopulations of macrophages within eutopic endometrium of endometriosis patients. Using IHC and anti- CD68 and anti-CDl63 they demonstrated that the endometriosis subject group had a significantly higher number of pan-macrophages . In our case in endometrium from endometriosis subject using IHC and % max scoring method we found a decrease expression of CD68 compared to control endometrium with a pvalue of 0,0128. r) to g) show results obtained on 15 endometrium from endometriosis subjects and 6 endometrium from controls chosen from the cohort used previously. IHC was performed for 6 of the biomarkers described above and staining was assessed using an automatic and quantitative interpretation software. r), s) pc-jun, t), u) AR, v), w) PR, x), y) ER, z), a) KI67, b), g) p53 we show here that the results obtained with a visual interpretation staining are in accordance with the results obtained with an automatic and quantitative interpretation for each of the 6 biomarkers chosen. Even with a small number of endometriosis subjects and controls we are able to demonstrate significantly different expressions of the biomarkers between the 2 populations and for some of them, like ER, PR or p53 the pvalue is equal or lower than 0,0001.

See Figure 2: Combination of biomarkers non-optimized with a variable number of biomarkers:

The previously described biomarkers were then combined in different manner described in the following paragraph and shown in Figure 2. First biomarkers were summed without a weight (non-optimized combination) and several combinations were assessed. The objective was to distinguish with a higher sensitivity and specificity the samples from the control population versus the endometriosis subjects. a/ first we combined without optimization the following 8 biomarkers using intensity score determined by visual analysis:

(AR.dens_glande xl) + (AR.dens_stroma xl) + (AR.int_glande xl) +

(AR.int_stroma xl) + (C.Erb.2.dens_glande xl) + (C.Erb.2.int_glande xl) + (c.Fos.dens_glande xl) + (c.Fos.dens_stroma xl) + (c.Fos.int_glande xl) +

(c.Fos.int_stroma xl) + (KI67.dens_glande xl) + (KI67.dens_stroma xl) +

(KI67.int_glande xl) + (KI67.int_stroma xl) + (p.c.jun.dens_glande xl) +

(p.c.jun.dens_stroma xl) + (p.c.jun.int_glande xl) + (p.c.jun.int_stroma xl) +

(P53.dens_glande xl) + (P53.dens_stroma xl) + (P53.int_glandexl) + (P53.int_stroma xl) + (PR.dens_stroma xl) + (PR.int_glande xl) +

(PR.int_stroma xl) + (Re.a.dens_glande xl) + (Re.a.dens_stroma xl) + (Re.a.int_glande xl) + (Re.a.int_stroma xl)

Using this combination, the performances of this model were a sensitivity of 87% and a specificity of 84%. These values could be even improved using another combination of biomarkers non op timized, like the following using intensity score determined by visual analysis:

(AR.dens_glande xl) + (AR.dens_stroma xl) + (AR.int_glande xl) + (AR.int_stroma xl) + (BCL2.int_glande xl) + (BCL2.int_stroma xl) + (C.Erb.2.dens_glande xl) + (C.Erb.2.int_glande xl) + (c.Fos.dens_glande xl) + (c.Fos.dens_stroma xl) + (c.Fos.int_glande xl) + (c.Fos.int_stroma xl) +

(C.Myc.dens_glande xl) + (C.Myc.dens_stroma xl) + (C.Myc.int_glande xl) +

(C.Myc.int_stroma xl) + (CD3l.int_stroma x(-l)) + (CD34.dens_stroma xl) + (CD34.int_stroma xl) + (CD45.int_glande xl) + (CD45.int_stroma xl) +

(CD68.dens_stroma xl) + (CD68.int_stroma xl) + (HOXAl0.int_stroma xl) + (KI67.dens_stroma xl) + (KI67.int_glande xl) + (KI67.int_stroma xl) +

(NFE2L3.int_glande xl) + (NFE2L3.int_stroma xl) + (p.c.jun.dens_glande xl) +

(p.c.jun.dens_stroma xl) + (p.c.jun.int_glande xl) + (p.c.jun.int_stroma xl) +

(P53.int_glande xl) + (P53.int_stroma xl) + (PR.int_glande xl) +

(Re.a.dens_stroma xl) + (Re.a.int_stroma xl) Using this combination, the performances of this model were increased up to 87% for sensitivity and 89% for specificity.

Then we used the Hscore determined by automatic and quantitative analysis to define another representation of the combination with the following 6 biomarkers: (pc-jun.Hscore_glande xl) + (pc-jun.Hscore_stroma xl) + (AR.Hscore_glande xl) + (AR.Hscore_stroma xl) + (PR.Hscore_glande xl) + (PR.Hscore_stroma xl) + (ER.Hscore_glande xl) + (ER.Hscore_stroma xl) + (KI67.Hscore_glande xl) + (KI67.Hscore_stroma xl) + (p53.Hscore_glande xl) + (p53.Hscore_stroma xl)

Using this combination and a cutoff value of 337 the performances were 100% in terms of sensitivity and 83% in terms of specificity.

Example 2: Combination of 2 biomarkers with algorithm optimization

Materials & Methods

See example 1

Results In this exemple only 2 biomarkers amongst all those described in Exemple 1 were used. Different set of 2 biomarkers were chosen and combined in differents ways to demonstrate their good performances independently of the 2 biomarkers and/or the optimized combination chosen. The values of sensitivity and specificity for each of the 4 combinations described below are represented in Figure 3. a) The combination of biomarkers consists of AR and Ki67 using intensity and or density score determined by visual analysis in the following combined manner:

(AR.dens_stroma x(-l)) + (AR.int_stroma xl5) + (KI67.dens_stroma xlO)

For this combination the performances obtained were sensitivity 94% and specificity 89%. b) the combination of biomarkers consists of AR and Ki67 using intensity and or density score determined by visual analysis for the same 2 biomarker than a) using another combination:

(AR.dens_stroma x(-2) + (AR.int_stroma x20) + (KI67.dens_stroma xlO) For this combination the performances obtained were sensitivity 90% and specificity

89% c) the combination of biomarkers consists of ERa and CD31 using intensity score and or density score determined by visual analysis for 2 others biomarkers:

(Re.a.dens_gland x(-2)) + (Re.a.int_stroma xlO) + (CD3l.int_stroma x(-20)) For this combination the performances obtained were sensitivity 87% and specificity

95% d) the combination of biomarkers consists of ERa and CD31 using intensity and or density score determined by visual analysis for the same 2 biomarkers than c) using another combination:

(Re.a.dens_gland x(-l)) + (Re.a.int_stroma xlO) + (CD3l.int_stroma c(-10))

For this combination the performances obtained were sensitivity 97% and specificity 79%

We here demonstrated that several combinations of 2 biomarkers amongst those described above can be used with good performances. The biomarkers can be used in different optimized combinations with good sensitivity and specificity. Example 3: Combination of biomarkers with algorithm to maximise diagnostic performance

Materials & Methods

See example 1 Results

This example illustrates combination of biomarkers optimized to match replacement test performances. Different set of biomarkers chosen from the list described above and used in different combinations are presented below. The test performances to be reached in order to be qualified as a replacement test are a specificity of at least 79% and a sensitivity of at least 94%

See Figure 4

First combination drawn is a set of 8 biomarkers using intensity score and or density score determined by visual analysis combined as follow:

(AR.dens_stroma x2) + (AR.int_stroma x4) + (C.Erb.2.dens_glande xl) +

(C.Erb.2.int_glande xl) + (c.Fos.dens_glande x3) + (c.Fos.dens_stroma x2) +

(c.Fos.int_stroma xl) + (CD3l.int_stroma xO) + (KI67.dens_stroma xl) +

(KI67.int_glande x3) + (KI67.int_stroma x3) + (p.c.jun.dens_glande xO) +

(p.c.jun.dens_stroma xl) + (p.c.jun.int_stroma xl) + (P53.int_glande xl) +

(PR.int_glande xl) + (PR.int_stroma x5) + (Re.a.dens_glande xl) +

(Re.a.int_glande x2) +( Re.a.int_stroma x4)

In this instance sensitivity is 87% and specificity is 95%.

ROC curve:

To illustrate these performances, a ROC curve was drawn. The accuracy of a test depends on its capacity to separate the control group and the endometriosis group. Accuracy is measured by the area under the ROC curve. An area of 1 represents a perfect test; an area of 0.5 represents a worthless test. Our test has an area under the ROC curve of 0,8734 which is good to excellent with a P value of 0,0001

Another representation is shown hereafter as a whisker plot. The cut off has been setup at 65 and with P value of 0,0001 our test has a sensitivity of 87% and a specificity of 95%. Second combination drawn is even more optimized. It is a set of 4 biomarkers using intensity score and or density score determined by visual analysis combined as follow:

(AR.dens_stroma x2) + (AR.int_stroma xlO) + (CD3l.int_stroma x(-l7)) +

(KI67.dens_stroma xlO) + (p.c.jun.dens_glande x7)

The sensitivity and specificity of this particular combinaison is 100% and 95% respectively.

Thus we have demonstrated here that the performance of our biomarkers associated in different manners and using different combinaison can match and/or are above replacement test performances criteria.

Example 4: Combination of 4 biomarkers with algorithm to maximise diagnostic sensitivity (rule out) + Combination of 4 biomarkers with algorithm to maximise diagnostic specificity (rule in)

Materials & Methods

See example 1

Results Here we proposed several combinations of 3 or 4 biomarkers with an optimized combination to maximize either diagnostic sensitivity (rule out test) or diagnostic specificity (rule in test). To reach the criteria for a rule out test the sensitivity must be abose 95% and specificity above 50%. For a rule in test, sensitivity must be above 50% and specificity above 95%. See Figure 5

For the rule out test, the combination shown here is a set of 4 biomarkers using intensity score and or density score determined by visual analysis combined as follow:

(AR.int_stroma xl6) + (c.Fos.int_stroma xlO) + (CD3l.int_stroma x(-l9)) + (P53.dens_stroma xl2) + (P53.int_stroma x(-l5))

The performances of this combination are a sensitivity of 1 and a specificity of 89%. The criteria for a rule out test are overreached and this combination could thus be used as such.

For the rule in test, 3 biomarkers were combined using intensity score and or density score determined by visual analysis as follow: (AR.Allred_glande x(-2)) + (AR.Allred_stroma x2) + (KI67.Allred_stroma x2) +

(p.c.jun.Allred_glande xl)

Sensitivity and specificity for this combination are 81% and 100% respectively. Thus, it could be proposed as a rule in test as it matches the minimal performances to be reached for this kind of test.

Claims

1 An in vitro method of diagnosing endometriosis in a subject comprising:

a) providing an endometrial sample from the subject,

b) measuring the levels of a combination of at least two biomarkers in said sample, thereby obtaining at least two scores.

c) combining said scores in a diagnostic score,

d) comparing said diagnostic score to a reference value sample, and

e) diagnosing the subject. 2 The in vitro method of claim 1 wherein

the diagnostic score is compared to a cut-off value, and

the subject is diagnosed with endometriosis when the diagnostic score is different than this cut-off value.

The in vitro method of claim 1 wherein

the diagnostic score is compared to a control score, and

the subject is diagnosed with endometriosis when the diagnostic score is different than this control score.

The in vitro method of any one of the preceding claims wherein the scores of step b) are combined in an algorithm. 5 The in vitro method of any one of the preceding claims wherein the algorithm is a mathematical function more particularly a logistic regression or any other predictive modeling method.

6 The in vitro method of any one of the preceding claims wherein the combination of biomarkers comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16 biomarkers. The in vitro method of any one of the preceding claims wherein, the biological marker is a protein.

The in vitro method of any one of the preceding claims wherein the biomarkers are chosen from the group comprising:

a human interleukin signaling marker, selected among the molecules listed in the Reactome pathway Knowledgebase“R-HSA-6785807 Interleukin- 4 and Interleukin- 13 signaling” (Fabregat A. et a , Nucleic Acids Res. 2016 Jan 4;44), including but not limited to p53, BCL2 or c-MYC;

a human intracellular signaling marker, selected among the molecules listed in the Reactome pathway Knowledgebase “R-HSA- 162582 Intracellular signaling by second messengers” (Fabregat A. et a , Nucleic Acids Res. 2016 Jan 4;44), including but not limited to c-Erb2 or CD34; a human nuclear signaling marker, selected among the molecules listed in the Reactome pathway Knowledgebase“R-HSA-8939211 ESR-mediated signaling” (Fabregat A. et a , Nucleic Acids Res. 2016 Jan 4;44), including but not limited to ERa, PR, c-Fos or c-Jun;

a human transcription marker, selected among the molecules listed in the Reactome pathway Knowledgebase“R-HSA-73857.5 RNA Polymerase II transcription” (Fabregat A. et a , Nucleic Acids Res. 2016 Jan 4;44), including but not limited to AR, HOXA10, Ki67 or NFE2L3; or a human immune system marker, selected among the molecules listed in the Reactome pathway Knowledgebase “R-HSA-6798695 Neutrophil degranulation” (Fabregat A. et ah, Nucleic Acids Res. 2016 Jan 4;44), including but not limited to CD31, CD45 or CD68.

The method of any one of the preceding claims wherein the combination of the at least two biomarkers is chosen among p53, BCF2, c-MYC, c-Erb2, CD34, ERa, PR, c-Fos, c-Jun, AR, HOXA10, Ki67, NFE2F3, CD31, CD45 and CD68.

10. The in vitro method of any one of the preceding claims wherein the combination of biomarkers consists of AR, c-Erb2, c-Fos, c-Jun, Ki67, p53, PR, ERa and CD31.

11. The in vitro method of any one of the preceding claims wherein the combination of biomarkers consists of AR, c-Jun, Ki67, p53, PR, ERa.

12. The in vitro method of any one of the preceding claims wherein the combination of biomarkers consists of AR and Ki67. 13. The in vitro method of any one of the preceding claims wherein the combination of biomarkers consists of ERa and CD31.

14. The in vitro method of any one of the preceding claims wherein the combination of biomarkers consists of c-Jun, CD31, Ki-67 and AR.

15. The in vitro method of any one of the preceding claims wherein the combination of biomarkers consists of p53, c-Fos, CD31 and AR.

16. The in vitro method of any one of the preceding claims wherein the combination of biomarkers consists of Ki67, c-Jun and AR.

17. The in vitro method of any one of the preceding claims wherein the measure of the biomarkers is made by any protein- specific detection method such as immunohistochemistry (IHC), immunofluorescence, flow cytometry, ELISA, protein immunoprecipitation, protein immune-electrophoresis, Western Blot, liquid chromatography, mass spectrometry or any combination thereof.

18. The in vitro method of claim 17 wherein the measure of the biomarkers is made by immunohistochemistry (IHC). 19. The in vitro method according to claim any one of the preceding claims, wherein the subject is (i) a-symptomatic (ii) pre- symptomatic for endometriosis or (iii) already displaying clinical symptoms of endometriosis.

20. A kit for the detection of endometriosis in a sample comprising reagents for measuring the levels of a combination of biomarkers reflecting endometriosis and/or material for collecting the sample(s) and/or reaction vessel and/or monoclonal and/or polyclonal antibodies against said biomarkers and secondary antibody and/or buffers and/or control samples for each biomarkers and solutions for use and instructions for use.

21. The kit of claim 19 wherein the combination of biomarkers is listed in anyone of claim 8 to 17.

22. The use of the kit of claim 19 or 20 for the implementation of a method of diagnostic of endometriosis.

23. Use of the combination of biomarkers as listed in any one of claim 8 to 17 as diagnostic biomarker for endometriosis.