EP3963094A1 - Classificateurs pour la détection de l'endométriose - Google Patents

Classificateurs pour la détection de l'endométriose

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Publication number
EP3963094A1
EP3963094A1 EP20799450.0A EP20799450A EP3963094A1 EP 3963094 A1 EP3963094 A1 EP 3963094A1 EP 20799450 A EP20799450 A EP 20799450A EP 3963094 A1 EP3963094 A1 EP 3963094A1
Authority
EP
European Patent Office
Prior art keywords
mir
endometriosis
women
mirna
machine learning
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP20799450.0A
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German (de)
English (en)
Other versions
EP3963094A4 (fr
Inventor
Hugh Taylor
Heather BOWERMAN
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Dot Laboratories Inc
Yale University
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Dot Laboratories Inc
Yale University
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Publication date
Priority claimed from PCT/US2019/059006 external-priority patent/WO2020092672A2/fr
Application filed by Dot Laboratories Inc, Yale University filed Critical Dot Laboratories Inc
Publication of EP3963094A1 publication Critical patent/EP3963094A1/fr
Publication of EP3963094A4 publication Critical patent/EP3963094A4/fr
Pending legal-status Critical Current

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
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    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6809—Methods for determination or identification of nucleic acids involving differential detection
    • G—PHYSICS
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    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/217—Validation; Performance evaluation; Active pattern learning techniques
    • G06F18/2193—Validation; Performance evaluation; Active pattern learning techniques based on specific statistical tests
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/20—Ensemble learning
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/112—Disease subtyping, staging or classification
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/16—Primer sets for multiplex assays
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/178—Oligonucleotides characterized by their use miRNA, siRNA or ncRNA
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/36—Gynecology or obstetrics
    • G01N2800/364—Endometriosis, i.e. non-malignant disorder in which functioning endometrial tissue is present outside the uterine cavity
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G06F18/24147—Distances to closest patterns, e.g. nearest neighbour classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
    • G06F18/24155—Bayesian classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/24323—Tree-organised classifiers
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation

Definitions

  • Endometriosis is a common condition affecting women of pubescent and reproductive age. The disease is thought to be caused by endometrial tissue which migrates from its normal position lining the uterus to other parts of the body, primarily within the abdominal cavity. The ovaries and gut wall are commonly affected. The displaced endometrial tissue, like that in its normal position, grows and declines according to the menstrual cycle as a result of the actions of the ovarian hormones. Endometriosis may cause many symptoms including, but not limited to, abdominal pain, gastrointestinal upset, excessive bleeding, infertility and menstrual disturbance.
  • Endometriosis an inflammatory disorder in which endometrial cells proliferate outside the uterus, affects nearly 10% of reproductive age women. It is seen in 50-60% of reproductive aged women with chronic pelvic pain and in up to 50% of women with infertility. Despite its prevalence, endometriosis often goes undiagnosed for years. The average time from the onset of symptoms to a correct diagnosis can range from 5-10 years. The disease can be difficult to recognize based on patients’ descriptions of symptoms, especially at early stages, and the definitive diagnosis of the condition presently requires laparoscopic examination, a surgical procedure. Laparoscopy is the current“gold standard” approach for visual confirmation of endometriosis pathology and collection of lesion tissue for histological analysis.
  • This disclosure addresses, among other things, a need in the art for minimally-invasive, accurate and more efficient methods of detecting, diagnosing, and monitoring endometriosis.
  • a method of detecting presence or absence of endometriosis in a female subject comprising: (a) detecting in a bodily fluid sample from the female subject an expression profile of a panel of miRNAs associated with endometriosis, wherein the panel of miRNAs associated with endometriosis comprises miR-342 or miR451a; (b) applying a machine learning algorithm to the expression profile of the panel of miRNAs associated with
  • the machine learning algorithm has importance measures assigned to miRNA features, and wherein: i. an importance measure is assigned to miR-342 and the importance measure assigned to miR-342 is greater than the importance measure assigned to at least one miRNA selected from the group consisting of: miR-150, miR-3613, miR-451a, let- 7b, and miR-125b; or ii. an importance measure is assigned to miR-451a and the importance measure assigned to miR-45 la is greater than the importance measure assigned to at least one miRNA selected from the group consisting of: miR-3613, miR-125b, and let-7b; and (c) using the machine learning algorithm to detect presence or absence of endometriosis in the female subject.
  • the female subject has symptoms of endometriosis. In some cases, the female subject has symptoms of endometriosis selected from the group consisting of: abdominal pain, gastrointestinal upset, excessive bleeding, infertility and menstrual disturbance. In some cases, the method comprises diagnosing endometriosis, monitoring endometriosis, prognosing endometriosis, or predicting the risk of endometriosis. In some cases, the subject has not been previously diagnosed with endometriosis. In some cases, the subject has been previously diagnosed with endometriosis and the method confirms presence of endometriosis in the female subject.
  • the method further comprises diagnosing endometriosis in the female subject when the presence of endometriosis is detected. In some cases, the method further comprises prognosing or monitoring endometriosis in the female subject when the presence of endometriosis is detected. In some cases, the method further comprises administering a treatment for endometriosis to the female subject when the presence of endometriosis is detected. In some cases, the method further comprises determining that the female subject has a condition that is not endometriosis when the absence of endometriosis is detected. In some cases, the method further comprises administering a treatment for a non-endometriosis condition to the female subject when the absence of endometriosis is detected.
  • the importance measure assigned to miR-342 is greater than the importance measure assigned to at least one miRNA selected from the group consisting of: miR-150, miR-3613, miR-451a, let- 7b, and miR- 125b. In some cases, the importance measure assigned to miR-342 is less than the importance measure assigned to at least one other miRNA.
  • the importance measure assigned to miR-342 is greater than the importance measure assigned to at least one miRNA selected from the group consisting of: miR-150, miR-3613, miR-451a, let- 7b, and miR-125b; and is less than the importance measure assigned to at least one miRNA selected from the group consisting of: miR-150, miR-3613, miR-451a, let- 7b, and miR-125b. In some cases, the importance measure assigned to miR-342 is less than the importance measure assigned to at least two other miRNA.
  • the importance measure assigned to miR-342 is greater than the importance measure assigned to at least two miRNA selected from the group consisting of: miR-150, miR- 3613, miR-451a, let-7b, or miR-125b. In some cases, the importance measure assigned to miR- 342 is greater than the importance measure assigned to at least three miRNA selected from the group consisting of: miR-150, miR-3613, miR-451a, let-7b, or miR-125b. In some cases, the importance measure assigned to miR-342 is greater than the importance measure assigned to at least four miRNA selected from the group consisting of: miR-150, miR-3613, miR-451a, let- 7b, and miR-125b.
  • the importance measure assigned to miR-342 is greater than the importance measure assigned to miR-150, miR-3613, miR-451a, let- 7b, and miR-125b.
  • the importance measure assigned to miR-45 la is greater than the importance measure assigned to at least one miRNA selected from the group consisting of: miR-3613, miR-125b and let-7b.
  • the importance measure assigned to miR-45 la is greater than the importance measure assigned to at least two miRNA selected from the group consisting of: miR- 3613, miR-125b and let-7b.
  • the importance measure assigned to miR-45 la is greater than the importance measure assigned to miR-3613, miR-125b and let- 7b.
  • the importance measures are assigned such that the ranking of the importance measures from highest to lowest is: miR-342, miR-451a, miR-3613, miR-125b, let- 7b, and miR-150.
  • the bodily fluid sample comprises cells.
  • the bodily fluid sample is a cell- free sample.
  • the bodily fluid sample is a blood sample, a plasma sample, a saliva sample, or a serum sample.
  • the panel of miRNA are cell-free miRNA.
  • the panel of miRNA are cell -associated miRNA or exosome-associated miRNA.
  • the applying a machine learning algorithm to the expression profile comprises applying a machine learning algorithm with specific importance measure rankings assigned to the miRNA features, wherein the ranking from highest to lowest is miR-342, miR-451a, miR-3613, miR- 125b, let-7b, and miR-150.
  • the machine learning algorithm is a random forest algorithm, k-nearest-neighbors algorithm (KNN), support vector machine (SVM), or Naive Bayes.
  • the machine learning algorithm is a random forest algorithm.
  • the method detects endometriosis in a population of women with a specificity of greater than 80%. In some cases, the population of women is premenopausal women.
  • the population of women comprises women with leiomyomas, cystadenomas, chronic pelvic infections, teratomas, endometriomas, or paratubal cysts. In some cases, the population of women comprises women with Stage I/II endometriosis. In some cases, the population of women comprises women with Stage III/IV endometriosis or women with all four stages of endometriosis (Stage I/II/III/IV). In some cases, the population of women comprises women having received hormone therapy within 3 months of the date on which the sample was obtained or women at any phase of their menstrual cycle. In some cases, the population of women comprises a cohort comprising at least 100 women. In some cases, the population of women comprises a cohort comprising at least 1000 women.
  • the machine learning algorithm is trained on expression data from at least 100 samples. In some cases, the machine learning algorithm is trained on expression data from at least 500 samples. In some cases, the machine learning algorithm is trained on expression data from at least 1000 samples. In some cases, the machine learning algorithm is trained on a population of women comprising women having stages I-IV endometriosis. In some cases, the method has an AUC for detecting endometriosis of greater than 0.85 in a population of women. In some cases, the method has an AUC for detecting endometriosis of greater than 0.90 in a population of women. In some cases, the method has an AUC for detecting endometriosis of greater than 0.92 in a population of women.
  • the method detects endometriosis in a population of women with a specificity of greater than 80%. In some cases, the method detects endometriosis in a population of women with a specificity of greater than 85%. In some cases, the method detects
  • the method detects endometriosis in a population of women with a specificity of greater than 90%. In some cases, the method detects endometriosis in a population of women with a specificity of greater than 92%. In some cases, the method detects endometriosis in a population of women with a specificity of greater than 95%. In some cases, the method detects endometriosis in a population of women with a sensitivity of greater than 80%. In some cases, the method detects endometriosis in a population of women with a sensitivity of greater than 85%. In some cases, the method detects endometriosis in a population of women with a sensitivity of greater than 90%.
  • the method detects endometriosis in a population of women with a specificity of greater than 90% and a sensitivity of less than 85%. In some cases, the method detects endometriosis in a population of women with a specificity of greater than 95% and a sensitivity of less than 85%. In some cases, the method detects endometriosis in a population of women with a sensitivity of less than 90%. In some cases, the method detects endometriosis in a population of women with a sensitivity of less than 85%. In some cases, the method detects endometriosis in a population of women with a sensitivity of less than 80%.
  • the method further comprises treating the female subject with a treatment that does not involve surgery when the absence of endometriosis is detected.
  • the method further comprises administering a treatment to the female subject when the presence of endometriosis is detected, wherein the treatment comprises a hormonal treatment, surgery, laparoscopic surgery, a statin, a non-steroidal anti-inflammatory drug (NSAID), an oral contraceptive, a progestin, a gonadotrophin releasing (GnRH) agonist, a GnRH antagonist, an androgen, an antiprogesterone, a selective estrogen receptor modulator (SERM), a selective progesterone receptor modulator (SPRM), atorvastatin, cerivastatin, fluvastatin, lovastatin, mevastatin, pitavastatin, pravastatin, rosuvastatin, simvastatin, paracetamol, a COX-2 inhibitor, or aspirin.
  • the miRNA expression level is detected
  • a method of classifying endometriosis in a female subject comprising: (a) obtaining a bodily fluid sample comprising miRNA wherein the bodily fluid sample is from a female subject; (b) performing quantitative real-time polymerase chain reaction, microarray assay or sequencing assay on a set of miRNA within the bodily fluid sample, wherein the set of miRNA comprises two or more different miRNA associated with endometriosis; (c) comparing to an amount of a control RNA, an amount of the two or more different miRNA associated with endometriosis in the biological sample to determine a normalized miRNA level for the two or more different miRNAs in the bodily fluid sample; (d) classifying the female subject as positive or negative for endometriosis by inputting the normalized miRNA levels to a trained algorithm, wherein the trained algorithm has importance rankings of the two or more different miRNA and wherein the trained algorithm is optimized for a specificity that is higher than sensitivity by selecting an optimal cut
  • the trained algorithm is optimized to detect endometriosis with a specificity of greater than 80% in a population of women. In some cases, the trained algorithm is optimized to detect endometriosis with a specificity of greater than 90% and a sensitivity less than 85% in a population of women. In some cases, the trained algorithm detects Stage I/II endometriosis with a specificity of greater than 90%, or greater than 95% in a population of women. In some cases, the method has an AUC for detecting endometriosis of greater than 0.85 in a population of women. In some cases, the method has an AUC for detecting endometriosis of greater than 0.9 in a population of women.
  • the method has an AUC for detecting endometriosis of greater than 0.92 in a population of women. In some cases, the method detects endometriosis in a population of women with a specificity of greater than 85%. In some cases, the method detects endometriosis in a population of women with a specificity of greater than 90%. In some cases, the method detects endometriosis in a population of women with a specificity of greater than 95%. In some cases, the method detects endometriosis in a population of women with a sensitivity of greater than 80%. In some cases, the method detects endometriosis in a population of women with a sensitivity of greater than 90%. In some cases, the method detects
  • the method has an area under curve (AUC) value greater than 0.85 irrespective of endometriosis stage or hormonal treatment.
  • the method further comprises administering a treatment for endometriosis to the female subject after the report identifies the female subject as being negative for endometriosis.
  • the method further comprises administering a treatment for a non-endometriosis condition to the female subject after the report identifies the female subject as being negative for endometriosis. In some cases, the method further comprises repeating (a)-(e) on an additional bodily fluid sample obtained at least three months after the report identifies the female subject as being negative for endometriosis. In some cases, the trained algorithm assigns an importance measure to miR-342 that is greater than the importance measure assigned to at least one miRNA selected from the group consisting of: miR-150, miR- 3613, miR-451a, let-7b, and miR-125b.
  • the importance measure assigned to miR- 342 is less than the importance measure assigned to at least one other miRNA. In some cases, the importance measure assigned to miR-342 is greater than the importance measure assigned to at least two miRNA selected from the group consisting of: miR-150, miR-3613, miR-451a, let- 7b, or miR-125b. In some cases, the importance measure assigned to miR-342 is greater than the importance measure assigned to at least three miRNA selected from the group consisting of: miR-150, miR-3613, miR-451a, let- 7b, or miR-125b.
  • the importance measure assigned to miR-342 is greater than the importance measure assigned to at least four miRNA selected from the group consisting of: miR-150, miR-3613, miR-451a, let-7b, and miR-125b. In some cases, the importance measure assigned to miR-342 is greater than the importance measure assigned to miR-150, miR-3613, miR-451a, let-7b, and miR-125b. In some cases, the importance measure assigned to miR-45 la is greater than the importance measure assigned to at least one miRNA selected from the group consisting of: miR-3613, miR-125b and let- 7b.
  • the importance measure assigned to miR-45 la is greater than the importance measure assigned to at least two miRNA selected from the group consisting of: miR-3613, miR- 125b and let-7b. In some cases, the importance measure assigned to miR-45 la is greater than the importance measure assigned to miR-3613, miR-125b and let- 7b. In some cases, the importance measures are assigned such that the ranking of the importance measures from highest to lowest is: miR-342, miR-451a, miR-3613, miR-125b, let- 7b, and miR-150.
  • the bodily fluid sample comprises cells. In some cases, the bodily fluid sample is a cell-free sample.
  • the bodily fluid sample is a blood sample, a plasma sample, a saliva sample, or a serum sample.
  • the applying a machine learning algorithm to the expression profile comprises applying a machine learning algorithm with specific importance measure rankings assigned to the miRNA features, wherein the ranking from highest to lowest is miR- 342, miR-451a, miR-3613, miR-125b, let-7b, and miR-150.
  • the machine learning algorithm is a random forest algorithm, k-nearest-neighbors algorithm (KNN), support vector machine (SVM), or Naive Bayes.
  • the machine learning algorithm is a random forest algorithm.
  • the population of women is premenopausal women.
  • the population of women comprises women with leiomyomas, cystadenomas, chronic pelvic infections, teratomas, endometriomas, or paratubal cysts, in any combination.
  • the population of women comprises women with Stage I/II endometriosis.
  • the population of women comprises women with Stage III/IV endometriosis or women with all four stages of endometriosis (Stage I/II/III/IV).
  • the population of women comprises women having received hormone therapy within 3 months of the date on which the sample was obtained or women at any phase of their menstrual cycle.
  • the population of women comprises a cohort comprising at least 100 women.
  • the population of women comprises a cohort comprising at least 500 women. In some cases, the population of women comprises a cohort comprising at least 1000 women. In some cases, the machine learning algorithm is trained on expression data from at least 100 samples. In some cases, the machine learning algorithm is trained on expression data from at least 1000 samples.
  • the machine learning algorithm is trained on a population of women comprising women having stages I-IV endometriosis.
  • the method further comprises treating the female subject with a treatment that does not involve surgery when the absence of endometriosis is detected.
  • the method further comprises administering a treatment to the female subject when the presence of endometriosis is detected, wherein the treatment comprises a hormonal treatment, surgery, laparoscopic surgery, a statin, a non steroidal anti-inflammatory drug (NSAID), an oral contraceptive, a progestin, a gonadotrophin releasing (GnRH) agonist, a GnRH antagonist, an androgen, an antiprogesterone, a selective estrogen receptor modulator (SERM), a selective progesterone receptor modulator (SPRM), atorvastatin, cerivastatin, fluvastatin, lovastatin, mevastatin, pitavastatin, pravastatin, rosuvastatin, simvastatin, paracetamol, a COX-2 inhibitor, or aspirin.
  • NSAID non steroidal anti-inflammatory drug
  • SPRM selective progesterone receptor modulator
  • the method comprises diagnosing endometriosis, monitoring endometriosis, prognosing endometriosis, or predicting the risk of endometriosis.
  • the subject has not been previously diagnosed with endometriosis.
  • a method of diagnosing and treating endometriosis in a female subject comprising: (a) detecting in a saliva sample from the female subject an expression profile of a panel of miRNAs associated with endometriosis, wherein the panel of miRNAs associated with endometriosis comprises miR-125b and at least one other miRNA; (b) applying a machine learning algorithm to the expression profile of the panel of miRNAs associated with endometriosis, wherein the machine learning algorithm has importance measures assigned to miRNA features, and wherein the importance measure of miR-125b is greater than the importance measure of miR-150, miR-3613, miR-451a, let- 7b, or miR-342; (c) using the machine learning algorithm to diagnose endometriosis in the female subject; and (d) treating the endometriosis diagnosed in the female subject with a treatment for endometriosis.
  • applying a machine learning algorithm to the expression profile comprises applying a machine learning algorithm with specific importance measures assigned to the miRNA features, wherein the importance measure of miR-125b is greater than at least one of miR-150, let-7b, miR-451a, or miR-3613.
  • applying a machine learning algorithm to the expression profile comprises applying a machine learning algorithm with specific importance measures assigned to the miRNA features, wherein the importance measure of miR-125b is greater than at least two of miR-150, let-7b, miR-451a, or miR-3613.
  • applying a machine learning algorithm to the expression profile comprises applying a machine learning algorithm with specific importance measures assigned to the miRNA features, wherein the importance measure of miR- 125b is greater than at least three of miR-150, let- 7b, miR-451a, or miR-3613.
  • applying a machine learning algorithm to the expression profile comprises applying a machine learning algorithm with specific importance measures assigned to the miRNA features, wherein the importance measure of miR-125b is greater than miR-150, let-7b, miR-451a, and miR-3613.
  • a method of characterizing a female subject as having endometriosis comprising: (a) obtaining a bodily fluid sample comprising miRNA wherein the bodily fluid sample is from a female subject; (b) performing quantitative real-time polymerase chain reaction, microarray assay, or sequencing of a set of miRNA within the bodily fluid sample, wherein the set of miRNA comprises two or more different miRNA associated with endometriosis; (c) comparing to an amount in a control RNA, an amount of the two or more different miRNA associated with endometriosis in the biological sample to determine a normalized miRNA level for the two or more different miRNAs in the bodily fluid sample; (d) classifying the female subject as positive or negative for endometriosis by inputting the normalized miRNA levels to a trained algorithm, wherein the trained algorithm is optimized for a sensitivity of at least 80% by selecting an optimal cutoff point on a receiver operating characteristic (ROC) curve for the two or more
  • ROC receiver operating characteristic
  • a method of characterizing a female subject as having or not having endometriosis comprising: (a) obtaining a bodily fluid sample comprising miRNA wherein the bodily fluid sample is from a female subject with abdominal pain, gastrointestinal upset, excessive bleeding, infertility or menstrual disturbance and said subject has not been previously diagnosed with endometriosis; (b) performing quantitative real-time polymerase chain reaction or sequencing of a set of miRNA within the bodily fluid sample, wherein the set of miRNA comprises two or more different miRNA associated with
  • the trained algorithm calculates a voting score indicative of likelihood of having endometriosis. In some cases, the trained algorithm classifies the female subject as having endometriosis when the voting score is greater than 36%. In some cases, the method has an area under curve (AUC) value greater than 0.85 for Stage I/II endometriosis. In some cases, the method has an area under curve (AUC) value greater than 0.85 for distinguishing between endometriosis and leiomyomas. In some cases, the method has an area under curve (AUC) value greater than 0.85 irrespective of endometriosis stage or hormonal treatment. In some cases, the method is optimized for a specificity greater than 90% and a sensitivity less than 85%.
  • a method of detecting endometriosis in a female subject comprising: (a) detecting in a sample comprising miRNA from the subject an expression profile of a panel of miRNAs associated with endometriosis; (b) applying a machine learning algorithm to the expression profile to detect endometriosis in the sample from the subject, wherein the machine learning algorithm is trained on a group of miRNA features selected from the group consisting of: (i) miR-342, miR-451a, and miR-3613; (ii) miR-342, miR- 451a, miR-3613, and miR-125b; (iii) miR-342, miR-451a, miR-3613, miR-125b, and let- 7b; (iv) miR-342, miR-451a, let-7b, and miR-125b; (v) miR-342, miR-451a, let-7b, and miR-3613;
  • the method further comprises obtaining the sample comprising miRNA from the subject, prior to (a).
  • the sample is a saliva sample.
  • the sample is a serum sample.
  • the machine learning algorithm is a random forest algorithm, k-nearest-neighbors algorithm (KNN), support vector machine (SVM), or Naive Bayes.
  • the method has an AUC for detecting endometriosis of greater than 0.85 in a population of women.
  • the population of women is premenopausal women.
  • the population of women is premenopausal and over 18.
  • the population of women is
  • the population of women is negative for critical anemia, hyperplasia, polyps, and malignancy. In some cases, the population of women includes women having received hormone therapy within 3 months of the date on which the sample was obtained. In some cases, the hormone therapy includes birth control pills or GnRH agonists. In some cases, the machine learning algorithm is trained on expression data of at least 100 samples. In some cases, the machine learning algorithm is trained on a population of women with surgically-confirmed endometriosis. In some cases, the machine learning algorithm is trained on a population of women comprising women having stage I or II endometriosis.
  • the machine learning algorithm is trained on a population of women comprising women having stages I-IV endometriosis.
  • applying a machine learning algorithm to the expression profile comprises applying a machine learning algorithm with specific importance measures assigned to the miRNA features, wherein the importance measure of miR-342 is greater than at least one of miR-150, let-7b, or miR-125.
  • applying a machine learning algorithm to the expression profile comprises applying a machine learning algorithm with specific importance measures assigned to the miRNA features, wherein the importance measure of miR-125b is greater than one of miR-150, let-7b, miR-451a, or miR-3613.
  • applying a machine learning algorithm to the expression profile comprises training the machine learning algorithm on the group of miRNA features of (viii) and applying a machine learning algorithm with specific importance measures assigned to the miRNA features, wherein the ranking of the miRNA features from highest to lowest is miR-125b, let-7b, miR-3613, miR-150, miR-342, and miR-451a.
  • applying a machine learning algorithm to the expression profile comprises training the machine learning algorithm on the group of miRNA features in (viii) and applying a machine learning algorithm with specific importance measure rankings assigned to the miRNA features, wherein the ranking from highest to lowest is miR-342, miR- 451a, miR-3613, miR-125b, let-7b, and miR-150.
  • applying a machine learning algorithm to the expression profile comprises training the machine learning algorithm on the group of miRNA features in (viii) and applying an machine algorithm with specific importance measures assigned to the miRNA features, wherein the importance measure ranking of the miRNA features is according to any column in Table 9.
  • a method comprising: (a) storing information related to the condition of a female patient in a standardized format in a plurality of network-based non- transitory storage devices; (b) providing remote access to users over a network so that at least one user can update the information related to the condition of a female patient in real time through a graphical user interface, wherein the at least one user provides the updated information in the form of an expression profile of miRNAs from the female patient; (c) converting, by a content server, the expression profile of the miRNAs from the female patient to a likelihood of the female patient having endometriosis using the application of a machine learning algorithm; (d) storing the likelihood of the female patient having endometriosis; (e) automatically generating a message containing the likelihood of the female patient having endometriosis by the content server whenever the updated information has been stored; and (f) transmitting the message to all of the users over the computer network in real time, so that each user has immediate
  • the machine learning algorithm is trained on a group of miRNA features selected from the group consisting of: (i) miR-342, miR-451a, and miR-3613; (ii) miR-342, miR-451a, miR-3613, and miR-125b; (iii) miR-342, miR-451a, miR-3613, miR-125b, and let- 7b; (iv) miR-342, miR-451a, let- 7b, and miR- 125b; (v) miR-342, miR-451a, let- 7b, and miR-3613; (vi) miR-342, miR-451a, and let- 7b; (vii) miR-125b, miR-150, miR-342, miR-451a, and let-7b; and (viii) miR-125b, miR-150, miR-342, miR-3613, miR-451a, and let- 7b.
  • the method further comprises obtaining a sample comprising miRNA from the subject.
  • the sample is a blood sample, plasma sample, or serum sample.
  • the sample is a saliva sample.
  • the sample is a serum sample.
  • the machine learning algorithm is a random forest algorithm, k-nearest-neighbors algorithm (KNN), support vector machine (SVM), or Naive Bayes.
  • the method has an AUC for detecting endometriosis of greater than 0.85 in a population of women.
  • the population of women includes women having received hormone therapy within 3 months of the date on which the sample was obtained.
  • the hormone therapy includes birth control pills or GnRH agonists.
  • the sample is a cell-free serum sample. In some cases, the sample is a cell-free saliva sample. In some cases, the method further comprises administering a treatment to the subject to treat the endometriosis based on the likelihood reported in (e).
  • the treatment comprises a hormonal treatment, a statin, or a non-steroidal anti-inflammatory drug (NSAID).
  • the hormonal treatment comprises an oral contraceptive, a progestin, a GnRH agonist, a GnRH antagonist, an androgen, an antiprogesterone, a SERM, or SPRM.
  • the statin comprises atorvastatin, cerivastatin, fluvastatin, lovastatin, mevastatin, pitavastatin, pravastatin, rosuvastatin, or simvastatin.
  • the NSAID comprises paracetamol, a COX-2 inhibitor, or aspirin.
  • the present disclosure provides for a method of detecting
  • endometriosis in a female subject comprising: (a) detecting in a sample from the subject an expression profile of a panel of miRNAs associated with endometriosis; (b) applying a machine learning algorithm to the expression profile to detect endometriosis in the sample from the subject, wherein the machine learning algorithm is trained on a group of miRNA features selected from the group consisting of: (i) miR-342, miR-451a, and miR-3613; (ii) miR-342, miR- 451a, miR-3613, miR-125b; (iii) miR-342, miR-451a, miR-3613, miR-125b, let-7b; (iv) miR- 342, miR-451a, let-7b, miR-125b; (v) miR-342, miR-451a, let- 7b, miR-3613; (vi) miR-342, miR- 451a, let-7b; (vii) miR-
  • the method comprises obtaining a sample comprising miRNA from the subject.
  • the sample is a blood sample.
  • the sample is a serum sample.
  • the sample is a plasma sample.
  • the sample is a cell-free or acellular blood, plasma, or serum sample.
  • the sample is a blood sample collected by venipuncture into a collection tube without additional additives, followed by centrifugation to remove cells.
  • the blood sample is centrifuged or filtered to remove cells.
  • the sample is a blood, plasma or serum sample.
  • the sample is a urine sample.
  • the sample is a bodily fluid sample.
  • the bodily fluid is sweat, saliva, tears, urine, blood, plasma, serum, vaginal fluid, cervico-vaginal fluid, whole blood, menstrual effluent, menstrual blood, spinal fluid, pulmonary fluid, or sputum.
  • the machine learning algorithm is a random forest algorithm, k-nearest-neighbors algorithm (KNN), support vector machine (SVM), or Naive Bayes.
  • the algorithm is a random forest algorithm.
  • the method has an AUC for detecting endometriosis of greater than 0.85 in a population of women. In some embodiments, the population of women is premenopausal women.
  • the population of women is premenopausal and over 18. In some embodiments, the population of women is premenopausal and under 49. In some embodiments, the population of women is negative for critical anemia, hyperplasia, polyps, and malignancy. In some embodiments, the population of women includes women having received hormone therapy within 3 months of the date on which the sample was obtained. In some embodiments, the hormone therapy includes birth control pills and/or GnRH agonists. In some embodiments, the algorithm is trained on expression data from at least 100 samples. In some embodiments, the algorithm is trained on expression data from at least 50 samples. In some embodiments, the algorithm is trained on expression data from at least 200 samples.
  • the algorithm is trained on expression data from at least 500 samples or at least 1000 sample. In some embodiments, the algorithm is trained on a population of women with surgically- confirmed endometriosis. In some embodiments, the algorithm is trained on a population of women comprising women having stage I or II endometriosis. In some embodiments, the algorithm is trained on a population of women comprising women having stages I-IV
  • applying a machine learning algorithm e.g., random forest algorithm
  • applying a machine learning algorithm to the expression profile comprises assigning importance measures to the miRNA features, wherein the importance measure of miR-342 is greater than one of miR-150, let-7b, or miR-125.
  • applying a machine learning algorithm to the expression profile comprises assigning importance measures the miRNA features, wherein the importance measure of miR-125b is greater than one of miR-150, let- 7b, or miR-125.
  • applying a machine learning algorithm to the expression profile comprises training the machine learning algorithm on the features in (viii) and assigning importance measures the miRNA features, wherein the importance measure ranking of the miRNA features from highest to lowest is miR-125b, let- 7b, miR-2613, miR-150, miR-342, and miR-451a.
  • applying a machine learning algorithm to the expression profile comprises training the machine learning algorithm on the features in (viii) and assigning importance measures the miRNA features, wherein the importance measure (e.g., feature importance) ranking of the miRNA features from highest to lowest is miR-342, miR-451a, miR-3613, miR-125b, let-7b, and miR-150.
  • applying a machine learning algorithm to the expression profile comprises training the machine learning algorithm on the features in (viii) and assigning importance measures the miRNA features, wherein the importance measure ranking of the miRNA features is according to Table 9.
  • the method further comprises detecting, diagnosing, or assessing risk of endometriosis in the female subject using a supervised learning algorithm having feature importances assigned in Table 9.
  • the methods further comprises administering an endometriosis treatment to treat the endometriosis detected or diagnosed in the female subject.
  • the present disclosure provides for a method comprising: (a) storing information related to the condition of a female patient in a standardized format in a plurality of network-based non-transitory storage devices; (b) providing remote access to users over a network so that at least one user can update the information related to the condition of a female patient in real time through a graphical user interface, wherein the at least one user provides the updated information in the form of an expression profile of miRNAs from the female patient; (c) converting, by a content server, the expression profile of the miRNAs from the female patient to a likelihood of the female patient having endometriosis using the application of a machine learning algorithm; (d) storing the likelihood of the female patient having endometriosis; (e) automatically generating a message containing the likelihood of the female patient having endometriosis by the content server whenever the updated information has been stored; and (f) transmitting the message to at least some of the users over the computer network in real time, so that
  • the machine learning algorithm is trained on a group of miRNA features selected from the group consisting of: (i) miR-342, miR-451a, and miR-3613; (ii) miR-342, miR- 451a, miR-3613, miR-125b; (iii) miR-342, miR-451a, miR-3613, miR-125b, let-7b; (iv) miR- 342, miR-451a, let-7b, miR-125b; (v) miR-342, miR-451a, let- 7b, miR-3613; (vi) miR-342, miR- 451a, let-7b; (vii) miR-125b, miR-150, miR-342, miR-451a, let-7b; and (viii) miR-125b, miR- 150, miR-342, miR-3613, miR-451a, and let-7b.
  • miRNA features selected from the group consisting of: (i
  • the method comprises obtaining a sample comprising miRNA from the subject.
  • the sample is a saliva sample.
  • the sample is a serum sample.
  • the sample is a blood, plasma or serum sample.
  • the sample is a urine sample.
  • the sample is a bodily fluid sample.
  • the bodily fluid is sweat, saliva, tears, urine, blood, plasma, serum, vaginal fluid, cervico-vaginal fluid, whole blood, menstrual effluent, menstrual blood, spinal fluid, pulmonary fluid, or sputum.
  • the machine learning algorithm is a random forest algorithm, k-nearest- neighbors algorithm (KNN), support vector machine (SVM), and Naive Bayes. In some embodiments the machine learning algorithm is a random forest algorithm. In some embodiments the machine learning algorithm is a random forest algorithm. In some embodiments the machine learning algorithm is a random forest algorithm. In some embodiments the machine learning algorithm is a random forest algorithm. In some embodiments the machine learning algorithm is a random forest algorithm. In some
  • the method has an AUC for detecting endometriosis of greater than 0.85 in a population of women.
  • the population of women includes women having received hormone therapy within 3 months of the date on which the sample was obtained.
  • the hormone therapy comprises birth control pills or GnRH agonists.
  • the method further comprises detecting, diagnosing, or assessing risk of endometriosis in the female subject based on the importance measures.
  • the methods further comprises administering an endometriosis treatment to treat the
  • Figure 1 depicts scatter and box plots of miRNA expression in control vs.
  • Data shows expression levels of six miRNAs (miR-125b, miR- 451a, miR-3613, miR-150, miR-342, and let- 7b), normalized relative to levels of the small nuclear RNA gene U6.
  • Data are plotted with the median indicated by a line and the interquartile range (IQR) marked by the box. Whiskers and outliers are displayed according to the Tukey method, which plots whiskers at the points falling less than or equal to 1.5 times IQR (25th percentile minus IQR or 75th percentile plus IQR), with points falling outside this range plotted individually.
  • Figure 2 depicts scatter/box plots of miRNA expression during proliferative or secretory phase, showing that expression of the six miRNAs do not change significantly in proliferative vs secretory phase of the menstrual cycle.
  • Data shows miRNA expression levels in control subjects, separated by phase in menstrual cycle at the time of serum sampling, and normalized relative to levels of the small nuclear RNA gene U6.
  • Data are plotted with the median indicated by a line and the interquartile range (IQR) marked by the box. No significant differences were found (p>0.05, Mann-Whitney U test).
  • FIG. 3 depicts scatter/box plots of miRNA expression with or without hormonal treatment, demonstrating that the miRNA expression levels do not meaningfully differ depending on hormone-administration status to the subject in the study.
  • Data shows miRNA expression levels in endometriosis subjects, analyzed by presence or absence of hormonal treatment (HT). Levels were normalized relative to levels of the small nuclear RNA gene U6. Data are plotted with the median indicated by a line and the interquartile range (IQR) marked by the box. No significant differences were found (p>0.05, Mann-Whitney U test).
  • Figure 4 depicts scatter/box plots of miRNA expression according to rASRM staging, demonstrating that while all markers distinguish between control and severe (III/IV) patients, the ability to distinguish between subsets (e.g., control vs I/I I or I/I I vs III/IV) varies.
  • Data shows miRNA expression levels in endometriosis subjects, divided by stages of endometriosis: LII, minimal/mild; III/IV, moderate/severe according to rASRM guidelines. Levels were normalized relative to levels of the small nuclear RNA gene U6. Data are plotted with the median indicated by a line and the interquartile range (IQR) marked by the box.
  • IQR interquartile range
  • Figure 5 depicts a receiver operating characteristic (ROC) curve showing performance of the classifier algorithm in an independent data set.
  • ROC receiver operating characteristic
  • Graph shows analysis of the Random Forest model using six miRNA biomarkers (miR-125b-5p, miR-150-5p, miR-342-3p, miR-451a, miR-3613-5p, let-7b).
  • Figure 6 depicts an example of a computer system for execution of the methods described herein.
  • Figure 7 depicts a histogram the distribution of voting percentages from the RF model in the Retrospective data set (Cosar); black bars indicate subjects with surgically defined endometriosis, white bars subjects without endometriosis. Using a diagnostic threshold (cut-off) of 43%, shown by the dashed vertical line, results in a 96% specificity and 83% sensitivity for the RF model in this data set.
  • Analyzing a combination of biomarkers may improve detection of endometriosis.
  • Serum cancer antigen CA-125 has been utilized as a circulating marker for the disease, however it does not have sufficient diagnostic sensitivity or specificity, since increased CA-125 levels mainly reflect advanced stages of endometriosis, and are also elevated in other diseases (e.g., fibroids, ovarian cancer, pelvic inflammatory disorder).
  • the present disclosure provides novel methods for characterizing, monitoring, and analyzing samples from subjects having a symptom of endometriosis, having endometriosis, at risk of having endometriosis, or suspected of having endometriosis.
  • This disclosure also provides methods of detecting, diagnosing, monitoring, and/or prognosing such subjects, as well as methods of treating such subjects.
  • the methods provided herein involve the detection or quantitation of biomarkers in a sample from a subject, particularly non-coding RNA (e.g., miRNA).
  • the methods provided herein involve application of a machine learning algorithm.
  • nucleic acids when used in reference to a nucleic acid, refers to a nucleic acid that was not associated with a cell at the time the nucleic acid was obtained from the body.
  • nucleic acids may be present in a body fluid such as blood or saliva in a cell-free state in that they are not associated with a cell.
  • the cell-free nucleic acids may have originally been associated with a cell, such as an endometrial cell prior to entering the bloodstream or other body fluid.
  • nucleic acids that are solely associated with cells in the body are generally not considered to be“cell-free.”
  • nucleic acids extracted directly from cells are generally not considered“cell-free” as the term is used herein.
  • a“cell-free sample” generally refers to a biological sample, particularly a biological fluid sample, in which cells are absent or are present in such low amounts that the miRNA level determined reflects its level in the liquid portion of the sample, rather than in the cellular portion.
  • the cell-free portion of the sample is obtained by centrifugation, filtration, fractionation column, or other method.
  • the body fluid may be naturally cell-free.
  • a cell-free body fluid sample contains no intact cells; however, it may contain cell fragments, exosomes, or cellular debris.
  • the sample is processed or used immediately following sample collection; in some cases, the sample is stored for later use. Any suitable storage method known in the art may be used to store the body fluid sample, for example, the sample may be frozen at about -20. degrees C. to about - 70. degrees C.
  • a“cell-free serum” sample is generally a serum sample that is processed almost immediately following collection in order to avoid disruption of cells, such as by a clotting mechanism.
  • Conventional notation is used herein to describe polynucleotide sequences: the left- hand end of a single-stranded polynucleotide sequence is the 5 '-end; the leftward direction of a double-stranded polynucleotide sequence is referred to as the 5 '-direction.
  • the terms“subject,”“patient,”“individual,” and the like are used interchangeably herein, and refer to any animal, amenable to the methods described herein.
  • the patient, subject, or individual is a human.
  • the methods provided herein may involve cells from such subject, patient or individual.
  • the method may be conducted, at least in part, in vitro or in situ.
  • RNAi RNA interference
  • RNAi describes a phenomenon whereby the presence of an RNA sequence that is complementary or antisense to a sequence in a target gene messenger RNA (mRNA) results in inhibition of expression of the target gene.
  • miRNAs are generally processed from hairpin precursors of about 70 or more nucleotides (pre-miRNA) which are derived from primary transcripts (pri-miRNA) through sequential cleavage by RNAse III enzymes.
  • miRBase is a comprehensive microRNA database located at www.mirbase.org.
  • miRNA genes are transcribed into a precursor or pre miRNA that is processed into mature miRNA.
  • pre-miRNA generally occurs in a hairpin form, wherein the hairpin contains a 5' arm (or side) connected to a loop that is then connected to a 3' arm (or side).
  • Processing of the precursor miRNA can result in the formation of two mature forms of miRNA, including a 5p form that is derived from the 5' side or arm of the precursor miRNA loop and a 3p form that is derived from the 3' side or arm of the precursor miRNA hairpin.
  • “or” may refer to“and”,“or,” or“and/or” and may be used both exclusively and inclusively.
  • the term“A or B” may refer to“A or B”,“A but not B”,“B but not A”, and“A and B”. In some cases, context may dictate a particular meaning.
  • the term“a” may refer to a singular of plural form. In other words,“a” generally refers to“one or more.” Similarly, the term“an” may refer to a singular or plural form.
  • RNA and RNAs are used interchangeably and may refer to a singular RNA or multiple RNA.
  • miRNA and miRNAs are used interchangeably and may refer to a singular miRNA or multiple miRNA.
  • ncRNA non-coding RNA
  • ncRNA generally refers to an endogenous RNA molecule that is not translated into a protein in a cell.
  • testing for endometriosis as described herein may involve determining the level of one or more ncRNA that is not a miRNA in addition to the specific microRNAs described herein.
  • random forest refers to an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Implementation of random forest for data classification has been described in a variety of contexts.
  • the term“support-vector machine” refers to a supervised learning method that analyzes data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting).
  • An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on the side of the gap on which they fall.
  • the term“sequencing,” as used herein, generally refers to methods and technologies for determining the sequence of nucleotide bases in one or more polynucleotides.
  • the polynucleotides can be, for example, nucleic acid molecules such as deoxyribonucleic acid (DNA) or ribonucleic acid (RNA), including variants or derivatives thereof (e.g., single stranded DNA). Sequencing can be performed by various systems currently available, such as, without limitation, a sequencing system by Illumina®, Pacific Biosciences (PacBio®), Oxford
  • sequencing may be performed using nucleic acid amplification, polymerase chain reaction (PCR) (e.g., digital PCR, quantitative PCR, or real time PCR), or isothermal amplification.
  • PCR polymerase chain reaction
  • Such systems may provide a plurality of raw genetic data corresponding to the genetic information of a subject (e.g., human), as generated by the systems from a sample provided by the subject.
  • sequencing reads also“reads” herein).
  • a read may include a string of nucleic acid bases corresponding to a sequence of a nucleic acid molecule that has been sequenced.
  • an adaptor or tag can be coupled to a polynucleotide sequence by any approach, including ligation, hybridization, primer extension, or other approaches.
  • Adaptors or tags may be added to a nucleic acid to facilitate attachment to a sequencing flow cell, to facilitate binding of sequencing primers to a nucleic acid, or for counting individual copies of a nucleic acid sequence in a mixture (e.g., used as a unique molecular identifier).
  • the amount of a target analyte may be normalized to a normalizer control using the "delta CT method" or "ACT method,” which involves calculating the ACT.
  • the ACT is calculated by subtracting the CT (cycle threshold) of a quantitative nucleic acid detection assay (e.g., a qPCR assay) used to detect a normalizer control from the CT of a quantitative nucleic acid detection assay (e.g., a qPCR assay) used to detect a target analyte.
  • the fold difference in the amounts of the normalizer control and target analyte is calculated from the ACT- In certain embodiments, the fold difference in the amounts of the normalizer control and target analyte is calculated from the ACT according to the formula 2 Da .
  • the normalizer control is a housekeeping nucleic acids (e.g., DNA or RNA encoding a housekeeping polypeptide. In some cases, the normalizer control is small nuclear RNA gene U6.
  • the methods and compositions described herein are applicable to human and non human subjects, including veterinary subjects.
  • Preferred subjects are "patients”— living humans that are receiving medical care for a disease or condition (e.g., endometriosis), or who are suspected of having such disease or condition or who are at risk of having such disease or condition. This includes persons with no defined illness who are being investigated for signs of pathology (e.g., endometriosis).
  • Preferred patients or subjects for the methods and compositions described herein are female patients that are at pubescent or post-pubescent ages, pre-menopausal, peri-menopausal, menopausal, or post-menopausal (as endometriosis may persist after menopause).
  • the methods and compositions provided herein may be useful for female subjects within a large range of ages, generally over the age of 10 or over the age of 18. Often, the subject is under the age of 49. In some cases, the subject is premenopausal. In some cases, the subject is premenopausal and over 18. In some cases, the subject is premenopausal and under 49.
  • the subject may be at any phase of the menstrual cycle, e.g., luteal or proliferative.
  • a subject may be at risk of having endometriosis.
  • a subject at risk of having endometriosis may, for example, have a family history of endometriosis, symptoms of endometriosis, or a past medical history of endometriosis.
  • the endometriosis may be any stage of endometriosis.
  • the subject has, or is suspected of having, Stage I, II, III, or IV endometriosis.
  • the subject has, or is suspected of having, endometrioma.
  • the subject has endometriosis at any stage (e.g., Stage I-IV) but a method provided herein detects endometriosis as a general condition in the subject, without specifying the stage.
  • the subject has early stage endometriosis.
  • the subject as Stage I/II endometriosis.
  • the subject has Stage III/IV endometriosis.
  • a subject may be suspected of having endometriosis. Such a subject may display no symptoms of endometriosis. But in other cases, such subject may display symptoms of endometriosis such as dysmenorrhea, pain with bowel movements or urination, deep
  • the subject may be suspected of having endometriosis due to the results of a previous or concurrent test for endometriosis.
  • a subject is suspected of having endometriosis due to multiple factors.
  • the subject may be suspected of having endometriosis due to the presence of symptoms in an overall clinical context consistent with endometriosis.
  • a subject may have, or be suspected of having, a non-endometriosis condition.
  • non-endometriosis condition refers to an abnormal reproductive condition that is not endometriosis.
  • Nonlimiting examples of a non endometriosis condition include fibroids, leiomyomas, cysts, dermoid cysts, serous
  • cystadenomas cystadenomas, cystadenomas, ovarian cysts, mucous cystadenomas, pelvic infection, teratoma, and/or paratubal cysts.
  • such subject may display symptoms of endometriosis such as dysmenorrhea, pain with bowel movements or urination, deep dyspareunia, chronic lower abdominal pain, chronic lower back pain, adnexal masses, infertility, or excessive bleeding.
  • the non-endometriosis condition is benign. In some cases, the non-endometriosis condition is malignant.
  • the subject may be receiving a hormonal treatment.
  • the hormonal treatment may include GnRH (gonadotrophin releasing hormone) agonists (with or without estrogen /progesterone replacement therapy or tibolone treatment) and antagonists,
  • levonorgestrel-releasing intrauterine devices e.g., Mirena
  • danazol e.g., danazol
  • antiprogesterones e.g., gestrinone
  • aromatase inhibitors e.g., aromatase inhibitors
  • SERMs selective estrogen receptor modulators
  • SPRMs selective progesterone receptor modulators
  • the sample is preferably a bodily fluid sample.
  • the bodily fluid may be sweat, saliva, tears, urine, blood, plasma, serum, vaginal fluid, cervico-vaginal fluid, whole blood, menstrual effluent (e.g., menstrual blood), spinal fluid, pulmonary fluid, sputum, or any other bodily fluid.
  • the sample is a saliva or menstrual effluent (e.g., menstrual blood) sample.
  • the sample comprises white blood cells (WBCs).
  • WBCs white blood cells
  • the sample is a plasma sample.
  • the sample is a cell-free or acellular blood, plasma, or serum sample.
  • the sample is a blood sample collected by venipuncture into a collection tube without additives (e.g., without anticoagulants or coagulants) , followed by centrifugation (e.g., at 2500xg) to remove cells.
  • the sample e.g., blood, serum, plasma, etc.
  • the sample comprises peripheral blood mononuclear cells (PBMCs); in some cases, the sample comprises peripheral blood lymphocytes (PBLs).
  • PBMCs peripheral blood mononuclear cells
  • PBLs peripheral blood lymphocytes
  • saliva, peripheral blood sample or menstrual effluent may be separated into cellular and non-cellular fractions by suitable methods (e.g., centrifugation, filtration).
  • nucleic acids may be extracted from the cellular (e.g., cell-containing) or non-cellular (e.g., non cell- containing) fractions.
  • analysis as described herein of miRNA or ncRNA expression may be performed on the cell-containing or non-cellular fractions of any of the samples (e.g., blood, plasma, serum, saliva, menstrual blood, menstrual effluent, etc.).
  • the sample comprises tissue, such as tissue from a biopsy.
  • tissue is endometrial tissue.
  • the sample comprises cell-free non-coding RNA (e.g., cell-free miRNA).
  • the sample comprises purified or extracted non-coding RNA (e.g., miRNA).
  • the sample comprises exosome-encapsulated non-coding RNA (e.g., miRNA).
  • the sample comprises cell-encapsulated (e.g., by white blood cells) non-coding RNA (e.g., miRNA).
  • obtaining a sample includes obtaining a sample directly or indirectly, including having a sample obtained (e.g., from a third party who directly obtained the sample from the subject).
  • the sample is taken from the subject by the same party (e.g., a testing laboratory) that subsequently acquires biomarker data from the sample.
  • the sample is received (e.g., by a testing laboratory) from another entity that collected it from the subject (e.g., a physician, nurse, phlebotomist, or other medical care provider).
  • the sample is taken from the subject by a medical professional under direction of a separate entity (e.g., a testing laboratory) and subsequently provided to said entity (e.g., the testing laboratory).
  • a separate entity e.g., a testing laboratory
  • the sample is taken by the subject or the subject’s caregiver (e.g., family member, home health aide) at home and subsequently provided to the party that acquires biomarker data from the sample (e.g., a testing laboratory).
  • caregiver e.g., family member, home health aide
  • test samples of blood may be obtained from a subject.
  • the blood sample is a peripheral blood sample.
  • the blood sample is a whole blood sample.
  • the sample is a blood sample and comprises whole blood, peripheral blood, serum, plasma, PBLs, PBMCs, T cells, CD4 T cells, CD8 T cells, or macrophages.
  • the blood sample may be obtained by a minimally-invasive method such as a blood draw.
  • the blood sample may be obtained by venipuncture.
  • test samples of saliva may be obtained from a subject.
  • Methods of obtaining saliva samples may include, but are not limited to ejection from the subject’s mouth (e.g., spitting), aspiration, or removal by a swab or other collection tool.
  • Methods for extracting RNA molecules from saliva can be found in e.g, Pandit, P et al. Clin Chem. 2013 Jul;
  • kits which collect the sample in a clean manner and provide for the stabilization of nucleic acids in the sample
  • DNA Genotek e.g., Oragene- RNA and products described in US20110212002A1 and W02008040126A1
  • Norgen Biotek e.g., Norgen Biotek
  • the sample e.g., saliva
  • antimicrobial agents e.g., Normocin, sodium azide
  • RNase inhibitors e.g., Polyvinylsulfonic acid, RNasin®, RNaseOUTTM
  • organic solution e.g., Trizol, phenol- chloroform, phenol-chloroform-isoamyl alcohol
  • broad-spectrum proteases e.g., SDS with Proteinase K
  • RNA e.g., miRNA
  • RNAs e.g., ncRNAs, miRNAs
  • RNAs may be isolated from the biological samples.
  • RNAs e.g., miRNAs, ncRNAs
  • RNAs may be isolated from a cell-free source.
  • expression levels are determined by a hybridization-based method, such as Northern blot, Southern blot, molecular beacon, molecular inversion probe, or microarray hybridization.
  • the hybridization-based method involves hybridization of a probe to a target RNA (e.g., ncRNA, miRNA), or hybridization of multiple different probes to different target RNAs (e.g., ncRNAs, miRNAs)
  • expression levels are determined by an amplification process or by polymerase chain reaction (PCR). In some cases, expression levels are determined by quantitative PCR, real-time PCR, reverse-transcriptase PCR, or other type of PCR. The PCR may include use of a probe, such as a TaqMan probe. [0056] In some cases, the expression levels are determined by sequencing.
  • sequencing may include Sanger sequencing, high-throughput sequencing, pyrosequencing, sequencing-by-ligation, sequencing by synthesis, sequencing-by-hybridization, RNA-Seq (Illumina), Digital Gene Expression (Helicos), next generation sequencing, single molecule sequencing by synthesis (SMSS) (Helicos), massively-parallel sequencing, clonal single molecule Array (Solexa), shotgun sequencing, Maxim-Gilbert sequencing, primer walking, or any combination thereof.
  • sequencing may first involve a reverse- transcriptase and/or PCR amplification step to increase abundance of miRNAs to be analyzed or to add appropriate sequencing adaptors.
  • Sequencing can be performed by various systems currently available, such as, without limitation, a sequencing system by Illumina®, Pacific Biosciences (PacBio®), Oxford Nanopore®, or Life Technologies (Ion Torrent®). Alternatively or in addition, sequencing may be performed using nucleic acid amplification, polymerase chain reaction (PCR) (e.g., digital PCR, quantitative PCR, or real time PCR), or isothermal amplification. Examples of sequencing may include Sanger sequencing, Next Generation sequencing, and RNA sequencing.
  • PCR polymerase chain reaction
  • Biomarker RN As (e.g., miRNA, ncRNA )
  • the methods and compositions herein may involve the detection of one or more ncRNA (e.g., miRNA) associated with endometriosis (e.g., detection of presence or absence of the at least one ncRNA) or measurement of a level one or more miRNA or ncRNA associated with endometriosis from a patient sample to detect, predict, or monitor the severity of endometriosis.
  • the detection of the more than one miRNAs further comprises applying a trained algorithm to the expression levels of the more than one miRNA or ncRNA associated with endometriosis.
  • Trained algorithms suitable for application comprise any of the classification algorithms described herein.
  • the trained algorithm is a machine learning algorithm.
  • the machine learning algorithm is a random forest algorithm, k-nearest-neighbors algorithm (KNN), support vector machine (SVM), and Naive Bayes.
  • the classifier set of miRNAs used comprises at least one of miR-125b, miR-150, miR-342, miR-3613, miR-451a, and let- 7b, in any number or combination.
  • the classifier set of miRNAs used is miR-342, miR-451, and miR-3613.
  • the classifier set of miRNAs used is miR-342, miR-451, miR-3613, and miR-125.
  • the classifier set of miRNAs used is miR-342, miR-451, miR-3613, miR-125, and let-7.
  • the classifier set of miRNAs used is miR-342, miR-451, let-7, and miR-125. In some cases, the classifier set of miRNAs used is miR-342, miR-451, let-7, and miR-3613. In some cases, the classifier set of miRNAs used is miR-342, miR-451, and let-7. In some cases, the classifier set of miRNAs used is miR-125, miR-150, miR-342, miR-451, and let-7. In some cases, the classifier set of miRNAs used is miR-125, miR-150, miR-342, miR-3613, miR-451, and let-7.
  • the classifier set of miRNAs used is miR-342, miR-451 a, and miR- 3613. In some cases, the classifier set of miRNAs used is miR-342, miR-451a, miR-3613, and miR-125b. In some cases, the classifier set of miRNAs used is miR-342, miR-451a, miR-3613, miR-125b, and let-7b. In some cases, the classifier set of miRNAs used is miR-342, miR-451a, let-7b, and miR-125b. In some cases, the classifier set of miRNAs used is miR-342, miR-451a, let-7b, and miR-3613.
  • the classifier set of miRNAs used is miR-342, miR-451a, and let- 7b. In some cases, the classifier set of miRNAs used is miR-125b, miR-150, miR-342, miR-451a, and let- 7b. In some cases, the classifier set of miRNAs used is miR-125b, miR-150, miR-342, miR-3613, miR-451a, and let-7b. In some cases, the classifier set of miRNAs used is miR-342-3p, miR-451a, and miR-3613-5p.
  • the classifier set of miRNAs used is miR-342-3p, miR-451a, miR-3613-5p, and miR-125b-5p. In some cases, the classifier set of miRNAs used is miR-342-3p, miR-451a, miR-3613-5p, miR-125b-5p, and let-7b-5p. In some cases, the classifier set of miRNAs used is miR-342-3p, miR-451a, let-7b-5p, and miR-125b-5p. In some cases, the classifier set of miRNAs used is miR-342-3p, miR-451a, let-7b-5p, and miR- 3613-5p.
  • the classifier set of miRNAs used is miR-342-3p, miR-451a, and let-7b- 5p. In some cases, the classifier set of miRNAs used is miR-125b-5p, miR-150-5p, miR-342-3p, miR-451a, and let-7b-5p. In some cases, the classifier set of miRNAs used is miR-125b-5p, miR-150-5p, miR-342-3p, miR-3613-5p, miR-451a, and let-7b-5p.
  • the methods of the present disclosure include assigning or administering treatment to a patient having, at risk of developing, or suspected of having endometriosis.
  • the appropriate treatment can be assigned or administered to a patient suffering from endometriosis.
  • These treatments can include, but are not limited to, hormone therapy, chemotherapy, immunotherapy, and surgical treatment.
  • the methods of the current disclosure can be used to assign or administer treatment to a patient with reduced fertility due to endometriosis. In this fashion, by determining the degree to which the patient's fertility has been reduced, through the detection of biomarkers found herein, the appropriate treatment can be assigned or administered.
  • Relevant treatments include, but are not limited to, hormone therapy, chemotherapy, immunotherapy, and surgical treatment.
  • the level of one or more miRNA e.g., circulating miRNAs
  • a classifier outputting a clinical condition determined therefrom in a biological sample of a patient is used to detect, diagnose, monitor, or prognose disease (e.g., endometriosis) in the patient.
  • the level of one or more miRNAs (e.g., circulating miRNA) in a test sample obtained from a patient can be compared to the level from a reference sample obtained from that patient at a prior timepoint.
  • the patient is clinically monitored; and the patient may, in some cases, serve as her own baseline control.
  • a change in level of one or more miRNAs may indicate the development of endometriosis in the patient.
  • test samples are obtained at multiple time points.
  • measurement of the level of one or more miRNAs (e.g., circulating miRNA) in the test samples provides an indication of whether the patient has, or is at risk of having endometriosis.
  • the level of one or more miRNAs (e.g., circulating miRNA) in a test sample obtained from a patient is compared to the level from a reference sample from a different patient, or a composite or average of different patients.
  • the level of one or more miRNA e.g., circulating miRNAs
  • a classifier outputting a clinical condition determined therefrom in a biological sample of a patient is used to monitor the effectiveness of treatment or the prognosis of disease.
  • the level of one or more miRNAs (e.g., circulating miRNA) in a test sample obtained from a treated patient can be compared to the level from a reference sample obtained from that patient prior to initiation of a treatment.
  • Clinical monitoring of treatment typically entails that a patient serve as his or her own baseline control.
  • test samples are obtained at multiple time points following administration of the treatment.
  • measurement of the level of one or more one or more miRNAs (e.g., circulating miRNA) in the test samples provides an indication of the extent and duration of an in vivo effect of the treatment.
  • the level of one or more miRNAs (e.g., circulating miRNA) in a test sample obtained from a treated patient is compared to the level from a reference sample from a different patient, or a composite or average of different patients.
  • Measurement of biomarker levels may allow for the course of treatment of a disease to be monitored.
  • the effectiveness of a treatment regimen for a disease can be monitored by detecting one or more biomarkers in an effective amount from samples obtained from a subject over time and comparing the amount of biomarkers detected. For example, a first sample can be obtained prior to the subject receiving treatment and one or more subsequent samples are taken after or during treatment of the subject. Changes in biomarker levels across the samples may provide an indication as to the effectiveness of the therapy.
  • the disclosure provides a method for monitoring the levels of miRNAs (or clinical condition, such as endometriosis or non-endometriosis) in response to treatment.
  • the disclosure provides for a method of determining the efficacy of treatment in a subject, by measuring the levels of one or more miRNAs described herein.
  • the level of the one or more miRNAs can be measured over time, where the level at one timepoint after the initiation of treatment is compared to the level at another timepoint after the initiation of treatment.
  • the level of the one or more miRNAs can be measured over time, where the level at one timepoint after the initiation of treatment is compared to the level prior to the initiation of treatment.
  • the present disclosure provides therapies (e.g., drug, surgical) for the treatment or prevention of endometriosis.
  • therapies e.g., drug, surgical
  • a non-limitative list of known methods and materials for endometriosis treatment include, but are not limited to, pain killers, hormonal treatments, chemotherapy, and surgical treatments.
  • a patient or subject in which the therapies e.g., drug, surgical
  • therapies e.g., drug, surgical
  • endometriosis is detected by a method provided herein, may undergo additional tests or procedures to confirm the endometriosis.
  • the patient or subject may have surgery (e.g., laparoscopic surgery) to further diagnose or characterize the endometriosis, e.g., the stage of endometriosis; or to treat the endometriosis. Pain killers used for the treatment of
  • endometriosis include both simple analgesics, such as paracetamol, COX-2 inhibitors, aspirin, and other non-steroidal anti-inflammatory drugs well known in the art, and narcotic analgesics, such as morphine, codeine, oxycodone, and others well known in the art.
  • Hormonal treatments include, but are not limited to, oral contraceptives, progestins (such as Dydrogesterone,
  • GnRH agonists such as leuprorelin, buserelin, goserelin, histrelin, deslorelin, nafarelin, and triptorelin
  • androgens and synthetic androgens like Danazol
  • GnRH agonists e.g., Elagolix
  • levonorgestrel-releasing intrauterine devices e.g., Mirena
  • danazol antiprogesterones
  • gestrinone gestrinone
  • SERMs selective estrogen receptor modulators
  • SPRMs selective progesterone receptor modulators
  • Surgical treatments include, but are not limited to, laparoscopic surgery, hysterectomy, and oophorectomy. Other treatments particularly well suited for use in the present disclosure are well known in the art.
  • the patient can be treated using a statin, including but not limited to, atorvastatin, cerivastatin, fluvastatin, lovastatin, mevastatin, pitavastatin, pravastatin, rosuvastatin and simvastatin.
  • the present disclosure provides therapies (e.g., drugs, surgery) for the treatment or prevention of a non-endometriosis condition.
  • therapies e.g., drugs, surgery
  • a non-limitative list of known methods and materials for non-endometriosis treatment include, but are not limited to, pain killers, antibiotics, chemotherapy, and surgical treatments. Pain killers may include both simple analgesics, such as paracetamol, COX-2 inhibitors, aspirin, and other non-steroidal anti-inflammatory drugs well known in the art, and narcotic analgesics, such as morphine, codeine, oxycodone, and others well known in the art.
  • Surgical treatments include, but are not limited to, laparoscopic surgery, hysterectomy, and oophorectomy.
  • the patient or subject may undergo additional testing or monitoring following a negative result from an endometriosis test or assay described herein.
  • Such subject or patient may, for example, be tested for a non-endometriosis disease or condition e.g., cancer, benign cyst, fibroids, etc.
  • the subject or patient may wait a reasonable amount of time (e.g., more than a week, more than a month, more than 6 months) and be tested again for endometriosis.
  • such testing may be by a method provided herein.
  • the subject or patient in response to a negative endometriosis result, the subject or patient may avoid undergoing a surgical intervention (e.g., laparoscopic surgery).
  • miRNA presence and/or expression data can be used to classify a sample.
  • a sample can be classified as, or predicted to be: a) from a patient having endometriosis or b) from a patient not having endometriosis .
  • Many statistical classification techniques are suitable as approaches to perform such a classification.
  • supervised learning approaches a group of samples from two or more groups (e.g., endometriosis or not) are analyzed or processed with a statistical classification method.
  • miRNA absence/presence or expression level can be used as a basis for classifier that differentiates between the two or more groups. A new sample can then be analyzed or processed so that the classifier can associate the new sample with one of the two or more groups.
  • Commonly used supervised classifiers include without limitation the neural network (multi-layer perceptron), support vector machines, k-nearest neighbours, Gaussian mixture model, Gaussian, naive Bayes, decision tree and radial basis function (RBF) classifiers.
  • Linear classification methods include Fisher's linear discriminant, logistic regression, naive Bayes classifier, perceptron, and support vector machines (SVMs).
  • Other classifiers for use with methods according to the disclosure include quadratic classifiers, k-nearest neighbor, boosting, decision trees, random forests, neural networks, pattern recognition, Bayesian networks and Hidden Markov models.
  • Other classifiers, including improvements or combinations of any of these, commonly used for supervised learning can also be suitable for use with the methods described herein.
  • Classification using supervised methods is generally performed by the following methodology:
  • [0068] Gather a training set. These can include, for example, expression levels of one or more miRNAs described herein from a sample from a patient having endometriosis or expression levels of one or more miRNAs described herein from a sample from a patient not having endometriosis.
  • the training samples are used to“train” the classifier.
  • [0069] Determine the input“feature” representation of the learned function.
  • the accuracy of the learned function depends on how the input object is represented.
  • the input object is transformed into a feature vector, which contains a number of features that are descriptive of the object.
  • the features might include a set of miRNAs detected in a sample from a patient or subject.
  • [0070] Determine the structure of the learned function and corresponding learning algorithm.
  • a learning algorithm is chosen, e.g., artificial neural networks, decision trees, Bayes classifiers or support vector machines. The learning algorithm is used to build the classifier.
  • the learning algorithm is run on the gathered training set. Parameters of the learning algorithm may be adjusted by optimizing performance on a subset (called a validation set) of the training set, or via cross-validation. After parameter adjustment and learning, the performance of the algorithm may be measured on a test set of naive samples that is separate from the training set.
  • the built model can involve feature coefficients or importance measures assigned to individual features.
  • the individual features are miRNA or levels of miRNA.
  • the level of miRNA is a normalized value, an average value, a median value, a mean value, an adjusted average, or other adjusted level or value.
  • the individual features may comprise or consist of sets or panels of miRNA, such as the sets provided herein
  • the machine learning algorithm has importance measures or feature importances assigned to miRNA features.
  • the importance measures or feature importances are predetermined; for example, the importance measure may have been arrived at via training on a previous data set.
  • the importance measure assigned to miR-342 is greater than importance measures or feature importances assigned to one or more additional miRNA (e.g., miR-150, miR-3613, miR-451a, let- 7b, or miR-125b, or other miRNA associated with endometriosis).
  • the importance measure assigned to miR-451a is greater than the importance measure assigned to one or more of miR-3613, miR-125b, or let-7b.
  • applying a machine learning algorithm e.g., random forest algorithm
  • applying a machine learning algorithm comprises applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure of miR-342 is greater than at least one of miR-150, let-7b, or miR-125.
  • applying a machine learning algorithm to the expression profile comprises applying a machine learning algorithm with specific importance measures or feature importances assigned the miRNA features, wherein the importance measure of miR-125b is greater than at least one of miR-150, let-7b, or miR-125.
  • the importance measure or feature importance ranking of the miRNA features from highest to lowest is miR-125b, let-7b, miR-2613, miR-150, miR-342, and miR-451a (or any subset thereof).
  • the importance measure (e.g., feature importance) ranking of the miRNA features from highest to lowest is miR-342, miR-451a, miR-3613, miR-125b, let-7b, and miR-150.
  • a machine learning algorithm e g., random forest algorithm
  • a machine learning algorithm is applied to the expression profile of miRNAs derived from a saliva sample.
  • applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance of miR-125b is greater than at least one of let-7b, miR-3613, miR-150, miR-342, or miR-451a.
  • applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of let-7b is greater than at least one of miR-3613, miR-150, miR-342, or miR-451a.
  • applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miR-3613 is greater than at least one of miR-150, miR-342, or miR-451a. In some embodiments, applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miR-150 is greater than at least one of miR-342 or miR.451a.
  • applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miR-342 is greater than miR-451a. In some embodiments, applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miR-451a is less than at least one of miR-341, miR-150, miR-3613, let-7b, or miR-125b.
  • applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miR-342 is less than at least one of miR-125b, let- 7b, miR-3613, or miR-150.
  • applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miR-150 is less than at less than at least one of miR-3613, let-7b, or miR-125b.
  • applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miR3613 is less than miR-125b or let-7b. In some embodiments, applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of let7b is less than miR-125b.
  • applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miRNAs is in order miR-125b, let-7b, miR- 3613, miR-150, miR-342, and miR-451a.
  • a machine learning algorithm (e g., random forest algorithm) is applied to the expression profile of miRNAs derived from a sample (e.g., serum sample).
  • applying such a machine learning algorithm involves applying a machine learning algorithm that has importance measures or feature importances assigned to particular miRNA features, wherein the importance measure or feature importance of miR-342 is greater than at least one of miR-125b, miR-451a, miR-3613, miR-150, or let- 7b.
  • the importance measure or feature importance of miR-125b is greater than at least one of miR- 451a, miR-3613, miR-150, or let- 7b.
  • the importance measure or feature importance of miR-451a is greater than at least one of miR3613, miR-150, or let-7b. In some embodiments, the importance measure or feature importance of miR-3613 is greater than miR- 150 or let- 7b. In some embodiments, the importance measure or feature importance of miR-150 is greater than let-7b. In some embodiments, the importance measure or feature importance of let-7b is less than at least one of miR-150, miR-3613, miR-451a, miR-125b, or miR-342. In some embodiments, the importance measure or feature importance of miR-150 is less than at least one of miR-3613, miR-451a, miR-125b, or miR-342.
  • importance measure or feature importance of miR-3613 is less than at least one of miR-451a, miR-125b, or miR-342. In some embodiments, the importance measure or feature importance of miR-451a is less than miR-125b or miR-342. In some embodiments, the importance measure or feature importance of miR-125b is less than miR-342. In some embodiments, the importance measure or feature importance measure or feature importance of miRNAs is in order miR-342, miR-125b, miR-451a, miR-3613, miR-150, and let-7b.
  • a machine learning algorithm (e g., random forest algorithm) is applied to the expression profile of miRNAs derived from a sample (e.g., serum sample).
  • applying such a machine learning algorithm involves applying a machine leaming algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miR-342 is greater than at least one of miR-451a, miR-3613, miR-125b, let-7b, or miR-150.
  • applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miR-451a is greater than at least one of miR- 3613, miR-125b, let-7b, or miR-150.
  • applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miR-3613 is greater than at least one of miR-125b, let-7b, or miR-150.
  • applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miR-125b is greater than let- 7b or miR-150. In some embodiments, applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of let- 7b is greater than miR-150.
  • applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miR-150 is less than at least one of let-7b, miR-125b, miR-3613, miR-451a, or miR-342.
  • applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of let- 7b is less than at least one of miR-125b, miR-3613, miR-451a, or miR-342.
  • applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miR-125b is less than at least one of miR-3613, miR451a, or miR-342.
  • applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miR-3613 is less than miR-342 or miR-451a.
  • applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miR-45 la is less than miR-342.
  • applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miRNAs is in order miR-342, miR-451a, miR-3613, miR-125b, let-7b, and miR-150.
  • a machine learning algorithm (e g., random forest algorithm) is applied to the expression profile of miRNAs derived from a sample (e.g., serum sample).
  • applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miR-125b is greater than at least one of miR-3613, miR-451a, miR-150, miR-342, or let- 7b.
  • applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miR-3613 is greater than at least one of miR- 451a, miR-150, miR-342, or let- 7b.
  • applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miR-45 la is greater than at least one of miR-150, miR-342, or let-7b.
  • applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miR-150 is greater than miR- 342 or let- 7b. In some embodiments, applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miR- 342 is greater than let- 7b.
  • applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of let-7b is less than at least one of miR-342, miR-150, miR-45 la, miR-3613, or miR-125b. In some embodiments, applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miR-342 is less than at least one of miR-150, miR-45 la, miR-3613, or miR-125b.
  • applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miR-150 is greater than at least one of miR- 45 la, miR-3613, or miR-125b. In some embodiments, applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miR-451a is less than miR-3613 or miR-125b.
  • applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miR-3613 is less than miR-125b. In some embodiments, applying such a machine learning algorithm involves applying a machine learning algorithm with specific importance measures or feature importances assigned to the miRNA features, wherein the importance measure or feature importance of miRNAs is in order miR- 125b, miR-3613, miR-451a, miR-150, miR-342, and let- 7b.
  • the classifier e.g., classification model
  • a sample e.g., a patient sample comprising miRNAs that is analyzed or processed according to methods described herein.
  • Unsupervised learning approaches can also be used with methods described herein.
  • Clustering is an unsupervised learning approach wherein a clustering algorithm correlates a series of samples without the use the labels. The most similar samples are sorted into“clusters.” A new sample could be sorted into a cluster and thereby classified with other members that it most closely associates.
  • the methods provided herein may include using a trained classifier or algorithm to analyze sample data, particularly to detect endometriosis.
  • the levels of RNA e.g, miRNA, ncRNA
  • the levels of RNA (e.g., miRNA, ncRNA) from a sample are used to develop or train an algorithm or classifier provided herein.
  • RNA levels e.g., miRNA, ncRNA levels
  • a classifier or algorithm e.g., trained algorithm
  • Training of multi-dimensional classifiers may be performed using numerous samples.
  • training of the multi-dimensional classifier may be performed using at least about 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170,
  • training of the multi-dimensional classifier may be performed using at least about 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 350,
  • training of the multi-dimensional classifier may be performed using at least about 525, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 2000 or more samples.
  • the classifier set may comprise one or more RNAs (e.g., miRNAs, ncRNA), such as let-7a, let-7b, let-7c, let-7d, let-7e, let-7f, miR-135a, miR-135b, miR-18a, miR-125b, miR- 143, miR-145, miR-150, miR-342, miR-451a, miR-500a, miR-3613, and miR-6755 individually or in any combination.
  • the classifier set of miRNAs used is miR-342, miR-451a, and miR-3613.
  • the classifier set of miRNAs used is miR-342, miR-451a, miR- 3613, and miR-125b. In some cases, the classifier set of miRNAs used is miR-342, miR-451a, miR-3613, miR-125b, and let- 7b. In some cases, the classifier set of miRNAs used is miR-342, miR-451a, let- 7b, and miR-125b. In some cases, the classifier set of miRNAs used is miR-342, miR-451a, let- 7b, and miR-3613. In some cases, the classifier set of miRNAs used is miR-342, miR-451a, and let- 7b.
  • the classifier set of miRNAs used is miR-125b, miR-150, miR-342, miR-451a, and let-7b. In some cases, the classifier set of miRNAs used is miR-125b, miR-150, miR-342, miR-3613, miR-451a, and let- 7b.
  • Classifiers and/or classifier probe sets may be used to either rule-in or rule-out a sample as healthy (e.g., as being derived from a healthy subject).
  • a classifier may be used to classify a sample as being from a healthy subject.
  • a classifier may be used to classify a sample as being from an unhealthy subject (e.g., as being derived from an unhealthy subject).
  • classifiers may be used to either rule-in or rule- out a sample as endometriosis (e.g., as being derived from a subject with endometriosis).
  • a classifier may be used to classify a sample as being from a subject suffering from endometriosis.
  • a classifier may be used to classify a sample as being from a subject that is not suffering from endometriosis.
  • the methods disclosed herein may comprise assigning a classification to one or more samples from one or more subjects. Assigning the classification to the sample may comprise applying an algorithm to the level of one or more RNA (e.g., miRNA, ncRNA) from the sample.
  • RNA e.g., miRNA, ncRNA
  • the algorithm may provide a record of its output including a classification of a sample and/or a confidence level.
  • the output of the algorithm can be the possibility of the subject of having a condition, such as endometriosis.
  • the algorithm may be a trained algorithm.
  • the algorithm may comprise a linear classifier.
  • the linear classifier may comprise one or more linear discriminant analysis, Fisher’s linear discriminant, Naive Bayes classifier, Logistic regression, Perceptron, Support vector machine, or a combination thereof.
  • the linear classifier may be a Support vector machine (SVM) algorithm.
  • the algorithm may comprise one or more linear discriminant analysis (LDA), Basic perceptron, Elastic Net logistic regression, logistic regression, (Kernel) Support Vector Machines (SVM), Diagonal Linear Discriminant Analysis (DLDA), Golub Classifier, Parzen-based, (kernel) Fisher Discriminant Classifier, k-nearest neighbor, Iterative RELIEF, Classification Tree, Maximum Likelihood Classifier, Random Forest, Nearest Centroid, Prediction Analysis of Microarrays (PAM), k-medians clustering, Fuzzy C-Means Clustering, Gaussian mixture models, or a combination thereof.
  • the algorithm may comprise a Diagonal Linear Discriminant Analysis (DLDA) algorithm.
  • the algorithm may comprise a Nearest Centroid algorithm.
  • the algorithm may comprise a Random Forest algorithm.
  • the methods provided herein can help determine whether the patient has endometriosis with a high degree of accuracy, sensitivity, and/or specificity.
  • the predictive accuracy e.g., for detecting endometriosis, or for distinguishing endometriosis from non endometriosis
  • the predictive accuracy is greater than 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 98.5%, 99.0%, 99.1%, 99.2%, 99.3%, 99.4%, 99.5%, 99.6%,
  • the predictive accuracy is 100%.
  • the sensitivity e.g., for detecting endometriosis, or for distinguishing endometriosis from non-endometriosis
  • the sensitivity is greater than 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 98.5%, 99.0%, 99.1%, 99.2%, 99.3%,
  • the sensitivity is 100%.
  • the specificity e.g., for detecting endometriosis, or for distinguishing endometriosis from non-endometriosis
  • the specificity is greater than 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 98.5%, 99.0%, 99.1%, 99.2%, 99.3%, 99.4%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, 99.95%, or 99.99%.
  • the specificity is 100%.
  • the positive predictive value (e.g., for detecting endometriosis, or for distinguishing endometriosis from non-endometriosis) of the method is greater than 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 98.5%, 99.0%, 99.1%, 99.2%, 99.3%, 99.4%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, 99.95%, or 99.99%.
  • the positive predictive value is 100%.
  • the AUC after thresholding in any of the methods provided herein may be greater than 0.9, 0.91, 0.92, 0.93,
  • the method may predict or determine whether a subject does not have or is at reduced risk of endometriosis.
  • the negative predictive value (e e.g., for detecting endometriosis, or for distinguishing endometriosis from non-endometriosis) may be greater than 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 98.5%, 99.0%, 99.1%, 99.2%, 99.3%, 99.4%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, 99.95%, or 99.99%. In some cases, the negative predictive value is substantially equal to 100%.
  • the methods provided herein are optimized for specificity by selecting an optimal cutoff point.
  • the cutoff point may be a threshold or cutoff, above which a sample may be characterized as being from a subject with endometriosis and below which the sample is identified as being from a subject negative for endometriosis.
  • the sample is identified as being from a subject positive for endometriosis when below the cutoff, and negative for endometriosis when above the cutoff.
  • the cutoff point or threshold is on a receiver operating characteristic (ROC) curve or on a voting percentage distribution.
  • ROC receiver operating characteristic
  • the method is optimized to attain a particular specificity, e.g., greater than 80% specificity, greater than 85% specificity, greater than 90% specificity, greater than 95% specificity, greater than 98% specificity.
  • the specificity is optimized for a value above the sensitivity of the assay.
  • the specificity is optimized to be above a certain value, e.g., above 85%, which may then result in a sensitivity of the assay that is less than a value, e.g., less than 95%, less than 90%, less than 85%, less than 80%.
  • the specificity is optimized to be above a certain value, e.g., above 90%, which may then result in a sensitivity of the assay that is less than a value, e.g., less than 95%, less than 90%, less than 85%, less than 80%. In some cases, the specificity is optimized to be above a certain value, e.g., above 95%, which may then result in a sensitivity of the assay that is less than a value, e.g., less than 95%, less than 90%, less than 85%, less than 80%.
  • Such assays may be especially suited for non-life threatening conditions, as they may help the subject avoid unnecessary intervention, such as surgical intervention, that can occur when the assay is associated with a high percentage of false positives.
  • the method attains a specificity described herein (e.g., greater than 80%, greater than 90%) over a diverse population of women.
  • the population may be a population of over 50 women, over 100 women, over 500 women, over 1000 women, etc.
  • the population may include, for example, women with a range of benign conditions, e.g. cysts, fibroids, leiomyomas, cystadenomas, chronic pelvic infections, teratomas, paratubal cysts.
  • the methods provided herein are optimized for sensitivity by selecting an optimal cutoff point.
  • the cutoff point may be a threshold or cutoff, above which a sample may be characterized as being from a subject with endometriosis and below which the sample is identified as being from a subject negative for endometriosis.
  • the sample is identified as being from a subject positive for endometriosis when below the cutoff, and negative for endometriosis when above the cutoff.
  • the cutoff point or threshold is on a receiver operating characteristic (ROC) curve or on a voting percentage distribution.
  • ROC receiver operating characteristic
  • the method is optimized to attain a particular sensitivity, e.g., greater than 80% sensitivity, greater than 85% sensitivity, greater than 90% sensitivity, greater than 95% sensitivity, greater than 98% sensitivity.
  • the sensitivity is optimized for a value above the specificity of the assay.
  • the sensitivity is optimized to be above a certain value, e.g., above 85%, which may then result in a specificity of the assay that is less than a value, e.g., less than 95%, less than 90%, less than 85%, less than 80%.
  • Such assays may be especially suited for screening tests for endometriosis.
  • the method attains a sensitivity described herein (e.g., greater than 80%, greater than 90%) over a diverse population of women.
  • the population may be a population of over 50 women, over 100 women, over 500 women, over 1000 women, etc.
  • the population may include, for example, women with a range of benign conditions, e.g. cysts, fibroids, leiomyomas, cystadenomas, chronic pelvic infections, teratomas, paratubal cysts.
  • the methods disclosed herein may comprise assigning a classification to one or more samples from one or more subjects. Assigning the classification to the sample may comprise applying an algorithm to the level of one or more RNA (e g., miRNA, ncRNA) from the sample.
  • RNA e g., miRNA, ncRNA
  • the algorithm may provide a record of its output including a classification of a sample and/or a confidence level.
  • the output of the algorithm can be the possibility of the subject of having a condition, such as endometriosis.
  • the present disclosure provides for administration of a treatment to the subject to treat the endometriosis detected herein based on the classification generated using the machine learning algorithms described herein.
  • Treatments for endometriosis include, but are not limited to, pain killers (e.g., NSAIDs), hormonal treatments, chemotherapy, and surgical treatments.
  • Pain killers used for the treatment of endometriosis include both simple analgesics, such as paracetamol, COX-2 inhibitors, aspirin, and other non-steroidal anti-inflammatory drugs well known in the art, and narcotic analgesics, such as morphine, codeine, and oxycodone.
  • Hormonal treatments include, but are not limited to, oral contraceptives, progestins, such as Dydrogesterone, Medroxyprogesterone acetate, Depot medroxyprogesterone acetate,
  • Surgical treatments include, but are not limited to, laparoscopic surgery, hysterectomy, and oophorectomy.
  • a GnRH antagonist is administered to treat the endometriosis detected herein.
  • GnRH A variety of antagonists of GnRH suitable for clinical administration, both peptide (goserelin acetate, buserelin, histrelin, deslorelin, nafarelin, and triptorelin, leuproreolin) and non-peptide (Elagolix/ABT-620, NBI-56418, see for e.g., Taylor et al. N Engl J Med. 2017 Jul 6;377(l):28-40), are available for second-line treatment of endometriosis in individuals with refractory endometriosis.
  • the fluid sample collection and diagnosis described above are performed after a defined period of time (e.g., 1 month, 6 month, or 1 year) and the initial dose of the drug used to treat endometriosis (e.g., any of the hormone analogs or antagonists described herein) is adjusted downward when endometriosis is not detected.
  • the fluid sample collection and diagnosis described above are performed after a defined period of time (e.g., 1 month, 6 month, or 1 year) and the initial dose of the drug is adjusted upward when endometriosis is detected.
  • the fluid sample collection and diagnosis described above are performed after a defined period of time (e.g., 1 month, 6 month, or 1 year) and administration of the drug is terminated when endometriosis is not detected.
  • RNAs e.g., miRNA, ncRNA
  • a digital computer is directly linked to a scanner or the like (e.g., a qPCR system, a multiplex fluorescent plate reader, FACS instrument, or a sequencer) receiving experimentally determined signals related to miRNA or ncRNA expression levels.
  • a scanner or the like e.g., a qPCR system, a multiplex fluorescent plate reader, FACS instrument, or a sequencer
  • expression levels can be inputted by other means.
  • the computer can be programmed to convert raw signals into expression levels (absolute or relative), compare measured expression levels with one or more reference expression levels, or a scale of such values, as described above.
  • the computer can also be programmed to assign values or other designations to expression levels based on the comparison with one or more reference expression levels, and to aggregate such values or designations for multiple genes in an expression profile.
  • the computer can also be programmed to output a value or other designation providing an indication of presence of endometriosis as well as any of the raw or intermediate data used in determining such a value or designation.
  • a typical computer may include a bus which interconnects major subsystems such as a central processor, a system memory, an input/output controller, an external device such as a printer via a parallel port, a display screen via a display adapter, a serial port, a keyboard, a fixed disk drive and a floppy disk drive operative to receive a floppy disk.
  • major subsystems such as a central processor, a system memory, an input/output controller, an external device such as a printer via a parallel port, a display screen via a display adapter, a serial port, a keyboard, a fixed disk drive and a floppy disk drive operative to receive a floppy disk.
  • Many other devices can be connected such as a scanner via EO controller, a mouse connected to serial port or a network interface.
  • the computer contains computer readable media holding codes to allow the computer to perform a variety of functions. These functions include controlling automated apparatus, receiving input and delivering output as described above.
  • the automated apparatus can include
  • the methods, systems, kits, and compositions provided herein may also be capable of generating and transmitting results through a computer network.
  • a sample is first collected from a subject (e.g., a patient with one or more symptoms of endometriosis, or a non- symptomatic patient).
  • the sample is assayed and RNA (e.g., miRNA, ncRNA) levels are measured.
  • RNA e.g., miRNA, ncRNA
  • a computer system may be used in analyzing the data and making classification of the sample.
  • the result may be capable of being transmitted to different types of end users via a computer network.
  • the subject e.g., patient
  • the subject may be able to access the result by using standalone software and/or a web-based application on a local computer capable of accessing the internet.
  • the result can be accessed via a mobile application provided to a mobile digital processing device (e.g., mobile phone, tablet, etc.).
  • the result may be accessed by medical care provider (e.g., physician) and help them identify and track conditions of their patients.
  • the result may be used for other purposes such as education and research.
  • the methods, kits, and systems disclosed herein may include at least one computer program, or use of the same.
  • a computer program may include a sequence of instructions, executable in the digital processing device’s CPU, written to perform a specified task.
  • Computer readable instructions may be implemented as program modules, such as functions, objects, Application Programming Interfaces (APIs), data structures, and the like, that perform particular tasks or implement particular abstract data types.
  • APIs Application Programming Interfaces
  • the functionality of the computer readable instructions may be combined or distributed as desired in various environments.
  • the computer program may normally provide a sequence of instructions from one location or a plurality of locations.
  • a computer program includes, in part or in whole, one or more web applications, one or more mobile applications, one or more standalone applications, one or more web browser plug-ins, extensions, add-ins, or add-ons, or combinations thereof.
  • the system may comprise (a) a digital processing device comprising an operating system configured to perform executable instructions and a memory device; (b) a computer program including instructions executable by the digital processing device to classify a sample from a subject comprising: (i) a first software module configured to receive a RNA (e.g., miRNA, ncRNA) expression profile of one or more RNA (e.g., miRNA, ncRNA) from the sample from the subject; (ii) a second software module configured to analyze the RNA (e.g., miRNA, ncRNA) expression profile from the subject; and (iii) a third software module configured to classify the sample from the subject based on a classification system comprising two or more classes.
  • a RNA e.g., miRNA, ncRNA
  • a second software module configured to analyze the RNA (e.g., miRNA, ncRNA) expression profile from the subject
  • a third software module configured to classify the sample from the subject based
  • At least one of the classes may be selected from endometriosis.
  • Analyzing the gene expression profile from the subject may comprise applying an algorithm.
  • Analyzing the gene expression profile may comprise normalizing the RNA (e.g., miRNA, ncRNA) expression profile from the subject (e.g., to a constitutive RNA such as small nuclear RNA U6, RNU48, RNU44, U47, or RNU6B, or any combination thereof).
  • Figure 6 shows a computer system (also“system” herein) 601 programmed or otherwise configured for implementing the methods of the disclosure, such as producing a selector set, and/or for data analysis.
  • the system 601 includes a central processing unit (CPU, also“processor” and“computer processor” herein) 605, which can be a single core or multi core processor, or a plurality of processors for parallel processing.
  • the system 601 also includes memory 610 (e.g., random-access memory, read-only memory, flash memory), electronic storage unit 615 (e g., hard disk), communications interface 620 (e.g., network adapter) for communicating with one or more other systems, and peripheral devices 625, such as cache, other memory, data storage and/or electronic display adapters.
  • the memory 610, storage unit 615, interface 620 and peripheral devices 625 are in communication with the CPU 605 through a communications bus (solid lines), such as a motherboard.
  • the storage unit 615 can be a data storage unit (or data repository) for storing data.
  • the system 601 is operatively coupled to a computer network (“network”) 630 with the aid of the communications interface 620.
  • the network 630 can be the Internet, an internet and/or extranet, or an intranet and/or extranet that is in communication with the Internet.
  • the network 630 in some instances is a telecommunication and/or data network.
  • the network 630 can include one or more computer servers, which can enable distributed computing, such as cloud computing.
  • the network 630 in some instances, with the aid of the system 601, can implement a peer-to-peer network, which may enable devices coupled to the system 601 to behave as a client or a server.
  • the system 601 may be in communication with a processing system 635.
  • the processing system 635 can be configured to implement the methods disclosed herein.
  • the processing system 635 is a multiplex fluorescent plate reader, a qPCR machine, or a nucleic acid sequencing system, such as, for example, a next generation sequencing system (e.g., Illumina sequencer, Ion Torrent sequencer, Pacific Biosciences sequencer).
  • the processing system 635 can be in communication with the system 601 through the network 630, or by direct (e.g., wired, wireless) connection.
  • the processing system 635 can be configured for analysis, such as nucleic acid sequence analysis.
  • Methods as described herein can be implemented by way of machine (or computer processor) executable code (or software) stored on an electronic storage location of the system 601, such as, for example, on the memory 610 or electronic storage unit 615.
  • the code can be executed by the processor 605.
  • the code can be retrieved from the storage unit 615 and stored on the memory 610 for ready access by the processor 605.
  • the electronic storage unit 615 can be precluded, and machine-executable instructions are stored on memory 610.
  • the methods, kits, and systems disclosed herein may include a digital processing device or use of the same.
  • the digital processing device includes one or more hardware central processing units (CPU) that carry out the device’s functions.
  • the digital processing device further comprises an operating system configured to perform executable instructions.
  • the digital processing device is optionally connected to a computer network.
  • the digital processing device is optionally connected to the Internet such that it accesses the World Wide Web.
  • the digital processing device is optionally connected to a cloud computing infrastructure.
  • the digital processing device is optionally connected to an intranet.
  • the digital processing device is optionally connected to a data storage device.
  • suitable digital processing devices include, by way of non-limiting examples, server computers, desktop computers, laptop computers, notebook computers, sub-notebook computers, netbook computers, netpad computers, set-top computers, handheld computers, Internet appliances, mobile smartphones, tablet computers, personal digital assistants, video game consoles, and vehicles.
  • server computers desktop computers, laptop computers, notebook computers, sub-notebook computers, netbook computers, netpad computers, set-top computers, handheld computers, Internet appliances, mobile smartphones, tablet computers, personal digital assistants, video game consoles, and vehicles.
  • smartphones are suitable for use in the system described herein.
  • Suitable tablet computers include those with booklet, slate, and convertible configurations, known to those of skill in the art.
  • the digital processing device may generally include an operating system configured to perform executable instructions.
  • the operating system may be, for example, software, including programs and data, which manages the device’s hardware and provides services for execution of applications.
  • suitable server operating systems include, by way of non-limiting examples, FreeBSD, OpenBSD, NetBSD®, Linux, Apple® Mac OS X Server®, Oracle® Solaris®, Windows Server®, and Novell® NetWare®.
  • suitable personal computer operating systems include, by way of non limiting examples, Microsoft® Windows®, Apple® Mac OS X®, UNIX®, and UNIX-like operating systems such as GNU/Linux®.
  • the operating system is provided by cloud computing.
  • suitable mobile smart phone operating systems include, by way of non-limiting examples, Nokia® Symbian® OS, Apple® iOS®, Research In Motion® BlackBerry OS®, Google® Android®, Microsoft® Windows Phone® OS, Microsoft® Windows Mobile® OS, Linux®, and Palm® WebOS®.
  • the device generally includes a storage and/or memory device.
  • the storage and/or memory device may be one or more physical apparatuses used to store data or programs on a temporary or permanent basis.
  • the device is volatile memory and requires power to maintain stored information.
  • the device is non-volatile memory and retains stored information when the digital processing device is not powered.
  • the non-volatile memory comprises flash memory.
  • the non volatile memory comprises dynamic random-access memory (DRAM).
  • the non-volatile memory comprises ferroelectric random-access memory (FRAM).
  • the non-volatile memory comprises phase-change random access memory (PRAM).
  • the device is a storage device including, by way of non-limiting examples, CD-ROMs, DVDs, flash memory devices, magnetic disk drives, magnetic tapes drives, optical disk drives, and cloud computing-based storage.
  • the storage and/or memory device is a combination of devices such as those disclosed herein.
  • a display to send visual information to a user may generally be initialized.
  • displays include a cathode ray tube (CRT, a liquid crystal display (LCD), a thin film transistor liquid crystal display (TFT-LCD, an organic light emitting diode (OLED) display.
  • CTR cathode ray tube
  • LCD liquid crystal display
  • TFT-LCD thin film transistor liquid crystal display
  • OLED organic light emitting diode
  • on OLED display is a passive-matrix OLED (PMOLED) or active-matrix OLED (AMOLED) display.
  • the display may be a plasma display, a video projector or a combination of devices such as those disclosed herein.
  • the digital processing device may generally include an input device to receive information from a user.
  • the input device may be, for example, a keyboard, a pointing device including, by way of non-limiting examples, a mouse, trackball, track pad, joystick, game controller, or stylus; a touch screen, or a multi-touch screen, a microphone to capture voice or other sound input, a video camera to capture motion or visual input or a combination of devices such as those disclosed herein.
  • the methods, kits, and systems disclosed herein may include one or more non- transitory computer readable storage media encoded with a program including instructions executable by the operating system to perform and analyze the test described herein; preferably connected to a networked digital processing device.
  • the computer readable storage medium may be a tangible component of a digital that may be optionally removable from the digital processing device.
  • the computer readable storage medium includes, by way of non-limiting examples, CD- ROMs, DVDs, flash memory devices, solid state memory, magnetic disk drives, magnetic tape drives, optical disk drives, cloud computing systems and services, and the like.
  • the program and instructions are permanently, substantially permanently, semi permanently, or non-transitorily encoded on the media.
  • a non-transitory computer-readable storage media may be encoded with a computer program including instructions executable by a processor to create or use a classification system.
  • the storage media may comprise (a) a database, in a computer memory, of one or more clinical features of two or more control samples, wherein (i) the two or more control samples may be from two or more subjects; and (ii) the two or more control samples may be differentially classified based on a classification system comprising three or more classes; (b) a first software module configured to compare the one or more clinical features of the two or more control samples; and (c) a second software module configured to produce a classifier set based on the comparison of the one or more clinical features.
  • At least two of the classes may be selected from endometriosis, non-endometriosis, and healthy.
  • a computer program includes a web application.
  • a web application in various embodiments, utilizes one or more software frameworks and one or more database systems.
  • a web application is created upon a software framework such as Microsoft® .NET or Ruby on Rails (RoR).
  • a web application utilizes one or more database systems including, by way of non-limiting examples, relational, non-relational, object oriented, associative, and XML database systems.
  • suitable relational database systems include, by way of non-limiting examples, Microsoft® SQL Server, mySQLTM, and Oracle®.
  • a web application in various embodiments, is written in one or more versions of one or more languages.
  • a web application may be written in one or more markup languages, presentation definition languages, client-side scripting languages, server-side coding languages, database query languages, or combinations thereof.
  • a web application is written to some extent in a markup language such as Hypertext Markup Language (HTML), Extensible Hypertext Markup Language (XHTML), or extensible Markup Language (XML).
  • XHTML Extensible Hypertext Markup Language
  • XML extensible Markup Language
  • a web application is written to some extent in a presentation definition language such as Cascading Style Sheets (CSS).
  • a web application is written to some extent in a client- side scripting language such as Asynchronous Javascript and XML (AJAX), Flash®
  • a web application is written to some extent in a server-side coding language such as Active Server Pages (ASP), ColdFusion®, Perl, JavaTM, JavaServer Pages (JSP), Hypertext Preprocessor (PHP), PythonTM, Ruby, Tel, Smalltalk, WebDNA®, or Groovy.
  • a web application is written to some extent in a database query language such as Structured Query Language (SQL).
  • SQL Structured Query Language
  • a web application integrates enterprise server products such as IBM® Lotus Domino®.
  • a web application includes a media player element.
  • a media player element utilizes one or more of many suitable multimedia technologies including, by way of non-limiting examples, Adobe® Flash®, HTML 5, Apple® QuickTime®, Microsoft® Silverlight®, JavaTM, and Unity®.
  • a computer program includes a mobile application provided to a mobile digital processing device.
  • the mobile application is provided to a mobile digital processing device at the time it is manufactured.
  • the mobile application is provided to a mobile digital processing device via the computer network described herein.
  • a mobile application may be created by techniques known to those of skill in the art using hardware, languages, and development environments known to the art. Those of skill in the art will recognize that mobile applications are written in several languages. Suitable programming languages include, by way of non limiting examples, C, C++, C#, Objective-C, JavaTM, Javascript, Pascal, Object Pascal, PythonTM, Ruby, VB.NET, WML, and XHTML/HTML with or without CSS, or combinations thereof.
  • Suitable mobile application development environments are available from several sources. Commercially available development environments include, by way of non-limiting examples, AirplaySDK, alcheMo, Appcelerator®, Celsius, Bedrock, Flash Lite, .NET Compact Framework, Rhomobile, and WorkLight Mobile Platform. Other development environments are available without cost including, by way of non-limiting examples, Lazarus, MobiFlex, MoSync, and Phonegap. Also, mobile device manufacturers distribute software developer kits including, by way of non-limiting examples, iPhone and iPad (iOS) SDK, AndroidTM SDK, BlackBerry® SDK, BREW SDK, Palm® OS SDK, Symbian SDK, webOS SDK, and Windows® Mobile SDK.
  • iOS iPhone and iPad
  • a computer program includes a standalone application, which is a program that is run as an independent computer process, not as an add-on to an existing process, e.g., not a plug-in.
  • a compiler is a computer program(s) that transforms source code written in a programming language into binary object code such as assembly language or machine code. Suitable compiled programming languages include, by way of non-limiting examples, C, C++, Objective-C, COBOL, Delphi, Eiffel, JavaTM, Lisp, PythonTM, Visual Basic, and VB .NET, or combinations thereof. Compilation is often performed, at least in part, to create an executable program.
  • a computer program includes one or more executable complied applications.
  • the computer program includes a web browser plug-in.
  • a plug-in is one or more software components that add specific functionality to a larger software application. Makers of software applications support plug-ins to enable third- party developers to create abilities which extend an application, to support easily adding new features, and to reduce the size of an application. When supported, plug-ins enable customizing the functionality of a software application. For example, plug-ins are commonly used in web browsers to play video, generate interactivity, scan for viruses, and display particular file types. Those of skill in the art will be familiar with several web browser plug-ins including, Adobe ® Flash ® Player, Microsoft ® Silverlight ® , and Apple ® QuickTime ® .
  • the toolbar comprises one or more web browser extensions, add-ins, or add-ons.
  • the toolbar comprises one or more explorer bars, tool bands, or desk bands.
  • plug-in frameworks are available that enable development of plug-ins in various programming languages, including, by way of non-limiting examples, C++, Delphi, JavaTM, PHP, PythonTM, and VB .NET, or combinations thereof.
  • Web browsers are software applications, designed for use with network-connected digital processing devices, for retrieving, presenting, and traversing information resources on the World Wide Web. Suitable web browsers include, by way of non limiting examples, Microsoft® Internet Explorer®, Mozilla® Firefox®, Google® Chrome, Apple® Safari®, Opera Software® Opera®, and KDE Konqueror. In some embodiments, the web browser is a mobile web browser. Mobile web browsers (also called microbrowsers, mini browsers, and wireless browsers) are designed for use on mobile digital processing devices including, by way of non-limiting examples, handheld computers, tablet computers, netbook computers, subnotebook computers, smartphones, music players, personal digital assistants (PDAs), and handheld video game systems.
  • PDAs personal digital assistants
  • Suitable mobile web browsers include, by way of non-limiting examples, Google® Android® browser, RIM BlackBerry® Browser, Apple® Safari®, Palm® Blazer, Palm® WebOS® Browser, Mozilla® Firefox® for mobile, Microsoft® Internet Explorer® Mobile, Amazon® Kindle® Basic Web, Nokia® Browser, Opera Software® Opera® Mobile, and Sony® PSPTM browser.
  • the methods, kits, and systems disclosed herein may include software, server, and/or database modules, or use of the same.
  • software modules are created by techniques known to those of skill in the art using machines, software, and languages known to the art.
  • the software modules disclosed herein are implemented in a multitude of ways.
  • a software module comprises a file, a section of code, a programming object, a programming structure, or combinations thereof.
  • a software module comprises a plurality of files, a plurality of sections of code, a plurality of programming objects, a plurality of programming structures, or combinations thereof.
  • the one or more software modules comprise, by way of non limiting examples, a web application, a mobile application, and a standalone application.
  • software modules are in one computer program or application. In other embodiments, software modules are in more than one computer program or application. In some embodiments, software modules are hosted on one machine. In other embodiments, software modules are hosted on more than one machine. In further embodiments, software modules are hosted on cloud computing platforms. In some embodiments, software modules are hosted on one or more machines in one location. In other embodiments, software modules are hosted on one or more machines in more than one location.
  • the methods, kits, and systems disclosed herein may comprise one or more databases, or use of the same.
  • suitable databases include, by way of non-limiting examples, relational databases, non-relational databases, object- oriented databases, object databases, entity-relationship model databases, associative databases, and XML databases.
  • a database is internet-based.
  • a database is web-based.
  • a database is cloud computing-based.
  • a database is based on one or more local computer storage devices.
  • the methods, kits, and systems disclosed herein may be used to transmit one or more reports.
  • the one or more reports may comprise information pertaining to the classification and/or identification of one or more samples from one or more subjects.
  • the one or more reports may comprise information pertaining to a disease status (e.g., endometriosis or non-endometriosis).
  • the one or more reports may comprise information pertaining to therapeutic regimens for use in treating endometriosis in a subject in need thereof.
  • the one or more reports may be transmitted to a subject or a medical representative of the subject.
  • the medical representative of the subject may be a physician, physician’s assistant, nurse, or other medical care provider.
  • the medical representative of the subject may be a family member of the subject.
  • a family member of the subject may be a parent, guardian, child, sibling, aunt, uncle, cousin, or spouse.
  • the medical representative of the subject may be a legal representative of the subject.
  • Inclusion criteria were women aged 18-49 years. Exclusion criteria consisted of post-menopausal patients, pregnancy, critical anemia, hyperplasia or polyps, or malignancy. Subjects were stratified into the disease group if visual findings at surgery (and pathology when required), confirmed the presence of endometriosis, and the control group if surgery revealed other benign pathology. All stages of endometriosis as well as untreated and treated subjects were included to provide a full spectrum of disease resulting in varied miRNA levels.
  • endometriosis study population and 59 were categorized as the control study population.
  • the endometriosis group was categorized based on visual diagnosis and histological verification of disease.
  • the control group was categorized based on absence of visual disease at time of surgery.
  • the demographics and clinical characteristics of the study subjects are summarized in Table 1.
  • the mean age of the study population was 34.1 ⁇ 7.1 for the endometriosis group and 36.9 ⁇ 8.2 for the control group.
  • Body mass index was 28.1 ⁇ 7.5 for the endometriosis group and 30.4 ⁇ 7.5 for the control group.
  • There was no statistically significant difference between ages of the women in the two groups (Student’s t-test), or between their BMI values (Student’s t- test).
  • Study subjects identified predominantly as Caucasian followed by Black/ African American and Hispanic.
  • the endometriosis group consisted of varying degrees of disease as categorized by rASRM stage. The 41 endometriosis subjects were divided into 11 (29%) Stage I, 7 (17%) Stage II, 15 (36%) Stage III, and 8 (19%) Stage IV. Endometriomas were reported in 13 patients.
  • the control group consisted of varying benign pathologies. The 59 control subjects were divided into the following categories: 23 (39%) leiomyomas, 4 (7%) cystadenomas, 5 (8%) chronic pelvic infections, 3 (5%) teratomas, 6 (10%) paratubal cysts, and 18 (31%) without abnormal pathology noted (Table 1).
  • Phase of menstrual cycle and presence of hormonal medications were recorded and can also be seen in Table 1.
  • the phase of the menstrual cycle could not be accurately determined based on either a history of irregular cycles (data absent) or due to the use of hormonal medication.
  • 8 (19%) were in proliferative phase and 15 (36%) were in secretory phase for the endometriosis group, and 14 (24%) were in proliferative phase and 13 (22%) in secretory phase for the control group.
  • Many study subjects were using hormonal agents at the time of serum collection.
  • hormonal agent categories included 10 (24%) combined oral contraception, 5 (12%) progesterone only, 0 (0%) estrogen only, 6 (15%) GnRH agonist, 1 (2%) aromatase inhibitors, and the remaining 19 (46%) were not using any hormonal agent.
  • hormonal agent categories included 10 (17%) combined oral contraception, 16 (27%) progesterone only, 1 (2%) estrogen only, 5 (8%) GnRH agonist, 1 (2%) aromatase inhibitors, and the remaining 26 (44%) were not using any hormonal agent.
  • Table 1 Patient Demographics and Clinical Characterisrics
  • Example 2. miRNA expression analysis from serum samples and saliva samples
  • Total miRNA was extracted from 300 mI of serum sample collected as in Example 1 using the miRNeasy mini Kit from Qiagen (Valencia, CA, USA) and reverse transcribed using TaqMan Advanced miRNA cDNA synthesis Kit from Applied Biosystems by Life Technologies (Carlsbad, CA) according to the manufacturer’s specifications. MicroRNA levels were quantified with qRT-PCR using SYBR Green (Bio-Rad Laboratories, Hercules, CA) with the MyiQ Single Color Real-Time PCR Detection System (Bio-Rad). The specificity of the amplified transcript and absence of primer-dimers was confirmed by a melting curve analysis. Primers for miRNAs and the U6 gene were obtained from the W. M.
  • ROC receiver operating characteristic
  • Example 4. Multivariate Model-building and Analysis based on miRNA expression in Serum
  • AUC scores for the model performance in the training and testing datasets are shown in Figure 5.
  • An AUC of 0.939 for the 6-marker classifier algorithm was attained in an independent validation experiment, after performing an independent re-quantitation of miRNAs from patient samples followed by application of the algorithm.
  • Examples 1-4 The study of Examples 1-4 was designed to demonstrate the ability of circulating miRNAs to reliably differentiate endometriosis from other gynecologic pathologies, with robust diagnostic performance in an independent test dataset of a diverse (real-world-like) population.
  • the control cases also comprised a greater variety of diseases than in our previous study, in which women in the control group were all diagnosed with different types of cysts (dermoid, ovarian, paratubal cysts, and serous or mucinous cystadenoma).
  • cysts dermoid, ovarian, paratubal cysts, and serous or mucinous cystadenoma
  • fibroids leiomyomas
  • were the leading pathology found in the control patients (n 23), with the second most common being absence of abnormal pathology (Table 1). Evaluation of these markers amongst a cohort of patients with varied pelvic pathologies supports the utility of using these markers in a general population to distinguish endometriosis from other conditions.
  • the 6-miRNA random forest model was optimized for specificity by selecting a different cutoff point on the ROC curve, yielding a model with 96% specificity and 83% sensitivity.
  • optimizing values of both sensitivity and specificity to be close to 90% can be achieved using yet a different cutoff on the ROC curve.
  • Higher sensitivity (and a low false negative rate) could be appropriate for use of the biomarker panel as a screening test.
  • Example 6 Generation of Trained Algorithm Models based on Data Derived Herein
  • Table 4 Mean (SD) of overall accuracy measures using Random Forest (RF) or Penalized Regression (PR) approaches.
  • Figure 7 shows a histogram where the distribution of voting percentages from the RF model in the Retrospective data set (Cosar); black bars indicate subjects with surgically defined endometriosis, white bars subjects without endometriosis.
  • a blood, blood plasma, blood serum, menstrual blood, menstrual effluent, urine, or saliva sample is taken from a female patient with suspicion of endometriosis.
  • the quantity of a microRNA associated with endometriosis (for example, miR-125b, miR-451a, miR-3613, miR- 150, miR-342, and let- 7b) is then determined in the sample, and any of the trained algorithm classifiers used herein are utilized to detect endometriosis If endometriosis is detected, the patient is treated with a therapeutically effective dose of a GnRH antagonist or agonist therapy (e.g., Elagolix). The compound causes a reduction in the symptoms of endometriosis.
  • a GnRH antagonist or agonist therapy e.g., Elagolix
  • the patient is assessed for levels of a microRNA signature associated with endometriosis. If the microRNA signature associated with endometriosis indicates the presence of endometriosis, the dose of the GnRH agonist or antagonist therapy (e.g., Elagolix) is adjusted upward, and the treatment/testing process is repeated until biomarkers indicate the absence of endometriosis.
  • the GnRH agonist or antagonist therapy e.g., Elagolix

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Abstract

L'invention concerne des procédés améliorés pour la détection de l'endométriose. De manière générale, les procédés comprennent, sans y être limités, l'application d'un algorithme d'apprentissage automatique à des niveaux de miARN afin de détecter, prédire, diagnostiquer ou surveiller la présence ou l'absence d'endométriose.
EP20799450.0A 2019-04-29 2020-04-28 Classificateurs pour la détection de l'endométriose Pending EP3963094A4 (fr)

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