Methods and compositions for assessing RNA levels in biological samples

Abstract

This disclosure provides methods for assessing miRNA levels in bodily samples involve extracting RNA from the samples no more than 50 days after sample collection. This disclosure further provides methods of creating sample training sets in which RNA degradation is minimized. The methods involve extracting RNA from a biological sample, such as saliva, serum or plasma, in no more than 50 days from collection of the sample, for example, no more than 30 days, 110 more than seven days or no more than 1 day. This produces a collection of samples that is more uniform in the state of RNA present.

Description

Docket No. DOT-708-PCT METHODS AND COMPOSITIONS FOR ASSESSING RNA LEVELS IN BIOLOGICAL SAMPLES REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of the priority date of US Provisional patent application 63 / 528,534, filed July 24, 2023, the contents of which are incorporated herein by reference in their entirety. BACKGROUND OF THE INVENTION

[0002] MicroRNAs (miRNAs) are an evolutionarily conserved gene class of short non- protein-coding regulatory RNAs and have recently gained attention in healthcare management for their potential as non-invasive biomarkers for human diseases. SUMMARY OF THE INVENTION

[0003] The increasing interest of miRNAs in clinical practice has led to an unmet need to accurately measure miRNAs and understand the stability of: (a) miRNAs overall, and (b) specific miRNAs, in minimally invasive patient samples such as blood (including, e.g., blood fractions such as plasma or serum).

[0004] RNA, in particular miRNA, from a biological sample tends to degrade if not separated from other biological macromolecules in the sample. Degraded RNA in a set of training samples used to quantify RNA can interfere with identifying biomarkers and producing a reliable diagnostic.

[0005] Accordingly, this disclosure provides methods of creating sample training sets in which RNA degradation is minimized. The methods involve extracting RNA from a biological sample, such as saliva, serum or plasma, in no more than 50 days from collection of the sample, for example, no more than 30 days, no more than seven days or no more than 1 day. This produces a collection of samples that is more uniform in the state of RNA present.

[0006] Furthermore, in samples taken for diagnostic testing, the time from sample collection to RNA extraction can be matched with that of the training samples. For example, the gap from collection to extraction can be no more than the average time gap for the training samples. Also, the time gap can be no more than 50 days from collection of the sample, for example, no more than 30 days, no more than seven days or no more than 1 day.Docket No. DOT-708-PCT

[0007] RNA, such as miRNA, is measured from the RNA extracted from the samples. After extraction, the samples can be frozen until use, e.g., at no more than -20 degrees C, or no more than -80 degrees C.

[0008] Classifiers developed on data obtained from the training samples can then be used to diagnose subjects from whom a sample has been collected. A positive diagnosis can be followed with treatment for the condition. INCORPORATION BY REFERENCE

[0009] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings of which:

[0011] FIGURES 1A, 1B, and 1C (FIGs.1A, 1B, and 1C) depict the change in miRNA levels during storage for selected miRNAs assessed in Example 1. The X axis represents days, the Y axis represents ddCt.

[0012] FIGURE 2 (FIG.2) depicts the change in miRNA levels during storage for selected miRNAs assessed in Example 1.

[0013] FIGURE 3 (FIG.3) depicts a Principal Component Analysis (PCA) on the expression data showing a distinction between samples processed at day 0 and samples processed at the later time points in Example 2.

[0014] FIGURE 4A through FIGURE 4AB (FIGs.4A through 4AB) depict the change of miRNAs during storage for selected miRNAs assessed in Example 2.

[0015] FIGURE 5 depicts an example computer system for executing computer-implemented methods of the disclosure.Docket No. DOT-708-PCT DETAILED DESCRIPTION OF THE INVENTION I. Definitions

[0016] As used herein, the term “cell-free”, 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. For example, 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. However, 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. In contrast, nucleic acids that are solely associated with cells in the body are generally not considered to be “cell-free.” For example, nucleic acids extracted directly from cells are generally not considered “cell-free” as the term is used herein.

[0017] As 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. In some instances, the cell-free portion of the sample is obtained by centrifugation, filtration, fractionation column, or other method. In some cases, the body fluid may be naturally cell-free. Typically, a cell-free body fluid sample contains no intact cells; however, it may contain cell fragments, exosomes, or cellular debris. In some cases, 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, or below -70 degrees C..

[0018] As used herein, 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.

[0019] 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.

[0020] The terms “subject,” “patient,” “individual,” and the like are used interchangeably herein, and refer to any animal, amenable to the methods described herein. The term “patient” typically refers to a subject under the care of a healthcare provider. In certain non-limiting embodiments, the patient, subject, or individual is a human. In some cases, the methodsDocket No. DOT-708-PCT provided herein may involve cells from such subject, patient, or individual. In some cases, the method may be conducted, at least in part, in vitro or in situ.

[0021] As used herein, “microRNA” or “miRNA” describes small non-coding RNA molecules, generally about 15 to about 50 nucleotides in length, preferably 17-23 nucleotides, which can play a role in regulating gene expression through, for example, a process termed RNA interference (RNAi). 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. In general, 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.

[0022] As used herein, RNA and RNAs are used interchangeably and may refer to a singular RNA or multiple RNA. Similarly, miRNA and miRNAs are used interchangeably and may refer to a singular miRNA or multiple miRNA.

[0023] As used herein, “non-coding RNA” (ncRNA) generally refers to an endogenous RNA molecule that is not translated into a protein in a cell. Exemplary types of ncRNAs include transfer RNAs (tRNAs), ribosomal RNAs (rRNAs), microRNAs (miRNAs), piRNAs, snoRNAs, snRNAs, exRNAs, scRNAs and long ncRNAs (such as Xist and HOTAIR). In some embodiments, 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.

[0024] As used herein, “or” may refer to “and”, “or,” or “and / or” and may be used both exclusively and inclusively. For example, 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.

[0025] As used herein, 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.Docket No. DOT-708-PCT

[0026] The phrase “at least one” includes “one”, “one or more”, “one or a plurality”, and, therefore, contemplates the use of the term “a plurality”.

[0027] The term “any of” between a modifier and a sequence means that the modifier modifies each member of the sequence. So, for example, the phrase “at least any of 1, 2 or 3” means “at least 1, at least 2 or at least 3”.

[0028] The term “about” refers to a range that is 5% plus or minus from a stated numerical value within the context of the particular usage.

[0029] Ct refers to rtPCR cycle threshold, the number of cycles required for the fluorescent signal to cross the detection threshold. In various embodiments, the amount of a target analyte may be normalized to a normalizer control using the "delta CT method" or "^CT method," which involves calculating the ^CT. In certain embodiments, the ^CT 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. In certain embodiments, the fold difference in the amounts of the normalizer control and target analyte is calculated from the ^CT- In certain embodiments, the fold difference in the amounts of the normalizer control and target analyte is calculated from the ^CT according to the formula 2-In some embodiments, the normalizer control is a housekeeping nucleic acids (e.g., DNA or RNA encoding a housekeeping polypeptide). The normalizing molecule can be, for example, miR-30b-5p, miR-484, RNU6-1 (U6 snRNA), RNU48 (U48 snRNA), or Cell39M. Figures herein show ^^Ct as a function of time from sample collection. ^^Ct is a measure of relative gene expression. It is calculated by measuring the Ct of both the target species and the reference gene in both the test and control samples. ^Ct is determined as the Ct difference between the Ct of the target and the Ct of the reference. ^ ^Ct determined as the difference between ^Ct test and ^Ct control. II. Subjects

[0030] 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).Docket No. DOT-708-PCT

[0031] 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). As such, in general, 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.

[0032] In some cases, a subject may be at risk of having endometriosis. A subject is “at risk” of endometriosis is their risk of endometriosis is greater than average risk for the population in which risk was measured, e.g., in the 51stpercentile, the 75thpercentile, the 80thpercentile, the 90thpercentile or the 95thpercentile. 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. In some cases, the subject has, or is suspected of having, Stage I, II, III, or IV endometriosis. In some cases, the subject has, or is suspected of having, endometrioma. In some cases, 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. In some cases, the subject has early stage endometriosis. In some cases, the subject as Stage I / II endometriosis. In some cases, the subject has Stage III / IV endometriosis.

[0033] In some cases, 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 dyspareunia, chronic lower abdominal pain, chronic lower back pain, adnexal masses, infertility, or excessive bleeding. In some cases, the subject may be suspected of having endometriosis due to the results of a previous or concurrent test for endometriosis. In some cases, a subject is suspected of having endometriosis due to multiple factors. For example, the subject may be suspected of having endometriosis due to the presence of symptoms in an overall clinical context consistent with endometriosis.

[0034] In some cases, a subject may have, or be suspected of having, a non-endometriosis condition. In general, as used herein, the term “non-endometriosis condition” refers to an abnormal reproductive condition that is not endometriosis. Nonlimiting examples of a non-Docket No. DOT-708-PCT endometriosis condition include fibroids, leiomyomas, cysts, dermoid cysts, serous cystadenomas, cystadenomas, ovarian cysts, mucous cystadenomas, pelvic infection, teratoma, and / or paratubal cysts. In some cases, 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. In some cases, the non-endometriosis condition is benign. In some cases, the non-endometriosis condition is malignant.

[0035] In some cases, the subject may be receiving a hormonal treatment. The hormonal treatment may include GnRH (gonadotrophin releasing hormone) analogs (agonists or antagonists), with or without estrogen / progesterone replacement therapy or tibolone treatment, levonorgestrel-releasing intrauterine devices (e.g., Mirena), danazol, antiprogesterones, gestrinone, aromatase inhibitors, selective estrogen receptor modulators (SERMs), or selective progesterone receptor modulators (SPRMs). III. Biological Samples

[0036] The term “biological sample” refers to a sample that includes material of biological origin, such as cells, biological macromolecules (e.g., nucleic acids, proteins, carbohydrates or lipids), metabolites, or their derivatives. The biological sample is preferably a bodily fluid sample. The bodily fluid may be sweat, saliva, tears, urine, blood (or a fraction thereof, such as plasma or serum), vaginal fluid, cervico-vaginal fluid, whole blood, menstrual effluent (e.g., menstrual blood), spinal fluid, pulmonary fluid, sputum, or any other bodily fluid. In preferred embodiments, the sample is a saliva or menstrual effluent (e.g., menstrual blood) sample. In some cases, the sample comprises white blood cells (WBCs). In some embodiments, the sample is a plasma sample. In some embodiments, the sample is a cell-free or acellular blood, plasma, or serum sample. In some embodiments, 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. In some cases, the sample (e.g., blood, serum, plasma, etc.) is subjected to centrifugation, filtration, fractionation, or other method In some cases, the sample comprises peripheral blood mononuclear cells (PBMCs); in some cases, the sample comprises peripheral blood lymphocytes (PBLs). Plasma can be prepared by treating blood with an anticoagulant and centrifuging the blood sample to remove red and white blood cells. Serum can be prepared by allowing blood to clot and centrifuging the sample to separate the clot from the serum. As used herein, the term “saliva” does not include sputum, since sputumDocket No. DOT-708-PCT pertains to mucus or phlegm samples. In some embodiments, the saliva, peripheral blood sample or menstrual effluent (e.g., menstrual blood) may be separated into cellular and non-cellular fractions by suitable methods (e.g., centrifugation, filtration). In some embodiments, nucleic acids (e.g., miRNA or ncRNA) may be extracted from the cellular (e.g., cell-containing) or non- cellular (e.g., non cell-containing) fractions. In some embodiments, 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.).

[0037] In some embodiments, the sample comprises cell-free non-coding RNA (e.g., cell- free miRNA). In some cases, the sample comprises purified or extracted non-coding RNA (e.g., miRNA). In some embodiments, the sample comprises exosome-encapsulated non-coding RNA (e.g., miRNA). In some embodiments, the sample comprises cell-encapsulated (e.g., by white blood cells) non-coding RNA (e.g., miRNA). IV. Sample Collection

[0038] As used herein “obtaining a sample” includes obtaining a sample directly (e.g., collecting the sample) or indirectly, including having a sample obtained (e.g., from a third party who directly obtained the sample from the subject). In some embodiments, the sample is taken from the subject by the same party (e.g., a testing laboratory) that subsequently acquires biomarker data from the sample. In some embodiments, 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). In some embodiments, 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). In some embodiments, 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).

[0039] In some embodiments, test samples of blood may be obtained from a subject. In some embodiments, the blood sample is a peripheral blood sample. In some embodiments, the blood sample is a whole blood sample. In some embodiments, the sample is a blood sample and comprises whole blood, peripheral blood, serum, plasma, PBLs (peripheral blood lymphocytes), PBMCs (peripheral blood mononuclear cells), T cells, CD4 T cells, CD8 T cells, orDocket No. DOT-708-PCT 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. V. Sample Processing

[0040] It has been discovered that miRNA in a sample tends to degrade over time, if the miRNA has not been extracted from a biological sample comprising other biological molecules. In particular, miRNA in a blood sample or blood derivative, such as plasma or serum, experiences degradation over time. This is true even if the biological sample has been frozen, e.g., stored at -80° C. Such degradation infers that measurements of miRNA from clinical samples, either for test development or diagnostic purposes, may not accurately reflect amounts of the analyte in the sample when collected.

[0041] Accordingly, provided herein are methods of preparing RNA, e.g. miRNA, from a sample so as to reduce analyte degradation. The methods involve obtaining a biological sample collected from a subject at a first time point, and extracting the analyte molecules from the sample before they experience significant degradation. The time from sample collection to RNA extraction can be no more than any of 50 days, 30 days, 14 days, 10 days, 9 days, 8 days, 7 days, 6 days, 5 days, 4 days, 3 days, 48 hours, or 24 hours. In extracted form, miRNA have normalized rates of decay of at most 0.001, 0.002, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 2, 3, 4, or 5 Ct / day.

[0042] During the period between sample collection and RNA extraction, the biological sample can remain at room temperature or can be frozen, e.g., placed on ice or stored at a temperature below -70°C, e.g., -80°C.

[0043] Extracting RNA from a sample involves separating the RNA from one or more other biological macromolecules (e.g., proteins, polypeptides, other nucleic acids and other polynucleotides) so that the RNA is the predominant species present (i.e., on a molar basis, more abundant than any other individual macromolecular species in the composition), e.g., the RNA comprises at least about 50% (on a molar basis) of all macromolecular species present. Extracting typically also comprises separating the RNA from undesired inorganic molecules, such as metals or salts. Substantially purifying RNA, to create a substantially pure composition, involves separating the RNA from one or more other molecules in the sample so that the RNA represents at least 90% of the macromolecular species present in the composition. Essentially purifying RNA, to create an essential homogenous composition, involves separating the RNA from one or more other molecules in the sample so that the RNA represents at least 99.5% of theDocket No. DOT-708-PCT macromolecular species in the composition. Solvent species, small molecules (<500 Daltons), stabilizers (e.g., BSA), and elemental ion species are not considered macromolecular species for purposes of this definition.

[0044] Several methods are known in the art for extracting microRNA from a sample. These include, for example, organic extraction (phenol-chloroform) isolation, solid phase extraction on a silica substrate, magnetic bead extraction and polyethylene glycol (PEG) precipitation.

[0045] In organic extraction methods a sample is contacted with phenol, chloroform and clonidine isolate thiocyanate. RNA partitions to the aqueous phase and can be precipitated using isopropanol or ethanol. One version of this is, TRIzol extraction

[0046] In solid phase extraction, a sample is contacted with a solid phase, such as silica, and contaminants are removed. This can be done on the column.

[0047] Magnetic beads that attract RNA can capture RNA from a sample. Immobilizing the beads and washing them extracts the RNA.

[0048] miRNA also can be extracted from a sample using polyethylene glycol in a salt solution to precipitate the RNA.

[0049] After extracting the RNA, it can be frozen for long periods of time without experiencing significant degradation.

[0050] Accordingly, the methods of this disclosure contemplate obtaining a sample and extracting the RNA from the sample within the aforementioned time periods, in particular, within 48 hours, within 24 hours, within 12 hours within six hours, or within 12 hours of collection before any freezing of the sample. Then, the RNA can be measured or otherwise analyzed or frozen for subsequent measurement or analysis.

[0051] Alternatively, the biological sample can be frozen at some time point after collection, and the RNA can be extracted from the frozen sample (e.g., by first thawing the sample), again, within the aforementioned time periods after sample collection. The extracted RNA can then be measured or otherwise analyzed. VI. RNA (e.g., miRNA) Expression Profiling

[0052] The methods, kits, and systems disclosed herein may comprise specifically detecting, profiling, or quantitating RNAs (e.g., ncRNAs, miRNAs) that are within the biological samples to determine an expression profile. In some instances, RNAs (e.g., miRNAs, ncRNAs) may beDocket No. DOT-708-PCT isolated from the biological samples. In some cases, RNAs (e.g., miRNAs, ncRNAs) may be isolated from a cell-free source.

[0053] In some cases, expression levels are determined by a hybridization-based method, such as Northern blot, Southern blot, molecular beacon, molecular inversion probe, or microarray hybridization. In some cases, 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).

[0054] In some cases, 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.

[0055] In some cases, the expression levels are determined by sequencing. Examples of 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. In some embodiments, sequencing may first involve a reverse- transcriptase and / or PCR amplification step to increase the 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. VII. Model Building and Classification

[0056] Data on RNA collected by the methods disclosed herein can be used in building models to classify test samples.

[0057] A categorical variable is a variable that can be defined by two or more, typically nonoverlapping, categorical states. For example, the categorical variable can be a pathological condition, and the categorical states can be status of the condition, such as different diagnoses,Docket No. DOT-708-PCT disease stage, risk of developing, likelihood of having, prognosis. Categorical states include status of the categorical variable or diagnosis of the categorical variable.

[0058] A measurement of a variable can be any combination of numbers and words. A measure can be any scale, including nominal (e.g., name or category), ordinal (e.g., hierarchical order of categories), interval (distance between members of an order), ratio (interval compared to a meaningful “0”), or a cardinal number measurement that counts the number of things in a set. Measurements of a variable on a nominal scale indicate a name or category, e.g., category into which the sequencing read is classified. Measurements of a variable on an ordinal scale produce a ranking, such as “first”, “second”, “third”. Measurements on a ratio scale include, for example, any measure on a pre-defined scale, absolute number of reads, normalized or estimated numbers, as well as statistical measurements such as frequency, mean, median, standard deviation, or quantile. Measurements that involve quantification are typically determined at the ratio scale level.

[0059] A measurement of a variable can be any combination of numbers and words. A measure can be any scale, including nominal (e.g., name or category, e.g., composite), ordinal (e.g., hierarchical order of categories), interval (distance between members of an order), ratio (interval compared to a meaningful “0”), or a cardinal number measurement that counts the number of things in a set. Measurements of a variable on a nominal scale indicate a name or category, e.g., category into which the sequencing read is classified. Measurements of a variable on an ordinal scale produce a ranking, such as “first”, “second”, “third”. Measurements on a ratio scale include, for example, any measure on a pre-defined scale, absolute number of reads, normalized or estimated numbers, as well as statistical measurements such as frequency, mean, median, standard deviation, or quantile. Measurements that involve quantification are typically determined at the ratio scale level.

[0060] Accordingly, in the case of endometriosis as a pathological condition, the status of the condition can be a nominal (e.g., present / absent; positive / negative; Stage I, Stage II, Stage III or Stage IV), an ordinal (e.g., likelihood (e.g., high, medium, low)), or a ratio (e.g., risk percentage / probability). Determining a categorical state of a pathological condition can also be referred to as “diagnosing” or “making a diagnosis.” The state can be expressed as any measure as described herein, e.g., a binary, a category (e.g., likely, neutral, and unlikely), or a probability (e.g., a continuous variable). Determining a categorical state of a pathological condition can also be referred to as “diagnosing” or “making a diagnosis.”Docket No. DOT-708-PCT

[0061] Each sample can be analyzed to measure amounts, e.g., expression, of RNA species, in particular, one or more forms of miRNA, using methods known in the art and described herein.

[0062] Datasets can comprise measures of one or a plurality of different analytes, e.g., RNA forms, e.g., miRNA. For purposes of training, a dataset typically provides, for each instance or sample, a categorical state of a categorical variable. For example, presence or absence of endometriosis.

[0063] Datasets can be analyzed using traditional statistical methods, such as, multivariate analysis of variance multivariate regression, principal component analysis, and discriminant analysis. However, more complex datasets for example, those including multidimensional measures of each instance and a large number of instances, require computerized methods for analysis and, in particular, artificial intelligence or machine learning.

[0064] Machine learning algorithms are trained on training datasets to generate models that predict the categorical state of a sample based on subject data, such as expression data. Predicted categorical state can be translated into recommendations to a subject for actions to be taken, for example, health interventions, such as therapeutic treatments.

[0065] Methods of generating models to predict a categorical state can involve providing a training dataset on which a machine learning algorithm can be trained to develop one or more models to predict or infer a categorical state. The training dataset will include a plurality of training examples, typically for each of a plurality of subjects and typically in the form of a vector. Each training example will include a plurality of features or data points and, for each feature, data, e.g., in the form of numbers or descriptors. Where learning is to be supervised, the data will include a classification of the sample or subject into a category of a categorical variable to be inferred. For example, the categorical variable may be “endometriosis diagnosis” and the categories or classifications of this variable can be “present” and “absent”. For machine learning, the training examples can have at least 5, at least 10, at least 100, at least 1000, at least 10,000, different features. The features selected are those on which prediction will be based.

[0066] In certain embodiments, a plurality of instances among the collection of instances in the training set can be assigned to each of a plurality of different categorical states for a categorical variable. So, for example, given 100 instances, 50 might be assigned to a first categorical state and 50 might be assigned to a second categorical state for a categorical variable.Docket No. DOT-708-PCT

[0067] The machine learning algorithm can be any suitable supervised machine learning algorithm, parametric or non-parametric. Machine learning algorithms include, without limitation, artificial neural networks (e.g., back propagation networks), decision trees (e.g., recursive partitioning processes, CART), random forests, discriminant analyses (e.g., Bayesian classifier or Fischer analysis), linear classifiers (e.g., multiple linear regression (MLR), partial least squares (PLS) regression, principal components regression (PCR)), mixed or random- effects models, non-parametric classifiers (e.g., k-nearest neighbors), support vector machines, and ensemble methods (e.g., bagging, boosting).

[0068] Using abundance data from a plurality of samples classified into the categorical states to be predicted, the machine learning algorithm generates a classifier that predicts, for an individual sample, the categorical state, based on abundance data collected from the sample.

[0069] In a classification or diagnostic step, a biological sample is collected from a subject. With or without prior freezing, RNA is extracted from the sample within the specified time periods after collection as described herein. One or more forms of RNA, e.g., miRNA, are measured to produce a dataset. Using a classifier as described above, an operator can classify the categorical state of a particular categorical variable of a subject based on the dataset. The classifier can classify conditions according to any classification scheme useful to the operator.

[0070] Classifiers that use miRNA data are most robust when the samples from which the training data is derived have a high degree of integrity, that is, in which degradation of the analyte is suppressed in the samples used. Accordingly, in the methods provided herein, RNA is extracted from each sample of a collection of samples in no more than a specified time period after collection, as described herein. In certain embodiments, that time period is no more than seven days, no more than 48 hours, no more than twenty four (24) hours, no more than twelve (12) hours, no more than six (6) hours, or no more than three (3) hours. Extracting RNA from a biological sample reduces degradation of that RNA. Furthermore, RNA from a sample from a subject to be tested also is extracted within the specified time after collection.

[0071] Accordingly, in some embodiments, the timing of RNA extraction from a collected sample to be tested is matched with the timing of RNA extraction from the collected samples used to develop the classifier. For example, the timing from collection to RNA extraction of a test sample can be no greater than the average time from collection to RNA extraction of the reference samples used to develop the classifier.Docket No. DOT-708-PCT VIII. Treatment

[0072] Measurement of RNA levels (or classification status) 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.

[0073] In some embodiments, the disclosure provides a method for monitoring the levels of miRNAs (or clinical condition, such as endometriosis or non-endometriosis) in response to treatment. For example, in certain embodiments, 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. In some embodiments, 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. In some embodiments, 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.

[0074] The present disclosure provides therapies (e.g., drug, surgical) for the treatment or prevention of endometriosis. A non-limitative list of known methods and materials for endometriosis treatment include, but are not limited to, pain killers (e.g., NSAIDs), hormonal treatments (e.g., oral contraceptive pills, GnRH analogs), chemotherapy, and surgical treatments. In some cases, a patient or subject in which endometriosis is detected by a method provided herein, may undergo additional tests or procedures to confirm the endometriosis. For example, 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 by excision. 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, Medroxyprogesterone acetate, Depot medroxyprogesterone acetate, Norethisterone, Levonorgestrel), progesterone and progesterone-like substances, gonadotropin-releasing hormone (GnRH) agonists (such as leuprorelin, buserelin, goserelin,Docket No. DOT-708-PCT histrelin, deslorelin, nafarelin, and triptorelin), androgens and synthetic androgens like Danazol, gonadotropin-releasing hormone antagonist medication (GnRH) antagonists (e.g., elagolix, relugolix) with or without estrogen / progesterone replacement therapy or tibolone treatment, levonorgestrel-releasing intrauterine devices (e.g., Mirena), danazol, antiprogesterones, gestrinone, selective estrogen receptor modulators (SERMs), or selective progesterone receptor modulators (SPRMs), 17^-hydroxysteroid dehydrogenase type 1 (HSD17B1) inhibitors, and aromatase inhibitors. Surgical treatments include, but are not limited to, laparoscopic surgery, laparotomy, hysterectomy, and oophorectomy. Other treatments particularly well suited for use in the present disclosure are well known in the art. In some embodiments, the patient can be treated using a statin, including but not limited to, atorvastatin, cerivastatin, fluvastatin, lovastatin, mevastatin, pitavastatin, pravastatin, rosuvastatin and simvastatin.

[0075] The present disclosure provides therapies (e.g., drugs, surgery) for the treatment or prevention of a non-endometriosis condition. 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, laparotomy, hysterectomy, and oophorectomy. In some cases, the patient or subject may undergo additional testing or monitoring following a negative result from an endometriosis test or assay described herein. Such a subject or patient may, for example, be tested for a non-endometriosis disease or condition e.g., cancer, benign cyst, fibroids, etc. In some cases, following a negative result, 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. In some cases, such testing may be by a method provided herein. In some cases, in response to a negative endometriosis result, the subject or patient may avoid undergoing a surgical intervention (e.g., laparoscopic surgery). IX. Computer-implemented methods

[0076] Expression levels of one or more RNAs (e.g., miRNA, ncRNA) can be analyzed and associated with the status of a subject (e.g., endometriosis, non-endometriosis) in a digital computer. Optionally, such a 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) receivingDocket No. DOT-708-PCT experimentally determined signals related to miRNA or ncRNA expression levels. Alternatively, 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.

[0077] A typical computer (see US 6,785,613 Figs.4 and 5) 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. Many other devices can be connected such as a scanner via I / O controller, a mouse connected to a 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 a robotic arm for delivering reagents for determining expression levels, as well as small vessels, e.g., microtiter wells for performing the expression analysis.

[0078] The methods, systems, kits, and compositions provided herein may also be capable of generating and transmitting results through a computer network. In some cases, a sample is first collected from a subject (e.g., a patient with one or more symptoms of endometriosis, or a non- symptomatic patient). In some instances, the sample is assayed and RNA (e.g., miRNA, ncRNA) levels are measured. 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. In some instances, the subject (e.g., patient) 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. In some instances, the result can be accessed via a mobile application provided to a mobile digital processing device (e.g., mobile phone, tablet, etc.). In some instances, the result may be accessed by a medical care provider (e.g., physician)Docket No. DOT-708-PCT and help them identify and track conditions of their patients. In some instances, the result may be used for other purposes such as education and research. X. Computer program

[0079] 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. In light of the disclosure provided herein, those of skill in the art may recognize that a computer program may be written in various versions of various languages.

[0080] 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. In various embodiments, 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.

[0081] Further disclosed herein are systems for classifying one or more samples and uses 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. 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).Docket No. DOT-708-PCT

[0082] Figure 5 shows a computer system (also “system” herein) 501 programmed or otherwise configured for implementing the methods of the disclosure, such as producing a selector set, and / or for data analysis. The system 501 includes a central processing unit (CPU, also “processor” and “computer processor” herein) 505, which can be a single core or multi core processor, or a plurality of processors for parallel processing. The system 501 also includes memory 510 (e.g., random-access memory, read-only memory, flash memory), electronic storage unit 515 (e.g., hard disk), communications interface 520 (e.g., network adapter) for communicating with one or more other systems, and peripheral devices 525, such as cache, other memory, data storage and / or electronic display adapters. The memory 510, storage unit 515, interface 520 and peripheral devices 525 are in communication with the CPU 105 through a communications bus (solid lines), such as a motherboard. The storage unit 515 can be a data storage unit (or data repository) for storing data. The system 501 is operatively coupled to a computer network (“network”) 530 with the aid of the communications interface 520. The network 530 can be the Internet, an internet and / or extranet, or an intranet and / or extranet that is in communication with the Internet. The network 530 in some instances is a telecommunication and / or data network. The network 530 can include one or more computer servers, which can enable distributed computing, such as cloud computing. The network 530 in some instances, with the aid of the system 501, can implement a peer-to-peer network, which may enable devices coupled to the system 501 to behave as a client or a server.

[0083] The system 501 may be in communication with a processing system 635. The processing system 535 can be configured to implement the methods disclosed herein. In some examples, the processing system 535 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 535 can be in communication with the system 501 through the network 530, or by direct (e.g., wired, wireless) connection. The processing system 535 can be configured for analysis, such as nucleic acid sequence analysis.

[0084] 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 501, such as, for example, on the memory 510 or electronic storage unit 515. During use, the code can be executed by the processor 505. In some examples, the code can be retrieved from the storage unit 515 and stored on the memory 510 for ready access by the processor 505. In someDocket No. DOT-708-PCT situations, the electronic storage unit 515 can be precluded, and machine-executable instructions are stored on memory 510. XI. Digital processing device

[0085] The methods, kits, and systems disclosed herein may include a digital processing device or use of the same. In further embodiments, the digital processing device includes one or more hardware central processing units (CPU) that carry out the device’s functions. In still further embodiments, the digital processing device further comprises an operating system configured to perform executable instructions. In some embodiments, the digital processing device is optionally connected to a computer network. In further embodiments, the digital processing device is optionally connected to the Internet such that it accesses the World Wide Web. In still further embodiments, the digital processing device is optionally connected to a cloud computing infrastructure. In other embodiments, the digital processing device is optionally connected to an intranet. In other embodiments, the digital processing device is optionally connected to a data storage device.

[0086] In accordance with the description herein, 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. Those of skill in the art will recognize that many smartphones are suitable for use in the system described herein. Those of skill in the art will also recognize that select televisions, video players, and digital music players with optional computer network connectivity 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.

[0087] 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. Those of skill in the art will recognize that 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®. Those of skill in the art will recognize that suitable personal computer operating systems include, by way of non-limiting examples, Microsoft® Windows®, Apple® Mac OS X®, UNIX®, andDocket No. DOT-708-PCT UNIX-like operating systems such as GNU / Linux®. In some embodiments, the operating system is provided by cloud computing. Those of skill in the art will also recognize that 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®.

[0088] 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. In some embodiments, the device is volatile memory and requires power to maintain stored information. In some embodiments, the device is non-volatile memory and retains stored information when the digital processing device is not powered. In further embodiments, the non-volatile memory comprises flash memory. In some embodiments, the non- volatile memory comprises dynamic random-access memory (DRAM). In some embodiments, the non-volatile memory comprises ferroelectric random-access memory (FRAM). In some embodiments, the non-volatile memory comprises phase-change random access memory (PRAM). In other embodiments, 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. In further embodiments, the storage and / or memory device is a combination of devices such as those disclosed herein.

[0089] A display to send visual information to a user may generally be initialized. Examples of 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. In various further embodiments, on OLED display is a passive-matrix OLED (PMOLED) or active-matrix OLED (AMOLED) display. In some embodiments, the display may be a plasma display, a video projector or a combination of devices such as those disclosed herein.

[0090] 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.Docket No. DOT-708-PCT XII. Non-transitory computer readable storage medium

[0091] 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. In some instances, the program and instructions are permanently, substantially permanently, semi- permanently, or non-transitorily encoded on the media.

[0092] 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.

[0093] At least two of the classes may be selected from endometriosis, non-endometriosis, and healthy. XIII. Web application

[0094] In some embodiments, a computer program includes a web application. In light of the disclosure provided herein, those of skill in the art will recognize that a web application, in various embodiments, utilizes one or more software frameworks and one or more database systems. In some embodiments, a web application is created upon a software framework such as Microsoft® .NET or Ruby on Rails (RoR). In some embodiments, 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. In further embodiments, suitable relational database systems include, by way of non-limiting examples, Microsoft® SQL Server, mySQL™, and Oracle®. Those of skill in the art will also recognize that a web application, inDocket No. DOT-708-PCT 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. In some embodiments, 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). In some embodiments, a web application is written to some extent in a presentation definition language such as Cascading Style Sheets (CSS). In some embodiments, a web application is written to some extent in a client-side scripting language such as Asynchronous Javascript and XML (AJAX), Flash® Actionscript, Javascript, or Silverlight®. In some embodiments, a web application is written to some extent in a server-side coding language such as Active Server Pages (ASP), ColdFusion®, Perl, Java™, JavaServer Pages (JSP), Hypertext Preprocessor (PHP), Python™, Ruby, Tcl, Smalltalk, WebDNA®, or Groovy. In some embodiments, a web application is written to some extent in a database query language such as Structured Query Language (SQL). In some embodiments, a web application integrates enterprise server products such as IBM® Lotus Domino®. In some embodiments, a web application includes a media player element. In various further embodiments, 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®, Java™, and Unity®. XIV. Mobile application

[0095] In some embodiments, a computer program includes a mobile application provided to a mobile digital processing device. In some embodiments, the mobile application is provided to a mobile digital processing device at the time it is manufactured. In other embodiments, the mobile application is provided to a mobile digital processing device via the computer network described herein.

[0096] In view of the disclosure provided 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, Java™, Javascript, Pascal, Object Pascal, Python™, Ruby, VB.NET, WML, and XHTML / HTML with or without CSS, or combinations thereof.Docket No. DOT-708-PCT

[0097] 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, Android™ SDK, BlackBerry® SDK, BREW SDK, Palm® OS SDK, Symbian SDK, webOS SDK, and Windows® Mobile SDK.

[0098] Those of skill in the art will recognize that several commercial forums are available for distribution of mobile applications including, by way of non-limiting examples, Apple® App Store, Android™ Market, BlackBerry® App World, App Store for Palm devices, App Catalog for webOS, Windows® Marketplace for Mobile, Ovi Store for Nokia® devices, Samsung® Apps, and Nintendo® DSi Shop. XV. Standalone application

[0099] In some embodiments, 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. Those of skill in the art will recognize that standalone applications are often compiled. 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, Java™, Lisp, Python™, Visual Basic, and VB .NET, or combinations thereof. Compilation is often performed, at least in part, to create an executable program. In some embodiments, a computer program includes one or more executable complied applications. XVI. Web browser plug-in

[0100] In some embodiments, the computer program includes a web browser plug-in. In computing, 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 webDocket No. DOT-708-PCT 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®. In some embodiments, the toolbar comprises one or more web browser extensions, add-ins, or add-ons. In some embodiments, the toolbar comprises one or more explorer bars, tool bands, or desk bands.

[0101] In view of the disclosure provided herein, those of skill in the art will recognize that several plug-in frameworks are available that enable development of plug-ins in various programming languages, including, by way of non-limiting examples, C++, Delphi, Java™, PHP, Python™, and VB .NET, or combinations thereof.

[0102] Web browsers (also called Internet 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. 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® PSP™ browser. XVII. Software modules

[0103] The methods, kits, and systems disclosed herein may include software, server, and / or database modules, or use of the same. In view of the disclosure provided herein, 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. In various embodiments, a software module comprises a file, a section of code, a programming object, a programming structure, or combinations thereof. In further various embodiments, 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 combinationsDocket No. DOT-708-PCT thereof. In various embodiments, the one or more software modules comprise, by way of non- limiting examples, a web application, a mobile application, and a standalone application. In some embodiments, 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. XVIII. Databases

[0104] The methods, kits, and systems disclosed herein may comprise one or more databases, or use of the same. In view of the disclosure provided herein, those of skill in the art will recognize that many databases are suitable for storage and retrieval of information pertaining to miRNA or ncRNA expression profiles, sequencing data, classifiers, classification systems, therapeutic regimens, or a combination thereof. In various embodiments, 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. In some embodiments, a database is internet-based. In further embodiments, a database is web-based. In still further embodiments, a database is cloud computing-based. In other embodiments, a database is based on one or more local computer storage devices. XIX. Data transmission

[0105] 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.Docket No. DOT-708-PCT

[0106] While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. It is not intended that the invention be limited by the specific examples provided within the specification. While the invention has been described with reference to the aforementioned specification, the descriptions and illustrations of the embodiments herein are not meant to be construed in a limiting sense. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. Furthermore, it shall be understood that all aspects of the invention are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is therefore contemplated that the invention shall also cover any such alternatives, modifications, variations or equivalents. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby. EXEMPLARY EMBODIMENTS

[0107] 1. A sample processing method comprising: a) obtaining a biological sample collected from a subject at an initial time point; and b) extracting RNA from the biological sample in no more than any of 50 days, 30 days, 14 days, 10 days, 9 days, 8 days, 7 days, 6 days, 5 days, 4 days, 3 days, 48 hours, 24 hours, 12 hours, six hours or three hours after the initial time point.

[0108] 2. The sample processing method of embodiment 1, comprising extracting the RNA no more than seven days after the initial time point.

[0109] 3. The sample processing method of embodiment 1, comprising extracting the RNA no more than 48 hours after the initial time point.

[0110] 4. The sample processing method of embodiment 1, comprising extracting the RNA no more than 24 hours after the initial time point.

[0111] 5. The sample processing method of any of embodiments 1-4, further comprising, before extracting, freezing the biological sample.

[0112] 6. The sample processing method of embodiment 5, wherein the freezing of the biological sample obtained from the subject is at a temperature less than or equal to -20° C or less than or equal to -70° C.Docket No. DOT-708-PCT

[0113] 7. The sample processing method of embodiment 5, wherein the freezing occurs continuously until the extracting of the RNA.

[0114] 8. The sample processing method of embodiment 5, comprising thawing the frozen biological sample within any of 40 days, 30 days 20 days, 10 days or 7 days of the freezing of the biological sample, extracting the RNA.

[0115] 9. The sample processing method of embodiment 5, wherein the thawing of the frozen extracted RNA occurs at least 24 hours or at least 12 hours from the initial time of collection.

[0116] 10. The sample processing method of any of embodiments 1-9, further comprising, after extracting, freezing the extracted RNA.

[0117] 11. The sample processing method of embodiment 1, wherein the biological sample is a bodily fluid sample, e.g., blood or saliva.

[0118] 12. The sample processing method of embodiment 1, wherein the biological sample comprises plasma or serum.

[0119] 13. The sample processing method of embodiment 1, wherein the subject is a human.

[0120] 14. The sample processing method of embodiment 1, wherein the subject has signs or symptoms of a pathological condition.

[0121] 15. The sample processing method of embodiment 1, wherein the subject has signs or symptoms of endometriosis.

[0122] 16. The sample processing method of embodiment 1, wherein extracting comprises substantially purifying or essentially purifying the RNA.

[0123] 17. The sample processing method of embodiment 1, wherein the extracting the RNA comprises extracting total RNA.

[0124] 18. The sample processing method of embodiment 1, wherein the extracting the RNA comprises selectively extracting miRNA.

[0125] 19. The sample processing method of embodiment 1, wherein the extracted RNA comprises at least one miRNA selected from the group consisting of: let-7e-5p, miR-141-3p, miR-155-5p, miR-199a-5p, miR-22a-3p, miR-3615, miR-628-3p, and miR-99b-3p.Docket No. DOT-708-PCT

[0126] 20. The sample processing method of embodiment 1, wherein the extracted RNA comprises at least one miRNA selected from the group consisting of: let-7b-5p, miR-125b-5p, miR342-3p, miR3631-5p, miR-150, miR451a, miR-141-3p, miR-98-5p, and miR-339-3p.

[0127] 21. The sample processing method of embodiment 1, wherein the extracted miRNA comprises at least one miRNA selected from the group consisting of: miR-30b-5p, miR-484, let- 7b-5p, miR-125b-5p, miR-150-5p, miR-342-3p, miR-3613-5p, miR-451a, let-7a-5p, let-7d-5p, let-7e-5p, let-7f-5p, miR-130a-3p, miR-141-3p, miR-142-3p, miR-145-5p, miR-155-5p, miR- 15b-5p, miR-182-5p, miR-185-5p, miR-186-5p, miR-18a-5p, miR-199a-3p, miR-199a-5p, miR- 19b-5p, miR-20a-5p, miR-22-3p, miR-23a-3p, miR-24-3p, miR-25-3p, miR-26a-5p, miR-29b- 3p, miR-324-5p, miR-339-3p, miR-3615, miR-3613-5p, miR-501-3p, miR-628-3p, miR-98-5p, miR-99b-5p, and miR-122-5p.

[0128] 22. The sample processing method of embodiment 1, wherein the extracted RNA does not comprise at least one miRNA selected from the group consisting of: let-7b-5p, miR- 125b-5p, miR342-3p, miR3613-5p, miR-150-5p and miR451a.

[0129] 23. The sample processing method of embodiment 1, wherein the extracted RNA comprises miR-342-3p.

[0130] 24. The sample processing method of embodiment 1, wherein the miRNA has decayed by less than 80% since the initial time point, by less than 70% since the initial time point, by less than 60% since the initial time point, or by less than 50% since the initial time point.

[0131] 25. The sample processing method of embodiment 1, wherein the miRNA has decayed by less than 30% since the initial time point.

[0132] 26. The sample processing method of embodiment 1, wherein the miRNA has decayed by less than 20% since the initial time point.

[0133] 27. The sample processing method of embodiment 1, wherein the miRNA has decayed by less than 10%, less than 5%, less than 2%, or less than 1% since the initial time point.

[0134] 28. The sample processing method of embodiment 1, further comprising amplifying or sequencing the extracted RNA, e.g., the miRNA.

[0135] 29. The method of embodiment 28, wherein the amplifying the miRNA from the RNA comprises performing quantitative PCR on the miRNA.Docket No. DOT-708-PCT

[0136] 30. The method of embodiment 28, wherein the amplifying the miRNA from the RNA comprises performing RNAseq on the miRNA.

[0137] 31. The sample processing method of any of embodiments 1-30, further comprising: c) measuring one or more specific RNA forms in the extracted RNA.

[0138] 32. The sample processing method of embodiment 31, wherein the measuring comprises obtaining raw values of the specific miRNA.

[0139] 33. The sample processing method of embodiment 32, wherein the measuring comprises normalizing the raw values of the specific miRNA using miRNA normalizers.

[0140] 34. The sample processing method of embodiment 33, wherein the miRNA normalizers comprise miR-30b-5p or miR-484.

[0141] 35. The sample processing method of embodiment 31, wherein the specific miRNA comprise at least two different miRNA.

[0142] 36. The sample processing method of embodiment 32, wherein the method further comprises obtaining RNA decay data about the at least two different miRNA prior to thawing the frozen extracted RNA.

[0143] 37. The sample processing method of embodiment 36, wherein a yield of the extracted miRNA is greater than any of 50%, 60%, 70%, 80%, 90%, 95%, 98% or 99% of a yield of extracted miRNA when miRNA is extracted directly prior to freezing the biological sample.

[0144] 38. The sample processing method of embodiment 31, comprising measuring at least five different miRNA forms.

[0145] 39. The method of embodiment 38, wherein the at least five different miRNA have rates of decay of at most 0.001, 0.002, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 2, 3, 4, or 5 Ct / day.

[0146] 40. The method of embodiment 38, wherein the at least five different miRNA have rates of decay of at most 0.25 Ct / day.

[0147] 41. A method of generating a classifier from RNA data comprising: a) obtaining a biological sample from each of a plurality of subjects, wherein a plurality of the subjects are assigned to each of a plurality of different categorical states for a categorical variable; b) extracting RNA from the biological samples, wherein the RNA is extracted from a biological sample no more than any of 50 days, 30 days, 14 days, 10 days, 9 days, 8 days,Docket No. DOT-708-PCT 7 days, 6 days, 5 days, 4 days, 3 days, 48 hours, 24 hours, 12 hours, six hours or three hours after a time of collection of the biological sample; c) measuring one or more specific RNA forms in the extracted RNA from the samples to create a training dataset; and d) using the training dataset, developing a classifier to classify a subject into a categorical state.

[0148] 42. The method of any of embodiments 41-42, wherein the classifier is developed by training a machine learning algorithm on the training dataset.

[0149] 43. The method of embodiment 42, wherein the machine learning algorithm comprises an artificial neural network (e.g., back propagation networks), a decision tree (e.g., recursive partitioning processes, CART), a random forest, discriminant analysis (e.g., Bayesian classifier or Fischer analysis), a linear classifier (e.g., multiple linear regression (MLR), partial least squares (PLS) regression, principal components regression (PCR)), a mixed or random- effects model, a non-parametric classifier (e.g., k-nearest neighbors), a support vector machine, or an ensemble method (e.g., bagging, boosting).

[0150] 44. The method of any of embodiments 41-43, wherein the categorical variable is a pathological condition and the categorical state is a status of the pathological condition.

[0151] 45. The method of embodiment 44, wherein the pathologic condition is endometriosis.

[0152] 46. The method of embodiment 45, wherein the categorical states are selected from a nominal (e.g., binary (e.g., present or absent / positive or negative) or class (e.g., Stage 1, Stage II, Stage III, Stage IV), an ordinal (e.g., a hierarchy of statuses) and a ratio (e.g., a probability of positive diagnosis or a risk of developing a condition).

[0153] 47. The method of any of embodiments 41-46, wherein the number of subjects assigned to each different categorical state is at least any of 5, 10, 25, 50, 100, or 250.

[0154] 48. The method of any of embodiments 41-47, wherein the plurality of biological samples is at least any of 10, 20, 50, 100, 200 or 500.

[0155] 49. The method of any of embodiments 41-48, wherein the one or more specific RNA forms comprise at least any of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 50, 100 or 200 RNA (e.g., miRNA) species.Docket No. DOT-708-PCT

[0156] 50. The method of any of embodiments 41-49, wherein the RNA is extracted no more than seven days, no more than 48 hours, no more than 24 hours, or no more than 12 hours after the time of collection.

[0157] 51. The method of any of embodiments 41-50, wherein the RNA comprises miRNA.

[0158] 52. The method of any of embodiments 41-51, wherein the training dataset is sufficiently large to require a machine learning algorithm to develop the classifier.

[0159] 53. The method of any of embodiments 41-52, wherein the classifier uses at least any of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, and 20 different specific RNA forms.

[0160] 54. The method of embodiment 53, wherein the different specific forms include 1, 2, 3, 4, 5 or 6 of let-7b, miR-125b-5p, miR342-3p, miR3613-5p, miR-150-5p and miR451a.

[0161] 55. A method of classifying a subject comprising: a) obtaining a biological sample collected from a subject at an initial time point; b) extracting RNA from the biological sample in no more than any of 50 days, 30 days, 14 days, 10 days, 9 days, 8 days, 7 days, 6 days, 5 days, 4 days, 3 days, 48 hours, 24 hours, 12 hours, six hours or three hours after the initial time point; c) measuring one or more specific RNA forms in the extracted RNA from the sample to create a test dataset; and d) classifying the subject into a categorical state of a categorical variable using a classifier developed using a training dataset comprising measures of one or more specific RNA forms measured from a plurality of training biological samples, wherein the specific RNA forms from the training biological samples are extracted from training biological samples no more than any of 50 days, 30 days, 10 days, 9 days, 8 days, 7 days, 6 days, 5 days, 4 days, 3 days, 48 hours, 24 hours 12 hours, six hours or three hours after a time of collection of the training biological sample.

[0162] 56. The method of embodiment 55, wherein the categorical variable is a pathological condition and the categorical state is a status of the pathological condition.

[0163] 57. The method of embodiment 56, wherein the categorical variable is endometriosis, and the categorical state is endometriosis status.Docket No. DOT-708-PCT

[0164] 58. The method of embodiment 57, wherein the categorical state is selected from presence / absence (positive / negative), stage, risk level, or likelihood of endometriosis for the subject a positive or negative diagnosis for endometriosis.

[0165] 59. The method of embodiment 58, wherein the classifier classifies a sample as positive or likely endometriosis, and the method further comprises: e) treating the subject for endometriosis.

[0166] 60. The method of embodiment 59, wherein treating comprises at least one treatment selected from the group consisting of 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, and aspirin.

[0167] 61. The method of any of embodiments 55-60, wherein the classifier is a classifier developed by the method of embodiment 41.

[0168] 62. A collection of at least 5, at least 10, at least 20, at least 50 at least 100, or at least 200 extracted RNA samples, wherein each RNA in the sample is degraded no more than 0.001, 0.002, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 2, 3, 4, or 5 ^Ct compared with a sample in which RNA is extracted no more than 1 hour after collection.

[0169] 63. A collection of at least 20 substantially pure RNA samples, wherein the RNA samples are extracted from biological samples, and extraction is performed no more than any of 50 days, 30 days, 10 days, 9 days, 8 days, 7 days, 6 days, 5 days, 4 days, 3 days, 48 hours, 24 hours 12 hours, six hours or three hours after a time of collection of the biological sample.

[0170] 64. The collection of any of embodiments 61 or 62, comprising at least any of 50, 100, 200 or 500 samples.

[0171] 65. The collection of any of embodiments 61-63, wherein the samples are collected from subjects assigned to a categorical state for a categorical variable, and wherein a plurality of the subjects are assigned to each of a plurality of different categorical states.

[0172] 66. The collection of any of embodiments 61-64, wherein the samples are aliquoted into different containers.Docket No. DOT-708-PCT

[0173] 67. The collection of any of embodiments 61-65, wherein the samples are collected over a period of seven days, 14 days, 30 days, 60 days, 120 days, 365 days or 730 days.

[0174] 68. A method of processing a plurality of samples comprising: a) obtaining a plurality of biological samples from a plurality of subjects; b) extracting RNA from the biological samples no more than any of 50 days, 30 days, 14 days, 10 days, 9 days, 8 days, 7 days, 6 days, 5 days, 4 days, 3 days, 48 hours, 24 hours, 12 hours, six hours or three hours after a time of collection of the biological sample from the subject; and c) measuring one or more specific RNA forms in the extracted RNA from the samples.

[0175] 69. A sample processing method comprising: a. providing a biological sample obtained from a subject; b. at an initial time point, freezing the biological sample obtained from the subject to obtain a frozen biological sample; c. thawing the frozen biological sample within 50 days of the initial time point to obtain a thawed sample; and d. extracting RNA from the thawed sample to obtain extracted RNA, wherein the extracted RNA comprises miRNA that has decayed by less than 99.9% since the initial time point.

[0176] 70. A method of processing a plurality of samples comprising: a. providing biological samples obtained from at least 50 different subjects; b. freezing the biological samples obtained from the at least five subjects to obtain frozen biological samples; c. thawing the frozen biological samples within a set time period of the freezing of the biological sample to obtain thawed samples; and d. extracting RNA from the thawed samples to obtain extracted RNA, wherein the extracted RNA comprises at least 5 different miRNA with rates of decay of at most 5 Ct / day over the set time period.

[0177] 71. A sample processing method comprising: a. providing a biological sample obtained from a subject; b. freezing the biological sample obtained from the subject to obtain a frozen biological sample;Docket No. DOT-708-PCT c. thawing the frozen biological sample within 1-30 days of the freezing of the biological sample to obtain a thawed sample; d. extracting RNA from the thawed sample; and e. amplifying miRNA from the RNA.

[0178] 72. A sample processing method comprising: a) obtaining a biological sample collected from a subject at an initial time point; and b) extracting RNA from the biological sample in no more than any of 50 days, 30 days, 14 days, 10 days, 9 days, 8 days, 7 days, 6 days, 5 days, 4 days, 3 days, 48 hours, 24 hours, 12 hours, six hours or three hours after the initial time point.

[0179] 73. A sample processing method comprising: a) obtaining a biological sample collected from a subject at an initial time point; and b) extracting RNA from the biological sample in no more than any of 50 days, 30 days, 14 days, 10 days, 9 days, 8 days, 7 days, 6 days, 5 days, 4 days, 3 days, 48 hours, 24 hours, 12 hours, six hours or three hours after the initial time point. EXAMPLES I. Example 1. –Differential miRNA Stability in Blood Fractions from EMPOWER Study

[0180] The EMPOWER study measured miRNA expression of individual miRNAs in both serum and plasma in 250 subjects.

[0181] Blood was drawn from subjects by venipuncture. Two red top serum tubes (No-clot Activator BD PN366430) and 2 plasma Streck RNA BCT tubes were collected from each subject, 10mL of blood per tube. For serum samples, samples were processed in the following way: (1) After collection, blood was allowed to clot in an upright position at room temperature for 30 to 90 minutes; (2) Tubes were centrifuged in a horizontal rotor (swing-out head) for 20 minutes at 1100- 1300 x g at room temperature; (3) Immediately after completion of centrifugation, a pipet was used to remove all serum from each tube and combined into one 15 mL centrifuge tube and the tube was inverted to mix, avoiding the red cells; and (4) Serum was aliquoted into cryovials.

[0182] For plasma samples, samples were processed in the following way:Docket No. DOT-708-PCT (1) Blood was allowed to stand for 60 to 90 minutes; (2) Whole blood collected in Streck tubes was centrifuged at 300 x g for 20 minutes at room temperature, and for each subject isolated plasma from the two Streck tubes after initial centrifugation was combined into one 15 mL tube and the plasma was centrifuged at 5000 x g for 10 minutes.; and (3) Plasma was aliquoted into cryovials.

[0183] Samples were stored at -20°C to -80°C. After a variable storage time (which differed by subject and ranged from 36 to 250 days for serum and from 8 to 225 for plasma), miRNAs were extracted from the samples and levels of specific miRNAs (e.g. miRNAs from Table 1A below such as let-7b-5p, miR-125b-5p, miR342-3p, miR3631-5p, miR-150, miR451a, miR-141- 3p, miR-98-5p, and miR-339-3p) were assessed relative to normalizer markers miR-30b-5p and miR-484. II. Table 1A: Levels of miRNAs observed in Example 1

[0184] To extract miRNAs, the MagMAX™ mirVana™Total RNA Isolation Kit (Thermo Fisher Scientific Part Number A27828) was used largely according to the manufacturer protocol. Using the KingFisher ™ Duo Prime System (Thermo Fisher Scientific Part Number 54001100), with only minor modifications (Proteinase K digestion on deck, mixing speed alterations). From extracted miRNAs, cDNA synthesis was performed using the TaqMan™ Advanced miRNADocket No. DOT-708-PCT cDNA Synthesis kit (Thermo Fisher Scientific Part Number A28007), incubating samples at room temperature instead of on ice. miRNAs (as cDNA amplicons of miRNAs) were then quantitated using qPCR on a Quantstudio 7 Flex (Applied Biosystems Part Number 4485688) using 2x TaqMan Fast Advanced Master Mix (Thermo Fisher Scientific Part Number 4444558 / 4444557) and TaqMan Custom Array plates (Thermo Fisher Scientific Part Number 4398985).

[0185] The miRNA decay rates were then calculated for each individual miRNA by examining the miRNA abundance over time.

[0186] Based on data from our EMPOWER” study, a trend towards a miRNA level decrease over time was detectable for all the measurements, especially in plasma (FIGURES 1A through 1C). The miRNA decay rates were generally higher in plasma than in serum (FIGURE 2). We observed that the miRNA decay affects the diagnostic, prognostic, and predictive model development and performance. We also observed that degradation rates varied for different miRNAs and follow a linear relationship with storage time (Table 1), such that the decay rate maybe expressed as a slope. Table 1. miRNA level change slope upon storage during the experiment in Example 1 (expressed in Ct change per day)Docket No. DOT-708-PCT

[0187] Notably, several miRNAs observed minimal decay under storage as serum samples. These were miR-342-3p, miR39-3p, miR-339-3p, miR-18a-5p, miR-98-5p, miR-182-5p. III. Example 2.- Study for short-term miRNA stability analysis

[0188] As the EMPOWER 250 study described in Example 1 examined miRNA stability starting at day 36, a further study was performed to assess miRNA stability (e.g. of miR-30b-5p, miR-484, let-7b-5p, miR-125b-5p, miR-150-5p, miR-342-3p, miR-3613-5p, miR-451a, let-7a- 5p, let-7d-5p, let-7e-5p, let-7f-5p, miR-122-5p, miR-130a-3p, miR-141-3p, miR-142-3p, miR- 145-5p, miR-155-5p, miR-15b-5p, miR-182-5p, miR-185-5p, miR-186-5p, miR-18a-5p, miR- 199a-3p, miR-199a-5p, miR-19b-5p, miR-20a-5p, miR-22-3p, miR-23a-3p, miR-24-3p, miR-25- 3p, miR-26a-5p, miR-29b-3p, miR-30b-5p, miR-324-5p, miR-339-3p, miR-342-3p, miR-3615, miR-3613-5p, miR-501-3p, miR-628-3p, miR-98-5p, miR-99b-5p, or miR-122-5p) in blood fractions at earlier time points, expressed relative to specific normalizer miRNAs such as miR- 30b-5p and miR-484 .

[0189] Blood and serum samples were prepared by the same protocol detailed above in Example 1. miRNA extraction (as detailed above in Example 1) was immediately performed on “Day 0” samples, and the remaining plasma and serum samples were then stored at -80°C for 1, 3, 5, 7, 10, 14, 21, and 28 days post-draw.

[0190] For each plasma or serum sample, cDNA synthesis and qPCR was also performed using the protocol detailed in Example 1 above to assess the level of each miRNA marker in serum and plasma over time. IV. Results

[0191] The miRNA expression variations between day 0 and later time points were observed. Principal Component Analysis (PCA) on the expression data clearly distinguished betweenDocket No. DOT-708-PCT samples processed at day 0 and samples processed at the later time points (Figure 3). The variations could also be found by comparing individual miRNA levels at Day 0 with them at later time points (FIGURE 4A through FIGURE 4AB). This suggested that models for detection of specific diseases such as endometriosis developed with samples processed at day 0 might not be suitable for detection in samples processed at the later time points, and vice versa.

[0192] In general, both plasma and serum miRNAs were observed to be qualitatively stable between day 1 and day 10. PCA analysis showed samples processed at day 1, day 5, and day 10 were clustered together (FIGURE 3) and showed little miRNA variation for each individual (FIGURE 4A through FIGURE 4AB). We observed some variations on day 28. These findings indicate a limit of the sample processing time; namely that processing within 10 days can obtain more accurate and consistent results. In addition, we found that miRNA levels in plasma showed a larger variability compared to serum (FIGURE 4A through FIGURE 4AB).

[0193] We also found that the rate of decay varied between different miRNAs. Some miRNAs showed large inter-subject variation even within 10 days, while others did not. Table 2 lists miRNAs with large inter-subject variabilities (FIGURE 2A through FIGURE 2AB). These miRNAs may not be suitable for use within the identified storage time period (e.g.1-28 days of freezer storage) without computational correction for the RNA decay rate. Table 2. A list of miRNAs with large inter-subject variation over the 1-28 day time course in Example 2Docket No. DOT-708-PCT V. Individual miRNAs (A) miR-30b-5p and miR-484

[0194] miR-30b-5p and miR-484 were detected in all samples and individuals. The Ct values between plates from the same day and individual are overall relatively consistent, indicating good technical reproducibility. (B) let-7b-5p, miR-125b-5p, miR342-3p, miR3631-5p, miR-150 and miR451a

[0195] These six markers exhibited some stability for up to 28 days, expressed as an average Ct variation of all 6 markers over time for each individual (not-normalized) being 2.7 for plasma [min – max: 1 – 4.5] and 2.3 for serum [1 – 3.5) (Fig.5 ). No clear trend across individuals was observed for any of the markers over time. This indicated that the variation observed may be more related to process variability i.e. RNA extraction and miRAmp efficiency, rather than a decay due to miRNA degradation.

[0196] After normalization of the miRNA signal against 30b-5p, we observed a reduction in the variability as expected from a normalizing step. The ^Ct from days 1, 5, 10 or 28 with respect to day 0 is on average less than 2, which is less than a 4-fold change between time points for the same marker and individual. Thus, the normalized results confirm the overall lack of clear decay trend discussed above for the time frame observed (e.g. up to 28 days).

[0197] While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.

Claims

Docket No. DOT-708-PCT WHAT IS CLAIMED IS:

1. A method of generating a classifier from RNA data comprising: a) obtaining a biological sample from each of a plurality of subjects, wherein a plurality of the subjects are assigned to each of a plurality of different categorical states for a categorical variable; b) extracting RNA from the biological samples, wherein the RNA is extracted from a biological sample no more than any of 50 days, 30 days, 14 days, 10 days, 9 days, 8 days, 7 days, 6 days, 5 days, 4 days, 3 days, 48 hours, 24 hours, 12 hours, six hours or three hours after a time of collection of the biological sample; c) measuring one or more specific RNA forms in the extracted RNA from the samples to create a training dataset; and d) using the training dataset, developing a classifier to classify a subject into a categorical state.

2. The method of any of claims 1-1, wherein the classifier is developed by training a machine learning algorithm on the training dataset.

3. The method of claim 2, wherein the machine learning algorithm comprises an artificial neural network (e.g., back propagation networks), a decision tree (e.g., recursive partitioning processes, CART), a random forest, discriminant analysis (e.g., Bayesian classifier or Fischer analysis), a linear classifier (e.g., multiple linear regression (MLR), partial least squares (PLS) regression, principal components regression (PCR)), a mixed or random-effects model, a non-parametric classifier (e.g., k-nearest neighbors), a support vector machine, or an ensemble method (e.g., bagging, boosting).

4. The method of any of claims 1-3, wherein the categorical variable is a pathological condition and the categorical state is a status of the pathological condition.

5. The method of claim 4, wherein the pathologic condition is endometriosis.

6. The method of claim 5, wherein the categorical states are selected from a nominal (e.g., binary (e.g., present or absent / positive or negative) or class (e.g., Stage 1, Stage II, Stage III, Stage IV), an ordinal (e.g., a hierarchy of statuses) and a ratio (e.g., a probability of positive diagnosis or a risk of developing a condition).

7. The method of any of claims 1-6, wherein the number of subjects assigned to each different categorical state is at least any of 5, 10, 25, 50, 100, or 250.

8. The method of any of claims 1-7, wherein the plurality of biological samples is at least any of 10, 20, 50, 100, 200 or 500.Docket No. DOT-708-PCT 9. The method of any of claims 1-8, wherein the one or more specific RNA forms comprise at least any of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 50, 100 or 200 RNA (e.g., miRNA) species.

10. The method of any of claims 1-9, wherein the RNA is extracted no more than seven days, no more than 48 hours, no more than 24 hours, or no more than 12 hours after the time of collection.

11. The method of any of claims 1-10, wherein the RNA comprises miRNA.

12. The method of any of claims 1-11, wherein the training dataset is sufficiently large to require a machine learning algorithm to develop the classifier.

13. The method of any of claims 1-12, wherein the classifier uses at least any of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, and 20 different specific RNA forms.

14. The method of claim 13, wherein the different specific forms include 1, 2, 3, 4, 5 or 6 of let-7b, miR-125b-5p, miR342-3p, miR3613-5p, miR-150-5p and miR451a.

15. A method of classifying a subject comprising: a) obtaining a biological sample collected from a subject at an initial time point; b) extracting RNA from the biological sample in no more than any of 50 days, 30 days, 14 days, 10 days, 9 days, 8 days, 7 days, 6 days, 5 days, 4 days, 3 days, 48 hours, 24 hours, 12 hours, six hours or three hours after the initial time point; c) measuring one or more specific RNA forms in the extracted RNA from the sample to create a test dataset; and d) classifying the subject into a categorical state of a categorical variable using a classifier developed using a training dataset comprising measures of one or more specific RNA forms measured from a plurality of training biological samples, wherein the specific RNA forms from the training biological samples are extracted from training biological samples no more than any of 50 days, 30 days, 10 days, 9 days, 8 days, 7 days, 6 days, 5 days, 4 days, 3 days, 48 hours, 24 hours 12 hours, six hours or three hours after a time of collection of the training biological sample.

16. The method of claim 15, wherein the categorical variable is a pathological condition and the categorical state is a status of the pathological condition.

17. The method of claim 16, wherein the categorical variable is endometriosis, and the categorical state is endometriosis status.Docket No. DOT-708-PCT 18. The method of claim 17, wherein the categorical state is selected from presence / absence (positive / negative), stage, risk level, or likelihood of endometriosis for the subject a positive or negative diagnosis for endometriosis.

19. The method of claim 18, wherein the classifier classifies a sample as positive or likely endometriosis, and the method further comprises: e) treating the subject for endometriosis.

20. The method of claim 19, wherein treating comprises at least one treatment selected from the group consisting of 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, and aspirin.

21. The method of any of claims 15-20, wherein the classifier is a classifier developed by the method of claim 1.

22. A collection of at least 5, at least 10, at least 20, at least 50 at least 100, or at least 200 extracted RNA samples, wherein each RNA in the sample is degraded no more than 0.001, 0.002, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 2, 3, 4, or 5 ^Ct compared with a sample in which RNA is extracted no more than 1 hour after collection.

23. A collection of at least 20 substantially pure RNA samples, wherein the RNA samples are extracted from biological samples, and extraction is performed no more than any of 50 days, 30 days, 10 days, 9 days, 8 days, 7 days, 6 days, 5 days, 4 days, 3 days, 48 hours, 24 hours 12 hours, six hours or three hours after a time of collection of the biological sample.

24. The collection of any of claims 22 or 23, comprising at least any of 50, 100, 200 or 500 samples.

25. The collection of any of claims 22-24, wherein the samples are collected from subjects assigned to a categorical state for a categorical variable, and wherein a plurality of the subjects are assigned to each of a plurality of different categorical states.

26. The collection of any of claims 22-25, wherein the samples are aliquoted into different containers.

27. The collection of any of claims 22-26, wherein the samples are collected over a period of seven days, 14 days, 30 days, 60 days, 120 days, 365 days or 730 days.

28. A sample processing method comprising:Docket No. DOT-708-PCT a) obtaining a biological sample collected from a subject at an initial time point; and b) extracting RNA from the biological sample in no more than any of 50 days, 30 days, 14 days, 10 days, 9 days, 8 days, 7 days, 6 days, 5 days, 4 days, 3 days, 48 hours, 24 hours, 12 hours, six hours or three hours after the initial time point.

29. A sample processing method comprising: providing a biological sample obtained from a subject; at an initial time point, freezing the biological sample obtained from the subject to obtain a frozen biological sample; thawing the frozen biological sample within 50 days of the initial time point to obtain a thawed sample; and extracting RNA from the thawed sample to obtain extracted RNA, wherein the extracted RNA comprises miRNA that has decayed by less than 99.9% since the initial time point.

30. A method of processing a plurality of samples comprising: providing biological samples obtained from at least 50 different subjects; freezing the biological samples obtained from the at least five subjects to obtain frozen biological samples; thawing the frozen biological samples within a set time period of the freezing of the biological sample to obtain thawed samples; and extracting RNA from the thawed samples to obtain extracted RNA, wherein the extracted RNA comprises at least 5 different miRNA with rates of decay of at most 5 Ct / day over the set time period.

31. A sample processing method comprising: providing a biological sample obtained from a subject; freezing the biological sample obtained from the subject to obtain a frozen biological sample; thawing the frozen biological sample within 1-30 days of the freezing of the biological sample to obtain a thawed sample; extracting RNA from the thawed sample; and amplifying miRNA from the RNA.

32. A method of processing a plurality of samples comprising: a) obtaining a plurality of biological samples from a plurality of subjects;Docket No. DOT-708-PCT b) extracting RNA from the biological samples no more than any of 50 days, 30 days, 14 days, 10 days, 9 days, 8 days, 7 days, 6 days, 5 days, 4 days, 3 days, 48 hours, 24 hours, 12 hours, six hours or three hours after a time of collection of the biological sample from the subject; and c) measuring one or more specific RNA forms in the extracted RNA from the samples.

33. A sample processing method comprising: a) obtaining a biological sample collected from a subject at an initial time point; and b) extracting RNA from the biological sample in no more than any of 50 days, 30 days, 14 days, 10 days, 9 days, 8 days, 7 days, 6 days, 5 days, 4 days, 3 days, 48 hours, 24 hours, 12 hours, six hours or three hours after the initial time point.

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