RNA biomarkers for use in barrett's esophagus

Abstract

Methods of detecting microRNA biomarkers in a patient having Barrett's esophagus and methods of diagnosing, monitoring, or treating Barrett's esophagus in a patient are conducted by detecting elevated expression levels of microRNA biomarkers in a biological sample from the patient. Exemplary microRNA biomarkers include miR-106b, miR-146a, miR-15a, miR-18a, miR-21, and miR-93.

Description

RNA BIOMARKERS FOR USE IN BARRETT’S ESOPHAGUSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of priority to US Application No. 63 / 711,824 filed October 25, 2024, the disclosure of which is incorporated by reference herein in its entirety.BACKGROUND

[0002] Esophageal adenocarcinoma (EAC), the second most lethal gastrointestinal malignancy after pancreatic cancer, is linked to chronic exposure to gastroesophageal reflux disease (GERD) and the development of Barrett's esophagus (BE) [1-3], The progression from BE to low-grade esophageal dysplasia (LGD), high-grade esophageal dysplasia (HGD), and ultimately EAC unfolds slowly over approximately 20 years (1-3% risk per year) [4-6], This extended timeframe theoretically offers ample opportunities for cancer prevention. However, fewer than 20% of patients with BE receive a diagnosis before they are diagnosed with EAC [7-9], Therefore, most EAC cases are diagnosed de novo, bypassing the window for preventive interventions [10,11], Moreover, the lethality’ of EAC is further explained by its rapid progression from a localized stage to regional and distant metastases because the esophageal anatomy, devoid of a serosa but rich in a dense lymphatic network, offers minimal resistance against the rapid and early spread of cancer

[0012] ,

[0003] EAC represents a public health threat: its incidence rose dramatically since the 1980s and, despite advances in therapy, the overall 5-year survival has remained below 20% [13-15], These observations, coupled with evidence for the cost-effectiveness of endoscopic screening [16-20], have led to recommendations for endoscopy for patients with persistent GERD or risk factors for BE and EAC [21-25], While early detection of EAC alone would reduce mortality but not incidence, early detection of BE. followed by BE surveillance and treatment for LGD / HGD, can halt the progression to EAC with low rates of recurrence [26-28], However, the current reliance on endoscopy has limitations that include its invasive nature, costs, and potential discomfort, contributing to poor patient adherence to screening programs

[0029] , Given the prevalence of GERD and the growing concern over EAC. new clinical strategies complementing current guidelines could be highly beneficial. A minimally invasive approach, such as a liquid biopsy targeting both pre-cancerous lesions and early-stage EAC, may improve patient compliance. This study aimed to address this need by developing a diagnostic model of EAC and its precursor lesions, leveraging state-of-the-art machine learning (ML) driven by biological and clinical data.

[0004] ML involves identifying patterns within data and fitting models to the endpoint of interest

[0030] , Ensemble classifiers combine multiple weak learners (z.e., the boosting procedure) to achieve higher accuracy

[0030] , To enhance the accuracy of these models, stacking involves creating a “meta-model” on the predictions of the base-line models

[0030] , Importantly, models like XGBoost and AdaBoost prioritize accuracy alongside interpretability, enabling analysis of biomarker importance with techniques like SHAP values analysis

[0030] ,

[0005] MicroRNAs (miRNAs), non-coding single-stranded RNAs regulating gene expression and various cellular processes, have been involved in EAC pathogenesis

[0031] . Due to their stability in bodily fluids and disease specificity, circulating miRNAs are potentially promising candidates for developing non-invasive liquid biopsies

[0032] , Previous reports described potential circulating miRNAs for the diagnosis of EAC, but these studies lacked a systematic and comprehensive biomarker discovery approach, or have not validated these biomarkers in multiple independent patient cohorts with adequate statistical power [33,34], In addition, most of these studies have focused essentially on a single or handful of biomarkers, which has resulted in limited sensitivities and specificities [33,34], Thus, there is an urgent need in the art to identify' and develop diagnostic methods and assays capable of accurately detecting Barrett’s esophagus and esophageal adenocarcinoma. The disclosure is directed to this, as well as other, important ends.BRIEF SUMMARY

[0006] Provided herein is a method of detecting microRNA in a human patient having Barrett’s esophagus or suspected of having Barrett’s esophagus comprising detecting an elevated expression level, relative to a control, of microRNA in a biological sample obtained from the human patient, wherein the microRNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93. or a combination of two or more thereof.

[0007] Provided herein is a method of treating Barrett’s esophagus in a human patient in need thereof comprising: (i) detecting an elevated expression level, relative to a control, of microRNA in a biological sample obtained from a human patient, wherein the microRNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of two or more thereof; and (ii) administering to the human patient an effective amount of a therapeutic agent.

[0008] Provided herein is a method of diagnosing a human patient with Barrett’s esophagus, the method comprising detecting an elevated expression level, relative to a control, of microRNA in a biological sample obtained from the human patient, thereby diagnosing thehuman patient with Barrett’s esophagus; wherein the microRNA comprises miR-106b, miR-146a. miR-15a, miR-18a, miR-21. miR-93, or a combination of two or more thereof.

[0009] Provided herein is a method of monitoring a human patient at risk for developing Barrett’s esophagus, the method comprising: (i) detecting an expression level of microRNA in a biological sample obtained from the human patient at a first point in time; (ii) detecting an expression level of microRNA in a biological sample obtained from the human patient at a second point in time, wherein the second point in time is later than the first point in time; wherein the microRNA comprises miR-106b, miR-146a, miR-15a, miR-18a. miR-21, miR-93, or a combination of two or more thereof.

[0010] These and other embodiments of the disclosure are described herein.BRIEF DESCRIPTION OF THE FIGURES

[0011] FIGS. 1A-1C: In silico discovery and prioritization of candidate miRNAs. FIG. 1A:Volcano plot of differentially expressed microRNAs between cases and controls using TCGA miRNA expression dataset. Contrast grading follows significance. 22 miRNAs were differentially expressed and 14 miRNAs were prioritized as initial candidates for further analysis. FIG. IB: Ridgeline plot of the initial pool of 14 microRNAs. FIG. 1C: A heatmap with unsupervised clustering illustrates the expression levels of the 14 candidate miRNAs in the TCGA miRNA expression dataset.

[0012] FIG. 2: Assessment of the 6 miRNAs diagnostic potential in the US (development) cohort. Six circulating miRNAs were significantly upregulated in cases (EAC, HGD, TGD, and BE) compared to NDCs and demonstrated AUROC values of diagnostic interest, ranging from 62.1% to 80.8%. In sub-group analysis (inserts), all candidate miRNAs demonstrated a significant differential expression between NDCs vs. EAC / HGD. five for NDCs vs. BE / LGD, and two for BE / LGD vs. EAC / HGD (* p<0.05, ** p<0.01, *** p<0.001; two-sided Student’s t-tests). Abbreviations: AUROC, area under the receiver operating characteristic curve; Spe, Specificity; Sen, sensitivity'

[0013] FIGS. 3A-3D: Establishment and external testing of the 6-circulating-miRNA signature. FIG. 3A: AUROC and performance metrics of the two first-level classifiers (XGB: XGBoost; ADA. AdaBoost) and the resulting stacked model (EMERALD) in the Italian (training) cohort. FIG. 3B: AUROC and performance metrics of the two first-level classifiers (XGB: XGBoost; ADA, AdaBoost) and the resulting stacked model (EMERALD) in the UK / Irish (testing) cohort. FIGS.3C-3D: Density-scatter plots: the background gradient is adensity plot and highlights the areas where most observations are encountered, without knowledge of them being cases and controls. The dots represent the scatter plot. The same patterns can be seen in both the Italian (training, FIG. 3C) and UK / Irish (testing, FIG.3D) cohorts, with two clusters where cases and controls appear to separate (top right and bottom left, respectively).

[0014] FIGS. 4A-4D: Evaluation of the diagnostic performance of the 6-circulating-miRNA signature. FIGS.4A-4B: Raincloud plots with super-imposed box and whisker plots demonstrating the distribution of EMERALD values between cases and controls in the Italian (training, FIG. 4A) and UK / Irish (testing, panel FIG. 4B) cohorts. FIGS. 4C-4D: Odds ratios for the presence of a compound endpoint of EAC, HGD, LGD, or BE with restricted cubic splines in the Italian (training, FIG. 4C) and UK / Irish (testing, FIG. 4D) cohorts.Abbreviations: EC, Esophageal cases; NDC, Non-disease controls

[0015] FIGS. 5A-5D: Evaluation of the EMERALD assay among symptomatic controls with GERD. FIG. 5A: Waterfall plot of the EMERALD values in the UK / Irish (testing) cohort, with controls sub-labeled by the presence of symptoms. FIG. 5B: Violin plot of EMERALD values according to the presence of GERD-related symptoms, with and without reflux esophagitis vs. cases with BE or LGE and cases with EAC or HGD (**** pO.OOOl; two-sided Student's t-tests). FIGS. 5C-5D: AUROCs of the two first-level classifiers (XGB: thin line; ADA, dashed line) and the stacked model (thick line with 95% confidence intervals) in a sub-analysis of the UK / Irish (testing) cohort which only included symptomatic controls vs. Barrett’s esophagus cases, with or without low-grade esophageal dysplasia (FIG. 5C) and cases with either highgrade esophageal dysplasia or adenocarcinoma (FIG.5D). Abbreviations: BE, Barrett's esophagus; GERD. Gastroesophageal reflux disease: RE, Reflux esophagitis; LGD, Low-grade Dysplasia; HGD, High-grade esophageal dysplasia; EAC, Esophageal Adenocarcinoma;AUROC, area under the receiver operating characteristic curve.

[0016] FIGS. 6A-6I: Markov simulation. FIG. 6A: Markov model structure: for each strategy, the Markov chain assumes a progression through pre-malignant disease, malignant disease, cancer recovery, recurrence, and eventually death. Mortality can be from any cause (cardiovascular, other cancers, etc.) or stage-specific mortality rates. Early detection of Barrett’s esophagus with esophageal dysplasia is assumed to trigger its eradication. EAC diagnosis, whether due to symptoms, endoscopic screening uptake, or non-invasive screening uptake, is assumed to trigger treatment. Costs and utility values are derived from the literature or, if unavailable, from internal estimates. The starting condition of each individual in the Markovchain is a 45-year-old with chronic GERD who is followed up for 30 years or until death. Five screening strategies are tested: non-invasive, EMERALD-based screening at 45% compliance every 5, 3, or 1 year(s), endoscopy-based screening at 10% compliance every 10-15 years, or no screening. FIG. 6B: Number of stage I EAC diagnoses per screening strategy. FIG. 6C:Number of stage II EAC diagnoses per screening strategy. FIG. 6D: Number of stage III EAC diagnoses per screening strategy. FIG. 6E: Number of stage IV EAC diagnoses per screening strategy. FIG. 6F: Number of deaths from all causes (both EAC and non-EAC related) per screening strategy. FIG. 6G: Annual costs associated with each screening strategy. FIG. 6H: Quality -adjusted life years per year by screening strategy, with a call-out box to demonstrate the effects of different screening strategies. FIG. 61: Incremental costs and effectiveness of each screening strategy against the most cost-effective screening strategy and interval. Abbreviations: EAC, Esophageal adenocarcinoma; USD, U.S. Dollars

[0017] FIG. 7. Consort diagram for study cohorts allocation.

[0018] FIG.8. Study design. The overall study design was divided into two parts. A first discovery' phase, based on tissue, which was then transitioned to a blood-based part for the development and independent validation of a liquid biopsy assay. The TCGA dataset provided expression-level data from 2,082 miRNAs. 14 of these miRNAs were significantly upregulated in cases with EAC compared to controls and had a discriminatory AUROC >70%. To confirm that these were differentially expressed not only in EAC but also in its precursor lesions, the expression level of these miRNAs was assessed in the GSE 16456 data set to confirm their upregulation in EAC, BE-HGD, and BE-LGD vs. matched normal healthy mucosa. 10 miRNAs passed these preliminary quality measures and were given full consideration as EAC-specific biomarkers. To assess whether these tissue-based results could be replicated with a different quantification method (RT-qPCR vs. sequencing vs. microarray), their expression level was internally assessed in 84 tissue samples from EAC tissue vs. normal matched mucosa, and we could confirm differential expression for nine. We then tested whether these candidates could be detected in blood. We assessed the delta CT values of these miRNAs against a normalizer miRNA (U6) in a cohort comprising 108 blood specimens and confirmed their detectability and differential expression for 6 miRNAs, which, ultimately, constituted the foundation of the diagnostic assay. The final diagnostic model (termed “EMERALD"’) is constituted of a two-level stacked model. In the first level, two independent ML models (XGBoost and AdaBoost) are allowed to learn independently from the miRNA expression levels. In the second level, a logistic regression function is fit on these tw o risk scores. At the end of this process, the diagnosticmodel is fully locked and independently applied to a second independent cohort of 297 individuals. Abbreviations: BE, Barrett’s Esophagus; EAC. Esophageal Adenocarcinoma; HGD, Barrett’s Esophagus with High-Grade Dysplasia: LGD, Barrett’s Esophagus with Low-Grade Dysplasia; RT-qPCR, Reverse Transcription quantitative polymerase chain reaction; NDC, Nondisease controls.

[0019] FIG.9. Trajectory testing for the miRNA candidates in GSE16456 ten of the 14 miRNAs identified by differential gene expression in the TCGA dataset, which only includes EAC vs. controls, were also differentially expressed between the normal mucosa vs. LGD. HGD, and EAC (all patient-matched). These 10 miRNAs were carried over to the next phase of the study.

[0020] FIG. 10. Tissue validation for miRNA candidates in a clinical cohort of esophageal cancer patients with matching adjacent normal mucosae. 9 of the total 10 miRNA candidates discovered during the in silico analysis were significantly upregulated in EAC tissue samples (n = 42) compared to patient-matched adjacent normal tissues (n = 42), when measured by RT-qPCR (* p<0.05, ** p<0.01, *** p<0.001, ns, not significant; two-sided paired Student’s t-tests).

[0021] FIG. 11. Expression level of the 6 circulating miRNAs in the Italian (training) cohort. In the Italian cohort (N=160, 96 vs. 64), 20 had stage 0 disease (pT;s). The 6 miRNAs (hsa-miR-106b-5p, hsa-miR-146a-5p, hsa-miR-15a-5p, hsa-miR-18a-5p, hsa-miR-21-5p, hsa-miR-93-5p) were all significantly upregulated in EAC / HGD compared with healthy controls in the training cohort (**** p<0.0001; two-sided Student’s t-tests).

[0022] FIGS. 12A-12E: Final model architecture and contribution of each miRNA to the final model structure. FIG. 12A: EMERALD relies on a stacked machine-learning approach. Two different boosting-based models, XGBoost and AdaBoost, are both allowed to learn patterns from the Delta-CT values of the 6 miRNA candidates. At the end of their training, a logistic regression function is passed on to their results, constituting the final ‘EMERALD’ model. This model is entirely developed, trained, and fine-tuned in the Italian cohort, fully locked, and then passed to the UK / Irish cohort for final testing. FIG. 12B: Beeswarm plot of SHAP values of each microRNA for XGBoost: values further from 0 contributed more towards the accuracy of the prediction; absolute miRNA measurements are represented from lowest to highest. FIG. 12C: Gain is the relative contribution of each miRNA to the model, calculated by taking each miRNA’ s contribution for each tree. miRNAs with higher gain metrics are more important and contribute more to the overall accuracy of the model. FIG. 12D: Beeswarm plot of SHAP values of each microRNA for AdaBoost: values further from 0 contributed more towards the accuracyof the prediction: absolute miRNA measurements are represented from lowest to highest. FIG.12E: Importance of each miRNA to AdaBoost. miRNAs with a higher importance contribute more to the overall accuracy of the AdaModel. Abbreviations: XGB, extreme Gradient Boosting; ADA, Adaptive Boosting; SHAP, SHapley Additive exPlanations

[0023] FIGS. 13A-13F: Symptom-specific evaluation of the EMERALD blood-based test in the external and independent testing cohort. FIG. 13A: Violin plot of EMERALD values according to the presence of epigastralgia-related symptoms, with and without reflux esophagitis or hiatal hernia vs. cases with BE or LGE and cases with EAC or HGD (**** p<0.0001; two-sided Student’s t-tests). FIGS. 13B-13C: AUROCs of the two first-level classifiers (XGB: thin line; ADA, dashed line) and the stacked model (thick line with 95% confidence intervals) in a sub-analysis of the UK / Irish (testing) cohort which only included symptomatic controls vs. Barrett’s esophagus cases, with or without low-grade dysplasia (FIG. 13B) and cases with either high-grade dysplasia or adenocarcinoma (FIG. 13C). FIG. 13D: Violin plot of EMERALD values according to the presence of dysphagia, regurgitation, or odynophagia symptoms, with and without reflux esophagitis or hiatal hernia vs. cases with BE or LGE and cases with EAC or HGD (**** pO.OOOl; two-sided Student's t-tests). FIGS. 13E-13F: AUROCs of the two first-level classifiers (XGB: thin line; ADA, dashed line) and the stacked model (thick line with 95% confidence intervals) in a sub-analysis of the UK / Irish (testing) cohort which only included symptomatic controls vs. Barrett’s esophagus cases, with or without low-grade dysplasia (FIG.13E) and cases with either high-grade dysplasia or adenocarcinoma (FIG. 13F).

[0024] FIGS. 14A-14B: Multivariate logistic regression to predict whether EMERALD-positive individuals have low-risk positive results (BE / LGD) or high-risk positive results (EAC / HGD) (results from the independent and external validation cohort). FIG. 14A: AUROC of the ability to discriminate between EAC / HGD vs. BE / LGD among the EMERALD testpositive individuals. FIG. 14B: Confusion matrix of the EMERALD test results in the external and independent testing cohort.DETAILED DESCRIPTION

[0025] Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by a person of ordinary skill in the art. See, e.g., Singleton et al., Dictionary of Microbiology and Molecular Biology. 2nd ed., J. Wiley & Sons (New York, NY 1994); Sambrook et al.. Molecular Cloning, A Laboratory Manual, Cold Springs Harbor Press (Cold Springs Harbor, NY 1989). Any methods, devices and materials similar or equivalent to those described herein can be used in the practice of this disclosure. The followingdefinitions are provided to facilitate understanding of certain terms used frequently herein and are not meant to limit the scope of the present disclosure.

[0026] “Nucleic acid” refers to nucleotides (e.g., deoxyribonucleotides or ribonucleotides) and polymers thereof in either single-, double- or multiple-stranded form, or complements thereof; or nucleosides (e.g., deoxyribonucleosides or ribonucleosides). In embodiments, “nucleic acid” does not include nucleosides. The terms “polynucleotide,” “oligonucleotide,” “oligo” or the like refer, in the usual and customary sense, to a linear sequence of nucleotides. The term “nucleoside” refers, in the usual and customary sense, to a glycosylamine including a nucleobase and a five-carbon sugar (ribose or deoxyribose). Non limiting examples, of nucleosides include, cytidine, uridine, adenosine, guanosine, thymidine and inosine. The term “nucleotide” refers, in the usual and customary sense, to a single unit of a polynucleotide, i.e., a monomer. Nucleotides can be ribonucleotides, deoxyribonucleotides, or modified versions thereof. Examples of polynucleotides contemplated herein include single and double stranded DNA, single and double stranded RNA, and hybrid molecules having mixtures of single and double stranded DNA and RNA. Examples of nucleic acid, e.g., polynucleotides, contemplated herein include any ty pes of RNA, e.g. mRNA, siRNA, miRNA, and guide RNA and any types of DNA. genomic DNA. plasmid DNA. and minicircle DNA, and any fragments thereof. The term “duplex” in the context of polynucleotides refers, in the usual and customary sense, to double strandedness. Nucleic acids can be linear or branched. For example, nucleic acids can be a linear chain of nucleotides or the nucleic acids can be branched, e.g., such that the nucleic acids comprise one or more arms or branches of nucleotides. Optionally, the branched nucleic acids are repetitively branched to form higher ordered structures such as dendrimers and the like.

[0027] A polynucleotide is typically composed of a specific sequence of four nucleotide bases: adenine (A); cytosine (C); guanine (G); and thymine (T) (uracil (U) for thymine (T) when the polynucleotide is RNA). Thus, the term “polynucleotide sequence” is the alphabetical representation of a polynucleotide molecule; alternatively, the term may be applied to the polynucleotide molecule itself. This alphabetical representation can be input into databases in a computer having a central processing unit and used for bioinformatics applications such as functional genomics and homology searching. Polynucleotides may optionally include one or more non-standard nucleotide(s), nucleotide analog(s) and / or modified nucleotides.

[0028] A “microRNA,” “microRNA nucleic acid sequence,” “miR,” “miRNA” as used herein, refers to a nucleic acid that functions in RNA silencing and post-transcriptional regulation of gene expression. The term includes all forms of a miRNA, such as the pri-. pre-, and matureforms of the miRNA. In embodiments, microRNAs (miRNAs) are short (20-24 nt) non-coding RNAs that are involved in post-transcriptional regulation of gene expression in multicellular organisms by affecting both the stability and translation of mRNAs. miRNAs are transcribed by RNA polymerase II as part of capped and poly adenylated primary transcripts (pri-miRNAs) that can be either protein-coding or non-coding. The primary' transcript is cleaved by the Drosha ribonuclease III enzyme to produce an approximately 70-nt stem-loop precursor miRNA (pre-miRNA), which is further cleaved by the cytoplasmic Dicer ribonuclease to generate the mature miRNA and antisense miRNA star (miRNA*) products. The mature miRNA is incorporated into aRNA-induced silencing complex (RISC), which recognizes target mRNAs through imperfect base pairing with the miRNA and most commonly results in translational inhibition or destabilization of the target mRNA. In embodiments, the miRNA are human miRNA, alternatively referred to as hsa-miRNA, e.g., hsa-miR-106b-5p, hsa-miR-146a-5p, hsa-miR-15a-5p, hsa-miR-18a-5p, hsa-miR-21-5p, hsa-miR-93-5.

[0029] The term “gene” means the segment of DNA involved in producing a protein; it includes regions preceding and following the coding region (leader and trailer) as well as intervening sequences (introns) between individual coding segments (exons). The leader, the trailer as well as the introns include regulatory- elements that are necessary during the transcription and the translation of a gene.

[0030] The word “expression” or “expressed” as used herein in reference to a gene means the transcriptional and / or translational product of that gene. The level of expression of a DNA molecule in a cell may be determined on the basis of either the amount of corresponding RNA that is present yvithin the cell or the amount of protein encoded by that DNA produced by the cell. The level of expression of non-coding nucleic acid molecules (e.g., miRNA, mRNA) may be detected by standard PCR or Northern blot methods well knoyvn in the art.

[0031] The terms “expression level,” “amount,” or “level” of a biomarker is a detectable level in a biological sample. “Expression” generally refers to the process by which information (e.g., gene-encoded and / or epigenetic) is converted into the structures present and operating in the cell. Therefore, “expression” may refer to transcription into a polynucleotide, translation into a polypeptide, or even polynucleotide and / or polypeptide modifications (e.g., posttranslational modification of a polypeptide). Fragments of the transcribed polynucleotide, the translated polypeptide, or polynucleotide and / or polypeptide modifications (e.g., post-translational modification of a polypeptide) shall also be regarded as expressed yvhether they originate from a transcript generated by alternative splicing or a degraded transcript, or from a post-translationalprocessing of the polypeptide, e.g., by proteolysis. "Expressed genes'’ include those that are transcribed into a polynucleotide as mRNA and then translated into a polypeptide, and also those that are transcribed into RNA but not translated into a polypeptide (for example, miRNA, transfer RNA, ribosomal RNA, non-coding RNA). Expression levels can be measured by methods known to one skilled in the art and also disclosed herein.

[0032] The terms an “elevated expression level” or “elevated level” or “increased level” of gene expression is an expression level of the gene that is higher than the expression level of the gene in a control. The control may be any suitable control, as described herein. In embodiments, an “elevated expression level” of the biomarker gene compared to the control (when the expression level of the biomarker is greater than the corresponding control) is, for example, an increase in the expression level of about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98% or 99% or greater relative to the control. In embodiments, an “elevated expression level” of the biomarker gene is an amount that is statistically significantly greater than the expression level of the control.

[0033] The terms “biomarker gene” and “biomarker” are used interchangeably and in accordance with their plain and ordinary' meaning. In embodiments, a biomarker is a gene or a set of genes (i.e., a biomarker gene). Biomarkers include, but are not limited to, polynucleotides (e.g., DNA, and / or RNA), polynucleotide copy number alterations (e g., DNA copy numbers), polypeptides, or polypeptide and polynucleotide modifications (e.g., posttranslational modifications). In embodiments, a biomarker refers to RNA. In embodiments, a biomarker refers to miRNA.

[0034] Biomarker levels may be detected at either the protein or gene expression level.Proteins expressed by biomarkers can be quantified by immunohistochemistry (IHC) or flow' cytometry with an antibody that detects the proteins. Biomarker expression can be quantified by multiple platforms such as real-time polymerase chain reaction (rtPCR), Nanostring, RNAseq, or in situ hybridization. There is a range of biomarker expression across as measured by Nanostring. In embodiments, quantitative rtPCR, Nanostring, RNAseq, and in situ hybridization are platforms to quantitate biomarker gene expression. For Nanostring, RNA is extracted from a biological sample and a known quantity of RNA is placed on the Nanostring machine for gene expression detection using gene specific probes. The number of counts of biomarkers within a sample is determined and normalized to a set of housekeeping genes. To determine a threshold for increased or decreased biomarker levels, one skilled in the art could assess biomarker levels in a control group of samples and select the 10th, 20th, 25th, 30th, 40th, 50th, 60th, 70th, 75th,80th or 90th percentile of biomarker gene expression. In embodiments, the increased or decreased expression of biomarkers may be determined by calculating the H-score for the expression of the biomarkers. Thus, the increased or decreased expression of biomarkers may have an H-score. As used herein, an “H-score” or “Histoscore” is a numerical value determined by a semi-quantitative method commonly known for immunohistochemically evaluating protein expression in tumor samples. The H-score may be calculated using the following formula: [1 x (% cells 1+) + 2 x (% cells 2+) + 3 x (% cells 3+)]. According to this formula, the H-score is calculated by determining the percentage of cells having a given staining intensity level (i.e., level 1+, 2+, or 3+ from lowest to highest intensity level), weighting the percentage of cells having the given intensity level by multiplying the cell percentage by a factor (e.g., 1, 2, or 3) that gives more relative weight to cells with higher-intensity membrane staining, and summing the results to obtain a H-score. Commonly H-scores range from 0 to 300. Further description on the determination of H-scores in tumor cells can be found in Hirsch et al, J Clin Oncol 21: 3798-3807, 2003 and John et al, Oncogene 28:S14-S23, 2009. IHC or other methods known in the art may be used for detecting biomarker expression.

[0035] “Control” is used in accordance with its plain ordinary meaning and refers to an assay, comparison, or experiment in which the patients or reagents of the experiment are treated as in a parallel experiment except for omission of a procedure, reagent, or variable of the experiment. Tn embodiments, the control is used as a standard of comparison in evaluating experimental effects. In embodiments, a control is the measurement of the activity or level of RNA, such as miRNA. In embodiments, a control is a healthy patient or a healthy population of patients. In embodiments, a control is an average value from a population of similar patients, e.g., healthy patients with a similar medical background, age, weight, etc. In embodiments, the control is a healthy patient or a population of healthy patients. In embodiments, a healthy patient can be referred to as a non-diseased patient or non-diseased control. In embodiments, the control is a population of non-diseased patient. In embodiments, a non-diseased patient is a patient that does not have Barrett’s esophagus. In embodiments, the control is a patient that does not have Barrett’s esophagus or a population of patients that do not have Barrett’s esophagus. In embodiments, the control is a patient or a population of patients that do not have Barrett's esophagus or esophageal adenocarcinoma. In embodiments, the control is a patient or a population of patients that do not have Barrett’s esophagus, esophageal dysplasia, or esophageal adenocarcinoma. In embodiments, the control is a patient or population of patients that have a negative endoscopic evaluation of the foregut (i.e., a doctor determines that a patient does not have any health issues based on an upper endoscopy, an endoscopic ultrasonography, or anendoscopic classification of the esophagogastric junction, preferably a doctor determines that a patient does not have any health issues based on an upper endoscopy). In embodiments, the control is an average value from population of healthy patients. A control can also be obtained from the same patient, e.g., from an earlier-obtained sample, prior to disease, prior to treatment, or a normalized miRNA expression level relative to the expression level of a reference RNA (e.g., U6). One of skill will recognize that controls can be designed for assessment of any number of parameters. In embodiments, a control is a negative control. In embodiments, such as some embodiments relating to detecting the level of expression of a gene / protein or a subset of genes / proteins, a control comprises the average amount of expression (e.g., protein or mRNA) in a population of patients (e.g., with Barret’s esophagus or in a healthy or general population. In embodiments, the control comprises an average amount (e.g. amount of expression) in a population in which the number of patients (n) is 5 or more, 20 or more, 50 or more, 100 or more, 1 ,000 or more, and the like. In embodiments, a control is a level of expression of the biomarker (e.g., RNA, miRNA) that has been correlated with the diagnosis of Barret’s esophagus in a patient. In embodiments, a control is a level of expression of the biomarker (e.g., RNA, miRNA) that has been correlated with a healthy patient (i.e., a patient that does not have Barret’s esophagus). One of skill in the art will understand which controls are valuable in a given situation and be able to analyze data based on comparisons to control values. Controls are also valuable for determining the significance of data. For example, if values for a given parameter are widely variant in controls, variation in test samples will not be considered as significant. In embodiments, the patient is a human patient.

[0036] The term '‘healthy patient” refers to a non-diseased patient. In embodiments, a healthy patient is a patient that does not have Barrett’s esophagus. In embodiments, a healthy patient is a patient that does not have Barret’s esophagus, esophageal dysplasia, or esophageal adenocarcinoma. In embodiments, a healthy patient is a patient that has a negative endoscopic evaluation of the foregut.

[0037] The term “about” means a range of values including the specified value, which a person of ordinary skill in the art would consider reasonably similar to the specified value. In embodiments, “about” means within a standard deviation using measurements generally acceptable in the art. In embodiments, “about” means a range extending to + / - 10% of the specified value. In embodiments, '‘about” includes the specified value.

[0038] The singular terms “a,” “an,” and “the” include the plural reference unless the context clearly indicates otherwise.

[0039] “Biological sample” or “sample” refer to materials obtained from or derived from a patient. A biological sample includes sections of tissues such as biopsy samples, and frozen sections taken for histological purposes. A biological sample include bodily fluids such as blood and blood fractions or products (e.g., serum, plasma, platelets, red blood cells, and the like), sputum, tissue, cultured cells (e.g., primary cultures, explants, and transformed cells) stool, urine, synovial fluidjoint tissue, synovial tissue, synoviocytes, fibroblast-like synoviocytes, macrophage-like synoviocytes, immune cells, hematopoietic cells, fibroblasts, macrophages, T cells, etc. In embodiments, a biological sample is blood. In embodiments, a biological sample is a serum sample (e.g., the fluid and solute component of blood without the clotting factors). In embodiments, a biological sample is a plasma sample (e.g, the liquid portion of blood). In embodiments, a biological sample is esophageal tissue or esophagael mucosa. In embodiments, a biological sample is esophageal tissue. In embodiments, a biological sample is esophagael mucosa.

[0040] “Liquid biological sample” refers to liquid materials obtained or derived from a patient. Liquid biological samples include bodily fluids such as blood and blood fractions or products (e.g., serum, plasma, platelets, red blood cells, and the like), sputum, urine, synovial fluid, and the like. In embodiments, a liquid biological sample is a blood sample.

[0041] The term “diagnosis” is used in accordance with its plain and ordinary meaning and refers to an identification or likelihood of the presence of a disease (e.g., Barrett’s esophagus) or outcome in a patient.

[0042] The terms “patient” is used in accordance with its plain and ordinary meaning and refer to a living organism suffering from or prone to a disease that can be treated by administration of a pharmaceutical composition, such as the therapeutic agents described herein. Non-limiting examples include humans, other mammals, dogs, cats, monkeys, and other non-mammalian animals. In embodiments, a patient is human. In embodiments, a patient is human with gastroesophageal reflux disease (GERD). In embodiments, a patient is human with reflux esophagitis. In embodiments, a patient is human with GERD and reflux esophagitis. In embodiments, a patient is human with long-segment Barrett’s esophagus. In embodiments, a patient is human with Barrett’s esophagus and low-grade esophageal dysplasia. In embodiments, a patient is human with Barrett’s esophagus that does not have esophageal dysplasia (e.g., low-grade esophageal dysplasia or high-grade esophageal dysplasia). In embodiments, a patient is human that does not have esophageal dysplasia. In embodiments, a patient is human that does not have low-grade esophageal dysplasia. In embodiments, a patient is human that does not havehigh-grade esophageal dysplasia. In embodiments, a patient is human that does not have esophageal adenocarcinoma. In embodiments, a patient is human that does not have eosinophilic esophagitis.

[0043] Methods of Detecting RNA

[0044] Provided herein is a method of detecting RNA in a patient having Barrett's esophagus or suspected of having Barrett’s esophagus comprising detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient, wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of two or more thereof. In embodiments, the patient has Barrett’s esophagus. In embodiments, the patient is suspected of having Barrett’s esophagus. In embodiments, the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, and miR-93. In embodiments, the RNA consists of miR-106b, miR-146a, miR-15a, miR-18a, miR-21, and miR-93. In embodiments, the RNA comprises miR-106b-5p, miR-146a-5p, miR-15a-5p, miR-18a-5p, miR-21-5p, and miR-93-5p. In embodiments, the RNA consists of miR-106b-5p, miR-146a-5p, miR-15a-5p, miR-18a-5p, miR-21-5p, and miR-93-5p. In embodiments, the biological sample is blood. In embodiments, the biological sample is plasma. In embodiments, the biological sample is serum. In embodiments, the control is a patient or population of patients that do not have Barrett’s esophagus. In embodiments, the control is a patient or population of patients that have a negative endoscopic evaluation of the foregut. In embodiments, the method further comprising administering to the patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof. In embodiments, the method further comprising administering to the patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, or a combination of two or more thereof.

[0045] Provided herein is a method of detecting RNA in a patient having Barrett’s esophagus comprising detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient, wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of two or more thereof. In embodiments, the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, and miR-93. In embodiments, the RNA consists of miR-106b, miR-146a, miR-15a, miR-18a, miR-21, and miR-93. In embodiments, the RNA comprises miR-106b-5p, miR-146a-5p, miR-15a-5p, miR-18a-5p, miR-21-5p, and miR-93-5p. In embodiments, the RNA consists of miR-106b-5p, miR-146a-5p. miR-15a-5p, miR-18a-5p, miR-21-5p, and miR-93-5p. In embodiments, the biological sample is blood. In embodiments, the biological sample is plasma. In embodiments, the biological sample is serum. In embodiments, the control is a patient or population of patients that do not have Barrett’s esophagus. In embodiments, the control is a patient or population of patients that have a negative endoscopic evaluation of the foregut. In embodiments, the method further comprising administering to the patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, aprokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof. In embodiments, the method further comprising administering to the patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, or a combination of two or more thereof.

[0046] Provided herein is a method of detecting RNA in a patient suspected of having Barrett’s esophagus comprising detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient, wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of two or more thereof. In embodiments, the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, and miR-93. In embodiments, the RNA consists of miR-106b, miR-146a, miR-15a, miR-18a, miR-21, and miR-93. In embodiments, the RNA comprises miR-106b-5p, miR-146a-5p, miR-15a-5p, miR-18a-5p, miR-21-5p, and miR-93-5p. In embodiments, the RNA consists of miR-106b-5p, miR-146a-5p, miR-15a-5p, miR-18a-5p, miR-21-5p, and miR-93-5p. In embodiments, the biological sample is blood. In embodiments, the biological sample is plasma. In embodiments, the biological sample is serum. In embodiments, the control is a patient or population of patients that do not have Barrett’s esophagus. In embodiments, the control is a patient or population of patients that have a negative endoscopic evaluation of the foregut. In embodiments, the method further comprising administering to the patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof. In embodiments, the method further comprising administering to the patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, or a combination of two or more thereof.

[0047] Methods of Treating

[0048] Provided herein is a method of treating Barrett’s esophagus in a patient in need thereof comprising: (i) detecting an elevated expression level, relative to a control, of RNA in abiological sample obtained from a patient, wherein the RNA comprises miR-106b, miR-146a, miR-15a. miR-18a, miR-21, miR-93, or a combination of two or more thereof; and (ii) administering to the patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, aprokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof. In embodiments, the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, and miR-93. In embodiments, the RNA consists of miR-106b, miR-146a, miR-15a, miR-18a, miR-21, and miR-93. In embodiments, the RNA comprises miR-106b-5p, miR-146a-5p, miR-15a-5p, miR-18a-5p, miR-21-5p, and miR-93-5p. In embodiments, the RNA consists of miR-106b-5p, miR-146a-5p, miR-15a-5p, miR-18a-5p, miR-21-5p, and miR-93-5p. In embodiments, the biological sample is blood. In embodiments, the biological sample is plasma. In embodiments, the biological sample is serum. In embodiments, the control is a patient or population of patients that do not have Barrett’s esophagus. In embodiments, the control is a patient or population of patients that have a negative endoscopic evaluation of the foregut. In embodiments, the method comprising administering to the patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, or a combination of two or more thereof.

[0049] Provided herein is a method of treating Barrett’s esophagus in a patient in need thereof comprising administering to the patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof; wherein a biological sample obtained from the patient comprises an elevated expression level, relative to a control, of RNA; wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of two or more thereof. In embodiments, the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a. miR-21, and miR-93. In embodiments, the RNA consists of miR-106b, miR-146a, miR-15a, miR-18a, miR-21, and miR-93. In embodiments, the RNA comprises miR-106b-5p, miR-146a-5p, miR-15a-5p, miR-18a-5p, miR-21-5p, and miR-93-5p. In embodiments, the RNA consists of miR-106b-5p, miR-146a-5p, miR-15a-5p, miR-18a-5p, miR-21-5p, and miR-93-5p. In embodiments, the biological sample is blood. In embodiments, the biological sample is plasma. In embodiments, the biological sample is serum. In embodiments, the control is a patient or population of patients that do not have Barrett’s esophagus. In embodiments, the control is a patient or population of patients that have a negative endoscopic evaluation of the foregut. In embodiments, the method comprising administering to the patient an effective amount of a proton pump inhibitor, apotassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, or a combination of two or more thereof.

[0050] Methods of Diagnosing

[0051] Provided herein is a method of diagnosing a patient with Barrett’s esophagus, the method comprising detecting an elevated expression level, relative to a control, of an RNA in a biological sample obtained from the patient, thereby diagnosing the patient with Barrett’s esophagus; wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of two or more thereof.. In embodiments, the RNA comprises miR-106b, miR-146a, miR-15a. miR-18a, miR-21, and miR-93. In embodiments, the RNA consists of miR-106b, miR-146a. miR-15a, miR-18a, miR-21. and miR-93. In embodiments, the RNA comprises miR-106b-5p, miR-146a-5p, miR-15a-5p, miR-18a-5p, miR-21-5p, and miR-93-5p. In embodiments, the RNA consists of miR-106b-5p, miR-146a-5p, miR-15a-5p, miR-18a-5p, miR-21-5p, and miR-93-5p. In embodiments, the biological sample is blood. In embodiments, the biological sample is plasma. In embodiments, the biological sample is serum. In embodiments, the control is a patient or population of patients that do not have Barrett’s esophagus. In embodiments, the control is a patient or population of patients that have a negative endoscopic evaluation of the foregut. In embodiments, the method further comprising administering to the patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof. In embodiments, the method comprising administering to the patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, or a combination of two or more thereof.

[0052] Methods of Monitoring

[0053] Provided herein is a method of monitoring a patient at risk for developing Barrett’s esophagus, the method comprising: (i) detecting an expression level of an RNA in a biological sample obtained from the patient at a first point in time; (ii) detecting an expression level of an RNA in a biological sample obtained from the patient at a second point in time, wherein the second point in time is later than the first point in time; wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a. miR-21, miR-93, or a combination of two or more thereof. In embodiments, an elevated expression level of RNA at the second point in time when compared to the expression level of RNA at the first point in time indicates that the patient has an increased risk of developing Barrett's esophagus or has Barrett's esophagus. In embodiments, anelevated expression level of RNA at the second point in time when compared to the expression level of RNA at the first point in time indicates that the patient has an increased risk of developing Barrett’s esophagus. In embodiments, an elevated expression level of RNA at the second point in time when compared to the expression level of RNA at the first point in time indicates that the patient has Barret's esophagus. In embodiments, the RNA comprises miR-106b, miR-146a, miR-15a. miR-18a, miR-21, and miR-93. In embodiments, the RNA consists of miR-106b, miR-146a, miR-15a, miR-18a, miR-21, and miR-93. In embodiments, the RNA comprises miR-106b-5p, miR-146a-5p, miR-15a-5p, miR-18a-5p, miR-21-5p, and miR-93-5p. In embodiments, the RNA consists of miR-106b-5p, miR-146a-5p, miR-15a-5p, miR-18a-5p, miR-21-5p, and miR-93-5p. In embodiments, the biological sample is blood. In embodiments, the biological sample is plasma. In embodiments, the biological sample is serum. In embodiments, the control is a patient or population of patients that do not have Barretf s esophagus. In embodiments, the control is a patient or population of patients that have a negative endoscopic evaluation of the foregut. In embodiments, the method further comprising administering to the patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, aprokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof. In embodiments, the method comprising administering to the patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, or a combination of two or more thereof.

[0054] Provided herein is a method of monitoring a patient at risk for developing esophageal adenocarcinoma, the method comprising: (i) detecting an expression level of an RNA in a biological sample obtained from the patient at a first point in time; (ii) detecting an expression level of an RNA in a biological sample obtained from the patient at a second point in time, wherein the second point in time is later than the first point in time; wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93. or a combination of two or more thereof; and wherein the patient has Barrett’s esophagus. In embodiments, an elevated expression level of RNA at the second point in time when compared to the expression level of RNA at the first point in time indicates that the patient has an increased risk of developing esophageal adenocarcinoma. In embodiments, an elevated expression level of RNA at the second point in time when compared to the expression level of RNA at the first point in time indicates that the patient has an esophageal adenocarcinoma. In embodiments, the RNA comprises miR-106b, miR-146a. miR-15a, miR-18a, miR-21, and miR-93. In embodiments, the RNA consists of miR-106b, miR-146a, miR-15a, miR-18a, miR-21, and miR-93. Inembodiments, the RNA comprises miR-106b-5p, miR-146a-5p, miR-15a-5p, miR-18a-5p, miR-21-5p, and miR-93-5p. In embodiments, the RNA consists of miR-106b-5p, miR-146a-5p. miR-15a-5p, miR-18a-5p, miR-21-5p, and miR-93-5p. In embodiments, the biological sample is blood. In embodiments, the biological sample is plasma. In embodiments, the biological sample is serum. In embodiments, the control is a patient or population of patients that do not have Barrett’s esophagus. In embodiments, the control is a patient or population of patients that have a negative endoscopic evaluation of the foregut. In embodiments, the method further comprising administering to the patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof. In embodiments, the method comprising administering to the patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, or a combination of two or more thereof.

[0055] Methods of Treatment

[0056] Provided herein is a method of treating Barrett’s esophagus in a patient in need thereof comprising: (i) selecting a patient having a diagnosis of Barrett’s esophagus based on a Barrett’s esophagus risk score or an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient, wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of two or more thereof; and (ii) treating the patient from step (i) by administering to the patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, radiofrequency ablation, photodynamic therapy , endoscopic mucosal resection, or a combination of two or more thereof. In embodiments, the method further comprises detecting the expression level of reference RNA, as described herein.

[0057] Provided herein is a method of treating Barrett’s esophagus in a patient in need thereof comprising: (i) receiving or obtaining a Barrett’s esophagus risk score or an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient, wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, and miR-93, or a combination of two or more thereof; and (ii) diagnosing a patient with Barrett’s esophagus based on the Barrett’s esophagus risk score or the elevated expression level of RNA, monitoring a patient who is at risk of developing Barrett’s esophagus based on the Barrett's esophagus risk score or the elevated expression level of RNA, or monitoring efficacy of treatment for Barrett's esophagus in a patient based on the Barrett’s esophagus risk score or the elevated expressionlevel of RNA; and (iii) treating the patient from step (ii) by administering to the patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof. In embodiments, the method further comprises detecting the expression level of reference RNA, as described herein.

[0058] Provided herein is a method of treating Barrett’s esophagus in a patient in need thereof comprising: (i) receiving or obtaining a Barrett’s esophagus risk score or an elevated expression level of RNA, wherein the Barrett’s esophagus risk score or elevated expression level of RNA is produced by a non-transitory computer-readable storage medium having instructions stored thereon which, when executed by a processor, causes the processor to perform an operation comprising applying an algorithm to the results of a method which comprises detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient, wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of two or more thereof; and (ii) diagnosing a patient with Barrett’s esophagus based on the Barrett’s esophagus risk score or the elevated expression level of RNA, monitoring a patient who is at risk of developing Barrett’s esophagus based on the Barrett's esophagus risk score or the elevated expression level of RNA, or monitoring efficacy of treatment for Barrett’s esophagus in a patient based on the Barrett’s esophagus risk score or the elevated expression level of RNA; and (iii) treating the patient from step (ii) by administering to the patient an effective amount of proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof. In embodiments, the method further comprises detecting the expression level of RNA, as described herein.

[0059] Provided herein is a computer-implemented method of administering an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof to a patient with Barrett’s esophagus, the method comprising: (i) obtaining a sample data set comprising an expression level of RNA from a biological sample obtained from the patient with Barrett’s esophagus, wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of two or more thereof; (ii) obtaining a reference data set comprising an expression level of a reference RNA from the biological sample; (iii) normalizing the expression level of the RNA to the expression level of the reference RNA; (iv) determining anelevated expression level of the normalized expression level of the RNA; and (v) administering to the patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, aprokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof.

[0060] Provided herein is a computer-implemented system for administering an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof to a patient with Barrett’s esophagus, wherein the computer-implemented system comprises: (i) a computer-readable medium holding a control data set of normalized expression levels of RNA from a population of patients that do not have Barrett’s esophagus; (ii) a processing device; (iii) a computer-readable medium containing programming instructions that are configured to instruct the processing device to: (a) receive a sample data set comprising an expression level of RNA from a biological sample obtained from a patient with Barrett’s esophagus, wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of tw o or more thereof; (b) receive a reference data set comprising an expression level of a reference RNA from the biological sample; (c) normalize the expression level of the RNA to the expression level of the reference RNA; (d) receive the control data set of normalized expression levels of RNA from the population of patients that do not have Barrett’s esophagus; and (e) determine an elevated expression level of the normalized expression level of the RNA in the sample data set when compared to the normalized expression level of RNA in the control data set. In embodiments, the normalized expression level of the RNA is elevated relative to the expression level of the reference RNA. In embodiments, the computer-readable medium generates a report providing results and instructions for administering to the patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of tw o or more thereof. In embodiments, the computer-readable medium generates a report providing results and instructions (and / or recommendations) providing the ty pe of drug to administer to the patient.

[0061] Provided herein are methods of processing data generated from the RNA levels in the biological sample obtained from a patient for establishing a Barrett’s esophagus risk score (composite risk score), e.g., a score indicative Barret’s esophagus. In embodiments, the method comprises the steps of (i) normalizing and / or scaling numeric values of the RNA level data, (ii)refining the discriminatory power of individual RNA by statistically weighting some of the numeric values associated therewith, and (iii) summating the numeric values obtained from step (ii) to provide a composite risk score. In embodiments, the composite risk score obtained from step (iii) is compared to a control and the comparison allows the sample to be designated as positive or negative for Barrett’s esophagus or a scale of likelihood of Barrett’s esophagus. In embodiments, the composite risk score is normalized. In embodiments, the composite risk score is scaled. In embodiments, the composite risk score is weighted. Weighted refers to the relevant value being adjusted to more appropriately reflect its contribution to the risk score. The Barrett’s esophagus risk score can be based on a comparison to a control, such as a healthy patient, a population of healthy patients, a patient with Barrett’s esophagus, or a population of patients with Barrett’s esophagus.

[0062] In embodiments, the Barrett's esophagus risk score is produced by a non-transitory computer-readable storage medium having instructions stored thereon which, when executed by a processor, causes the processor to perform an operation comprising applying an algorithm to the protein levels.

[0063] In embodiments of the methods described herein, a medical provider instructs a patient to obtain laboratory' tests. The laboratory analyzes a biological sample provided by the patient to produce test results, e.g.. levels of RNA and / or Barrett’s esophagus risk scores. The medical provider receives the test results from the laboratory- or obtains the test results from a patient so that the medical provider can use the test results to treat a patient with Barrett’s esophagus, diagnose a patient with Barrett’s esophagus, monitor a patient who is at risk of developing Barrett’s esophagus, or monitoring efficacy of treatment for Barrett’s esophagus in a patient. Receiving and obtaining can be used interchangeably herein and can refer to physically receiving / obtaining paper documents or receiving / obtaining files via an electronic device (e.g., computer, phone). Medical provider refers to any person or entity that provides medical services to a patient. In embodiments, the medical provider is a medical doctor, a nurse, a nurse practitioner, a physician’s assistant, a hospital, a doctor’s office, and the like.

[0064] miRNA

[0065] In embodiments of the methods described herein, the RNA is miRNA.

[0066] In embodiments of the methods described herein, the RNA comprises miR-106b, miR-146a. miR-15a, miR-18a, miR-21, and miR-93. In embodiments, the RNA comprises miR-106b-5p, miR-146a-5p, miR-15a-5p, miR-18a-5p, miR-21-5p, and miR-93-5p.

[0067] In embodiments of the methods described herein, the RNA comprises miR-106b, miR-18a, and miR-93. In embodiments, the RNA comprises: (i) miR-106b, miR-18a, and miR-93, and (ii) miR-146a, miR-15a, miR-21, or a combination of two or more thereof. In embodiments, the RNA comprises: (i) miR-106b, miR-18a, and miR-93, and (ii) at least one of miR-146a, miR-15a, and miR-21. In embodiments, the RNA comprises: (i) miR-106b, miR-18a, and miR-93, and (ii) one of miR-146a, miR-15a, and miR-21. In embodiments, the RNA comprises: (i) miR-106b, miR-18a, and miR-93, and (ii) at least two of miR-146a, miR-15a, and miR-21. In embodiments, the RNA comprises: (i) miR-106b, miR-18a, and miR-93, and (ii) two of miR-146a, miR-15a, and miR-21.

[0068] In embodiments of the methods described herein, the RNA comprises miR-106b-5p, miR-18a-5p, and miR-93-5p. In embodiments, the RNA comprises: (i) miR-106b-5p, miR-18a-5p, and miR-93-5p, and (ii) miR-146a-5p, miR-15a-5p, miR-21-5p, or a combination of two or more thereof. In embodiments, the RNA comprises: (i) miR-106b-5p, miR-18a-5p, and miR-93-5p, and (ii) at least one of miR-146a-5p, miR-15a-5p, and miR-21-5p. In embodiments, the RNA comprises: (i) miR-106b-5p, miR-18a-5p, and miR-93-5p, and (ii) one of miR-146a-5p, miR-15a-5p. and miR-21-5p. In embodiments, the RNA comprises: (i) miR-106b-5p, miR-18a-5p, and miR-93-5p, and (ii) at least two of miR-146a-5p, miR-15a-5p, and miR-21-5p. In embodiments, the RNA comprises: (i) miR-106b-5p, miR-18a-5p, and miR-93-5p, and (ii) two of miR-146a-5p, miR-15a-5p, and miR-21-5p.

[0069] In embodiments of the methods described herein, the RNA consists of miR-106b, miR-146a, miR-15a, miR-18a, miR-21, and miR-93. In embodiments, the RNA consists of miR-106b-5p, miR-146a-5p, miR-15a-5p, miR-18a-5p, miR-21-5p, and miR-93-5p.

[0070] In embodiments of the methods described herein, the RNA consists of miR-106b, miR-18a, and miR-93. In embodiments, the RNA consists of: (i) miR-106b, miR-18a, and miR-93, and (ii) one of miR-146a, miR-15a, and miR-21. In embodiments, the RNA consists of: (i) miR-106b, miR-18a, and miR-93, and (ii) two of miR-146a, miR-15a, and miR-21.

[0071] In embodiments of the methods described herein, the RNA consists of miR-106b-5p, miR-18a-5p, and miR-93-5p. In embodiments, the RNA consists of: (i) miR-106b-5p, miR-18a-5p, and miR-93-5p, and (ii) one of miR-146a-5p, miR-15a-5p, and miR-21-5p. In embodiments, the RNA consists of: (i) miR-106b-5p, miR-18a-5p. and miR-93-5p, and (ii) two of miR-146a-5p, miR-15a-5p, and miR-21 -5p.

[0072] In embodiments of the methods described herein, the RNA does not include miR-135b,miR-196a, miR-335, miR-15b, miR-17, miR-181a, miR-181b, miR-196b, or a combination of two or more thereof. In embodiments, the RNA does not include miR-135b, miR-196a, miR-335, or miR-15b. In embodiments, the RNA does not include miR-17. In embodiments, the RNA does not include miR-181a, miR-181b, or miR-196b. In embodiments, the RNA does not include miR-135b, miR-196a, miR-335, miR-15b, miR-17, miR-181a, miR-181b, or miR-196b. In embodiments, miR-135b is miR-135b-5p, miR-196ais miR-196a-l-5p, miR-335 is miR-335-3p, miR-15b is miR-15b-5p, miR-17 is miR-17-5p, miR-181ais miR-181a-5p, miR-181b is miR-181b-5p, and miR-196b is miR-196b-5p.

[0073] Treatments

[0074] Provided herein are methods of treating Barrett’s esophagus comprising administering to a patient an effective amount of a therapeutic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof. In embodiments, the methods of treating Barrett's esophagus comprising administering to a patient an effective amount of a therapeutic agent.

[0075] A “therapeutic agent" as used herein refer to an agent (e.g., compound, pharmaceutical composition) that when administered to a patient will have the intended therapeutic effect, e.g., treatment or amelioration of Barrett’s esophagus, or Barrett’s esophagus symptoms including any objective or subjective parameter of treatment such as abatement; remission; diminishing of symptoms or making the Barrett’s esophagus more tolerable to the patient; slowing in the rate of degeneration or decline; making the final point of degeneration less debilitating; or improving a patient’s physical or mental well-being. Exemplary therapeutic agents include a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, and a prokinetic agent.

[0076] Provided herein are methods of treating Barrett’s esophagus comprising administering to a patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof. In embodiments, the methods of treating Barrett’s esophagus comprising administering to a patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, or a combination of two or more thereof.

[0077] In embodiments, the methods of treating Barrett’s esophagus comprising administering to a patient an effective amount of a proton pump inhibitor. In embodiments, the proton pumpinhibitor is dexlansoprazole, esomeprazole, lansoprazole, omeprazole, pantoprazole, or rabeprazole.

[0078] In embodiments, the methods of treating Barrett’s esophagus comprising administering to a patient an effective amount of a potassium competitive acid blocker. In embodiments, the potassium competitive acid blocker is fexuprazan, keverprazan, linaprazan, revaprazan, soraprazan, tegoprazan, vonoprazan, or zestaprazan.

[0079] In embodiments, the methods of treating Barrett’s esophagus comprising administering to a patient an effective amount of an antacid. In embodiments, the antacid is aluminum hydroxide, calcium carbonate, magnesium carbonate, magnesium hydroxide, magnesium trisilicate, magaldrate, sodium bicarbonate, or sodium citrate.

[0080] In embodiments, the methods of treating Barrett’s esophagus comprising administering to a patient an effective amount of a histamine 2 blocker. In embodiments, the histamine 2 blocker is cimetidine, famotidine, nizatidine, ranitidine, or roxatidine.

[0081] In embodiments, the methods of treating Barrett’s esophagus comprising administering to a patient an effective amount of a prokinetic agent. In embodiments, the prokinetic agent is cisapride, domperidone, levosulpiride, metoclopramide, or mosapride.

[0082] In embodiments, the methods of treating Barrett’s esophagus comprising administering to a patient an effective amount of dexlansoprazole, esomeprazole, lansoprazole, omeprazole, pantoprazole, rabeprazole, fexuprazan, keverprazan, linaprazan, revaprazan, soraprazan, tegoprazan, vonoprazan, zestaprazan, aluminum hydroxide, calcium carbonate, magnesium carbonate, magnesium hydroxide, magnesium trisilicate, magaldrate, sodium bicarbonate, sodium citrate, cimetidine, famotidine, nizatidine, ranitidine, roxatidine, cisapride, domperidone, levosulpiride, metoclopramide, or mosapride, or a combination of two or more thereof.

[0083] The terms “treating” or “treatment” are used in accordance with their plain and ordinary meaning and broadly includes any approach for obtaining beneficial or desired results in a patient’s condition, including clinical results. Beneficial or desired clinical results can include, but are not limited to, alleviation or amelioration of one or more symptoms or conditions, diminishment of the extent of a disease, stabilizing (i.e., not worsening) the state of disease, delay or slowing of disease progression, amelioration or palliation of the disease state, and remission, whether partial or total and whether detectable or undetectable. Treatment may inhibit the disease’s spread; relieve the disease’s symptoms, fully or partially remove the disease’s underlying cause, shorten a disease’s duration, or do a combination of these things.Treatment methods include administering to a patient a therapeutically effective amount of an active agent. The term “treating’7does not including preventing.

[0084] An “effective amount” is an amount sufficient to accomplish a stated purpose (e.g. achieve the effect for which it is administered, treat a disease). An example of an “effective amount” is an amount sufficient to contribute to the treatment, prevention, or reduction of a symptom or symptoms of a disease, which could also be referred to as a “therapeutically effective amount.” A “reduction” of a symptom or symptoms (and grammatical equivalents of this phrase) means decreasing of the severity or frequency of the symptom(s), or elimination of the symptom(s). The exact amounts will depend on the purpose of the treatment, and will be ascertainable by one skilled in the art using know n techniques. In embodiments, “therapeutically effective amount” refers to the amount of the therapeutic agent sufficient to treat or ameliorate Barrett’s esophagus, as described above. For any therapeutic agent described herein, the therapeutically effective amount can be initially determined from cell culture assays. Target concentrations will be those concentrations of active compound(s) that are capable of achieving the methods described herein, as measured using the methods described herein or know n in the art. As is well known in the art, therapeutically effective amounts for use in humans can also be determined from animal models. For example, a dose for humans can be formulated to achieve a concentration that has been found to be effective in animals. The dosage in humans can be adjusted by monitoring compounds effectiveness and adjusting the dosage upwards or downwards, as described above. Adjusting the dose to achieve maximal efficacy in humans based on the methods described above and other methods is well within the capabilities of the ordinarily skilled artisan. Dosages may be varied depending upon the requirements of the patient and the therapeutic agent being employed. The dose administered to a patient should be sufficient to effect a beneficial therapeutic response in the patient over time. The size of the dose also will be determined by the existence, nature, and extent of any adverse side-effects.Determination of the proper dosage for a particular situation is within the skill of the practitioner. Generally, treatment is initiated with smaller dosages which are less than the optimum dose of the compound. Thereafter, the dosage is increased by small increments until the optimum effect under circumstances is reached. Dosage amounts and intervals can be adjusted individually to provide levels of the administered compound effective for the particular clinical indication being treated. This will provide a therapeutic regimen that is commensurate with the severity of the patient's disease state. A “therapeutically effective amount” can also be found on the label or Prescribing Information for commercially available therapeutic agents.

[0085] The term “administering'’ means oral administration, administration as a suppository, topical contact, intravenous, parenteral, intraperitoneal, intramuscular, intralesional, intrathecal, intranasal or subcutaneous administration, or the implantation of a slow-release device, e.g., a mini-osmotic pump, to a patient. Administration is by any route, including parenteral and transmucosal (e.g., buccal, sublingual, palatal, gingival, nasal, vaginal, rectal, or transdermal). Parenteral administration includes, e.g., intravenous, intramuscular, intra- arteriole, intradermal, subcutaneous, intraperitoneal, intraventricular, and intracranial. Other modes of delivery include, but are not limited to, the use of liposomal formulations, intravenous infusion, transdermal patches, etc. In embodiments, the administering does not include administration of any active agent other than the recited active agent.

[0086] miR Controls

[0087] The term “reference RNA” or “housekeeping RNA” refers to typically constitutive RNA that is required for the maintenance of basal cellular function and that is expected to maintain constant expression levels in all cells. The expression of one or multiple reference RNA is used as a reference point for the analysis of expression levels of other RNA (e.g., miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93). The key criterion for the use of a reference RNA in this manner is that the chosen reference RNA is uniformly expressed with low variance under both control and experimental conditions (e.g., in both healthy patients and patients with Barrett’s esophagus). In embodiments, the reference RNA is U6 (RNU6-1).

[0088] In embodiments, the methods described herein further comprise detecting the expression level of U6 in the biological sample obtained from the patient. In embodiments, the methods described herein further comprise normalizing the expression levels of miRNA (e.g., miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93) to the expression level of the reference RNA (e.g.. U6). In embodiments, the expression level of the reference RNA are used as a control to the expression level of the miRNA (e.g., miR-106b, miR-I46a, miR-15a, miR-I8a, miR-21, miR-93). In embodiments, the methods described herein further comprise normalizing the expression level of the miRNA (e.g., miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93) to the expression level of the reference RNA (e.g., U6), thereby obtaining a normalized expression level of the miRNA (e.g., miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93). In embodiments, the expression level of the reference RNA (e.g., U6) is used as a control to the normalized expression level of the miRNA (e.g., miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93).

[0089] In embodiments, an elevated expression level refers to a normalized expression level ofmiRNA (e.g., miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93) that is at least 1.1 times greater than the expression level of the reference RNA (e.g., U6). In embodiments, an elevated expression level refers to a normalized expression level of miRNA that is at least 1.2 times greater than the expression level of the reference RNA (e.g., U6). In embodiments, an elevated expression level refers to a normalized expression level of miRNA that is at least 1.3 times greater than the expression level of the reference RNA (e g., U6). In embodiments, an elevated expression level refers to a normalized expression level of miRNA that is at least 1.4 times greater than the expression level of the reference RNA (e g., U6). In embodiments, an elevated expression level refers to a normalized expression level of miRNA that is at least 1.5 times greater than the expression level of the reference RNA (e.g., U6). In embodiments, an elevated expression level refers to a normalized expression level of miRNA that is at least 1.6 times greater than the expression level of the reference RNA (e g., U6). In embodiments, an elevated expression level refers to a normalized expression level of miRNA that is at least 1.7 times greater than the expression level of the reference RNA (e.g., U6). In embodiments, an elevated expression level refers to a normalized expression level of miRNA that is at least 1.8 times greater than the expression level of the reference RNA (e.g., U6). In embodiments, an elevated expression level refers to a normalized expression level of miRNA that is at least 1.9 times greater than the expression level of the reference RNA (e.g., U6). In embodiments, an elevated expression level refers to a normalized expression level of miRNA that is at least 2 times greater than the expression level of the reference RNA (e.g., U6). In embodiments, an elevated expression level refers to a normalized expression level of miRNA that is at least 2.5 times greater than the expression level of the reference RNA (e.g., U6). In embodiments, an elevated expression level refers to a normalized expression level of miRNA that is at least 3 times greater than the expression level of the reference RNA (e.g., U6). In embodiments, an elevated expression level refers to a normalized expression level of miRNA that is at least 3.5 times greater than the expression level of the reference RNA (e.g., U6). In embodiments, an elevated expression level refers to a normalized expression level of miRNA that is at least 4 times greater than the expression level of the reference RNA (e.g., U6). In embodiments, an elevated expression level refers to a normalized expression level of miRNA that is at least 5 times greater than the expression level of the reference RNA (e.g., U6). In embodiments, an elevated expression level refers to a normalized expression level of miRNA that is at least 6 times greater than the expression level of the reference RNA (e.g., U6). In embodiments, an elevated expression level refers to a normalized expression level of miRNA that is at least 7 times greater than the expression level of the reference RNA (e g., U6). In embodiments, anelevated expression level refers to a normalized expression level of miRNA that is at least 8 times greater than the expression level of the reference RNA (e.g., U6). In embodiments, an elevated expression level refers to a normalized expression level of miRNA that is at least 9 times greater than the expression level of the reference RNA (e.g., U6). In embodiments, an elevated expression level refers to a normalized expression level of miRNA that is at least 10 times greater than the expression level of the reference RNA (e g., U6). In embodiments, an elevated expression level refers to a normalized expression level of miRNA that is statistically significantly greater than the expression level of the reference RNA (e g., U6).

[0090] Kits

[0091] Provided herein are kits comprising reagents capable of detecting an expression level of RNA from a biological sample; wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, and miR-93. Provided herein are kits comprising reagents capable of detecting an expression level of RNA from a biological sample, wherein the RNA consists of miR-106b, miR-146a, miR-15a, miR-18a. miR-21, and miR-93.

[0092] Provided herein are kits comprising reagents capable of detecting an expression level of RNA from a biological sample; wherein the RNA comprises miR-106b-5p, miR-146a-5p, miR-15a-5p, miR-18a-5p, miR-21-5p, and miR-93-5p. Provided herein are kits comprising reagents capable of detecting an expression level of RNA from a biological sample, wherein the RNA consists of miR-106b-5p, miR-146a-5p, miR-15a-5p, miR-18a-5p, miR-21-5p, and miR-93-5p.

[0093] Provided herein are kits comprising components, such as reagents and reaction mixtures, to conduct the assays to detect the miRNA as described herein (e.g., miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93). As part of the kit, materials and instruction are provided, e.g., for storage and use of kit components. In embodiments, the kits comprise one or more of the following: a RNA probe that can hybridize to a RNA biomarker, pairs of primers that under appropriate reaction conditions can prime amplification of at least a portion of a RNA marker or a RNA encoding a polypeptide marker (e g., by PCR), instructions on how to use the kit, and a label or insert indicating regulatory approval for diagnostic or therapeutic use. In embodiments, the kit further includes RNA microarrays comprising RNA of the disclosure or molecules which specifically bind to the RNA described herein. In embodiments, standard techniques of microarray technology are utilized to -assess expression of the RNA.Polynucleotide arrays, particularly arrays that bind RNA described herein, also can be used for diagnostic applications.

[0094] “Assaying’' or “detecting” means using an analytic procedure to qualitatively assess or quantitatively measure the presence or amount or the functional activity of a target entity (e.g.. miRNA). For example, detecting the level of RNA (such as miRNA) means using an analytic procedure (such as an in vitro procedure) to qualitatively assess or quantitatively measure the presence or amount of the miRNA. In embodiments, raw expression values are normalized by performing quantile normalization relative to the reference distribution and subsequent log 10-transformation. In embodiments, when miRNA expression is detected using the nCounter® Analysis System marketed by Nanostring Technologies, the reference distribution is generated by pooling reported (i.e., raw) counts for the test sample and one or more control samples (preferably at least 2 samples, more preferably at least any of 4, 8 or 16 samples) after excluding values for technical (both positive and negative control) probes and without performing intermediate normalization relying on negative (background-adjusted) or positive (synthetic sequences spiked with known titrations).

[0095] The terms “probe” or “primer” refer to one or more nucleic acid fragments whose specific hybridization to a sample can be detected. A probe or primer can be of any length depending on the particular technique it will be used for. For example, PCR primers are generally between 10 and 40 nucleotides in length, while nucleic acid probes for. e.g., a Southern blot, can be more than a hundred nucleotides in length. The probe or primers can be unlabeled or labeled as described below so that its binding to a target sequence can be detected (e.g., with a FRET donor or acceptor label). The probe or primer can be designed based on one or more particular (preselected) portions of a chromosome, e.g., one or more clones, an isolated w hole chromosome or chromosome fragment, or a collection of polymerase chain reaction (PCR) amplification products. One of skill can adjust these factors to provide optimum hybridization and signal production for a given hybridization and detection procedures, and to provide the required resolution among different genes or genomic locations.

[0096] Probes and primers can also be immobilized on a solid surface (e.g., nitrocellulose, glass, quartz, fused silica slides), as in an array. Techniques for producing high density arrays can also be used for this purpose. One of skill w ill recognize that the precise sequence of particular probes and primers can be modified from the target sequence to a certain degree to produce probes that are “substantially identical” or “substantially complementary to” a target sequence, but retain the ability to specifically bind to (i.e., hybridize specifically to) the same targets from which they were denved.

[0097] The term “capable of hybridizing to” refers to a polynucleotide sequence that formsWatson-Crick bonds with a complementary sequence. One of skill will understand that the percent complementarity need not be 100% for hybridization to occur, depending on the length of the polynucleotides, length of the complementary region (e.g. 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, or more bases in length), and stringency of the conditions. For example, a polynucleotide (e.g., primer or probe) can be capable of binding to a polynucleotide having 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%. 94%. 95%. 96%. 97%. 98%. 99% or 100% complementarity over the stretch of the complementary region.

[0098] In embodiments, methods include detecting a level of a biomarker with a specific binding agent (e.g., an agent that binds to a nucleic acid molecule). Exemplary binding agents include an antibody or a fragment thereof, a detectable protein or a fragment thereof, a nucleic acid molecule such as an oligonucleotide / polynucleotide comprising a sequence that is complementary’ to patient genomic DNA, mRNA or a cDNA produced from patient mRNA, or any combination thereof. In embodiments, an antibody is labeled with detectable moiety, e.g., a fluorescent compound, an enzyme or functional fragment thereof, or a radioactive agent. In embodiments, an antibody is detectably labeled by coupling it to a chemiluminescent compound. In embodiments, the presence of the chemiluminescent-tagged antibody is then determined by detecting the presence of luminescence that arises during the course of chemical reaction. Nonlimiting examples of useful chemiluminescent labeling compounds are luminol, isoluminol, theromatic acridinium ester, imidazole, acridinium salt and oxalate ester.

[0099] In embodiments, the subject matter provides a composition comprising a binding agent, wherein the binding agent is attached to a solid support, (e.g., a strip, a polymer, a bead, a nanoparticle, a plate such as a multiwell plate, or an array such as a microarray). In embodiments relating to the use of a nucleic acid probe attached to a solid support (such as a microarray), a nucleic acid in a test sample may be amplified (e.g., using PCR) before or after the nucleic acid to be measured is hybridized with the probe. In embodiments, reverse transcription polymerase chain reaction (RT-PCR) is used to detect miRNA levels. In embodiments, a probe on a solid support is used, and miRNA (or a portion thereof) in a biological sample is converted to cDNA or partial cDNA and then the cDNA or partial cDNA is hybridized to a probe (e.g., on a microarray), hybridized to a probe and then amplified, or amplified and then hybridized to a probe. In embodiments, a strip may be a nucleic acid-probe coated porous or non-porous solid support strip comprising linking a nucleic acid probe to a carrier to prepare a conjugate and immobilizing the conjugate on a porous solid support. In embodiments, the support or carrier comprises glass, polystyrene, polypropylene, polyethylene,dextran, nylon, amylases, natural and modified celluloses, polyacrylamides, gabbros, and magnetite. In embodiments, the nature of the carrier can be either soluble to some extent or insoluble for the purposes of the present subject matter. In embodiments, the support material may have any structural configuration so long as the coupled molecule is capable of binding to a binding agent (e.g., an antibody). In embodiments, the support configuration may be spherical, as in a bead, or cylindrical, as in the inside surface of a test tube, or the external surface of a rod. In embodiments, the surface may be flat such as a plate (or a well within a multiwell plate), sheet, test strip, polystyrene beads. Those skilled in the art will know many other suitable carriers for binding antibody or antigen, or will be able to ascertain the same by use of routine experimentation.

[0100] In embodiments, a solid support comprises a polymer, to which an agent is chemically bound, immobilized, dispersed, or associated. In embodiments, a polymer support may be, e g., a network of polymers, and may be prepared in bead form (e.g., by suspension polymerization). In embodiments, the location of active sites introduced into a polymer support depends on the type of polymer support. In embodiments, in a swollen-gel-bead polymer support the active sites are distributed uniformly throughout the beads, whereas in a macroporous-bead polymer support they are predominantly on the internal surfaces of the macropores. In embodiments, the solid support, e g., a device, may contain a biomarker binding agent alone or together with a binding agent for at least one, two, three or more other biomarkers.

[0101] In embodiments, the cells in a biological sample are lysed to release a protein or nucleic acid. Numerous methods for lysing cells and assessing protein and nucleic acid levels are known in the art. In embodiments, cells are physically lysed, such as by mechanical disruption, liquid homogenization, high frequency sound waves, freeze / thaw cycles, with a detergent, or manual grinding. Non-limiting examples of detergents include Tween 20, Triton X-100, and sodium dodecyd sulfate (SDS). Non-limiting examples of assays for determining the level of a protein include HPLC, LC / MS, ELISA, immunoelectrophoresis, Western blot, immunohistochemistry, and radioimmunoassays. Non-limiting examples of assays for determining the level of an mRNA include Northern blotting, RT-PCR, RNA sequencing, and qRT-PCR.

[0102] In embodiments, once a suitable biological sample has been obtained, it is analyzed to quantitate the expression level of each of the biomarker genes. In embodiments, determining the expression level of a gene comprises detecting and quantifying RNA transcribed from that gene or a protein translated from such RNA. In embodiments, the RNA includes miRNA transcribedfrom the gene, and / or specific spliced variants thereof and / or fragments of such miRNA and spliced variants.

[0103] In embodiments, raw expression values are normalized to a reference RNA (e.g., U6). In embodiments, raw expression values are normalized by performing quantile normalization relative to the reference distribution and subsequent log 10-transformation. In embodiments, when the gene expression is detected using the nCounter® Analysis System marketed by NanoString® Technologies, the reference distribution is generated by pooling reported (i.e., raw) counts for the test sample and one or more control samples (preferably at least 2 samples, more preferably at least any of 4, 8 or 16 samples) after excluding values for technical (both positive and negative control) probes and without performing intermediate normalization relying on negative (background-adjusted) or positive (synthetic sequences spiked with known titrations).

[0104] A “detectable agent’ ' or “detectable moiety’' is a compound or composition detectable by appropriate means such as spectroscopic, photochemical, biochemical, immunochemical, chemical, magnetic resonance imaging, or other physical means. The RNA described herein and the expression level of the RNA described herein may be accomplished through the use of a detectable moiety in an assay or kit. A detectable moiety is a monovalent detectable agent or a detectable agent bound (e.g.. covalently and directly or via a linking group) with another compound, e.g., nucleic acid. Exemplary detectable agents / moi eties for use in the present disclosure include an antibody ligand, peptide, nucleic acid, radioisotopes, paramagnetic metal ions, fluorophore (e.g., fluorescent dyes), electron-dense reagents, enzymes (e.g., used in an ELISA), biotin, biotin-avidin complex, biotin-streptavidin complex, digoxigenin, magnetic beads, paramagnetic molecules, paramagnetic nanoparticles, ultrasmall superparamagnetic iron oxide nanoparticles or nanoparticle aggregates, superparamagnetic iron oxide nanoparticles or nanoparticle aggregates, monocrystalline iron oxide nanoparticles, monocrystalline iron oxide, nanoparticle contrast agents, liposomes or other delivery' vehicles containing Gadolinium chelate molecules, gadolinium, radionuclides, fluorodeoxyglucose, gamma ray -emitting or positronemitting radionuclides, radiolabeled glucose, water, or ammonia, biocolloids, microbubbles, iodinated contrast agents, barium sulfate, thorium dioxide, gold, gold nanoparticles or nanoparticle aggregates, fluorophores, two-photon fluorophores, or haptens and proteins or other entities which can be made detectable, e.g., by incorporating a radiolabel into a peptide or antibody specifically reactive with a target peptide.

[0105] In embodiments, oligonucleotides in kits are capable of specifically hybridizing to atarget region of a polynucleotide, such as for example, an RNA transcript or cDNA generated therefrom. As used herein, specific hybridization means the oligonucleotide forms an antiparallel double-stranded structure with the target region under certain hybridizing conditions, while failing to form such a structure with non-target regions when incubated with the polynucleotide under the same hybridizing conditions. The composition and length of each oligonucleotide in the kit will depend on the nature of the transcript containing the target region as well as the type of assay to be performed with the oligonucleotide and is readily determined by the skilled artisan.

[0106] In embodiments, the disclosure provides a kit for detecting the RNA (e.g., miRNA) described herein. In embodiments, the kit is an assay system including any one of assay reagents, assay controls, protocols, exemplary assay results, or combinations of these components designed to provide the user with means to evaluate the expression level of the RNA (e.g., miRNA) described herein. In embodiments, the disclosure provides a kit for diagnosing Barrett s esophagus in a patent, including reagents for detecting miRNA markers in a biological (e.g., blood) sample from a patient.

[0107] In embodiments, the kits comprise one or more of the following: a RNA probe that can hybridize to a RNA biomarker, pairs of primers that under appropriate reaction conditions can prime amplification of at least a portion of a RNA marker or a RNA encoding a polypeptide marker (e.g., by PCR), instructions on how to use the kit, and a label or insert indicating regulatory approval for diagnostic or therapeutic use. In embodiments, the kit further includes RNA microarrays comprising RNA of the disclosure or molecules which specifically bind to the RNA described herein. In embodiments, standard techniques of microarray technology are utilized to assess expression of the RNA. Polynucleotide arrays, particularly arrays that bind RNA described herein, also can be used for diagnostic applications.

[0108] miRNA Expression

[0109] In embodiments of the methods described herein, the elevated level of gene expression is an elevated level of RNA (e.g., miRNA) expression. Levels of gene expression can be determined by methods known in the art, such as those described herein. In embodiments, the RNA is miRNA. In embodiments, RNA expression is detected by direct digital counting of nucleic acids, RNA sequencing (RNA-seq), quantitative reverse transcriptase polymerase chain reaction (RT-qPCR), quantitative polymerase chain reaction (qPCR), multiplex qPCR, microarray analysis, or a combination thereof. In embodiments, RNA expression is detected by RNA sequencing. RNA sequencing is a sequencing technique which uses next-generationsequencing (NGS) to reveal the presence and quantity of RNA in a biological sample. In embodiments, the gene expression level is an average of the gene expression level of the biomarker genes. In embodiments, the average of the gene expression level of the biomarker genes is an average of the normalized gene expression level of the biomarker genes. In embodiments, the gene expression level of the biomarker genes is a median of the gene expression level of the biomarker genes. In embodiments, the median of the gene expression level of the biomarker genes is a median of a normalized gene expression level of the biomarker genes. In embodiments, the gene expression level of the biomarker genes is the gene expression level of the biomarker genes normalized to a reference gene (e.g., reference RNA (e.g., U6)).

[0110] In embodiments of the methods described herein, the individual elevated expression level of the miRNA described herein are used. In embodiments, the elevated expression levels of the miRNA are weighted and combined to form a risk score. In embodiments, the expression levels of the miRNA are normalized to the expression level of reference RNA (e.g., U6), and the normalized expression levels of the miRNA are weighted.

[0111] Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as the control (e.g., healthy individuals). The 2v tmethod is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The Ct (threshold cycle) method quantification was used for the evaluation of the expression level of each miRNA. The threshold cycle (Ct) is defined as the PCR cycle at which the fluorescent signal of the reporter dye crosses an arbitrarily placed threshold. This method quantifies the absolute expression of each miRNAs in each sample analyzed and then calculates the different expression of each miRNA in sample versus the controls. These expression values of the RNA can be used individually to produce a risk score, can be added together to produce a risk score, or logistic regression analysis can be applied to produce a risk score based on weighted values of the expression levels of the RNA.

[0112] In embodiments, the disclosure provides methods of processing miRNA expression data generated from the expression levels of the miRNA in the biological sample obtained from a patient as described herein, for establishing the presence of a signature indicative of Barret’s esophagus), comprising the steps of (i) normalizing and / or scaling numeric values of the RNA expression data, (ii) refining the discriminatory power of individual miRNA by statistically weighting some of the numeric values associated therewith, and (iii) summating the numeric values obtained from step (ii) to provide a composite expression score. In embodiments, the composite expression score obtained from step (iii) is compared to a control and the comparisonallows the sample to be designated as positive or negative for Barrett’s esophagus. In embodiments, the composite expression score is normalized. In embodiments, the composite expression score is scaled. In embodiments, the composite expression score is weighted.Weighted refers to the relevant value being adjusted to more appropriately reflect its contribution to the profile. In embodiments, the expression of level of each miRNA is calculated using 2'ACtmethod, the normalized expression values are log10transformed, and then used in the equations herein.

[0113] Embodiments 1-37

[0114] Embodiment 1. A method of detecting RNA in a human patient having Barrett’s esophagus or suspected of having Barrett’s esophagus, the method comprising detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the human patient, wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of two or more thereof.

[0115] Embodiment 2. The method of Embodiment 1, wherein the human patient has Barrett’s esophagus.

[0116] Embodiment 3. The method of Embodiment 1, wherein the human patient is suspected of having Barrett’s esophagus.

[0117] Embodiment 4. The method of any one of Embodiments 1 to 3. further comprising administering to the human patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof.

[0118] Embodiment 5. A method of treating Barrett’s esophagus in a human patient in need thereof, the method comprising: (i) detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from a human patient, wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of two or more thereof; and (ii) administering to the human patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof.

[0119] Embodiment 6. A method of treating Barrett’s esophagus in a human patient in need thereof, the method comprising administering to the human patient an effective amount of aproton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof; wherein a biological sample obtained from the human patient comprises an elevated expression level, relative to a control, of RNA; wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of two or more thereof.

[0120] Embodiment 7. A method of diagnosing a human patient with Barrett’s esophagus, the method comprising detecting an elevated expression level, relative to a control, of an RNA in a biological sample obtained from the human patient, thereby diagnosing the human patient with Barrett’s esophagus; wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of two or more thereof.

[0121] Embodiment 8. A method of monitoring a human patient at risk for developing Barrett’s esophagus, the method comprising: (i) detecting an expression level of an RNA in a biological sample obtained from the human patient at a first point in time; (ii) detecting an expression level of an RNA in a biological sample obtained from the human patient at a second point in time, wherein the second point in time is later than the first point in time; wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of two or more thereof.

[0122] Embodiment 9. The method of Embodiment 8, wherein an elevated expression level of RNA at the second point in time when compared to the expression level of RNA at the first point in time indicates that the human patient has an increased risk of developing Barrett’s esophagus or has Barrett’s esophagus.

[0123] Embodiment 10. The method of any one of Embodiments 7 to 9, further comprising administering to the human patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof.

[0124] Embodiment 11. The method of any one of Embodiments 4-6 and 10, comprising administering to the human patient the effective amount of the proton pump inhibitor, the potassium competitive acid blocker, the antacid, the histamine 2 blocker, the prokinetic agent, or the combination of two or more thereof.

[0125] Embodiment 12. The method of any one of Embodiments 1 to 11, wherein the RNAcomprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21. and miR-93.

[0126] Embodiment 13. The method of any one of Embodiments 1 to 12, wherein the RNA does not include miR-135b, miR-196a, miR-335, miR-15b, miR-17, miR-181a, miR-181b, or miR-196b.

[0127] Embodiment 14. The method of any one of Embodiments 1 to 11, wherein the RNA consists of miR-106b, miR-146a, miR-15a, miR-18a, miR-21, and miR-93.

[0128] Embodiment 15. The method of any one of Embodiments 1 to 14, wherein miR-106b is miR-106b-5p; miR-146ais miR-146a-5p; miR-15ais miR-15a-5p; miR-18ais miR-18a-5p; miR-21 is miR-21-5p; and miR-93 is miR-93-5p.

[0129] Embodiment 16. The method of any one of Embodiments 1 to 14, wherein miR-106b is hsa-miR-106b-5p; miR-146a is hsa-miR-146a-5p; miR-15a is hsa-miR-15a-5p; miR-18a is hsa-miR-18a-5p; miR-21 is hsa-miR-21-5p; and miR-93 is hsa-miR-93-5p.

[0130] Embodiment 17. The method of any one of Embodiments 1 to 16, wherein the biological sample is a liquid biological sample.

[0131] Embodiment 18. The method of any one of Embodiments 1 to 17, wherein the biological sample is a blood.

[0132] Embodiment 19. The method of any one of Embodiments 1 to 17, wherein the biological sample is plasma.

[0133] Embodiment 20. The method of any one of Embodiments 1 to 17, wherein the biological sample is serum.

[0134] Embodiment 21. The method of any one of Embodiments 1 to 16, wherein the biological sample is esophageal mucosa.

[0135] Embodiment 22. The method of any one of Embodiments 1 to 16, wherein the biological sample is esophageal tissue.

[0136] Embodiment 23. The method of any one of Embodiments 1 to 22, wherein the control is a human patient or population of human patients that do not have Barrett’s esophagus.

[0137] Embodiment 24. The method of any one of Embodiments 1 to 23, wherein the control is a human patient or population of human patients that have a negative endoscopic evaluation of the foregut.

[0138] Embodiment 25. The method of any one of Embodiments 1 to 24, wherein the humanpatient has gastroesophageal reflux disease.

[0139] Embodiment 26. The method of any one of Embodiments 1 to 25, wherein the human patient has reflux esophagitis.

[0140] Embodiment 27. The method of any one of Embodiments 1 to 26, wherein the Barrett's esophagus is long-segment Barrett's esophagus.

[0141] Embodiment 28. The method of any one of Embodiments 1 to 27, wherein the human patient is at risk of developing esophageal adenocarcinoma.

[0142] Embodiment 29. The method of any one of Embodiments 1 to 28, wherein the human patient has low-grade esophageal dysplasia.

[0143] Embodiment 30. The method of any one of Embodiments 1 to 28, wherein the human patient does not have esophageal dysplasia.

[0144] Embodiment 31. The method of any one of Embodiments 1 to 28, wherein the human patient does not have low-grade esophageal dysplasia.

[0145] Embodiment 32. The method of any one of Embodiments 1 to 28, wherein the human patient does not have high-grade esophageal dysplasia.

[0146] Embodiment 33. The method of any one of Embodiments 1 to 28, wherein the human patient does not have esophageal adenocarcinoma.

[0147] Embodiment 34. The method of any one of Embodiments 1 to 33, wherein the human patient does not have eosinophilic esophagitis.

[0148] Embodiment 35. The method of any one of Embodiments 4-6 and 10-34, wherein: the proton pump inhibitor is dexlansoprazole, esomeprazole, lansoprazole, omeprazole, pantoprazole, or rabeprazole; the potassium competitive acid blocker is fexuprazan, keverprazan, linaprazan, revaprazan, soraprazan, tegoprazan, vonoprazan, or zestaprazan; the antacid is aluminum hydroxide, calcium carbonate, magnesium carbonate, magnesium hydroxide, magnesium trisilicate, magaldrate, sodium bicarbonate, or sodium citrate; the histamine 2 blocker is cimetidine, famotidine, nizatidine, ranitidine, or roxatidine; and the prokinetic agent is cisapride, domperidone, levosulpiride, metoclopramide, or mosapride.

[0149] Embodiment 36. A kit comprising reagents capable of detecting an expression level of RNA from a biological sample; wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, and miR-93.

[0150] Embodiment 37. The kit of Embodiment 35, wherein the RNA consists of miR-106b, miR-146a, miR-15a, miR-18a. miR-21, and miR-93.

[0151] Embodiments N1-N20

[0152] Embodiment NE A method of detecting RNA in a human patient having Barrett's esophagus or suspected of having Barrett’s esophagus, the method comprising detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the human patient, wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of tw o or more thereof.

[0153] Embodiment N2. The method of Embodiment 1, wherein the human patient has Barrett’s esophagus.

[0154] Embodiment N3. The method of Embodiment 1, w herein the human patient is suspected of having Barrett’s esophagus.

[0155] Embodiment N4. A method of treating Barrett’s esophagus in a human patient in need thereof, the method comprising: (i) detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from a human patient, wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of tw o or more thereof; and (ii) administering to the human patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof.

[0156] Embodiment N5. A method of treating Barrett’s esophagus in a human patient in need thereof, the method comprising administering to the human patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof; wherein a biological sample obtained from the human patient comprises an elevated expression level, relative to a control, of RNA; wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of two or more thereof.

[0157] Embodiment N6. A method of treating Barrett’s esophagus in a patient in need thereof, the method comprising: (i) selecting a patient having a diagnosis of Barrett’s esophagus based on a Barrett’s esophagus risk score or an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient, wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of two or more thereof; and (ii) treating the patient from step (i) by administering to the patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof.

[0158] Embodiment N7. A method of diagnosing a human patient with Barrett’s esophagus, the method comprising detecting an elevated expression level, relative to a control, of an RNA in a biological sample obtained from the human patient, thereby diagnosing the human patient with Barrett’s esophagus; wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of two or more thereof.

[0159] Embodiment N8. A method of monitoring a human patient at risk for developing Barrett’s esophagus, the method comprising: (i) detecting an expression level of an RNA in a biological sample obtained from the human patient at a first point in time; (ii) detecting an expression level of an RNA in a biological sample obtained from the human patient at a second point in time, wherein the second point in time is later than the first point in time; wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of two or more thereof; and wherein an elevated expression level of RNA at the second point in time when compared to the expression level of RNA at the first point in time indicates that the human patient has an increased risk of developing Barrett’s esophagus or has Barrett’s esophagus.

[0160] Embodiment N9. The method of any one of Embodiments 1 to 8, wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, and miR-93.

[0161] Embodiment N10. The method of any one of Embodiments 1 to 9, wherein the RNA does not include miR-135b-5p, miR-196a-l-5p, miR-335-3p, miR-15b-5p, miR-17-5p, miR-181a-5p, miR-181b-5p, or miR-196b-5p.

[0162] Embodiment N11. The method of any one of Embodiments 1 to 10, wherein the RNA consists of miR-106b, miR-146a, miR-15a, miR-18a, miR-21, and miR-93.

[0163] Embodiment N12. The method of any one of Embodiments 1 to 11, wherein miR- 106b is miR-106b-5p; miR-146ais miR-146a-5p; miR-15ais miR-15a-5p; miR-18ais miR-18a-5p; miR-21 is miR-21-5p; and miR-93 is miR-93-5p.

[0164] Embodiment N 13. The method of any one of Embodiments 1 to 12, wherein the biological sample is a blood, esophageal mucosa, or esophageal tissue.

[0165] Embodiment N14. The method of any one of Embodiments 1 to 13, wherein the control is a human patient or population of human patients that do not have Barrett's esophagus or that have a negative endoscopic evaluation of the foregut.

[0166] Embodiment N15. The method of any one of Embodiments 1 to 14. wherein the human patient has gastroesophageal reflux disease, reflux esophagitis, or low-grade esophageal dysplasia.

[0167] Embodiment N16. The method of any one of Embodiments 1 to 15, wherein the Barrett’s esophagus is long-segment Barrett’s esophagus.

[0168] Embodiment N17. The method of any one of Embodiments 1 to 16, wherein the human patient is at risk of developing esophageal adenocarcinoma.

[0169] Embodiment N18. The method of any one of Embodiments 1-14, 16, and 17, wherein the human patient does not have esophageal dysplasia, low-grade esophageal dysplasia, highgrade esophageal dysplasia, esophageal adenocarcinoma, or eosinophilic esophagitis.

[0170] Embodiment N 19. The method of any one of Embodiments 4-6 and 9-18. wherein the proton pump inhibitor is dexlansoprazole, esomeprazole, lansoprazole, omeprazole, pantoprazole, or rabeprazole: the potassium competitive acid blocker is fexuprazan, keverprazan, linaprazan, revaprazan, soraprazan, tegoprazan, vonoprazan, or zestaprazan; the antacid is aluminum hydroxide, calcium carbonate, magnesium carbonate, magnesium hydroxide, magnesium trisilicate, magaldrate, sodium bicarbonate, or sodium citrate: the histamine 2 blocker is cimetidine, famotidine, nizatidine, ranitidine, or roxatidine; and the prokinetic agent is cisapride, domperidone, levosulpiride, metoclopramide, or mosapride.

[0171] Embodiment N20. A kit comprising reagents capable of detecting an expression level of RNA from a biological sample; wherein the RNA comprises miR-106b-5p, miR-146a-5p, miR-15a-5p, miR-18a-5p, miR-21-5p. and miR-93-5p.EXAMPLES

[0172] Objective: There is no clinically relevant serological marker for the early detection of esophageal adenocarcinoma (EAC) and its precursor lesion, Barrett’s esophagus (BE).

[0173] Design: EMERALD was a large, international, multi-center biomarker cohort study involving 792 patient samples from four countries (NCT06381583) to develop and validate a circulating miRNA signature for the early detection of EAC and high-risk BE. Tissue-based miRNA sequencing and microarray datasets (n=134) were used to identify candidate miRNAs ofdiagnostic potential, followed by validation using 42 pairs of matched cancer and nornial tissues. The usefulness of the candidate miRNAs was initially assessed using 108 sera (44 EAC, 34 EAC precursors, and 30 non-disease controls). A machine learning model (XGBoost+AdaBoost) was trained on RT-qPCR results from circulating miRNAs from a training cohort (n=160) and independently tested it in an external cohort (n=295).

[0174] Results: After a strict process of biomarker discovery and selection, 6 miRNAs were identified that were over-expressed in all sera of patients compared to non-disease controls from three independent cohorts of different nationalities (miR-106b, miR-146a, miR-15a, miR-18a, miR-21, and miR-93). A 6-miRNA diagnostic signature was established using the training cohort (AUC: 97.6%), and tested it in an independent cohort (AUC: 91.9%). This assay could also identify patients with BE among patients with gastroesophageal reflux disease (AUC:94.8%, sensitivity: 92.8%, specificity: 85.1%).

[0175] Conclusion: Using a comprehensive approach integrating unbiased genome-wide biomarker discovery and several independent experimental validations, we developed and validated a blood test to complement screening options for BE / EAC.

[0176] Introduction

[0177] By integrating a systematic genome-wide biomarker discovery and clinical validation approach in more than 750 tissue and blood specimens from multiple independent patient cohorts with EAC, HGD, LGD, BE, and healthy patients from five countries (USA, UK, Ireland, Italy, Netherlands), we have identified, developed, and established a novel liquid biopsy assay ( ’EMERALD" - Esophageal MicroRNAs of BaRRett, Adenocarcinoma, and Dysplasia) to complement EAC screening and prevention. This non-invasive circulating miRNA-based signature will be transformative in the clinic and lead to improved survival outcomes.

[0178] Results

[0179] Characteristics of patients with EAC, LGD, and HGD, and healthy patients

[0180] The characteristics of the study participants are summarized in Table 2. There were no significant differences in the distribution of age and sex between cases and NDCs in any cohort. Tissue and serum specimens from patients with EAC, HGD, LGD, and BE, as well as healthy subjects used in this study, were primarily collected prior to surgery or chemotherapy treatment. Overall, our study included 792 individual patient-derived specimens, of whom 134 were from in-silico studies, 84 were from a tissue cohort (42 vs. 42), and 574 were from the blood cohorts (565: 258 EAC, 139 BE, and 168 NDCs). All participants labeled as NDCs underwent upperendoscopic examination to exclude foregut diseases.

[0181] Identification of candidate miRNAs

[0182] In the first part of the tissue-based phase, we interrogated the TCGA miRNA expression dataset to identify candidate biomarkers that can distinguish patients with EAC from healthy subjects. We initially identified 22 differentially expressed miRNAs based on differential gene expression and significance level (FIG. 1A). We then ranked them based on AUC values and selected only the candidates that demonstrated a discriminative AUG value of > 70% and, finally, excluded the miRNAs with a low expression level. After this initial selection, we identified a pool of 14 candidate biomarkers of potential diagnostic interest (FIG. IB). Applying the unweighted pair-group Ward-D2 method for unsupervised clustering, only the cancer status was co-segregated with unsupervised clustering, while other clinical characteristics (biological sex, stage, histological differentiation, and race) did not (FIG. 1C). Next, we tested whether these biomarkers were also differentially expressed during the malignant progression from BE to LGD, HGD, and EAC. Using a public dataset (GSE16456) of 32 tissue biospecimens (6 EAC, 5 HGD, and 5 LGD with corresponding patient-matched normal mucosa), we assessed the miRNA expression levels of these 14 candidates, quantified with microarrays (as opposed to sequencing). We observed a statistically significant trend of progressively increasing expression from normal mucosa towards EAC (Anova, p<0.05) for 10 of the 14 biomarkers. Because the overall aim of this study was to develop a biomarker signature capable of detecting EAC and its precursor lesions, four miRNAs were excluded (hsa-miR-135b-5p, hsa-miR-196a-l-5p, hsa-miR-335-3p, and hsa-miR-15b-5p, FIG. 9)

[0183] Transitioning from in-silico analyses to RT-qPCR, we evaluated the expression levels of the remaining 10 candidates in a clinical cohort of 42 EAC patients with patient-matched adjacent normal tissue specimens. We confirmed the robustness of the in-silico predictions by verifying that nine were significantly overexpressed in EAC tissues also in the first clinical cohort of our study (p-value < 0.05, two-sided paired Student’s / -tests; FIG. 10). Therefore, after the exclusion of one miRNA (hsa-miR-17-5p), nine biomarker candidates were given full consideration for their ability' to differentiate EAC from NDCs and were therefore carried over to the blood-based phase of our study.

[0184] In summary’, the tissue-based discovery phase employed a systematic approach to identify' a panel of 9 candidate miRNAs independently associated with both EAC and its precursor lesions across three quantification methods (sequencing, microarray, and RT-qPCR) in three independent cohorts.

[0185] Development of the Circulating miRNAs Panel

[0186] Upon transitioning our tissue-based discovery7phase into a blood-based assay, we sought to confirm whether the 9 tissue-derived candidate miRNAs could be measured in blood and if these were also upregulated in the serum collected from EAC patients. In our development cohort (N=108; 78 cases vs. 30 NDCs), three miRNAs (hsa-miR-I81a-5p, hsa-miR-181b-5p, and hsa-miR-196b-5p) had an expression level below the detection limit (average cycle threshold >35) and were excluded. The remaining six miRNAs (hsa-miR-106b-5p, hsa-miR-146a-5p, hsa-miR-15a-5p, hsa-miR-18a-5p, hsa-miR-21-5p, and hsa-miR-93-5p) were abundant in blood and could be carried over to the subsequent phases of our study (model training and independent testing). Interestingly, these biomarkers all demonstrated the potential to discriminate cases vs. NDCs, with AUC values ranging from 62.1% to 80.8% (FIG.2). More importantly, we observed that five of these biomarkers were significantly upregulated in the EAC / HGD vs. NDC pair-wise comparison and in patients with BE / LGD compared to NDCs, supporting their potential as non-invasive biomarkers of EAC pre-cancerous lesions (p-value <0.05, one-sided Student’s Wests). Four of these biomarkers were expressed at similar levels in BE / LGD and EAC / HGD, potentially indicating that they represent neoplastic processes that occur early during esophageal malignant transformation and may be of diagnostic potential for both EAC and its precursor lesions.

[0187] Development and Independent Testing of a Circulating miRNA Signature

[0188] Next, we assessed the expression of these six miRNAs in the training cohort (N=160, 96 EAC patients, of whom 20 with stage 0 disease, pTis; 64 NDCs). In this cohort, too, all six miRNAs were significantly upregulated in the serum of patients compared with NDCs (p-value <0.05, two-sided Student’s f-tests; FIG. 11). We then fit a stacked machine learning model (XGBoost + AdaBoost, FIG. 12A) based on ACt values. The resulting classifier. EMERALD, was based on the contributions from both XGBoost and AdaBoost. The most important contributors to both models were hsa-miR-106b-5p, hsa-miR-93-5p, and hsa-miR-18a-5p (FIGS. 12B-12C for XGBoost; FIGS. 12D-12E for AdaBoost). This approach allowed the stratification of patients in the training cohort into high and low-risk groups based on Youden's index (-0.103). As a result, the blood-based test achieved a high performance in distinguishing EAC patients from NDCs with an AUC of 97.6% (95% CI, 95.5%-99.6%), with a corresponding sensitivity of 95.8% and a specificity of 95.2% (FIG.3 A).

[0189] To independently test the diagnostic accuracy and performance of our EMERALD liquid biopsy, we evaluated the signature's robustness in an external testing cohort. Among the297 serum samples included in the testing cohort (118 EAC, 105 BE, 74 NDCs), we observed substantial repl i cabi 1 i ty of our training efforts, where the 6-miRNA signature maintained a strong ability to distinguish patients from NDCs, with an AUC value of 91.9% (FIG. 3B) and corresponding sensitivity' and specificity7values of 82.5% and 90.5%, respectively (Table 1).More interestingly, the key hypothesis of our machine-learning approach was that combining two algorithms would enhance assay robustness. The density plots (FIGS. 3C-3D, respectively) confirm this, as the distribution of cases and NDCs in both training and testing cohorts closely resemble each other. Two distinct clusters emerge in both cohorts - one enriched with cases (green) in the top-right comer and another enriched with NDCs (pink) in the bottom-left comer.

[0190] Table 1. Statistical evaluations of the EMERALD test for differentiating cases from controls in the training and validation cohorts.Training cohort [N= 160] External and independent testing cohort [N=297]Area under the 97.6% 91.9%receiver operating (95.5 -99.6%) (88.3 -95.5%) characteristic curve(95% C.I.)No. No. Sensitivity No. No. Sensitivity detected (95%> C.I.) detected (95% C.I.) Esophageal 96 92 95.8% 223 184 82.5% adenocarcinoma and (89.8-98.4%) (77.0 - 86.9%) Barrett’s esophagus,with or withoutdysplasiaEAC / HGD 96 92 95.8% 125 105 84.0%(89.8-98.4%) (76.6 - 90.4%) BE / LGD (A) — — — 98 79 80.6%(71.7- 87.2%) No. No. Specificity No. No. Specificity negative (95% C.I.) negative (95% C.I.) Non-disease controls, 64 61 95.2% 74 67 90.5% all (87.1 -98.4%) (81.7-95.3%) Non-disease controls, — — 47 44 93.6% with long-standing (82.8 -97.8%) or refractory heartburn(B)Non-disease controls, — — 39 37 94.9% with dysphagia, (83.1 -98.6%) odynophagia, orregurgitation (B)Non-disease controls, — — 51 50 98.0%with epigastralgia (B) (89.7-99.7%)

[0191] Table 1 Footnotes: (A) The training cohort enrolled several Tis, but not BE / LGD; (B) The training cohort did not include symptom-specific data other than the indication for BEscreening. No. = number.

[0192] It is noteworthy that while the training cohort primarily consisted of patients with early -stage or in situ EAC. the testing cohort included a significant number of patients with pre-cancerous lesions (BE. LGD, or HGD). Despite this compositional difference, patients with either EAC or pre-cancerous lesions consistently displayed higher EMERALD values compared to NDCs in both cohorts (FIGS.4A-4B, respectively). Furthermore, we investigated whether higher EMERALD scores correlated with a greater likelihood of esophageal disease.Interestingly, in both cohorts, the odds ratios of having the disease progressively increased with higher EMERALD values (FIGS.4C-4D, respectively). The overall trend of the spline curves supports the reproducibility of the assay across both cohorts despite their differences.

[0193] Discriminatory Capacity Among Controls with GERD-Related Symptoms

[0194] In the testing cohort, 63.5% of the NDCs had long-standing GERD-related symptoms, with a few having reflux esophagitis (FIG. 5A). Given that EAC screening is currently recommended for individuals with chronic GERD or risk factors for BE and EAC [21-25], we specifically evaluated our model's performance in this high-risk population. The results were promising, demonstrating a clear stepwise increase in EMERALD values. With chronic GERD patients without esophagitis as the baseline, we observed a progressive rise in EMERALD scores with increasing disease severity7(for both BE / LGD and EAC / HGD, FIG. 5B). Consistent with the prior observation, EMERALD scores exhibited a statistically significant stepwise increase (p<0.0001 for all comparisons, Student's t-tests) between chronic GERD controls with esophagitis and patients with BE / LGD or EAC / HGD. There was no statistically significant difference between GERD patients with vs. without reflux esophagitis (p=0.79) and between BE / LGD vs. EAC / HGD (p=0.45). Indeed, the circulating 6-miRNA signature maintained a robust discriminatory power for the aggregate measure of esophageal cases vs. symptomatic controls (N=270; AUC = 95.0%, Cl95%: 92.2 - 97.9%, sensitivity: 92.8%, specificity: 85.1%) and, most importantly, it distinguished pre-malignant lesions from symptomatic controls (n = 145; AUC: 94.8%, CI95% 91.3-98.2%, sensitivity7: 92.8%, same specificity7; FIG. 5C). In addition, the performance of the 6-miRNA signature was superior to individual miRNAs when discriminating EAC / HGD from symptomatic controls (N=172, FIG.5D), achieving an AUC value of 95.3% (95% CI 92.2 - 98.3%) with 93.6% sensitivity and the same specificity. Similar results were observed among those who reported symptoms other than heartbum, including epigastralgia (FIG. 13A-13C) or dysphagia, odinophagia, and regurgitation (FIGS. 13D-13F).Finally, to assist clinicians in prioritizing endoscopic evaluations, we conducted a multinomialmultivariate logistic regression analysis. This analysis identified high-risk and low-risk subgroups within EMERALD-positive subjects, providing valuable information for risk stratification (FIG. 14). Collectively, these data confirmed the diagnostic significance of our non-invasive, 6-miRNA signature and highlighted its ability to separate symptomatic patients with pre-malignant or malignant diseases from those without.

[0195] Finally, using the sensitivity and specificity calculated in the testing cohort, we constructed a Markov-based decision model to simulate the course of events for five cohorts of patients with chronic GERD, aged 45 at simulation start, undergoing no screening, endoscopybased screening every 10 years, or EMERALD-based screening every five-, three-, or one-year for 30 years (FIG.6A). The results of our cost-effectiveness analysis support that both an endoscopy -first approach and a non-invasive approach would lead to a stage-shift effect, where an increase in early-stage diagnoses would be observed after a few years, with a subsequent decrease in the number of late-stage diagnoses (FIGS. 6B-6E), which would result in a reduction in mortality (FIG.6F). While the costs of a more compliant and non-invasive approach would initially surpass the costs of a less compliant endoscopy-first approach (especially at more frequent intervals of testing), the overall costs would be in favor of a non-invasive program after 15 years, justified by a reduction in the number of patients with more advanced-stage and more expensive diagnoses (FIG. 6G), with a corresponding increase in QALYs (FIG. 6H). Finally, we estimated the cost-effective frontier to be in favor of a non-invasive, EMERALD-based approach at 5-year intervals (FIG. 61), which would reduce the number of cancer-attributable deaths with a stage-shift effect for a lower price than other intervals of testing.

[0196] Discussion

[0197] This multicenter study is the first to successfully develop and externally validate a liquid biopsy for the noninvasive diagnosis of EAC and BE (with and without esophageal dysplasia). We employed a multi-step approach with multiple patient cohorts to ensure our final assay could capture the full spectrum of the disease, ranging from GERD to EAC. Leveraging ML and rigorously selected biomarkers, the EMERALD assay was developed and independently tested in two large cohorts. The assay effectively distinguished BE with and without esophageal dysplasia (LGD / HGD), as well as EAC, from control groups, including non-disease controls and chronic GERD patients with and without esophagitis. This offers a valuable addition to the existing screening options for this aggressive and deadly cancer. Furthermore, using a Markov model, we estimated that implementing the EMERALD assay at a 5-year interval will offer themost cost-effective strategy to reduce EAC mortality and incidence.

[0198] Once a rare cancer, EAC has become the dominant form of esophageal cancer in developed countries [13,14], While treatment advancements have modestly improved survival rates, half of patients die within a year of their diagnosis

[0015] , A key limitation of the current approach lies in its reactive nature, focusing only on EAC. A more strategic approach would prioritize identifying individuals at high risk, those with BE [16-20], Though largely benign, BE represents a precursor lesion for EAC. Early detection and intervention at this pre-malignant stage, with esophageal dysplasia surveillance and treatment, can interrupt the disease's natural history and prevent it [26-28], The current screening strategies could benefit from additional, non-invasive options. The CYTOSPONGE® represents such an option, detecting the trefoil factor 3 (TFF3) through a patient-swallowed sponge that captures esophageal epithelial cells

[0041] , In a pragmatic, multi-centric trial, it demonstrated encouraging patient interest rates (39% expressing interest), with high sensitivity for BE (80 to 90%, depending on the BE segment length) and specificity (92%) [42,43], A blood-based test expands the arsenal of screening tools by offering a readily repeatable, minimally invasive approach that will improve overall screening participation.

[0199] Materials and Methods

[0200] Study Populations. This study analyzed data from 792 patients, both from publicly available miRNA expression datasets (N=134) and four prospectively collected independent clinical cohorts (N=658, FIG. 7). One clinical cohort was comprised entirely of histological biospecimens, and three were based on blood for the development, training, and independent evaluation of the liquid biopsy assay (Table 2).

[0201] The in-silico discovery phase utilized expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO, accession number: GSE164560). It was designed to identify biomarkers differentially expressed between EAC tissue and normal esophageal mucosa (N=134), and then select those demonstrating a statistically significant increase across disease stages (Anova p <0.05) from patient-matched normal mucosa to LGD, HGD, and EAC (N=32). We further excluded miRNAs lacking significant expression differences between EAC tissues (Stage I-III) and patient-matched normal mucosa (N=42 each) from a separate clinical cohort (Radboud University Medical Center, Netherlands). The remaining miRNAs were examined in our “Development cohort” (N=108), encompassing serum specimens from 51 patients with EAC / HGD, 27 with BE / LGD, and 30 NDCs (Norton Thoracic Institute at St. Joseph's Hospital and Medical Center, Phoenix, AZ, USA; and Baylor Universify MedicalCenter, Dallas, TX, USA).

[0202] The diagnostic assay was developed in the “Training cohort” (N=160), which included 96 patients with EAC / HGD (Veneto Institute of Oncology’ IOV-IRCCS, Padova, Italy) and 64 NDCs. Finally, the blood-based assay was externally and independently tested in the “Testing cohort” (N=306), which included 125 patients with EAC / HGD, 98 BE / LGD, and 74 NDCs (Queen's University Belfast, United Kingdom, and the National Cancer Registry Ireland, Ireland, for the FINBAR study; The Johns Hopkins Hospital, Baltimore, MD, USA; Translational Genomics Research Institute, Phoenix, AZ, USA; 9 excluded after quality control).

[0203] All individuals with a diagnosis of EAC, HGD, LGD, or BE were considered cases. All individuals with a negative endoscopic evaluation of the foregut were considered NDCs. whether or not they had GERD-associated symptoms

[0035] , The presence of esophageal dysplasia was attested and confirmed by a second pathologist with expertise in esophageal diseases [21,36].

[0204] Study Design. This study was an international, multi-institutional, retro-prospective, multi-phase biomarker study covering phases I, II, and III (PRoBE classification), STARD compliant and included both tissue- and the blood-based biospecimens (FIG. 8). Three independent tissue-based cohorts were utilized to discover biomarkers with diagnostic potential for EAC and BE, and prioritize the biomarkers with the highest diagnostic potential (phase I). The serum biomarker panel was finalized in the blood-based biomarker “development cohort.” Subsequently, we established an EAC risk-score formula ('EMERALD') using a two-levels machine learning approach (phase II). After training on qRT-PCR data from the training cohort (N=160), the model w as fully locked and then tested in an independent, non-overlapping, external testing cohort (N=306, phase III).

[0205] The study w as approved by the Institutional Review Boards of each participating institution (Supplementary Methods and Table 2), conducted in accordance with the Declaration of Helsinki, and registered and completed on ClinicalTrials.gov (NCT06381583). All participants provided written informed consent.

[0206] Assay. Tissue samples were collected therapy-naive, placed in RNAlater immediately after surgery and stored at -80°C. Whole blood samples were collected primarily before treatment, centrifuged at 3000 g for ten minutes within 12 hours after collection, and stored in RNase-free Eppendorf tubes at -80°C.

[0207] RNA was isolated from tissue and serum using the RNeasy Mini and miRNeasySerum / Plasma Kits, respectively (Qiagen, Valencia, CA). RNA was then reverse-transcribed using the TaqMan MicroRNA Reverse Transcription Kit (Applied Biosystems). Real-time PCRs were conducted using MicroRNA Assay Kits and TaqMan Universal Master Mix II using QuantStudio 6 Flex Real-Time PCR System (Applied Biosystems). The expression of miRNAs was normalized against U6, a commonly used endogenous control (Ambion, Austin, TX), and the data was 2’ACttransformed.

[0208] Statistical Approaches. Candidate biomarkers were selected in the TCGA dataset based on the following criteria: log2(Fold-Change) >1 (EAC / HGC vs NDCs). a Benjamini -Hochberg-adjusted p-value <0.00001, an individual candidate AUC >70%, and an average miRNA expression level higher than the median of all differentially expressed miRNAs. This phase was adequately powered (power=0.94) to detect a 2-fold change (p = 200%) at a false discovery7rate of 5% under conservative specifications of the depth of coverage for the transcript (ko=3Ox) and coefficient of variation in expression between samples (CV=0.5).

[0209] In all qPCR experiments, expression levels were compared using two-sided Student’s t-tests for paired comparisons and ANOVA for comparisons between multiple groups. A p-value < 0.05 was considered statistically significant. The area under the receiver operating characteristic curves (AUROCs) with 95% confidence intervals (CI) were computed by the method of DeLong, with optimal cutoff thresholds determined by Youden’s index. The odds ratios of disease as a function of the EMERALD score were computed with restricted cubic spline curves.

[0210] The final diagnostic model, named EMERALD, involved two independently trained ML models using six candidate biomarkers. The two algorithms used, XGBoost and AdaBoost, are popular models that employ a sequential iterative boosting strategy from weaker learners (decision trees for XGBoost and decision stumps for AdaBoost). Multivariate logistic regression was employed to derive a formula to predict EAC risk from the two models. This model was fully locked for independent and external testing.

[0211] A Markov model was used to simulate five cohorts of patients with chronic GERD, all aged 45 years, undergoing one of five screening options: endoscopy every 10 years, EMERALD-based screening every7five, three, or one year, vs. no screening. The model utilizes a cycle time of one year and runs for 30 years, allowing to observe early detection, disease prevention, and disease stage anticipation (stage shift). Quality-adjusted life years (QALYs) and costs serve as the primary outcome measures. The model incorporates the possibility7of progression to BE, dysplastic BE, and, ultimately, EAC. The natural history7of EAC, thecompliance with screening, the treatment outcomes, and the associated costs were derived from the literature or, when unavailable, were internally derived [37-40], Full details on the Markov model assumptions are presented in the Supplementary Methods. All analyses were performed in R.

[0212] Supplementary Methods

[0213] Supplementary Study populations. This study included 792 patients, both from publicly available miRNA expression datasets (N=134) and four independent clinical cohorts (N=658). One cohort was comprised entirely of histological bio-specimens, and three were based on blood for the development, training, and independent evaluation of a liquid biopsy assay (Table 2 and FIG. 7).

[0214] Supplementary In-silico discovery cohorts. Initially, we interrogated The Cancer Genome Atlas (TCGA) miRNA expression dataset for biomarker discovery. The TCGA dataset consisted of tissue-based small RNA-seq data from 89 stage I-IV EAC patients and 13 normal esophageal mucosae. The level-3 expression data of 2082 miRNAs and the corresponding clinical data were downloaded from The Firehose Broad GDAC portal (gdac.broadinstitute.org). The miRNA expression levels, measured by reads per million miRNAs mapped (RPM), were log2-transformed and then used for subsequent analyses. Given the sample size, this phase of our study was adequately powered (powei^0.94) to detect a 2-fold change (p = 200%) at a false discovery' rate of 5% under conservative specifications of the depth of coverage for the transcript (Xo=3Ox) and coefficient of variation in expression between samples (CV=0.5). Another public tissue miRNA dataset was downloaded from Gene Expression Omnibus (GEO, accession number: GSE16456), with 32 paired diseased and normal tissues from 16 patients diagnosed with EAC, BE with HGD, or BE with LGD to evaluate which candidate microRNAs increase from normal, to LGD, HGD. and EAC and, hence, tracked with the neoplastic transition events [1].

[0215] Supplementary Clinical Cohorts Characteristics. To evaluate the potential of these candidate miRNAs as specific biomarkers for EAC and its precursor lesions, we assessed their expression levels and confirmed their bona fide biomarker status in four patient cohorts encompassing 649 clinical specimens. This evaluation involved three steps: first, we evaluated the expression of the biomarkers discovered during the in-silico discovery phase in a tissue cohort using RT-qPCR. Second, in a blood-based cohort, we identified which tissue markers were also present in blood. Finally, two additional blood cohorts were used to develop and test a diagnostic assay for EAC and its precursor lesions.

[0216] For tissue validation of the candidate miRNAs identified in the TCGA and GSE16456 datasets, we used a single-center case-only study conducted in the Netherlands in patients who had received esophagectomy for EAC (Ethical Committee approval obtained from the Radboud University Medical Centre). This cohort included 42 EAC tissue slides and 42 matched normal mucosal tissues collected from stage I-III EAC patients undergoing esophageal resection without pre-operative therapy. All participants to this cohort were enrolled at the Radboud University’ Medical Center, Netherlands, between 1999 and 2009.

[0217] The candidate miRNAs identified from the tissue cohort were first examined in our “Development cohort” (N=108), which was a multi-institutional case-control study conducted at the Norton Thoracic Institute at St. Joseph's Hospital and Medical Center, Phoenix, AZ, USA, and the Baylor University Medical Center, Dallas, TX, USA (Ethical Committee approval obtained from both institutions separately). This study phase included serum specimens from 51 EAC / HGD patients, and 27 serum specimens from BE / LGD patients, and 30 NDCs. All study participants were enrolled between 2011 and 2015 at the Norton Thoracic Institute at St.Joseph's Hospital and Medical Center, and at Baylor University Medical Center, both in the USA.

[0218] After the final panel of serum biomarkers was established, a diagnostic assay was developed in our “Training cohort” (N=160). This study cohort was conducted in Italy (Ethical Committee approval obtained from the University of Padua), and it included 96 specimens from EAC / HGD patients, all enrolled between 2011 and 2015 at the Veneto Institute of Oncology IOV-IRCCS, Padova, Italy, and 64 from NDCs. Finally, the miRNA signature was validated using our “Testing cohort” (N=306), which was a multinational case control study that drew participants from several institutions and also leveraged samples from the FINBAR study. Our “Testing cohort”, utilized a total of 297 serum specimens from 125 EAC / HGD patients, 98 BE / LGD patients, and 74 NDC subjects from different institutes in the UK, Ireland (FINBAR), and the USA. The FINBAR (Factors Influencing the Barrett's Adenocarcinoma Relationship) Study was an all-Ireland population-based case-control study for the etiology of EE, BE. and EAC [2,3] . Incident cases of EAC, BE, and population NDCs were recruited from Northern Ireland and the Republic of Ireland between March 2002 and July 2004, while EE cases were recruited from Northern Ireland only between 2004 and 2005. BE, EE, and NDCs were matched within 5-year age and sex strata to EAC (maximum age: 85 years). This study was compliant with the Declaration of Helsinki, and all procedures were approved by the Research Ethics Committee of the Queen’s University Belfast, Northern Ireland, Clinical Research EthicsCommitee of Cork Teaching Hospitals and Research Ethics Committee Board of St. James's Hospital, Dublin. All the subjects provided written informed consent to participate in the study. Specifically for the EMERALD study, 206 patient derived samples were drawn from the FINBAR study (EAC, N=118; BE, N=88), all recruited from both Northern Ireland and the Republic of Ireland. The remaining 91 subjects were derived from another multi-institutional case-control study that involved the Johns Hopkins Hospital, Baltimore, MD, and the Translational Genomics Research Institute, Phoenix, AZ (Ethical Commitee approval obtained from both institutions separately). All subjects were enrolled between 2002 and 2004. In total, only 9 samples in the “Testing cohort” were excluded from the analyses by quality control (QC).

[0219] The study was approved by the Institutional Review Boards of each participating institution (previously reported [4-6] and detailed in Table 2), conducted in accordance with the Declaration of Helsinki, and registered and completed on ClinicalTrials.gov (NCT06381583). All participants provided writen informed consent.

[0220] Supplementary Study Design. Our study was STARD compliant and encompassed two major phases, one tissue- and the other blood-based, respectively (FIG. 8). The tissue phase consisted of two sub-studies: an in-silico approach to biomarker discovery in two cohorts and a clinical validation for biomarker prioritization. The blood-based segment consisted of three independent phases in three non-overlapping patient cohorts: blood-phase biomarker panel development, and subsequent liquid biopsy training, and independent testing.

[0221] During the in-silico phase, tissue-based genome-wide miRNA expression datasets from TCGA and GSE16456 were used to identify miRNAs that were overexpressed in the EAC tissue specimens compared to normal mucosa. Candidate miRNAs were then prioritized using the following criteria: log2(Fold-Change) >1 (EAC / HGC vs non-disease controls, NDCs), a strict statistical level of significance of Benjamini -Hochberg-adjusted p-value <0.00001. an individual candidate AUC >70%, and an average miRNA expression level higher than the median of all differentially expressed miRNAs. Because our intention was to generate an assay capable of detecting both EAC and its precursor lesions, the expression levels of the candidate miRNAs were then evaluated in a second, microarray -based tissue phase that utilized a cohort of EAC, HGD, and LGD with matching normal mucosa. Only the candidate biomarkers that demonstrated a statistically significant (Anova p <0.05) progressive increase in expression from normal to LGD, HGD, and EAC were carried over to the following phase of tissue validation. To confirm the results from sequencing and microarray methods, we further tested the differential expression of the remaining candidates also by RT-qPCR in a cohort of matchedtumors and adjacent normal tissues from 42 EAC patients.

[0222] In order to test whether miRNAs overexpressed in tissues were also detectable and differentially expressed in blood, we assessed their expression levels and potential as biomarkers in a serum cohort of 51-EAC / HGD, 27 LGD / BE, and 30 NDC. After this final biomarker selection step, the efficacy of the candidate miRNAs was examined in the training and independent validation phases. To establish an EAC risk-score formula, we employed a two-level machine learning approach (details provided in subsequent sections) using qRT-PCR data from the training cohort (N=160, 96 cases vs. 64 NDCs). After the efforts to prioritize biomarkers that would detect both EAC and its precursor lesions, the choice to train an algorithm primarily on EAC / HGD was strategic to ensure that the resulting diagnostic model would be particularly effective in detecting the highest risk lesions. After training, the model was fully locked and validated in an independent, non-overlapping, external cohort (N=306), which included, after QC, 125 EAC / HGD, 98 BE / LGD, and 74 NDCs, where performance and robustness were evaluated.

[0223] Supplementary Definitions and inclusion / exclusion criteria. All individuals with a diagnosis of EAC, HGD, LGD, or BE were considered cases. All individuals with a negative endoscopic evaluation of the foregut were considered NDCs, whether or not they had GERD-associated symptoms [7-9], The presence or absence of esophageal dysplasia, whether of low- or high-grade, was attested and confirmed by a second pathologist with expertise in esophageal diseases [10,11],

[0224] Supplementary RNA isolation and quantitative real time-PCR. Tissue samples from our clinical tissue cohort (tumor and the corresponding normal mucosa) were obtained from patients subjected to esophagectomy without any pre-operative therapy, and they were immediately placed in RNAlater (Qiagen, Germany) and then stored at -80°C. Whole blood samples from each participant were collected primarily before treatment and centrifuged at 3000 g for ten minutes within 12 hours after collection. The serum samples were stored in an RNase-free Eppendorf tube at -80°C.

[0225] RNA was isolated from tissue specimens using the RNeasy Mini Kit (Qiagen, Valencia, CA), and for serum RNA isolation, the miRNeasy Serum / Plasma Kit (Qiagen) kit was used according to the manufacturer’s instructions to extract RNA enriched in small RNAs. Briefly, serum samples were thawed on ice and centrifuged at 10,000 rpm for five minutes to remove cellular debris. 200 pL of supernatant was lysed in 1,000 pL of Qiazol Lysis Reagent. For normalization of sample-to-sample variation during the RNA isolation procedures, syntheticC. elegctns miRNA (cel-miR-39, Qiagen) was added to each denatured sample

[0012] . For miRNA-based qRT-PCR assays, RNA from tissue and serum specimens was reverse-transcribed using the TaqMan MicroRNA Reverse Transcription Kit (Applied Biosystems) according to manufacturer’s instructions in a total reaction volume of 6 pL. Real-time PCRs were conducted using MicroRNA Assay Kits and TaqMan Universal Master Mix II, no UNG (Applied Biosystems) using QuantStudio 6 Flex Real-Time PCR System (Applied Biosystems). The expression of miRNAs was normalized using U6 (Ambion, Austin, TX), an endogenous control commonly used to normalize miRNA expression data

[0013]

[0014] , All data were represented as 2" ACt

[0226] Supplementary Model Architecture and Hyperparameters. The final diagnostic model, named EMERALD, is a stacked model. Two independent ML models were allow ed to leam patterns from the final six candidate miRNAs. The two algorithms used, XGBoost and AdaBoost, are popular models that employ a sequential iterative boosting strategy to convert weaker learners (decision trees for XGBoost and decision stumps for AdaBoost) into a robust model. XGBoost is generally preferred for complex, high-dimensional data due to its extensive system optimization options and faster computations. However, AdaBoost performs well for data with low noise levels, as was the case in our dataset after our consistent efforts in candidate miRNA selection. As both approaches allow for extracting meaningful information from data, but in different ways, both were used independently. In the second step, multivariate logistic regression was employed to derive a formula to predict EAC risk from the tw o models (‘ghn’ function of R) and. at this time, the resulting model was fully locked before independent and external validation.

[0227] The XGBoost model was allowed to undergo a maximum of 500 training rounds with gradient boosting trees implemented by the 'xgboosf package in R (Version 0.1.3). Given our goal to optimize both sensitivity7and specificity, we utilized the area under the curve (AUC) and precision-recall (‘aucpr’ function) as the evaluation metrics. To balance model complexity and avoid overfitting, we allowed the tree depth to reach a maximum of five branches, imposed a 75% subsample parameter for training data selection, and employed a high-pruning strategy (y=5). Finally, we determined model explainability by analyzing feature importance and SHAP values using the 'SHAPforxgboosf (Version 0.1.3) and 'fmsb' (Version 0.7.6) packages in R. The AdaBoost model, implemented with the 'ada' package in R (Version 2.0-5), underwent up to 50 training iterations with a sampling fraction of 55% and, to control overtraining, the learning rate was reduced to 20% with the default boosting strategy. For this model, too, we analyzedfeature importance and SHAP values, using the ‘varplof function within the 'ada' package and the ‘kemelshap' package (Version 0.4.1) in R, respectively.

[0228] Supplementary Statistical Analysis. During the in-silico biomarker discovery phase, differential miRNA expression analysis was performed using an empirical Bayes method by ‘limma’ package in R, and the resulting p-values were adjusted using the Benjamini -Hochberg method. For visual processing of sequencing data, volcano plots, heatmaps, and ridgeline plots were generated in R using the ‘EnhancedVolcano’ (Release 3.18), ‘pheatmap’ (Version 1.0.12), and ‘ggridges’ (Version 0.5.6) packages, respectively. The statistical power for identification of differentially expressed miRNAs from the TCGA dataset was calculated using the Bioconductor ‘RNASeqPower’ package in R (Version 2.12.0).

[0229] In all qPCR experiments, differential miRNA expression was analyzed using two-sided Student’s / -tests for paired comparisons and ANOVA for comparisons between multiple groups, with a p-value of < 0.05 considered statistically significant. A receiver operating characteristic (ROC) curve was generated, and the area under the ROC curve (AUC) with 95% confidence intervals (CI) was computed by the method of DeLong with 2000 stratified bootstrap replicates ('pROC,' Version 1.18.5), with optimal cutoff thresholds determined by Youden’s index ('cutpointr,1Version 1.1.2). The odds ratios of disease as a function of the EMERALD score were computed with restricted cubic spline curves (‘plotRCS’, Version 0.1.4).

[0230] We employed a Markov model-based decision analysis to evaluate the potential clinical and economic impact of the EMERALD assay. This approach simulates the course of events for five cohorts of patients with chronic GERD, all aged 45 years at the simulation start. Each cohort underwent one of five screening options: endoscopy every 10 years or EMERALD-based screening every five, three, or one year (implemented using the 'heemod' package, Version 1.0.1). The model utilizes a cycle time of one year and runs for 30 years, allowing sufficient time to observe the impact of early detection, disease prevention, and disease stage anticipation at presentation (stage shift). Quality-adjusted life years (QALYs) and associated costs sen e as the primary outcome measures. The base-case patient is a 45-year-old with chronic reflux for at least five years. The model incorporates the possibility of progression to BE, dysplastic BE, and, ultimately, EAC. The annual incidence rates for BE (3%) and malignant transformation (0.3%), as well as the prevalence of BE at simulation start (8%), were derived from the literature [15-17], Age-specific mortality' rates for the general population were obtained from the WHO mortality registry. We opted to focus on a single age group to limit model complexity and because of limitations in high-quality data for assigning precise age-related risks. Acomprehensive PubMed search identified relevant articles on the natural history' of EAC. including treatment outcomes and associated costs for both conservative and surgical approaches [9,18-21], Data from these sources informed the model's parameters regarding progression rates, cure rates, clinical characteristics, costs, and QALYs. Internally derived costs were used when data was unavailable [9,18-21], Importantly, the model accounts for the anticipated differences in patient compliance between screening strategies. Compliance with endoscopy was estimated at 10% based on prior studies [22-24], For the non-invasive EMERALD assay, we conservatively estimated a 45% compliance rate, drawing from data on adherence observed in other liquid biopsy studies [25,26], All analyses were performed in R.

[0231] With reference to Table 2, the abbreviations are: BE, Barret’s esophagus; GERD, Gastro-Esophageal Reflux Disease; HGD, High-grade dysplasia; LGD, Low-grade dysplasia; RE, Reflux Esophagitis. Footnotes: a = Sixteen are adjacent normal esophageal tissue matched to an equal number of cases; b = All are adjacent normal esophageal tissue; c = Numbers are provided without the 9 samples that did not pass quality7control; d = Sources: TCGA (n=89) and GSE16456 (n=32); e = A single-center case-only study conducted in the Netherlands in patients who had received esophagectomy (Ethical Committee approval obtained from the Radboud University Medical Centre); f = A multi-institutional case-control study that involved the Norton Thoracic Institute at St. Joseph's Hospital and Medical Center, USA, and the Baylor University Medical Center, USA (Ethical Commitee approval obtained from both institutions separately); g = A single-center case-control study conducted in Italy, case-control study (Ethical Committee approval obtained from the University of Padua); h = The FINBAR (Factors Influencing the Barret's Adenocarcinoma Relationship) Study was an all-Ireland population-based case-control study (n=206), all recruited from both Northern Ireland and the Republic of Ireland (Ethical commitee approval obtained from the Research Ethics Commitee of Queen's University7, Belfast, Northern Ireland; Clinical Research Ethics Commitee of the Cork Teaching Hospitals; and Research Ethics Commitee Board of St. James's Hospital. Ethical approval was separately obtained from Queen's University for the recruitment of reflux esophagitis patients); i = A multi-institutional case-control study (n=91) that involved the Johns Hopkins Hospital, Baltimore, MD, and the Translational Genomics Research Institute, Phoenix, AZ (Ethical Commitee approval obtained from both institutions separately).

[0232] Table 2Tissue (N=218) Blood (N=565)0In silico Tissue Development Training Validation Total0Cohorts Cohort Cohort Cohort Cohort0N=783 (N = 134)d(N = 84)e(N = 108)f(N = 160)8(N = 297)h,‘ Cases0(N=544) 105 42 78 96 223° SexMen 90 31 44 90 181 Women 15 11 20 6 42 Undisclosed 0 0 14 0 0Age (years)Median (range) 69 (27-86) 60 (35-70) 59 (28-87) 62 (27-83) 66 (34-84) EAC Precursors(N=149)BE 0 0 20 0 88 LGD 5 0 7 0 10 HGD 5 0 7 0 7 Total 10 0 34 0 105 EAC (N=395)0 (T1S) 0 0 0 20 0IA 3 5 0 4 0IB 11 4 2 11 0ILA 7 12 3 15 0IIB 20 3 7 10 0IIIA 13 5 5 12 0IIIB 8 10 5 9 0 me 11 3 8 11 0IV 11 0 5 1 0 Unavailable 11 0 9 0 118 Total 95 42 44 96 118 Non-disease controls 29a42b30 64 74° (N=239)0SexMen 23 31 22 55 58 Women 6 11 8 9 14 Undisclosed 0 0 0 0 2Age (years)Median (range) 73 (45-83) 60 (35-70) 55 (35-72) 58 (35-74) 59 (38-78) SymptomsGERD — — 0 0 23RE — — 0 0 24 None — — 0 64 27Not available — — 30 0 0

[0233] While various embodiments and aspects of the present invention are shown and described herein, it will be obvious to those skilled in the art that such embodiments and aspects 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 understoodthat various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described. All documents, or portions of documents, cited in the application including, without limitation, patents, patent applications, articles, books, manuals, and treatises are hereby expressly incorporated by¬ reference in their entirety- for any purpose.

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Claims

CLAIMSWhat is claimed is:

1. A method of detecting RNA in a human patient having Barrett’s esophagus or suspected of having Barrett’s esophagus, the method comprising detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the human patient, wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of two or more thereof.

2. The method of claim 1 , wherein the human patient has Barrett’s esophagus.

3. The method of claim 1, wherein the human patient is suspected of having Barrett’s esophagus.

4. A method of treating Barrett’s esophagus in a human patient in need thereof, the method comprising:(i) detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from a human patient, wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of two or more thereof; and(ii) administering to the human patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof.

5. A method of treating Barrett’s esophagus in a human patient in need thereof, the method comprising administering to the human patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof; wherein a biological sample obtained from the human patient comprises an elevated expression level, relative to a control, of RNA; wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of tw o or more thereof.

6. A method of treating Barrett's esophagus in a patient in need thereof, the method comprising:(i) selecting a patient having a diagnosis of Barrett’s esophagus based on a Barret’s esophagus risk score or an elevated expression level, relative to a control, ofRNA in a biological sample obtained from the patient, wherein the RNA comprises miR-106b. miR-146a, miR-15a. miR-18a, miR-21, miR-93, or a combination of two or more thereof; and(ii) treating the patient from step (i) by administering to the patient an effective amount of a proton pump inhibitor, a potassium competitive acid blocker, an antacid, a histamine 2 blocker, a prokinetic agent, radiofrequency ablation, photodynamic therapy, endoscopic mucosal resection, or a combination of two or more thereof.

7. A method of diagnosing a human patient with Barrett’s esophagus, the method comprising detecting an elevated expression level, relative to a control, of an RNA in a biological sample obtained from the human patient, thereby diagnosing the human patient with Barrett’s esophagus; wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of two or more thereof.

8. A method of monitoring a human patient at risk for developing Barrett’s esophagus, the method comprising:(i) detecting an expression level of an RNA in a biological sample obtained from the human patient at a first point in time;(ii) detecting an expression level of an RNA in a biological sample obtained from the human patient at a second point in time, wherein the second point in time is later than the first point in time;wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, miR-93, or a combination of two or more thereof; andwherein an elevated expression level of RNA at the second point in time when compared to the expression level of RNA at the first point in time indicates that the human patient has an increased risk of developing Barret's esophagus or has Barret's esophagus.

9. The method of claim 1, wherein the RNA comprises miR-106b, miR-146a, miR-15a, miR-18a, miR-21, and miR-93.

10. The method of claim 1, wherein the RNA does not include miR-135b-5p, miR-196a-l-5p, miR-335-3p, miR-15b-5p, miR-17-5p, miR-181a-5p, miR-181b-5p, or miR-196b-5p.

11. The method of claim 1, wherein the RNA consists of miR-106b, miR-146a, miR-15a, miR-18a, miR-21, and miR-93.

12. The method of claim 1, wherein miR-106b is miR-106b-5p; miR-146a is miR-146a-5p; miR-15ais miR-15a-5p; miR-18ais miR-18a-5p; miR-21 is miR-21-5p; and miR-93 is miR-93-5p.

13. The method of claim 1. wherein the biological sample is a blood, esophageal mucosa, or esophageal tissue.

14. The method of claim 1. wherein the control is a human patient or population of human patients that do not have Barrett’s esophagus or that have a negative endoscopic evaluation of the foregut.

15. The method of claim 1 , wherein the human patient has gastroesophageal reflux disease, reflux esophagitis, or low-grade esophageal dysplasia.

16. The method of claim 1, wherein the Barrett’s esophagus is long-segment Barrett’s esophagus.

17. The method of claim 1, wherein the human patient is at risk of developing esophageal adenocarcinoma.

18. The method of claim 1 , wherein the human patient does not have esophageal dysplasia, low-grade esophageal dysplasia, high-grade esophageal dysplasia, esophageal adenocarcinoma, or eosinophilic esophagitis.

19. The method of claim 4. wherein the proton pump inhibitor is dexlansoprazole, esomeprazole, lansoprazole, omeprazole, pantoprazole, or rabeprazole; the potassium competitive acid blocker is fexuprazan, keverprazan, linaprazan, revaprazan, soraprazan, tegoprazan, vonoprazan, or zestaprazan; the antacid is aluminum hydroxide, calcium carbonate, magnesium carbonate, magnesium hydroxide, magnesium trisilicate, magaldrate. sodium bicarbonate, or sodium citrate; the histamine 2 blocker is cimetidine, famotidine, nizatidine, ranitidine, or roxatidine; and the prokinetic agent is cisapride, domperidone, levosulpiride, metoclopramide, or mosapride.

20. A kit comprising reagents capable of detecting an expression level of RNA from a biological sample; wherein the RNA comprises miR-106b-5p, miR-146a-5p, miR-15a-5p, miR-18a-5p, miR-21-5p. and miR-93-5p.

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