Biomarkers for use in pancreatic cancer

Abstract

Elevated expression levels of RNA biomarkers can be used to diagnose, monitor, or treat pancreatic cancer in a patient. Exemplary RNA biomarkers include miR-30c, miR-142, miR-340, miR-335, miR-1260b, miR-145, miR-200a, miR-200b, miR-429, and miR-145. In embodiments, the RNA biomarkers include cell-free miR-30c-5p, cell-free miR-142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR-429, exosomal miR-145-3p, or a combination of two or more thereof.

Description

Docket No. 048440-206001 WO / TEC 24-026BIOMARKERS FOR USE IN PANCREATIC CANCERCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to US Application No. 63 / 687,134 filed August 26, 2024, the disclosure of which is incorporated by reference herein in its entirety.STATEMENT AS TO RIGHTS TO INVENTIONS MADE UNDER FEDERALLY SPONSORED RESEARCH AND DEVELOPMENT

[0002] This invention was made with government support under CA214254 awarded by the National Institutes of Health. The government has certain rights in the invention.BACKGROUND

[0003] Pancreatic ductal adenocarcinoma (PDAC) is the most lethal solid malignancy worldwide, with a steadily rising incidence. PDAC is projected to become the second leading cause of cancer-related deaths before 2030, with an estimated 51,750 patients in the United States dying of PDAC in 2024. Survival rates for PDAC have demonstrated modest improvement in recent years, with the overall 5-year survival rate now approaching 12%. The dismal survival rates in PDAC are primarily attributed to late-stage diagnoses, with approximately 80% of patients being identified at a late and incurable stage. Despite the poor prognosis of PDAC, early detection has been demonstrated to significantly improve patient sunrival. A recent study revealed that individuals with screen detected PDAC have a median overall sunrival of 9.8 years, significantly higher than cases diagnosed outside of surveillance programs.

[0004] Given the relatively low prevalence of PDAC. effective screening in the general population necessitates both high sensitivity and super high specificity to minimize the risk of overdiagnosis. Blood-based early detection assays could enable the identification of patients who would gain the most benefit from imaging or endoscopic procedures. Yet to date, carbohydrate antigen 19-9 (CAI 9-9) is the sole Food and Drug Administration (FDA)-approved blood-based biomarker for PDAC diagnosis. Although CA19-9 is known as the main biomarker for PDAC, its levels are elevated in only 50% of PDAC cases smaller than 3 cm. This limited sensitivity often leads to diagnoses being made after the optimal window for surgical resection has closed. Additionally, CAI 9-9 exhibits limited specificity for PDAC, as its levels can also be elevated in some benign conditions, such as pancreatitis, cholangitis, biliary obstruction, and other gastrointestinal cancers. Furthermore, approximately 5% - 10% of PDAC patients withLewis’s antigen-negative blood type are unable to synthesize CA19-9 at all, contributing to increased false negative rates. Meanwhile, its utility as a screening marker is limited due to its low positive predictive value of 0.5% - 0.9%. Thus, there is an urgent need in the art to identify and develop diagnostic methods capable of accurately detecting PDAC at an early stage, when the disease is still localized and remains operable. The disclosure is directed to this, as well as other, important ends.BRIEF SUMMARY

[0005] Provided herein is a method of detecting RNA in a patient having pancreatic cancer or minimal residual disease or suspected of having pancreatic cancer or minimal residual diseases, the method comprising detecting an elevated expression level, relative to a control, of an RNA in a biological sample obtained from the patient, wherein the RNA comprises miR-30c, miR- 142, miR-340, miR-335, miR- 1260b, miR-145, miR-200a, miR-200b, miR-429, miR-145, or a combination of two or more thereof. In embodiments, the RNA comprises the RNA comprises cell-free miR-30c-5p, cell-free miR-142-3p. cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR-429, exosomal miR-145-3p, or a combination of two or more thereof.

[0006] Provided herein is a method of treating pancreatic cancer or minimal residual disease in a patient in need thereof, the method comprising: detecting an elevated expression level, relative to a control, of an RNA in a biological sample obtained from a patient, wherein the RNA comprises miR-30c, miR-142. miR-340. miR-335, miR-1260b. miR-145. miR-200a. miR-200b, miR-429, miR-145, or a combination of two or more thereof; and (ii) administering to the patient an effective amount of an anti-cancer agent, and optionally further comprising administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the pancreas of the patient, or a combination of two or more thereof. In embodiments, the RNA comprises the RNA comprises cell-free miR-30c-5p, cell-free miR-142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR-429, exosomal miR-145-3p, or a combination of two or more thereof.

[0007] Provided herein is a method of diagnosing a patient with pancreatic cancer or minimal residual disease, 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 pancreatic cancer; wherein the RNA comprises miR-30c, miR-142, miR-340, miR- 335, miR-1260b, miR-145, miR-200a, miR-200b, miR-429, miR-145, or a combination of twoor more thereof. In embodiments, the RNA comprises the RNA comprises cell-free miR-30c-5p. cell-free miR-142-3p, cell-free miR-340-5p. cell-free miR-335-5p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR-429, exosomal miR-145-3p, or a combination of two or more thereof.

[0008] Provided herein is a method of monitoring a patient at risk for developing pancreatic cancer or monitoring for minimal residual disease or recurrence of pancreatic cancer, 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-30c, miR-142, miR-340, miR- 335, miR-1260b, miR-145, miR-200a, miR-200b, miR-429, miR-145, or a combination of two or more thereof. In embodiments, the RNA comprises the RNA comprises cell-free miR-30c-5p. cell-free miR-I42-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR-429, exosomal miR-145-3p, or a combination of two or more thereof.

[0009] These and other embodiments of the disclosure are described herein.BRIEF DESCRIPTION OF THE FIGURES

[0010] FIGS. 1A-1F: Training, validation, and testing of the PANXEON signature. FIG. 1A: Violin plots depict the PANXEON levels in the training cohort, by participant status. FIG. IB: ROC curves in detecting all stages of PDAC in the training cohort. FIG. 1C: Violin plots of PANXEON levels in the validation cohort, by participant status. FIG. ID: ROC curves for all stages of PDAC in the validation cohort. FIG. IE: Violin plots of PANXEON levels in the testing cohort, by participant status. FIG. IF: ROC curve for all stages PDAC in the testing cohort, ns p > 0.05. * p < 0.05. *** p < 0.001. Abbreviations: PDAC, pancreatic ductal adenocarcinoma. ROC, receiver operating characteristic.

[0011] FIGS. 2A-2E: PANXEON signature before, during, and after treatment. FIG. 2A: Fitted curves illustrating the relationship between time (at diagnosis, during neo-adjuvant chemotherapy, before surgery, and after surgery) and PANXEON levels. FIG. 2B: Whisker and superimposed dot plots of paired PANXEON measurements before and after neo-adjuvant chemotherapy (upper). The cloud plot depicts the paired PANXEON reduction after neoadjuvant chemotherapy (3-month course, bottom). FIG. 2C: Swimmer plot illustrating recurrence-free and overall survival outcomes among patients who received 3 months of neoadjuvantchemotherapy, categorized into high PANXEON reduction (PANXEON levels reduced by > 0.71, shown in blue) and low PANXEON reduction (PANXEON levels reduced by < 0.71, shown in red) groups. FIG. 2D: The whisker and superimposed dot plots illustrating the reduction in PANXEON levels over the 4-month period following surgery in 30 matched pre- and post-surgery plasma samples from 15 patients. FIG. 2E: A spider plot depicting PANXEON changes before surgery and during post-surgery follow-up. * p < 0.05. *** p < 0.001. Abbreviations: NAC, Neoadjuvant chemotherapy. A [NAC] = Pre-NAC minus post-NAC PANXEON score.

[0012] FIGS. 3A-3F: Combining PANXEON with CA19-9 improves its performance for the early detection of pancreatic ductal adenocarcinoma. FIG. 3A: Boxplots showing PAXEON levels and CAI 9-9 in non-disease controls and patients with pancreatic cancer from the final independent testing cohort. FIG. 3B: Bar plots compare the specificity and sensitivity of PANXEON versus CA19-9 for pancreatic cancer. FIG. 3C: ROC curves of PANXEON, CA19- 9, and their combination for detecting all-stage pancreatic cancer in the testing cohort. FIG. 3D: ROC curves of PANXEON, CA19-9, and their combination for stage I-II pancreatic cancer in the test cohort. FIG. 3E: Raincloud plots illustrating the distribution of risk scores for the combination of PANXEON and CA19-9 in patients with pancreatic cancer and non-disease controls. FIG. 3F: Standardized net-benefit of three early-detection strategies, ns p > 0.05. * p < 0.05. *** p < 0.001. Abbreviations: NDC, non-disease control. PDAC, pancreatic ductal adenocarcinoma. ROC, receiver operating characteristic.

[0013] FIGS. 4A-4B: Workflow chart of this study. FIG. 4A: The detailed clinical samples used in the training, validation, testing and specificity evaluation phase. FIG. 4B: Schematic diagram illustrating the four phases of signature development: training, validation, testing, and specificity evaluation.

[0014] FIGS. 5A-5C: Cell free and exosomal miRNA panels robustly identify patients with pancreatic ductal adenocarcinoma. Receiver operating characteristic curve (ROC) curve analysis evaluates the performance of 4 cf-miRNAs panel (FIG. 5A) and 6 exo-miRNAs panel (FIG.5B) that robustly distinguished PDAC from the NDC individuals. FIG. 5C: The density plots of the cell free and exosomal miRNA panels for the non-disease controls and PDAC. NDC. non- disease control; PDAC, pancreatic ductal adenocarcinoma.

[0015] FIGS. 6A-6F: Evaluation of the PANXEON signature for pancreatic ductal adenocarcinoma detection. A restricted cubic splines plot to depict the odds ratios for the presence of PDAC based on PANXEON scores in the training (FIG. 6A), validation (FIG. 6B)and testing cohort (FIG. 6C). Sensitivity and specificity plots versus probability cutoff points for the PANXEON signature in the training (FIG. 6D), validation (FIG. 6E) and testing cohort (FIG. 6F). NDC, non-disease control; PDAC, pancreatic ductal adenocarcinoma.

[0016] FIGS. 7A-7I: PANXEON signature robustly identifies patients with pancreatic ductal adenocarcinoma regardless of tumor location. Raincloud plot with super-imposed box and whisker plot to illustrate the distribution of PANXEON scores across NDC individuals, patients with head / uncinate (H / U) PDAC, and body / tail (B / T) PDAC in the training (FIG. 7A), validation (FIG. 7B), and testing (FIG. 7C) cohorts. Receiver Operating Characteristic (ROC) curve analysis evaluates the performance of PANXEON in robustly distinguishing patients with H / U PDAC, from NDC individuals in the training (FIG. 7D), validation (FIG. 7E), and testing (FIG. 7F) cohorts. ROC curv e analysis evaluates the performance of PANXEON in robustly distinguishing patients with B / T PDAC, from NDC individuals in the training (FIG. 7G), validation (FIG. 7H), and testing (FIG. 71) cohorts. H / U, head / uncinate; BT, body / tail. NDC, non-disease control; PDAC, pancreatic ductal adenocarcinoma.

[0017] FIGS. 8A-8B: PANXEON signature robustly identifies patients with pancreatic ductal adenocarcinoma across different country cohorts. FIG. 8A: Receiver Operating Characteristic (ROC) curv e analysis evaluates the performance of PANXEON in robustly distinguishing patients with PDAC from NDC individuals from Japan. South Korea, and the USA in the training cohort, respectively. FIG. 8B: ROC curve analysis evaluates the performance of PANXEON in robustly distinguishing patients with PDAC from NDC individuals from Japan, South Korea, and the USA in the validation cohort, respectively. NDC, non-disease control; PDAC, pancreatic ductal adenocarcinoma.

[0018] FIGS. 9A-9B: PANXEON signature robustly identifies patients with pancreatic ductal adenocarcinoma across different ethnicity population in the testing cohort. FIG. 9A: Receiver Operating Characteristic (ROC) curve analysis evaluates the performance of PANXEON in robustly distinguishing patients with PDAC from NDC individuals in Caucasian American populations within the testing cohort. FIG. 9B: ROC curve analysis evaluates the performance of PANXEON in robustly distinguishing patients with PDAC from NDC individuals in African American populations within the testing cohort. NDC, non-disease control; PDAC. pancreatic ductal adenocarcinoma.

[0019] FIGS. 10A-10B: Comparison of PANXEON signature performance in detecting various gastrointestinal cancers. FIG. 10A: ROC curve analysis comparing the performance of PANXEON in detecting PDAC versus other gastrointestinal cancers (ie, ESCC, CRC, GC,HCC, and CCA). FIG. 10B: Bar plot to illustrate the sensitivity of PANXEON in detecting gastrointestinal cancers and PDAC. ESCC, Esophageal squamous cell carcinoma; CRC, Colorectal cancer; GC, Gastric cancer; Hepatocellular carcinoma. HCC; CCA, Cholangiocarcinoma; NDC, non-disease control; PDAC, pancreatic ductal adenocarcinoma.

[0020] FIG. 11 shows Kaplan-Maier recurrence free survival curves for PANXEON reduction high and PANXEON reduction low patients received neoadjuvant chemotherapy.

[0021] FIGS. 12A-12B: the performance of CA19-9 in detecting patients with pancreatic ductal adenocarcinoma. FIG. 12A: Receiver Operating Characteristic (ROC) curve analysis evaluates the performance of CAI 9-9 in detecting patients with PDAC across all stages in the testing cohort. FIG. 12B: ROC curve analysis reveals the performance of PANXEON in individuals with CAI 9-9 levels under the cutoff value (37 U / rnL) from the test cohort. The accompanying box plot displays the CA19-9 levels between CA19-9 negative (CA19-9 < 37 U / mL) NDC and PDAC patients in the testing cohort. NDC, non-disease control; PDAC, pancreatic ductal adenocarcinoma.

[0022] FIG. 13 provides a summary of the diagnostic performance of PANXEON in detecting PDAC patients.

[0023] FIG. 14 shows the diagnostic performance of PANXEON, CAI 9-9 and their combination at varying specificity thresholds.

[0024] FIG. 15 shows the clinicopathologic characteristics of samples from training, validation, testing and pre-surgery and post-surgery cohort

[0025] FIG. 16 shows the clinicopathologic characteristics of samples from gastrointestinal cancer cohorts.

[0026] FIG. 17 provides a summary7of the SYBR® Green-based miRNA primers.

[0027] FIG. 18 provides a summan of the diagnostic performance of normalizers-based XGBoost classifiers in detecting PDAC patients in the training cohort.

[0028] FIG. 19 provides a summary of the performance of PANXEON in detecting PDAC patients regardless of tumor locationDETAILED DESCRIPTION

[0029] Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by a person of ordinary7skill 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 following definitions are provided to facilitate understanding of certain terms used frequently herein and are not meant to limit the scope of the present disclosure.

[0030] “Tumor-derived exosome” or “exosome” refers to a small (between 20-300 nm in diameter) vesicle comprising a lipid bilayer membrane that encloses an internal space, and which is generated from a cancer cell by direct plasma membrane budding or by fusion of the late endosome with the plasma membrane. The components of tumor-derived exosomes include proteins, DNA, mRNA, microRNA (miRNA), long noncoding RNA, circular RNA, and the like, which play a role in regulating tumor growth, metastasis, and angiogenesis in the process of cancer development.

[0031] “Exosomal RNA” refers to RNA within a tumor-derived exosome or RNA obtained from within a tumor-derived exosome. In embodiments, “exosomal RNA” is exosomal miRNA. In embodiments, “exosomal RNA” is exosomal mRNA. Exosomal RNA can be detected and measured by methods known in the art, such as those described herein. In embodiments, exosomal RNA is exosomal miRNA. In embodiments, exosomal RNA is exosomal hsa- miRNA, where “hsa” refers to homo sapiens.

[0032] “Cell-free RNA” or “cf-RNA” refers to RNA that is not within a tumor-derived exosome or RNA that has not been obtained from within a tumor-derived exosome. Cell-free RNA can be detected and measured by methods known in the art, such as those described herein. In embodiments, cell-free RNA is cell-free miRNA. In embodiments, cell-free RNA is cell-free hsa-miRNA, where “hsa” refers to homo sapiens.

[0033] A “cell” refers to a cell carrying out metabolic or other function sufficient to preserve or replicate its genomic DNA. A cell can be identified by well-known methods in the art including, for example, presence of an intact membrane, staining by a particular dye. ability to produce progeny or, in the case of a gamete, ability to combine with a second gamete to produce a viable offspring. Cells may include prokaryotic and eukaryotic cells. Eukaryotic cells include but are not limited to yeast cells and cells derived from plants and animals, for example mammalian (e.g. human) cells.

[0034] “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 types 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.

[0035] 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.

[0036] 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 mature forms 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 polyadenylated primary transcripts (pri-miRNAs) thatcan 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 a RNA-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.

[0037] “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. Further, a “protein gene product” is a protein expressed from a particular gene.

[0038] “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 within 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 known in the art. See, Sambrook et al., 1989 Molecular Cloning: A Laboratory Manual, 18.1-18.88.

[0039] “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 whether they’ originate from a transcript generated by alternative splicing or a degraded transcript, or from a post-translational processing 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 bymethods known to one skilled in the art and also disclosed herein.

[0040] 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.

[0041] “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, e.g., miRNA.

[0042] 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, 80thor 90thpercentile 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 proteinexpression 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 intensity7level by multiplying the cell percentage by a factor (e.g., 1, 2, or 3) that gives more relative weight to cells with higher-intensity7membrane 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.

[0043] “Control” is used in accordance with its plain ordinary7meaning and refers to an assay, comparison, or experiment in which the subjects or reagents of the experiment are treated as in a parallel experiment except for omission of a procedure, reagent, or variable of the experiment. In embodiments, the control is used as a standard of comparison in evaluating experimental effects. In embodiments, a control is the measurement of the activity7or 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 anon-diseased patient or non-diseased control. In embodiments, the control is a population of non-diseased patients. In embodiments, a non-diseased patient is a patient that does not have cancer. In embodiments, a non-diseased patient is a patient that does not have pancreatic cancer. In embodiments, the control is a patient that does not have cancer or a population of patients that do not have cancer. In embodiments, the control is a patient that does not have pancreatic cancer or a population of patients that do not have pancreatic cancer. In embodiments, the control is a patient that does not have PDAC or a population of patients that do not have PDAC. 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 miRNA. 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 subjects (e.g., with cancer) or in a healthyor general population. In embodiments, the control comprises an average amount (e.g. amount of expression) in a population in which the number of subjects (n) is 5 or more, 20 or more. 50 or more, 100 or more, 1,000 or more, and the like. In embodiments, the control is a standard control. In embodiments, a standard control is a level of expression of the biomarker (e.g., RNA, miRNA) that has been correlated w ith the diagnosis of pancreatic cancer in a subject. In embodiments, a standard control is a level of expression of the biomarker (e.g.. RNA, miRNA) that has been correlated with ahealthy subject (i.e., a subject that does not have pancreatic cancer). 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.

[0044] “Healthy patient” refers to a non-diseased patient. In embodiments, a healthy patient is a patient that does not have cancer. In embodiments, a healthy patient is a patient that does not have pancreatic cancer (e.g., PDAC). In embodiments, the healthy patient is a control.

[0045] As used herein, “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.

[0046] The singular terms “a,” “an,” and “the” include the plural reference unless the context clearly indicates otherwise.

[0047] A “therapeutic agent” or “anticancer agent” as used herein refer to an agent (e.g.. compound, pharmaceutical composition) that when administered to a subject will have the intended therapeutic effect, e.g., treatment or amelioration of pancreatic cancer, or pancreatic cancer symptoms including any objective or subjective parameter of treatment such as abatement; remission; diminishing of symptoms or making the cancer 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 w ell-being.

[0048] “Biological sample” or “sample” refer to materials obtained from or derived from a subject or 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 thelike), sputum, tissue, cultured cells (e.g.. primary' cultures, explants, and transformed cells) stool, urine, synovial fluid, joint 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 cell-free miRNA obtained from blood. In embodiments, a biological sample is an exosome obtained from a blood sample, wherein the exosome comprises miRNA. In embodiments, a biological sample is an exosome obtained from a serum sample, wherein the exosome comprises miRNA. In embodiments, a biological sample is an exosome obtained from a plasma sample, wherein the exosome comprises miRNA.

[0049] “Liquid biological sample” refers to liquid materials obtained or derived from a subject or 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.

[0050] “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., pancreatic cancer) or outcome in a subject.

[0051] “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 subject'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 subject a therapeutically effective amount of an active agent. The term “treating” does not including preventing.

[0052] 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 “therapeuticallyeffective 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 known techniques. In embodiments, “therapeutically effective amount” refers to the amount of the therapeutic agent sufficient to treat or ameliorate pancreatic cancer, 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 known 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.

[0053] “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 subject. 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.

[0054] "‘Patient” or “subject” are 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 anti-cancer agents and chemotherapeutic agents. Non-limiting examples include humans, other mammals, dogs, cats, monkeys, and other non-mammalian animals. In embodiments, a patient is human. In embodiments, the human patient is at least 45 years old. In embodiments, the human patient is at least 50 years old. In embodiments, the human patient is at least 55 years old. In embodiments, the human patient is at least 60 years old. In embodiments, the human patient is at least 45 years old and has new-onset diabetes. In embodiments, the human patient is at least 50 years old and has new-onset diabetes. In embodiments, the human patient is at least 55 years old and has new- onset diabetes. In embodiments, the human patient has a family history of pancreatic cancer. In embodiments, the human patient has obesity7. In embodiments, the human patient has a history7of pancreatitis. In embodiments, the human patient has a history of chronic pancreatitis.

[0055] “PDAC” refers to pancreatic ductal adenocarcinoma, a type of pancreatic cancer. PDAC accounts for more than 90% of cases of pancreatic cancer. Symptoms of pancreatic cancer include diabetes, hyperglycemia, jaundice, sudden weight loss, abdominal pain, back pain, persistent loss of appetite, light-colored stools, bloating, nausea, vomiting, or diarrhea. In embodiments, a symptom of pancreatic cancer is new-onset diabetes. In embodiments, a symptom of pancreatic cancer is new-onset hyperglycemia. In embodiments, a symptom of pancreatic cancer is diabetes. In embodiments, a symptom of pancreatic cancer is hyperglycemia. In embodiments, diabetes is new-onset diabetes or pre-existing diabetes.

[0056] Stages of pancreatic cancer are well known in the art. For example, “Stage 1” pancreatic cancer refers to pancreatic cancer that is only found in the pancreas. Stage 1 encompasses Stages IA and IB, wherein Stage IA is defined as Tl, NO, M0, and Stage IB is defined as T2, NO, M0). “Stage 2” pancreatic cancer refers to pancreatic cancer may have metastasized to nearby tissue and organs or lymph nodes near the pancreas. Stage 2 pancreatic cancer encompasses Stages IIA and IIB, wherein Stage IIA is defined as T3, NO, M0, and Stage IIB is defined as T1 / T2 / T3, Nl, M0. “Stage 3” pancreatic cancer refers to pancreatic cancer that has spread to the major blood vessels near the pancreas and may have spread to nearby lymph nodes, but not too distant sites (T4, any N, M0). “Stage 4” pancreatic cancer refers to pancreaticcancer that may be of any size and have spread to distant organs (e.g., liver, lung, peritoneal cavity) and may have spread to lymph nodes or organs and tissues near the pancreas (any T, any N, Ml). The term “Tl” means the tumor is only in the pancreas and is 2 cm or smaller in size. The term “T2” means the tumor is only in the pancreas and is larger than 2 cm and smaller than 4 cm. The term “T3” means the tumor is larger than 4 cm and that extends beyond the pancreas, but does not involve major arteries or veins near the pancreas. The term ’T4" means the tumor extends beyond the pancreas into major arteries or veins near the pancreas. The term “NO” means the cancer was not found in the regional lymph nodes. The term “Nl” means the cancer has spread to 1-3 regional lymph nodes. The term “N2” means the cancer has spread to 4 or more regional lymph nodes. The term “MO” means the cancer has not spread to other parts of the body. The term “Ml” means the cancer has spread to another part of the body, including distant lymph nodes.

[0057] “Pancreatic head / uncinate cancer” refers to a pancreatic tumor in the pancreatic head or in the ucinate process (PHU group). “Pancreatic body / tail cancer” refers to a pancreatic tumor in the pancreatic body or the tail group (PBT group).

[0058] “Minimal residual disease” refers to the presence of a very small number of pancreatic cancer cells in the body after treatment, when standard tests (like imaging or routine blood counts) cannot detect any cancer. These residual pancreatic cancer cells, though undetectable by conventional methods, can potentially lead to relapse.

[0059] The term “carbohydrate antigen 19-9” or “CA19-9” refers to serum CA19-9 which is the most well -documented and widely used serum biomarker in patients with PDAC. Although CAI 9-9 is commonly used to monitor disease progression and therapeutic response in pancreatic cancer, it lacks satisfactory sensitivity or specificity for screening and early detection of patients with PDAC. 15-25% of patients with PDAC, including those at early-stages (e.g., Stage 1 and 2), often exhibit CA19-9 levels less than 37 U / ml, which is considered normal. Furthermore, 5- 10% of the general population is Lewis antigen-negative with no or low secretion of CAI 9-9. The phrase “a normal level of CA19-9” refers to CA19-9 levels less than 37 U / ml. The phrase “an elevated level of CA19-9” refers to CA19-9 levels of 37 U / ml or higher. The phrase “a rising carbohydrate antigen 19-9 level” refers to a level of CA19-9 taken in a patient that is elevated when compared to the level of CA19-9 taken at an earlier point in time. “A rising carbohydrate antigen 19-9 level” can be greater than or less than 37 U / ml at any time point.

[0060] Methods of Detecting

[0061] Provided herein is a method of detecting RNA in a patient having pancreatic cancer or suspected of having pancreatic cancer 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-30c, miR-142, miR-340, miR-335, miR-1260b, miR-145, miR-200a, miR-200b, miR-429, miR-145, or a combination of two or more thereof. In embodiments, the RNA comprises cell-free miR-30c-5p, cell-free miR-142-3p, cell-free miR-340-5p. cell-free miR-335- 5p. exosomal miR-1260b, exosomal miR-145-5p. exosomal miR-200a-3p, exosomal miR-200b- 3p, exosomal miR-429, exosomal miR-145-3p, or a combination of two or more thereof. In embodiments, the patient has pancreatic cancer. In embodiments, the patient is suspected of having pancreatic cancer. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA comprises cell-free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p, exosomal miR-30e-5p, or a combination of two or more thereof. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA comprises cell-free miR-15b-5p. cell-free miR-23a-3p, cell-free miR-30e-5p. exosomal miR- I5b-5p, exosomal miR-23a-3p, and exosomal miR-30e-5p. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA consists of cell-free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p. and exosomal miR-30e-5p. In embodiments, the method further comprises normalizing the expression level of the RNA to the expression level of the reference RNA. In embodiments, the methods further comprise administering to the patient an effective amount of an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the pancreas of the patient, or a combination of two or more thereof. In embodiments, the methods further comprise administering to the patient an effective amount of an anti-cancer agent.

[0062] Provided herein is a method of detecting RNA in a patient having minimal residual disease, the method 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-30c, miR-142, miR-340, miR-335, miR-1260b, miR-145, miR-200a, miR-200b. miR-429, miR-145, or a combination of two or more thereof; wherein the patient has minimal residual disease while being treated for pancreatic cancer or has minimal residual disease after completing treatment for pancreatic cancer. In embodiments, the RNA comprises cell-free miR-30c-5p, cell-free miR- 142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR- 1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR-429, exosomal miR-145-3p, or a combination of two or more thereof. In embodiments, the methods comprise detecting RNA in a patient having minimal residual disease while being treated for pancreatic cancer. In embodiments, the methods comprise detecting RNA in a patient having minimal residual disease after completing treatment for pancreatic cancer. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA comprises cell-free miR-15b-5p. cell-free miR-23a-3p, cell-free miR-30e-5p. exosomal miR-15b-5p. exosomal miR-23a-3p, exosomal miR-30e-5p, or a combination of two or more thereof. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA comprises cell-free miR-15b-5p, cell -free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p, and exosomal miR-30e-5p. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA consists of cell-free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p. exosomal miR-23a-3p, and exosomal miR-30e-5p. In embodiments, the method further comprises normalizing the expression level of the RNA to the expression level of the reference RNA. In embodiments, the methods further comprise administering to the patient an effective amount of an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the pancreas of the patient, or a combination of two or more thereof. In embodiments, the methods further comprise administering to the patient an effective amount of an anti-cancer agent.

[0063] Methods of Treatment

[0064] Provided herein is a method of treating pancreatic cancer in a 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 patient, wherein the RNA comprises miR-30c, miR-142, miR-340, miR-335, miR-1260b, miR-145, miR-200a, miR-200b, miR-429. miR-145, or a combination of two or more thereof; and (ii) administering to the patient an effective amount of an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the pancreas of the patient, or a combination of two or more thereof. In embodiments, the RNA comprises cell-free miR-30c-5p, cell-free miR-142-3p, cell-free miR-340-5p. cell-free miR-335- 5p. exosomal miR-1260b, exosomal miR-145-5p. exosomal miR-200a-3p, exosomal miR-200b- 3p, exosomal miR-429, exosomal miR-145-3p, or a combination of two or more thereof. Inembodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA comprises cell-free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p, exosomal miR-30e-5p, or a combination of two or more thereof. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA comprises cell-free miR- 15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a- 3p. and exosomal miR-30e-5p. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA consists of cell-free miR-15b- 5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p, and exosomal miR-30e-5p. In embodiments, the method further comprises normalizing the expression level of the RNA to the expression level of the reference RNA. In embodiments, the method comprises administering to the patient an effective amount of an anti -cancer agent.

[0065] Provided herein is a method of treating pancreatic cancer or minimal residual disease in a patient in need thereof, the method comprising administering to the patient an effective amount of an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the pancreas of the patient, 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-30c, miR-142, miR-340, miR-335, miR-1260b, miR-145, miR-200a, miR-200b, miR-429, miR-145, or a combination of two or more thereof. In embodiments, the RNA comprises cell-free miR-30c-5p, cell-free miR-142-3p. cell-free miR- 340-5p, cell-free miR-335-5p, exosomal miR- 1260b, exosomal miR-145-5p, exosomal miR- 200a-3p, exosomal miR-200b-3p, exosomal miR-429, exosomal miR-145-3p, or a combination of two or more thereof. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA comprises cell-free miR-15b-5p, cell-free miR-23a-3p. cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p, exosomal miR-30e-5p, or a combination of two or more thereof. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA comprises cell-free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p. exosomal miR- 15b-5p, exosomal miR-23a-3p, and exosomal miR-30e-5p. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA consists of cell-free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p, and exosomal miR-30e-5p. In embodiments, the method further comprises normalizing the expression level of the RNA to the expression level of the referenceRNA. In embodiments, the method comprises administering to the patient an effective amount of an anti-cancer agent.

[0066] Provided herein is a method of treating minimal residual disease in a 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 patient, wherein the RNA comprises miR-30c, miR-142, miR-340, miR-335, miR-1260b, miR-145, miR-200a, miR-200b, miR-429, miR-145, or a combination of two or more thereof; and (ii) administering to the patient an effective amount of an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the pancreas of the patient, or a combination of two or more thereof. In embodiments, the RNA comprises cell-free miR-30c-5p, cell-free miR-142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR-429, exosomal miR-145-3p, or a combination of two or more thereof. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA comprises cell-free miR-15b-5p, cell-free miR-23a- 3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p, exosomal miR-30e-5p, or a combination of two or more thereof. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA comprises cell-free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p, and exosomal miR-30e-5p. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA consists of cell- free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p, and exosomal miR-30e-5p. In embodiments, the method further comprises normalizing the expression level of the RNA to the expression level of the reference RNA. In embodiments, the method comprises administering to the patient an effective amount of an anticancer agent.

[0067] Provided herein is a method of treating minimal residual disease in a patient in need thereof, the method comprising administering to the patient an effective amount of an anticancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the pancreas of the patient, 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-30c, miR-142, miR-340, miR-335, miR-1260b, miR-145, miR-200a, miR-200b,miR-429. miR-145, or a combination of two or more thereof. In embodiments, the RNA comprises cell-free miR-30c-5p. cell-free miR-142-3p, cell-free miR-340-5p. cell-free miR-335- 5p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b- 3p, exosomal miR-429, exosomal miR-145-3p, or a combination of two or more thereof. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA comprises cell-free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p. exosomal miR-15b-5p. exosomal miR-23a-3p, exosomal miR-30e-5p, or a combination of two or more thereof. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA comprises cell-free miR- 15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a- 3p, and exosomal miR-30e-5p. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA consists of cell-free miR-15b- 5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p, and exosomal miR-30e-5p. In embodiments, the method further comprises normalizing the expression level of the RNA to the expression level of the reference RNA. In embodiments, the method comprises administering to the patient an effective amount of an anti -cancer agent.

[0068] Methods of Diagnosis

[0069] Provided herein is a method of diagnosing a patient with pancreatic cancer 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 pancreatic cancer; wherein the RNA comprises miR-30c, miR-142, miR-340, miR-335, miR-1260b, miR-145, miR-200a, miR- 200b, miR-429, miR-145, or a combination of two or more thereof. In embodiments, the RNA comprises cell-free miR-30c-5p. cell-free miR-142-3p, cell-free miR-340-5p. cell-free miR-335- 5p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b- 3p, exosomal miR-429, exosomal miR-145-3p, or a combination of two or more thereof. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA comprises cell-free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p, exosomal miR-30e-5p, or a combination of two or more thereof. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA comprises cell-free miR- 15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a- 3p. and exosomal miR-30e-5p. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA consists of cell-free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p. exosomal miR-23a-3p, and exosomal miR-30e-5p. In embodiments, the method further comprises normalizing the expression level of the RNA to the expression level of the reference RNA. In embodiments, the method further comprises administering to the patient an effective amount of an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the pancreas of the patient, or a combination of two or more thereof. In embodiments, the method further comprises administering to the patient an effective amount of an anti-cancer agent.

[0070] Provided herein is a method of diagnosing a patient with minimal residual disease comprising detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient, thereby diagnosing the patient with minimal residual disease; wherein the RNA comprises miR-30c. miR-142, miR-340, miR-335, miR-1260b, miR-145, miR-200a, miR-200b, miR-429, miR-145, or a combination of two or more thereof. In embodiments, the RNA comprises cell-free miR-30c-5p, cell-free miR-142-3p, cell-free miR- 340-5p, cell-free miR-335-5p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR- 200a-3p, exosomal miR-200b-3p, exosomal miR-429, exosomal miR-145-3p, or a combination of two or more thereof. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA comprises cell-free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p, exosomal miR-30e-5p, or a combination of two or more thereof. In embodiments, the method further comprises detecting the expression level of reference RNA. wherein the reference RNA comprises cell-free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR- 15b-5p, exosomal miR-23a-3p, and exosomal miR-30e-5p. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA consists of cell-free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p. and exosomal miR-30e-5p. In embodiments, the method further comprises normalizing the expression level of the RNA to the expression level of the reference RNA. In embodiments, the method further comprises administering to the patient an effective amount of an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the pancreas of the patient, or a combination of two or more thereof. In embodiments, the method further comprises administering to the patient an effective amount of an anti-cancer agent.

[0071] Methods of Monitoring

[0072] Provided herein is a method of monitoring a patient at risk for developing pancreatic cancer 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-30c, miR-142, miR- 340, miR-335, miR-1260b, miR-145, miR-200a, miR-200b, miR-429, miR-145, or a combination of two or more thereof. In embodiments, the RNA comprises cell-free miR-30c-5p. cell-free miR-142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR-429, exosomal miR-145-3p, 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 pancreatic cancer. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA comprises cell-free miR-15b-5p, cell-free miR- 23a-3p. cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p, exosomal miR- 30e-5p. or a combination of two or more thereof. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA comprises cell-free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p, and exosomal miR-30e-5p. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA consists of cell- free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p, and exosomal miR-30e-5p. In embodiments, the method further comprises normalizing the expression level of the RNA to the expression level of the reference RNA. In embodiments, the method further comprises administering to the patient an effective amount of an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the pancreas of the patient, or a combination of two or more thereof. In embodiments, the method further comprises administering to the patient an effective amount of an anti-cancer agent.

[0073] Provided herein is a method of monitoring for minimal residual disease or recurrence of pancreatic cancer in a patient in need thereof, 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 thepatient 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-30c. miR-142, miR-340, miR-335, miR-1260b, miR- 145, miR-200a, miR-200b, miR-429, miR-145, or a combination of two or more thereof. In embodiments, the RNA comprises cell-free miR-30c-5p, cell-free miR-142-3p, cell-free miR- 340-5p, cell-free miR-335-5p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR- 200a-3p, exosomal miR-200b-3p, exosomal miR-429, exosomal miR-145-3p, or a combination of two or more thereof. In embodiments, the method is for monitoring for minimal residual disease. In embodiments, the method is for monitoring for recurrence of pancreatic cancer. 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 minimal residual disease or a recurrence of pancreatic cancer. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA comprises cell-free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR- 15b-5p, exosomal miR-23a-3p, exosomal miR-30e-5p, or a combination of two or more thereof. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA comprises cell-free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p, and exosomal miR-30e-5p. In embodiments, the method further comprises detecting the expression level of reference RNA, wherein the reference RNA consists of cell-free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p. exosomal miR-23a-3p, and exosomal miR-30e-5p. In embodiments, the method further comprises normalizing the expression level of the RNA to the expression level of the reference RNA. In embodiments, the method further comprises administering to the patient an effective amount of an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the pancreas of the patient, or a combination of two or more thereof. In embodiments, the method further comprises administering to the patient an effective amount of an anti-cancer agent.

[0074] Methods of Treatment

[0075] Provided herein is a method of treating pancreatic cancer in a patient in need thereof comprising: (i) selecting a patient having a diagnosis of pancreatic cancer based on a pancreatic cancer 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-30c, miR-142. miR-340, miR-335, miR-1260b, miR-145, miR-200a, miR-200b, miR-429, miR-145, or a combination oftwo or more thereof, 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 an anti-cancer agent. administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the stomach of the patient, or a combination of two or more thereof. In embodiments, step (ii) comprises administering to the patient an effective amount of an anti-cancer agent. In embodiments, the method further comprises detecting the expression level of reference RNA. as described herein. In embodiments, the method of treating pancreatic cancer is a method of treating minimal residual disease. In embodiments, the RNA comprises cell-free miR-30c-5p, cell-free miR-142-3p, cell- free miR-340-5p, cell-free miR-335-5p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR-429, exosomal miR-145-3p. or a combination of two or more thereof.

[0076] Provided herein is a method of treating pancreatic cancer in a patient in need thereof comprising: (i) receiving or obtaining a pancreatic cancer 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-30c, miR-142, miR-340, miR-335, miR-1260b, miR-145, miR-200a, miR- 200b, miR-429, miR-145, or a combination of two or more thereof; and (ii) diagnosing a patient with pancreatic cancer based on the pancreatic cancer risk score or the elevated expression level of RNA, monitoring a patient who is at risk of developing pancreatic cancer based on the pancreatic cancer risk score or the elevated expression level of RNA, monitoring efficacy of treatment for pancreatic cancer in a patient based on the pancreatic cancer 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 an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the stomach of the patient, or a combination of two or more thereof. In embodiments, the pancreatic cancer is early pancreatic cancer. In embodiments, the RNA comprises cell-free miR-30c-5p, cell-free miR-142-3p, cell-free miR-340-5p, cell-free miR-335- 5p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b- 3p, exosomal miR-429, exosomal miR-145-3p, or a combination of two or more thereof. In embodiments, step (iii) comprises administering to the patient an effective amount of an anticancer agent. In embodiments, the method further comprises detecting the expression level of reference RNA, as described herein. In embodiments, the method of treating pancreatic cancer is a method of treating minimal residual disease.

[0077] Provided herein is a method of treating pancreatic cancer in a patient in need thereof comprising: (i) receiving or obtaining a pancreatic cancer risk score or an elevated expression level of RNA, wherein the pancreatic cancer 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-30c, miR-142, miR-340, miR-335, miR- 1260b, miR- 145, miR-200a, miR-200b, miR-429, miR-145, or a combination of two or more thereof; and (ii) diagnosing a patient with pancreatic cancer based on the pancreatic cancer risk score or the elevated expression level of RNA. monitoring a patient who is at risk of developing pancreatic cancer based on the pancreatic cancer risk score or the elevated expression level of RNA, monitoring efficacy of treatment for pancreatic cancer in a patient based on the pancreatic cancer 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 an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the stomach of the patient, or a combination of two or more thereof. In embodiments, the RNA comprises cell-free miR-30c-5p, cell-free miR- 142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR- 1260b, exosomal miR-145- 5p. exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR-429. exosomal miR-145-3p, or a combination of two or more thereof. In embodiments, step (iii) comprises administering to the patient an effective amount of an anti-cancer agent. In embodiments, the method further comprises detecting the expression level of RNA, as described herein. In embodiments, the method of treating pancreatic cancer is a method of treating minimal residual disease.

[0078] Provided herein is a computer-implemented method of administering an effective amount of an anti-cancer agent to a patient with pancreatic cancer, the method comprising: (i) obtaining a sample data set comprising an expression level of oncogenic RNA from a biological sample obtained from the patient with pancreatic cancer, wherein the oncogenic RNA comprises cell-free miR-30c-5p, cell-free miR-142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR-1260b, exosomal miR-145-5p. exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR-429, exosomal miR-145-3p, 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 oncogenic RNA to the expression level of the reference RNA; (iv) determining an elevated expression level of the normalizedexpression level of the oncogenic RNA; and (v) administering to the patient an effective amount of the anti-cancer agent based on the determined normalized expression level. In embodiments, the oncogenic RNA comprises cell-free miR-30c-5p, cell-free miR-142-3p, cell-free miR-340- 5p, cell-free miR-335-5p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR-429, exosomal miR-145-3p, or a combination of two or more thereof

[0079] Provided herein is a computer-implemented system for administering an effective amount of an anti-cancer agent to a patient with pancreatic cancer, wherein the computer- implemented system comprises: (i) a computer-readable medium holding a control data set of normalized expression levels of oncogenic RNA from a population of patients that do not have cancer; (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 oncogenic RNA from a biological sample obtained from a patient with cancer, wherein the oncogenic RNA comprises cell-free miR-30c-5p, cell-free miR- 142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR-1260b, exosomal miR-145- 5p, exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR-429, exosomal miR-145-3p, or a combination of two 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 oncogenic RNA to the expression level of the reference RNA; (d) receive the control data set of normalized expression levels of oncogenic RNA from the population of patients that do not have cancer; and (e) determine an elevated expression level of the normalized expression level of the oncogenic RNA in the sample data set when compared to the normalized expression level of oncogenic RNA in the control data set. In embodiments, the patient is administered an effective amount of the anti-cancer agent based on the elevated expression level of the normalized expression level of the oncogenic RNA in the sample data set received from the computer-readable medium. In embodiments, the normalized expression level of the oncogenic 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 the anti-cancer agent. In embodiments, the computer-readable medium generates a report providing results and instructions (and / or recommendations) providing the type of anti-cancer agent to administer to the patient.

[0080] Provided herein are methods of processing data generated from the RNA levels in the biological sample obtained from a patient for establishing a pancreatic cancer risk score(composite risk score), e.g., a score indicative pancreatic cancer. 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 pancreatic cancer or a scale of likelihood of pancreatic cancer. 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 pancreatic cancer risk score can be based on a comparison to a control, such as a healthy patient, a population of healthy patients, a patient with pancreatic cancer, or a population of patients with pancreatic cancer.

[0081] In embodiments, the pancreatic cancer 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.

[0082] 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 pancreatic cancer 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 pancreatic cancer, diagnose a patient with pancreatic cancer, monitor a patient who is at risk of developing pancreatic cancer, or monitoring efficacy of treatment for pancreatic cancer 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.

[0083] miRNA

[0084] In embodiments of the methods described herein, the RNA comprises miR-30c, miR- 142, miR-340, miR-335, miR- 1260b, miR-145, miR-200a, miR-200b, miR-429, miR-145, or a combination of two or more thereof. In embodiments, the RNA comprises miR-30c, miR-142,miR-340. miR-335, miR-1260b, miR-145, miR-200a, miR-200b, miR-429, and miR-145. In embodiments, the RNA consists of miR-30c, miR-142, miR-340, miR-335, miR-1260b, miR- 145, miR-200a, miR-200b, miR-429, and miR-145.

[0085] In embodiments of the methods described herein, the RNA comprises cell-free miR- 30c-5p, cell-free miR-142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR- 1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR- 429, exosomal miR-145-3p, or a combination of two or more thereof.

[0086] In embodiments of the methods described herein, the RNA comprises cell-free miR- 30c-5p. cell-free miR-142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR- 1260b, exosomal miR-145-5p. exosomal miR-200a-3p. exosomal miR-200b-3p, exosomal miR- 429, and exosomal miR-145-3p.

[0087] In embodiments of the methods described herein, the RNA consists of cell-free miR- 30c-5p, cell-free miR-142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR- 1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR- 429, and exosomal miR-145-3p.

[0088] In embodiments of the methods described herein, the RNA comprises cell-free miR- 30c-5p. cell-free miR-142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR- 1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR- 429, exosomal miR-145-3p, cell-free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p, and exosomal miR-30e-5p.

[0089] In embodiments of the methods described herein, the RNA consists of cell-free miR- 30c-5p. cell-free miR-142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR- 1260b, exosomal miR-145-5p. exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR- 429, exosomal miR-145-3p, cell-free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p, and exosomal miR-30e-5p.

[0090] In embodiments of the methods described herein, the RNA does not comprise cell-free miR-23b-3p, exosomal miR-216b-5p, exosomal miR-217-5p, cell-free let-7e-5p, cell-free miR- 26a-5p. cell-free miR-223-3p, cell-free miR-340-3p, exosomal miR-1260a, exosomal miR-141- 3p, exosomal miR-143-3p. exosomal miR-148a-3p. exosomal miR-200c-3p, exosomal miR- 216a-5p, exosomal miR-34a-5p, cell-free let-7f-5p, cell-free miR-369-3p, cell-free miR-125a- 5p, cell-free miR-495-3p, exosomal miR-375-3p, or exosomal miR-199a-5p.

[0091] In embodiments of the methods described herein, the RNA does not comprise cell-freemiR-23b-3p, exosomal miR-216b-5p, or exosomal miR-217-5p.

[0092] In embodiments of the methods described herein, the RNA comprises cell-free miR- 30c-5p. cell-free miR-142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR- 1260b, exosomal miR-145-5p. exosomal miR-200a-3p. exosomal miR-200b-3p, exosomal miR- 429, exosomal miR-145-3p, cell-free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p, and exosomal miR-30e-5p; and the RNA does not comprise cell-free miR-23b-3p, exosomal miR-216b-5p, exosomal miR-217-5p, cell-free let- 7e-5p, cell-free miR-26a-5p. cell-free miR-223-3p, cell-free miR-340-3p, exosomal miR-1260a, exosomal miR-141-3p, exosomal miR-143-3p, exosomal miR-148a-3p, exosomal miR-200c-3p, exosomal miR-216a-5p, exosomal miR-34a-5p, cell-free let-7f-5p, cell-free miR-369-3p, cell- free miR-125a-5p, cell-free miR-495-3p, exosomal miR-375-3p, or exosomal miR-199a-5p.

[0093] In embodiments of the methods described herein, the RNA comprises cell-free miR- 30c-5p. cell-free miR-142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR- 1260b, exosomal miR-145-5p. exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR- 429, exosomal miR-145-3p, cell-free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p, and exosomal miR-30e-5p; and the RNA does not comprise cell-free miR-23b-3p, exosomal miR-216b-5p, or exosomal miR-217-5p.

[0094] In embodiments of the methods described herein, the RNA comprises cell-free miR- 30c-5p. cell-free miR-142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR- 1260b, exosomal miR-145-5p. exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR- 429, and exosomal miR-145-3p; and the RNA does not comprise cell-free miR-23b-3p, exosomal miR-216b-5p, exosomal miR-217-5p, cell-free let-7e-5p, cell-free miR-26a-5p, cell- free miR-223-3p, cell-free miR-340-3p, exosomal miR-1260a, exosomal miR-141-3p, exosomal miR-143-3p, exosomal miR-148a-3p, exosomal miR-200c-3p, exosomal miR-216a-5p, exosomal miR-34a-5p, cell-free let-7f-5p, cell-free miR-369-3p, cell-free miR-125a-5p, cell-free miR-495-3p, exosomal miR-375-3p, or exosomal miR-199a-5p.

[0095] In embodiments of the methods described herein, the RNA comprises cell-free miR- 30c-5p, cell-free miR-142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR- 1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR- 429, and exosomal miR-145-3p; and the RNA does not comprise cell-free miR-23b-3p, exosomal miR-216b-5p, or exosomal miR-217-5p.

[0096] miR Controls

[0097] The term “reference RNA” or “normalizer RNA” or “housekeeping RNA” or “control 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. For experimental purposes, 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., oncogenic RNA). 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 cancer patients). In embodiments, the reference RNA is miRNA. In embodiments, the reference RNA is hsa-miRNA.

[0098] ‘ ‘Reference RNA” described herein include miR-15b-5p, miR-23a-3p, and exosomal miR-30e-5p. In embodiments, the reference RNA comprises exosomal miR-15b-5p, cell free miR-15b-5p, exosomal miR-23a-3p. cell-free miR-23a-3p, exosomal miR-30e-5p, and cell-free miR-30e-5p.

[0099] In embodiments, the methods described herein (including embodiments thereof) further comprise detecting the expression level of a reference RNA, wherein the reference RNA comprises exosomal miR-15b-5p, cell free miR-15b-5p, exosomal miR-23a-3p, cell-free miR- 23a-3p. exosomal miR-30e-5p, and cell-free miR-30e-5p, or a combination of two or more thereof in the biological sample obtained from the patient. In embodiments, the methods described herein further comprising detecting the expression level of exosomal miR-15b-5p, cell free miR-15b-5p, exosomal miR-23a-3p, cell-free miR-23a-3p, exosomal miR-30e-5p, and cell- free miR-30e-5p in the biological sample obtained from the patient. In embodiments, the methods described herein (including embodiments thereof) further comprise normalizing the expression levels of oncogenic RNA to the expression level of the reference RNA. In embodiments, the expression level of the reference RNA are used as a control to the expression level of the RNA. In embodiments, RNA is miRNA.

[0100] In embodiments, the methods described herein (including embodiments thereof) further comprise detecting the expression level of a reference miRNA in the biological sample, wherein the reference miRNA is as described herein. In embodiments, the methods described herein (including embodiments thereof) further comprise normalizing the expression level of the miRNA to the expression level of the reference miRNA, thereby obtaining a normalized expression level of the miRNA. In embodiments, the expression level of the reference miRNA is used as a control to the normalized expression level of the miRNA.

[0101] In embodiments, an elevated expression level refers to a normalized expression level ofmiRNA that is at least 1.1 times greater than the expression level of the reference miRNA. 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 miRNA. 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 miRNA. 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 miRNA. 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 miRNA. 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 miRNA. 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 miRNA. 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 miRNA. 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 miRNA. 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 miRNA. 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 miRNA. 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 miRNA. 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 miRNA. 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 miRNA. 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 miRNA. 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 miRNA. 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 miRNA. In embodiments, an elevated expression level refers to a normalized expression level of miRNAthat is at least 8 times greater than the expression level of the reference miRNA. 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 miRNA. 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 miRNA. 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 miRNA.

[0102] Methods

[0103] In embodiments of the methods described herein, the biological sample is any biological sample. In embodiments, the biological sample is a liquid biological sample. In embodiments, the biological sample is a blood sample or a tissue sample. In embodiments, the biological sample is a tissue sample. In embodiments, the tissue sample is a tumor tissue sample. In embodiments, the biological sample is a stool sample. In embodiments, the biological sample is a liquid biological sample. In embodiments, the biological sample is a blood sample. In embodiments, the blood sample is a serum sample or a plasma sample. In embodiments, the biological sample is a serum sample. In embodiments, the biological sample is a plasma sample.

[0104] In embodiments of the methods described herein, the pancreatic cancer is pancreatic ductal adenocarcinoma. In embodiments, the pancreatic cancer is Stage 1 pancreatic cancer or Stage 2 pancreatic cancer. In embodiments, the pancreatic cancer is Stage 1 pancreatic cancer. In embodiments, the pancreatic cancer is Stage IA. In embodiments, the pancreatic cancer is Stage IB. In embodiments, the pancreatic cancer is Stage 2 pancreatic cancer. In embodiments, the pancreatic cancer is Stage IIA. In embodiments, the pancreatic cancer is Stage IIB. In embodiments, the pancreatic cancer is Stage 3 pancreatic cancer or Stage 4 pancreatic cancer. In embodiments, the pancreatic cancer is Stage 3 pancreatic cancer. In embodiments, the pancreatic cancer is Stage 4 pancreatic cancer. In embodiments, the pancreatic cancer is Stage 1 pancreatic cancer, Stage 2 pancreatic cancer, or Stage 3 pancreatic cancer. In embodiments, the pancreatic cancer is Stage 1 pancreatic cancer, Stage 2 pancreatic cancer, Stage 3 pancreatic cancer, or Stage 4 pancreatic cancer. In embodiments, the biological sample obtained from the patient has a normal level of CAI 9-9. In embodiments, the biological sample obtained from the patient has an elevated level of CAI 9-9. In embodiments, the biological sample obtained from the patient is Lewis antigen-negative. In embodiments, the biological sample is a blood sample. In embodiments, the blood sample is a serum sample. In embodiments, the blood sample is a plasma sample. In embodiments, the control is the average expression level of RNA in apopulation of healthy patients. In embodiments, the method comprises administering to the patient an effective amount of an anti-cancer agent.

[0105] In embodiments of the methods described herein, the patient has a symptom of pancreatic cancer. In embodiments, the patient has new-onset diabetes. In embodiments, the patient has diabetes. In embodiments, the patient is at least 45 years old. In embodiments, the patient has new-onset diabetes and is at least 50 years old. In embodiments, the human patient is at least 55 years old. In embodiments, the human patient is at least 60 years old. In embodiments, the patient has diabetes (e.g., new-onset diabetes or pre-existing diabetes), has obesity, has a rising carbohydrate antigen 19-9 level, has a family history of pancreatic cancer, has ahistory of pancreatitis, is > 40 years old, smokes cigarettes, has a history of smoking cigarettes, or a combination of two or more thereof. In embodiments, the patient has obesity. In embodiments, the patient smokes nicotine (e.g., cigarettes, cigars, e-cigarettes) or has a history of smoking nicotine. In embodiments, the patient has a history of pancreatitis. In embodiments, the patient has a history of chronic pancreatitis. In embodiments, the biological sample obtained from the patient is Lewis antigen-negative.

[0106] In embodiments of the methods described herein, the method further comprises detecting a level of carbohydrate antigen 19-9 (CAI 9-9) in the biological sample obtained from the patient. In embodiments, the biological sample obtained from the patient has an elevated level of carbohydrate antigen 19-9 (CAI 9-9). In embodiments, the method further comprises detecting an elevated level of carbohydrate antigen 19-9 (CAI 9-9), relative to a control, in the biological sample obtained from the patient.

[0107] In embodiments of the methods described herein, the method further comprises detecting a level of Lewis antigen, carcinoembryonic antigen, hepatocyte growth factor, osteopontin, or a combination of two or more thereof in the biological sample obtained from the patient. In embodiments, the method comprises detecting an elevated level, relative to a control, of Lewis antigen, carcinoembryonic antigen, hepatocyte grow th factor, osteopontin, or a combination of two or more thereof in the biological sample obtained from the patient. In embodiments, the method comprises detecting a level of Lewis antigen in the biological sample obtained from the patient. In embodiments, the method comprises detecting an elevated level of Lewis antigen, relative to a control, in the biological sample obtained from the patient. In embodiments, the method comprises detecting a level of carcinoembryonic antigen in the biological sample obtained from the patient. In embodiments, the method comprises detecting an elevated level of carcinoembryonic antigen, relative to a control, in the biological sampleobtained from the patient. In embodiments, the method comprises detecting a level of hepatocyte growth factor in the biological sample obtained from the patient. In embodiments, the method comprises detecting an elevated level of hepatocyte growth factor, relative to a control, in the biological sample obtained from the patient. In embodiments, the method comprises detecting a level of osteopontin in the biological sample obtained from the patient. In embodiments, the method comprises detecting an elevated level of osteopontin, relative to a control, in the biological sample obtained from the patient.

[0108] Anti-Cancer Agents

[0109] In embodiments, the methods described herein comprise administering to a patient an effective amount of an anti-cancer agent. The anticancer treatment can be any drug known in the art as useful for treating cancer, such as chemotherapy, immunotherapy, or a combination thereof. In embodiments, the anti-cancer agent is a chemotherapeutic agent. In embodiments, the anti-cancer agent comprises everolimus, erlotinib, olaparib, mitomycin, sunitinib, gemcitabine, 5-fluorouracil, irinotecan, oxaliplatin, paclitaxel, capecitabine, cisplatin, docetaxel, leucovorin, lanreotide, dabrafenib, trametinib, larotrectinib, entrectinib, selpercatinib, adagrasib, sotorasib, or a combination of two or more thereof.

[0110] In embodiments, the chemotherapeutic agent is an alkylating agent, an antimetabolite compound, an anthracy cline compound, an antitumor antibiotic, a platinum compound, a topoisomerase inhibitor, a vinca alkaloid, a taxane compound, an epothilone compound, or a combination of two or more thereof. In embodiments, the alkylating agent is carboplatin, chlorambucil, cyclophosphamide, melphalan, mechlorethamine, procarbazine, or thiotepa. In embodiments, the antimetabolite compound is azacitidine, capecitabine, cytarabine, gemcitabine, doxifluridine, hydroxyurea, methotrexate, pemetrexed, 6-thioguanine, 5- fluorouracil, or 6-mercaptopurine. In embodiments, the anthracy cline compound is daunorubicin, doxorubicin, idarubicin, epirubicin, or mitoxantrone. In embodiments, the antitumor antibiotic is actinomycin, bleomycin, mitomycin, or valrubicin. In embodiments, the platinum compound is cisplatin or oxaliplatin. In embodiments, the topoisomerase inhibitor is irinotecan, topotecan, amsacrine, etoposide, teniposide, or eribulin. In embodiments, the vinca alkaloid is vincristine, vinblastine, vinorelbine, or vindesine. In embodiments, the taxane compound is paclitaxel or docetaxel. In embodiments, the epothilone compound is epothilone, ixabepilone, patupilone, or sagopilone.[Dili] In embodiments, the method comprises administering to the subject an effective amount of gemcitabine, 5-fluorouracil, irinotecan, oxaliplatin, paclitaxel, capecitabine, cisplatin,docetaxel, or a combination of two or more thereof. In embodiments, the method comprises administering to the subject an effective amount of gemcitabine. In embodiments, the method comprises administering to the subject an effective amount of 5-fluorouracil. In embodiments, the method comprises administering to the subject an effective amount of irinotecan. In embodiments, the method comprises administering to the subject an effective amount of oxaliplatin. In embodiments, the method comprises administering to the subject an effective amount of paclitaxel. In embodiments, the method comprises administering to the subject an effective amount of capecitabine. In embodiments, the method comprises administering to the subject an effective amount of cisplatin. In embodiments, the method comprises administering to the subject an effective amount of docetaxel. In embodiments, the method comprises or further comprises administering to the subject an effective amount of leucovorin.

[0112] “Chemotherapeutic” or “chemotherapeutic agent” is used in accordance with its plain ordinary meaning and refers to a chemical composition or compound having antineoplastic properties or the ability to inhibit the grow th or proliferation of cells.

[0113] “Anti-cancer agent” is used in accordance with its plain ordinary meaning and refers to a composition (e.g. compound, drug, antagonist, inhibitor, modulator) having antineoplastic properties or the ability to inhibit the growth or proliferation of cells. In some embodiments, an anti-cancer agent is a chemotherapeutic. In embodiments, an anti-cancer agent is an agent identified herein having utility in methods of treating cancer. In embodiments, an anti-cancer agent is an agent approved by the FDA or similar regulatory agency of a country other than the USA, for treating cancer. Examples of anti-cancer agents include, but are not limited to, MEK (e.g. MEK1, MEK2, or MEK1 and MEK2) inhibitors (e.g. XL518, CI-1040, PD035901, selumetinib, trametinib, GDC-0973. ARRY-162, ARRY-300. AZD8330. PD0325901, U0126. PD98059, TAK-733, PD318088, AS703026, BAY 869766), alkylating agents (e g., cyclophosphamide, ifosfamide, chlorambucil, busulfan, melphalan, mechlorethamine, uramustine, thiotepa. nitrosoureas, nitrogen mustards (e.g., mechloroethamine, cyclophosphamide, chlorambucil, meiphalan). ethylenimine and methylmelamines (e.g., hexamethlymelamine, thiotepa), alkyl sulfonates (e.g., busulfan), nitrosoureas (e.g., carmustine, lomusitne, semustine, streptozocin), triazenes (decarbazine)), anti-metabolites (e.g., 5- azathioprine, leucovorin, capecitabine, fludarabine, gemcitabine, pemetrexed, raltitrexed, folic acid analog (e.g.. methotrexate), or pyrimidine analogs (e.g., fluorouracil, floxouridine, Cytarabine), purine analogs (e.g., mercaptopurine, thioguanine, pentostatin), etc.), plant alkaloids (e.g., vincristine, vinblastine, vinorelbine, vindesine, podophyllotoxin, paclitaxel,docetaxel, etc.), topoisomerase inhibitors (e.g., irinotecan, topotecan, amsacrine, etoposide (VP16), etoposide phosphate, teniposide, etc.), antitumor antibiotics (e.g., doxorubicin, adriamycin, daunorubicin, epirubicin, actinomycin, bleomycin, mitomycin, mitoxantrone, plicamycin, etc.), platinum-based compounds (e.g. cisplatin, oxaloplatin, carboplatin), anthracenedione (e.g., mitoxantrone), substituted urea (e.g., hydroxyurea), methyl hydrazine derivative (e.g., procarbazine), adrenocortical suppressant (e.g.. mitotane, aminoglutethimide), epipodophyllotoxins (e.g.. etoposide), antibiotics (e.g., daunorubicin. doxorubicin, bleomycin), enzymes (e.g., L-asparaginase), inhibitors of mitogen-activated protein kinase signaling (e g. U0126, PD98059, PD184352, PD0325901, ARRY-142886, SB239063, SP600125, BAY 43- 9006, wortmannin, or LY294002). mTOR inhibitors, antibodies (e.g., rituxan), 5-aza-2'- deoxy cytidine, doxorubicin, vincristine, etoposide, gemcitabine, imatinib, geldanamycin, 17-N- Allylamino-17-demethoxygeldanamycin (17-AAG), bortezomib, trastuzumab, anastrozole; angiogenesis inhibitors; antiandrogen, antiestrogen; antisense oligonucleotides; apoptosis gene modulators; apoptosis regulators; arginine deaminase: BCR / ABL antagonists; beta lactam derivatives; bFGF inhibitor; bicalutamide; camptothecin derivatives; casein kinase inhibitors (ICOS); clomifene analogues; cytarabine dacliximab; dexamethasone; estrogen agonists; estrogen antagonists; etanidazole; etoposide phosphate; exemestane; fadrozole; finasteride; fludarabine; fluorodaunorunicin hydrochloride; gadolinium texaphyrin; gallium nitrate; gelatinase inhibitors; gemcitabine; glutathione inhibitors; hepsulfam; immunostimulant peptides; insulin-like growth factor- 1 receptor inhibitor; interferon agonists; interferons; interleukins; letrozole; leukemia inhibiting factor; leukocyte alpha interferon; leuprolide+estrogen+progesterone; leuprorelin; matrilysin inhibitors; matrix metalloproteinase inhibitors; MIF inhibitor; mifepristone; mismatched double stranded RNA; monoclonal antibody.; mycobacterial cell wall extract; nitric oxide modulators; oxaliplatin; panomifene; pentrozole; phosphatase inhibitors; plasminogen activator inhibitor; platinum complex; platinum compounds; prednisone; proteasome inhibitors; protein A-based immune modulator; protein kinase C inhibitor; protein kinase C inhibitors, protein ty rosine phosphatase inhibitors; purine nucleoside phosphorylase inhibitors; ras famesyl protein transferase inhibitors; ras inhibitors; ras-GAP inhibitor; ribozymes; signal transduction inhibitors; signal transduction modulators; single chain antigen-binding protein; stem cell inhibitor; stem-cell division inhibitors; stromelysin inhibitors; synthetic glycosaminoglycans; tamoxifen methiodide; telomerase inhibitors; thyroid stimulating hormone; translation inhibitors; tyrosine kinase inhibitors: urokinase receptor antagonists; steroids (e.g., dexamethasone), finasteride, aromatase inhibitors, gonadotropin-releasing hormone agonists (GnRH) such as goserelin or leuprolide,adrenocorticosteroids (e.g., prednisone), progestins (e.g., hydroxy progesterone caproate, megestrol acetate, medroxyprogesterone acetate), estrogens (e.g., diethly stilbestrol, ethinyl estradiol), antiestrogen (e.g., tamoxifen), androgens (e.g., testosterone propionate, fluoxymesterone), antiandrogen (e.g., flutamide), immunostimulants (e.g., Bacillus Calmette- Guerin, levamisole, interleukin-2, alpha-interferon, etc.), monoclonal antibodies (e.g., anti- CD20, anti-HER2, anti-CD52, anti-HLA-DR, and anti-VEGF monoclonal antibodies), immunotoxins (e.g., anti-CD33 monoclonal antibody-calicheamicin conjugate. anti-CD22 monoclonal antibody-pseudomonas exotoxin conjugate, etc ), radioimmunotherapy (e.g., anti- CD20 monoclonal antibody conjugated toniIn,90Y, or131I, etc.), triptolide, homoharringtonine, dactinomycin, doxorubicin, epirubicin, topotecan, itraconazole, vindesine, cerivastatin, vincristine, deoxyadenosine, sertraline, pitavastatin. irinotecan, clofazimine, 5- nonyloxytryptamine, vemurafenib, dabrafenib, erlotinib, gefitinib, EGFR inhibitors, epidermal growth factor receptor (EGFR)-targeted therapy or therapeutic (e.g. gefitinib, erlotinib, cetuximab, lapatinib, panitumumab, vandetanib, afatinib, canertinib, neratinib, CP-724714, TAK-285, AST-1306, ARRY334543, ARRY-380, AG-1478, dacomitinib, desmethyl erlotinib, AZD8931, AEE788, pelitimb, CUDC-101, WZ8040, WZ4002, WZ3146, AG-490, XL647, PD153035, BMS-599626), sorafenib, imatinib, sunitinib, dasatinib, or the like.

[0114] Kits

[0115] Provided here are kits comprising components, such as reagents and reaction mixtures, to conduct the assays to detect the miRNA as described herein. 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 regulatory7approval 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.

[0116] “Assaying’’ or “detecting” means using an analytic procedure to qualitatively assess or quantitatively measure the presence or amount or the functional activity7of a target entity (e.g.. miRNA). For example, detecting the level of RNA (such as miRNA) means using an analyticprocedure (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).

[0117] 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 whole 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.

[0118] 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 will 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 derived.

[0119] The term “capable of hybridizing to” refers to a polynucleotide sequence that forms Watson-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.

[0120] 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 enzy me 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.

[0121] 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 materialmay 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, polysty rene 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 routine experimentation.

[0122] 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 distnbuted 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.

[0123] 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 dodecyl 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.

[0124] 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 transcribed from the gene, and / or specific spliced variants thereof and / or fragments of such miRNA and spliced variants.

[0125] In embodiments, raw expression values are normalized to a reference miRNA. Inembodiments, 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).

[0126] 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., a nucleic acid. Exemplary' detectable agents / moieties for use in the present disclosure include an antibody ligand, a peptide, a nucleic acid, radioisotopes, paramagnetic metal ions, fluorophore (e.g. fluorescent dyes), electron-dense reagents, enzymes (e g., as commonly used in an ELISA), biotin, a biotin-avidin complex, a biotin-streptavidin complex, digoxigenin, magnetic beads, paramagnetic molecules, paramagnetic nanoparticles, ultrasmall superparamagnetic iron oxide nanoparticles, ultrasmall superparamagnetic iron oxide nanoparticle aggregates, superparamagnetic iron oxide nanoparticles, superparamagnetic iron oxide nanoparticle aggregates, monocrystalline iron oxide nanoparticles, monocrystalline iron oxide, nanoparticle contrast agents, liposomes or other delivery vehicles containing Gadolinium chelate molecules, gadolinium, radionuclides, fluorodeoxy glucose, any gamma ray emitting radionuclides, positron-emitting radionuclide, radiolabeled glucose, radiolabeled water, radiolabeled ammonia, biocolloids, microbubbles, iodinated contrast agents, barium sulfate, thorium dioxide, gold, gold nanoparticles, gold 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.

[0127] In embodiments, oligonucleotides in kits are capable of specifically hybridizing to a target region of a polynucleotide, such as for example, an RNA transcript or cDNA generatedtherefrom. 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.

[0128] 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 pancreatic cancer in a patent, including reagents for detecting miRNA markers in a biological (e.g., blood) sample from a patient.

[0129] 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.

[0130] miRNA Expression

[0131] 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-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample. Inembodiments, 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 miRNA).

[0132] In embodiments of the methods described herein, the individual elevated expression level of the miRNA described herein are used. In embodiments, the individual elevated expression level of the exosomal miRNA described herein are used. In embodiments, the individual elevated expression level of the cell-free miRNA described are used. In embodiments, the individual elevated expression level of the cell-free miRNA and exosomal miRNA described herein are used.

[0133] In embodiments, the elevated expression levels of the miRNA are weighted and combined to form a risk score. In embodiments, the elevated expression levels of the exosomal RNA described herein are weighted and combined to form a risk score. In embodiments, the elevated expression levels of the cell-free miRNA described herein are weighted and combined to form a risk score. In embodiments, the elevated expression levels of the cell-free and exosomal miRNA described herein 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 miRNA, and the normalized expression levels of the miRNA (cell-free miRNA and / or exosomal miRNA) are weighted.

[0134] 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 2ACtmethod 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 allows for the quantification of the absolute expression of each miRNA in each sample analyzed and then for the calculation of 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 regressionanalysis can be applied to produce a risk score based on weighted values of the expression levels of the RNA.

[0135] 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 pancreatic cancer), comprising the steps of (i) normalizing and / or scaling numeric values of the RNA expression data (e.g., the exosomal miRNA expression data and / or cell-free miRNA 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 comparison allows the sample to be designated as positive or negative for pancreatic cancer and / or minimal residual disease. 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.

[0136] Embodiments 1 to 51

[0137] Embodiment 1. A method of detecting RNA in a patient having pancreatic cancer or suspected of having pancreatic cancer, the method comprising detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient, wherein the RNA comprises cell-free miR-30c-5p. cell-free miR-142-3p, cell-free miR-340-5p, cell-free miR-335-5p. exosomal miR- 1260b, exosomal miR-145-5p. exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR-429, exosomal miR-145-3p, or a combination of two or more thereof.

[0138] Embodiment 2. The method of Embodiment 1, wherein the patient has pancreatic cancer.

[0139] Embodiment 3. The method of Embodiment 1, wherein the patient is suspected of having pancreatic cancer.

[0140] Embodiment 4. A method of detecting RNA in a patient having minimal residual disease, the method comprising detecting an elevated expression level, relative to a control, ofRNA in a biological sample obtained from the patient, wherein the RNA comprises cell-free miR-30c-5p, cell-free miR-142-3p, cell-free miR-340-5p. cell-free miR-335-5p, exosomal miR- 1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR- 429, exosomal miR-145-3p, or a combination of two or more thereof; wherein the patient has minimal residual disease while being treated for pancreatic cancer or has minimal residual disease after completing treatment for pancreatic cancer.

[0141] Embodiment 5. The method of any one of Embodiments 1 to 4, further comprising administering to the patient an effective amount of an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the pancreas of the patient, or a combination of two or more thereof.

[0142] Embodiment 6. A method of treating pancreatic cancer in a 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 patient, wherein the RNA comprises cell-free miR-30c-5p, cell-free miR-142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR-429, exosomal miR-145-3p. or a combination of two or more thereof; and (ii) administering to the patient an effective amount of an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the pancreas of the patient, or a combination of two or more thereof.

[0143] Embodiment 7. A method of treating pancreatic cancer or minimal residual disease in a patient in need thereof, the method comprising administering to the patient an effective amount of an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the pancreas of the patient, 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 cell-free miR-30c-5p, cell-free miR-142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b-3p. exosomal miR-429, exosomal miR-145-3p, or a combination of two or more thereof.

[0144] Embodiment 8. A method of treating minimal residual disease in a 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 patient, wherein the RNA comprises cell-freemiR-30c-5p, cell-free miR-142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR- 1260b, exosomal miR-145-5p. exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR- 429, exosomal miR-145-3p, or a combination of two or more thereof; and (ii) administering to the patient an effective amount of an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the pancreas of the patient, or a combination of two or more thereof.

[0145] Embodiment 9. A method of treating minimal residual disease in a patient in need thereof, the method comprising administering to the patient an effective amount of an anticancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the pancreas of the patient, 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 cell-free miR-30c-5p, cell-free miR-142-3p, cell-free miR-340-5p, cell-free miR-335- 5p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b- 3p, exosomal miR-429, exosomal miR-145-3p, or a combination of two or more thereof.

[0146] Embodiment 10. The method of any one of Embodiments 5 to 9, comprising administering to the patient the effective amount of the anti-cancer agent.

[0147] Embodiment 11. A method of diagnosing a patient with pancreatic cancer, 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 pancreatic cancer; wherein the RNA comprises cell-free miR-30c-5p, cell-free miR-142-3p, cell-free miR- 340-5p, cell-free miR-335-5p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR- 200a-3p, exosomal miR-200b-3p, exosomal miR-429, exosomal miR-145-3p, or a combination of two or more thereof.

[0148] Embodiment 12. A method of diagnosing a patient with minimal residual disease, the method comprising detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient, thereby diagnosing the patient with pancreatic cancer; wherein the RNA comprises cell-free miR-30c-5p, cell-free miR-142-3p, cell-free miR- 340-5p, cell-free miR-335-5p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR- 200a-3p, exosomal miR-200b-3p, exosomal miR-429, exosomal miR-145-3p, or a combination of two or more thereof.

[0149] Embodiment 13. A method of monitoring a patient at nsk for developing pancreaticcancer, 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 cell-free miR-30c-5p, cell-free miR-142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR- 1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR- 429, exosomal miR-145-3p, or a combination of two or more thereof.

[0150] Embodiment 14. The method of Embodiment 13, 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 patient has an increased risk of developing pancreatic cancer.

[0151] Embodiment 15. A method of monitoring for minimal residual disease or recurrence of pancreatic cancer in a patient in need thereof, 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 cell-free miR-30c-5p, cell-free miR-142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR-429, exosomal miR-145-3p, or a combination of two or more thereof.

[0152] Embodiment 16. The method of Embodiment 15, wherein the method is for monitoring for minimal residual disease.

[0153] Embodiment 17. The method of Embodiment 15, wherein the method is for monitoring for recurrence of pancreatic cancer.

[0154] Embodiment 18. The method of any one of Embodiments 15 to 17, 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 patient has minimal residual disease or a recurrence of pancreatic cancer.

[0155] Embodiment 19. The method of any one of Embodiments 11 to 18, further comprising administering to the patient an effective amount of an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the pancreas of the patient, or a combination of two or more thereof.

[0156] Embodiment 20. The method of any one of Embodiments 11 to 18, further comprising administering to the patient an effective amount of an anti-cancer agent.

[0157] Embodiment 21. The method of any one of Embodiments 1 to 20, wherein the RNA comprises cell-free miR-30c-5p. cell-free miR-142-3p, cell-free miR-340-5p, cell-free miR-335- 5p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b- 3p, exosomal miR-429, and exosomal miR-145-3p.

[0158] Embodiment 22. The method of any one of Embodiments 1 to 20, wherein the RNA consists of cell-free miR-30c-5p, cell-free miR-142-3p, cell-free miR-340-5p, cell-free miR-335- 5p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b- 3p. exosomal miR-429, and exosomal miR-145-3p.

[0159] Embodiment 23. The method of any one of Embodiments 1 to 22, wherein the RNA does not comprise cell-free miR-23b-3p, exosomal miR-216b-5p, or exosomal miR-217-5p.

[0160] Embodiment 24. The method of any one of Embodiments 1 to 22, wherein the RNA does not comprises cell-free miR-23b-3p, exosomal miR-216b-5p, exosomal miR-217-5p, cell- free let-7e-5p, cell-free miR-26a-5p, cell-free miR-223-3p, cell-free miR-340-3p, exosomal miR-1260a, exosomal miR-141-3p, exosomal miR-143-3p, exosomal miR-148a-3p, exosomal miR-200c-3p, exosomal miR-216a-5p, exosomal miR-34a-5p, cell-free let-7f-5p, cell-free miR- 369-3p, cell-free miR-125a-5p, cell-free miR-495-3p, exosomal miR-375-3p, or exosomal miR- 199a-5p.

[0161] Embodiment 25. The method of any one of Embodiments 1 to 24, further comprising detecting the expression level of reference RNA, wherein the reference RNA comprises cell-free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p. exosomal miR-23a-3p, exosomal miR-30e-5p. or a combination of two or more thereof.

[0162] Embodiment 26. The method of any one of Embodiments 1 to 24, further comprising detecting the expression level of reference RNA, wherein the reference RNA comprises cell-free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p, and exosomal miR-30e-5p.

[0163] Embodiment 27. The method of any one of Embodiments 1 to 24, further comprising detecting the expression level of reference RNA, wherein the reference RNA consists of cell- free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p, and exosomal miR-30e-5p.

[0164] Embodiment 28. The method of any one of Embodiments 25 to 27, further comprisingnormalizing the expression level of the RNA to the expression level of the reference RNA.

[0165] Embodiment 29. The method of any one of Embodiments 1 to 28, further comprising detecting a level of carbohydrate antigen 19-9 (CAI 9-9) in the biological sample obtained from the patient.

[0166] Embodiment 30. The method of any one of Embodiments 1 to 29, wherein a biological sample obtained from the patient has an elevated level of carbohydrate antigen 19-9 (CAI 9-9).

[0167] Embodiment 31. The method of any one of Embodiments 1 to 30, further comprising detecting a level of Lewis antigen, carcinoembryonic antigen, hepatocyte grow th factor, osteopontin, or a combination of two or more thereof in the biological sample obtained from the patient.

[0168] Embodiment 32. The method of any one of Embodiments 1 to 31, w herein the biological sample is a liquid biological sample.

[0169] Embodiment 33. The method of any one of Embodiments 1 to 31, wherein the biological sample is a blood sample.

[0170] Embodiment 34. The method of any one of Embodiments 1 to 31, wherein the biological sample is a plasma sample.

[0171] Embodiment 35. The method of any one of Embodiments 1 to 31, wherein the biological sample is a serum sample.

[0172] Embodiment 36. The method of any one of Embodiments 1 to 35, wherein the pancreatic cancer is a pancreatic head / uncinate cancer.

[0173] Embodiment 37. The method of any one of Embodiments 1 to 35, wherein the pancreatic cancer is a pancreatic body / tail cancer.

[0174] Embodiment 38. The method of any one of Embodiments 1 to 37, wherein the pancreatic cancer is Stage 1.

[0175] Embodiment 39. The method of any one of Embodiments 1 to 37, wherein the pancreatic cancer is Stage 2.

[0176] Embodiment 40. The method of any one of Embodiments 1 to 37, wherein the pancreatic cancer is Stage 3 or Stage 4.

[0177] Embodiment 41. The method of any one of Embodiments 1 to 40, w herein the pancreatic cancer is pancreatic ductal adenocarcinoma.

[0178] Embodiment 42. The method of any one of Embodiments 1 to 41, wherein the control is a patient or population of patients that do not have cancer.

[0179] Embodiment 43. The method of any one of Embodiments 1 to 41, wherein the control is a patient or population of patients that do not have pancreatic cancer.

[0180] Embodiment 44. The method of any one of Embodiments 1 to 43, wherein the patient is a human patient.

[0181] Embodiment 45. The method of any one of Embodiments 5-10 and 19-44, wherein the anti-cancer agent comprises everolimus, erlotinib, olaparib, mitomycin, sunitinib, gemcitabine, 5-fluorouracil, irinotecan, oxaliplatin, paclitaxel, capecitabine, cisplatin, docetaxel, leucovorin, lanreotide, dabrafenib, trametinib, larotrectinib, entrectinib, selpercatinib, adagrasib, sotorasib. or a combination of two or more thereof.

[0182] Embodiment 46. The method of any one of Embodiments 5-10 and 19-44, wherein the anti-cancer agent is a chemotherapeutic agent.

[0183] Embodiment 47. The method of Embodiment 46, wherein the chemotherapeutic agent comprises gemcitabine, 5-fluorouracil, irinotecan, oxaliplatin, paclitaxel, capecitabine, cisplatin, docetaxel, or a combination of two or more thereof.

[0184] Embodiment 48. The method of Embodiment 46, wherein the chemotherapeutic agent is an alkylating agent, an antimetabolite compound, an anthracycline compound, an antitumor antibiotic, a platinum compound, a topoisomerase inhibitor, a vinca alkaloid, a taxane compound, an epothilone compound, or a combination of two or more thereof.

[0185] Embodiment 49. The method of Embodiment 48, wherein the alkylating agent is carboplatin, chlorambucil, cyclophosphamide, melphalan, mechlorethamine, procarbazine, or thiotepa; the antimetabolite compound is azacitidine, capecitabine, cytarabine, gemcitabine, doxifluridine, hydroxyurea, methotrexate, pemetrexed, 6-thioguanine, 5-fluorouracil, or 6- mercaptopurine; the anthracycline compound is daunorubicin, doxorubicin, idarubicin, epirubicin, or mitoxantrone; the antitumor antibiotic is actinomycin, bleomycin, mitomycin, or valrubicin; the platinum compound is cisplatin or oxaliplatin; the topoisomerase inhibitor is irinotecan, topotecan, amsacrine, etoposide, teniposide, or eribulin; the vinca alkaloid is vincristine, vinblastine, vinorelbine, or vindesine; the taxane compound is paclitaxel or docetaxel; and the epothilone compound is epothilone, ixabepilone. patupilone, or sagopilone.

[0186] Embodiment 50. A kit comprising reagents capable of detecting an expression level of RNA from a biological sample; wherein the RNA cell-free miR-30c-5p, cell-free miR-142-3p.cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR-1260b, exosomal miR-145-5p. exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR-429. and exosomal miR-145- 3p.

[0187] Embodiment 51. The kit of Embodiment 50. wherein the RNA further comprises cell- free miR-15b-5p, cell-free miR-23a-3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p, exosomal miR-30e-5p, or a combination of two or more thereof.EXAMPLES

[0188] The inventors utilized extreme Gradient Boosting (XGBoost), an advanced machine learning approach, to develop an accurate assay using biomarkers for detecting PDAC. We developed and validated an RT-qPCR-based assay, PANXEON (PANcreatic cancer eXosome Early detectiON), which can accurately identify patients with PDAC in a large-scale, multicenter, multinational prospective patient cohort. This assay was robustly validated in an independent cohort, yielding an AUC value of 95.0% in detecting patients with stage I / II PDAC. Notably, its performance exhibited consistency across various tumor locations and countries, indicating robust applicability in diverse clinical settings. Specifically, its potential tumorspecificity was evidenced by decreased postoperative levels and the highest accuracy in detecting PDAC among gastrointestinal cancers. Furthermore, the accuracy of PANXEON was enhanced when combined with CA19-9 in the early detection of this malignancy

[0189] Results

[0190] Baseline Cohort Characteristics

[0191] This multicenter prospective study comprised four phases: the training phase, internal validation phase, independent testing phase, and final specificity evaluation phase. This study included data from 1494 plasma samples coming from 1386 unique patients (FIG. 4A). Participants were recruited from 11 institutions across the USA, Japan, and South Korea. This multicenter prospective study comprised the following four phases: a training phase, an internal validation phase, an independent testing phase, and the final specificity evaluation phase (FIG. 4B). Detailed clinical characteristics of the training, validation, test, and pre- / post-surgery cohorts can be found in FIG. 2.

[0192] Establishment of PANXEON signature leveraging cell-free and exosomal miRNAs

[0193] The primary objective of this study w as to establish a robust liquid biopsy for detecting PDAC patients by leveraging cf- and exo-miRNAs using machine-learning XGBoost algorithms. To achieve this goal, we performed qRT-PCR to interrogate the expression levels ofthe identified 5 cf- and 8 exo-miRNAs in plasma specimens from the training cohort described in WO 2023 / 239920, including cell-free miR-30c-5p, cell-free miR-142-3p, cell-free miR-340- 5p, cell-free miR-335-5p, cell-free miR-23b-3p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR-216b-5p, exosomal miR-217- 5p, exosomal miR-429, and exosomal miR-145-3p. We observed that cf-miR-23b-3p, exo-miR- 217-5p, and exo-miR-216b exhibited opposite patterns across the Korea, Japan, and USA cohorts and were therefore excluded from further analysis. Our machine-learning architecture consists of three layers: the first layer was built on XGBoost classifiers (three for cf- and three for exo-classifiers), which are then stacked to form the second-level classifiers (cf- and exopanels), and these are finally stacked again to form the final PANXEON signature (FIG. 1). The XGBoost classifiers, based on 4 cell-free and 6 exosome miRNAs, could robustly distinguish PDAC patients from NDC in the training cohort (AUC for cf-classifiers: 93.6% - 95.2%: AUC for exo-classifiers: 84.9% - 87.9%, FIG. IB). The second-level, cf- and exo- panels were built on the base-level classifiers. Interestingly, the density plot revealed distinct clustering patterns of cf- and exo- panels, with NDC subjects primarily situated in the bottom left comer and PDAC patients in the top right comer (FIG. 5). These findings highlight the potential cooperative potential of the cf- and exo-miRNA panels in distinguishing PDAC from NDC. Consequently, the ROC analysis demonstrated a remarkable performance of the cf-miRNAs panel (AUC: 96.2% [Cl95%: 94.9 - 97.5%], Sensitivity = 95.8%, Specificity = 83.8%, FIG. 13) and the exo- miRNAs panel (AUC: 91.8% [Cl95%: 89.8 - 93.8%], Sensitivity = 80.2%, Specificity = 87.9%, FIG. 13) in the training cohort, highlighting their capability to accurately discriminate patients with PDAC from NDC subjects. In alignment with our original hypothesis, these findings indicate that the cf-miRNAs panel provided superior sensitivity, whereas the exo-miRNAs panel offered higher specificity in identifying patients with PDAC.

[0194] Additionally, we then proceeded to combine cf-miRNAs and exo-miRNAs panels to synergize their diagnostic accuracies. The final signature. PANXEON. leveraged both cf- and exo-miRNAs panels and PANXEON levels were significantly higher in patients wi th PDAC compared to NDC individuals within the training cohort (p < 0.001, FIG. 1). Moreover, the PANXEON signature displayed a notably linear association with the odds ratio (OR) values of PDAC. indicating a proportional increase in PDAC risk with elevated risk score levels (FIG. 6). Therefore, PANXEON was able to accurately discriminate between PDAC and NDC compared to either panel alone (AUC: 98.4% [CI95%: 97.7 - 99.1%]). The diagnostic performance of PANXEON in terms of its diagnostic accuracy, sensitivity', specificity', PPV, and NPV of training cohort are summarized in FIG. 13. Additionally, the favorable calibration attested to thealignment between the predicted probability of PANXEON and the actual probability of PDAC in the training cohort (FIG. 6). Furthermore, the Hosmer-Lemeshow test indicated no significant deviation from the ideal fit ( / 2 = 7.97, degrees of freedom = 8, p = 0.44). Taken together, these results highlight the clinical value of PANXEON as a non-invasive liquid biopsy assay for the early detection of PDAC patients. Therefore, the final PANXEON signature was comprehensively developed and locked in the training cohort.

[0195] Successful validation and testing of PANXEON signature

[0196] Having established the PANXEON signature, qRT-PCR was performed in both the validation and independent test cohorts, with subsequent application of the final PANXEON signature to both cohorts (FIG. 1). When applied to the validation and test cohorts, the PANXEON scores were observed to be significantly higher in PDAC patients compared to NDC subjects within both cohorts (p < 0.001, FIG. 1). Consistent with the findings from the training cohort, the ROC curve analysis revealed that the PANXEON signature was able to reliably distinguish patients with PDAC from NDC individuals in both cohorts (AUC in validation cohort: 96.9% [CI95%: 95.2 - 98.6%], FIG. 1; AUC in test cohort: 94.5% [CI95%: 91.2 - 97.8%], FIG. 1). Specifically, the plots of sensitivity and specificity versus probability cut-off points illustrated the performance of the PANXEON signature in the validation and test cohorts (FIG. 13). A higher cutoff setting results in higher specificity and lower sensitivity, and vice versa. At the cutoff point of 1.617 (locking a specificity of 97.5% in the training cohort), the PANXEON signature demonstrated a robust capability for distinguishing patients with PDAC from NDC participants in both the validation cohort (accuracy: 88.9%, specificity7: 94.2%, sensitivity: 86.1%) and the test cohort (accuracy: 87.6%, specificity : 81.8%, sensitivity : 93.3%). At a higher cutoff point of 2.748 (locking a specificity7of 99.0% in the training cohort), the PANXEON signature exhibited increased specificity for distinguishing patients with PDAC from NDC participants in both the validation cohort (specificity7: 97.1%, sensitivity: 79.6%) and the test cohort (specificity: 93.2%, sensitivity: 84.3%). The performance of PANXEON in terms of its diagnostic accuracy, sensitivity, specificity, PPV, and NPV of validation and test cohorts are summarized in FIG. 13. Collectively, we successfully validated the performance PANXEON signature in both cohorts, demonstrating its robust capability7for detecting PDAC.

[0197] PANXEON signature robustly identifies patients with pancreatic ductal adenocarcinoma even at the earliest disease stages

[0198] In PDAC, the stage at diagnosis has been demonstrated to be a critical determinant of patient survival. Therefore, we assessed the stage-specific performance of the PANXEON assay.We observed a consistent elevation of the PANXEON levels across all stages of PDAC compared to NDC subjects across the training, validation, and test cohorts (p < 0.001, FIG. 1). Because we did not observe any significant difference in PANXEON levels between stage I / II and stage III / IV patients, this suggests its equal capability in detecting PDAC patients, even at the earliest stages. As a result, the ROC cune analysis revealed that the PANXEON signature reliably distinguished patients with stage I / II PDAC from NDC individuals in all cohorts (AUC in training cohort: 98.4% [CI95%: 97.6 - 99.2%]. FIG. 1A; AUC in validation cohort: 97.3% [CI95%: 95.6 - 99.0%], FIG. 1C; AUC in test cohort: 95.0% [CI95%: 91.5 - 98.5%], FIG. ID). At the cutoff point of 2.748, the PANXEON signature exhibited consistent accuracy with relatively high specificity in detecting patients with stage I / II PDAC across all three cohorts [(training cohort: specificity. 99.0%. sensitivity. 80.3%. accuracy, 88.9% (FIG. 13); validation cohort: specificity, 97.1%, sensitivity, 83.7%; accuracy, 89.2% (FIG. 13); test cohort: specificity, 93.2%, sensitivity, 84.4%, accuracy, 89.5% (FIG. 13)]. The performance of PANXEON in terms of its performance in detecting patients wi th stage I / II PDAC was summarized in FIG. 13. These findings underscore the robustness of PANXEON signature even in distinguishing patients with stage I / II PDAC from NDC individuals. Collectively, these results highlight its clinical significance as a diagnostic assay for clinical translation, facilitating the non-invasive detection of patients with early-stage PDAC.

[0199] PANXEON signature robustly identifies patients with pancreatic ductal adenocarcinoma regardless of tumor location, country

[0200] Anatomically, PDAC can be classified into head / uncinate (H / U) and body / tail (B / T) cancers. PDAC in the B / T region of the pancreas are often diagnosed later because less symptomatic and, therefore, have a worse prognosis. SEER data, comprising 43,946 cases of pancreatic cancer from 1973 to 2002, indicate that patients with pancreatic B / T cancer were more likely to present with distant metastases compared to those with pancreatic H / U cancer (72.7% versus 39.2%). The 3-year survival rate is 20.0% for localized pancreatic B / T cancer, while it is 9% for localized pancreatic H / U cancer. These results highlight the clinical significance of diagnostic biomarkers for detecting pancreatic B / T cancer to improve survival rates. Importantly, we observed a significant elevation in PANXEON levels across all PDAC locations compared to NDC subjects in the training, validation, and test cohorts (p < 0.001, FIG. 5). No significant differences were observed between H / U and B / T PDAC, indicating that PANXEON performs consistently in detecting PDAC regardless of tumor location. Indeed, ROC curve analysis demonstrated that the PANXEON signature distinguished patients with H / U andB / T PDAC from NDC individuals across all cohorts: training cohort (H / U vs. B / T, AUC 98.4% vs. 98.4%, FIG. 19). validation cohort (H / U vs. B / T, AUC 96.5% vs. 97.5%. FIG. 19), and test cohort (H / U vs. B / T, AUC 94.5% vs. 94.3%, FIG. 19). At the cutoff point of 2.748, the PANXEON signature exhibited similar specificity in detecting NDC individuals while demonstrating relatively high accuracy in detecting patients with B / T PDAC across all cohorts: training cohort (H / U vs. B / T, 90.1% vs. 92.5%, FIG. 19). validation cohort (H / U vs. B / T, 86.7% vs. 90.9%, FIG. 19). and test cohort (H / U vs. B / T, 88.3% vs. 92.7%. FIG. 19)..

[0201] Moreover, the PANXEON signature exhibited consistent accuracy in detecting patients with PDAC across all three different country cohorts in the training (Japan cohort: 98.3% [CI95%: 96.8% - 99.8%]; South Korea cohort: 97.8% [CI95%: 96.% - 99.5%]; USA cohort: 98.3% [CI95%: 96.9% - 99.6%], FIG. 8), and validation cohort (Japan: 96.2% [CI95%: 91.2% - 100%]; South Korea: 96.4% [CI95%: 93.1% - 99.8%]; USA: 95.2% [CI95%: 90.7% - 99.8%], FIG. 8). Furthermore, PANXEON demonstrates consistent performance in testing populations sourced from an independent institute in the USA. Specifically, we observed that PANXEON could distinguish patients with PDAC from NDC in both Caucasian (AUC, 94.6% [CI95%: 91.2% - 97.9%], FIG. 8) and Africa American (AUC. 98.0% [CI95%: 95.7% - 100%], FIG. 8) populations within the test cohort. These findings underscore the robust and consistent ability of the PANXEON signature to identify patients with PDAC, irrespective of tumor location, country, ethnicity'.

[0202] PANXEON signature exhibits highest accuracy in detection PDAC among gastrointestinal cancers and significantly decreased post-surgical levels

[0203] To further evaluate the specificity of PANXEON for PDAC, we compared its diagnostic performance in patients with other gastrointestinal cancers (FIG. 2), including colorectal cancer (CRC). esophageal squamous cell carcinoma (ESCC), gastric cancer (GC). hepatocellular carcinoma (HCC), and cholangiocarcinoma (CCA). The results indicate that PANXEON had the highest capability for detecting patients with PDAC (AUC, 94.5%; Sensitivity, 84.3%) compared to all other GI cancers using the cutoff of 2.748 (CRC: AUC, 55.8%; Sensitivity. 20.7%; ESCC: AUC, 50.5%; Sensitivity, 20.8%; GC: AUC, 51.5%; Sensitivity. 10.3%; HCC: AUC. 53.2%; Sensitivity. 16.7%; and CCA: AUC. 75.7%; Sensitivity. 55.0%; FIG. 4).

[0204] We further investigated whether PANXEON levels at diagnosis would change during or after treatment with neoadjuvant chemotherapy (NAC), surgery, during follow-up, and at recurrence in patient-matched samples. 29 unique patients (20 with and 9 without recurrentPDAC during the time of follow-up) provided a total of 137 plasma samples that were collected at diagnosis, during NAC, before surgery, after surgery, and during follow-up. The scatter plot of PANXEON levels illustrates a general decrease during NAC and after surgery, especially in patients who did not develop a recurrence (FIG. 2). However, during long-term postoperative follow-up, PANXEON levels trended upwards, with significantly higher levels in patients who developed a recurrence after curative-intent treatment (FIG. 2). These intriguing results compelled us to investigate whether NAC -induced changes to PANXEON levels could predict outcomes. Therefore, we evaluated the changes in PANXEON levels before and after NAC in 19 patients (11 recurrent and 8 non-recurrent) who underwent a complete 3-month course of treatment, with plasma samples collected both prior to and following NAC. We observed that the PANXEON levels decreased after NAC in patients who did not develop a recurrence. However, NAC did not reduce the PANXEON levels in patients who later developed a recurrence (FIG. 2). This finding suggests that the reduction in PANXEON score before and after NAC might be a predictor of recurrence-free survival (RFS). Interestingly, we observed that when PANXEON levels decreased by > 0.71 after NAC, the RFS outcomes were significantly better. In fact, patients that achieved a PANXEON decrease > 0.71 after NAC had a substantially higher chance of achieving 6-month RFS (85.7% vs. 41.7%, p < 0.05, FIG. 2). We further investigated whether surgery-induced changes to PANXEON levels could predict survival outcomes. We observed a significant decrease in the PANXEON score in 35 matched pre-surgery and within 4 months post-surgery plasma samples from unique 15 patients (p < 0.001, FIG. 2). To further investigate whether PANXEON could monitor the recurrence of PDAC, we tracked the PANXEON levels in all cases that underwent multiple post-surgical follow-up plasma collections. Interestingly, the PANXEON score showed a notable increase before recurrence (post 3-month surgery), surpassing the cutoff of 2.748 in most recurrent cases (FIG. 2). Collectively, the pre- and post-surgical PANXEON pattern highlights its tumor specificity and ability for detecting minimal residual disease or monitoring disease recurrence.

[0205] Combining PANXEON with CAI 9-9 improves its performance for the early detection of pancreatic ductal adenocarcinoma

[0206] In routine clinical practice, CAI 9-9 is the only blood-based biomarker currently used for managing PDAC patients. However, it does not provide the sensitivity' and specificity' needed for early detection in the general population. We investigated whether the inclusion of CAI 9-9 could further enhance the overall diagnostic performance of PANXEON. To support this investigation, we first measured plasma CAI 9-9 levels in patients with PDAC enrolled in thetest cohort. CAI 9-9 was elevated in PDAC patients compared to NDC individuals (p < 0.001, FIG. 3) and achieved an AUC value of 83.7% (FIG. 3). We also observed a significantly positive correlation between the PANXEON score and CA19-9 levels (p < 0.01, R = 0.42, [CI95%: 0.29 - 0.53], FIG. 3). Interestingly, 95 individuals (72 with NDC and 23 with PDAC) had CA19-9 levels below737 U / mL, a threshold generally considered negative for PDAC in clinical settings. The PANXEON exhibited significant upregulation in PDAC among these negative individuals (FIG. 3) and could robustly identify these PDAC cases, yielding an AUC value of 91.0% [CI95%: 82.5 - 99.6%] (FIG. 3). These findings highlight the potential complementary effect of combining PANXEON with CAI 9-9 to enhance diagnostic accuracy. As a result, the diagnostic performance was significantly improved when combining PANXEON with CA19-9 levels, achieving an AUC value of 97.0% [CI95%: 94.5 - 99.4%], compared to either marker alone (FIG. 3). Specifically, the plots of sensitivity and specificity versus probability cut-off points elucidated the performance of the PANXEON plus CAI 9-9 in the test cohort (FIG. 3). The plot displayed that by setting the assay's specificity at 95% and 97.5%, the combination of PANXEON and CA19-9 could achieve sensitivity values of 89.9% [CI95%: 82.0 - 97.8%] and 87.6% [CI95%: 64.0 - 95.5%], respectively (FIG. 3).

[0207] We next compared the CA19-9 levels in patients with stage I / II vs. stage III / IV PDAC from the test cohort. We observed that CA19-9 expression levels w ere significantly elevated in patients with stage III / IV PDAC compared to patients with stage I / II PDAC (FIG. 3). As a result, unlike the PANXEON scores, the diagnostic performance of CAI 9-9 in detecting stage I / II PDAC was lower compared to stage 111 / IV, with AUC values of 81.2% and 89.6%. respectively (FIG. 3). These findings indicate that PANXEON could overcome the diagnostic limitations of CAI 9-9, particularly its low sensitivity for early-stage PDAC detection (FIG. 3). Therefore, we evaluated the diagnostic efficacy of combining PANXEON with CAI 9-9 for stage I / II PDAC using ROC analysis, which yielded a remarkable AUC value of 96.6% [CI95%: 93.4 - 99.7%] (FIG. 3). The decision curve analysis (DCA) indicated that the combination of PANXEON and CAI 9-9 offered a superior net benefit compared to CAI 9-9 or PANXEON alone in the test cohort across most ranges of threshold probability for distinguishing patients with stage I / II PDAC from NDC individuals (FIG. 3). For instance, at a 43% threshold probability, which is recommended for diagnosing suspicious pancreatic masses, the combined use of PANXEON and CA19-9 demonstrated a higher net benefit (0.352) in identifying stage I / II PDAC patients compared to using PANXEON (0.309) or CA19-9 (0.215) alone (FIG. 3).

[0208] Finally, the efficacy of combining PANXEON with CAI 9-9 for detecting stage I / IIPDAC was further assessed by locking the assay's specificity at 95% and 97.5%. When specificity was fixed at these levels, CAI 9-9 alone exhibited significantly lower sensitivity, at 53.1% and 32.8% for stage I / II PDAC patients, respectively. In contrast, the combination of PANXEON and CA19-9 resulted in markedly higher sensitivity rates of 89.1% and 87.5% (FIG. 3). These findings clearly demonstrate that the PANXEON assay significantly enhances diagnostic accuracy, emphasizing its use in clinical implementation in the early detection of PDAC.

[0209] Discussion

[0210] Early detection of PDAC remains critical to improve the outcomes of this malignancy. Currently, PDAC surveillance in high-risk individuals hinges on endoscopic ultrasonography and magnetic resonance. However, their application in PDAC screening is limited due to the invasive nature, poor patient compliance, high costs, and low sensitivity for detecting small pancreatic lesions. CAI 9-9 remains the only FDA-approved biomarker for PDAC and is primarily used for disease monitoring rather than screening, due to its limited sensitivity and specificity. Therefore, more accurate and inexpensive liquid biopsy assays are needed to complement the diagnostic options currently available, ultimately leading to better outcomes.

[0211] We present PANXEON, an XGBoost-based liquid biopsy signature developed, validated, and independently tested in multi-center and international prospective cohorts. PANXEON was able to distinguish patients with PDAC from NDC, achieving AUC values of 98.4%. 96.9%, and 94.5% in the training, validation, and independent test cohorts, respectively. It maintained stable performance across populations from different countries. This liquid biopsy test offers an opportunity to complement existing screening and surveillance strategies, thereby improving prognoses for PDAC.

[0212] Multiple studies have sought to incorporate additional markers with CA19-9 to improve its diagnostic accuracy. However, a recent meta-analysis revealed that incorporating additional protein biomarkers with CAI 9-9 resulted in only minor improvements in clinical utility. KRAS mutations in ctDNA combined with four proteins (CAI 9-9, carcinoembryonic antigen, hepatocyte grow th factor, and osteopontin) detected patients with stage I-III PDAC with a sensitivity of 64%. These and other tests have promising diagnostic characteristics, but detecting PDAC while still surgically resectable remains the greater challenge. In contrast.RNA-based tests leverage the advantages of both cell-free and exosome-derived RNA to provide high sensitivity7and specificity in cancer detection. In the current study, PANXEON effectively distinguishes patients with stage I / II PDAC from NDC, with a sensitivity of 84.3% and aspecificity of 93.2%. Importantly, PANXEON could identify the PDAC patients among the individuals with CA19-9 below 37 U / mL, yielding an AUC value of 91.0%. These findings highlight the capability of PANXEON to complement the performance of CAI 9-9 in PDAC detection. As a result, PANXEON combined with CAI 9-9 significantly improved diagnostic performance in detecting patients with stage I / II PDAC, achieving sensitivities of 87.5% at specificities of 97.5%. Additionally, PANXEON holds the highest capability for detecting patients with PDAC compared to all other G1 cancers. Besides, the pre- and post-surgical PANXEON pattern indicates its ability to detect minimal residual disease after surgery and monitoring disease recurrence during follow-up. Collectively, these findings indicate that PANXEON will significantly augment existing strategies in detecting patients with PDAC.

[0213] By leveraging the advantages of biological features (cf- and exo-miRNAs) and machine learning algorithms, we established, validated, and independently tested this assay in a large-scale, multi-center, international prospective study. It will serve to augment existing strategies for detecting patients with PDAC, offering a more inexpensive, noninvasive, and accurate approach.

[0214] Online Methods

[0215] Ethics approval. The prospective collection of the patient datasets in each cohort was approved by the institutional review7board (IRB) at each institution with a waiver for informed consent: the Translational Genomics Research Institute (TGen) IRB, Hoag Family Center Institute IRB, Medical College of Wisconsin (MCW) IRB, HonorHealth Research Institute IRB. Nagoya University Graduate School of Medicine IRB, Mie University Graduate School of Medicine IRB, Samsung Medical Center IRB, Ochsner Clinic Foundation IRB, Asan Medical Center IRB, Allegheny Health Network (AHN) IRB, Baylor Scott & White Research Institute IRB. The study protocol for Pancreatic Cancer Detection Consortium (PCDC) prospective cohorts’ collection was registered at clinicaltrials.gov (ID: NCT06271291). The study was conducted in accordance with the principles of the Declaration of Helsinki, and all participants provided written informed consent.

[0216] Study Design and Patient Cohorts.

[0217] The study workflow design is depicted in FIG. 4. This multicenter prospective study comprised the following four phases: a training phase, on which the PANXEON signature were established; an internal validation phase, on which the PANXEON signature performance was successfully assessed; an independent test phase, on which the PANXEON signature wasindependently validated; an final specificity evaluation phase, on which the PANXEON signature was assessed on pre- and post-surgical samples from PDAC participants, and patients with several other gastrointestinal cancers, including CRC, ESCC, GC, HCC, CCA.

[0218] Candidate miRNAs were assessed in blood using RT-qPCR. The initial biomarker panel included 5 cf-miRNAs and 8 exo-miRNAs. Cf-miR-23b-3p, exo-miR-217-5p, and exo- miR-216b displayed opposite expression patterns across the population from different countries and were therefore excluded from further analysis. The PANXEON training phase involved 434 patients with PDAC and 273 NDCs. Next, in the validation phase, qRT-PCR assays were performed to assess the capability of PANXEON in a validation cohort comprising plasma specimens from 201 patients with PDAC and 104 NDCs. All the 1012 participants in both the training and validation cohorts were enrolled at TGen (Phoenix, Arizona, USA), Hoag (Newport Beach, California, USA), MCW (Milwaukee, Wisconsin, USA), HonorHealth (Scottsdale, Arizona, USA), AHN (Pittsburgh, PA, USA), Ochsner Clinic Foundation (New Orleans, LA, USA), Baylor Scott & White Research Institute (Dallas, TX, USA), Nagoya University Graduate School of Medicine (Nagoya, Japan), Mie University Graduate School of Medicine (Mie. Japan). Samsung Medical Center (Seoul, Korea), and Asan Medical Institute (Seoul, Korea) between 2021 and 2023. The participants were randomly assigned to the training and validation cohorts in a 7:3 ratio, ensuring balance by sex, country, TNM stage, and ethnicity using the "splitTools" package in R. Subsequently, we tested the performance of the final PANXEON signature in an independent cohort consisting of 89 patients with PDAC and 88 NDCs enrolled at the Ochsner Clinic Foundation, New Orleans, Louisiana, between 2020 and 2023. The detailed clinical characteristics of training, validation and test cohorts are provided in FIG. 15.

[0219] To further evaluate the specificity of PANXEON, we evaluated its expression levels in 137 pre- and post-surgical samples from 29 unique patients enrolled at AHN Institute. The detailed clinical characteristics of the pre- and post-surgical samples are provided in FIG. 15. Additionally, we compared its performance in patients with other gastrointestinal cancers, including ESCC (n = 24), CRC (n = 29), GC (n = 29), HCC (n = 24), CCA (n = 20) and NDC (n = 42). These individuals are collected from Nagoya University Graduate School of Medicine and Mie University' Graduate School of Medicine. The detailed clinical characteristics of this gastrointestinal cancer cohort are provided in FIG. 16.

[0220] Definitions, inclusion and exclusion criteria, and study endpoints

[0221] Eligible cases were defined as individuals with a histological diagnosis of PDACaccording to the AJCC Staging System, 8th edition. NDCs were defined as individuals without PDAC at the time of blood collection and with at least one year of negative follow-up thereafter. All blood samples used for training, validation, and testing of PANXEON were collected before the administration of any treatment. In addition, multi-time point blood samples were specifically collected and utilized after neoadjuvant chemotherapy and surgery to evaluate the specificity of PANXEON. The primary exclusion criterion for cases was a history of other cancers. The primary objective of this study is to assess the sensitivity and specificity for detecting patients with PDAC, specifically for Stage I / II cases.

[0222] Cell-free and exosomal RNA extraction

[0223] Total circulating cell-free RNA was isolated from 200 pL of plasma using the miRNeasy serum / plasma Kit (Qiagen, Valencia, CA, USA). Exosome-derived total RNA was isolated in a two-step process involving exosome isolation and subsequent RNA extraction. The intact exosomes were isolated from 200 pL of plasma using the total exosome isolation kit (Waltham, MA, USA), followed by its RNA extraction using the miRNeasy serum / plasma Kit (Qiagen).

[0224] Real-time quantitative polymerase chain reaction assays

[0225] The complementary DNA (cDNA) was synthesized from the total cell free and exosomal RNA using miRCURY LNA RT Kit (Qiagen), respectively. Subsequently, the Realtime quantitative polymerase chain reaction (RT-qPCR) was performed to assess the miRNA expression level of candidates using the SensiFASTTM SYBR® LO-ROX Kit (Bioline, London, UK) on a QuantStudio 7 Flex Real-Time PCR system (Thermo Fisher Scientific, Irwindale, CA. USA). All the SYBR® Green-based miRNA primers were provided by Qiagen (FIG. 13). The expressions of hsa-miR-30e-5p, hsa-miR-15b-5p and " were used as endogenous controls for data normalization. The expression level of each target miRNA was calculated using 2-ACt method.

[0226] Plasma CAI 9-9 Measurement

[0227] Plasma CAI 9-9 levels were measured using the CAI 9-9 Human ELISA Kit (#EHCA199. Thermo Fisher Scientific), following the manufacturer's instructions.

[0228] Model Architecture and Hyperparameters

[0229] Our biomarker architecture consists of three layers: three nonnalizers-based XGBoost classifiers, cf- and exo-miRNA panels, and a final signature combining the two panels (FIG. 14). Our final liquid biopsy diagnostic assay, named PANXEON. was developed using machine-learning XGBoost algorithms and stacked with logistic regression algorithms. XGBoost is an efficient and scalable gradient boosting system that incorporates algorithmic innovations such as approximate greedy search and parallel learning, along with various hyper-parameters to enhance learning and avoid overfitting. XGBoost has become the leading model for structural data analysis, leveraging its capacity to elevate weak learners to strong learners through sequential ensemble learning techniques. By combining multiple learning algorithms, XGBoost achieves higher prediction accuracy than individual algorithms, making it widely employed across various fields. In the present study, the XGBoost machine learning algorithm was employed to develop three normalizer-based classifiers for the cell-free and exosome miRNAs in the training cohort, respectively, the XGBoost classifier underwent a maximum of 100 training rounds with gradient boosting trees implemented using the 'xgboost' package in R (version 1.7.7.1). To enhance accuracy in detecting PDAC, we utilized the AUC / precision-recall (‘aucpr’) evaluation metric to train a binary discriminatory assay. The final XGBoost classifier had the following hyperparameters: mounds = 100, max_depth = 2. colsample_bytree = 1, subsample = 0.1. colsample_bytree = 1, eta = 0.3, verbosity = 1, min_child_weight = 1, scale_pos_weight = 1. Subsequently, we established two panels by integrating the three cell-free and exosomal normalizer-based XGBoost classifiers using logistic regression, respectively. Ultimately, we developed and locked the final PANXEON signature by combining these two cf- and exo-panels using logistic regression. Following the training phase, PANXEON was comprehensively developed, trained, and locked in. and was subsequently applied to the validation, independent testing, specificity evaluation phase.

[0230] Statistical Analyses

[0231] All statistical analyses were conducted within the R environment (version 4.4. 1 ). The performance of diagnostic biomarker was evaluated in terms of the receiver operating characteristic curve (ROC) analysis, sensitivity, specificity, positive and negative predictive values (PPV and NPV), accuracy using the “pROC’ package (version 1.18.5). The decision curve analysis (DCA) was conducted to determine the net benefit value of diagnostic biomarkers, utilizing the “rmda” package (version 1.5). Calibration curve analysis was employed to evaluate the calibration of diagnostic biomarkers, utilizing the ’‘CalibrationCurves” package (version 2.0.1) in R. A restricted cubic spline plot (RCS) was generated using the 'plotRCS' package (version 0.1.4) to illustrate potential relationships between PANXEON score and the risk of PDAC.

[0232] It is understood that the examples described herein are for illustrative purposes onlyand that various modifications or changes in light thereof will be suggested to persons skilled in the art and are to be included within the spirit and scope of this application and claims. 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 are expressly incorporated by reference herein in their entirety and for all purposes.

[0233] References1. Rahib, L„ et al. Cancer Res 74, 2913-2921 (2014).2. Siegel, et al, CA Cancer J Clin 74, 12-49 (2024).3. Placido, D , et al. Nat Med 29, 1113-1122 (2023).4. Dbouk, M„ et al. J Clin Oncol 40, 3257-3266 (2022).5. Vasen, H„ et al J Clin Oncol 34, 2010-2019 (2016).6. Blackford, A.L., et al. JAMA Oncol (2024).7. Cohen, J.D., et al. Science 359, 926-930 (2018).8. Chen, Y.L., et al. Am J Cancer Res 8, 2548-2563 (2018).9. Guler, G.D., et al. Nat Commun 11, 5270 (2020).10. Kartal, E„ et al Gut 71. 1359-1372 (2022).11. Conroy, T. et al. Ann Oncol 34, 987-1002 (2023).12. Force, U.S.RS.T., et al. JAMA 322, 438-444 (2019).13. Henrikson, N.B., et al. JAMA 322, 445-454 (2019).14. Aslanian, et al, Gastroenterology 159, 358-362 (2020).15. Cortez, M.A., et al. MicroRNAs in body fluids— the mix of hormones and biomarkers. Nat Rev Clin Oncol 8, 467-477 (2011).16. Nakamura, K., et al. A Liquid Biopsy Signature for the Detection of Patients With Early - Onset Colorectal Cancer. Gastroenterology 163, 1242-1251 el 242 (2022).17. Pardini, B., et al. A Fecal MicroRNA Signature by Small RNA Sequencing Accurately Distinguishes Colorectal Cancers: Results From a Multicenter Study. Gastroenterology 165, 582-599 e588 (2023).18. So, J.B.Y, et al. Development and validation of a serum microRNA biomarker panel for detecting gastric cancer in a high-risk population. Gut 70, 829-837 (2021).19. Kalluri. R. & LeBleu, V.S. The biology, function, and biomedical applications of exosomes. Science 367(2020).20. Li, K„ et al. Gastroenterology 165, 932-945 e939 (2023).21. Li, Y, Sui, S. & Goel, A. Extracellular vesicles associated microRNAs: Their biology and clinical significance as biomarkers in gastrointestinal cancers. Semin Cancer Biol 99, 5-23(2024).22. Nakamura, K., et al. An Exosome-based Transcriptomic Signature for Noninvasive, Early Detection of Patients With Pancreatic Ductal Adenocarcinoma: A Multicenter Cohort Study. Gastroenterology 163, 1252-1266 el252 (2022).23. Lau, M.K., et al, Pancreas 39, 458-462 (2010).24. Lennon, A.M., et al. Science 369(2020).25. Stoffel. E.M. et al, Gastroenterology 164, 752-765 (2023).26. Goggins, M„ et al. Gut 69, 7-17 (2020).27. Overbeek, K.A., et al. Gastroenterology 162, 772-785 e774 (2022).28. Boyd, L.N.C., et al. Diagnostic accuracy and added value of blood-based protein biomarkers for pancreatic cancer: A meta-analysis of aggregate and individual participant data. EClinicalMedicine 55, 101747 (2023).29. Cohen, J.D., et al. Combined circulating tumor DNA and protein biomarker-based liquid biopsy for the earlier detection of pancreatic cancers. Proc Natl Acad Sci U S A 114, 10202- 10207 (2017).30. Han, J.W., et al. Clin Cancer Res (2024).31. Yew, P.Y., et al. Alzheimers Dement (2024).32. Clift, A.K., et al. BMJ 381, e073800 (2023).33. Khera, R„ et al. JAMA Cardiol 6, 633-641 (2021).

Claims

CLAIMSWhat is claimed is:

1. A method of detecting RNA in a patient having pancreatic cancer, a patient suspected of having pancreatic cancer, or a patient having minimal residual disease, the method 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-30c, miR-142, miR-340, miR-335, miR-1260b, miR-145, miR-200a, miR-200b, miR-429, miR-145, or a combination of two or more thereof.

2. A method of diagnosing a patient with pancreatic cancer or minimal residual disease, 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 pancreatic cancer; wherein the RNA comprises miR-30c, miR-142, miR-340, miR-335, miR- 1260b, miR-145, miR-200a, miR-200b, miR-429, miR-145, or a combination of two or more thereof.

3. A method of treating pancreatic cancer or minimal residual disease in a 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 patient, wherein the RNA comprises miR-30c, miR-142, miR-340, miR-335, miR-1260b, miR-145, miR-200a, miR-200b, miR- 429, miR-145, or a combination of two or more thereof; and(ii) administering to the patient an effective amount of an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the pancreas of the patient, or a combination of two or more thereof.

4. A method of treating pancreatic cancer in a patient in need thereof, the method comprising:(i) selecting a patient having a diagnosis of pancreatic cancer based on a pancreatic cancer 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- 30c, miR-142, miR-340, miR-335, miR-1260b, miR-145, miR-200a, miR-200b, miR-429, miR-145, or a combination of two or more thereof; and(ii) treating the patient from step (i) by administering to the patient an effectiveamount of an anti-cancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the stomach of the patient, or a combination of two or more thereof.

5. A method of treating pancreatic cancer or minimal residual disease in a patient in need thereof, the method comprising administering to the patient an effective amount of an anticancer agent, administering to the patient an effective amount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the pancreas of the patient, 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-30c, miR-142, miR-340, miR-335, miR-1260b, miR-145, miR-200a, miR-200b, miR-429, miR-145, or a combination of two or more thereof.

6. A method of monitoring a patient at risk for developing pancreatic cancer or monitoring for minimal residual disease or recurrence of pancreatic cancer, 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-30c, miR-142. miR-340, miR- 335, miR-1260b, miR-145, miR-200a, miR-200b, miR-429, miR-145, or a combination of two or more thereof;7. The method of claim 6, 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 patient has an increased risk of developing pancreatic cancer.

8. The method of claim 6, 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 patient has minimal residual disease or a recurrence of pancreatic cancer.

9. The method of claim 1. wherein the RNA comprises cell-free miR-30c-5p, cell- free miR-142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR-429, exosomal miR-145-3p, or a combination of two or more thereof10. The method of claim 1, wherein the RNA comprises cell-free miR-30c-5p, cell- free miR-142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR-1260b. exosomalmiR-145-5p, exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR-429, and exosomal miR-145-3p.

11. The method of claim 1. wherein the RNA consists of cell-free miR-30c-5p, cell- free miR-142-3p, cell-free miR-340-5p, cell-free miR-335-5p, exosomal miR-1260b, exosomal miR-145-5p, exosomal miR-200a-3p, exosomal miR-200b-3p, exosomal miR-429, and exosomal miR-145-3p.

12. The method of claim 1, wherein the RNA does not comprise cell-free miR-23b- 3p, exosomal miR-216b-5p, or exosomal miR-217-5p.

13. The method of claim 1, wherein the RNA does not comprises cell-free miR-23b- 3p, exosomal miR-216b-5p, exosomal miR-217-5p, cell-free let-7e-5p, cell-free miR-26a-5p, cell-free miR-223-3p, cell-free miR-340-3p. exosomal miR-1260a, exosomal miR-141-3p. exosomal miR-143-3p, exosomal miR-148a-3p, exosomal miR-200c-3p, exosomal miR-216a- 5p, exosomal miR-34a-5p, cell-free let-7f-5p, cell-free miR-369-3p, cell-free miR-125a-5p, cell- free miR-495-3p, exosomal miR-375-3p, or exosomal miR-199a-5p.

14. The method of claim 1, further comprising detecting the expression level of reference RNA, wherein the reference RNA comprises cell-free miR-15b-5p. cell-free miR-23a- 3p, cell-free miR-30e-5p, exosomal miR-15b-5p, exosomal miR-23a-3p, exosomal miR-30e-5p. or a combination of two or more thereof.

15. The method of claim 14, further comprising normalizing the expression level of the RNA to the expression level of the reference RNA.

16. The method of claim 1, wherein the biological sample is a liquid biological sample.

17. The method of claim 1 , wherein the pancreatic cancer is a pancreatic head / uncinate cancer.

18. The method of claim 1, wherein the pancreatic cancer is a pancreatic body / tail cancer.

19. The method of claim 1, wherein the pancreatic cancer is pancreatic ductal adenocarcinoma.

20. A kit comprising reagents capable of detecting an expression level of RNA from a biological sample; wherein the RNA comprises miR-30c, miR-142. miR-340, miR-335, miR- 1260b, miR-145. miR-200a. miR-200b, miR-429. miR-145, or a combination thereof.

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