RNA biomarkers for use in treating, diagnosing, and monitoring early-onset colorectal cancer

Abstract

Methods of treating colorectal cancer, diagnosing colorectal cancer, monitoring colorectal cancer, treating high grade dysplasia in the colon, diagnosing high grade dysplasia in the colon, and monitoring high grade dysplasia in the colon can be conducted with biomarkers. Such biomarkers include miR-32, miR-625, miR-486, miR-550a, miR-30d, or a combination of two or more thereof. In embodiments, the biomarkers include exosomal miR-32-5p, cell-free miR- 625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, cell-free miR- 30d-5p, or a combination of two or more thereof.

Description

Docket No. 048440-203001 WO / TEC 24-027RNA BIOMARKERS FOR USE IN TREATING, DIAGNOSING, AND MONITORING EARLY-ONSET COLORECTAL CANCERCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of priority to US Application No. 63 / 687,117 filed August 26, 2024, the disclosure of which is incorporated by reference herein in its entirety.BACKGROUND

[0002] Early -onset colorectal cancer (EOCRC), defined as a CRC diagnosis at younger than age 50 years, comprises 15-20% of all newly diagnosed CRC cases. Once rare, it now stands as the leading cause of cancer-related death in young men and the second in women, with continuously increasing incidence trends documented globally. Most cases of EOCRC remain undiagnosed until they reach an advanced stage, while an earlier diagnosis, ideally at an asymptomatic stage, would make the disease more curable. In response, several societies have lowered the recommended age for initiating CRC screening. However, persistent barriers to screening keep compliance rates with the new age of screening below 20%.

[0003] Screening preferences lean towards minimally invasive options, and young adults are especially reluctant for traditional colonoscopy screening. Non-invasive, biomarker-based methods offer several advantages to complement traditional colonoscopy screening. Bloodbased genomic biomarkers can increase screening participation and identify individuals who are more likely to have abnormal findings, allowing colonoscopy efforts to focus on higher-risk patients. There is currently only one RNA-based molecular test approved for CRC screening in average-risk individuals, which analyzed eight RNA transcripts in stool. These limitations highlight the imperative need to develop robust, non-invasive biomarkers that can help overcome the challenges of the current generation of diagnostic assays, and facilitate the identification of patients with EOCRC. The present disclosure is directed to these important needs.BRIEF SUMMARY

[0004] Provided herein are methods of detecting RNA in a patient with colorectal cancer or high grade dysplasia in the colon 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-32, miR-625, miR-486, miR-550a, miR-30d, or a combination thereof. In embodiments, the RNA comprises exosomal miR-32-5p. cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, cell-free miR-30d-5p, or a combination of two or morethereof.

[0005] Provided herein are methods of treating colorectal cancer high grade dysplasia in the colon 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 the patient; wherein the RNA comprises miR-32, miR-625, miR-486, miR-550a, miR-30d, or a combination 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 colon of the patient, or a combination of two or more thereof. In embodiments, the RNA comprises exosomal miR-32-5p, cell-free miR- 625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, cell-free miR- 30d-5p, or a combination of two or more thereof.

[0006] Provided herein are methods of diagnosing a patient with colorectal cancer or high grade dysplasia in the colon comprising: (i) detecting the expression level of RNA in a biological sample obtained from the patient; and (ii) diagnosing the patient as having colorectal cancer when the biological sample has an elevated expression level, relative to a control, of the RNA; wherein the RNA comprises miR-32, miR-625, miR-486, miR-550a, miR-30d, or a combination thereof. In embodiments, the RNA comprises exosomal miR-32-5p, cell-free miR- 625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, cell-free miR- 30d-5p, or a combination of two or more thereof.

[0007] Provided herein are methods of monitoring treatment in a patient having colorectal cancer or high grade dysplasia in the colon or monitoring risk for developing colorectal cancer or high grade dysplasia in the colon in a patient, the method comprising: (i) detecting the expression level of RNA in a biological sample obtained from the patient at a first time point;(ii) detecting the expression level of the RNA in a biological sample obtained from the patient at a second time point, wherein the second time point is later than the first time point; and (iii) comparing the expression level of the RNA at the second time point to the expression level of the RNA at the first time point, thereby monitoring treatment or monitoring risk; wherein the RNA comprises miR-32. miR-625, miR-486, miR-550a, miR-30d, or a combination thereof. In embodiments, the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, exosomal miR- 486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, cell-free miR-30d-5p, or a combination of two or more thereof.

[0008] These and other embodiments of the disclosure are described herein.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] FIGS. 1A-1E: Discovery7and prioritization of the 12-cfRNA and the 11-exoRNA panels. FIG. 1A: Volcano plot of differentially expressed cfRNAs between EOCRC and age- and sex-matched controls; color grading follows significance. FIG. IB: Volcano plot of differentially expressed RNA transcripts between cancerous and adjacent normal mucosas, derived from the same patients with EOCRC; color grading follows significance. FIG. 1C: Volcano plot of differentially expressed exoRNAs between EOCRC and age- and sex-matched controls; color grading follows significance. FIG. ID: Heatmap of the 12 cfRNAs selected for further analyses (unsupervised clustering based on weighted pair group method with arithmetic mean). FIG. IE: Heatmap of the 11 exoRNAs selected for further analyses (unsupervised clustering based on weighted pair group method with arithmetic mean).

[0010] FIGS. 2A-2F: Results of the training and validation cohorts for the cf / exoRNA blood assay. ROC curves with the corresponding AUC values for the final 4-exoRNA panel and the 2- cfRNA panels in the training (FIG. 2A) and validation (FIG. 2B) cohorts (95% confidence intervals estimated based on 2000 bootstraps are presented as shaded areas); The density plots of the cfRNA and exoRNA panels for EOCRC and the non-disease controls demonstrate a clear clustering of the EOCRC cases on the top-right comer of the diagram, while NDCs clustered in the bottom-left comer in both the training (FIG. 2C) and validation (FIG. 2D) cohorts; ROC Curve with the 95% confidence intervals of the final classifier, named ENCODE, with the corresponding AUC value based on a combined panel of four exoRNAs and two cfRNAs in the training (FIG. 2E) and validation (FIG. 2F) cohorts (95% confidence intervals estimated based on 2000 bootstraps are presented as shaded areas).

[0011] FIGS. 3A-3F : Performance of the cf / exoRNA blood test in the training and validation cohorts. Raincloud plot with super-imposed box and whisker plot demonstrate that patients with EOCRC have higher values of the blood-based cf / exoRNA test than NDCs in both the training (FIG. 3A) and validation (FIG. 3B) cohorts; Waterfall plot of the values of the cf / exoRNA blood test, normalized on the positivity7threshold, in individuals with and without EOCRC in the training (FIG. 3C) and validation (FIG. 3D) cohorts; Odds ratios for the presence of EOCRC with restricted cubic splines as a function of the values of the cf / exoRNA blood test in the training (FIG. 3E) and validation (FIG. 3F) cohorts (95% confidence intervals for odds ratios are presented as dashed lines).

[0012] FIGS. 4A-4E: Validation Cohort Sub-Analyses Based on Stage. FIG. 4A: Modified box-whisker plot of the values of the cf / exoRNA blood test in the validation cohort based on thedisease stage (the dots represent the mean values, the boxes the first and third quartiles, the thick lines the 95% confidence intervals, and the thin lines the range of values). ROC curves with the corresponding AUC values for high grade dysplasia (HGD) (FIG. 4B), stage I / II EOCRC (FIG. 4C), stage III EOCRC (FIG. 4D), and stage IV EOCRC (FIG. 4E). In all ROC curves, 95% confidence intervals are estimated based on 2000 bootstraps and presented as shaded areas.

[0013] FIGS. 5A-5I: Validation Cohort Sub- Analyses Based on Age. FIG. 5A: Average values of the cf / exoRNA blood test among patients with EOCRC (red) vs. NDCs (blue). 99-5% confidence intervals are presented as shaded areas. FIGS. 5B-5C: Violin plot and ROC curves with the corresponding AUC values for the 18-35 age group. FIGS. 5D-5E Violin plot and ROC curves with the corresponding AUC values for the 35-40 age group. FIGS. 5F-5G: Violin plot and ROC curves with the corresponding AUC values for the 41-45 age group. FIGS. 5H-5I: Violin plot and ROC curves with the corresponding AUC values for the 46-49 age group. In all ROC curves, 95% confidence intervals are estimated based on 2000 bootstraps and presented as shaded areas. NDC, Non disease controls; EOCRC, Early-Onset Colorectal Cancer; AUC, Area Under the Curv e; ****, pO OOOl

[0014] FIGS. 6A-6F: Pairwise Comparison of ENCODE Levels Before and After Curative Intent Surgical Treatment. FIG. 6A: The values of the cf / exoRNA blood test decreased significantly after surgery compared to their pre-surgical values. FIG. 6B: Hybrid parallel -line waterfall plot: most patients experienced a decrease in the value of their cf / exoRNA blood test after surgery. However, the decrease appeared to be more substantial for those whose blood sample was drawn four or more days after surgery'. Density' plot of the cfRNA and exoRNA panels demonstrates that at diagnosis, most patients with EOCRC clustered in the top right comer (FIG. 6C) Individuals whose blood was drawn in the first 1-3 days after surgery moved slightly towards the left (FIG. 6D), while those whose blood was drawn at day four or later moved significantly more towards the bottom left comer of the plot (FIG. 6E). FIG. 6F: Timespecific representation of the values of the cf / exoRNA blood test based on how many days after surgery the blood was drawn. In the first three days after surgery, these values decreased slightly, while for those collected at day 4-to-7 the values decreased significantly more and, in some instances, became even negative.

[0015] FIG. 7 shows the performance characteristics of the ENCODE blood-based test.

[0016] FIG. 8 shows the clinical cohorts characteristics.(A)149 tissue samples were included from 80 unique patients: of these 80 patients, 18 provided unique data entries, 55 had both neoplastic and matched normal adjacent tissue, and 7 had neoplastic tissue as well as twomatched normal adjacent tissues;(B)For each of the 19 individuals with EOCRC included in the discovery’ cohort, we separately analyzed cf-miRNA, exo-miRNA, EOCRC-miRNA, and adjacent normal mucosa’s miRNAs;(C)41 samples collected at diagnosis and 41 after surgery, from the same patients.

[0017] FIG. 9 shows miRNA probes.

[0018] FIG. 10 shows the age-specific ENCODE performance characteristics.

[0019] FIG. 11: Cohort allocation flow-chart.

[0020] FIG. 12: Study design.

[0021] FIGS. 13A-13D: Refinement of the cf-miRNA and exo-miRNA Candidate Biomarkers Based on RNA Sequencing Expression Levels. FIG. 13A: The 12 cf-miRNAs were reduced to 9 cf-miRNAs based on their average expression level in blood (5-fold increase versus the average expression levels). FIG. 13B: The 11 exo-miRNAs were not reduced based on their average expression level in blood (because the expression level of exo-miRNAs was significantly lower, in general, than that of cf-miRNAs, a lower 2-fold increase threshold was utilized). FIG. 13C: The 9 cf-miRNAs were reduced to 6 cf-miRNAs based on their average expression level in cancerous tissue. FIG. 13D: The 11 exo-miRNAs were reduced to 10 exo-miRNAs based on their average expression level in cancerous tissue;

[0022] FIGS. 14A-14B: Refinement of the cf-miRNA and exo-miRNA Candidate Biomarkers Based on Reverse Transcription. Quantitative Polymerase Chain Reaction Results. FIG. 14A: Transitioning the 6 cf-miRNAs from sequencing (i.e.. discovery cohort) to RT-qPCR (i.e.. training cohort), the correlation plot demonstrated a statistically significant association with the presence of EOCRC for only 2 cf-miRNA biomarkers. All others were discarded for model construction. FIG. 14B: Transitioning the 10 exo-miRNAs from sequencing (i.e., discovery cohort) to RT-qPCR (i.e., training cohort), the correlation plot demonstrated a statistically significant association with the presence of EOCRC for only 4 exo-miRNA biomarkers. All others were discarded for model construction.

[0023] FIGS. 15A-15D: Machine Learning Approaches, Architecture, and Inner Workings. FIG. 15A: Schematic representation of the XGBoost ensemble model. ENCODE is built on an XGBoost platform and it employs a sequential iterative strategy based on decision trees to minimize the classification errors. During each training round, the next tree has the task to reduce the mistakes of the previous tree. FIG. 15B: SHAP beeswarm plot displays an information-dense summary of how each cf / exoRNA in the training dataset impacts the model’soutput. Each dot represents a patient and the x-position of the dot is determined by the SHAP value of that cf / exo-miRNA for that patient. SHAP values assign an importance value to each feature, and absolute magnitude of this measure estimates how strong that features influenced the final ENCODE value for that patient. The further a dot is from the 0-line, the greater the impact on the model. The biomarkers are ranked top to bottom based on their mean absolute SHAP values, which represents the average impact across all instances. FIG. 15C: Gain is defined as the relative contribution of each cf / exo-miRNA to the model and it is calculated by taking each cf / exo-miRNAs’ contribution for each tree in the model. The lollipop plot presents the average gain of each cf / exo-miRNA when it is used in a branch of the tree. Compared to SHAP values, the cf / exo-miRNAs’ gain pertains to the entire cohort, rather than the individual patients, and it therefore represents an aggregate measure. FIG. 15D: Frequency represents the relative number of times each cf / exo-miRNA occurs in the trees of ENCODE. The lollipop plot presents how frequently each cf / exo-miRNA appears on the branches of the trees.DETAILED DESCRIPTION

[0024] Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by a person of ordinary skill in the art. See, e.g., Singleton et al., Dictionary7of 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.

[0025] The term “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, 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.

[0026] “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. 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.

[0027] “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.

[0028] A “cell” refers to a cell carry ing 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.

[0029] Exemplary RNA described herein include the RNA in Table A and Table B.

[0030] Table A

[0031] Table B

[0032] “Nucleic acid” refers to nucleotides (e.g., deoxyribonucleotides or ribonucleotides) and polymers thereof in either single-, double- or multiple-stranded form, or complements thereof; or nucleosides (e.g., deoxyribonucleosides or ribonucleosides). In embodiments, “nucleic acid” does not include nucleosides. The terms “polynucleotide,” “oligonucleotide,” “oligo” or the like refer, in the usual and customary' sense, to a linear sequence of nucleotides. The term “nucleoside” refers, in the usual and customary sense, to a glycosylamine including a nucleobase and a five-carbon sugar (ribose or deoxyribose). Non limiting examples, of nucleosides include, cytidine, uridine, adenosine, guanosine, thymidine and inosine. The term “nucleotide” refers, in the usual and customary sense, to a single unit of a polynucleotide, i.e., a monomer. Nucleotides can be ribonucleotides, deoxyribonucleotides, or modified versions thereof. Examples of polynucleotides contemplated herein include single and double stranded DNA, single and double stranded RNA, and hybrid molecules having mixtures of single and double stranded DNA and RNA. Examples of nucleic acid, e.g., polynucleotides, contemplated herein include any ty pes of RNA, e.g. mRNA, siRNA, miRNA, and guide RNA and any types of DNA, genomic DNA. plasmid DNA, and minicircle DNA, and any fragments thereof. The term “duplex” in the context of polynucleotides refers, in the usual and customary sense, to double strandedness. Nucleic acids can be linear or branched. For example, nucleic acids can be a linear chain of nucleotides or the nucleic acids can be branched, e.g., such that the nucleic acids comprise one or more arms or branches of nucleotides. Optionally, the branched nucleic acids are repetitively branched to form higher ordered structures such as dendrimers and the like.

[0033] 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.

[0034] 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 ofgene 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) that can be either protein-coding or non-coding. The primary’ transcript is cleaved by the Drosha ribonuclease III enzyme to produce an approximately 70-nt stem-loop precursor miRNA (pre- miRNA), which is further cleaved by the cytoplasmic Dicer ribonuclease to generate the mature miRNA and antisense miRNA star (miRNA*) products. The mature miRNA is incorporated into 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.

[0035] The term “gene” means the segment of DNA involved in producing a protein; it includes regions preceding and following the coding region (leader and trailer) as well as intervening sequences (introns) between individual coding segments (exons). The leader, the trailer as well as the introns include regulatory elements that are necessary during the transcription and the translation of a gene. Further, a "protein gene product" is a protein expressed from a particular gene.

[0036] The word “expression” or “expressed” as used herein in reference to a gene means the transcriptional and / or translational product of that gene. The level of expression of a DNA molecule in a cell may be determined on the basis of either the amount of corresponding RNA that is present 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) may be detected by standard PCR or Northern blot methods well known in the art.

[0037] The terms “expression level,” “amount,” or “level” of a biomarker is a detectable level in a biological sample. “Expression” generally refers to the process by which information (e.g., gene-encoded and / or epigenetic) is converted into the structures present and operating in the cell. Therefore, “expression” may refer to transcription into a polynucleotide, translation into a polypeptide, or even polynucleotide and / or polypeptide modifications (e.g., posttranslational modification of a polypeptide). Fragments of the transcribed polymucleotide, 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-translationalprocessing of the polypeptide, e.g., by proteolysis. "Expressed genes'’ include those that are transcribed into a polynucleotide as mRNA and then translated into a polypeptide, and also those that are transcribed into RNA but not translated into a polypeptide (for example, miRNA, transfer RNA, ribosomal RNA, IncRNA). Expression levels can be measured by methods known to one skilled in the art and also disclosed herein.

[0038] The terms an “elevated expression level” or “elevated 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.

[0039] The terms “biomarker gene” and “biomarker” are used interchangeably and in accordance with their plain and ordinary' meaning. In embodiments, a biomarker is a gene or a set of genes (i.e., a biomarker gene). Biomarkers include, but are not limited to, polynucleotides (e.g., DNA, and / or RNA), polynucleotide copy number alterations (e g., DNA copy numbers), polypeptides, or polypeptide and polynucleotide modifications (e.g., posttranslational modifications). In embodiments, a biomarker refers to RNA, e.g., miRNA.

[0040] Biomarker levels may be detected at either the protein or gene expression level. Proteins expressed by biomarkers can be quantified by immunohistochemistry (IHC) or flow cytometry' with an antibody that detects the proteins. Biomarker expression can be quantified by multiple platforms such as real-time polymerase chain reaction (rtPCR), NanoString. RNAseq, or in situ hybridization. There is a range of biomarker expression across as measured by NanoString. In embodiments, quantitative rtPCR, NanoString, RNAseq, and in situ hybridization are platforms to quantitate biomarker gene expression. For NanoString, RNA is extracted from a biological sample and a known quantity of RNA is placed on the NanoString machine for gene expression detection using gene specific probes. The number of counts of biomarkers within a sample is determined and normalized to a set of housekeeping genes. To determine a threshold for increased or decreased biomarker levels, one skilled in the art could assess biomarker levels in a control group of samples and select the 10th. 20th, 25th, 30th, 40th, 50th, 60th, 70th, 75th, 80th or 90th percentile of biomarker gene expression. In embodiments,the increased or decreased expression of biomarkers may be determined by calculating the Id- score for the expression of the biomarkers. Thus, the increased or decreased expression of biomarkers may have an H-score. As used herein, an "H-score " or "Histoscore" is a numerical value determined by a semi-quantitative method commonly known for immunohistochemically evaluating protein expression in tumor samples. The H-score may be calculated using the following formula: [1 x (% cells 1+) + 2 x (% cells 2+) + 3 x (% cells 3+)]. According to this formula, the H-score is calculated by determining the percentage of cells having a given staining intensity level (i.e., level 1+, 2+, or 3+ from lowest to highest intensity level), weighting the percentage of cells having the given intensity level by multiplying the cell percentage by a factor (e.g., 1, 2, or 3) that gives more relative weight to cells with higher-intensity membrane staining, and summing the results to obtain a H-score. Commonly H-scores range from 0 to 300. Further description on the determination of H-scores in tumor cells can be found in Hirsch et al, J Clin Oncol 21 : 3798-3807, 2003 and John et al, Oncogene 28:S14-S23, 2009. IHC or other methods known in the art may be used for detecting biomarker expression.

[0041] “Control” is used in accordance with its plain ordinary meaning and refers to an assay, comparison, or experiment in which the subj ects 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 activity or expression level of RNA. In embodiments, a control is the measurement of the activity or expression level of 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, a healthy patient can be referred to as a non-diseased patient or non-diseased control. In embodiments, the control is a population of non-diseased 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 colorectal cancer. In embodiments, anon-diseased patient is a patient that does not have colorectal 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 colorectal cancer or a population of patients that do not have colorectal cancer. In embodiments, the control is a patient that does not have colorectal or a population of patients that do not have colorectal. 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 areference 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., miRNA) in a population of subjects (e g., with cancer) or in a healthy or general population. In embodiments, the control comprises an average amount (e.g. amount of expression) in a population in which the number of 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 with the diagnosis of colorectal 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 a healthy subject (i.e., a subject that does not have colorectal 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.

[0042] The term "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 colorectal cancer (e.g., EOCRC or LOCRC). In embodiments, a healthy patient is a patient that does not have early-onset colorectal cancer. In embodiments, a healthy patient is a patient that does not have late-onset colorectal cancer

[0043] The term "about” means a range of values including the specified value, which a person of ordinary skill in the art would consider reasonably similar to the specified value. In embodiments, “about” means within a standard deviation using measurements generally acceptable in the art. In embodiments, “about” means a range extending to + / - 10% of the specified value. In embodiments, “about” includes the specified value.

[0044] The singular terms "a," "an," and "the" include the plural reference unless the context clearly indicates otherwise.

[0045] 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 colorectal cancer, or their 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; slowingin the rate of degeneration or decline; making the final point of degeneration less debilitating; or improving a patient’s physical or mental well-being.

[0046] “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 the like), 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.

[0047] “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.

[0048] The term “diagnosis” is used in accordance with its plain and ordinary meaning and refers to an identification or likelihood of the presence of a disease (e.g., colorectal cancer) or outcome in a subject.

[0049] “Image-based screening” refers to methods using imaging technology to detect a cancer or tumor in a patient. Exemplary types of image-based screening include x-rays, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and ultrasound. In embodiments, the image-based screening is CT, MRI, or ultrasound. In embodiments, the ultrasound is endoscopic ultrasonography (EUS). In embodiments, the image-based screening is CT, MRI, or EUS. In embodiments, the image-based screening is MRI or EUS. In embodiments, the image-based screening is CT. In embodiments, the image-based screening is MRI. In embodiments, the image-based screening is EUS.

[0050] The terms “treating” or “treatment” are used in accordance with their plain and ordinary meaning and broadly includes any approach for obtaining beneficial or desired results in a 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.

[0051] An “effective amount” is an amount sufficient to accomplish a stated purpose (e.g. achieve the effect for which it is administered, treat a disease). An example of an “effective amount” is an amount sufficient to contribute to the treatment, prevention, or reduction of a symptom or symptoms of a disease, which could also be referred to as a “therapeutically effective amount.” A “reduction” of a sy mptom 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 colorectal 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 interv als 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 individual's disease state. A “therapeutically effective amount” can also be found on the label or Prescribing Information for commercially available therapeutic agents.

[0052] The term “administering” means oral administration, administration as a suppository, topical contact, intravenous, parenteral, intraperitoneal, intramuscular, intralesional, intrathecal, intranasal or subcutaneous administration, or the implantation of a slow-release device, e.g., a mini-osmotic pump, to a 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 delivery7include, 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.

[0053] The terms “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, bovines, rats, mice, dogs, cats, monkey’s, and other non-mammalian animals. In embodiments, a patient is human patient. In embodiments, the human patient is less than 50 years old. In embodiments, the human patient is less than 45 years old. In embodiments, the human patient is less than 40 years old. In embodiments, the human patient has Lynch syndrome.

[0054] The term “NDC” or “non-diseased control” or “healthy patient” refer to a non-diseased or healthy patient. In embodiments, a healthy patient is a patient that does not have cancer. In embodiments, a healthy patient is a patient less than 50 years old. In embodiments, a healthy patient is a patient that does not have colorectal cancer. In embodiments, a healthy patient is a patient less than 50 years old that does not have cancer. In embodiments, a healthy patient is a patient less than 50 years old that does not have colorectal cancer. In embodiments of the methods described herein, a control is a healthy patient.

[0055] “Colorectal cancer"’ or “CRC” refers to a cancer that generally begins as grow th (e.g., polyp) on the inner lining of the colon or rectum. Over time, the polyps can grow into the wall of the colon or rectum and into blood vessels or lymph nodes. The stage (extent of spread) of a colorectal cancer depends on how deeply it grows into the wall and if it has spread outside the colon or rectum. Colorectal cancer generally occurs when the patient is at least 50 years old, in which case it can also be referred to as late-onset colorectal cancer (LOCRC). The term “colorectal cancer” encompasses colon cancer and rectal cancer.

[0056] “Early-onset colorectal cancer” or “EOCRC” refers to colorectal cancer in a patient less than 50 years old. In embodiments, the patient is less than 50 years old and does not have a familial or hereditary’ disposition for colorectal cancer.

[0057] Methods for treating colorectal cancer, including early-onset colorectal cancer and late- onset colorectal cancer, include: (a) administering to the patient an effective amount of an anticancer agent, (b) administering to the patient an effective amount of radiation therapy, (c) administering to the patient image-based screening, (d) surgically removing all or a portion of the colon of the patient, or (e) a combination of two or more thereof. Surgery to remove all or portion of the colon of the patient are known in the art and include, for example, polypectomy, local excision via colonoscope, transanal excision (TAE), transanal endoscopic microsurgery' (TEM). low anterior resection (LAR), proctectomy, abdominoperineal resection (APR), pelvic exenteration, and the like. The term “removing all or a portion of the colon” includes: (i) removing all or a portion of the colon, (ii) removing all or a portion of the rectum, and (iii) removing all or a portion of the rectum and all or a portion of the colon.

[0058] In “Stage 1” colorectal cancer, the cancer has grown through the muscularis mucosa into the submucosa (Tl). and it may also have grown into the muscularis propria (T2), but it has not spread to nearby lymph nodes (NO) or to distant sites (M0).

[0059] “Stage 2" colorectal cancer is generally identified by one of the following: (i) the cancer has grown into the outermost layers of the colon or rectum but has not gone through them (T3); it has not reached nearby organs; and it has not spread to nearby lymph nodes (NO) or to distant sites (M0); (ii) the cancer has grown through the w all of the colon or rectum but has not grown into other nearby tissues or organs (T4a). and has not yet spread to nearby lymph nodes (NO) or to distant sites (M0); or (iii) the cancer has grown through the wall of the colon or rectum and is attached to or has grown into other nearby tissues or organs (T4b), but it has not yet spread to nearby lymph nodes (NO) or to distant sites (M0).

[0060] “Stage 3’' colorectal cancer is generally identified by one of the following: (i) the cancer has grown through the mucosa into the submucosa (T 1 ). and it may also have grown into the muscularis propria (T2); it has spread to 1 to 3 nearby lymph nodes (Nl) or into areas of fat near the lymph nodes but not the nodes themselves (Nlc); and it has not spread to distant sites (MO); (ii) the cancer has grown through the mucosa into the submucosa (T 1 ); it has spread to 4 to 6 nearby lymph nodes (N2a); and it has not spread to distant sites (MO); (iii) the cancer has grown into the outermost layers of the colon or rectum (T3) or through the visceral peritoneum (T4a) but has not reached nearby organs; it has spread to 1 to 3 nearby lymph nodes (Nla or Nib) or into areas of fat near the lymph nodes but not the nodes themselves (Nlc); and it has not spread to distant sites (MO); (iv) the cancer has grown into the muscularis propria (T2) or into the outermost layers of the colon or rectum (T3); it has spread to 4 to 6 nearby lymph nodes (N2a); and it has not spread to distant sites (MO); (v) the cancer has grown through the mucosa into the submucosa (Tl), and it might also have grown into the muscularis propria (T2); it has spread to 7 or more nearby lymph nodes (N2b); and it has not spread to distant sites (MO); (vi) the cancer has grown through the wall of the colon or rectum (including the visceral peritoneum) but has not reached nearby organs (T4a); it has spread to 4 to 6 nearby lymph nodes (N2a); and it has not spread to distant sites (MO); (vi) the cancer has grown into the outermost layers of the colon or rectum (T3) or through the visceral peritoneum (T4a) but has not reached nearby organs; it has spread to 7 or more nearby lymph nodes (N2b); and it has not spread to distant sites (MO); or (viii) the cancer has grown through the wall of the colon or rectum and is attached to or has grown into other nearby tissues or organs (T4b); it has spread to at least one nearby lymph node or into areas of fat near the lymph nodes (N 1 or N2); and it has not spread to distant sites (MO).

[0061] “Stage 4’' colorectal cancer is generally identified by one of the following: (i) the cancer may or may not have grown through the wall of the colon or rectum (Any T); it might or might not have spread to nearby lymph nodes (Any N); it has spread to 1 distant organ (such as the liver or lung) or distant set of lymph nodes, but not too distant parts of the peritoneum (the lining of the abdominal cavity) (Mia); (ii) the cancer might or might not have grown through the wall of the colon or rectum (Any T); it might or might not have spread to nearby lymph nodes (Any N); it has spread to more than 1 distant organ (such as the liver or lung) or distant set of lymph nodes, but not too distant parts of the peritoneum (the lining of the abdominal cavity) (Mlb); or (iii) the cancer might or might not have grown through the w all of the colon or rectum (Any T); it might or might not have spread to nearby lymph nodes (Any N); it has spread to distant parts of the peritoneum (the lining of the abdominal cavity), and may or may not havespread to distant organs or lymph nodes (Mlc).

[0062] “High grade dysplasia” or “HGD” generally refers to colon polyps / abnormal cells in the colon that have an advanced histology and / or appear to be cancerous and / or are associated with a higher risk of developing colorectal cancer. The World Health Organization (WHO) has defined high-grade dysplasia on the basis of both cytological and architectural features, such as high nuclear to cytoplasmic ratio, nuclear pleomorphism, loss of nuclear polarity and intraluminal cribriforming in at least two (or more) glands.

[0063] Methods of Detecting

[0064] Provided herein is a method of detecting RNA in a patient with colorectal cancer or with high grade dysplasia in the colon 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-32, miR-625, or a combination thereof. In embodiments, the method of detecting RNA in a patient with colorectal cancer 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-32. miR-625, miR-486, miR-550a, miR-30d, 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.

[0065] Provided herein is a method of detecting RNA in a patient with colorectal 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 exosomal miR-32-5p, cell-free miR-625-3p, or a combination thereof. In embodiments, the method of detecting RNA in a patient with colorectal cancer comprises detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, cell-free miR-30d-5p, 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.

[0066] Provided herein is a method of detecting RNA in a patient with high grade dysplasia in the colon comprising detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, or a combination thereof. In embodiments, the method of detecting RNA in a patient with high grade dysplasia in the colon comprises detecting an elevated expressionlevel, relative to a control, of RNA in a biological sample obtained from the patient; wherein the RNA comprises exosomal miR-32-5p. cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, cell-free miR-30d-5p, 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.

[0067] Methods of Treatment

[0068] Provided herein is a method of treating colorectal cancer in a patient in need thereof or high grade dysplasia in the colon in a patient in need thereof comprising: (i) detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient; wherein the RNA comprises miR-32, miR-625. or a combination 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 colon of the patient, or a combination of two or more thereof. In embodiments, the method of treating colorectal cancer in a patient in need thereof comprises: (i) detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient; wherein the RNA comprises miR-32, miR-625, miR-486. miR-550a, miR-30d, 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 colon of the patient, or a combination of two or more thereof. In embodiments, the method comprises administering to the patient an effective amount of an anti-cancer agent.

[0069] Provided herein is a method of treating colorectal cancer in a patient in need thereof or high grade dysplasia in the colon in a patient in need thereof 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 colon 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 a RNA; wherein the RNA comprises miR-32-5p, miR-625-3p, or a combination thereof. In embodiments, the method of treating colorectal cancer in a patient in need thereof 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 colon of the patient, or acombination of two or more thereof; wherein a biological sample obtained from the patient comprises an elevated expression level, relative to a control, of a RNA; wherein the RNA comprises miR-32, miR-625, miR-486, miR-550a, miR-30d, or a combination of two or more thereof. In embodiments, the method comprises administering to the patient an effective amount of an anti-cancer agent.

[0070] Provided herein is a method of treating colorectal cancer in a patient in need thereof comprising: (i) detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, or a combination 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 colon of the patient, or a combination of two or more thereof. In embodiments, the method of treating colorectal cancer in a patient in need thereof comprises: (i) detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, cell-free miR-30d-5p, 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 colon of the patient, or a combination of two or more thereof. In embodiments, the RNA comprises exosomal miR-32-5p. cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR- 550a-3-5p, exosomal miR-625-3p, and cell-free miR-30d-5p. In embodiments, the method comprises administering to the patient an effective amount of an anti-cancer agent.

[0071] Provided herein is a method of treating colorectal cancer in a patient in need thereof 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 colon 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 a RNA; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, or a combination thereof. In embodiments, the method of treating colorectal cancer in a patient in need thereof 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-basedscreening, surgically removing all or a portion of the colon 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 a RNA; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, cell-free miR-30d-5p, or a combination of two or more thereof. In embodiments, the RNA comprises exosomal miR-32-5p. cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p. exosomal miR-625-3p. and cell-free miR-30d-5p. In embodiments, the method comprises administering to the patient an effective amount of an anti-cancer agent.

[0072] Provided herein is a method of treating high grade dysplasia in the colon in a patient in need thereof comprising: (i) detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient; wherein the RNA comprises exosomal miR-32- 5p, cell-free miR-625-3p, or a combination 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 colon of the patient, or a combination of two or more thereof. In embodiments, the method of treating high grade dysplasia in the colon in a patient in need thereof comprises: (i) detecting an elevated expression level, relative to a control, of RNA in a biological sample obtained from the patient; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625- 3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, cell-free miR-30d- 5p, 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 colon of the patient, or a combination of two or more thereof. In embodiments, the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR- 550a-3-5p, exosomal miR-625-3p. and cell-free miR-30d-5p. In embodiments, the method comprises administering to the patient an effective amount of an anti-cancer agent.

[0073] Provided herein is a method of treating high grade dysplasia in the colon in a patient in need thereof 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 colon 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 a RNA; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, or a combination thereof. Inembodiments, the method of treating high grade dysplasia in the colon in a patient in need thereof 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 colon 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 a RNA; wherein the RNA comprises exosomal miR-32-5p. cell-free miR-625-3p, exosomal miR-486-3p. exosomal miR- 550a-3-5p, exosomal miR-625-3p, cell-free miR-30d-5p, or a combination of two or more thereof. In embodiments, the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, and cell-free miR- 30d-5p. In embodiments, the method comprises administering to the patient an effective amount of an anti-cancer agent.

[0074] Methods of Diagnosis

[0075] Provided herein is a method of diagnosing a patient with colorectal cancer or high grade dysplasia in the colon comprising: (i) detecting the expression level of RNA in a biological sample obtained from the patient; and (ii) diagnosing the patient as having colorectal cancer when the biological sample has an elevated expression level, relative to a control, of the RNA; wherein the RNA comprises miR-32, miR-625. or a combination thereof. In embodiments, a method of diagnosing a patient with colorectal cancer comprises: (i) detecting the expression level of RNA in a biological sample obtained from the patient; and (ii) diagnosing the patient as having colorectal cancer when the biological sample has an elevated expression level, relative to a control, of the RNA; wherein the RNA comprises miR-32, miR- 625, miR-486, miR-550a, miR-30d, or a combination of two or more thereof.

[0076] Provided herein is a method of diagnosing a patient with colorectal cancer comprising:(i) detecting the expression level of RNA in a biological sample obtained from the patient; and(ii) diagnosing the patient as having colorectal cancer when the biological sample has an elevated expression level, relative to a control, of the RNA; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p. or a combination thereof. In embodiments, a method of diagnosing a patient with colorectal cancer comprises: (i) detecting the expression level of RNA in a biological sample obtained from the patient; and (ii) diagnosing the patient as having colorectal cancer when the biological sample has an elevated expression level, relative to a control, of the RNA; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p. exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, cell-free miR-30d-5p,or a combination of two or more thereof. In embodiments, the RNA comprises exosomal miR- 32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR- 625-3p, and cell-free miR-30d-5p. In embodiments, the method further comprises administering to the patient an effective amount of an anti-cancer agent.

[0077] Provided herein is a method of diagnosing a patient with high grade dysplasia in the colon comprising: (i) detecting the expression level of RNA in a biological sample obtained from the patient; and (ii) diagnosing the patient as having high grade dysplasia in the colon when the biological sample has an elevated expression level, relative to a control, of the RNA; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, or a combination thereof. In embodiments, a method of diagnosing a patient with high grade dysplasia in the colon comprises: (i) detecting the expression level of RNA in a biological sample obtained from the patient; and (ii) diagnosing the patient as having high grade dysplasia in the colon when the biological sample has an elevated expression level, relative to a control, of the RNA; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, cell-free miR-30d-5p, or a combination of two or more thereof. In embodiments, the RNA comprises exosomal miR-32-5p, cell-free miR-625- 3p. exosomal miR-486-3p. exosomal miR-550a-3-5p. exosomal miR-625-3p, and cell-free miR- 30d-5p. In embodiments, the method further comprises administering to the patient an effective amount of an anti-cancer agent.

[0078] Methods of Monitoring

[0079] Provided herein is a method of monitoring treatment in a patient having colorectal cancer or monitoring risk for developing colorectal cancer in a patient comprising: (i) detecting the expression level of RNA in a biological sample obtained from the patient at a first time point; (ii) detecting the expression level of the RNA in a biological sample obtained from the patient at a second time point, wherein the second time point is later than the first time point; and (iii) comparing the expression level of the RNA at the second time point to the expression level of the RNA at the first time point, thereby monitoring treatment or monitoring risk; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p. or a combination thereof. In embodiments, the method of monitoring treatment in a patient having colorectal cancer or monitoring risk for developing colorectal cancer in a patient comprises: (i) detecting the expression level of RNA in a biological sample obtained from the patient at a first time point; (ii) detecting the expression level of the RNA in a biological sample obtained from the patient at a second time point, wherein the second time point is later than the first time point;and (iii) comparing the expression level of the RNA at the second time point to the expression level of the RNA at the first time point, thereby monitoring treatment or monitoring risk; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, cell-free miR-30d-5p, or a combination of two or more thereof. In embodiments, the RNA comprises exosomal miR-32-5p, cell-free miR-625- 3p, exosomal miR-486-3p. exosomal miR-550a-3-5p, exosomal miR-625-3p, and cell-free miR- 30d-5p. 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 is not responding to treatment. In embodiments, the method is for monitoring treatment in a patient having colorectal 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 an increased risk of developing colorectal cancer. In embodiments, the method is for monitoring risk for developing colorectal cancer in the patient. In embodiments, the method comprises administering to the patient an effective amount of an anti-cancer agent.

[0080] Provided herein is a method of monitoring treatment in a patient having high grade dysplasia in the colon or monitoring risk for developing high grade dysplasia in the colon in a patient comprising: (i) detecting the expression level of RNA in a biological sample obtained from the patient at a first time point; (ii) detecting the expression level of the RNA in a biological sample obtained from the patient at a second time point, wherein the second time point is later than the first time point; and (iii) comparing the expression level of the RNA at the second time point to the expression level of the RNA at the first time point, thereby monitoring treatment or monitoring risk; wherein the RNA comprises exosomal miR-32-5p, cell-free miR- 625-3p, or a combination thereof. In embodiments, a method of monitoring treatment in a patient having high grade dysplasia in the colon or monitoring risk for developing high grade dysplasia in the colon in a patient comprises: (i) detecting the expression level of RNA in a biological sample obtained from the patient at a first time point; (ii) detecting the expression level of the RNA in a biological sample obtained from the patient at a second time point, wherein the second time point is later than the first time point; and (iii) comparing the expression level of the RNA at the second time point to the expression level of the RNA at the first time point, thereby monitoring treatment or monitoring risk; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, cell-free miR-30d-5p, or a combination of two or more thereof. In embodiments, the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, exosomal miR- 486-3p, exosomal miR-550a-3-5p. exosomal miR-625-3p, and cell-free miR-30d-5p. Inembodiments, 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 is not responding to treatment for high grade dysplasia in the colon. In embodiments, the method is for monitoring treatment in a patient having high grade dysplasia in the colon. 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 high grade dysplasia in the colon. In embodiments, the method is for monitoring risk for developing high grade dysplasia in the colon in the patient. In embodiments, the method comprises administering to the patient an effective amount of an anti-cancer agent.

[0081] Methods of Treatment

[0082] Provided herein is a method of treating colorectal cancer in a patient in need thereof or high grade dysplasia in the colon in a patient in need thereof comprising: (i) selecting a patient having a diagnosis of colorectal cancer based on a colorectal 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-32, miR-625, or a combination 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 colon 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 RNA comprises miR- 32-5p, miR-625-3p, or a combination thereof In embodiments, the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p. cell-free miR-30d-5p, or a combination of two or more thereof. In embodiments, the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, and cell-free miR-30d-5p. In embodiments, the method further comprises detecting the expression level of reference RNA, as described herein.

[0083] Provided herein is a method of treating colorectal cancer in a patient in need thereof or high grade dysplasia in the colon in a patient in need thereof comprising: (i) receiving or obtaining a colorectal 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-32, miR-625, or a combination thereof; and (ii) diagnosing a patient with colorectal cancer based on the colorectal cancer risk score or the elevated expression level of RNA, monitoring a patientwho is at risk of developing colorectal cancer based on the colorectal cancer risk score or the elevated expression level of RNA. monitoring efficacy of treatment for colorectal cancer in a patient based on the colorectal 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 miR-32-5p, miR-625-3p, or a combination thereof In embodiments, the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR- 550a-3-5p, exosomal miR-625-3p. cell-free miR-30d-5p, or a combination of two or more thereof. In embodiments, the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p. exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, and cell-free miR- 30d-5p. 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 reference RNA, as described herein.

[0084] Provided herein is a method of treating colorectal cancer in a patient in need thereof comprising: (i) receiving or obtaining a colorectal cancer risk score or an elevated expression level of RNA, wherein the colorectal 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-32, miR-625, or a combination thereof; and (ii) diagnosing a patient with colorectal cancer based on the colorectal cancer risk score or the elevated expression level of RNA, monitoring a patient who is at risk of developing colorectal cancer based on the colorectal cancer risk score or the elevated expression level of RNA. monitoring efficacy of treatment for colorectal cancer in a patient based on the colorectal 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 miR-32-5p, miR-625-3p, or a combination thereof In embodiments, the RNA comprises exosomal miR-32-5p, cell-free miR- 625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, cell-free miR-30d-5p, or a combination of two or more thereof. In embodiments, the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p. exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, and cell-free miR-30d-5p. 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.

[0085] Provided herein is a computer-implemented method of administering an effective amount of an anti-cancer agent to a patient with colorectal 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 colorectal cancer, wherein the RNA comprises miR-32, miR-625, or a combination 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 normalized expression 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 RNA comprises miR-32-5p, miR-625-3p, or a combination thereof In embodiments, the RNA comprises exosomal miR-32-5p, cell-free miR- 625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, cell-free miR- 30d-5p, or a combination of two or more thereof. In embodiments, the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p. and cell-free miR-30d-5p.

[0086] Provided herein is a computer-implemented system for administering an effective amount of an anti-cancer agent to a patient with colorectal 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 wherein the RNA comprises miR- 32, miR-625, or a combination 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 theoncogenic RNA in the sample data set when compared to the normalized expression level of oncogenic RNA in the control data set. In embodiments, the RNA comprises miR-32-5p, miR- 625-3p, or a combination thereof In embodiments, the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, cell-free miR-30d-5p, or a combination of two or more thereof. In embodiments, the RNA comprises exosomal miR-32-5p. cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR- 550a-3-5p. exosomal miR-625-3p. and cell-free miR-30d-5p. 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.

[0087] Provided herein are methods of processing data generated from the RNA levels in the biological sample obtained from a patient for establishing a colorectal cancer risk score (composite risk score), e.g., a score indicative colorectal 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 colorectal cancer or a scale of likelihood of colorectal 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 colorectal 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 colorectal cancer, or a population of patients with colorectal cancer.

[0088] In embodiments, the colorectal cancer risk score is produced by anon-transitory computer-readable storage medium having instructions stored thereon which, when executed bya processor, causes the processor to perform an operation comprising applying an algorithm to the protein levels.

[0089] 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 colorectal 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 colorectal cancer, diagnose a patient with colorectal cancer, monitor a patient who is at risk of developing colorectal cancer, or monitoring efficacy of treatment for colorectal 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.

[0090] RNA Biomarkers

[0091] In embodiments of the methods described herein, the RNA biomarkers are miRNA. In embodiments, the RNA comprises miR-32 and miR-625. In embodiments, the RNA comprises miR-32-5p and miR-625-3p. In embodiments, the RNA comprises exosomal miR-32 and cell- free miR-625.

[0092] In embodiments of the methods described herein, the RNA comprises exosomal miR- 32-5p and cell-free miR-625-3p. In embodiments, the RNA consists of exosomal miR-32-5p and cell-free miR-625-3p. In embodiments, the RNA comprises exosomal miR-32-5p. In embodiments, the RNA comprises cell-free miR-625-3p.

[0093] In embodiments of the methods described herein, the RNA comprises exosomal miR- 32-5p, cell-free miR-625-3p. or a combination thereof, and the RNA further comprises exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p. cell-free miR-30d-5p, or a combination of two or more thereof. In embodiments, the RNA comprises exosomal miR-32-5p and cell-free miR-625-3p, and the RNA further comprises exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, cell-free miR-30d-5p, or a combination of two or more thereof. In embodiments, the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p. exosomal miR-625-3p, and cell-free miR- 30d-5p. In embodiments, the RNA consists of exosomal miR-32-5p, cell-free miR-625-3p,exosomal miR-486-3p. exosomal miR-550a-3-5p, exosomal miR-625-3p, and cell-free miR- 30d-5p. In embodiments, the RNA comprises exosomal miR-32-5p, and the RNA further comprises exosomal miR-486-3p, exosomal miR-550a-3-5p, and exosomal miR-625-3p. In embodiments, the RNA consists of exosomal miR-32-5p, exosomal miR-486-3p, exosomal miR- 550a-3-5p, and exosomal miR-625-3p. In embodiments, the RNA comprises the cell-free miR- 625-3p and cell-free miR-30d-5p. In embodiments, the RNA consists of cell-free miR-625-3p and cell-free miR-30d-5p.

[0094] In embodiments of the methods described herein, the RNA comprises exosomal miR- 32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR- 625-3p, and cell-free miR-30d-5p, and further comprises one or more RNA selected from the group consisting of cell-free miR-4659b-3p, cell-free miR-296-5p, cell-free miR-4685-3p, cell- free miR-550a-5p, cell-free miR-4446-3p, cell-free miR-432-5p, cell-free miR-151a-3p, cell-free miR-199a-3p, cell-free miR-146a-5p, cell-free miR-24-3p, cell-free miR-223-3p, exosomal miR- 556-3p, exosomal miR-2355-5p, exosomal miR-181a-3p, exosomal miR-3120-3p, exosomal miR-7-l-3p, exosomal miR-99b-3p, and exosomal miR-425-3p. In embodiments of the methods described herein, the method further comprises detecting an elevated expression level, relative to a control, of one or more RNA selected from the group consisting of cell-free miR-4659b-3p, cell-free miR-296-5p, cell-free miR-4685-3p, cell-free miR-550a-5p, cell-free miR-4446-3p, cell-free miR-432-5p, cell-free miR-151a-3p, cell-free miR-199a-3p, cell-free miR-146a-5p, cell-free miR-24-3p, cell-free miR-223-3p, exosomal miR-556-3p, exosomal miR-2355-5p, exosomal miR-181a-3p, exosomal miR-3120-3p, exosomal miR-7-l-3p, exosomal miR-99b-3p, and exosomal miR-425-3p, in the biological sample obtained from the patient. Exemplary miRNA are shown in Table B.

[0095] In embodiments of the methods described herein, the RNA comprises exosomal miR- 32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR- 625-3p, and cell-free miR-30d-5p, and further comprises one or more RNA selected from the group consisting of cell-free miR-223-3p, cell-free miR-24-3p, cell-free miR-151a-3p, cell-free miR-199a-3p, exosomal 425-3p, exosomal 99b-3p, exosomal miR-191-3p, exosomal miR-7-1- 3p, miR-181a-3p, and exosomal miR-2355-5p. In embodiments of the methods described herein, the method further comprises detecting an elevated expression level, relative to a control, of one or more RNA selected from the group consisting of cell-free miR-223-3p, cell-free miR-24-3p, cell-free miR-151a-3p. cell-free miR-199a-3p, exosomal 425-3p. exosomal 99b-3p, exosomal miR-191-3p, exosomal miR-7-l-3p, miR-181a-3p, and exosomal miR-2355-5p, in the biologicalsample obtained from the patient. Exemplary' miRNA are shown in FIG. 12.

[0096] In embodiments of the methods described herein, the RNA comprises exosomal miR- 32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR- 625-3p, and cell-free miR-30d-5p. and further comprises one or more RNA selected from the group consisting of cell-free miR-223-3p, cell-free miR-24-3p, cell-free miR-151a-3p, cell-free miR-199a-3p, cell-free miR-432-5p, cell-free miR-4446-3p, cell-free miR-191-3p, cell-free miR-550a-5p, cell-free miR-146a-3p, cell-free miR-4685-3p, exosomal 425-3p, exosomal 99b- 3p, exosomal miR-191-3p. exosomal miR-7-l-3p. miR-181a-3p, exosomal miR-2355-5p, and miR-556-3p. In embodiments of the methods described herein, the method further comprises detecting an elevated expression level, relative to a control, of one or more RNA selected from the group consisting of cell-free miR-223-3p, cell-free miR-24-3p, cell-free miR-151a-3p, cell- free miR-199a-3p, cell-free miR-432-5p, cell-free miR-4446-3p, cell-free miR-191-3p, cell-free miR-550a-5p, cell-free miR-146a-3p, cell-free miR-4685-3p, exosomal 425-3p, exosomal 99b- 3p, exosomal miR-191-3p, exosomal miR-7-l-3p, miR-181a-3p, exosomal miR-2355-5p, and miR-556-3p, in the biological sample obtained from the patient. Exemplary miRNA are shown in FIG. 12

[0097] In embodiments of the methods described herein, the RNA comprises exosomal miR- 32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR- 625-3p, and cell-free miR-30d-5p, and further comprises one or more RNA selected from the group consisting of cell-free miR-223-3p, cell-free miR-24-3p, cell-free miR-151a-3p, cell-free miR-199a-3p, cell-free miR-432-5p, cell-free miR-191-3p, cell-free miR-550a-5p, cell-free miR- 146a-3p, and cell-free miR-4685-3p. In embodiments of the methods described herein, the method further comprises detecting an elevated expression level, relative to a control, of one or more RNA selected from the group consisting of cell-free miR-223-3p, cell-free miR-24-3p, cell-free miR-151a-3p, cell-free miR-199a-3p, cell-free miR-432-5p, cell-free miR-191-3p, cell- free miR-550a-5p, cell-free miR-146a-3p, and cell-free miR-4685-3p, in the biological sample obtained from the patient. Exemplary miRNA are shown in FIG. 13A.

[0098] In embodiments of the methods described herein, the RNA comprises exosomal miR- 32-5p, cell-free miR-625-3p. exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR- 625-3p, and cell-free miR-30d-5p, and further comprises one or more RNA selected from the group consisting of exosomal miR-425-3p, exosomal miR-99b-3p, exosomal miR-191-3p, exosomal miR-7-l-3p, exosomal miR-181a-3p, exosomal miR-2355-5p, and exosomal miR- 556-3p. In embodiments of the methods described herein, the method further comprisesdetecting an elevated expression level, relative to a control, of one or more RNA selected from the group consisting of exosomal miR-425-3p, exosomal miR-99b-3p, exosomal miR-191-3p, exosomal miR-7-l-3p, exosomal miR-181a-3p, exosomal miR-2355-5p, and exosomal miR- 556-3p, in the biological sample obtained from the patient. Exemplary miRNA are shown in FIG. 13B

[0099] In embodiments of the methods described herein, the RNA comprises exosomal miR- 32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR- 625-3p, and cell-free miR-30d-5p. and further comprises one or more RNA selected from the group consisting of cell-free miR-24-3p, cell-free miR-199a-3p, cell-free miR-151a-3p, cell-free miR-223-3p, cell-free miR-223-3p, cell-free miR-625-3p, cell-free miR-191-3p, cell-free miR- 432-5p, and cell-free miR-4446-3p. In embodiments of the methods described herein, the method further comprises detecting an elevated expression level, relative to a control, of one or more RNA selected from the group consisting of cell-free miR-24-3p, cell-free miR-199a-3p, cell-free miR-151a-3p, cell-free miR-223-3p, cell-free miR-223-3p, cell-free miR-625-3p, cell- free miR-191-3p, cell-free miR-432-5p, and cell-free miR-4446-3p, in the biological sample obtained from the patient. Exemplary miRNA are shown in FIG. 13C.

[0100] In embodiments of the methods described herein, the RNA comprises exosomal miR- 32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR- 625-3p, and cell-free miR-30d-5p, and further comprises one or more RNA selected from the group consisting of exosomal miR-7-l-3p, exosomal miR-425-3p, exosomal miR-99b-3p, exosomal miR-181a-3p, exosomal miR-191-3p, exosomal miR-556-3p, and exosomal miR- 2355-5p. In embodiments of the methods described herein, the method further comprises detecting an elevated expression level, relative to a control, of one or more RNA selected from the group consisting of exosomal miR-7-l-3p, exosomal miR-425-3p, exosomal miR-99b-3p, exosomal miR-181a-3p, exosomal miR-191-3p, exosomal miR-556-3p, and exosomal miR- 2355-5p, in the biological sample obtained from the patient. Exemplary miRNA are shown in FIG. 13D

[0101] In embodiments of the methods described herein, the RNA comprises exosomal miR- 32-5p, cell-free miR-625-3p. exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR- 625-3p, and cell-free miR-30d-5p, and further comprises one or more RNA selected from the group consisting of cell-free miR-223-3p, cell-free miR-24-3p, cell-free miR-151a-3p, and cell- free miR-199a-3p. In embodiments of the methods described herein, the method further comprises detecting an elevated expression level, relative to a control, of one or more RNAselected from the group consisting of cell-free miR-223-3p, cell-free miR-24-3p, cell-free miR- 15 la-3p, and cell-free miR-199a-3p. in the biological sample obtained from the patient.Exemplary miRNA are shown in FIG. 14A.

[0102] In embodiments of the methods described herein, the RNA comprises exosomal miR- 32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR- 625-3p, and cell-free miR-30d-5p, and further comprises one or more RNA selected from the group consisting of exosomal miR-425-3p, exosomal miR-99b-3p, exosomal miR-191-3p, exosomal miR-7-l-3p, exosomal miR-181a-3p, and exosomal miR-556-3p. In embodiments of the methods described herein, the method further comprises detecting an elevated expression level, relative to a control, of one or more RNA selected from the group consisting of exosomal miR-425-3p, exosomal miR-99b-3p, exosomal miR-191-3p, exosomal miR-7-l-3p, exosomal miR-181a-3p, and exosomal miR-556-3p, in the biological sample obtained from the patient. Exemplary miRNA are shown FIG. 14B.

[0103] miR Controls

[0104] 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 are 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.

[0105] ‘ ‘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.

[0106] 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 methodsdescribed 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.

[0107] 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.

[0108] In embodiments, an elevated expression level refers to a normalized expression level of miRNA 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 miRNAthat 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 miRNA that 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.

[0109] Methods

[0110] In embodiments of all 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. In embodiments, the biological sample is a stool sample.[OHl] In embodiments of all of the methods described herein, the colorectal cancer is early - onset colorectal cancer or late-onset colorectal cancer. In embodiments, the colorectal cancer is early-onset colorectal cancer. In embodiments, the colorectal cancer is late-onset colorectal cancer. In embodiments, the colorectal cancer is Stage I or Stage II. In embodiments, the colorectal cancer is Stage III or Stage IV. In embodiments, the colorectal cancer is Stage I. In embodiments, the colorectal cancer is Stage II. In embodiments, the colorectal cancer is Stage III. In embodiments, the colorectal cancer is Stage IV. In embodiments, the early-onset colorectal cancer is Stage I or Stage II. In embodiments, the early-onset colorectal cancer is Stage III or Stage IV. In embodiments, the early-onset colorectal cancer is Stage I. In embodiments, the early-onset colorectal cancer is Stage II. In embodiments, the early-onset colorectal cancer is Stage III. In embodiments, the early-onset colorectal cancer is Stage IV. In embodiments, the late-onset colorectal cancer is Stage I or Stage II. In embodiments, the late- onset colorectal cancer is Stage III or Stage IV. In embodiments, the late-onset colorectal cancer is Stage I. In embodiments, the late-onset colorectal cancer is Stage II. In embodiments, the late- onset colorectal cancer is Stage III. In embodiments, the late-onset colorectal cancer is Stage IV.

[0112] In embodiments of all of the methods described herein, the patient is a human patient. In embodiments, the patient is less than 50 years old. In embodiments, the patient is less than 45 years old. In embodiments, the patient is less than 40 years old. In embodiments, the patient is < 45 years old. In embodiments, the patient is < 40 years old. In embodiments, the patient is > 50 years old.

[0113] Anti-Cancer Agents

[0114] 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 is a chemotherapeutic agent. In embodiments, the anti-cancer agent is adagrasib, bevacizumab, irinotecan, capecitabine, ramucirumab, oxaliplatin, cetuximab, fluorouracil, fruquintinib, ipilimumab. pembrolizumab, leucovorin, trill uridine, tipiracil, nivolumab. panitumumab. regorafenib, tucatinib, ziv-aflibercept, encorafenib, trastuzumab, pertuzumab, lapatinib, larotrectinib, entrectinib, selpercatinib, sotorasib, regorafenib, or a combination of two or more thereof.

[0115] In embodiments, the chemotherapeutic agent is an alkylating agent, an antimetabolite compound, an anthracy cline compound, an antitumor antibiotic, a platinum compound, atopoisomerase 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 anthracycline 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. the chemotherapeutic agent comprises 5-fluorouracil, leucovorin, oxaliplatin, irinotecan, capecitabine, or a combination of two or more thereof.

[0116] In embodiments, the chemotherapeutic agent comprises 5-fluorouracil, leucovorin, oxaliplatin, irinotecan, capecitabine, or a combination of two or more thereof. In embodiments, the chemotherapeutic agent comprises everolimus, erlotinib, olaparib, mitomycin, sunitinib, gemcitabine, 5-fluorouracil, irinotecan, oxaliplatin, paclitaxel, capecitabine, cisplatin, docetaxel, or a combination of two or more thereof. In embodiments, the chemotherapeutic agent comprises 5-fluorouracil, oxaliplatin, irinotecan, capecitabine, or a combination of two or more thereof. In embodiments, the chemotherapeutic agent comprises gemcitabine, 5-fluorouracil. irinotecan, oxaliplatin, paclitaxel, capecitabine, cisplatin, docetaxel, or a combination of two or more thereof. In embodiments, the chemotherapeutic agent comprises gemcitabine. In embodiments, the chemotherapeutic agent comprises 5-fluorouracil. In embodiments, the chemotherapeutic agent comprises irinotecan. In embodiments, the chemotherapeutic agent comprises oxaliplatin. In embodiments, the chemotherapeutic agent comprises paclitaxel. In embodiments, the chemotherapeutic agent comprises capecitabine. In embodiments, the chemotherapeutic agent comprises cisplatin. In embodiments, the chemotherapeutic agent comprises docetaxel. In embodiments, the chemotherapeutic agent comprises further leucovorin.

[0117] “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.

[0118] “Anti-cancer agent” is used in accordance with its plain ordinary meaning and refers toa composition (e.g. compound, drug, antagonist, inhibitor, modulator) having antineoplastic properties or the ahi 1 i ty 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 / AZD6244, GSK.1120212 / trametinib, GDC-0973, ARRY-162, ARRY-300, AZD8330, PD0325901, U0126, PD98059, TAK-733, PD318088, AS703026, BAY 869766), alky lating 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., hexamethly melamine, 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, fl oxouridine, 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, 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), enzy mes (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 tyrosine 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., hydroxyprogesterone caproate, megestrol acetate, medroxyprogesterone acetate), estrogens (e.g.. diethlystilbestrol, 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, or1?1I. 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, dacomitimb. desmethyl erlotinib, AZD8931, AEE788, pelitinib, CUDC-101, WZ8040, WZ4002, WZ3146, AG-490, XL647,PD153035, BMS-599626), sorafenib, imatinib, sunitinib, dasatinib, or the like.

[0119] Kits

[0120] 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 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 technology7are utilized to assess expression of the RNA. Polynucleotide arrays, particularly arrays that bind RNA described herein, also can be used for diagnostic applications.

[0121] “Assaying" or “detecting" means using an analytic procedure to qualitatively assess or quantitatively measure the presence or amount or the functional activity of a target entity (e.g.. miRNA). For example, detecting the level of RNA (such as miRNA) means using an analytic procedure (such as an in vitro procedure) to qualitatively assess or quantitatively measure the presence or amount of the miRNA. In embodiments, raw expression values are normalized by performing quantile normalization relative to the reference distribution and subsequent log 10- transformation. In embodiments, when miRNA expression is detected using the nCounter® Analysis System marketed by Nanostring Technologies, the reference distribution is generated by pooling reported (i.e., raw) counts for the test sample and one or more control samples (preferably at least 2 samples, more preferably at least any of 4. 8 or 16 samples) after excluding values for technical (both positive and negative control) probes and without performing intermediate normalization relying on negative (background-adjusted) or positive (synthetic sequences spiked with known titrations).

[0122] 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.

[0123] 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 denved.

[0124] The term “capable of hybridizing to” refers to a polynucleotide sequence that forms Watson-Crick bonds wi th 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.

[0125] 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, miRNA 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.

[0126] In embodiments, the subject matter provides a composition comprising a binding agent, wherein the binding agent is attached to a solid support, (e.g., a strip, a polymer, a bead, a nanoparticle, a plate such as a multiwell plate, or an array such as a microarray). In embodiments relating to the use of a nucleic acid probe attached to a solid support (such as a microarray), a nucleic acid in a test sample may be amplified (e.g., using PCR) before or after the nucleic acid to be measured is hybridized with the probe. In embodiments, reverse transcription polymerase chain reaction (RT-PCR) is used to detect miRNA levels. In embodiments, a probe on a solid support is used, and miRNA (or a portion thereof) in a biological sample is converted to cDNA or partial cDNA and then the cDNA or partial cDNA is hybridized to a probe (e.g., on a microarray), hybridized to a probe and then amplified, or amplified and then hybridized to a probe. In embodiments, a strip may be a nucleic acid-probe coated porous or non-porous solid support strip comprising linking a nucleic acid probe to a carrier to prepare a conjugate and immobilizing the conjugate on a porous solid support. In embodiments, the support or carrier comprises glass, polystyrene, polypropylene, polyethylene, dextran, nylon, amylases, natural and modified celluloses, polyacrylamides, gabbros, and magnetite. In embodiments, the nature of the carrier can be either soluble to some extent or insoluble for the purposes of the present subject matter. In embodiments, the support material may have any structural configuration so long as the coupled molecule is capable of binding to a binding agent (e.g., an antibody). In embodiments, the support configuration may be spherical, as in a bead, or cylindrical, as in the inside surface of a test tube, or the external surface of a rod. In embodiments, the surface may be flat such as a plate (or a well within a multiwell plate), sheet, test strip, polystyrene beads. Those skilled in the art will know many other suitable carriers for binding antibody or antigen, or will be able to ascertain the same by use of routine experimentation.

[0127] In embodiments, a solid support comprises a polymer, to which an agent is chemically bound, immobilized, dispersed, or associated. In embodiments, a polymer support may be. e.g., a network of polymers, and may be prepared in bead form (e.g., by suspension polymerization). In embodiments, the location of active sites introduced into a polymer support depends on the type of polymer support. In embodiments, in a swollen-gel-bead polymer support the active sites are distributed uniformly throughout the beads, whereas in a macroporous-bead polymer support they are predominantly on the internal surfaces of the macropores. In embodiments, the solid support, e.g., a device, may contain a biomarker binding agent alone or together with a binding agent for at least one, two, three or more other biomarkers.

[0128] 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 miRNA include Northern blotting, RT-PCR, RNA sequencing, and qRT-PCR.

[0129] 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.

[0130] In embodiments, raw expression values are normalized to a reference 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 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).

[0131] 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 presentdisclosure 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.

[0132] 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 generated therefrom. As used herein, specific hybridization means the oligonucleotide forms an antiparallel double-stranded structure with the target region under certain hybridizing conditions, while failing to form such a structure with non-target regions when incubated with the polynucleotide under the same hybridizing conditions. The composition and length of each oligonucleotide in the kit will depend on the nature of the transcript containing the target region as well as the type of assay to be performed with the oligonucleotide and is readily determined by the skilled artisan.

[0133] In embodiments, the kit comprises reagents capable of detecting an expression level of RNA from a biological sample; wherein the RNA is as described herein.

[0134] 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 colorectal cancer in a patent, including reagents for detecting miRNA markers in a biological (e.g., blood) sample from a patient.

[0135] 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.

[0136] miRNA Expression

[0137] 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. In embodiments, the gene expression level is an average of the gene expression level of the biomarker genes. In embodiments, the average of the gene expression level of the biomarker genes is an average of the normalized gene expression level of the biomarker genes. In embodiments, the gene expression level of the biomarker genes is a median of the gene expression level of the biomarker genes. In embodiments, the median of the gene expression level of the biomarker genes is a median of a normalized gene expression level of the biomarker genes. In embodiments, the gene expression level of the biomarker genes is the gene expression level of the biomarker genes normalized to a reference gene (e.g.. reference miRNA).

[0138] 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.

[0139] 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.

[0140] Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as the control (e.g., healthy individuals). The 2v tmethod is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The Ct (threshold cycle) method quantification was used for the evaluation of the expression level of each miRNA. The threshold cycle (Ct) is defined as the PCR cycle at which the fluorescent signal of the reporter dye crosses an arbitrarily placed threshold. This method allows to quantify the absolute expression of each miRNAs in each sample analyzed and then to calculate the different expression of each miRNA in sample versus the controls. These expression values of the RNA can be used individually to produce a risk score, can be added together to produce a risk score, or logistic regression analysis can be applied to produce a risk score based on weighted values of the expression levels of the RNA.

[0141] 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 colorectal 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 colorectal cancer. 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. Inembodiments, the expression of level of each miRNA is calculated using 2'ACtmethod, the normalized expression values are log10transformed.

[0142] Embodiments 1 to 77

[0143] Embodiment 1. A method of detecting RNA in a patient with colorectal 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 exosomal miR-32-5p, cell-free miR-625-3p, or a combination thereof.

[0144] Embodiment 2. A method of detecting RNA in a patient with high grade dysplasia in the colon, 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 exosomal miR-32-5p, cell-free miR-625-3p. or a combination thereof.

[0145] Embodiment 3. The method of embodiment 1 or 2, further comprising administering to the patient an effective amount of an anti-cancer agent.

[0146] Embodiment 4. A method of treating colorectal 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 the patient; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, or a combination 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 colon of the patient, or a combination of two or more thereof.

[0147] Embodiment 5. A method of treating colorectal cancer 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 colon 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 a RNA; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, or a combination thereof.

[0148] Embodiment 6. A method of treating high grade dysplasia in the colon 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 the patient; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, or a combination thereof; and (ii) administering to the patient an effective amount of an anti-cancer agent, administering to the patient an effectiveamount of radiation therapy, administering to the patient image-based screening, surgically removing all or a portion of the colon of the patient, or a combination of two or more thereof.

[0149] Embodiment 7. A method of treating high grade dysplasia in the colon 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 colon 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 a RNA; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, or a combination thereof.

[0150] Embodiment 8. The method of any one of embodiments 4 to 7, comprising administering to the patient the effective amount of the anti -cancer agent.

[0151] Embodiment 9. A method of diagnosing a patient with colorectal cancer, the method comprising: (i)detecting the expression level of RNA in a biological sample obtained from the patient; and (ii) diagnosing the patient as having colorectal cancer when the biological sample has an elevated expression level, relative to a control, of the RNA; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p. or a combination thereof.

[0152] Embodiment 10. A method of diagnosing a patient with high grade dysplasia in the colon, the method comprising: (i) detecting the expression level of RNA in a biological sample obtained from the patient; and (ii) diagnosing the patient as having high grade dysplasia in the colon when the biological sample has an elevated expression level, relative to a control, of the RNA; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, or a combination thereof.

[0153] Embodiment 11. A method of monitoring treatment in a patient having colorectal cancer or monitoring risk for developing colorectal cancer in a patient, the method comprising: (i) detecting the expression level of RNA in a biological sample obtained from the patient at a first time point; (ii) detecting the expression level of the RNA in a biological sample obtained from the patient at a second time point, wherein the second time point is later than the first time point; and (iii) comparing the expression level of the RNA at the second time point to the expression level of the RNA at the first time point, thereby monitoring treatment or monitoring risk; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, or a combination thereof.

[0154] Embodiment 12. The method of embodiment 11, wherein an elevated expression levelof 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 is not responding to treatment.

[0155] Embodiment 13. The method of embodiment 11 or 12 for monitoring treatment in a patient having colorectal cancer.

[0156] Embodiment 14. The method of embodiment 11, 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 colorectal cancer.

[0157] Embodiment 15. The method of embodiment 11 or 14 for monitoring risk for developing colorectal cancer in the patient.

[0158] Embodiment 16. A method of monitoring treatment in a patient having high grade dysplasia in the colon or monitoring risk for developing high grade dysplasia in the colon in a patient, the method comprising: (i) detecting the expression level of RNA in a biological sample obtained from the patient at a first time point; (ii) detecting the expression level of the RNA in a biological sample obtained from the patient at a second time point, wherein the second time point is later than the first time point; and (iii) comparing the expression level of the RNA at the second time point to the expression level of the RNA at the first time point, thereby monitoring treatment or monitoring risk; wherein the RNA comprises exosomal miR-32-5p, cell-free miR- 625-3p, or a combination thereof.

[0159] Embodiment 17. The method of embodiment 16, 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 is not responding to treatment for high grade dysplasia in the colon.

[0160] Embodiment 18. The method of embodiment 16 or 17 for monitoring treatment in a patient having high grade dysplasia in the colon.

[0161] Embodiment 19. The method of embodiment 16, 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 high grade dysplasia in the colon.

[0162] Embodiment 20. The method of embodiment 16 or 19 for monitoring risk for developing high grade dysplasia in the colon in the patient.

[0163] Embodiment 21. The method of any one of embodiments 9 to 20, further comprisingadministering to the patient the effective amount of an anti-cancer agent.

[0164] Embodiment 22. The method of any one of embodiments 1 to 21, wherein the RNA comprises exosomal miR-32-5p and cell-free miR-625-3p.

[0165] Embodiment 23. The method of any one of embodiments 1 to 21, wherein the RNA consists of exosomal miR-32-5p and cell-free miR-625-3p.

[0166] Embodiment 24. The method of any one of embodiments 1 to 21, wherein the RNA comprises exosomal miR-32-5p.

[0167] Embodiment 25. The method of any one of embodiments 1 to 21, wherein the RNA comprises cell-free miR-625-3p.

[0168] Embodiment 26. The method of any one of embodiments 1 to 21. wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, or a combination thereof, and wherein the RNA further comprises exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR- 625-3p, cell-free miR-30d-5p, or a combination of two or more thereof.

[0169] Embodiment 27. The method of any one of embodiments 1 to 21, wherein the RNA comprises exosomal miR-32-5p and cell-free miR-625-3p, and wherein the RNA further comprises exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, cell-free miR-30d-5p, or a combination of two or more thereof.

[0170] Embodiment 28. The method of any one of embodiments 1 to 21, wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR- 550a-3-5p, exosomal miR-625-3p, and cell-free miR-30d-5p.

[0171] Embodiment 29. The method of embodiment 28, wherein the RNA consists of exosomal miR-32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, and cell-free miR-30d-5p.

[0172] Embodiment 30. The method of any one of embodiments 1 to 21, wherein the RNA comprises exosomal miR-32-5p. and wherein the RNA further comprises exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p.

[0173] Embodiment 31. The method of embodiment 30, wherein the RNA consists of exosomal miR-32-5p, exosomal miR-486-3p, exosomal miR-550a-3-5p, and exosomal miR- 625-3p

[0174] Embodiment 32. The method of any one of embodiments 1 to 21, wherein the RNA comprises the cell-free miR-625-3p, and wherein the RNA further comprises cell-free miR-30d-5p.

[0175] Embodiment 33. The method of embodiment 32, wherein the RNA consists of cell-free miR-625-3p and cell-free miR-30d-5p.

[0176] Embodiment 34. The method of any one of embodiments 1 to 33, wherein the method further comprises detecting an elevated expression level, relative to a control, of one or more RNA selected from the group consisting of cell-free miR-4659b-3p, cell-free miR-296-5p, cell- free miR-4685-3p, cell-free miR-550a-5p, cell-free miR-4446-3p, cell-free miR-432-5p, cell- free miR-151a-3p, cell-free miR-199a-3p, cell-free miR-146a-5p, cell-free miR-24-3p, cell-free miR-223-3p, exosomal miR-556-3p. exosomal miR-2355-5p, exosomal miR-181a-3p, exosomal miR-3120-3p, exosomal miR-7-l-3p, exosomal miR-99b-3p, and exosomal miR-425-3p, in the biological sample obtained from the patient.

[0177] Embodiment 35. The method of any one of embodiments 1 to 33, wherein the method further comprises detecting an elevated expression level, relative to a control, of one or more RNA selected from the group consisting of cell-free miR-223-3p, cell-free miR-24-3p, cell-free miR-151a-3p, cell-free miR-199a-3p, exosomal 425-3p, exosomal 99b-3p, exosomal miR-191- 3p. exosomal miR-7-l-3p. miR-181a-3p, and exosomal miR-2355-5p, in the biological sample obtained from the patient.

[0178] Embodiment 36. The method of any one of embodiments 1 to 33, wherein the method further comprises detecting an elevated expression level, relative to a control, of one or more RNA selected from the group consisting of cell-free miR-223-3p, cell-free miR-24-3p, cell-free miR-151a-3p, cell-free miR-199a-3p, cell-free miR-432-5p, cell-free miR-4446-3p, cell-free miR-191-3p. cell-free miR-550a-5p. cell-free miR-146a-3p, cell-free miR-4685-3p, exosomal 425-3p, exosomal 99b-3p, exosomal miR-191-3p, exosomal miR-7-l-3p, miR-181a-3p, exosomal miR-2355-5p, and miR-556-3p, in the biological sample obtained from the patient.

[0179] Embodiment 37. The method of any one of embodiments 1 to 33, wherein the method further comprises detecting an elevated expression level, relative to a control, of one or more RNA selected from the group consisting of cell-free miR-223-3p. cell-free miR-24-3p. cell-free miR-151a-3p, cell-free miR-199a-3p, cell-free miR-432-5p, cell-free miR-191-3p, cell-free miR- 550a-5p, cell-free miR-146a-3p, and cell-free miR-4685-3p, in the biological sample obtained from the patient.

[0180] Embodiment 38. The method of any one of embodiments 1 to 33, wherein the method further comprises detecting an elevated expression level, relative to a control, of one or moreRNA selected from the group consisting of exosomal miR-425-3p, exosomal miR-99b-3p, exosomal miR-191-3p, exosomal miR-7-l-3p, exosomal miR-181a-3p. exosomal miR-2355-5p, and exosomal miR-556-3p, in the biological sample obtained from the patient.

[0181] Embodiment 39. The method of any one of embodiments 1 to 33. wherein the method further comprises detecting an elevated expression level, relative to a control, of one or more RNA selected from the group consisting of cell-free miR-24-3p, cell-free miR-199a-3p, cell-free miR-151a-3p, cell-free miR-223-3p, cell-free miR-223-3p, cell-free miR-625-3p, cell-free miR- 19 l-3p, cell-free miR-432-5p, and cell-free miR-4446-3p, in the biological sample obtained from the patient.

[0182] Embodiment 40. The method of any one of embodiments 1 to 33. wherein the method further comprises detecting an elevated expression level, relative to a control, of one or more RNA selected from the group consisting of exosomal miR-7-l-3p, exosomal miR-425-3p, exosomal miR-99b-3p, exosomal miR-181a-3p, exosomal miR-191-3p, exosomal miR-556-3p, and exosomal miR-2355-5p. in the biological sample obtained from the patient.

[0183] Embodiment 41. The method of any one of embodiments 1 to 33, wherein the method further comprises detecting an elevated expression level, relative to a control, of one or more RNA selected from the group consisting of cell-free miR-223-3p, cell-free miR-24-3p, cell-free miR-151a-3p, and cell-free miR-199a-3p, in the biological sample obtained from the patient.

[0184] Embodiment 42. The method of any one of embodiments 1 to 33, wherein the method further comprises detecting an elevated expression level, relative to a control, of one or more RNA selected from the group consisting of exosomal miR-425-3p, exosomal miR-99b-3p, exosomal miR-191-3p. exosomal miR-7-l-3p. exosomal miR-181a-3p. and exosomal miR-556- 3p, in the biological sample obtained from the patient.

[0185] Embodiment 43. The method of any one of embodiments 1 to 42, further comprising detecting the expression level of a reference RNA in the biological sample, wherein the reference RNA comprises miR-15b-5p, miR-23a-3p, miR-30e-5p, or a combination of two or more thereof.

[0186] Embodiment 44. The method of any one of embodiments 1 to 42, further comprising detecting the expression level of a reference RNA in the biological sample, wherein the reference RNA comprises miR-15b-5p, miR-23a-3p, and miR-30e-5p.

[0187] Embodiment 45. The method of any one of embodiments 1 to 42, further comprising detecting the expression level of a reference RNA in the biological sample, wherein thereference RNA comprises exosomal miR-15b-5p, cell free miR-15b-5p, exosomal miR-23a-3p, cell-free miR-23a-3p, exosomal miR-30e-5p, cell-free miR-30e-5p. or a combination of two or more thereof.

[0188] Embodiment 46. The method of any one of embodiments 1 to 42. further comprising detecting the expression level of a reference RNA in the biological sample, 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.

[0189] Embodiment 47. The method of any one of embodiments 1 to 42, further comprising detecting the expression level of a reference RNA in the biological sample, wherein the reference RNA consists 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.

[0190] Embodiment 48. The method of any one of embodiments 1 to 47, wherein the biological sample is a liquid biological sample.

[0191] Embodiment 49. The method of any one of embodiments 1 to 47, wherein the biological sample is a blood sample.

[0192] Embodiment 50. The method of any one of embodiments 1 to 47, wherein the biological sample is a plasma sample.

[0193] Embodiment 51. The method of any one of embodiments 1 to 47, wherein the biological sample is a serum sample.

[0194] Embodiment 52. The method of any one of embodiments 1 to 47, wherein the biological sample is a tissue sample.

[0195] Embodiment 53. The method of any one of embodiments 1 to 52, wherein the colorectal cancer is early onset colorectal cancer.

[0196] Embodiment 54. The method of any one of embodiments 1 to 53, wherein the patient is asymptomatic for colorectal cancer.

[0197] Embodiment 55. The method of any one of embodiments 1 to 54, wherein the colorectal cancer is Stage 0 colorectal cancer.

[0198] Embodiment 56. The method of any one of embodiments 1 to 54. wherein the colorectal cancer is Stage I colorectal cancer.

[0199] Embodiment 57. The method of any one of embodiments 1 to 54, wherein thecolorectal cancer is Stage II colorectal cancer.

[0200] Embodiment 58. The method of any one of embodiments 1 to 54, wherein the colorectal cancer is Stage III colorectal cancer.

[0201] Embodiment 59. The method of any one of embodiments 1 to 54, wherein the colorectal cancer is Stage IV colorectal cancer.

[0202] Embodiment 60. The method of any one of embodiments 1 to 59, wherein the patient is less than 50 years old.

[0203] Embodiment 61. The method of any one of embodiments 1 to 59, wherein the patient is35 years old or younger.

[0204] Embodiment 62. The method of any one of embodiments 1 to 59. wherein the patient is 18 years old to 49 years old.

[0205] Embodiment 63. The method of any one of embodiments 1 to 59. wherein the patient is 18 years old to 35 years old.

[0206] Embodiment 64. The method of any one of embodiments 1 to 59, wherein the patient is36 years old to 40 years old.

[0207] Embodiment 65. The method of any one of embodiments 1 to 59, wherein the patient is 41 years old to 45 years old.

[0208] Embodiment 66. The method of any one of embodiments 1 to 59, wherein the patient is 46 years old to 49 years old.

[0209] Embodiment 67. The method of any one of embodiments 1 to 66, wherein the control is a healthy patient.

[0210] Embodiment 68. The method of any one of embodiments 1 to 66 wherein the control is a healthy patient less than 50 years old that does not have colorectal cancer.

[0211] Embodiment 69. The method of any one of embodiments 1 to 68 wherein the patient is a human patient.

[0212] Embodiment 70. The method of any one of embodiments 3-8 and 21-69, wherein the anti-cancer agent is adagrasib. bevacizumab, irinotecan, capecitabine, ramucirumab. oxaliplatin, cetuximab, fluorouracil, fruquintinib, ipilimumab, pembrolizumab, leucovorin, trifluridine, tipiracil, nivolumab, panitumumab, regorafenib, tucatinib, ziv-aflibercept, encorafenib, trastuzumab, pertuzumab, lapatinib, larotrectinib, entrectinib, selpercatinib, sotorasib,regorafenib, or a combination of two or more thereof.

[0213] Embodiment 71. The method of any one of embodiments 3-8 and 21-69, wherein the anti-cancer agent is a chemotherapeutic agent.

[0214] Embodiment 72. The method of embodiment 71, 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.

[0215] Embodiment 73. The method of embodiment 72, 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 vind esine; the taxane compound is paclitaxel or docetaxel; and the epothilone compound is epothilone, ixabepilone, patupilone. or sagopilone.

[0216] Embodiment 74. A kit comprising reagents capable of detecting an expression level of RNA from a biological sample; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, and cell- free miR-30d-5p.EXAMPLE

[0217] The incidence of early-onset colorectal cancer (EOCRC, i.e., diagnosed before age 50) continues to increase, now standing as the first cause of cancer-related deaths in young men. Screening participation in young adults remains low, but a non-invasive test may help.

[0218] To address the clinical needs and improve patient outcomes for the expanding population at risk for EOCRC, we have developed and validated a novel RNA-based blood test. We first identified a panel of cf / exo-miRNA biomarkers with a systematic RNA sequencing discovery' phase. Subsequently, we developed, trained, and independently validated an transcriptomic blood test using XGBoost (extreme Gradient Boosting, a machine learning approach that is tree-based). This assay (termed "ENCODE7’ - Early oNset COlorectal cancer DEtection) discriminated between EOCRC patients and age- and sex -matched controls with highspecificity and sensitivity, including for stages I and II, and to some extent, high-grade dysplasia. Notably, the test's levels decreased following surgery.

[0219] ENCODE was an international, multicentric cohort study involving 542 individuals from several institutes in four countries (U.S.A., Italy, Spain, and Japan; NCT06342401). We identified a panel circulating biomarkers through small RNA sequencing in the plasma and tissue, hyper-selected with Cox-LASSO regression and expression analysis. We then trained an advanced machine learning model (extreme Gradient Boosting) on RT-qPCR results from a training cohort (n=192) and independently validated it in an external cohort (n=191).

[0220] ENCODE represents the largest EOCRC study to date to develop, train, and externally validate a blood assay for the growing population at risk of EOCRC and it offers a complementary screening strategy.

[0221] We identified a panel of six cf / exo-miRNA biomarkers, which were highly accurate in detecting of EOCRC in training vs. external validation cohorts (AUC: 97-5% vs. 95 -6%). In the validation cohort sub-analysis, patients with EOCRC has significantly higher values of the cf / exo-miRNA blood test at all ages and could be readily distinguished from non-disease controls, even in the 20-35 age range (AUC: 98-5%). This blood test was validated with a sensitivity of 94- 1% for stage I EOCRC (CI95%=73 02-98-95%), 100% for stage II (CI95%=I00- 100%), and 96-8% for stage III (CI95%=83-81-99-43%), and 6T5% pre-malignant lesions with high grade dysplasia (CI95°O=35- 52-82- 29%). Finally, comparing the plasma samples collected before and after surgery, we demonstrate a reduction in the ENCODE values following surgery, reaching negativity after four days.

[0222] Materials and Methods

[0223] Study Design, Population, and Specimens

[0224] The ENCODE study was an Early Detection Research Network (ERDN)-phase I-III study designed to discover cf / exo-miRNA biomarkers, develop and test a non-invasive blood test to detect early-stage and asymptomatic EOCRC. Our study encompassed systematic RNA biomarker discovery (ERDN phase I), clinical assay development and training (ERDN phase II), and independent testing from prospectively collected patient-derived blood specimens (PRoBE- compliant ERDN phase III). The study was conducted in accordance with the Declaration of Helsinki, it was registered and completed on clinicaltrials.gov (NCT06342401), and it was STARD-compliant.

[0225] This study included data from 503 plasma samples and 187 tissue samples collectedfrom a total of 542 unique patients (FIG. 11). The data originated from both publicly available datasets (N=80) and four independent clinical cohorts (N=462, FIG. 8). The discovery phase of this study aimed to identify the most likely tumor-derived cf / exo-miRNA biomarkers. We utilized two independent cohorts, one publicly available (GSE115513) and one of our own. To translate our sequencing results into a cheaper and more practical RT-qPCR test, we evaluated which cf / exo-miRNA biomarkers could be readily detected using a first large, multi-centric clinical cohort (N=192; from San Raffaele Hospital, Milan. Italy; Hospital Universitario de Canarias, La Laguna, Spain; Hospital Clinic de Barcelona, Barcelona, Spain; Hospital Universitario de Donostia, Donostia, Spain; and Salamanca Biomedical Research Institute, Madrid, Spain). After prioritizing a panel of six cf / exo-miRNA transcripts, we trained and locked an XGBoost-based machine learning model on the RT-qPCR results. This model was then tested in a second, external, and independent prospective cohort (N=191; from National Cancer Center Hospital, Tokyo; Kawasaki University, Kawasaki; Mie University7, Mie; Yamagata University, Yamagata; and Tokyo Medical and Dental University, Tokyo).

[0226] This study involved participants aged 18 to 49 who provided written informed consent before blood draw. Cases received a histological diagnosis of CRC (TNM classification, 8thedition) before age 50. Cases with dysplastic changes confined to the epithelium, lamina propria, or muscolaris mucosae were collectively defined as “high-grade dysplasia”, following the USMSTF terminology recommendations. Non-disease controls (NDCs) were younger than age 50, free of CRC at the time of blood collection with at least one year of negative follow-up, and matched to cases on age (±2 years) and biological sex. Key exclusion criteria included a history of cancer, inflammatory bowel disease, hereditary CRC predisposition (identified through genetic testing), and a lack of endoscopic evaluation. All specimens were collected before any treatment administration. To evaluate post-treatment assay levels, post-surgical samples were collected during hospitalization.

[0227] Laboratory procedures for RNA sequencing (ERDN phase-I)

[0228] To identify candidate biomarkers, we isolated total cell-free and exosome-bound RNA, each from 200 pL of plasma using the miRNeasy and exoRNeasy Midi kits (Qiagen, Valencia, CA), respectively. Tissue RNA was extracted from ~30 pg of fresh-frozen tissue (tumor and adjacent healthy mucosa) using AllPrep DNA / RNA / miRNA Universal Kit (Qiagen). Nextgeneration sequencing libraries were constructed using the Small RNA-Seq Kit V3 (Revvity, Waltham, MA) and pair-end sequencing performed on an Illumina NovaSeq platform. Candidate cf / exo-miRNAs were selected through a multi-step selection process, including differentialexpression in blood between EOCRC and NDCs (|Log2(Fold-Change)| >1; Benjamini- Hochberg-adjusted p values < 0 05; and log2(CPM) > 2 0), concordance with patient-matched tissue expression patterns, and external validation in a separate publicly available dataset (GSE115513). Finally, we employed Cox-LASSO regression to prioritize the most promising biomarkers.

[0229] Laboratory procedures for the diagnostic assay based on RT-qPCR (ERDN phase- II / III)

[0230] Following biomarker discovery', we proceeded to develop a blood test based on the expression levels of the chosen cf / exo-miRNA transcripts, which were measured with RT-qPCR in patient plasma samples. For all thee clinical cohorts (training, testing, and pre- / post-surgery), total cfRNA was isolated from 200 pL of plasma using miRNeasy serum / plasma kit (Qiagen), while total exoRNA was isolated in a two-step process that involved centrifugation, precipitation, and resuspension of exosomes from 200 pL of plasma, followed by exosome lysis and RNA extraction (Total Exosome Precipitation Reagent by Invitrogen, Waltham, MA, USA, followed by miRNeasy serum / plasma kit). Complementary DNA was synthesized (miRCURY LNA RT Kit, Qiagen) and target cf / exoRNA expression was measured on a QuantStudio 7 Flex Real-Time PCR system (Thermo Fisher Scientific, Irwindale, CA) using high-quality probes (Qiagen, FIG. 9) and high-sensitivity SYBR Green Master Mix (Thermo Fisher Scientific). The relative abundance of target transcripts was determined using the 2’ACtmethod, which normalizes the Ct (cycle threshold) value of the target transcripts to the average Ct value of three reference genes (hsa-miR-15b-5p, hsa-miR-23a-3p, and hsa-miR-30e-5p). ACt refers to the difference of Ct values between the target transcripts and the average of the three normalizers.

[0231] In Nakamura et al (A Liquid Biopsy Signature for the Detection of Patients With Early-Onset Colorectal Cancer, Gastroenterology 2022; 163(5): 1242-51 e2), a blood test based on four cf-miRNAs and logistic regression achieved a sensitivity of 82%, specificity of 86%, and an AUC of 88%. This time, considering the decision to leverage a combination of cf / exo- miRNAs and more advanced machine-learning approaches, the sample sizes (N=192 and 191 for training and validation) are adequately powered to reach AUC values of 90% or higher (a=5%, precision 95.0%) with sensitivity and specificity values >90% and >85%. respectively.

[0232] Statistical Analyses

[0233] All analyses were computed in R. The final diagnostic assay incorporates XGBoost, a popular and powerful machine learning model that utilizes sequential iterative boosting as astrategy to convert weak learners (decision trees) into a more robust model. XGBoost is particularly suited for complex and high-dimensional data due to its faster computation, extensive optimization options, and built-in methods for handling missing data. After training, the model was locked to prevent modifications, and we explored its inner workings to understand how it arrives at its predictions by analyzing feature importance and SHAP values. The performance was evaluated in terms of AUC, sensitivity and specificity values, with the threshold for low vs. high-risk scores defined by minimizing the distance between the ROC curve and the point of perfect discrimination. Confidence intervals for proportions were computed with the Wilson method, while confidence intervals for the ROC curv es were estimates w ith 2000 stratified bootstrap replicates. We estimated the value-risk relationship between the XGBoost-derived risk scores and the risk of EOCRC by modeling the risk scores as restricted cubic splines with knots at the positivity7threshold (which approximated with the 50thpercentile) and at the 25thand 75thpercentile of the sample distribution. Statistical significance was defined at p<0 05 for all analyses and was tested by the Wilcoxon method for pairwise comparisons, by the student t-test for two groups, and by Anova for multiple groups.

[0234] Results

[0235] Identification Of Candidate Biomarkers

[0236] The discovery7phase first identified 442 cf-miRNAs and 206 exo-miRNAs as candidate biomarkers. Of these, 384 cf-miRNAs and 183 exo-miRNAs were also differentially expressed in patient-matched tissue samples (FIGS. 1A-1C). After assessing for external reproducibility in an independent dataset (GSE1 15513), 235 cf-miRNAs and 126 exo-miRNAs remained. We further excluded candidates with low average blood expression (log2CPM<2 0, which excluded 76 cf-miRNAs and 38 exo-miRNAs), an adjusted p- value >0 05 (which excluded 26 cf-miRNAs and 52 exo-miRNAs), and a log2FC<1 0 (which excluded 53 cf-miRNAs and 19 exo-miRNAs). The remaining 80 cf-miRNAs and 17 exo-miRNAs were further hyper-selected with LASSO- regression to 12 cf-miRNAs and 11 exo-miRNAs (FIG. 12). Unsupervised clustering of these candidate biomarkers revealed an ability to separate EOCRC from NDCs (both for the 12 cf- miRNAs in FIG. ID and 11 exo-miRNAs in FIG. IE). Finally, we further excluded candidates with a low expression in plasma or in tissue, excluding 6 cf-miRNAs and 1 exo-miRNA (FIG. 13). In conclusion, the discovery phase employed a systematic, genome-wide, and unbiased sequencing effort to identify a panel of 6 cf-miRNAs and 10 exo-miRNAs independently associated with EOCRC.

[0237] Development And Validation Of The Cf / Exoma Blood-Based Test

[0238] Evaluating these discovered cf / exo-miRNAs with RT-qPCR in the first clinical cohort ('training' cohort) revealed a statistically significant correlation with EOCRC for two cell-free transcripts (miR-625-3p and miR-30d-5p, FIG. 14A) and four exosome-bound transcripts (miR- 32-5p, miR-486-3p, miR-550a-3-5p, and miR-625-3p, FIG. 14B). These six cf / exo-miRNAs formed our formal and final biomarker panel. We then trained a machine learning model on the RT-qPCR results of these candidate cf / exo-miRNAs. The resulting classifier derived its accuracy primarily from exo-miR-32-5p and cf-miR-625-3p, while the other analytes contributed to the fine-tuning (FIG. 15). After completing the training process, the model was fully locked and tested in a second, external, and independent cohort with similar clinical characteristics for validation. From here on, the results of the training (N=192) and validation (N=191) cohorts are presented side by side.

[0239] We first evaluated the diagnostic potential of cf-miRNAs and exo-miRNAs individually. Both panels yielded robust results (exo-AUC = 94-6% vs. 92-3% in training and external testing, respectively; cf-AUC = 87-3% vs. 82-6%, FIGS. 2A-2B). Interestingly, combining the 2-cf-miRNA panel with the 4-exo-miRNA panel resulted in a more distinct separation of EOCRC and NDCs in both cohorts. Specifically, in both cohorts, patients with EOCRC clustered in the top-right part of the density plot, while the NDCs in the bottom left comer (FIGS. 2C-2D) The final model, termed ‘ ENCODE, ’ utilized two cf-miRNAs and four exo-miRNAs to achieve AUC values of 97-5% in training and 95-6% in external testing (FIGS. 2E-2F), thus improving the performance of the cell-free and exosomal assays alone.

[0240] Both cohorts showed higher cf / exo-miRNA levels in individuals with EOCRC compared to NDCs (FIGS. 3A-3B). Moreover, the distribution of the cf / exo-miRNA levels relative to the positivity threshold was similar in both cohorts (FIGS. 3C-3D). Interestingly, the model established a statistically significant association (p<0 001) with the odds ratios of EOCRC in both cohorts (FIGS. 3E-3F). At increasing values of the cf / exo-miRNA blood test, the odds ratios of EOCRC increased proportionately. Such relationship was linear for the first clinical cohort, while the second cohort showed a flection point for values higher than 0-6. The observation that the odds ratios of EOCRC plateaued in the second clinical cohort was expected. While the cf / exo-miRNA blood test effectively discriminated most EOCRC patients from controls, two NDC individuals exhibited high cf / exo-miRNA values (false positives). This resulted in high odds ratios (above 10), significantly greater than 1, but reaching a plateau due to a ceiling effect. Consequently, the association between cf / exo-miRNA levels and the odds ratio of EOCRC, while highly significant, exhibited a non-linear relationship.

[0241] Having established the repl i cabi li ty of the findings between the training and validation cohorts, all the following analyses are from the external testing cohort to truly establish the performance of the model.

[0242] High Stage-Specific Performance In The Independent Validation Cohort

[0243] Since early detection is crucial for reducing mortality in EOCRC. we evaluated the blood test's performance across different stages. Interestingly, we observed significantly higher values of the cf / exo-miRNA test in the plasma of EOCRC patients at all stages and even in young individuals with HGD compared to NDCs (FIG. 4A). This indicates the test's ability to detect pre-cancerous lesions. Furthermore, this blood test achieved high AUC values all stages of EOCRC, ranging from HGD (AUC = 93- 1%), stage I (AUC = 96-5%), stage II (AUC = 97-4%), stage III (AUC = 95 1%), to stage IV (AUC = 93-3%, FIGS. 4B-4E). In the independent and external testing cohort, this blood-based test achieved a sensitivity of 91 -6% (Cl95% = 84-25 - 95-67%) and a specificity- of 87-5% (CI95% = 79-4 - 92 7%). Importantly, it demonstrated high sensitivity values for all screening-relevant stages of EOCRC (97-3% for stages I-III, Cb5% = 90-6 - 99-3%). More specifically, the sensitivity for stage I EOCRC was 94- 1% (CI95% = 73-02 - 98-95%), for stage II 100-0% (CI95% = 100-100%), and for stage III 96-8% (CI95% = 83-81 - 99 43%). Moreover, a sensitivity of 61 5% was observed for HGD (CI95%= 35 -52 - 82-29%), indicating the test's ability to detect pre-invasive lesions amenable to endoscopic resection (FIG. 7).

[0244] Analyzing the test's performance across different age groups within our testing cohort (which included a wide age range), we observed consistently higher values of the blood-based test in patients with EOCRC (and HGD) compared to NDCs at all ages (FIG. 5A). We further evaluated the transcriptomic test characteristics for specific age groups. Patients with EOCRC showed significantly higher blood test values, ranging from those aged 35 or younger (FIGS. 5B-5C), 36-40 (FIG. 5D-5E), 41-45 (FIG. 5F-5G), to those aged 46-49 (FIG. 5H-5I). Finally, we investigated whether using age-specific cutoff values could improve the test's performance. While age-specific cutoffs resulted in modest improvements in sensitivity- for the 41-45 age group and specificity for the 35 or younger group, they did not significantly affect the overall accuracy of the model.

[0245] Blood Test Levels Decrease After Curative-Intent Surgery

[0246] To assess if the values of this blood test changed after surgery, we compared patient- matched samples collected at diagnosis and 2-7 days after surgery (FIG. 7). Plasma samplescollected after surgery demonstrated significantly lower values than those collected at diagnosis (p<0 001, FIG. 6A). Interestingly, samples collected four or more days post-surgery showed a more substantial decrease in cf / exo-miRNA levels, demonstrating a clearer distinction between pre- and post-operative levels as the time interval lengthened (FIG. 6B). This trend was reflected in the density plots, where samples collected at diagnosis and within the first three days tended to cluster in the top-right comer (FIGS. 6C-6D), while samples collected later postsurgery shifted closer to the bottom-left comer (FIG. 6E). Finally, analyzing the time-trend of levels according to the post-operative day, we observed that only blood samples collected after four days post-surgery had diminished levels reaching negative values.

[0247] Discussion

[0248] This translational study builds on our previous report to validate a cf / exo-miRNA blood test for detecting EOCRC in light of the growing risk of CRC among young adults2’5. This blood based test was powered by machine learning and it was developed and validated independently in two international, multi-centric, cohorts. The results demonstrated virtually no loss of performance when replicating the test on an independent cohort of prospectively collected plasma specimens. Furthermore, the evaluation of the blood test at diagnosis and after surgery supports the hypothesis that the core biomarkers are specific for EOCRC. For the expanding population at risk of CRC, this blood test offers an opportunity to complement existing screening strategies and expand access to screening.

[0249] While the incidence of EOCRC continues to rise, there is a scarcity of data on non- invasive screening tests for this patient population. The only FDA-approved cfDNA-based blood test (Shield by Guardant) reported a sensitivity of 76-5% (Cl95% = 52-7 - 90-4) for all-stage CRC in the 45-59 age group. This test also showed a sensitivity of just 7-9% (Cl95% = 5-8 - 10-8) for precancerous lesions (including HGD and Tis, similar to our study definitions) in the same age range. Furthermore, these sensitivity’ values were lower than those observed in older age group (88-2% in the 60-69 age group). The fact that DNA methylation-based tests (both blood- and stool-based) experience a decline in sensitivity measures for younger individuals has already been reported. This observation has been attributed to the fact that methylation patterns change with age. affecting test results in different age groups. In contrast, RNA-based tests, including a recent multi-target stool RNA test, seem less susceptible to age-related variations. Our blood test demonstrated a similar advantage. It maintained stable performance metrics rendering age-adjusted thresholds unnecessary for optimal test performance. Additionally, several studies have reported that EOCRC often presents at advanced stages compared to later-onset CRC, and this appears to be due to the inherently aggressive nature of the disease rather than diagnostic delays. This underscores the critical need for alternative strategies for early detection, ideally before the cancer becomes invasive. Stage-specific measures for EOCRC have not been reported comprehensively for other non-invasive tests, but this cf / exoRNA blood test appeared promising in this regard. The sensitivity values of this assay for stage I, II, and III EOCRC were 94- 1%, 100%. and 96-8%, respectively. Notably, the test even detected HGD with a sensitivity of 61 -5%. Collectively, these findings indicate that this blood-based test will complement existing strategies in the fight against EOCRC.

[0250] We would like to highlight a few strengths of this study. It gathered the largest cohort of EOCRC cases and controls to date, providing a robust foundation for the findings. Second, the study design extended beyond CRC by including individuals with HGD, demonstrating the ability of this blood test to prevent EOCRC by detecting (and then removing) pre-cancerous lesions. Furthermore, the study utilized an explainable machine learning model. This allowed us to understand the rationale behind the test’s predictions and pinpoint the key factors influencing its decisions. Lastly, the assay requires minimal plasma volumes, making it highly translatable to clinical settings. This characteristic has the abi 1 i ty to complement other screening options and expand access to screening, thus reaching individuals during routine check-ups.

[0251] In conclusion, this study builds upon our previous work to develop a more accurate, sensitive, and specific blood test for the growing population at risk of EOCRC. By leveraging biological data and machine learning, we developed and independently validated this assay in a large, multi-centric, international ERDN-phase-I / II / III study. While efforts to improve compliance with endoscopic screening in younger individuals continue, this blood test offers a complementary strategy. It may be a more appealing option for those hesitant about invasive procedures, thereby facilitating and encouraging participation in CRC screening among young adults who might otherwise avoid it.

[0252] Supplementary Materials and Methods

[0253] Study Design, Population, and Specimens

[0254] The ENCODE study was a ERDN-phase I / III study designed to evaluate the performance of a cf- / exo-miRNA blood based test to detect asymptomatic and early-stage CRC in a screening -relevant population. Our study encompassed systematic miRNA-based biomarker discovery’ (ERDN phase I), clinical assay development, machine-learning hyperparameter finetuning and training (ERDN phase II), independent validation from a cohort of prospectivelycollected and retrospectively analyzed samples (PRoBE compliant ERDN phase III). The study was conducted in accordance with the Declaration of Helsinki, it was registered and completed on clinicaltrials.gov (NCT06342401), and it was STARD-compliant (FIG. 8).

[0255] This study included data from 503 plasma samples and 187 tissue samples coming from 542 unique patients, both from publicly available miRNA expression datasets (N=80) and four independent clinical (N=462). All participants were enrolled from 10 institutions in Europe and Asia (FIG. 7).

[0256] The discovery' phase of this study was designed to identify the biomarkers that were most likely to be tumor-derived and it utilized two independent cohorts, one publicly available, and one of our own. The clinic-based discovery cohort included 38 individuals (EOCRC, N=19; Age- and sex-matched controls, N=19). More specifically all cases included in this cohort provided three patient-matched samples: plasma, tumor, and adjacent normal colonic mucosa, all features that we leveraged to guarantee a discovery strategy rooted in the tissue specificity of the discovered biomarkers. The discovery efforts were then assessed and further refined by comparison with an external in-silico approach (GSE115513). This second discovery cohort comprised 80 individuals diagnosed with EOCRC, for whom a total of 149 tissue samples were available.

[0257] Candidate biomarkers that demonstrated a statistically significant association with EOCRC in the discovery cohorts were carried over to the blood phases of our study. In transitioning our findings from small RNA sequencing to RT-qPCR, we first evaluated which of these biomarkers were readily detectable in a large, multi-centric, clinical cohort of European origin (N=192), which included 96 individuals with EOCRC and 96 NDCs (San Raffaele Hospital, Milan, Italy; Hospital Universitario de Canarias, La Laguna, Spain; Hospital Clinic de Barcelona, Barcelona, Spain; Hospital Universitario de Donostia. Donostia, Spain; and Salamanca Biomedical Research Institute, Madrid, Spain). After prioritization of a panel of cf- and exo-miRNAs independently associated with EOCRC, we trained a ML model on the RT- qPCR results. After the ML-based diagnostic assay was established and locked, based on a combination of cf- and exo-miRNAs, the assay was transitioned to an external and entirely independent cohort (N=191) that included 95 individuals with EOCRC and 96 NDCs from four Japanese centers (National Cancer Center Hospital, Tokyo; Kawasaki Universify, Kawasaki; Mie Universify, Mie; Yamagata Universify, Yamagata; and Tokyo Medical and Dental University, Tokyo).

[0258] For the biomarker discovery, this study employed fresh frozen tumor tissue andadjacent healthy mucosa, as well as plasma samples collected before the administration of any therapy. For biomarker training and validation, all samples were plasma samples. Initial pathological analyses, including tumor grading, lymphatic invasion, and vascular invasion, were conducted at the recruiting center. Subsequently, all samples were sequentially collected at participating sites and gathered at the principal study site for centralized analyses.

[0259] Definitions, Inclusion And Exclusion Criteria, And Study Endpoints

[0260] Eligible participants were 18 to 49 years of age at the time of consent. Cases were defined as having a histological diagnosis of CRC (TNM classification, 8thedition) before age 50. Cases included also individuals diagnosed with dysplastic changes confined to the epithelium, lamina propria, or muscolaris mucosae. While these were variously described by pathologists as “Tis,” ‘’carcinoma in-situ ” “intramucosal carcinoma,” or '‘high-grade dysplasia;”, following the USMSTF terminology7recommendations, these were here collectively defined as “high-grade dysplasia”. Controls were younger than age 50, without CRC at the time of blood collection, and with at least one year of negative follow-up thereafter. Cases and controls were matched on age (±2 years) and biological sex and provided written informed consent before their blood was draw n. Key exclusion criteria were a history of cancer, a know n diagnosis of inflammatory bow el disease, and a hereditary predisposition to CRC (identified through genetic testing). Cases with EOCRC underwent standard diagnostic, staging, and therapeutic procedures per local guidelines, received stage-specific curative-intent resection, with or without systemic therapy (as appropriate), or upfront systemic therapy (as appropriate). Importantly, all biospecimens used for the development, training, and validation of the assay were collected before the administration of any treatment. To evaluate the levels of the assay after treatment, post-surgical samples were utilized.

[0261] The co-primary outcomes were sensitivity for EOCRC and specificity7in individuals aged 49 or younger. Secondary outcomes for which sub-analyses w ere pre-planned included the sensitivity for early-stage CRC and the sensitivity7for pre-malignant lesions. Additionally, we had two exploratory7endpoints: whether the assay maintained the performance across four predetermined age groups (18-35; 36-40; 41-45; and 46-49) and an estimation of the assay half-life following surgery. For this specific sub-analysis, we had access to a small cohort of EOCRC patients for whom plasma samples were available before and after curative-intent surgical treatment.

[0262] Laboratory7procedures for RNA sequencing (discovery phase)

[0263] For the biomarker discovery phase, total RNA was isolated from ~30 pg of fresh frozen tissue (tumor and adjacent healthy mucosa) using AllPrep DNA / RNA / miRNA Universal Kit (Qiagen, Hilden, Germany). Total RNA was isolated from 200 pL of plasma using while exosomal-RNA content was isolated by first isolating the exosomes from 200 pL of plasma, and then extracting the RNA content of such exosomes using exoRNeasy Midi Kit (Qiagen) Construction of next-generation sequencing libraries for RNAs from tissue and plasma was performed using the Small RNA-Seq Kit V3 (Revvity, Waltham. MA, USA). After size selection, libraries’ quality and quantity was assessed using the TapeStation (Agilent, Santa Clara, CA, USA) and then equimolar-pooled prior to sequencing on an Illumina HighSeq 2500 with single-end 35-base read lengths at an average of 10 million reads per sample. Based on the discovery’ cohort sample size (N=l 18), this study phase was adequately powered (>80%) to detect at least 50 differentially expressed RNAs, under the assumptions of a 5% false discovery rate, 30x coverage (Xo), and a 5% significance level (a) ('RNASeqSampleSize', Version 2.12.0).

[0264] Illumina small RNA-seq 3' adapters were trimmed using cutadapt software, and all retained sequences were confirmed to contain high-quality scores and peaks concentrated at 22 nt, representing miRNAs. Preprocessed reads were aligned to a human reference genome (human genome build 38) and annotated using GENCODE miRNA annotation. For the biomarker discovery phase, the initial candidate biomarkers were selected based on differential gene expression (|Log2(Fold-Change)| >0-5 and Benjamini -Hochberg-adjusted p values < 0 05). From this initial dataset, we ensured that the blood-based expression was reflective of the tissuespecific characteristics and therefore we excluded all the candidates whose expression levels in cancer vs. matched mucosa were opposite to those observed in the blood. Next, to ensure that our findings were not cohort specific, we analyzed a publicly available dataset (GSE115513), and we further excluded all the candidate biomarkers with a differential gene expression that was opposite to the one we observed in out cohort in a statistically significant manner. We further refined our discovery strategy by prioritizing candidates with a larger |Log2(Fold- Change)| >1 and an average expression level log2(CPM) > 2 0 (CPM, counts per million, represents candidates that are more highly expressed in blood that the average of all other candidates), to guarantee the robustness of the findings. Finally, we employed Cox-LASSO regression to prioritize the most promising biomarkers, which were further refined based on their expression levels.

[0265] Laboratory procedures for the diagnostic assay based on RT-qPCR (training and validation)

[0266] After completion of the biomarker discovery phase, we proceeded to develop a plasmabased assay based on RT-qPCR. From the clinical cohorts (training, validation, surgical, respectively), total circulating cell-free RNA was isolated from 200 pL of plasma using miRNeasy serum / plasma kit (Qiagen). Exosome-bound total RNA was isolated in a two-step process that involved both centrifugation, precipitation, and resuspension of exosomes from 200 pL of plasma, followed by exosome lysis and RNA extraction with (Total Exosome Precipitation Reagent by Invitrogen. Waltham, MA. USA, followed by miRNeasy serum / plasma kit). Complementary DNA was synthesized from total RNA using the miRCURY LNA RT Kit (Qiagen) and then miRNA expression was assessed by quantitative reverse transcription PCR (RT-qPCR) on a QuantStudio 7 Flex Real-Time PCR system (Thermo Fisher Scientific, Irwindale, CA. USA) using high-quality miRNA probes (Qiagen. FIG. 9) and high-sensitivity SYBR Green Master Mix (Thermo Fisher Scientific). The relative abundance of target transcripts was determined and normalized to the expression levels of the average of hsa-miR- 15b-5p, hsa-miR-23a-3p, and hsa-miR-30e-5p as internal controls using the by 2'ACtmethod. ACt refers to the difference of Ct values between the transcript of interest and the average of the three normalizers.

[0267] In our previous publication, which was based on logistic regression for 4-cf-miRNA signature, we reported sensitivity and specificity values of 82% and 86%, respectively, with an AUC value of 88%. This time, considering the decision to leverage both cf-miRNAs and exo- miRNAs and ML approaches, we determined our sample size based on a minimum AUC value of 90% with specificity >85% and sensitivity >90% in both training and validation cohorts. The overall sample sizes (N=192 and 191 for training and validation) are both adequately powered to reach AUC values of 90% or higher (a=5%, precision 95 0%). Likewise, the number of cases and controls in both cohorts are adequately powered to reach sensitivity and specificity values >90% and >85%, respectively, with 90% precision at a significance level of 0 05.

[0268] Model Architecture and Hyperparameters

[0269] The final diagnostic assay, named ENCODE, employed XGBoost, a popular ML model that utilizes sequential iterative boosting as a strategy to convert weak learners (decision trees) into a more robust model. XGBoost is particularly suited for complex and highdimensional data because of the faster computation, its many optimization options, and its built- in methods to handle missing data. In this study, XGBoost model was allowed to undergo a maximum of 800 training rounds with gradient boosting trees implemented by the ‘xgboost’ package in R (Version 0.1.3). Given our goal to optimize AUC. sensitivity, and specificityvalues, we utilized the AUC / precision-recall (‘aucpr’) evaluation metric to train a binary discriminatory assay. Each tree was allowed to reach a maximum of 8 branches with a low- pruning strategy (y=2) but, to minimize overtraining, we slowed the learning rate (s=0- 1 %), and imposed each decision tree (i.e., each training round) to utilize only 75% of samples and 80% of the features (i.e., microRNAs) available. At the end of the training phase, the model was fully locked and we explored model explainability by analyzing feature importance and SHAP values.

[0270] Statistical Analyses

[0271] The ENCODE assay performance was evaluated in terms of ROC curve analysis ('pROC,' Version 1.18.5), sensitivity and specificity ('DTComPair,' Version 1.2.2), with the threshold for low vs. high-risk scores defined by minimizing the distance from the point of perfect discrimination (‘cutpointr’. Version 1.1.2). Confidence intervals for proportions were computed with the Wilson method, while confidence intervals for the ROC curv es were estimates with 2000 stratified bootstrap replicates. We estimated the value-risk relationship between ENCODE levels and the risk of EOCRC by modeling ENCODE levels as restricted cubic splines with knots at the positivity threshold (which approximated with the 50thpercentile) and at the 25thand 75thpercentile of the sample distribution (‘plotRCS’, Version 0.1.4). Statistical significance defined at p<0 05 for all analyses and was tested by the Wilcoxon method for pairwise comparisons, by the student t-test for two groups, and by anova for multiple groups. All analyses were computed in R.

[0272] It is understood that the examples described herein are for illustrative purposes only and 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.

[0273] REFERENCES1 . Araghi M, et al. Lancet Gastroenterol Hepatol 2019; 4(7): 511-8.2. Siegel RL, et al, Gut 2019; 68(12): 2179-85.3. Cavestro GM, et al, Clin Gastroenterol Hepatol 2023; 21(3): 581-603 e33.4. Siegel RL, et al, CA Cancer J Clin 2024; 74(1): 12-49.5. Siegel RL, et al, CA Cancer J Clin 2023; 73(3): 233-54.6. Patel SG, et al, Lancet Gastroenterol Hepatol 2022; 7(3): 262-74.7. Vuik FE, et al, Gut 2019; 68(10): 1820-6.8. Bailey CE, et al, JAMA Surg 2015; 150(1): 17-22.9. Chen FW, et al, Clin Gastroenterol Hepatol 2017; 15(5): 728-37 e3.10. Demb J, et al, JAMANetw Open 2024; 7(5): e2413157.Demb J, et al, Gut 2020; 70(8): 1529-37. Patel SG, et al, Gastroenterology 2022; 162(1): 285-99. loannou S, et al, BMC Cancer 2021; 21(1): 966. Bretthauer M, et al, N Engl J Med 2022; 387(17): 1547-56. Shaukat A, et al, Nat Rev Gastroenterol Hepatol 2022; 19(8): 521-31. Coronado GD, et al, Gut 2024; 73(4): 622-8. Imperiale TF, et al, N Engl J Med 2024; 390(11): 984-93. Chung DC, Gray DM, 2nd, Singh H, et al. A Cell-free DNA Blood-Based Test for Colorectal Cancer Screening. N Engl J Med 2024; 390(11): 973-83. Bamell EK, et al, JAMA 2023; 330(18): 1760-8. Nakamura K, Hernandez G, Sharma GG, et al. A Liquid Biopsy Signature for the Detection of Patients With Early-Onset Colorectal Cancer. Gastroenterology 2022; 163(5): 1242-51 e2. Yu W, Hurley J, Roberts D, et al. Exosome-based liquid biopsies in cancer: opportunities and challenges. Ann Oncol 2021; 32(4): 466-77. Miyazaki K, WadaY, Okuno K, et al. An exosome-based liquid biopsy signature for preoperative identification of lymph node metastasis in patients with pathological high-risk T1 colorectal cancer. Mol Cancer 2023; 22(1): 2. Nakamura K. et al. Gastroenterology’ 2022; 163(5): 1252-66 e2. Shaukat A, et al, Gastrointest Endosc 2020; 92(5): 997-1015 el. Imperiale TF. et al. Cancer Prev Res (Phila) 2021; 14(4): 489-96. Horvath S, et al, Nat Rev Genet 2018; 19(6): 371-84. Cercek A, et al, J Natl Cancer Inst 2021; 113(12): 1683-92. Castelo M, et al, Gastroenterology’ 2023; 164(7): 1152-64. Chen X, et al, EClinicalMedicine 2022; 49: 101460. Dharwadkar P, et al, Clin Gastroenterol Hepatol 2021; 19(1): 192-4 e3.

Claims

CLAIMSWhat is claimed is:

1. A method of detecting RNA in a patient with colorectal 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 exosomal miR-32-5p, cell-free miR-625-3p, or a combination thereof.

2. A method of detecting RNA in a patient with high grade dysplasia in the colon, 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 exosomal miR-32-5p, cell-free miR-625-3p, or a combination thereof.

3. A method of treating colorectal cancer in a patient in need thereof or high grade dysplasia in the colon 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 the patient; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, or a combination 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 colon of the patient, or a combination of two or more thereof.

4. A method of treating colorectal cancer in a patient in need thereof or high grade dysplasia in the colon 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 colon 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 a RNA; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, or a combination thereof.

5. A method of treating colorectal cancer in a patient in need thereof or high grade dysplasia in the colon in a patient in need thereof, the method comprising:(i) selecting a patient having a diagnosis of colorectal cancer or high grade dysplasia in the colon based on a colorectal cancer risk score or an elevated expressionlevel, relative to a control, of RNA in a biological sample obtained from the patient, wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p. or a combination 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 colon of the patient, or a combination of two or more thereof.

6. A method of diagnosing a patient with colorectal cancer, the method comprising:(i) detecting the expression level of RNA in a biological sample obtained from the patient; and(ii) diagnosing the patient as having colorectal cancer when the biological sample has an elevated expression level, relative to a control, of the RNA; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, or a combination thereof.

7. A method of diagnosing a patient with high grade dysplasia in the colon, the method comprising:(i) detecting the expression level of RNA in a biological sample obtained from the patient; and(ii) diagnosing the patient as having high grade dysplasia in the colon when the biological sample has an elevated expression level, relative to a control, of the RNA; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, or a combination thereof.

8. A method of monitoring treatment in a patient having colorectal cancer, monitoring risk for developing colorectal cancer in a patient, monitoring treatment in a patient having high grade dysplasia in the colon, or monitoring risk for developing high grade dysplasia in the colon in a patient, the method comprising:(i) detecting the expression level of RNA in a biological sample obtained from the patient at a first time point;(ii) detecting the expression level of the RNA in a biological sample obtained from the patient at a second time point, wherein the second time point is later than the first time point; and(iii) comparing the expression level of the RNA at the second time point to theexpression level of the RNA at the first time point, thereby monitoring treatment or monitoring risk; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, or a combination thereof.

9. The method of claim 1. wherein the RNA comprises exosomal miR-32-5p and cell-free miR-625-3p.

10. The method of claim 1, wherein the RNA comprises exosomal miR-32-5p and cell-free miR-625-3p, and wherein the RNA further comprises exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, cell-free miR-30d-5p, or a combination of two or more thereof.

11. The method of claim 10, wherein the RNA comprises exosomal miR-32-5p, cell- free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, and cell-free miR-30d-5p.

12. The method of claim 10, wherein the RNA consists of exosomal miR-32-5p, cell- free miR-625-3p, exosomal miR-486-3p, exosomal miR-550a-3-5p, exosomal miR-625-3p, and cell-free miR-30d-5p.

13. The method of claim 1. wherein the RNA comprises exosomal miR-32-5p, and wherein the RNA further comprises exosomal miR-486-3p. exosomal miR-550a-3-5p. exosomal miR-625-3p.

14. The method of claim 13, wherein the RNA consists of exosomal miR-32-5p, exosomal miR-486-3p, exosomal miR-550a-3-5p. and exosomal miR-625-3p15. The method of claim 1, wherein the RNA comprises the cell-free miR-625-3p, and wherein the RNA further comprises cell-free miR-30d-5p.

16. The method of claim 1, wherein the RNA consists of cell-free miR-625-3p and cell-free miR-30d-5p.

17. The method of claim 1. wherein the method further comprises detecting an elevated expression level, relative to a control, of one or more RNA in the biological sample obtained from the patient, wherein the one or more RNA are selected from the group consisting of:(a) cell-free miR-4659b-3p, cell-free miR-296-5p. cell-free miR-4685-3p, cell-free miR-550a-5p, cell-free miR-4446-3p, cell-free miR-432-5p. cell-free miR-151a- 3p, cell-free miR-199a-3p, cell-free miR-146a-5p, cell-free miR-24-3p, cell-free miR-223-3p, exosomal miR-556-3p, exosomal miR-2355-5p, exosomal miR- 181a-3p, exosomal miR-3120-3p, exosomal miR-7-l-3p, exosomal miR-99b-3p, and exosomal miR-425-3p;(b) cell-free miR-223-3p, cell-free miR-24-3p, cell-free miR-15 la-3p, cell-free miR- 199a-3p, exosomal 425-3p, exosomal 99b-3p, exosomal miR-191-3p, exosomal miR-7-l-3p, miR-181a-3p, and exosomal miR-2355-5p;(c) cell-free miR-223-3p, cell-free miR-24-3p, cell-free miR-151a-3p, cell-free miR- 199a-3p, cell-free miR-432-5p. cell-free miR-4446-3p, cell-free miR-191-3p. cell-free miR-550a-5p, cell-free miR-146a-3p, cell-free miR-4685-3p, exosomal 425-3p, exosomal 99b-3p, exosomal miR-191-3p, exosomal miR-7-l-3p, miR- 181a-3p, exosomal miR-2355-5p, and miR-556-3p;(d) cell-free miR-223-3p, cell-free miR-24-3p, cell-free miR-151a-3p, cell-free miR- 199a-3p, cell-free miR-432-5p, cell-free miR-191-3p, cell-free miR-550a-5p, cell-free miR-146a-3p, and cell-free miR-4685-3p;(e) exosomal miR-425-3p, exosomal miR-99b-3p, exosomal miR-191-3p, exosomal miR-7-l-3p, exosomal miR-181a-3p, exosomal miR-2355-5p. and exosomal miR-556-3p;(f) cell-free miR-24-3p, cell-free miR-199a-3p, cell-free miR-151a-3p, cell-free miR-223-3p, cell-free miR-223-3p, cell-free miR-625-3p, cell-free miR-191-3p, cell-free miR-432-5p, and cell-free miR-4446-3p;(g) exosomal miR-7-l-3p, exosomal miR-425-3p, exosomal miR-99b-3p. exosomal miR-181a-3p, exosomal miR-191-3p, exosomal miR-556-3p, and exosomal miR- 2355-5p;(h) cell-free miR-223-3p, cell-free miR-24-3p, cell-free miR-151a-3p, and cell-free miR-199a-3p; or(i) exosomal miR-425-3p, exosomal miR-99b-3p, exosomal miR-191-3p, exosomal miR-7-l-3p, exosomal miR-181a-3p, and exosomal miR-556-3p.

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

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

20. The method of claim 1, wherein the control is a healthy patient.

21. A kit comprising reagents capable of detecting an expression level of RNA from a biological sample; wherein the RNA comprises exosomal miR-32-5p, cell-free miR-625-3p, exosomal miR-486-3p. exosomal miR-550a-3-5p, exosomal miR-625-3p, and cell-free miR- 30d-5p.

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