Combining Expression Levels of KRT19 and COL1A2 to Generate a Score for Screening and Diagnosing Cancer

Abstract

The present invention provides a combination of epithelial and stromal biomarkers that are useful for determining whether a subject has a high or low probability of developing a tumor.In particular, the present invention provides a method for screening, detecting or monitoring tumors in a subject, the method comprising combining the expression levels of KRT19 and COL1A2 to generate a score, and determining whether a subject has a high or low probability of developing a tumor based on the score.

Description

[Technical Field]

[0001] This application claims priority from EP22383195.9, filed December 07, 2022, the contents and elements of which are incorporated herein by reference for all purposes.

[0002] FIELD OF THE INVENTION The present invention relates to methods for screening and diagnosing cancer, monitoring its clinical progression or response to treatment, and detecting recurrence based on a combination of tumor epithelial and tumor stromal biomarkers. [Background technology]

[0003] Background of the Invention Cancer is a major public health concern worldwide. According to the GLOBOCAN database, run by the International Agency for Research on Cancer, part of the World Health Organization, approximately 20 million cancer cases were diagnosed worldwide in 2020, and nearly 10 million cancer deaths occurred that same year.

[0004] The most common diagnostic method for diagnosing cancer when symptoms or other early markers are present is a tissue biopsy, which involves removing a small amount of body tissue for analysis.

[0005] Alternatively, liquid biopsies can be performed. These are performed on bodily fluids and are therefore less invasive than tissue biopsies. Currently, most liquid biopsy studies in cancer involve the collection of circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and extracellular vesicles (EVs) present in human plasma. 8 This is being carried out.

[0006] EVs are classified into two types: ectosomes and exosomes. Ectosomes [microvesicles (MVs) and oncosomes] are 100–1000 nm in size and are derived directly from budding plasma membranes. 12Exosomes are small vesicles formed during the endocytic pathway, ranging in size from 30 to 150 nm. 12 EVs can have different functions depending on their cell of origin; EVs have been intensively studied as facilitators of immune responses and as antigen presentation agents. 11 , programmed cell death, angiogenesis, inflammation, coagulation 13、14 In cancer, EVs promote tumorigenesis and progression, and are involved in the formation of premetastatic niches, tumor angiogenesis, and tumor immunosuppression. 15、16 .

[0007] Transport of tumor-derived extracellular vesicles (TD-EVs) has been observed in solid tumors 17 EVs are expressed in a mixture that depends on the microenvironment of the donor cells. 14 transport complex bioactive cargoes such as DNA, RNA, microRNA, and long non-coding RNA (lncRNA) 18 .

[0008] There are known biomarkers that predict cancer. For example, KRT19 is known as a marker for detecting tumor cell dissemination in breast, lung, and colon cancer. 19~21。 KRT19 is a low-molecular-weight type I keratin. It is part of the cytoskeleton of many epithelial cells in various organs and is highly expressed in epithelial tumors. Therefore, it is a biomarker for detecting micro- and macrometastases in several malignancies. 20 KRT19 was also detected in colon tumor-derived exosomes by RT-PCR. 22 , and has been detected in circulating breast tumor cells 23 .

[0009] The tumor microenvironment (TME) is formed by tumor stromal cells (stromal fibroblasts, endothelial cells, and immune cells) and non-cellular components of the extracellular matrix (ECM), such as collagen, fibronectin, and laminin. 24 Non-malignant cells present in the TME are known to be key to tumor growth and progression, promoting tumorigenesis at all stages of cancer development and metastasis.25。 ECM secreted by stromal cells also plays an important role in tumor progression, metastasis, and treatment resistance. 26 Therefore, TME components may reflect tumor status and therefore may be useful as diagnostic and prognostic biomarkers for cancer. Type I collagen (COL1A2) is an important component of the ECM, and its expression is increased in several cancer types, including pancreatic, colorectal, breast, and lung cancer. 27~29。 COL1A2 gene expression has been used to characterize tumor progression in vitro, but its clinical utility as a biomarker in human samples has been largely unexplored, with the exception of a few studies related to gastric and bladder cancer. 30~34 .

[0010] Although biomarkers for cancer detection and diagnosis exist, many lack the sensitivity and specificity required for accurate diagnosis. When biomarkers are detected in both healthy and malignant tissue, background noise can often drown out the signal from the malignant tissue. 42 Therefore, there is a need for cancer biomarkers with enhanced sensitivity. Summary of the Invention

[0011] Summary of the Invention We have found that a combination of epithelial cell biomarkers and stromal cell biomarkers is useful for cancer screening, detection, and monitoring. Specifically, the combination of the epithelial tumor cell marker KRT19 and the tumor stromal cell marker COL1A2 is useful for distinguishing cancer patients from healthy donors. The combination of both biomarkers achieves much greater sensitivity and specificity than either biomarker alone.

[0012] Accordingly, in a first aspect, the present invention provides a method of screening, detecting or monitoring a tumor in a subject, the method comprising: a) measuring the expression levels of KRT19 and COL1A2 in a sample obtained from a subject; and b) Combining the expression levels of KRT19 and COL1A2 to generate a score c) determining whether the subject has a high or low probability of having a tumor based on the score; Includes.

[0013] In some embodiments, the subject has or is at risk of developing cancer, e.g., the subject may have symptoms of cancer or may have other risk factors.

[0014] In some embodiments, the subject is at risk of developing a recurrence of the cancer, e.g., the subject may have symptoms of the cancer or may have other risk factors.

[0015] In some embodiments, the subject is a mammal. In preferred embodiments, the subject is a human.

[0016] In some embodiments, the sample obtained from the subject is a tissue sample.

[0017] In some embodiments, the sample obtained from the subject is a biological fluid (also referred to as a "liquid biopsy"). The biological fluid may be blood, serum, plasma, nipple aspirate, or urine. Such samples can be collected in a less invasive manner than tumor tissue biopsies, making the liquid biopsy approach more attractive to patients.

[0018] In some embodiments, the expression levels of KRT19 and COL1A2 are measured from plasma obtained from the subject.

[0019] The expression levels of KRT19 and COL1A2 can be measured from whole plasma and / or from any specific plasma-derived component. Non-limiting examples of plasma-derived components include exosomes, ectosomes, extracellular vesicles, platelets, microvesicles, circulating tumor cells, and circulating tumor DNA. Thus, in some embodiments, the expression levels of KRT19 and COL1A2 are measured from exosomes, ectosomes, extracellular vesicles, platelets, microvesicles, circulating tumor cells, and / or circulating tumor DNA derived from plasma obtained from a subject.

[0020] In some embodiments, the expression levels of KRT19 and COL1A2 are measured from plasma-derived exosomes obtained from the subject.

[0021] In some embodiments, the tumor is cancerous and the cancer is a solid cancer. In some preferred embodiments, the cancer is selected from prostate cancer, pancreatic cancer, bladder cancer, renal cancer, melanoma, ovarian cancer, colorectal cancer, lung cancer, prostate cancer, breast cancer, penile cancer, and leiomyosarcoma.

[0022] In some embodiments, the methods involve measuring the cDNA, RNA, or protein expression levels of KRT19 and COL1A2.

[0023] In some embodiments, the expression levels of KRT19 and COL1A2 are measured in separate assays or in a multiplex assay.

[0024] In some embodiments, the RNA expression levels of KRT19 and COL1A2 are measured using RT-PCR.

[0025] In some embodiments, high expression levels of KRT19 and COL1A2 positively correlate with high tumor likelihood.

[0026] In some embodiments, combining the expression levels of KRT19 and COL1A2 to generate a score comprises using a logistic regression model.

[0027] In some embodiments, the score is a probabilistic score.

[0028] In some embodiments, combining the expression levels of KRT19 and COL1A2 to generate a score comprises: a) Assigning weight values ​​to KRT19 expression to provide weighted KRT29 expression levels b) assigning weight values ​​to COL1A2 expression to provide weighted COL1A2 expression levels; c) Calculating the sum of the weighted KRT19 and COL1A2 expression levels d) Using the sum resulting from step c) to generate a probabilistic score. Includes.

[0029] In some embodiments, combining the expression levels of KRT19 and COL1A2 to generate a probabilistic score comprises using the following formula:

[0030]

number

[0031] β is the intercept weight, β is a vector of weights for each of the k variables, and x is a vector of variables associated with the sample, including KRT19 expression levels, COL1A2 expression levels, and optionally variables derived therefrom.

[0032] In some embodiments, the weights of the KRT19 expression level variable and the COL1A2 expression level variable have the same sign. In preferred embodiments, the weights of the KRT19 expression level variable and the COL1A2 expression level variable both have positive coefficients.

[0033] In some embodiments, determining whether a subject has a high or low likelihood of having a tumor based on the score comprises comparing the score to a reference value, which in some embodiments is obtained from a control sample or is a predetermined threshold value.

[0034] In some embodiments, determining whether a subject has a high or low probability of having a tumor based on the score includes comparing the score to one or more predetermined thresholds, and determining that the subject has a high probability of having a tumor if the score is above a first threshold, or determining that the subject has a low probability of having a tumor if the score is below a second predetermined threshold, where optionally the first and second predetermined thresholds are the same.

[0035] When the method is used to monitor a tumor, the score can be used to determine whether the tumor has progressed, regressed, or remained steady compared to a previous time point. Thus, in one embodiment, a method of monitoring a tumor is provided, the method comprising: a). Measuring the expression levels of KRT19 and COL1A2 in a sample obtained from the subject at an initial time point; b). Combining the expression levels of KRT19 and COL1A2 obtained at the first time point to form a baseline value; c). measuring the expression levels of KRT19 and COL1A2 in a sample obtained from the subject at a second time point; d). Combining the expression levels of KRT19 and COL1A2 obtained at the second time point to generate a score; e) Based on the score, determine whether the tumor is likely to have progressed, regressed, or remain in a steady state compared to the baseline value. Includes.

[0036] In some embodiments, the subject may be undergoing treatment for cancer, and thus the method can monitor the progression or regression of cancer in a subject previously diagnosed with cancer in response to treatment.

[0037] In some embodiments, if the score indicates that tumor progression is likely, it suggests that the treatment or dose of treatment should be modified.

[0038] In some embodiments, the treatment comprises chemotherapy, radiation therapy, treatment with a biologic agent such as an antibody, antibody conjugate, protein or nucleotide, brachytherapy, or surgery.

[0039] In a second aspect, there is provided a method comprising assessing the efficacy of a treatment for cancer in a subject, the method comprising: a). Measuring the expression levels of KRT19 and COL1A2 in a sample obtained from the subject at an initial time point; b). Combining the expression levels of KRT19 and COL1A2 obtained at the first time point to form a baseline value; c). measuring the expression levels of KRT19 and COL1A2 in a sample obtained from the subject at a second time point; d). Combining the expression levels of KRT19 and COL1A2 obtained at the second time point to generate a score; e) Based on the score, determine whether the tumor is likely to have progressed, regressed, or remain in a steady state compared to the baseline value. Includes. If the tumor progresses or remains steady, it indicates that the treatment is not effective, whereas if the tumor regresses, it indicates that the treatment is effective.

[0040] In some embodiments, the treatment comprises chemotherapy, radiation therapy, treatment with a biologic agent such as an antibody, antibody conjugate, protein or nucleotide, brachytherapy, or surgery.

[0041] The present inventors have found that when KRT19 and COL1A2 RNA expression levels are measured from plasma-derived exosomes from a subject, this provides a particularly powerful tool for predicting the likelihood of tumors in a subject. Thus, in some embodiments, provided herein are methods for screening, detecting, or monitoring tumors, the methods comprising: a) Measuring the expression levels of KRT19 and COL1A2 in plasma-derived exosome samples obtained from subjects using RT-PCR; b) Combining the expression levels of KRT19 and COL1A2, including using the following algorithm:

[0042]

number

[0043] c) determining whether the subject has a high or low probability of having a tumor based on the score; Includes.

[0044] In a third aspect, provided is a method of providing a tool for characterizing a test sample obtained from a subject, the method comprising: a). Providing expression data of KRT19 and COL1A2 for a plurality of training samples associated with known tumor conditions, thereby forming a labeled training dataset; b) training a machine learning model using the labeled training dataset to learn parameters for at least the variable KRT19 expression level and the variable COL1A2 expression level, wherein the trained machine learning model provides as an output a probabilistic score for predicting whether the test sample is from a tumor-bearing or tumor-free subject based on input data including the KRT19 expression level and the COL1A2 expression level of the test sample. Includes.

[0045] The method of providing a tool for characterising a test sample obtained from a subject as described above may have any of the (computer-implemented) features described in relation to an embodiment of either the first or second aspect of the invention.

[0046] In a fourth aspect, provided is a computer-implemented method for screening, detecting, or monitoring a tumor in a subject, the method comprising: a). Providing KRT19 and COL1A2 expression data from subjects; b). Combining the expression levels of KRT19 and COL1A2 to generate a score; d) classifying the subject as having a high or low probability of having a tumor based on the score. In some embodiments, the computer-implemented method uses a machine learning model trained with a labeled training dataset to learn parameters for at least the variables KRT19 expression level and COL1A2 expression level, and the trained machine learning model provides as output a probabilistic score for predicting whether a test sample is obtained from a tumor-bearing or tumor-free subject based on input data including the KRT19 expression level and COL1A2 expression level of the test sample.

[0047] In a fifth aspect, there is provided a system comprising: a). processor; and b) a computer-readable medium comprising instructions that, when executed by a processor, cause the processor to perform the computer-implemented method of the fourth aspect of the present invention. Includes.

[0048] In a sixth aspect, provided is a non-transitory computer-readable medium or media comprising instructions that, when executed by at least one processor, cause the at least one processor to perform the computer-implemented method of the fourth aspect of the present invention.

[0049] The computer-implemented method of the fourth aspect of the invention, the system of the fifth aspect of the invention and the non-transitory computer-readable medium or media of the sixth aspect of the invention may have any of the computer-implemented features described in relation to the embodiments of either the first or second aspects of the invention.

[0050] In a seventh aspect, the present invention provides a kit for detecting a tumor and / or monitoring the progression or regression of a tumor in a subject, the kit comprising reagents and specific antibodies, primers or probes for detecting and / or quantifying the expression of KRT19 and COL1A2.

[0051] In an eighth aspect, the present invention relates to the use of a kit comprising reagents for detecting tumors and / or monitoring their progression or regression and specific antibodies, primers or probes for the detection and / or quantification of KRT19 and COL1A2 protein or gene expression.

[0052] In a ninth aspect, the present invention relates to the use of a combination of protein, cDNA or RNA levels of the tumor epithelial biomarker KRT19 and the tumor stromal biomarker COL1A2 as markers for detecting tumors and monitoring their progression or regression.

[0053] In a tenth aspect, the present invention relates to the use of a particular algorithm that combines the expression levels of the tumor epithelial biomarker KRT19 and the tumor stromal biomarker COL1A2 as a tool for detecting tumors and monitoring their progression or remission.

[0054] In an eleventh aspect, the present invention relates to specific procedures and reagents for purifying KRT19 and COL1A2 RNA from plasma or tumor-derived exosomes present in plasma.

[0055] The present invention includes combinations of the described aspects and features except where such combinations are expressly not permitted or explicitly avoided. [Brief explanation of the drawings]

[0056] BRIEF DESCRIPTION OF THE DRAWINGS Embodiments and experiments illustrating the principles of the present invention will now be discussed with reference to the accompanying figures:

[0057] [Figure 1] STEM micrographs of plasma extracellular vesicles. A. Extracellular vesicles from an asymptomatic control (AC). A1. TEM micrograph of mixed microvesicles and exosome structures at 25.00K magnification. Scale bar: 1 μm. A2. TEM micrograph of spheroidal vesicles at 100.00K magnification. Scale bar: 200 nm. A3. SEM micrograph of spheroidal vesicles at 100.00K magnification. Scale bar: 200 nm. B. Plasma extracellular vesicles from a cancer patient (CP). B1. TEM micrograph of mixed microvesicles and exosome structures at 25.00K magnification. Scale bar: 1 μm. B2. TEM micrograph of spheroidal vesicles at 100.00K magnification. Scale bar: 200 nm. B3. SEM micrograph of spheroidal vesicles at 100.00K magnification. Scale bar: 200 nm. Images are representative of at least three independent experiments. [Figure 2]NTA of plasma extracellular vesicles. A. Concentration and size distribution of extracellular vesicles in plasma from patients with AC (left) and CP (right). B. Differences in concentration (B.1) and size distribution (B.2 and B.3) between AC and CP plasma samples. No differences were observed between AC and CP plasma extracellular vesicle samples. Data are expressed as mean ± SD from three independent experiments. Ns indicates no statistically significant difference when comparing CP and control groups. [Figure 3] ΔCt values ​​for KRT19, CEA, COL11A1, and COL1A2 in AC and CP individuals. A. Boxplot representation showing the distribution of ΔCt values ​​for each gene. The median ΔCt value is represented by the black line in the boxplot, dividing the ΔCt values ​​into lower and upper interquartile ranges. The whiskers represent the upper and lower data ranges for AC and CP samples. The data showed significant differences in ΔCt values ​​between the two patient cohorts. B. Comparison of ΔCt values ​​for KRT19, CEA, COL11A1, and COL1A2 in AC and PC samples. T-tests revealed significant differences in expression between the CP and AC groups for all biomarkers (KRT19, p<0.0001; CEA, p=0.0001; COL11A1, p<0.0001; COL1A2, p<0.0001). Data are presented as mean ± SE. Asterisks indicate significant differences between the AC and PC groups for each gene (****p-value<0.0001; ***p-value<0.001; **p-value<0.01; *p-value<0.05). [Figure 4]The graph shows the relative fold expression of epithelial and stromal biomarkers (KRT19, CEA, COL11A1, and COL1A2) between the AC and CP patient cohorts, calculated using the 2-ΔΔCt method. The data showed higher expression levels of KRT19, CEA, COL11A1, and COL1A2 in CP individuals. The Mann-Whitney U test showed significant differences between the groups for all biomarkers (KRT19, p<0.0001; CEA, p=0.0003; COL11A1, p<0.0001; COL1A2, p=0.0001). Data are expressed as mean ± SE. Asterisks indicate significant differences between the AC and PC groups for each gene (****p<0.0001; ***p<0.001; **p<0.01; *p<0.05). [Figure 5] ROC curves for KRT19, CEA, COL11A1, and COL1A2. [Figure 6] Amplification results of individual reactions for ATCB. Different amounts of starting material were used to decipher the detection limit for ACTB. [Figure 7] Amplification results of individual reactions for KRT19. Different amounts of starting material were used to decipher the detection limit for KRT19. [Figure 8] Amplification results of individual reactions for COL11A1. Different amounts of starting material were used to decipher the detection limit for COL11A1. [Figure 9] Amplification results for ACTB (purple), KRT19 (pink), and COL11A1 (blue) in a 3-plex assay. Different amounts of starting material were used to decipher the detection limits for all three of ACTB, KRT19, and COL11A1 in the same assay. [Figure 10] Amplification results of individual reactions for COL1A2. Different amounts of starting material were used to decipher the detection limit for COL1A2. [Figure 11] Amplification results for ACTB (purple), KRT19 (pink), and COL1A2 (green) in a 3-plex assay. Different amounts of starting material were used to decipher the detection limits for all three of ACTB, KRT19, and COL1A2 in the same assay. [Figure 12] Boxplot showing the distribution of delta Ct values ​​for tumor biomarkers (COL1A2 and KRT19) in AC and CP. The y-axis indicates delta Ct values, and the x-axis indicates the analysis group. The median (black line within the box), interquartile range (width of the box representing the middle 50% of the data), and total data range (whiskers) are shown. Tumor biomarker expression was significantly higher in cancer patients. ****p<0.0001 for COL1A2, **p=0.0031 for KRT19. B Bar graphs show the mRNA fold changes of COL1A2 and KRT19 in two independent groups: AC and CP. The LHS graphs represent the mean ± SD of COL1A2 for AC (1.40±1.25) and CP (9.68±15.07) [AC (n=12) and CP (n=30)]. The RHS graph shows the mean ± SD of AC (1.21 ± 0.78) and CP (26.61 ± 45.46) for KRT19 [AC (n = 13) and CP (n = 38)]. Significance analysis was performed using the Mann-Whitney U-test. COL1A2 and KRT19 showed significant differences, ****p < 0.0001 and **p = 0.0031, respectively. [Figure 13] Fold change of COL1A2 / KRT19 mRNA in the different tumor types included in this study. Mann-Whitney U-test comparing the mean fold change of controls in two independent groups: AC vs. cancer patient groups (CRC (A and B), RC (C and D), BC (E and F), PC (G and H)). Graphs represent mean ± SD. ****p<0.0001, ***p<0.001, **p<0.01, *p<0.05. [Figure 14] A. ROC curves for KRT19, B. COL1A2, C. KRT19 and COL1A2 combined. D. Overlay of the ROC curves for A, B, and C. DETAILED DESCRIPTION OF THE INVENTION

[0058] Detailed Description of the Invention The features disclosed in the foregoing description, or the following claims, or the accompanying drawings, whether expressed as appropriate in their specific form or in terms of means for performing a disclosed function or a method or process for obtaining a disclosed result, may be utilized to realize the invention in various of its forms, either separately or in any combination of such features.

[0059] While the present invention has been described in conjunction with the exemplary embodiments set forth above, many equivalent modifications and variations will be apparent to those skilled in the art given this disclosure. Accordingly, the exemplary embodiments of the invention set forth above are intended to be illustrative, not limiting. Various changes can be made to the described embodiments without departing from the spirit and scope of the invention.

[0060] For the avoidance of doubt, any theoretical explanations provided herein are provided for the purpose of enhancing the understanding of the reader, and the inventors do not wish to be bound by any of these theoretical explanations.

[0061] Any section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

[0062] Throughout this specification, including the claims which follow, unless the context requires otherwise, the words "comprise" and "include", and variations such as "comprises", "comprising", and "including", will be understood to mean the inclusion of a stated integer or step or group of integers or steps, but not to the exclusion of any other integer or step or group of integers or steps.

[0063] It should be noted that, as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Ranges may be expressed herein as from "about" one particular value and / or to "about" another particular value. When such a range is expressed, another embodiment includes from the one particular value and / or to the other particular value. Similarly, when values ​​are expressed as approximations, it will be understood that the particular value forms another embodiment by use of the antecedent "about." The term "about" in reference to numerical values ​​is arbitrary and means, for example, ±10%.

[0064] definition To facilitate the understanding of this patent application, the meaning of some terms and expressions used in connection with the present invention will be explained below.

[0065] The terms "cancer" or "carcinoma" refer to diseases characterized by unregulated growth of abnormal cells that can invade nearby tissues and spread to distant organs.

[0066] The term "specificity" refers to the ability to detect true negatives. 100% specificity means there are no false positives (subjects who do not have the disease but get a positive result).

[0067] The term "clinical progression" or "progression" generally refers to the progression of a patient's clinical condition throughout the course of disease treatment. More specifically, as used herein, the term refers to a tumor that grows in size and / or progresses to a more advanced stage of disease, decreases in cellular differentiation, or becomes more invasive.

[0068] The term "COL1A2" refers to the type I collagen α2 chain gene, ID1278, also known as OI4, EDSCV, and EDSARTH2, which encodes the COL1A2 protein, also known as collagen alpha-2(I) chain. Specifically, this gene encodes the pro-α2 chain of type I collagen, whose triple helix contains two α1 chains and one α2 chain. Type I is a fibrillar collagen present in most connective tissues, being abundant in bone, cornea, dermis, and tendons. Mutations in this gene are associated with osteogenesis imperfecta types I-IV, Ehlers-Danlos syndrome type VIIB, recessive Ehlers-Danlos syndrome classic type, idiopathic osteoporosis, and atypical Marfan syndrome. However, symptoms associated with mutations in this gene tend to be less severe than those of type I collagen α1 chain (COL1A1), reflecting the distinct role of the α2 chain in matrix integrity. Three transcripts have been identified for this gene, resulting from the use of alternating polyadenylation signals.

[0069] The term "exosome" refers to small, single-membrane secretory organelles, ~30 to ~200 nm in diameter, that have the same topology as cells and are enriched with selected proteins, lipids, nucleic acids, and glycoconjugates. Exosomes contain an array of membrane-bound, higher-order oligomeric protein complexes, exhibit remarkable molecular diversity, and are generated by budding at both the plasma membrane and the endosomal membrane. 35 .

[0070] The term "gene" refers to a molecular chain of deoxyribonucleotides that codes for a protein.

[0071] The term "KRT19" refers to the gene "keratin 19," ID3880, which encodes the cytokeratin type I cytoskeletal 19 protein, also known as cytokeratin 19, keratin 19, K19, or CK19. The protein encoded by this gene is a member of the keratin family. Keratins are intermediate filament proteins responsible for the structural integrity of epithelial cells and are classified as cytokeratins and hair keratins. Type I cytokeratins consist of acidic proteins arranged in pairs of heterotypic keratin chains and clustered on chromosome 17q12-q21.

[0072] Unlike related family members, this smallest known acidic cytokeratin is not paired with basic cytokeratins in epithelial cells. It is specifically expressed in the periderm, a transient superficial layer that envelops the developing epidermis. It is expressed in a defined zone of basal keratinocytes in the deep outer root sheath of hair follicles, and is also observed (at the protein level) in sweat gland cells, mammary ductal and secretory cells, bile ducts, gastrointestinal tract, bladder urothelium, oral epithelium, esophagus, and uterine cervix epithelium. It is also expressed in defined regions of epithelial basal cells, nipple epidermis, and hair follicles, in a subset of vascular mural cells in both human umbilical vein and artery, and in muscle fibers where it accumulates in the costamere of the sarcoplasm of umbilical vascular smooth muscle, a structure containing dystrophin and spectrin.

[0073] As used herein, the term "probability" or "likelihood" measures the frequency of a certain result (or set of results) when a random experiment is performed under sufficiently stable conditions, with all possible results known. The probability or likelihood of a particular result being obtained may be "high", i.e., the frequency of the result being obtained is greater than 50%, or "low", i.e., the frequency of such a result being obtained is less than 50%. As described herein, the probability of detecting KTR19 and / or COL1A2 expression levels (e.g., RNA, cDNA, or protein expression levels) in a subject's sample is higher when the subject has a tumor compared to a healthy subject. In addition, the probability of detecting KTR19 and / or COL1A2 levels (e.g., RNA, cDNA, or protein expression levels) is higher when the subject has an ongoing tumor rather than a remission state. As can be understood by those skilled in the art, the probability does not need to be 100% for all subjects evaluated, although it should preferably be so. However, in the context of the present invention, the term requires that a statistically significant portion of subjects who are at risk for having a tumor for some reason (risk factors, suspicious results on other tests, symptoms, or previous tumors that may recur) or who have an ongoing tumor can be identified as having a high probability of achieving a particular outcome. Those skilled in the art can easily determine whether an event is statistically significant using various known statistical evaluation tools, such as Student's t-test, Mann-Whitney test, determining confidence intervals, and determining p-values. Additional information about these statistical tools can be found in Dowdy and Wearden, *Statistics for Research*, John Wiley & Sons, New York, 1983. Preferred confidence intervals are at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, or at least 95%. P-values ​​are preferably 0.1, 0.05, 0.02, 0.01, or less.

[0074] The term "recurrence" generally refers to the recurrence of a lesion after a relatively long period of time in which the disease has been absent, including more than 1 month, more than 2 months, more than 3 months, more than 4 months, more than 5 months, more than 6 months, more than 7 months, more than 8 months, more than 9 months, more than 10 months, more than 11 months, more than 1 year, more than 2 years, more than 5 years, or more than 10 years.

[0075] As used herein, the term "risk" refers to the relative risk or probability that a subject has a tumor at any location. A subject may be considered at risk based on a variety of factors: clinically known risk factors (age, sex, family history of cancer, exposure to cancerous substances), symptoms, suspicious results in some tests (screening tests, imaging probes), or a clinical history of previous tumors (bladder, colon, etc.) with a high recurrence rate.

[0076] The term "sensitivity" refers to the detection of true positives (e.g., a positive diagnosis of a pathology when the patient has said pathology); 100% sensitivity means no false negatives (negative diagnoses in patients with said pathology).

[0077] "Subject" or "individual" refers to a member of a mammalian species, including, but not limited to, domestic animals, primates, and humans; subjects are preferably human beings, male or female, of any age and race.

[0078] The term "greater" as applied to RNA expression levels, specifically KRT19 and COL1A2, means that the amount or concentration of said RNA in a particular sample from a subject with a tumor or a subject with a progressing tumor is higher than in another sample considered as a control sample or reference value; thus, the KRT19 and / or COL1A2 expression level in a sample from a subject with a tumor is higher (greater) than the KRT19 and / or COL2A1 RNA expression level in a control sample obtained from a population of control subjects with no history of cancer, or in a previous sample from the same subject.

[0079] In the context of the present invention, the expression levels (e.g., RNA, cDNA, or protein expression levels) of KRT19 and COL2A1 in a sample from a subject with or progressing to a tumor can be considered greater than (or higher than) the expression levels in a control sample (reference value) if the expression levels in the sample from the subject are increased, for example, by 3%, 5%, 10%, 25%, 50%, 100%, or more compared to the reference value. In other embodiments, the expression levels can be increased, for example, by at least 1.1-fold, 1.5-fold, 2-fold, 5-fold, 10-fold, 20-fold, 30-fold, 40-fold, 50-fold, 60-fold, 70-fold, 80-fold, 90-fold, 100-fold, or more compared to the reference value.

[0080] As used herein, the term "treatment" generally refers to the application of therapy to alleviate or eliminate a pathology or to reduce or eliminate one or more symptoms associated with said pathology. Said therapy may include surgical procedures, pharmacological treatments, radiotherapy treatments, etc.

[0081] The term "tumor" refers to any abnormal mass of tissue resulting from a neoplastic process, either benign (non-cancerous) or malignant (cancerous).

[0082] The term "RT-PCR multiplex assay" refers to an assay in which amplification of multiple specific nucleic acid sequences from an RNA template by PCR occurs within a single assay reaction using unique primers and / or probes for each specific sequence. RT-PCR assays include purification, amplification, detection, and identification steps.

[0083] The term "purification" in the context of exosomes refers to manual, semi-automated, and / or fully automated methods for purifying tumor-derived exosomes from a sample. In a preferred embodiment, the processing method utilizes the ExoRNeasy Maxi Kit (QIAGEN, Hilden, Germany).

[0084] The term "amplification" refers to the generation of replicate copies of specific nucleic acid sequences present in a genome by providing target-specific primer sequences in a PCR reaction containing the components necessary for enzymatic replication, including dideoxynucleotides, magnesium, reverse transcriptase, and amplicase enzymes in a suitable buffer. Amplification involves subjecting the reaction to repeated temperature cycling to allow denaturation of the nucleic acid template, followed by annealing and extension of the primers from the primer sequences to generate replicate copies of the template.

[0085] The term "detection" in the context of a PCR assay refers to a method for measuring the amplification of a target sequence from a genetic template. Detection is achieved by a PCR instrument used for amplification. In one embodiment, the instrument used for detection is a real-time PCR detection system manufactured by any manufacturer. In a more preferred embodiment, the instrument used for detection is a CFX96 C1000 Thermal Cycler (Bio-Rad Laboratories, USA).

[0086] The term "sample" refers to human sample types, including, but not limited to, tissue samples and fluid samples, such as, for example, serum, blood, saliva, semen, sweat, urine, tissue, synovial fluid, and / or tears. In a preferred embodiment, the term "sample" refers to human serum and / or blood. The term "sample" may be human serum.

[0087] The term "reverse transcription polymerase chain reaction" or "RT-PCR" refers to real-time amplification performed in the presence of a fluorescently labeled target-specific probe that binds a gene template between target-specific primers. An intact probe does not emit any fluorescent signal. Cleavage of the fluorescent dye-labeled probe during target amplification removes structural components of the probe or the presence of an attached quencher dye that quenches the fluorescent dye signal on the intact probe. Once the probe is cleaved, it releases a detectable fluorescent signal that is measured by the PCR instrument. The result is reported as the PCR cycle at which the fluorescent dye is detectable above a threshold. This PCR cycle can be read off a reference standard curve, yielding a quantifiable template copy number in the original sample.

[0088] Methods for screening, detecting and monitoring tumors The present invention is based on the discovery that a combination of epithelial cell markers and stromal cell markers can be used as a sensitive biomarker for tumor screening, detection, and monitoring. Specifically, the combination of the expression of the epithelial cell marker KRT19 and the stromal cell marker COL1A2 is a powerful tool for tumor screening, detection, and monitoring in subjects. The biomarker can be determined at the protein, RNA, or cDNA level, and can be present on tumor tissue, released in plasma, or contained in plasma-derived extracellular vesicles.

[0089] Thus, in a first aspect, the present invention provides a method of screening, detecting or monitoring a tumor in a subject, the method comprising: a) measuring the expression levels of KRT19 and COL1A2 in a sample obtained from a subject; and b) Combining the expression levels of KRT19 and COL1A2 to generate a score c) determining whether the subject is likely or unlikely to have a tumor based on the score; Includes.

[0090] Tumor monitoring Despite advances in cancer treatment, drug resistance remains a major factor limiting patient success 40 One strategy to overcome this problem is to monitor tumor progression during treatment, so that treatment failure can be detected early, allowing changes in treatment or adaptations to current treatment as soon as possible.

[0091] The potential of tumor-derived macrovesicles for therapeutic monitoring has been previously reported in several cancer types, such as breast cancer, glioblastoma, and colorectal cancer, as well as non-solid tumors. 41 .

[0092] Accordingly, the present invention provides a method for monitoring a tumor in a subject, the method comprising: a). Measuring the expression levels of KRT19 and COL1A2 in a sample obtained from a subject; b). Combining the expression levels of KRT19 and COL1A2 to generate a score; c) Based on the score, determine whether the tumor is likely to have progressed, regressed, or remain in a steady state compared to the baseline value. Includes.

[0093] When the method is used to monitor cancer, the reference value can be the score obtained from the same subject at a previous time point.When the method is used to monitor cancer, the score obtained at a later time point, compared with the reference value, can indicate tumor progression, regression or steady state.Steady state refers to tumor that is not progressing or regressing.Therefore, in one embodiment, a method for monitoring tumor is provided, the method comprising: a). Measuring the expression levels of KRT19 and COL1A2 in a sample obtained from the subject at an initial time point; b). Combining the expression levels of KRT19 and COL1A2 obtained at the first time point to form a baseline value; c). Measuring the expression levels of KRT19 and COL1A2 in a sample obtained from the subject at a second time point; d). Combining the expression levels of KRT19 and COL1A2 obtained at the second time point to generate a score; e) Based on the score, determine whether the tumor is likely to have progressed, regressed, or remain stable compared to the baseline value. Includes.

[0094] In one embodiment, when the method is used to monitor tumors, the subject may be undergoing treatment for cancer, and thus the method can monitor the progression or regression of cancer in a subject previously diagnosed with cancer in response to treatment.

[0095] In some embodiments, if tumor progression is determined to be likely based on the score, this suggests that the treatment, administration schedule, or dose of treatment should be modified. In some embodiments, if tumor progression is determined to be likely based on the score, this suggests that the treatment or dose of treatment should be modified.

[0096] In some embodiments, the treatment comprises chemotherapy, radiation therapy, treatment with a biological agent such as an antibody, antibody conjugate, protein or nucleotide, brachytherapy, or surgery. One skilled in the art will appreciate that the subject may receive any treatment used to treat solid tumors.

[0097] In some embodiments, the subject is not undergoing treatment for cancer. If the subject is not undergoing treatment, the methods of the present invention may indicate that the subject needs to begin treatment.

[0098] Treatment effectiveness The combined expression of KRT19 and COL1A2 can also be used to assess the status of a tumor in response to a particular treatment, thereby determining the effectiveness of the treatment.

[0099] In a second aspect, provided herein is a method of assessing the efficacy of a treatment for cancer in a subject, comprising: a). Measuring the expression levels of KRT19 and COL1A2 in a sample obtained from the subject at an initial time point; b). Combining the expression levels of KRT19 and COL1A2 obtained at the first time point to form a baseline value; c). measuring the expression levels of KRT19 and COL1A2 in a sample obtained from the subject at a second time point; d). combining the KRT19 and COL1A2 expression levels obtained at the second time point to generate a score; e) Based on the score, determine whether the tumor is likely to have progressed, regressed, or remain stable compared to the baseline value. Includes.

[0100] If the tumor progresses or remains in a steady state, it indicates that the treatment is ineffective. If the tumor regresses, it indicates that the treatment is effective. In some embodiments, the treatment includes chemotherapy, radiation therapy, treatment with a biological agent such as an antibody, antibody conjugate, protein, or nucleotide, brachytherapy, or surgery. Those skilled in the art will understand that the effectiveness of any therapy used to treat solid tumors can be measured using the methods of the present invention.

[0101] subject The method of the present invention can be used to screen and detect tumors in subjects who have not previously been diagnosed with cancer. It can also be used in subjects who have previously been diagnosed with cancer but have experienced a period of remission. In this way, the method of the present invention can detect the recurrence of cancer.

[0102] In some embodiments, the subject has or is at risk of developing cancer. For example, the subject may have symptoms of cancer or may have other risk factors. Risk factors include clinically known risk factors (age, sex, family history of cancer, exposure to cancerous substances), symptoms, suspicious results in some tests (screening tests, imaging probes), or a clinical history of previous tumors (bladder, colon, etc.) with a high recurrence rate.

[0103] In some embodiments, the subject is at risk of developing a recurrence of cancer. For example, the subject may have symptoms of cancer or may have other risk factors. Risk factors include clinically known risk factors (age, sex, family history of cancer, exposure to cancerous substances), symptoms, suspicious results in some tests (screening tests, imaging probes), or a clinical history of a previous tumor (bladder, colon, etc.) with a high recurrence rate. The subject may have previously been diagnosed with cancer and been treated and in remission, for example, the subject may be in complete remission.

[0104] In some embodiments, the subject is a mammal. The subject may be any member of a mammalian species, including, but not limited to, domestic animals, primates, and humans. In preferred embodiments, the subject is a human. The subject may be a male or female human of any age and race.

[0105] In some embodiments, the tumor is a cancer. In some embodiments, the cancer is a solid cancer. Examples of solid cancers include AIDS-related cancer, acoustic neoma, adenocystic carcinoma, adrenocortical carcinoma, primary myelofibrosis, alopecia, alopecia, alveolar soft part sarcoma, anal cancer, angiosarcoma, aplastic anemia, astrocytoma, ataxia telangiectasia, basal cell carcinoma (bcc), bladder cancer, bone cancer, intestinal cancer, brainstem glioma, brain and central nervous system cancer, breast cancer, central nervous system cancer, carcinoid cancer, cervical cancer, pediatric brain cancer, pediatric cancer, pediatric soft tissue sarcoma, chondrosarcoma, choriocarcinoma, colorectal cancer, cutaneous T-cell lymphoma, dermatofibrosarcoma protuberans, desmoplastic small round cell carcinoma, ductal carcinoma, endocrine carcinoma, and intrauterine cancer. Membrane cancer, ependymoma, esophageal cancer, Ewing's sarcoma, extrahepatic bile duct cancer, eye cancer, eye melanoma, retinoblastoma, fallopian tube cancer, Fanconi anemia, fibrosarcoma, gallbladder cancer, gastric cancer, gastrointestinal cancer, gastrointestinal carcinoid cancer, genitourinary cancer, germ cell cancer, gestational trophoblastic disease, glioma, gynecological cancer, hematologic malignancies, head and neck cancer, hepatocellular carcinoma, hereditary breast cancer, histiocytosis, Hodgkin's disease, human papillomavirus, hydatidiform mole, hypercalcemia, hypopharyngeal cancer, intraocular melanoma, islet T-cell carcinoma, Kaposi's sarcoma, renal cancer, Langerhans cell histiocytosis, Laryngeal cancer, leiomyosarcoma, Li-Fraumeni syndrome, lip cancer, liposarcoma, liver cancer, lung cancer, lymphedema, lymphoma, Hodgkin's lymphoma, non-Hodgkin's lymphoma, male breast cancer, malignant rhabdoid carcinoma of the kidney, medulloblastoma, melanoma, Merkel cell carcinoma, mesothelioma, metastatic cancer, oral cancer, multiple endocrine neoplasia, mycosis fungoides, myelodysplastic syndrome, myeloma, myeloproliferative disorders, nasal cancer, nasopharyngeal cancer, nephroblastoma, neuroblastoma, neurofibromatosis, Nijmegen chromosomal instability syndrome, non-melanoma skin cancer, non-small cell lung cancer (NSCLC), eye cancer, esophageal Cancer, oral cancer, oropharyngeal cancer, osteosarcoma, ostomy ovarian cancer, pancreatic cancer, paranasal cancer, parathyroid cancer, parotid cancer, penile cancer, peripheral neuroectodermal cancer, pituitary cancer, polycythemia vera, prostate cancer, rare cancers and related diseases, kidney cancer, retinoblastoma, rhabdomyosarcoma, Rothmund-Thomson syndrome, salivary gland cancer, sarcoma, schwannoma, Sezary syndrome, skin cancer, small cell lung cancer (SCLC), small intestine cancer, soft tissue sarcoma, spinal cancer, squamous cell carcinoma (SEC), gastric cancer, synovial sarcoma, testicular cancer, thymic cancer, thyroid cancer, transitional cell carcinoma (bladder),These include transitional cell carcinoma (renal pelvis / ureter), choriocarcinoma, urethral cancer, urinary system cancer, uroplakin, uterine sarcoma, uterine cancer, vaginal cancer, vulvar cancer, Waldenström's macroglobulinemia, and Wilms' cancer.

[0106] In preferred embodiments, the solid cancer is selected from bladder cancer, breast cancer, colorectal cancer, leiomyosarcoma, lung cancer, melanoma, ovarian cancer, pancreatic cancer, penile cancer, prostate cancer and kidney cancer.

[0107] sample The expression of KRT19 and COL1A2 can be measured from a sample obtained from the subject.

[0108] In some embodiments, the sample obtained from the subject may be a tissue sample.

[0109] Tissue biopsies remain the primary diagnostic method for diagnosing cancer when symptoms or any other early markers are present. However, these types of biopsies are invasive, expensive, and harmful to the patient. Furthermore, they cannot be performed when the target lesion is difficult to access or when the patient's health is compromised. 1 Even if tissue could be directly observed, the diversity and plasticity of neoplastic cells, as well as genetic variation, would only paint a small picture of the malignancy at any given moment. 2 However, biopsies cannot fully reflect the nature of the tumor. Therefore, tissue biopsies may lack important information for proper, personalized management. 3 After initial diagnosis, tissue biopsies are often still required to identify genetic alterations in the tumor and to monitor response after treatment, thus perpetuating the problems described above.

[0110] Therefore, it can be advantageous to perform a liquid biopsy, a non-invasive technique performed on bodily fluids that can be performed on any patient, regardless of their health status. The non-invasive nature of liquid biopsies allows for repeated testing on the same patient, providing molecular information at different stages of disease progression, as well as providing early detection / screening. 5 , detection of recurrence in asymptomatic patients, and monitoring response to treatment.

[0111] Thus, in some embodiments, the specimen obtained from the subject is a biological sample. In some embodiments, the sample obtained from the subject is a biological fluid. Illustrative, non-limiting examples of such samples include any biological fluid, such as blood, serum, nipple aspirate, urine, etc. Some sample types can be frozen at -80°C after initial sample processing to simplify storage and handling.

[0112] In a preferred embodiment, the sample obtained from the subject is a plasma sample. Plasma contains many components. Examples of plasma components include exosomes, ectosomes, extracellular vesicles, platelets, microvesicles, circulating tumor cells, and circulating tumor DNA. The expression levels of KRT19 and COL1A2 can be measured from the entire plasma sample and / or from any specific plasma-derived component. Plasma-derived components can be isolated from plasma by any suitable method. Non-limiting examples of plasma-derived components include exosomes, ectosomes, extracellular vesicles, platelets, microvesicles, circulating tumor cells, and circulating tumor DNA. Thus, in some embodiments, the expression levels of KRT19 and COL1A2 are measured from exosomes, ectosomes, extracellular vesicles, platelets, microvesicles, circulating tumor cells, and / or circulating tumor DNA derived from plasma obtained from a subject.

[0113] In particular, the plasma sample comprises circulating tumor cells (CTCs), circulating tumor DNA (ctDNA) and extracellular vesicles (EVs).Extracellular vesicles include exosomes and ectosomes.In some embodiments, the expression levels of KRT19 and COL1A2 are measured from CTCs, ctDNA or EVs derived from the plasma sample.

[0114] EVs are secreted by living cells and are abundant in biological fluids. 9 , and because it is more stable under circulating physiological conditions 8,10 EVs are particularly superior to CTCs or ctDNA. EVs contain unique molecular cargo (nucleic acids, proteins, or lipids) from donor cells and play a crucial role in mediating intercellular communication. 11 .

[0115] Transport of tumor-derived extracellular vesicles (TD-EVs) has been observed in solid tumors 17 TD-EVs contain DNA, RNA, microRNA, and long non-coding RNA (lncRNA) in a mixture that depends on the donor cell microenvironment. 18 Carrying complex biologically active cargo such as 14 This signaling allows communication between malignant cells within tumors, while TD-EVs can travel to distant sites and modulate the microenvironment there to form premetastatic niches. Thus, RNA derived from epithelial and stromal EVs reflects the state of tumors and tumor microenvironments in real time and can be detected noninvasively, potentially making them highly useful tools for patients with active cancer as diagnostic, prognostic, and predictive biomarkers. 18 .

[0116] In some embodiments, the sample obtained from the subject is a plasma sample. Thus, the expression levels of KRT19 and COL1A2 can be measured directly from the plasma. Alternatively or additionally, the expression levels of KRT19 and COL1A2 can be measured from plasma-derived EVs obtained from the plasma sample. Alternatively or additionally, the expression levels of KRT19 and COL1A2 can be measured from plasma-derived exosomes obtained from the plasma sample. In a preferred embodiment, the expression levels of KRT19 and COL1A2 are measured from plasma-derived exosomes obtained from the subject.

[0117] To purify tumor-derived exosomes, plasma samples must be processed. One method for this purification is to use the ExoRNeasy Maxi Kit (QIAGEN, Hilden, Germany) according to the supplier's instructions.

[0118] Measurement of expression levels In some embodiments, the methods involve measuring cDNA, RNA, or protein expression of KRT19 and COL1A2.

[0119] Expression can be detected by any conventional method that can detect the presence of protein, cDNA or RNA.The illustrative, non-limiting examples of the method that can detect protein include ELISA, immunoprecipitation, Western blotting and bead-based multiplex immunofluorescence assay.The illustrative, non-limiting examples of the method that can detect RNA or cDNA include PCR, RT-PCR, multiplex PCR, nuclease protection assay and hybridization.Those skilled in the art will understand that any known technique for detecting the presence of protein, cDNA or RNA can be used.

[0120] Determining the possibility of a tumor Once the expression levels of KRT19 and COL1A2 are measured, they are combined to generate a score, which may be a probabilistic score.

[0121] High expression of KRT19 and COL1A2 positively correlates with high tumor potential, whereas low expression of KRT19 and COL1A2 indicates low tumor potential.

[0122] Any suitable algorithm can be used to combine the expression levels of KRT19 and COL1A2. For example, combining the expression levels can include using a logistic regression model.

[0123] Each of KRT19 and COL1A2 can be assigned a weighted value. The weighting can be based on the importance of the biomarker in relation to tumor detection. The weighting can be determined using an appropriate test data set. The test data includes expression scores based on the expression data of KRT19 and COL1A2 obtained from multiple samples with known tumor status, i.e., scores obtained from samples from subjects with or without tumors.

[0124] In some embodiments, combining the expression levels of KRT19 and COL1A2 to generate a score comprises: a) Assigning weight values ​​to KRT19 expression to provide weighted KRT29 expression levels b) assigning weight values ​​to COL1A2 expression to provide weighted COL1A2 expression levels; c) Calculating the sum of the weighted KRT19 and COL1A2 expression levels d) Using the sum resulting from step c) to generate a probabilistic score. Includes.

[0125] In some embodiments, combining the expression levels of KRT19 and COL1A2 to generate a score comprises using the following formula:

[0126]

number

[0127] β is the intercept weight, β is a vector of weights for each of the k variables, and x is a vector of variables associated with the sample, including KRT19 expression levels, COL1A2 expression levels, and optionally variables derived therefrom.

[0128] Derived variables include other parameters that may affect the likelihood that a subject has a tumor, including, but not limited to, the age of the subject, the sex of the subject, and the type of cancer.

[0129] In some embodiments, the weights of the KRT19 expression level variable and the COL1A2 expression level variable have the same sign. In a preferred embodiment, the weights of the KRT19 expression level variable and the COL1A2 expression level variable both have positive coefficients.

[0130] The score generated by any of the methods described above determines whether the subject is likely or unlikely to have a tumor. In some cases, the score indicates the presence or absence of a tumor, preferably any type of tumor.

[0131] Thus, in a subsequent step, the method includes correlating the score of the combined KRT19 and COL1A2 data with a high or low likelihood of tumor. In some cases, the method includes correlating the score of the combined KRT19 and COL1A2 data with the presence or absence of tumor.

[0132] The combined score representing the expression of KRT19 and COL1A2 can be compared with reference value.The score representing the expression level of KRT19 and COL1A2 that is higher than reference value can indicate that the subject has a high possibility of tumor, and the score representing the expression level of KRT19 and COL1A2 that is lower than reference value can indicate that the subject has a low possibility of tumor.In some cases, the score representing the expression level of KRT19 and COL1A2 that is higher than reference value indicates that the subject has tumor, and the score representing the expression level of KRT19 and COL1A2 that is lower than reference value indicates that the subject does not have tumor.

[0133] The reference value can be obtained from a control sample. For example, the control sample can be collected from a healthy individual, particularly from an individual with no history of cancer. When monitoring a tumor, the reference value can be obtained from the same subject at an earlier time point. When evaluating the effectiveness of a treatment, the reference value can be obtained from the same subject at an earlier time point. The reference value can represent the combined expression score of KRT19 and COL1A2 in a control sample (e.g., an individual with no history of cancer, or a sample from the same subject at an earlier time point).

[0134] The reference value may be a threshold value. For example, the reference value may be a predetermined threshold value. The predetermined threshold value may be one, or may be two or more. For example, the predetermined threshold value may be two.

[0135] Thus, in some embodiments, determining whether a subject has a high or low probability of having a tumor based on the score includes comparing the score to one or more predetermined thresholds and determining that the subject has a high probability of having a tumor if the score is above a first threshold, or that the subject has a low probability of having a tumor if the score is below a second predetermined threshold, optionally where the first and second predetermined thresholds are the same.

[0136] The threshold value can be determined using test data. The test data includes expression scores based on KRT19 and COL1A2 expression data from multiple samples with known tumor status, i.e., scores from samples from subjects with or without tumors. The threshold value can be selected to optimize the accuracy of tumor detection.

[0137] As mentioned above, the score obtained from the method of the present invention determines whether a subject is likely or unlikely to have a tumor. This information can be effectively used by a specialist to confirm a diagnosis and select the most appropriate test to characterize the tumor or the most appropriate treatment to administer to a subject diagnosed with cancer.

[0138] This method for monitoring tumor progression or regression involves comparing the combined score with a previous value representing the combined expression of KRT19 and COL1A2 obtained from the same subject at a previous time point. The comparison of the results indicates a high likelihood of cancer progression, regression, or steady state.

[0139] Thus, in a subsequent step, the method involves correlating increases or decreases in the combined KRT19 and COL1A2 score with progression or regression of tumors of all types and sites.

[0140] This information can be effectively used by specialists to make decisions about initiating or terminating treatment, or to modify current treatment doses, administration schedules, or even the medication itself.

[0141] In methods involving assessing the efficacy of treatment, the combined score is compared to a previous value representing the combined expression of KRT19 and COL1A2 obtained from the same subject at a previous time point, and the comparison of the results indicates a high likelihood of cancer progression, regression, or steady state.

[0142] Thus, in a subsequent step, the method involves correlating increases or decreases in the combined KRT19 and COL1A2 score with progression or regression of tumors of all types and locations. Tumor progression indicates ineffective treatment. Tumor regression indicates effective treatment.

[0143] This information can be effectively used by specialists to make decisions about initiating or terminating treatment, or to modify current treatment doses, administration schedules, or even the medication itself.

[0144] RT-PCR In some embodiments, the detection of KRT19 and COL2A1 is carried out by RT-PCR. This assay can be performed separately for each of the KRT19 and COL2A1 genes, or multiplexed. In either case, the value of each gene should be normalized with a housekeeping gene before analyzing the results. Any suitable housekeeping gene can be used. Non-limiting examples of housekeeping genes include beta-actin (ACTB), glyceraldehyde 3-phosphate dehydrogenase (GAPDH), 18S rRNA, β2-microglobin (β2M), etc.

[0145] Without being bound by any theory, it is believed that the results of the KRT19 and COL1A2 combination correlate with tumor size and tumor progression because as tumors grow, there is more shredding of epithelial and stromal-derived material and tumor-derived exosomes, resulting in higher expression levels of the KRT19 and COL1A2 combination than in stable or regressing tumors.

[0146] Accordingly, provided herein is an RT-PCR multiplex assay for detecting and quantifying KRT19 and COL1A2 RNA levels in tumor-derived exosomes from plasma, comprising: a) Exosome purification step; b) one or more amplification steps, including real-time reverse transcription polymerase chain reaction; c) a detection step; and d) Data combination step Includes.

[0147] In some embodiments, the amplification step involves a first reaction of KRT19 and COL1A2 RT-PCR reactions to determine the number of copies of RNA of each gene present in the sample. The KRT19 and COL1A2 RT-PCR reactions involve amplifying nucleic acid using a KRT19 forward primer (SEQ ID NO: 1), a KRT19 reverse primer (SEQ ID NO: 2), a KRT19 probe (SEQ ID NO: 3), a COL1A2 forward primer (SEQ ID NO: 4), a COL1A2 forward primer (SEQ ID NO: 5), and a COL1A2 probe (SEQ ID NO: 6). For data normalization purposes, expression of the ACTB gene is also measured simultaneously using an ACTB forward primer (SEQ ID NO: 7), an ACTB reverse primer (SEQ ID NO: 8), and an ACTB probe (SEQ ID NO: 9).

[0148] In some embodiments, the gene-specific primers and probes are fluorescently labeled, with preferred labels being 5' hexachlorofluorescein (HEX), 5' N-hydroxysuccinimide (LC610), 5' tetramethylindo(di)-carbocyanine (CY5), or 3' BlackBerry. TM Examples include QSY quenchers such as Quencher 650 or structural variants.

[0149] In another embodiment, the present invention provides alternative fluorescent dye and multiplex dye combinations attached to KRT19, COL1A2 and ACTB gene-specific probes, as well as alternative quenchers or structural variants to mask fluorescence while the probes are intact.

[0150] In some embodiments, provided is a kit for detecting tumors and / or monitoring tumor progression or regression in a subject, the kit comprising reagents and specific antibodies, primers, or probes for detecting and / or quantifying the expression of KRT19 and COL1A2. Any primers and probes capable of detecting and quantifying KRT19 in an RT-PCR assay can be used. Any primers and probes capable of detecting COL1A2 in an RT-PCR assay can be used. The kit can include instructions for use.

[0151] In one embodiment, the present invention provides a kit for detecting KRT19 and COL1A2 in a sample, the kit comprising primers and probes selected from SEQ ID NOs: 1 to 9, and instructions for use.

[0152] Novel primers and probes that can be used to detect and quantitate KRT19, COL1A2, and beta-actin are listed below: KRT19 RT-PCR: Forward primer 5′-CAGCCACTACTACACGACCATC-3′ (SEQ ID NO: 1); Reverse primer 5'-CAAACTTGGTTCGGAAGTCATC-3' (SEQ ID NO: 2): and Probe (TaqMan) 5'-CAGCCAGACGGGCATTGTCG-3' (SEQ ID NO: 3). COL1A2-PCR: Forward primer 5´-CCTGGACCAATGGGCTTA-3´ (SEQ ID NO: 4); Reverse primer 5'-GAAACCTTGAGGGCCTGG-3', (SEQ ID NO: 5); and Probe (TaqMan) 5'-TAGAGGCCCACCTGGTGCAGC-3 (SEQ ID NO: 6). ACTB-PCR: Forward primer 5´-CCACACTGTGCCCATCTACG -3´ (SEQ ID NO: 7); Reverse primer 5'-AGGATCTTCATGAGGTAGTCAGTCAG-3' (SEQ ID NO: 8); and Probe (TaqMan) 5'-CAGGTCCAGACGCAGGATGGC-3' (SEQ ID NO: 9). Fluorophores: 5'Hexachlorofluorescein (HEX) 5'N-Hydroxysuccinimide (LC610) 5'-Tetramethylindo(di)-carbocyanine (CY5) Quencher: 3' BlackBerry TM Quencher 650 (BBQ-650 TM )

[0153] The cycle numbers (Ct) quantified for KRT19 and COL1A2 can be normalized using the Ct values ​​obtained for a housekeeping gene (e.g., ACTB). The normalized results can then be combined to generate a score. The score can be a probabilistic score.

[0154] In certain embodiments, the data of KRT19 and COL1A2 are normalized using the ΔΔCt method.The ΔΔCt values ​​of KRT19 and COL1A2 are then combined to generate a score.The score can be a probabilistic score.

[0155] High expression of KRT19 and COL1A2 positively correlates with high tumor potential, whereas low expression of KRT19 and COL1A2 indicates low tumor potential. Any suitable algorithm can be used to combine the expression levels of KRT19 and COL1A2. For example, combining the expression levels can include using a logistic regression model.

[0156] Each of KRT19 and COL1A2 can be assigned a weighted value. The weighting can be based on the importance of the biomarker in relation to tumor detection. The weights can be determined using an appropriate test data set. The test data includes expression scores based on KRT19 and COL1A2 expression data obtained from multiple specimens with known tumor status, i.e., scores obtained from samples from subjects with or without tumors.

[0157] In some embodiments, combining the expression levels of KRT19 and COL1A2 to generate a score comprises: a) Assigning weight values ​​to KRT19 expression to provide weighted KRT29 expression levels b) assigning weight values ​​to COL1A2 expression to provide weighted COL1A2 expression levels; c) Calculating the sum of the weighted KRT19 and COL1A2 expression levels d) Using the sum resulting from step c) to generate a probabilistic score. Includes.

[0158] In some embodiments, combining the expression levels of KRT19 and COL1A2 to generate as a score comprises using the following formula:

[0159]

number

[0160] where β is the intercept weight, β is a vector of weights for each of the k variables, and x is a vector of variables associated with the sample, including KRT19 expression levels, COL1A2 expression levels, and optional variables derived therefrom.

[0161] In some embodiments, the weights of the KRT19 expression level variable and the COL1A2 expression level variable have the same sign. In a preferred embodiment, the weights of the KRT19 expression level variable and the COL1A2 expression level variable both have positive coefficients.

[0162] Derived variables include other parameters that may affect the likelihood that a subject has a tumor, including, but not limited to, the age of the subject, the sex of the subject, and the type of cancer.

[0163] In some embodiments, the ΔΔCt values ​​of KRT19 and COL1A2 are combined using the following formula:

[0164]

number

[0165] This algorithm was found to optimize the diagnostic power of combined KRT19 and COL1A2 expression level data obtained using an RT-PCR multiplex assay of RNA from a subject's plasma-derived exosome sample. The assay consists of an exosome purification step followed by three real-time reverse transcription polymerase chain reactions to detect KRT19, COL1A2, and beta-actin (ACTB) as a normalization gene. In one embodiment, exosomes are purified using the ExoRNeasy Maxi Kit (QIAGEN, Hilden, Germany). In a further embodiment, target amplification and detection are achieved using TaqMan chemistry on a CFX96 C1000 Thermal Cycler (Bio-Rad Laboratories, USA).

[0166] After detecting the presence of KRT19 and COL1A2 RNA in the sample, quantifying the RNA copy number, normalizing the data, and combining using a formula, the final test score can be correlated with the likelihood or low likelihood of tumor.Preferably, the score can be related to the presence or absence of any type of tumor.

[0167] Thus, in a subsequent step, the method includes correlating a high score in the combined KRT19 and COL1A2 data with a high probability of tumor, and a low score with a low probability of tumor, hi some embodiments, a high score in the combined KRT19 and COL1A2 data indicates the presence of tumor, and a low score indicates the absence of tumor.

[0168] The combined score representing the expression of KRT19 and COL1A2 can be compared with reference value.The score representing the expression level of KRT19 and COL1A2 that is higher than reference value can indicate that the subject has a high possibility of tumor, while the score representing the expression level of KRT19 and COL1A2 that is lower than reference value can indicate that the subject has a low possibility of tumor.In some cases, the score representing the expression level of KRT19 and COL1A2 that is higher than reference value indicates that the subject has tumor, while the score representing the expression level of KRT19 and COL1A2 that is lower than reference value indicates that the subject does not have tumor.

[0169] Reference value can be obtained from control sample.For example, healthy person, particularly person without history of cancer.When monitoring tumor, reference value can be obtained from the same subject at a previous time point.When evaluating the effectiveness of treatment, reference value can be obtained from the same subject at a previous time point.Reference value can represent the combined expression score of KRT19 and COL1A2 in control sample (for example, from the individual without history of cancer or from the sample from the same subject at a previous time point).

[0170] The reference value may be a threshold value. For example, the reference value may be a predetermined threshold value. The predetermined threshold value may be one, or may be two or more. For example, the predetermined threshold value may be two.

[0171] Thus, in some embodiments, determining whether a subject has a high or low probability of having a tumor based on the score includes comparing the score to one or more predetermined thresholds and determining that the subject has a high probability of having a tumor if the score is above a first threshold, or that the subject has a low probability of having a tumor if the score is below a second predetermined threshold, optionally where the first and second predetermined thresholds are the same.

[0172] The threshold value can be determined using test data. The test data includes expression scores based on the expression data of KRT19 and COL1A2 from multiple samples with known tumor status, i.e., scores obtained from samples of subjects with or without tumors. The threshold value can be selected to optimize the accuracy of tumor detection.

[0173] As mentioned above, the score obtained from the method of the present invention determines whether a subject is likely or unlikely to have a tumor. This information can be effectively used by a specialist to select the most appropriate test to confirm the diagnosis and characterize the tumor, or the most appropriate treatment to administer to a subject diagnosed with cancer.

[0174] From the above, those skilled in the art will understand that the present invention further enables classification of subjects into healthy individuals or patients with active tumors, which is useful for determining the most appropriate complementary testing, treatment and / or follow-up regimen for each subject depending on the subject's condition. Thus, the present invention provides a highly sensitive and specific in vitro method for detecting active tumors that may require treatment or resection.

[0175] Tools, computer-implemented methods and systems Determining whether a subject has a high or low likelihood of having a tumor based on the combined expression score of KRT19 and COL1A2 can include using a machine learning tool trained with test data, including expression scores based on KRT19 and COL1A2 expression data from multiple samples with known tumor status, i.e., scores obtained from samples from subjects with or without tumors.

[0176] Accordingly, there is also provided a method for providing a tool for characterizing a sample obtained from a subject, the method comprising: a). Providing expression data of KRT19 and COL1A2 for a plurality of training samples associated with known tumor conditions, thereby forming a labeled training dataset; b) training a machine learning model using the labeled training dataset to learn parameters for at least the variable KRT19 expression level and the variable COL1A2 expression level, wherein the trained machine learning model provides as an output a probabilistic score for predicting whether the test sample is from a tumor-bearing or tumor-free subject based on input data including the KRT19 expression level and the COL1A2 expression level of the test sample. Includes.

[0177] The method of providing a tool for characterising a test sample obtained from a subject as described above may have any of the (computer-implemented) features described in relation to an embodiment of either the first or second aspect of the invention.

[0178] The present invention also provides a computer-implemented method for screening, detecting, or monitoring a tumor in a subject, the method comprising: a). Provide the subject's KRT19 and COL1A2 expression data; b). Combining the expression levels of KRT19 and COL1A2 to generate a score; and d) classifying the subject as having a high or low probability of having a tumor based on the score. In some embodiments, the computer-implemented method uses a machine learning model trained with the labeled training dataset to learn parameters for at least the variables KRT19 expression level and COL1A2 expression level, and the trained machine learning model provides as output a probabilistic score for predicting whether a test sample is obtained from a tumor-bearing or tumor-free subject based on input data including the KRT19 expression level and COL1A2 expression level of the test sample.

[0179] The system also provides: a). processor; and b) a computer-readable medium containing instructions that, when executed by a processor, cause the processor to perform the computer-implemented method of the present invention. Includes.

[0180] The "system" may be a computer system, including hardware, software, and data storage devices for implementing the system or executing the method as described above. For example, the computer system may include a processor, such as a central processing unit (CPU) and / or a graphics processing unit (GPU), input means, output means, and data storage, which may be embodied as one or more connected computing devices. Preferably, the computer system includes a computing device having a display or a display for providing a visual output display. The data storage may include RAM, a disk drive, or other computer-readable media. The computer system may include multiple computing devices connected by a network and capable of communicating with each other via the network. It is expressly contemplated that the computer system may consist of or include a cloud computer. In certain embodiments, the computer system may be operably connected to one or more devices for measuring gene expression of KRT19 and / or COL1A2. For example, the computer system may be operably connected to one or more quantitative RT-PCR thermal cycler devices (such as a CFX96 C1000 Thermal Cycler) and may receive data representing gene expression levels (such as ΔCt values ​​for KRT19 and / or COL1A2) from the devices.

[0181] Also provided is a non-transitory computer-readable medium or media containing instructions that, when executed by at least one processor, cause the at least one processor to perform the computer-implemented method of the present invention.

[0182] As used herein, the term "computer-readable medium" includes, but is not limited to, any non-transitory medium or media that can be read and accessed directly by a computer or computer system. Media can include, but are not limited to, magnetic storage media such as floppy disks, hard disk storage media, and magnetic tape; optical storage media such as optical disks or CD-ROMs; electrical storage media such as memory, including RAM, ROM, and flash memory; and hybrids and combinations of the above, such as magnetic / optical storage media.

[0183] The computer-implemented method of the present invention can have any of the features described in connection with any embodiment of the first or second aspect of the present invention. Thus, the above-described system and non-transitory computer-readable medium or media can also have any of the features described in connection with any embodiment of the first or second aspect. For example, the provided expression data can be obtained by any means described herein and from any subject described herein. Combining the expression levels can be performed by any means described herein, and classifying the subject as having a high or low probability of tumor based on the score can be achieved using any means described herein.

[0184] The following examples are illustrative of the present invention and should not be construed as limiting the invention. [Example]

[0185] Example 1 Purification and characterization of tumor-derived exosomes from cancer patients material and method Peripheral whole blood was collected from each subject into 10 mL EDTA-K2 tubes and processed within 4 hours after centrifugation to avoid contamination with genomic DNA released from lysed blood cells. Samples were centrifuged at 2000 x g for 20 minutes, and 2–4 mL of plasma was collected. The resulting plasma was passed through a 0.8 μm filter and stored at -80°C. Plasma sample processing and RNA isolation were performed using the commercially available ExoRNeasy Maxi Kit (QIAGEN, Hilden, Germany) according to the manufacturer's protocol. Total RNA was eluted in 14 μl of RNase-free water. Purified RNA from each sample was assayed qualitatively and quantitatively using the Agilent RNA 6000 Pico Kit on an Agilent 2100 Bioanalyzer.

[0186] To characterize their ultrastructural morphology, EVs were suspended in 500 μl of XE buffer (QIAGEN, Hilden, Germany), and then samples were adsorbed onto 300-mesh carbon-coated copper grids for 1 minute in a humidified chamber at room temperature. EV-attached grids were examined at 20–30 kV under a Zeiss Gemini SEM 500 microscope equipped with a STEM detector (Carl Zeiss Microscopy GmbH, Jena, Germany).

[0187] The size distribution and concentration of plasma EVs were determined using nanoparticle tracking (NTA) with specific parameters according to the manufacturer's protocol on a Malvern NanoSight NS300 Analyzer (Malvern Panalytical Ltd., Malvern, UK). Capture and analysis were achieved using the built-in NanoSight Software NTA 3.3.301 (Malvern Panalytical Ltd., Malvern, UK). The nanoparticle detection threshold was fixed at 8 for all experiments. Samples were diluted with PBS to a final volume of 1 mL. For each measurement, five consecutive 60-second videos were recorded at 25°C using a continuous syringe pump with an injection rate of 40 (arbitrary units). Particles (EVs) were detected using a 488 nm blue laser and a scientific CMOS camera.

[0188] result To confirm the presence, morphology, and size of extracellular vesicles from plasma samples of cancer patients (CP) and healthy controls (AC), we observed samples using STEM microscopy. Vesicles ranging from 800 to 25 nm were analyzed. Micrographs revealed predominantly exosome and microvesicle structures in both samples (Figure 1). Morphologically, spheroidal extracellular vesicles were more prevalent, with some cup-shaped exosome structures also observed (Figure 1A.1 / 1A.2). No differences in structure or morphology were observed between control donors and cancer patients.

[0189] NTA analysis showed that the average size of the vesicles ranged from 161.6 ± 2.3 nm (AC sample) to 160.5 ± 0.7 nm (CP) (Figure 2). The modal values ​​were similar (AC: 127.6 ± 10.7 nm, CP: 156.5 ± 13.2 nm). Analysis revealed that exosome-sized structures were predominantly observed in both samples. In terms of concentration, the AC sample had a 1.08 e +11 ±5.09e +9 The mean particle counts per ml were shown. The plasma from cancer donors had an average of 1.02 e +11 ±1.11e +10Particles / ml were recorded. No statistically significant differences in concentration and size distribution were observed between the two cohorts of donors.

[0190] conclusion EVs isolated by STEM and NTA contained primarily exosomal structures with a significant proportion of microvesicles, and in agreement with other studies, the concentration and size distribution did not differ between the two cohorts of donors.

[0191] Example 2 Monoplex analysis of RNA gene expression. material and method Patients and samples This study included 30 plasma samples from 30 patients, including 10 healthy donors (AC) and 20 cancer patients (CP). All of them underwent blood sampling for health care purposes, and the samples were analyzed at the Department of Pathological Anatomy at the A Coruña University General Hospital. The demographic and clinicopathological characteristics of the population are summarized in Table 1.

[0192] For the control group representing healthy patients, 10 patients aged 25 to 55 years who had never previously presented with any oncological problems were analyzed. 40,41 and CEA 42-46 There was also no history of chronic pathologies such as chronic inflammatory bowel disease (Crohn's disease, ulcerative colitis...), pancreatitis, liver cirrhosis, chronic obstructive pulmonary disease (COPD) or hypothyroidism, as these have been shown to be related to changes in basal values ​​of IL-1. Of these 10 patients, 30% were men (n=3) and 70% were women (n=7).

[0193] In the cancer patient group, plasma samples were analyzed from eight prostate adenocarcinomas (seven stage IV and one stage IIb); three renal cell carcinomas (two stage IV and one stage III); three pancreatic neuroendocrine tumors (stage IV); two melanomas (both stage IV); one urothelial bladder cancer (stage IV); one patient with synchronous colorectal tumors, one adenocarcinoma and one small intestinal neuroendocrine tumor (stage I and stage IV, respectively); one serous ovarian cancer (stage III) and one small cell lung cancer (stage IV). Of these 20 patients, 85% were men (n=17) and 15% were women (n=3). The staging of the different tumors was assessed according to the 2017, 8th edition of the American Joint Committee on Cancer TNM classification. 47 .

[0194] Peripheral whole blood was collected from each subject into 10 mL EDTA-K2 tubes and processed within 4 hours after centrifugation to avoid contamination with genomic DNA released from lysed blood cells. Samples were centrifuged at 2000 x g for 20 minutes, and 2–4 mL of plasma was collected. The resulting plasma was passed through a 0.8 μm filter and stored at −80°C until needed.

[0195] [Table 1]

[0196] RNA measurement RNA was quantified using an Agilent 2100 Bioanalyzer, and total RNA samples were reverse transcribed to cDNA according to the protocol of the Quantinova Reverse Transcription kit (QIAGEN, Hilden, Germany). cDNA synthesis was performed using a thermal cycler according to the manufacturer's instructions: gDNA removal reaction at 45°C for 2 minutes, annealing step at 25°C for 3 minutes, reverse transcription step at 45°C for 10 minutes, and reverse transcriptase inactivation at 85°C for 5 minutes.

[0197] Quantitative RT-PCR was performed using a CFX96 C1000 Thermal Cycler (Bio-Rad Laboratories, USA). cDNA expression was evaluated using a QuantiNova SYBR Green PCR kit (QIAGEN, Hilden, Germany). PCR conditions were set according to the supplier's instructions. The initial activation was 95°C for 2 minutes, followed by 45 cycles of 95°C for 5 seconds and 60°C for 10 seconds. Melting curve analysis consisted of a 0.5°C increase from 55°C to 95°C. PCR was performed in a 20 μL reaction mixture (6 μL H2O, 10 μL QuantiNova SYBR Green PCR, 1 μL of each primer [20 μM forward and reverse primers (TIB Molbiol, Berlin, Germany), final concentration: 1 μM], and 2 μL of cDNA template). The forward and reverse primers are listed in Table 2.

[0198] [Table 2]

[0199] Relative gene expression was calculated as previously described by Pfaffl et al. 39 The threshold cycle (Ct) was calculated using a modified comparative threshold cycle (Ct) method (2-ΔΔCt). Two replicates of each sample were analyzed for each gene. For the housekeeping gene (ACTB), a Ct value of ≤28 was considered positive. An ACTB Ct value of ≥29 was considered negative and the result invalid. For epithelial and stromal markers, a Ct value of ≤39 on a sigmoidal curve was accepted as positive. After amplification, a representative sample from each set of amplified products was analyzed by agarose electrophoresis to confirm specificity. Sixteen microliters of PCR product was separated by electrophoresis on a 2% agarose gel.

[0200] statistical analysis Statistical analysis was performed using the IBM SPSS® Statistics v27 program. Descriptive statistics were used to characterize the clinical and pathological data of patients in this study. A Shapiro-Wilk normality test was performed for each dataset. A Grubbs and Dixon test was performed to detect atypical data (outliers). Boxplots were created to examine the distribution of ΔCt values ​​between the AC and PC groups. If statistical data followed a normal distribution, a t-test was performed for comparison between the two groups. The nonparametric Mann-Whitney U test was used to compare relative fold expression between patients in the two cohorts. ΔCt and 2-ΔΔCt values ​​were expressed as mean ± SEM. Statistical significance was determined at an α-limit of 5%.

[0201] result Details of the ΔCt values ​​obtained for each sample in the AC and CP cohorts are shown in Table 3(I) and (II), respectively. ΔCt values ​​were obtained for two epithelial markers and two stromal markers. The two epithelial markers were KRT19 and CEA. The two stromal markers were COL11A1 and COL1A2. All data were normalized to ACTB expression. ΔCt represents the difference between the mean of two replicates of the target gene's Ct and the mean of the Ct of the reference control gene (ACTB).

[0202] [Table 3-1]

[0203] [Table 3-2]

[0204] Boxplots were created to visualize the distribution of ΔCt results for the AC and CP cohorts and compare them between the two groups (Figure 3). The data distribution revealed that the median ΔCt values ​​for KRT19 in AC and CP were 8.66 (IQR: 1.21) and 3.66 (IQR: 1.24), respectively. The median ΔCt values ​​for CEA were 10.32 (IQR: 2.10; AC) and 5.32 (IQR: 6.92; PC). This last result demonstrated the significant dispersion and variability of the data in the CP group for CEA. For stromal markers, the median ΔCt values ​​for COL11A1 were 7.97 (IQR: 1.54; AC) and 2.39 (IQR: 2.03; PC), respectively, and the median ΔCt values ​​for COL1A2 were 8.29 (IQR: 1.64; AC) and 3.25 (IQR: 1.47; PC) (Figure 3A). The box plots also showed the presence of extreme ΔCt values ​​for KRT19 (AC and CP samples) and CEA AC results. Grubbs and Dixon outlier analysis of these values ​​confirmed that these were the most distant from the remaining data but were not significant outliers (p-value > 0.05; IC: 95%).

[0205] Figure 3A and B also show that the difference in ΔCt values ​​between the AC and CP groups was significantly different for all genes investigated. T-test results showed p-values ​​of <0.0001 for KRT19, p-value = 0.0001 for CEA, p-value <0.0001 for COL11A1, and p-value <0.0001 for COL1A2.

[0206] The relative gene expression of each biomarker was calculated as previously described by Pfaffl 39The fold change in expression was assessed using a modified comparative Ct method (2-ΔΔCt). Figure 4 shows the fold change in expression in AC and CP samples. Results showed increased KRT19 expression in CP patients (66.04 ± 14.29) compared with AC patients (1.36 ± 0.43). CEA levels were also higher in cancer patients (245.81 ± 84.63) compared with healthy donors (2.96 ± 1.80). However, high interindividual expression variability was observed for this gene in both the AC and CP cohorts, even among tumor patients with the same cancer type. Stroma markers also showed a high increase in the CP group. AC patients showed levels of 1.26 ± 0.25 (COL11A1) and 1.31 ± 0.30 (COL1A2), while CP patients showed 47.80 ± 7.51-fold and 40.64 ± 6.00-fold increases in COL11A1 and COL1A2 expression, respectively. For all epithelial and stromal biomarkers, significant differences were found between the two groups using nonparametric tests: KRT19 (p<0.0001), CEA (p=0.0003), COL11A1 (p<0.0001), and COL1A2 (p=0.0001) using the Mann-Whitney U test.

[0207] As a basis for deciding which markers to include in the final combination, ROC curves were generated to assess the diagnostic ability of epithelial and stromal markers (Fig. 5).

[0208] KRT19 was selected as the epithelial biomarker for constructing the final combination because its AUC was 1 (sensitivity and specificity reached 100%), whereas CEA's AUC was 0.66 (sensitivity and specificity ranged from 95% to 83%). Regarding stromal markers, both COL11A1 and COL2A1 showed an AUC value of 1, with sensitivity and specificity values ​​of 100%.

[0209] conclusion KRT19, CEA, COL11A1, and COL1A2 showed significant differences in expression between the control and cancer groups, confirming their potential as cancer biomarkers in plasma exosomes from patients with various tumor types at advanced stage (III / IV), including prostate cancer, renal cancer, pancreatic cancer, melanoma, ovarian cancer, bladder cancer, colorectal cancer, and lung cancer. However, for epithelial markers, KRT19 showed higher sensitivity and specificity than CEA.

[0210] Example 3 Multiplex assay development material and method For the development of the multiplex assay, Qiagen's Quantinova Multiplex RTPCR system was selected, and the reaction was performed in a CFX96 C1000 Thermal Cycler (Bio-Rad Laboratories, EEUU).

[0211] Primers as described herein for ACTB, KRT19, COL11A1 and COL1A2 were used with the following TaqMan probes: ACTB_P660 Length: 21 mer Cy5-CAggTCCAgACgCAggATggC-BBQ (SEQ ID NO: 9) KRT19_P610 Length: 20 mer LC610-CAgCCAgACgggCATTgTCg-BBQ (SEQ ID NO: 3) COL11A1_P530 Length: 25 mer 6FAM-CTCCAACACCACCAACTgAACCAAC-BBQ (SEQ ID NO: 18) COL1A2_P580 Length: 21 mer HEX-TAgAggCCCACCTggTgCAgC-BBQ (SEQ ID NO: 6)

[0212] result First, individual reactions were optimized. Both ACTB and KRT19 reactions were successful, with detection limits of 2 pg and 0.2 pg RNA, respectively (Figures 6 and 7).

[0213] However, the detection limit for COL11A1 was 200 pg RNA, indicating a rather low sensitivity (Fig. 8 ).

[0214] When all three reactions were multiplexed, the ACTB and KRT19 reactions maintained their performance, while COL11A1 became even less sensitive (Figure 9).

[0215] Several attempts to improve the detection of COL11A1 were unsuccessful, and different primers were tested, but COL11A1 was excluded, and COL1A2 was ultimately selected as the stromal marker for the multiplexed combination.

[0216] The COL1A2 reaction was successful both in individual reactions (Figure 10) and in a multiplex assay (ACTB, KRT19 and COL1A2) (Figure 11).

[0217] conclusion Reactions containing ACTB, KRT19, and COL1A2 performed well in multiplex assays, whereas reactions containing COL11A1 performed poorly. For these reasons, the combination of KRT19 and COL1A2, along with ACTB as a housekeeping gene, was selected for use in multiplex assays.

[0218] Example 4 Biomarker validation by multiplex RT-PCR material and method Patients and samples The study sample included 60 patients: 47 cancer patients (CPs) who underwent curative resection and 13 asymptomatic control volunteers (ACs). Patients with recurrent neoplasms or other cancers were excluded from the study. Diagnosis, surgery, and sample collection were performed at the A Coruña University General Hospital (CHUAC). Samples were analyzed at the hospital's Pathology Department (UNE-EN ISO 9001-2015 certified). Clinical data, such as gender, age, tumor histology, and stage, were obtained from medical records. This study was conducted in compliance with the Declaration of Helsinki. Approval from the Clinical Research Ethics Committee, storage of patients' written informed consent, and sample storage were managed by the A Coruña Biobank. The demographic and clinicopathological characteristics of the population are summarized in Table 4.

[0219] On the day of surgery, participants collected 6–10 mL of blood into EDTA (K3) collection tubes (Vacuette, Greiner bio-one, Kremsmunster, Austria). Blood was centrifuged at 2000 x g for 20 min at room temperature. Tubes were processed for the first 4 h, and plasma was stored at -80°C. RNA was extracted from plasma samples using the ExoRNeasy Maxi Kit (QIAGEN, Hilden, Germany) according to the supplier's instructions. For the elution step, the yield was increased by incubating the column with 14 μL of RNase-free water for 10 min.

[0220] [Table 4]

[0221] RNA measurement The genes of interest were co-amplified in a multiplex TaqMan assay using a QIAGEN® Multiplex PCR Kit (QIAGEN) on a CFX96 C1000 Thermal Cycler (Bio-Rad Laboratories, USA). Matched primer and probe sets specific for human gene targets were designed and synthesized by TIB Molbiol (Berlin, Germany).

[0222] A 72-bp COL1A2 amplicon was amplified with PCR primers 5′-CCTGGACCAATGGGCTTA-3′ (SEQ ID NO: 4) and 5′-GAAACCTTGAGGGCCTGG-3′ (SEQ ID NO: 5), a 130-bp KRT19 amplicon was amplified with PCR primers 5′-CAGCCACTACTACACGACCATC-3′ (SEQ ID NO: 1) and 5′-CAAACTTGGTTCGGAAGTCATC-3′ (SEQ ID NO: 2), and a 99-bp ACTB amplicon was amplified with PCR primers 5′-CCACACTGTGCCCATCTACG-3′ (SEQ ID NO: 7) and 5′-AGGATCTTCATGAGGTAGTCAGTCAG-3′ (SEQ ID NO: 8).

[0223] 5' hexachlorofluorescein (HEX) labeled probe, 3' Blackberry TM Quencher 650 (BBQ-650 TM ) and the sequence 5'-TAGAGGCCCACCTGGTGCAGC-3' (SEQ ID NO: 6) were used for the COL1A2 gene, a 5'N-hydroxysuccinimide (LC610) labeled probe, 3'BBQ-650 and the sequence 5'-CAGCCAGACGGGCATTGTCG-3' (SEQ ID NO: 3) was used for the KRT19 gene, and a 5'tetramethylindo(di)-carbocyanine (CY5) labeled probe, 3'BBQ-650 and the sequence 5'-CAGGTCCAGACGCAGGATGGC-3' (SEQ ID NO: 9) was used for the ACTB gene.

[0224] The reaction mixture (20 μL) contained the following reagents: 7.8 μL HO, 5 μL QuantiNova Multiplex PCR Master Mix (QIAGEN), 1 μL of each primer-probe mix (20x primer-probe mix contains 16 μM forward and reverse primers and 5 μM TaqMan probes), 0.2 μL QuantiNova Multiplex RT mix, and 4 μL RNA template. Multiplex RT-qPCR conditions included RT at 50°C for 10 min, an initial activation at 95°C for 2 min, and 40 cycles of 95°C for 5 s and 62°C for 30 s.

[0225] Relative quantification of COL1A2 and KRT19 expression was performed using the comparative 2-ΔΔCt method, with ACTB gene expression used to normalize the data. In all cases, duplicate measurements were performed and then averaged. A cycle threshold number ≤39 with a sigmoidal curve was accepted as positive.

[0226] A nonparametric Mann-Whitney U test was used for intergroup comparisons. ΔCt and 2-ΔΔCt are expressed as mean ± SD, and unpaired t-tests (two-tailed) were used to test for differences between groups. A binary logistic regression model was used to analyze the data using IBM SPSS® Statistics v27. For both biomarkers, cutoff values ​​were set using Youden's J index (the value that combines the highest sensitivity and specificity) by receiver operating characteristic (ROC) curve analysis, and the area under the curve (AUC) was calculated. When multiple values ​​met this condition, the cutoff value that allowed for greater sensitivity was selected. Statistical significance was determined at an α-limit of 5% in all analyses.

[0227] result The Ct values ​​(number of amplification cycles) of KRT19 and COL1A2 were normalized by subtracting the Ct values ​​obtained with ACTB for the same samples to obtain relative Ct values, ΔCt. Both ΔCt values ​​were higher in cancer patients than in healthy donors: the median ΔCt values ​​for COL1A2 in AC were 10.88 (IQR 10.31-11.70) and 7.64 (IQR 6.87-10.56) in cancer patients, and the median ΔCt values ​​for KRT19 in AC were 10.95 (IQR 10.73-12.66) and 9.12 (IQR 8.10-11.40) in cancer patients, both of which showed statistical significance (Figure 12A).

[0228] Relative gene expression levels (increase or decrease in expression) were evaluated using the 2-ΔΔCt formula. Figure 12B shows the mean ± SD of AC (1.40 ± 1.25) and CP (9.68 ± 15.07) for COL1A2 and the mean ± SD of AC (1.21 ± 0.78) and CP (26.61 ± 45.46) for KRT19. A nonparametric Mann-Whitney U-test revealed significant differences between both groups, with p values ​​of <0.0001 for COL1A2 and p values ​​of =0.0031 for KRT19.

[0229] Relative gene expression differences were also analyzed separately for different cancer types (Table 5 and Figure 13). In colorectal cancer (CRC), significant differences were observed for both COL1A2 (p-value < 0.0001) and KRT19 (p-value = 0.0054). In renal cancer (RC), significant differences were also observed for COL1A2 (p-value = 0.0088) and KRT19 (p-value = 0.0008). In bladder cancer, prostate cancer, and other cancer types, although a clear trend toward overexpression of all biomarkers was observed compared to the control group, the limited sample size prevented evaluation of individual biomarker differences or the differences were not significant (p-value > 0.1).

[0230] [Table 5]

[0231] Example 5 KRT19 and COL1A2 combination model method A binary logistic regression model was used to analyze the data using IBM SPSS® Statistics v27. For both biomarkers, a receiver operating characteristic (ROC) curve analysis was performed to set cutoff values ​​using Youden's J index (the value that combines the highest sensitivity and specificity), and the area under the curve (AUC) was calculated. When multiple values ​​met this condition, the cutoff value that allowed for higher sensitivity was selected. Statistical significance was determined at an α-limit of 5% in all analyses.

[0232] result To design a diagnostic tool, individual quantitative profiles were established based on the expression levels of biomarkers. Because not all patients express / amplify both markers, the absence of amplification of one or both biomarkers (COL1A2 or KRT19) was considered as the absence of mRNA and assigned a value of 0.

[0233] The algorithm that best classified patients was as follows:

[0234]

number

[0235] Because this quantitative expression profile was intended to be used as a patient classification tool, a cutoff value was required. To this end, a receiver operating characteristic (ROC) curve analysis was performed (Figure 14). Using both biomarkers, a running combination of the dataset using logistic regression was identified, achieving an AUC of 0.897 (0.815-0.979), as shown in Figure 14C. The AUC of the combined model (Figure 15C) was significantly higher than the individual biomarker models (Figures 14A and B: AUC = 0.624 (0.490-0.757) for KRT19 and AUC = 0.636 (0.501-0.771) for COL1A2). The two-biomarker model had an overall test sensitivity of 0.830 and specificity of 0.846 compared to the individual markers COL1A2 (S=0.617, E=0.845) and KRT19 (S=0.617, E=0.615) (Figures 14A and B, respectively).

[0236] conclusion The two-biomarker model showed higher sensitivity and specificity than individual biomarkers and performed better in patient classification. Because biomarker validation and model construction were based on data from patients with resectable tumors, this diagnostic system may be useful for early tumor detection and therefore may be a perfect screening tool for various types of cancer.

[0237] References cited herein Non-Patent Documents Cited in This Specification 1. Esposito A.; Criscitiello C.; Locatelli M. et al. Liquid biopsies for solid tumors: understanding tumor heterogeneity and real time monitoring of early resistance to targeted therapies. Pharmacol. Ther. (2016); 157:120-124. 2. Gerlinger M.; Rowan A.J.; Horswell S. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. (2012); 366:883-892. 3. Feller S.M.; Lewitzky M. Hunting for the ultimate liquid cancer biopsy-let the TEP dance begin. Cell Commun. Signal. (2016); 14:24. 4. Izzotti A.; Carozzo S.; Pulliero A. et al. Extracellular microRNA in liquid biopsy: applicability in cancer diagnosis and prevention. Am. J. Cancer Res. (2016); 6:1461-1493. 5. Santarpia, M.; Liguori, A.; D’Aveni, A.; Karachaliou, N.; Gonzalez-Cao, M.; Daffina, M.G.; Lazzari, C.; Altavilla, G.; Rosell, R. Liquid biopsy for lung cancer early detection. J. Thorac. Dis. 2018, 10, S882-S897; DOI:10.21037 / jtd.2018.03.81. 6. Moran, T.; Felip, E.; Bosch-Barrera, J.; de Aguirre, I.; Ramirez, J.L.; Moesia, C.; Carcereny, E.; Roa, D.; Sais, E.; Garcia, Y.; et al. Monitoring EGFR-T790M mutation in serum / plasma for prediction of response to third-generation EGFR inhibitors in patients with lung cancer. Oncotarget 2018, 9, 27074-27086; DOI:10.18632 / oncotarget.25478. 7. Huang, W.-L.; Chen, Y.-L.; Yang, S.-C.; Ho, C.-L.; Wei, F.; Wong, D.T.; Come on, WC; Lin, C.-C. Liquid biopsy genotyping in lung cancer: ready for clinical utility? Oncotarget 2017, 8, 18590-18608; DOI:10.18632 / oncotarget.14613. 8. Yu, D.; Li Y., Wang; M., Gu; J., Xu, W.; Cai, H.; Fang, X.; Zhang, X. Exosomes as a new frontier of cancer liquid biopsy. Mol Cancer. 2022 Feb 18;21(1):56. doi: 10.1186 / s12943-022-01509-9. 9. Cai, X; Janku, F.; Zhan, Q.; Fan, J.B. Accessing genetic information with liquid biopsies. Genetic Trends. 2015;31:564-75. 10. Yu, W.; Hurley, J.; Roberts, D.; Chakrabortty, S.; Enderle, D.; Noerholm, M.; et al. Exosome-based liquid biopsies in cancer: opportunities and challenges. Ann Oncol. 2021;32(4):466-77. 11. Moller, A.; Lobb, R.J. The evolving translational potential of small extracellular vesicles in cancer. Nat Rev Cancer. 2020 Dec;20(12):697-709. doi: 10.1038 / s41568-020-00299-w. 12. Thery, C.; Witwer, K.W.; Aikawa, E.; Alcaraz, M.J.; Anderson, J.D.; Andriantsitohaina, R.; Antoniou, A.; Arab, T.; Archer, F.; Atkin-Smith, G.K; et al. Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. J Extracell Vesicles. 2018 Nov 23;7(1):1535750. doi: 10.1080 / 20013078.2018.1535750. eCollection 2018. 13. Gyorgy, B.; Szabo, T.G.; Pasztoi, M.; Pal, Z.; Misjak, P.; Aradi, B.; Laszlo, V.; Pallinger, E.; Pap, E.; Kittel, A. Membrane vesicles, current state-of-the-art: emerging role of extracellular vesicles. Cell Mol Life Sci. 2011 Aug;68(16):2667-88. doi: 10.1007 / s00018-011-0689-3. 14. Narang, P.; Shah, M.; Beljanski, V. Exosomal RNAs in diagnosis and therapies. Noncoding RNA Res. 2022 Jan 14;7(1):7-15. doi: 10.1016 / j.ncrna.2022.01.001. eCollection 2022 Mar. 15. Becker, A.; Thakur, B.K.; Joshua Mitchell Weiss, J.M.; Kim, H.S.; Peinado, H.; Lyden, D. Extracellular Vesicles in Cancer: Cell-to-Cell Mediators of Metastasis. Cancer Cell. 2016 Dec 12;30(6):836-848. doi: 10.1016 / j.ccell.2016.10.009. 16. Hoshino, A.; Costa-Silva, B.; Shen, T-L.; Rodrigues, G.; Hashimoto, A.; Mark, M.T.; Molina, H.; Kohsaka, S.; Di Giannatale, A.; Ceder, S.; et al. Tumour exosome integrins determine organotropic metastasis. Nature. 2015 Nov 19;527(7578):329-35. doi: 10.1038 / nature15756. Epub 2015 Oct 28. 17. Qiao, F.; Pan, P.; Yan, J.; Sun, J.; Zong, Y.; Wu, Z.; Lu, X.; CheN, N.; Mi, R.; Ma, Y.; et al. Role of tumor-derived extracellular vesicles in cancer progression and their clinical applications. Int J Oncol. 2019 May;54(5):1525-1533. doi: 10.3892 / ijo.2019.4745. Epub 2019 Mar 12. 18. Kok, V.C.; Yu, C-C. Cancer-Derived Exosomes: Their Role in Cancer Biology and Biomarker Development. Int J Nanomedicine. 2020 Oct 19;15:8019-8036. doi: 10.2147 / IJN.S272378. eCollection 2020. 19. Xu, D.; Li, XF.; Zheng, S. et al. Quantitative real-time RT-PCR detection for CEA, CK20 and CK19 mRNA in peripheral blood of colorectal cancer patients. J Zhejiang Univ Sci B. (2006) 7(6):445-51. 20. Chu, PG, Weiss, LM. Keratin expression in human tissues and neoplasms. Histopathology 2002;40:403-39. 21. Masai K, Nakagawa K, Yoshida A, Sakurai H,. Watanabe S, Asamura H, Tsuta K. Cytokeratin 19 expression in primary thoracic tumors and lymph node metastasis. Lung Cancer. 2014; 86:3. 22. Xiao, Y.; Li, Y.; Yuan, Y.; Liu, B.; Pan, S.; Liu, Q.; Qi, X.; Zhou, H.; Dong, W.; Jia, L. The potential of exosomes derived from colorectal cancer as a biomarker. Clin. Chim. Acta 2019, 490, 186-193. 23. Bystricky, B.; Jurisova, S.; Karaba, M.; Minarik, G.; Benca, J.; Sedlackova, T.; Tothova, L.; Vlkova, B.; Cierna, Z.; Janega, P.; et al. Relationship Between Circulating Tumor Cells and Tissue Plasminogen Activator in Patients with Early Breast Cancer. Anticancer Res. 2017; 37(4):1787-1791. 24. Baghban, R.; Roshangar, L.; Jahanban-Esfahlan, R.; Seidi, K.; Ebrahimi-Kalan, A.; Jaymand, M.; Kolahian, S.; Javaheri, T.; Zare, P. Tumor microenvironment complexity and therapeutic implications at a glance. Cell Commun Signal. 2020 Apr 7;18:59. 25. Hanahan D, Coussens LM. Accessories to the crime: functions of cells recruited to the tumor microenvironment. Cancer Cell. 2012;21:309-22. 26. Korkaya, H.; Orsulic, S. Editorial: The tumor microenvironment: Recent advances and novel therapeutic approaches. Front Cell Dev Biol. 2020;8:586176. 27. Armstrong, T.; Packham, G.; Murphy, LB.; Bateman, AC.; Conti, JA.; Fine, DR.; et al. Type I collagen promotes the malignant phenotype of pancreatic ductal adenocarcinoma. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research. 2004; 10:7427-7437. 28. Kehlet, SN.; Sanz-Pamplona, R.; Brix, S.; Leeming, DJ.; Karsdal, MA.; Moreno, V. Excessive collagen turnover products are released during colorectal cancer progression and elevated in serum from metastatic colorectal cancer patients. Scientific Reports. 2016; 6(1). 29. Bager, CL.; Willumsen, N.; Leeming, DJ.; Smith, V.; Karsdal, MA.; Dornan, D.; Bay-Jensen, AC. Collagen degradation products measured in serum can separate ovarian and breast cancer patients from healthy controls: A preliminary study. Cancer Biomarkers: Section A of Disease Markers. 2015; 15:783-788. 30. Identification of COL1A1 and COL1A2 as candidate prognostic factors in gastric cancer. Li J, et al., World J Surg Oncol. 2016 Nov 29;14(1):297 31. Search for new biomarkers of gastric cancer through serial analysis of gene expression and its clinical implications. Yasui W, et al., Cancer Sci. 2004 May;95(5):385-92 32. Positive association of collagen type I with non-muscle invasive bladder cancer progression. Brooks M, et al., Oncotarget. 2016 Dec 13;7(50):82609-82619 33. Expression and network analysis of YBX1 interactors for identification of new drug targets in lung adenocarcinoma. Murugesan SN, et al., J Genomics. 2018 Jun 26;6:103-112 34. CpG hypermethylation of collagen type I alpha 2 contributes to proliferation and migration activity of human bladder cancer. Mori K, et al., Int J Oncol. 2009 Jun;34(6):1593-602. 35. Pegtel, DM et al., Exosomes. Annu Rev Biochem . 2019 Jun 20;88:487-514. 36. One-step nucleic acid amplification for detecting lymph node metastasis of head and neck squamous cell carcinoma. Goda H, et al., Oral Oncol. 2012 Oct;48(10):958-963. 37. One-Step Nucleic Acid Amplification (OSNA): A fast molecular test based on CK19 mRNA concentration for assessment of lymph-nodes metastases in early stage endometrial cancer. Fanfani F, et al., PLoS One. 2018 Apr 26;13(4):e0195877. 38.An accurate and rapid detection of lymph node metastasis in non-small cell lung cancer patients based on one-step nucleic acid amplification assay. Inoue M, et al., Lung Cancer. 2012 Dec;78(3):212-8. 39. Lymph node metastases in papillary thyroid cancer detected by quantitative real-time polymerase chain reaction for thyroglobulin and cytokeratine-19. Kaczka K, et al., Pol J Pathol. 2013 Jun;64(2):90-5 40. A view on drug resistance in cancer. Vasan, N. et al., Nature. 2019 Nov; 575(7782): 299-309 41. Monitoring Therapy Efficiency in Cancer through Extracellular Vesicles. Stevic, I. et al., Cells. 2020 Jan; 9(1): 13 42. Brooks JD. Translational genomics: the challenge of developing cancer biomarkers. Genome Res. 2012;22(2):183-187.

Claims

1. 1. A method for screening, detecting, or monitoring a tumor in a subject, comprising: a) Measuring the expression levels of KRT19 and COL1A2 in a sample obtained from the subject; and b) Combining the expression levels of KRT19 and COL1A2 to generate a score. c) Determining whether the subject has a high or low probability of having a tumor based on the score. A method comprising:

2. (a) the subject has or is at risk of developing a tumor; (b) the subject has or is at risk of developing a recurrence of the tumor; and / or (c) the subject is a human; A method for screening or detecting a tumor according to claim 1.

3. The method for screening, detecting or monitoring a tumor according to claim 1 or 2, wherein the sample obtained from the subject is a biological fluid, optionally the biological fluid is blood, serum, plasma, nipple aspirate or urine, and optionally the expression levels of KRT19 and COL1A2 are measured from plasma obtained from the subject, and / or the expression levels of KRT19 and COL1A2 are measured from exosomes derived from plasma obtained from the subject.

4. 10. The method of screening, detecting or monitoring a tumor according to any one of the preceding claims, wherein the tumor is cancerous, optionally the cancer is a solid cancer, and further optionally the cancer is selected from bladder cancer, breast cancer, colorectal cancer, leiomyosarcoma, lung cancer, melanoma, ovarian cancer, pancreatic cancer, penile cancer, prostate cancer and renal cancer.

5. 10. The method for screening, detecting or monitoring tumors according to any one of the preceding claims, wherein the method comprises measuring the cDNA, RNA and / or protein expression levels of KRT19 and COL1A2, and optionally the RNA expression levels of KRT19 and COL1A2 are measured by RT-PCR.

6. 2. The method of screening, detecting or monitoring a tumor according to any one of the preceding claims, wherein combining the expression levels of KRT19 and COL1A2 to generate a score comprises using a logistic regression model.

7. Combining the expression levels of KRT19 and COL1A2 to generate a score: a) Assigning a weight value to KRT19 expression to provide a weighted KRT29 expression level b) Assigning weight values ​​to COL1A2 expression to provide weighted COL1A2 expression levels. c) Calculating the sum of the weighted KRT19 expression level and COL1A2 expression level. d) Using the sum resulting from step c) to generate a probabilistic score 10. A method for screening, detecting or monitoring a tumor according to any one of the preceding claims, comprising:

8. wherein combining the expression levels of KRT19 and COL1A2 to generate a score comprises determining a probabilistic score using the following formula: [Equation 1] β ０ is an intercept weight, β is a vector of weights for each of the k variables, and x is a vector of variables associated with the sample, the variables including KRT19 expression level, COL1A2 expression level, and optionally variables derived therefrom; A method for screening, detecting or monitoring tumors according to any one of the preceding claims.

9. 10. The method of screening, detecting, or monitoring tumors according to any one of the preceding claims, wherein determining whether the subject has a high or low likelihood of having a tumor based on the score comprises comparing the score to a reference value, optionally the reference value being obtained from a control sample or a predetermined threshold, and further optionally determining whether the subject has a high or low likelihood of having a tumor based on the score comprises comparing the score to one or more predetermined thresholds, and determining whether the subject has a high likelihood of having a tumor if the score is above a first threshold or whether the subject has a low likelihood of having a tumor if the score is below a second predetermined threshold, optionally the first and second predetermined thresholds being the same.

10. A method for assessing the efficacy of a cancer treatment in a subject, a) Measuring the expression levels of KRT19 and COL1A2 in a sample obtained from the subject at an initial time point; b) combining the KRT19 and COL1A2 expression levels obtained at the first time point to form a baseline value; c) measuring the expression levels of KRT19 and COL1A2 in a sample obtained from the subject at a second time point; d) combining the KRT19 and COL1A2 expression levels obtained at the second time point to generate a score; e) Determining whether the tumor is more likely to have progressed, regressed, or remain at a steady state based on the score compared to the baseline value. A method comprising:

11. 1. A method for providing a tool for characterizing a sample obtained from a subject, the method comprising: a) Providing KRT19 and COL1A2 expression data for a plurality of training samples associated with known tumor conditions, thereby forming a labeled training dataset; b) training a machine learning model using the labeled training dataset to learn parameters of at least the variable KRT19 expression level and the variable COL1A2 expression level, wherein the trained machine learning model provides as an output a probabilistic score for predicting whether the test sample is from a tumor-bearing or tumor-free subject based on input data including the KRT19 expression level and the COL1A2 expression level of the test sample; The method comprising:

12. 1. A computer-implemented method for screening, detecting, or monitoring a tumor in a subject, comprising: a) Providing KRT19 and COL1A2 expression data from a subject; b) Combining the expression levels of said KRT19 and COL1A2 to generate a score; c) Classifying the subject as having a high or low probability of having a tumor based on the score. The method comprising:

13. a) a processor; and b) A computer-readable medium comprising instructions that, when executed by the processor, cause the processor to perform the method of claim 12. Including, the system.

14. 13. A non-transitory computer-readable medium or media comprising instructions that, when executed by at least one processor, cause the at least one processor to perform the method of claim 12.

15. Use of a kit for detecting a tumor in a subject and / or monitoring tumor progression or regression from a biological fluid sample obtained from the subject, the kit comprising reagents and specific antibodies, primers or probes for detecting and / or quantifying KRT19 and COL1A2 expression.

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