KR101390590B1 - Markers for pancreatic cancer recurrence prognosis prediction and its use - Google Patents

Abstract

Alpha-2-antiplasmin, Lumican, Apolipoprotein CIII, Alpha-2-macroglobulin, complement C4 gamma chain C4 gamma chain, lamin A / C, elongation factor Tu, triosephosphate isomerase and Carbonic anhydrase V A marker for predicting the prognosis of recurrence of pancreatic cancer. The proteins identified as overexpressed or less expressed in sera of patients with recurrent pancreatic cancer after surgical treatment of patients with pancreatic cancer according to the present invention are useful as biomarkers for predicting the prognosis of recurrence of pancreatic cancer before concurrent chemoradiotherapy Lt; / RTI >

Description

≪ Desc / Clms Page number 1 > Markers for Pancreatic Cancer Recurrence Prognosis < RTI ID = 0.0 >

The present invention relates to a marker for predicting the prognosis of recurrence of pancreatic cancer, and more particularly, to a biomarker capable of predicting the prognosis of recurrence of pancreatic cancer after surgical treatment of pancreatic cancer and concurrent chemoradiation therapy.

Pancreatic cancer is a very fatal cancer with a very poor treatment outcome compared to other cancers, with a morbidity rate close to that of the tumor, with a survival of only 14 months. According to 2008 National Cancer Registry statistics, pancreatic cancer accounts for 9th place among all cancer types (8.7 cases per 100,000 people), while it occupies fifth place in mortality rate. Although the 5-year survival rate of all pancreatic cancer patients in Korea is 7.6% on average, the survival rate of all cancer patients is steadily increasing due to the ongoing development of oncology. However, unlike other cancers, the survival rate of pancreatic cancer has been almost It did not improve.

The most important factor influencing the treatment of pancreatic cancer in Korea is early diagnosis. According to the analysis of the data of the major hospitals in Korea, the 5-year survival rate is 75% (stage IA) or 40% (stage IB) in the first stage and pancreatic cancer requires more early diagnosis 1). However, the early diagnosis is difficult to diagnose pancreatic cancer, the stage of the disease has already progressed significantly in the case of surgical treatment is possible only 20% of the patients, and after surgery, recurrence and metastasis more frequent, but significantly improved survival rate reported There is no situation. In addition, surgical resection and chemotherapy after chemotherapy alone or in combination with chemotherapy often leads to recurrence in the future, and in this case, the survival rate decreases sharply. Marker proteins that can predict the prognosis of pancreatic cancer patients may suggest recurrence before chemotherapy, and thus, the intensity of chemotherapy may be adjusted or replaced by other active interventions It can be used as a basis for maximizing the survival rate.

Korean Patent Laid-Open Publication No. 2009-0003308 discloses a method for diagnosing pancreatic cancer by detecting the expression level of REG4 protein in a blood sample of an individual, and Korean Patent Publication No. 2012-0009781 provides information necessary for diagnosis of pancreatic cancer in an individual Korean Patent Laid-Open No. 2007-0119250 discloses an assay method for measuring the expression level of XIST RNA in cancer tissues isolated from an individual, in order to compare the expression level of XIST RNA with that of normal human pancreatic tissues. The novel gene LBFL313 family expressed in human pancreatic cancer tissues Lt; / RTI > Also, U.S. Published Patent Application No. 2011/0294136 discloses a method for diagnosing pancreatic cancer using biomarkers such as keratin 8 protein.

However, the pancreatic cancer diagnosis-related substances disclosed or implied in these patent documents are different from those of the recurrence prognostic biomarkers of the present application using a single marker or are different from those of the present invention, and a biomarker for predicting the prognosis of post- It is important in that it is.

In this context, the present invention is based on the selection of candidates for pancreatic cancer-related markers through the use of iTRAQ (Isobaric tags for relative and absolute quantitation) and proteome and gene expression data mining using cancer tissues and normal tissues of six pancreatic cancer patients, (http://www.plasmaproteomedatabase.org) was used to screen for proteins detected in the blood, and MRM (Mutiple Reaction Monitoring) was performed to determine whether the blood of the patient after pancreatic cancer surgery, The present invention is based on the finding that prognostic markers for recurrence of pancreatic cancer before concurrent chemoradiotherapy can be identified based on the present invention. The present invention relates to a combination of one or more of these proteins according to their specificity and sensitivity, A marker for prediction and a use thereof.

However, the technical problem to be solved by the present invention is not limited to the above-mentioned problems, and other matters not mentioned can be clearly understood by those skilled in the art from the following description.

In one embodiment, the invention provides a pharmaceutical composition comprising Alpha-2-antiplasmin, Lumican, Apolipoprotein CIII, Alpha-2-macroglobulin, Complement C4 gamma chain, Lamin A / C, Elongation factor Tu, Triosephosphate isomerase and Carbonic anhydride anhydrase V). The present invention also provides a marker for predicting the prognosis of recurrence of pancreatic cancer. These proteins have been shown to have significant differences in protein expression as a result of MRM analysis of selected proteins from iTRAQ experiments and data mining results in biological samples from patients with recurrent pancreatic cancer and those without recurrence.

That is, in the present invention, qualitative and quantitative analysis was performed on cancer tissues and normal tissue regions of pancreatic cancer patients through iTRAQ (Isobaric tags for relative and absolute quantitation). As a result of iTRAQ analysis, expression of 440 proteins in pancreatic cancer tissues and normal tissues was 1.5 times or more higher than that of normal tissues, 204 of them were expressed in cancer tissues and 236 of proteins were decreased in comparison with normal tissues Respectively. In addition, proteome data mining and gene expression data mining were performed to synthesize iTRAQ experimental data. As a result, 167 proteins were found in all three fields of iTRAQ, proteome data mining and gene expression data mining. Finally, in order to select proteins that can be detected in blood, a plasmid proteome database (http: // www .plasmaproteomedatabase.org), and the final 154 proteins were selected as candidates for proteomic markers.

In addition, MRM (Mutiple Reaction Monitoring) was performed on blood samples as candidates for the selected candidate proteomic markers, and blood samples were collected after surgical treatment to show the difference in expression between the patient group in which no recurrence occurred and the patient group in which recurrence occurred Nine proteins were identified.

In another aspect, the present invention also provides a kit for predicting the prognosis of recurrence of pancreatic cancer, comprising the marker and a reagent capable of specifically detecting the marker. The detection reagent of the kit is capable of detecting the marker at the protein or mRNA level and can be used for detection of monoclonal antibody, polyclonal antibody, substrate, aptamer, receptor, ligand or cofactor, Reagents and the like. Reagents which can be detected at the mRNA level include a reverse transcription polymerase chain reaction, an RNase protection assay, and reagents used in northern blots. By analyzing the intensity of the final signal obtained in the above analysis process, that is, performing signal matching between the recurred sample and the unreacted sample, it is possible to predict the recurrence of pancreatic cancer in a patient who has undergone surgical treatment of the pancreatic cancer.

The present invention also provides a kit for predicting the prognosis of recurrence of pancreatic cancer, comprising a computer having reagents, devices and algorithms for detecting one or more markers according to the present invention, Prediction of the recurrence of pancreatic cancer.

According to still another aspect of the present invention, there is provided a method for detecting a marker for predicting recurrence prognosis from a test subject in order to provide information necessary for predicting a recurrence prognosis, Alpha-2-antiplasmin, Lumican, Apolipoprotein CIII, Alpha-2-macroglobulin, Complement C4 gamma chain chain elongation factor Tu, Triosephosphate isomerase and Carbonic anhydrase V, which are selected from the group consisting of Lamin A / C, Lamin A / C, Elongation factor Tu, Detecting the presence or amount of one or more markers to be detected; And correlating the presence or amount of the one or more markers with a predictive value of a pancreatic cancer recurrence prognosis for the patient.

According to another aspect of the present invention, there is provided a pharmaceutical composition comprising Alpha-2-antiplasmin, Lumican, Apolipoprotein CIII, Alpha-2-macroglobulin, Complement C4 gamma chain, Lamin A / C, Elongation factor Tu, Triosephosphate isomerase, And Carbonic anhydrase V. The present invention also relates to a microarray for predicting the prognosis of recurrence of pancreatic cancer comprising at least one protein selected from the group consisting of Carbonic anhydrase V,

According to the present invention, among the patients who have undergone surgical treatment of pancreatic cancer, proteins that have been specifically overexpressed or expressed in a patient / lesion in which pancreatic cancer has recurred can be usefully used as biomarkers for predicting the prognosis of recurrence of pancreatic cancer , And the marker proteins may suggest recurrence before the concurrent chemoradiotherapy of each patient. Therefore, the marker proteins may be used as a basis for maximizing the survival rate by adjusting the intensity of the concurrent chemoradiotherapy or substituting other active intervention .

1 is a graph showing the survival rate according to stage of pancreatic cancer patients (Jang Jin-young et al., Korean Simple Pancreatectomy Society 2004; 8: 85-91) (75% stage IA or 40% stage IB) 5 year survival rate).
2 is a process chart showing a schematic process of performing MRM in the present invention.
FIG. 3 is a screenshot illustrating a process of selecting a transition using the MIDAS workflow program.
4 is a schematic diagram of the entire experiment.
FIG. 5 is a schematic view showing a process of qualitative and quantitative analysis by performing three iTRAQ experiments on cancer tissues and normal tissue regions of six pancreatic cancer patients. FIG.
FIG. 6 is a graph showing the frequency according to the number of journals of the number of proteins analyzed in the results of proteome data mining and gene expression data mining.
7 is a Venn diagram comparing and analyzing the results of iTRAQ and data mining.
Figure 8 16 is a schematic view showing the process of separating peptides from serum samples before concurrent chemoradiotherapy and performing MRM analysis in 16 patients with recurrent pancreatic cancer and 19 patients with recurrent pancreatic cancer after surgical treatment of pancreatic cancer.
FIGS. 9A to 9I are graphs showing ROC (Receiver Operating Characteristic) curves and interactive graphs of nine proteins showing differences in expression in MRM analysis.
FIGS. 10A through 10G are graphs comparing ROC curves for marker combinations and curves of complement C4 gamma chains according to an embodiment of the present invention. 10a is a combination of complement C4 gamma chain and alpha-2-antiplasmin, and area under curve (AUC) sum is 0.812. 10b is a combination of complement C4 gamma chain, alpha-2-antiplasmin, Is 0.819, 10c is the combination of complement C4 gamma chain, alpha-2-antiplastin, lamin A / C and extension factor Tu, the AUC sum is 0.832 and 10d is the complement C4 gamma chain, alpha-2-antiparasmin , Lamin A / C, elongation factor Tu and triosulfate isomerase, the AUC sum is 0.836, 10e is the complement C4 gamma chain, alpha-2-antiplasmin, elongation factor Tu, lumican and triosulfate Isomerase, the AUC sum is 0.826, and 10f is the complement C4 gamma chain, alpha-2-antiplasmin, extension factor Tu, lumican, apolipoprotein CIII, triosulfate isomerase and carbonic acid hard Raiz V, 10g is a combination of complement C4 gamma chain, Al The AUC sum is 0.842 with par-2-antiplasmin, lamin A / C, lengthener Tu, lumican, apolipoprotein CIII, triosposter isomerase and alpha-2-macroglobulin.

The term "biological sample" or "sample" as used herein includes, but is not limited to, any solid or liquid sample obtained from human or mammals, such as tissue from a particular organ, urine, saliva, whole blood, It does not. According to a preferred embodiment of the present invention, the marker protein of the present invention is contained in a tissue or a blood sample.

In the specification of the present invention, pancreatic cancer includes pancreatic ductal adenocarcinoma, acinar cell carcinoma, neuroendocrine tumor and serous cystadenoma which is a cystic benign tumor, This is a rare case of malignant tumors of the pancreas, including mucinous cystic neoplasm, intraductal papillary mucinous neoplasm (IPMN), and solid pseudopapillary tumor. Of the pancreatic cancer.

The pancreatic cancer relapse prognostic marker of the present invention can be an indicator for the prediction of relapse of pancreatic cancer and can be used to predict the prognosis for recurrence of pancreatic cancer before chemotherapy.

According to a preferred embodiment of the present invention, the marker protein of the present invention can be used in a method for providing information necessary for predicting the recurrence of pancreatic cancer by using a composition or kit for predicting the recurrence of pancreatic cancer.

The term "prognosis" in the context of the present invention is intended to include an object for a particular disease or disorder, that is, to determine whether a subject will have a disease later, to determine the subject's responsiveness to treatment, (e. g., monitoring the status of an object to provide information about the therapeutic efficacy).

 The term "prognostic marker" or " prognostic marker " or "prognostic marker" in the present invention refers to a substance that predicts the recurrence of pancreatic cancer in advance and includes a recurrent pancreatic cancer Proteins or nucleic acids showing an increase or a decrease in the tissues or regions of a patient, organic biomolecules such as lipids, glycolipids, glycoproteins and the like. In the present invention, the marker for predicting the prognosis of pancreatic cancer may be Alpha-2-antiplasmin, Lumican, Apolipoprotein CIII, Alpha-2-macroglobulin, 2-macroglobulin, Complement C4 gamma chain, Lamin A / C, Elongation factor Tu, Triosephosphate isomerase and Carbonic anhydride. (Carbonic anhydrase V), and is a protein whose expression increases or decreases in tissues or blood. Among these markers, Alpha-2-antiplasmin, Lumican, Apolipoprotein CIII and Alpha-2-macroglobulin have been reported to cause recurrence (Lamin A / C), elongation factor Tu (Tu), triosephosphate isomerase (TMA), and the like. And Carbonic anhydrase V decrease expression. As a pancreatic cancer recurrence prognostic marker, any one of the above proteins may be used, but preferably a complex marker containing two or more of these markers.

In one embodiment, in particular, the markers may be used in combination of two or more to be used for predicting cancer recurrence in a pancreatic cancer patient from a control group. A combination of markers according to the present invention which exhibit the best effect for such use can be selected and used, and a person skilled in the art will be able to select an appropriate combination according to the use. In one embodiment, the combination of markers is combined such that the AUC value of the marker is equal to or greater than the AUC value of the complement C4 gamma chain, which is at most 0.763, for example, such a combination may include a complement C4 gamma chain and alpha-2- Combination of plasmin; A combination of complement C4 gamma chain, alpha-2-antiplasmin and extension factor Tu; A combination of complement C4 gamma chain, alpha-2-antiplasmin, lamin A / C and extension factor Tu; A combination of complement C4 gamma chain, alpha-2-antiplasmin, lamin A / C, elongation factor Tu and triosulfate isomerase; A combination of a complement C4 gamma chain, alpha-2-antiplasmin, an extension factor Tu, a lumican and a triosulfate isomerase; A combination of a complement C4 gamma chain, alpha-2-antiplasmin, an extension factor Tu, a lumican, an apolipoprotein CIII, a triosfosporate isomerase and a carnionic anhidase V; A combination of a complement C4 gamma chain, alpha-2-antiplasmin, lamin A / C, prolongation factor Tu, lumican, apolipoprotein CIII, triosfosporate isomerase and alpha-2-macroglobulin, The ROC curves for the combinations are described in Figures 10A-10G.

In another embodiment, the sample in which the marker for predicting the prognosis of recurrence of pancreatic cancer is used is at least one of pancreatic cancer tissue, whole blood, serum or plasma. In another embodiment, the specimen used in the present invention can be used not only for a specimen of a subject requiring prediction of recurrence prognosis of the pancreatic cancer but also for a specimen of a specific type of pancreatic cancer control for comparative analysis.

According to one embodiment of the present invention, the blood of the present invention was collected before the concurrent chemoradiotherapy treatment immediately after the operation, and was determined to be increased or decreased in the blood of the patient group in which recurrence occurred, It can be used for the prognosis of recurrence of pancreatic cancer. Especially, it can be useful for predicting the recurrence prognosis in pancreatic cancer stage 2 or stage 3 in normal control group.

For example, the sequence of each of the above-mentioned markers is a human-derived sequence. For example, the sequence of each DB number listed in [Table 6] may be used, but is not limited thereto. And the like.

The markers of the present invention can be detected at the protein or mRNA level. Methods for detecting protein or mRNA levels are well known, for example, the former may be an antigen-antibody reaction, a substrate that specifically binds to the marker, a nucleic acid or peptide aptamer, a receptor or ligand that specifically interacts with the marker Or may be detected through reaction with cofactors, or a mass spectrometer may be used. Reagents or substances that specifically interact or bind to the markers of the present invention can be used in conjunction with chip-based or nanoparticles.

Detection and expression levels at the mRNA level can be detected using a reverse transcriptase / polymerase chain reaction, RNase protection assay, or Northern blot. In the present invention, detection includes quantitative and qualitative analysis, including detection of presence and absence and detection of the amount of expression, and such methods are well known in the art, and those skilled in the art will appreciate that methods suitable for the practice of the present invention You will be able to choose.

In one embodiment, antibody molecules that specifically bind to a marker that predicts the prognosis for recurrence of pancreatic cancer of the invention are used.

The antibody used in the present invention is a polyclonal or monoclonal antibody, preferably a monoclonal antibody. Antibodies can be produced using methods commonly practiced in the art, such as the fusion method (Kohler and Milstein, European Journal of Immunology, 6: 511-519 (1976)), the recombinant DNA method (US Patent No. 4,816,56) Or phage antibody library methods (Clackson et al., Nature, 352: 624-628 (1991) and Marks et al., J. Mol. Biol., 222: 58, 1-597 (1991)). General procedures for antibody preparation are described in Harlow, E. and Lane, D., Using Antibodies: A Laboratory Manual, Cold Spring Harbor Press, New York, 1999; Zola, H., Monoclonal Antibodies: A Manual of Techniques, CRC Press, Inc., Boca Raton, Florida, 1984; And Coligan, CURRENT PROTOCOLS IN IMMUNOLOGY, Wiley / Greene, NY, 1991, the disclosures of which are incorporated herein by reference. For example, the preparation of hybridoma cells producing monoclonal antibodies is accomplished by fusing an immortal cell line with an antibody-producing lymphocyte, and the techniques required for this process are well known and readily practicable by those skilled in the art. Polyclonal antibodies can be obtained by injecting a protein antigen into a suitable animal, collecting the antiserum from the animal, and then separating the antibody from the antiserum using a known affinity technique.

Such immunoassays can be performed according to various quantitative or qualitative immunoassay protocols developed in the past. The immunoassay format may include, but is not limited to, radioimmunoassays, radioimmunoprecipitation, immunoprecipitation, immunohistochemical staining, enzyme-linked immunosorbant assay, capture-ELISA, inhibition or competitive assay, sandwich assay, flow cytometry, But are not limited to, fluorescent staining and immunoaffinity purification. Methods of immunoassay or immunostaining are described in Enzyme Immunoassay, E. T. Maggio, ed., CRC Press, Boca Raton, Florida, 1980; Gaastra, W., Enzyme-linked immunosorbent assay (ELISA), in Methods in Molecular Biology, Vol. 1, Walker, J.M. ed., Humana Press, NJ, 1984, which is incorporated herein by reference. By analyzing the intensity of the final signal by the above immunoassay procedure, that is, by performing signal contrast with the control sample, the prognosis of recurrence of pancreatic cancer can be predicted.

Reverse transcription PCR (polymerase chain reaction) is a method for detecting a specific gene in a specimen by separating RNA of a sample, specifically mRNA, synthesizing cDNA therefrom, and then using a specific primer or a combination of primers and probes , The presence / absence or expression level of a specific gene can be determined. Such a method is described in, for example, Han, H., Bearss, DJ; Browne, LW; Calaluce, R .; Nagle, RB; Von Hoff, DD. Identification of differentially expressed genes in pancreatic cancer cells using cDNA microarray. Cancer Res 2002 , 62, (10), 2890-6. / Kwang Hyuck Lee, Quatification of DNA as a tumor marker in patients with pancreatic cancer, Korean Journal of Gastroenterology 2005, 46, 226-32.

In the present invention, the marker can also be detected by mass spectrometry. After the protein is separated from the sample, it can be analyzed in the manner described in the examples of the present invention, for example, (Kim J Protein Res 2010, 9, (2), 689-99 (2008), pp. 219-222 / Anderson, L .; Hunter, CL, Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol Cell Proteomics 2006, 5, (4), 573-88.

The marker for recurrence prediction of pancreatic cancer according to the present invention may be used in combination with a plurality of markers. In this case, the marker can be analyzed using an algorithm to improve the accuracy of prediction of recurrence prognosis. The algorithm will be described later.

In another aspect, the present invention relates to a kit for predicting the prognosis of recurrence of pancreatic cancer, said kit comprising Alpha-2-antiplasmin, Lumican, Apolipoprotein CIII, Alpha-2-antiplasmin, Alpha-2-macroglobulin, Complement C4 gamma chain, Lamin A / C, Elongation factor Tu, Triosulfate isomerase A triosephosphate isomerase, and a carbonic anhydrase V. The reagent can specifically recognize one or more markers selected from the group consisting of triosephosphate isomerase and carbonic anhydrase V. The markers included in the kit according to the present invention can be used in combination, and the aforementioned combinations of the markers according to the embodiment of the present invention are as described above.

In the method of the present invention, the above-mentioned detection of the presence / absence or the expression level of the marker is as mentioned above, and the marker of the present invention may contain reagents necessary for detection at the protein or mRNA level. For example, reagents detectable at the protein level may include monoclonal antibodies, polyclonal antibodies, substrates, aptamers, receptors, ligands or cofactors, and the like. These reagents can be incorporated into nanoparticles or chips as needed. Proteins can also be detected using a mass spectrometer.

Reagents that can be detected at the mRNA level include reagents used in reverse transcription polymerase chain reaction, RNase protection assay, northern blotting, and include, for example, primers and probes specific to each marker.

The substance that specifically recognizes such a marker of the present invention may be present separately in a divided container, and in this sense, the present invention also encompasses a molecule capable of specifically recognizing the marker of the present invention / RTI >

The invention also encompasses a computer embedded with reagents, devices and algorithms for detecting one or more markers of the invention, wherein the algorithm associates the detection of the marker with a recurrence prognosis of pancreatic cancer, And a prediction kit. The associating step comprises training the algorithm to compare patterns of expression levels of the one or more markers in a patient group in which no recurrence has occurred or in a group of patients in which recurrence has occurred to derive a pattern of differences in the expression levels.

In one embodiment of the present invention, the algorithm training for deriving the pattern comprises: constructing an algorithm for mapping the marker expression amount given as an input value to a diagnostic or prognosis result given as a calculated value; Performing the algorithm to map the marker emission amount to a recurrence prognosis of pancreatic cancer; And performing the algorithm to implement an optimal algorithm mapping architecture by varying an input value of the constructed algorithm and an output value according to the algorithm, wherein the optimal algorithm mapping is performed in a patient group in which the recurrence has not occurred, The number of marker episodes in the patient group and the number of marker episodes in the subject were used for the prediction of recurrence of pancreatic cancer.

Known algorithms may be used in the present invention, including, but not limited to, linear or non-linear regression algorithms; Pre- or non-linear classification algorithms; ANOVA; Neural network algorithm; Genetic Algorithm; Support vector machine algorithm; Hierarchical analysis or clustering algorithm; A hierarchical algorithm using a decision tree, or a kernel principal components analysis algorithm; Markov Blanket algorithm; recursive feature elimination or entropy - basic recursive feature elimination algorithms; Multiple algorithms arranged in committee network; And a forward floating search or a back floating search algorithm.

A sample used in the kit of the present invention may be derived from one or more of pancreatic cancer tissue, whole blood, serum or plasma, and a sample derived from a patient in need of a diagnosis of recurrence of pancreatic cancer recurrence or a sample derived from a pancreatic cancer control group .

The kit of the present invention can be useful for predicting recurrence prognosis especially in patients with pancreatic cancer or in patients with stage 1 to stage 4, especially stage 2 or stage 3.

In another aspect, the present invention provides a method for detecting a marker from a sample of a subject that is required to be examined in order to provide information necessary for predicting the recurrence of pancreatic cancer, said method comprising administering to said subject a sample containing alpha-2-antiplasmin 2-antiplasmin, Lumican, Apolipoprotein CIII, Alpha-2-macroglobulin, Complement C4 gamma chain, lamin A / C The presence of one or more protein markers selected from the group consisting of Lamin A / C, elongation factor Tu, triosephosphate isomerase and Carbonic anhydrase V Or an amount of expression; And correlating the presence or expression level of the one or more markers with a prediction of a recurrence prognosis of the subject's pancreatic cancer.

The marker for predicting recurrence of pancreatic cancer of the present invention can be used in combination of a plurality of markers, and the combination of markers is as described above.

Detection of the presence / absence or expression level of the marker in the method of the present invention can be determined at the level of protein and / or mRNA expression, as mentioned above.

In addition to the marker analysis results, the method of the present invention can additionally use the non-protein clinical information of the patient, i.e., clinical information other than the marker, in order to provide information on the prediction of the recurrence prognosis of pancreatic cancer. Such non-protein clinical information includes, for example, age, sex, weight, diet, body mass, ultrasonography, CT, magnetic resonance imaging (MRI), angiography, endoscopic retrograde pancreatobiliary, Endoscopy, tumor marker, or laparoscopy.

In the method of the present invention, a marker for predicting the recurrence prognosis of pancreatic cancer can be used in combination of a plurality of markers. In this case, the marker can be analyzed using an algorithm to improve the accuracy of prediction.

In one embodiment, the step of correlating comprises comparing the amount of protein of each determined marker with a threshold value for each marker determined in a control group, wherein the step of correlating the detected marker results with a pancreatic cancer recurrence prognosis prediction, .

In other implementations, the associating step may be performed through a computer algorithm. The algorithm may be a linear or non-linear regression algorithm; Pre- or non-linear classification algorithms; ANOVA; Neural network algorithm; Genetic Algorithm; Support vector machine algorithm; Hierarchical analysis or clustering algorithm; A hierarchical algorithm using a decision tree, or a kernel principal components analysis algorithm; Markov Blanket algorithm; recursive feature elimination or entropy - basic recursive feature elimination algorithms; Multiple algorithms arranged in committee network; And a forward floating search or a back floating search algorithm.

In another embodiment, the step of associating in the present invention may also comprise the steps of: a) determining the expression level of the marker determined in the subject to be tested for recurrence of the pancreatic cancer, based on the expression level of the marker determined in the pancreatic cancer patient group or the recurrent pancreatic cancer patient group Comparing with a numerical value; And b) training the algorithm to derive a pattern of differences in the amount of expression associated with predicting recurrence of pancreatic cancer through the comparison. The amount of protein expressed by each marker can be determined through, for example, but not limited to, an antigen-antibody reaction, reverse transcription PCR, or mass spectrometry analysis, as described above.

In the present invention, the algorithm training for deriving the pattern includes constructing an algorithm for mapping the marker expression amount given as an input value to a recurrence prognosis prediction result given as a calculated value, and executing the constructed algorithm to calculate the marker amount and the pancreatic cancer Mapping a recurrence prognosis; And performing the algorithm to implement an optimal algorithm mapping architecture by varying an input value of the constructed algorithm and an output value according to the algorithm, wherein the optimal algorithm mapping is performed in a patient group in which the recurrence has not occurred, And the number of markers expressed by the subject is used to predict the prognosis of recurrence of pancreatic cancer.

The present invention also provides a microarray for predicting recurrence of pancreatic cancer, comprising an antibody recognizing a marker according to the present invention or a protein encoded by the marker. The microarray of the present invention can be easily produced by a method commonly used in the art using the marker or antibody of the present invention.

In the microarray of the present invention, the term "microarray" means a polynucleotide or an antibody group immobilized on a substrate at a high density, wherein the polynucleotide or antibody group is immobilized in a predetermined region. Such microarrays are well known in the art. Microarrays are described, for example, in U.S. Pat. Nos. 5,445,934 and 5,744,305, the contents of which are incorporated herein by reference. The markers are as described above.

The term "substrate" refers to any substrate on which the marker or oligonucleotide probe can be coupled under conditions that retain hybridization properties and maintain a low background level of hybridization. Typically, the substrate may be a microtiter plate, a film (e.g., nylon or nitrocellulose) or a microsphere (bead) or chip. Before application or fixation to the membrane, the nucleic acid probe may be modified to promote immobilization or improve hybridization efficiency. Such modifications may include homopolymer tailing, coupling with aliphatic groups, different reactive functional groups such as NH2, SH and carboxyl groups, or coupling with biotin, haptens or proteins.

Hereinafter, preferred embodiments of the present invention will be described in order to facilitate understanding of the present invention. However, the following examples are provided only for the purpose of easier understanding of the present invention, and the present invention is not limited by the following examples.

Example  One. iTRAQ  ( Isobaric tags for relative and absolute quantitation )

ITRAQ experiments were performed to analyze quantitative proteomics in pancreatic tissues of normal control and pancreatic tissues of PDAC patients. In other words, the change of protein in normal and cancer tissues showing direct phenotype was confirmed, and the purpose of protein candidates to identify protein changes in tissues was found.

The iTRAQ reagent (Applied Biosystems, USA) is an amine-specific isotope tag that labels the lysine and N-terminal of all peptides and consists of a reporter, a balance, and a peptide reactive group. When the labeled samples are analyzed and analyzed, they appear as a single peak in the MS spectrum due to the influence of the iTRAQ balance, even though each sample is labeled with a different tag (m / z 114-117). Following MS analysis, during the MS / MS analysis by collision induced dissociation (CID), cleavage occurred at the segment of the isotope tag in the middle, resulting in a reduction of the balance, resulting in an iTRAQ reporter group of 114-117 m / lt; RTI ID = 0.0 > z. < / RTI > By quantifying the peak area of the Aion and comparing the relative amounts of the given peptides in each sample, the amount of protein in the sample can be quantified. In addition to MS / MS results, you get b-Aion and y-Aion as well as reporter aion, which will be used to identify proteins.

Tissue sample preparation

To 300 [mu] l of cold PBS treated with pancreatic cancer pancreatic cancer tissue and adjacent normal tissue (100 [mu] g) and treated with a protease inhibitor cocktail (Roche Diagnostics Ltd, Mannheim, Germany) And then ultrasonicated. The supernatant was collected by centrifugation (10,000 g, 30 min, 4 ° C) and stored at -70 ° C until used for iTRAQ labeling. Bicinchoninic acid assay (Sigma-Aldrich, St. Louis, Mo., USA) was used before the test.

iTRAQ  sign

100 ㎍ of each protein separated from normal tissues and cancer tissues of pancreatic cancer patients was dried with speed vac and resuspended in 20 μl of dissolution buffer (0.5 M triethylammonium bicarbonate, 0.05% SDS). After treatment with 1 μl of a denaturant (2% SDS), 2 μl of a reducing agent (50 mM tris- (2-carboxyethl) phosphine) was treated and then kept at 60 ° C for 1 hour. After treating with 1 μL of cysteine blocking agent (200 mM methyl methanethiosulfonate in isopropanol), 20 μl of 0.5 μg / μl trypsin (Promega, Madison, WI, USA) was treated and reacted at 37 ° C for 16 hours. Each sample was mixed with four different iTRAQ reagents (m / z 114-117) and dried with speed vac.

SCX  ( Strong cation exchange ) Chromatography

The sample dried by Speed vac was resuspended in 500 μl of buffer A (5 mM ammonium formate, pH 2.7, 25% acetonitrile). 500 μl of the sample was injected into a Polysulfoethyl A column (PolyLC, Columbia, MD, USA) of HP 1100 series HPLC (Agilent Technologies, Palo Alto, Buffer A for 5 min and then linear gradient from 5% to 30% buffer B (1 M ammonium formate, pH 3, 25% acetonitrile) for 125 min. A total of 63 fractions were separated and dried in a speed vac.

LC - MS / MS  analysis

LC-MS / MS analysis was performed on the samples prepared above using QStar Elite (Applied Biosystems, USA). The dried peptide samples were resuspended in 20 μl Buffer A (water / ACN [98: 2 v / v] and 0.1% formic acid) and 5 μl of them were injected using a nanoLC auto-sampler. Peptides were desalted for 10 min using capillary flow (20 μl / min) using trap column (300 ㎛ id x 5 mm, 5 ㎛, 100 Å, Zorbax 300SB-C18, Agilent Technologies) (300 nl / min) by a Zorbax 300SB-C18 capillary column (75 ㎛ id x 150 mm, 3.5 ㎛, 100 Å) according to a gradient of [2:98 v / v] and 0.1% formic acid Respectively. Separated peptides were qualitatively and quantitatively analyzed using on-line hybrid Quadrupole-TOF LC-MS / MS spectrometer. The mass spectra were analyzed using ProteinPilot (ver.2.0.1), and the human CDS database (human_UnicombinedPANTHER_20061012.fasta) was used as the database.

Example  2: Data mining

Proteome data mining

Proteome data mining is a data table of a total of 18 proteome-related journals using five types of journals (Proteomics, Journal of proteome research, Molecular and cellular proteomics, Gastroenterology, Cancer research) Respectively. The information of the 18 journals used is shown in [Table 4].

Gene expression data mining

Gene expression data mining was performed using the Oncomine database (https://www.oncomine.org). Oncomine DB filter condition was analyzed by differential analysis (Cancer vs normal analysis) and Cancer type: panceratic cancer, and a total of 8 data sets were obtained. Of these, pancreatic carcinoma vs normal data were extracted only for the genes that were top 5% over-expression and top 5% under-expression. The information of the eight journals used in the Oncomine DB is shown in Table 5 below.

Example  3: Multiple Reaction Monitoring  ( MRM )

Blood sample preparation

After surgical treatment, blood samples from 29 patients with pancreatic cancer were collected before concurrent chemoradiotherapy. Serum samples from each patient were quantitated using Bradford, and 200 ug of plasma was taken for urea, And reduced and alkylated using DTT and iodoacetic acid. Here, trypsin was treated at a ratio of 50: 1 (protein: trypsin, w / w) for 16 hours to convert the denatured protein into a peptide, which was then desalted and freeze-dried using C18 ZipTip (Millipore, USA). The solution was dissolved in solution A (95% Distilled Water, 5% acetonitrile, 0.1% formic acid), and 50 moles of β-galactosidase peptide as an internal standard was added thereto.

Transition  selection

In order to perform the MRM analysis, a peptide having a characteristic charge-to-mass ratio (m / z) of a specific protein is selected (Q1) and the peptide / z are selected (Q3). The combination of Q1 and Q3 is a combination of ions specific to a specific protein. This is called a transition. A signal obtained when ions are sequentially passed through these characteristic Q1 and Q3 in a high-resolution (triple quadrupole) And the quantitative analysis is carried out. Transition selection was performed in 154 parts of proteome data mining and oncomine data mining results in iTRAQ. MS / MS analysis was performed for each of these proteins. Based on this, representative peptides for each protein were selected (Q1 transition) and the most intense fragmentation ions (Q3) were selected by electrically breaking the peptides. Two peptides were selected for each protein and two fragment ions were selected for each peptide. Q1 / Q3 was determined as four transitions for one protein. Transitions were selected using the MIDAS (MRM-Initiated Detection and Sequencing) workflow program (MRMPliot, version 2.0, Applied Biosystems, USA) for some of the transitions that were less likely to be selected experimentally. Transitions not captured by the MIDAS workflow program were selected using the PeptideAtlas database (www.peptideatlas.org) to select transitions with high numbers of observed peptides.

LC  And MRM

LC was a MDLC nanoflow Tempo LC from MDS. For the isolation of peptides, C18 resin with a diameter of 3 μm and a pore size of 200 Å was directly filled using a fused sillica capillary column with a length of 15 cm and an inner diameter of 100 μm. 1.0 μl of the peptide sample was injected directly into the analytical column directly without passing through the Trap column and the flow rate was 400 nl / min. The column was equilibrated with SolA (95% Distilled Water, 5% acetonitrile, 0.1% formic acid) for 10 minutes and then eluted with 5% to 85% with SolB (5% Distilled Water, 95% acetonitrile, 0.1% formic acid) The peptide was eluted through a gradient of 85% for 5 minutes.

The mass spectrometer was monitored in MRM mode for the transitions to the primary and secondary pancreatic cancer-specific candidate proteins using a hybrid triple quadrupole / linear ion trap of Applied Biosystems, 4000 QTrap instrument. Ion bolts were used at 2000V and the resolutions in Quadruple 1 (Q1) and Quadruple 3 (Q3) were set as units. The dwell time for the transition was set to 20 milliseconds so that the total cycle time was 2.5 seconds. The spraying gas (Nebulizing gas) was used in 5 units and the heater temperature was set at 150 ° C. 50 fmol beta-galactosidase peptides (Transition 411.3 / 495.3) spiked in each sample were also simultaneously monitored to demonstrate inter-batch variation. The MS run time was run for 60 minutes with LC and time synchronization, and MS and LC were run using Analyst 2.1.2.

Data Analysis

For relative quantification, MRM was quantified at eight concentration points in blank, 0.5, 1.0, 5.0, 10.0, 25.0, 50.0, and 100.0 fmol using a beta-galactosidase peptide (Transition 411.3 / 495.3) ). The MRM results of each individual were generated by ion chromatography (XIC, Extraction Chromatography) of corresponding MRM transitions using MultiQuant (Applied Biosystems, ver 1.0), and the peak area of each transition was calculated and plotted again with time Respectively. The area of one peak of each XIC was normailized to the XICed peak area of the internal standard, beta-galactosidase peptide (Transition 411.3 / 495.3), and relative quantitative analysis of each protein was performed with this value. For statistical analysis, Receiver Operating Characteristic (ROC) curves and interactive plots were generated using MedCalc (MedCalc Software, Belgium, vesion 11.3.3) and ANOVA (Analysis of variance) statistical analysis was performed. Sigma Plot (Systat Software Inc, USA, version 10.1) was used for some plotting and t-test analysis.

Experiment result ( Marker selection )

A summary of the entire experiment and information on the origin of the samples used in the iTRAQ and MRM analyzes are shown in FIG. 4 and Table 1.

Pancreatic cancer tissue Protein body  analysis

Qualitative and quantitative analysis was performed on three cancer tissues and normal tissues of six pancreatic cancer patients by performing three iTRAQ (Isobaric tags for relative and absolute quantitation) experiments (FIG. 5). A total of 1376 proteins were identified in the iTRAQ experiment, and 440 proteins showed a 1.5-fold difference in expression (Table 2).

Of these, 6, 4, 5, 12, 4, and 21, respectively, were increased in 4, 5, 6, and 14 patients, respectively. Protein was reduced in 8, and protein decreased in 3 patients (Table 3).

Data mining  result

18, and 5 kinds of proteome journals were analyzed for proteome data mining. The Journal, Volumn, Page Numbers, Year, Impact Factor (IF), Unique Protein Count, and Article of each journal are shown in Table 4. . As a result, a total of 901 proteins were mined in relation to pancreatic cancer. One of the proteins from 9 journals, 2 from 8 journals, 2 from 7 journals, 4 from 6 journals, and 13 from 5 journals. The number of proteins from four journals was 31. 752 of these were also detected in the Plasma Proteome Database (Fig. 6A).

Gene expression data mining was performed using the Oncomine database (https://www.oncomine.org). Journal, Journal, Impact Factor (IF), Title, Year, Volume, Volumes, and Sample (s) for the 8 journals used in the Oncomine DB. ) Are shown in Table 5. As a result, a total of 8,212 genes were mined in relation to pancreatic cancer. Among the seven journals, there were 3 genes, 17 from the 6 journals, 46 from the 5 journals, and 187 from the 4 journals. 4,281 of these were detected in the Plasma Proteome Database (Fig. 6b).

Comparing iTRAQ results with Proteome data mining, 278 proteins were analyzed in common, and 256 of them were detected in the Plasma Proteome Database. Also, compared with Oncomine data mining, there were 272 proteins in common, 252 of which were filtered in the Plasma Proteome Database. Finally, iTRAQ results showed that 167 proteins were present in all three parts of Proteome data mining and Oncomine data mining, and 154 of them were detected in the Plasma Proteome Database (Fig. 7).

MRM  result

After the surgical treatment, the peptides were isolated from sera from pancreatic cancer patients before the concurrent chemoradiotherapy, and then serum samples were taken from 16 patients who had no recurrence and 19 patients who had recurrence. After the MRM analysis, the peak area for the transition was extracted from the Wiff file to the MultiQuant (ABI, USA, version 1.1). Each peak area was normalized by using the peak area of the internal standard peptide to correct errors due to spray variation between batches. The results of the protein were divided into cancer types and sexes, and the results were analyzed by an interactive plot and ROC (Receiver Operating Characteristic) Curve was created. The ROC curve is widely used to determine the efficiency of diagnostic methods. The ROC curves represent the relationship between sensitivity and specificity on the two-dimensional plane. The larger the area under the ROC curve (AUC), the better the diagnostic method.

Figure 9 shows the ROC curve and interactive plot of each of the nine proteins. A total of 9 proteins showed differential expression, of which 4 were increased in recurrent pancreatic cancer and 5 were decreased. Table 6 below summarizes the AUC of patients with recurrence in patients who did not recur each protein. Proteins showing these differences in expression can be developed as markers for predicting the recurrence of pancreatic cancer by combining one or more of them according to their specificity and sensitivity.

It will be understood by those skilled in the art that the foregoing description of the present invention is for illustrative purposes only and that those of ordinary skill in the art can readily understand that various changes and modifications may be made without departing from the spirit or essential characteristics of the present invention. will be. It is therefore to be understood that the embodiments and experiments described above are illustrative in all aspects and not restrictive.

Claims (23)

A marker composition for predicting recurrence prognosis of pancreatic cancer, comprising a combination of a complement C4 gamma chain and an alpha-2-antiplasmin marker. delete The composition according to claim 1, wherein the composition comprises at least one marker selected from the group consisting of lamin A / C, extension factor Tu, rumicane, apolipoprotein CIII, carbonic acid hardase V and alpha-2-macroglobulin Wherein the marker composition further comprises at least one marker selected from the group consisting of: 2. The marker composition according to claim 1, wherein the prediction is based on one or more of tissue, whole blood, serum or plasma. The marker composition according to claim 1, wherein the marker combination predicts recurrence prognosis of pancreatic cancer after surgical treatment of pancreatic cancer patients before concurrent chemoradiation therapy. 1. A kit for predicting recurrence prognosis of pancreatic cancer, the kit comprising a reagent capable of detecting a combination of a complement C4 gamma chain and a marker of Alpha-2-antiplasmin, A kit for predicting recurrence of pancreatic cancer. 7. The kit for predicting recurrence of pancreatic cancer according to claim 6, wherein the detection reagent is a reagent capable of detecting the marker at a protein or mRNA level.
The method of claim 7, wherein the reagent detectable at the protein level is a monoclonal antibody, polyclonal antibody, substrate, aptamer, receptor, ligand or cofactor, or a reagent for detecting a mass spectrometer , A kit for prediction of recurrence of pancreatic cancer.
[Claim 7] The kit for predicting recurrence of pancreatic cancer according to claim 7, wherein the reagent which can be detected at the mRNA level is a reagent used for reverse transcription polymerase chain reaction, RNase protection assay, northern blotting.
delete 7. The kit according to claim 6, wherein the kit comprises one or more markers selected from the group consisting of lamin A / C, elongation factor Tu, lumican, apolipoprotein CIII, carbonic acid hardase V and alpha-2-macroglobulin Wherein the reagent further comprises a detectable reagent for detecting the prognosis of recurrence of pancreatic cancer. [Claim 7] The kit for predicting recurrence of pancreatic cancer according to claim 6, wherein the marker combination predicts recurrence of pancreatic cancer after surgical treatment of pancreatic cancer patients before concurrent chemoradiation therapy. A method for detecting a marker from a specimen of a test subject in order to provide information necessary for prediction of recurrence of pancreatic cancer,
The method comprises the steps of: detecting the presence or amount of a marker combination of a Complement C4 gamma chain and Alpha-2-antiplasmin in a sample of the test subject; And
And associating the presence or amount of the marker combination with a prediction of a recurrence prognosis of the subject's pancreatic cancer,
Wherein the prediction is diagnosed as a recurrence of pancreatic cancer at an increased expression level of the marker combination. 14. The method according to claim 13, wherein the specimen is tissue, whole blood, serum or plasma.
14. The method of claim 13, wherein the presence or amount of the marker combination is determined at a protein or mRNA expression level. 14. The method of claim 13, wherein the associating further uses non-marker clinical information of the subject.
The method according to claim 16, wherein the non-marker clinical information of the examinee includes at least one of age, sex, weight, dietary habit, body mass, ultrasonic wave, CT, magnetic resonance imaging (MRI), angiography, A planet pancreatic duct angiography, an ultrasound endoscopy, a tumor marker, or a laparoscopic examination.
14. The method of claim 13, wherein the associating step comprises comparing the amount of marker combination with a threshold value for a marker combination determined in a control. 14. The method of claim 13, wherein the associating step detects a marker combination performed using a computer algorithm. 20. The method of claim 19, wherein the algorithm comprises a linear or nonlinear regression algorithm; Pre- or non-linear classification algorithms; ANOVA; Neural network algorithm; Genetic Algorithm; Support vector machine algorithm; Hierarchical analysis or clustering algorithm; A hierarchical algorithm using a decision tree, or a kernel principal components analysis algorithm; Markov Blanket algorithm; recursive feature elimination or entropy - basic recursive feature elimination algorithms; Multiple algorithms arranged in committee network; And a forward floating search or a backward floating search algorithm.
14. The method of claim 13,
a) comparing the expression level of the marker combination determined in the subject to predict the recurrence of the pancreatic cancer with the expression level of the marker combination determined in the pancreatic cancer patient group in which no recurrence has occurred or in the recurrent pancreatic cancer patient group; And
b) training the algorithm to derive a pattern of the difference in the amount of expression associated with a recurrence of pancreatic cancer through the comparison. 22. The method of claim 21, wherein training the algorithm to derive the pattern comprises:
Constructing an algorithm for mapping an expression amount of the marker combination given as an input value to a recurrence prognosis prediction result given as a calculated value;
Mapping the amount of occurrence of the marker combination to whether the pancreatic cancer recurred or not by executing the constructed algorithm; And
Performing an algorithm mapping architecture by implementing the algorithm while varying an input value and an output value of the constructed algorithm, wherein the algorithm mapping is performed using a marker of the patient group in which the recurrence has not occurred, Wherein a significant difference is identified using the expression level of the combination of the marker and the marker combination of the subject to be tested and used for predicting the recurrence prognosis of pancreatic cancer. A microarray for predicting the prognosis of recurrence of pancreatic cancer comprising a combination of a complement C4 gamma chain and an alpha-2-antiplasmin marker.

Images