KR20180120615A - A high-throughput disease diagnostic system by fingerprinting of blood protein and metabolome based on MALDI-TOF mass spectrometry - Google Patents

Abstract

The present invention relates to a superhigh speed disease diagnostic system using fingerprinting of a blood protein and metabolome based on MALDI-TOF mass spectrometry, and according to the present invention, the analysis of the fingerprinting of the blood protein and metabolome based on the MALDI-TOF mass spectrometry enables epoch-making time shortening and large-scale analysis of disease diagnosis, less cost and time burden due to a diagnosis method through blood analysis, and eliminates the objection to diagnosis of the patient, so it is easy to access disease diagnosis. In addition, it is possible to perform diagnosis with high accuracy and reliability with double checking by comparing and analyzing the diagnosis result by metabolism extraction in blood and database construction and diagnosis result using protein based database in blood. In addition, the present invention can be applied to various diseases by a database-based diagnosis method to have excellent expandability of techniques, and since health conditions of people can be made into barcode through barcode database construction of mass analysis of blood analysis, can contribute to socially effective national health care. In addition, the system is a diagnosis method which can be performed through simple education, rather than a diagnosis method requiring a skilled expert, which has a merit that the barrier to entry of technology is low and the power of technology is strong.

Description

[0001] The present invention relates to a high-throughput disease diagnostic system using blood protein and metabolism fingerprinting based on MALDI-TOF mass spectrometry,

The present invention relates to a high-speed disease diagnosis system using blood protein and metabolism fingerprinting based on MALDI-TOF (matrix-assisted laser desorption / ionization time-of-flight mass spectrometry) mass spectrometry.

Human serum is one of the most important body fluids used clinically for the detection and diagnosis of cancer biomarkers. The sample may include proteins that are synthesized or secreted from cancer tissues and cells of the human body as well as serum-specific proteins. Thus, the serum protein profile can reflect an individual's health and disease status, and since it is relatively easy to collect serum samples, the cancer biomarker protein present in the serum is the most common target for diagnostic assays and treatments Can be used. For example, it has been shown that a-fetoprotein (AFP), carcinoembryonic antigen (CEA), prostate-specific antigen (PSA), immunoglobulin and chorionic gonadotropin Several cancer biomarkers present in serum, such as hCG, have generally been used as clinical monitoring parameters. However, specific cancer biomarker-based screening has disadvantages of false positives and low specificity due to other immunological events that can raise the level of biomarker protein. Therefore, it is urgent to develop an innovative and reliable method for serum-based diagnosis of cancer.

Most cancer diagnostic techniques have relied on immunoassays or mass spectrometry. A conventional ELISA (enzyme-linked immunosorbent assay; ELISA ) for, and determine the cancer biomarkers from a mixture of serum proteins using a specific antibody to quantify, which is accurate, specific and sensitive advantages (pg mL ^{- 1} level of detection limit). In addition, ELISA is being used in the target evaluation of biomarkers in many hospitals and diagnostic institutions because it can rapidly test large numbers of samples in parallel. However, ELISA has several disadvantages, such as quantitative determination of multiple biomarkers, high initial costs for developing methods, and the like. In particular, the number of serum protein biomarkers available for ELISA-based cancer screening and their specific antibodies is still limited.

Mass spectrometry, on the other hand, has been used as a diagnostic method for screening new serum protein biomarkers and identifying multiple biomarkers simultaneously. For this purpose, liquid chromatography tandem-mass spectrometry (LC-MS / MS) can be applied to both non-targeted and targeted serum protein biomarker screening. Recently, multi-biomarker identification and monitoring has been achieved with target proteomics (multi-reaction monitoring (MRM) or parallel reaction monitoring (PRM)) using high resolution LC-MS / MS. LC-MS / MS provides quantitative reproducibility through high selectivity, high sensitivity, and reliable identification of target peptides, but is not suitable for robust, high-throughput analysis to treat large numbers of patient serum samples.

Therefore, we would like to develop a simple yet powerful method of cancer diagnosis through full profiling of serum proteins based on MALDI-TOF (matrix-assisted laser desorption / ionization time-of-flight mass spectrometry). MALDI-TOF MS can provide fast processing time and high-speed processing analysis platform. The present invention provides a novel technique for diagnosing cancer without pretreatment of a serum sample or a MALDI plate using a serum protein fingerprinting method based on MALDI-MS.

Korean Registered Patent No. 10-1214317 (registered on December 13, 2012)

It is an object of the present invention to provide a system for diagnosing super-fast disease using blood protein and metabolism fingerprinting based on MALDI-TOF mass spectrometry.

In order to accomplish the above object, the present invention provides a method for analyzing serum protein spectral data by performing matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF) mass spectrometry on a serum sample separated from a patient group and a control group Data acquiring means for acquiring data; MSP library conversion means for converting the spectrum data into a main spectrum profile (MSP) library; Real-time matching score converting means for comparing the MSP library of serum samples separated from the subject suspected of being a patient with the MSP library of the patient group or the control group, respectively, to convert the MSP library into a matching score; And diagnostic means for diagnosing whether or not the object suspected to be a patient is diseased using the matching score.

According to the present invention, blood protein and metabolism fingerprinting analysis based on MALDI-TOF mass spectrometry enables epoch-making time shortening and mass analysis of disease diagnosis, and it is possible to reduce the cost and time burden by a diagnostic method through blood analysis It is easy to access disease diagnosis by eliminating the objection to the diagnosis of the patient. In addition, diagnosis and diagnosis with high accuracy and reliability can be performed by double checking by comparing and analyzing the diagnosis result using metabolism extraction and database construction in blood and the diagnosis result using protein based database in blood. In addition, since the present invention can be applied to various diseases by a database-based diagnosis method, the technology can be expanded, and a bar-code database of a mass analysis bar code database for blood analysis can be barcoded to secure a human health condition. It can contribute to health care. In addition, it is a diagnosis method that can be performed through a simple education rather than a diagnostic method requiring a skilled expert in the past.

FIG. 1 schematically illustrates a conventional diagnostic method.
2 shows a system for diagnosing a super-high-speed disease using blood protein and metabolic fingerprinting based on MALDI-TOF mass spectrometry.
FIG. 3 shows MALDI-TOF mass spectrometry using sera from the experimental group (liver cancer patient) and the control group (normal human) to collect protein profiles and convert them into a main spectrum profile (MSP) The relative strength of the group.
FIG. 4 is a graph showing the possibility of diagnosing a disease by an automated matching system by applying a serum sample to an experimental MSP and a control MSP.
FIG. 5 is a graph showing the possibility of diagnosing a disease by partial least squares discrimination analysis by applying a serum sample to an experimental group MSP and a control group MSP.

Currently, there are various diagnostic methods such as blood tests, ultrasonic tests, and histological examinations as typical diagnostic methods of diseases. However, conventional methods of diagnosis of diseases are divided into three types as blood test, image test, and biopsy as shown in Fig. First, a blood test for hepatic blood test was widely used to diagnose the difference between α-fetoprotein (AFP) and normal human blood. However, the above blood test is inferior in accuracy, and in addition to diseases, pregnancy, inflammation, etc., there is a problem that the accuracy is low due to the possibility of misdiagnosis. Secondly, there are two types of imaging methods: liver ultrasound, computerized tomography (CT), magnetic resonance imaging (MRI), and hepatic angiography. , And it has been widely used because of its high accuracy. Image examination methods such as CT and MRI have a disadvantage in that diagnosis cost is high, diagnosis takes a long time, diagnosis process is complicated, and skilled manpower such as skilled experts is needed. Finally, if the results are not obtained by either of the above methods, there is a method of diagnosing a tumor by conducting a histopathological analysis by performing a needle biopsy, which involves piercing a thin needle under ultrasound or computed tomography. However, as described above, the diagnostic method of the conventional disease is complicated and it is not suitable for analyzing a large amount of samples in a short time, and thus it is urgently required to develop a new disease diagnosis system.

Accordingly, the inventors of the present invention intend to provide a system for diagnosing a super-high-speed disease using blood proteins and fingerprinting based on MALDI-TOF mass spectrometry as shown in FIG.

The inventors of the present invention diluted the serum of the experimental group (liver cancer patients) and the control group (normal persons) with distilled water and collected the MALDI-MS protein profile through the Flexcontrol without further purification or separation process and measured them using MALDI Biotyper And converted to a main spectrum profile (MSP). The MSP of the experimental group and the control group can be used as a platform to diagnose diseases with high accuracy and reliability from a small amount of blood samples and to diagnose and classify diseases by high speed processing through an automated matching system.

Accordingly, the present invention provides data acquisition means for acquiring serum protein spectral data by performing mass-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF) mass spectrometry on a serum sample separated from a patient group and a control group; MSP library conversion means for converting the spectrum data into a main spectrum profile (MSP) library; Real-time matching score converting means for comparing the MSP library of serum samples separated from the subject suspected of being a patient with the MSP library of the patient group or the control group, respectively, to convert the MSP library into a matching score; And diagnostic means for diagnosing whether or not the object suspected to be a patient is diseased using the matching score.

The serum sample may be diluted to 2 to 8% by volume with distilled water, but it is not limited thereto.

The serum protein spectral data can be obtained by loading and drying the serum samples of the patient group and the control group on a MALDI plate, loading the MALDI matrix solution, and performing MALDI-TOF mass spectrometry.

The MALDI matrix solution may include acetonitrile, trifluoroacetic acid (TFA), and? -Cyano-4-hydroxycinnamic acid (HCCA) .

As shown in FIG. 3, the MSP library converting means for converting the mass spectrum obtained from the MALDI flexcontrol software may be, but is not limited to, Biotyper 3 software.

The real-time matching point conversion means, which can check the homology of the clinical sample with the main spectrum profile (MSP) of the experimental group and the control group converted through the Biotyper 3 software, may be Biotyper RTC software, It is not limited.

The matching score is displayed in a range of 0 to 3 and the diagnosis means can judge that the object suspected to be a patient is highly homologous with a group having a matching score close to 3 out of the matching scores of the patient group or the control group .

The patient may be a cancer patient, and the cancer may be cancer of the liver, lung cancer, skin cancer, non-small cell lung cancer, colon cancer, bone cancer, pancreatic cancer, head or neck cancer, uterine cancer, ovarian cancer, rectal cancer, gastric cancer, Endometrioid carcinoma, thyroid carcinoma, papillary carcinoma, soft tissue sarcoma, soft tissue sarcoma, urethral cancer, penile cancer, endometrioid carcinoma, endometrial carcinoma, endometrial carcinoma, cervical carcinoma, vaginal carcinoma, vulvar carcinoma, Hodgkin's disease, (CNS), primary central nervous system lymphoma, spinal cord tumor, brainstem glioma, and / or neuroendocrine carcinoma, in a mammal, including a human, Pituitary adenoma, and pituitary adenoma.

Hereinafter, the present invention will be described in more detail with reference to Examples. It is to be understood by those skilled in the art that these embodiments are only for describing the present invention in more detail and that the scope of the present invention is not limited by these embodiments in accordance with the gist of the present invention .

Example 1: Chemicals and reagents

Acetonitrile was purchased from Junsei Chemical Co. (Tokyo, Japan), and distilled water and trifluoroacetic acid (TFA) were purchased from Sigma-Aldrich (St. Louis, Mo., USA). α-cyano-4-hydroxycinnamic acid (HCCA), a peptide standard and a bacterial test were purchased from Bruker (Bremen, Germany).

Example 2: Serum collection of experimental group (liver cancer patient) and control group (normal person)

The extracted blood was left in the tube for at least 20 minutes for reaction with the coagulant. All serum samples were collected according to the standardized protocol of Kyungpook National University Hospital after approval by the institutional review board (IRB). Blood was added to a 10 cc tube (BD Vacutainer SST II Advance REF 367953) and serum was collected by centrifugation at 2,500 rpm for 5 minutes. Serum samples were dispensed into cryovials in 300 μl aliquots and stored at -80 ° C or in a liquid nitrogen tank until the experiment.

Serum samples were obtained from 54 liver cancer patients. The mean age of patients with liver cancer was 57.2 years (range, 32.6 to 90.3 years), and the experimental group (patients with liver cancer) included all subtypes of pathologically proven common subtypes. The control group (normal persons) was 99 healthy persons without cancer and the mean age was 50.0 (25.9 ~ 76.7) years.

Example 3: MALDI-TOF mass spectrometry and MALDI Biotyper

Serum from the experimental group (n = 40) and control group (n = 80) was diluted with distilled water to 4% (v / v). 1 μl of diluted serum was immediately loaded onto MSP 96 target polished steel MALDI plates and dried at room temperature. Thereafter, 1 μl of a solution obtained by dissolving 2 mg of α-cyano-4-hydroxycinnamic acid matrix in 250 μl of 47.5% by volume of distilled water-50% by volume of acetonitrile-2.5% by volume of trifluoroacetic acid was titrated.

After drying, the Bruker Daltonics Microflex LRF MALDI-TOF MS (Bruker, Bremen, Germany) was calibrated using a mixture of peptide standards and bacterial test standards (Bruker Daltonics, Bremen, Germany) : Cation and linear mode, detector value = 7.0, laser frequency = 60.0 Hz, laser output = 44%. A mixture of standard materials was applied to cover the low mass range (m / z 1,000 to 2,000). Each spectrum of the experimental and control groups was then obtained by collecting spots per 240 shots using AutoeXecute, with the following conditions: mass range (m / z 1,000-20,000), peak resolution> 400 ppm , Laser output = 52 ~ 56%. For equally detected peaks, including different peaks in each serum, the obtained spectra were visually evaluated for MSP compliance by a threshold according to the protocol provided by the manufacturer. That is, using the Flexanalysis 3.3 software (Bruker, Bremen, Germany), the spectra containing the peaks with the two most different spectral errors less than 500 ppm were used for the MSP, and the samples were reanalyzed for more than 500 ppm.

Then, using the Biotyper 3 software (Bruker, Bremen, Germany) as shown in FIG. 3, the spectra obtained from the sera of the experimental group and the control group were converted into line spectrums and unified into MSPs. That is, each MSP contains a precipitate of all sera from each experimental and control group. The MSP heterogeneity between the experimental group and the control group was also confirmed using the software and the matching score.

Finally, the integrated MSP was validated using a Biotyper RTC software (Bruker, Bremen, Germany) and a matching between the experimental set (n = 13) and the control (n = 18) blinded test set and the integrated MSP. As described above, the test set samples were automatically matched with the integrated MSPs converted by the Biotyper 3 software method using Biotyper RTC software. At this time, the agreement of the spectrum peaks and intensities of the integrated MSP in the line spectrum form and the blind test set is shown as the matching score in the range of 0 to 3. When the matching score is close to 3, the integrated MSP and the new spectrum (See FIG. 4).

Example 4: Multivariate analysis < RTI ID = 0.0 >

The acquired data set was processed with SIMCA-P + 12.0 software (Umetrics, Sweden) for multivariate statistical analysis. The data was adjusted to a pareto-scale to reduce the effects of baseline deviation and noise. Principal component analysis (PCA) was performed to identify outliers, and partial least squares analysis (PLS-DA) was performed to distinguish the control and experimental set of samples. Significant peaks associated with group separation were explained by variable importance in projections (VIP > 2) and fold change values between groups of 2 or more or less than 0.5.

Experimental Example 1: Pattern recognition system for MALDI mass spectrometry for diagnosis of liver cancer

To verify the cancer diagnosis platform, three steps of analysis verification, clinical verification and clinical application were performed. First, analytical verification was performed to determine whether the sample preparation process was appropriate. In addition, it was tested whether the mass spectral patterns were different between the experimental group and the control group. The collected serum was diluted with distilled water and then used for MALDI-MS profiling. The procedure was completed within 10 seconds for each serum sample, without additional purification steps that were time consuming. Compared to previous studies based on serum protein profiles using surface-enhanced laser desorption / ionization (SELDI), significant time savings were seen. In particular, SELDI has the disadvantage that it requires a process of fixing the antibody as a plate preparation. After sample preparation, mass spectral collection of sera from experimental and control groups was performed automatically. As a result, it was confirmed that the sample preparation process was suitable by confirming the spectral quality at more than 2,000 horizontal baselines and intensity per 40 shots. It was also confirmed that the experimental group and the control group had different mass patterns.

In the next phase of clinical validation, serum protein profiles were converted to integrated MSPs to detect control (n = 80) and experimental (n = 40), respectively. Subsequently, the heterogeneity of the integrated MSPs was confirmed by comparative scoring using Biotyper 3 software and the possibility of classification of liver cancer by Biotyper RTC was confirmed. In the final step, a clinical application of the Biotyper RTC was performed to confirm whether serum-based classification platforms for liver cancer provide definite results.

Experimental Example 2: Analysis and Clinical Validation

One of the important requirements for cancer screening platforms is high-speed processing, and the process and the type of sample (serum or tissue, etc.) also make up a significant portion of the availability of the platform. Various mass spectrometry methods have been used to diagnose cancer by the protein profile of the sample, but there are some disadvantages. According to a recent study (Geoffrey Barioude et al.), Tissue-based MALDI-TOF mass spectrometry, without any surface treatment on the chip, has been reported to show satisfactory results for lung cancer diagnosis. However, this method has a disadvantage that the preparation process is simple, but the tissue as an assay sample is not suitable for high-throughput screening.

Therefore, in the present invention, serum was used as a sample for high-speed processing and diluted with water to prepare. Subsequent samples (liver cancer patient (n = 40), control group (n = 80)) were analyzed by MALDI-TOF mass spectrometry and the entire profile of serum proteins was automatically collected by Autoexecute: resolution> 300 ppm, range: 1,000 to 8,000. The quality of the obtained spectra was confirmed with a horizontal baseline and intensity of 3,000 or more per 40 shots, and the spectrum is shown in FIG. As a result, it was confirmed that the spectra of the experimental group and the control group had different mass profiles before being converted to MSP. In particular, the peaks of the mass spectra obtained in the experimental and control sera were different in the low mass range (<10 kDa). In addition, the relative intensity of each mass peak in the experimental group serum was higher than that of the whole control serum.

Within the experimental group, the mass spectra of each sample were slightly different, but the major peaks detected were not different, and peaks with low detection frequencies in most mass spectra were not converted to MSP through the minimum required peak frequency parameter. Therefore, the serum spectrum of each experimental group and the control group was converted to the integrated MSP by Biotyper 3 with the minimum required peak frequency parameter set at 25% in the MSP generation method. This eliminates unnecessary peaks in the classification through the integrated MSP construction. Through the parameter setting, the integrated MSP contains the frequently detected major peaks, and then the integrated MSP was applied to the clinic.

Experimental Example 3: Clinical application

MALDI-MS analysis confirmed whether the test sample (liver cancer serum) could be accurately classified into experimental group or control group. Biotyper-RTC analysis results, commonly used to classify bacteria through automated MALDI assays, are scored. In this experiment, comparative scores were applied to compare the spectra of the MSP and the sample. Scores range from 0 to 3, with scores closer to 3 indicating a higher probability of homology between the integrated MSP and the analyzed spectrum from the database. A verification test of the integrated MSP was performed to confirm whether the classification method for diagnosis of liver cancer can provide reliable results.

The control group (n = 18) was used to validate the integrated MSP for liver cancer diagnosis. The control test set was consistent with the control serum total MSP with a high mean matching score of 2.558 and a standard deviation of 0.130 (see Tables 1 and 2 below). However, this was consistent with an integrated MSP of liver cancer serum with an average score of 1.632 (Table 1). As a result, it was confirmed that the specificity of the combined MSP was 100%, and automatic classification was performed using the liver cancer serum (n = 13) test to confirm the sensitivity. The liver cancer test set was consistent with liver cancer serum total MSP with an average matching score of 2.271 and a standard deviation of 0.123 (see Tables 1 and 3 below). The difference between the control and experimental groups was 0.614 to 1.206. However, this was consistent with the serum total MSP of the control group with an average matching score of 1.381. From the above results, the sensitivity of the integrated MSP to liver cancer was confirmed. FIG. 4 represents only the results of liver cancer sample 1 (Liver Cacer 1) and control serum 8 (Healthy Control 8).

On the other hand, the mean difference in match scores consistent with experimental MSP and control MSP was 0.891 in the experimental test set and 0.926 in the control test set. The difference in scores was considered sufficient to classify liver cancer according to Bruker's guideline and provides a degree of discrimination based on a 0.3 point difference for microbiological identification: score 2.3 to 3.0: highly probable species identification, 2.0 to 2.299 : Reliable identification or species identification, 1.7 to 1.99: Identification, 0.0 to 1.69: Unreliable identification. In addition, the reproducibility of the MALDI-based classification must be performed because the mass spectral patterns through MALDI analysis, i.e., intensity and m / z depend on the raster spot. Therefore, the classification was repeated three times and the reproducibility was confirmed to be 100%. This method was found to provide consistent results for the diagnosis of liver cancer, and was also optimized for RTC sample preparation, MSP conversion and classification methods. A clear difference in the peak patterns in the serum mass spectra of the experimental and control groups appeared to produce a stable reproducibility and therefore PLS-DA was performed to determine how different MSPs were.

Experimental Example 4: Partial least squares discriminant analysis (PLS-DA)

Partial least squares discriminant analysis was used to determine whether there was a statistically significant difference in the MALDI-MS profiles between the experimental group and the control serum samples. Referring to FIG. 5A, except for six healthy control sera, the experimental group and the control group were classified as the first component which obtains the spectrum information of each serum sample in the score chart, and it was confirmed that the experimental group was scattered more than the control group All test samples showed negative scores (about -20 to -48), whereas samples from the control group showed almost positive scores (about 2 to 10), which means that the experimental group and the control group clearly differentiated from each other.

Figure 5B shows a PLS-DA plot of a test set and an integrated MSP. The results show a pattern similar to FIG. 5A: control group-MSP and control group-T (control test set) were dense while experimental group-MSP and experimental group-T (experimental group test set) were widely scattered. In the blinded study, each database was found to be in close proximity and the statistical results completely separated the experimental MSP and the control MSP and supported the results of the MALDI-MS profile-based liver cancer classification.

In the present invention, a novel method for diagnosing liver cancer based on serum was developed by constructing a reliable integrated MSP using the modified Biotyper method. To validate the integrated MSP, a blinded test was completed within 30 minutes using a serum sample diluted with distilled water. The results were scored and confirmed that the combined MSP provided consistent results. The PLS-DA analysis also confirmed that the results were due to differences in peak patterns. Construction of an integrated MSP that reflects the difference in peak pattern of the serum protein mass spectrum and can be applied to the diagnosis of liver cancer enables the diagnosis of liver cancer using serum. This diagnostic method is easier to sample than the conventional diagnostic method, is simple preparation process, and is suitable for diagnosis of liver cancer because it establishes reliable MSP.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is clearly understood that the same is by way of illustration and example only and is not to be construed as limiting the scope of the invention. Accordingly, the actual scope of the present invention will be defined by the appended claims and their equivalents.

The scope of the present invention is defined by the appended claims, and all changes or modifications derived from the meaning and scope of the claims and their equivalents should be construed as being included within the scope of the present invention.

Claims (7)

Data acquisition means for performing mass-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF) mass spectrometry on serum samples isolated from patients and controls to obtain serum protein spectral data;
MSP library conversion means for converting the spectrum data into a main spectrum profile (MSP) library;
A real-time matching score converting means for comparing the MSP library of serum samples separated from the subject suspected of being a patient with the MSP library of the patient group or the control group, respectively, to convert the MSP library into a matching score; And
Diagnostic means for diagnosing whether or not the object suspected to be a patient is diseased using the matching score;
Wherein the disease diagnosis system comprises: 2. The disease diagnosis system according to claim 1, wherein the serum sample is diluted with 2 to 8 vol% of serum using distilled water. The method according to claim 1, wherein the serum protein spectral data is obtained by loading and drying a serum sample of a patient group and a control group on a MALDI plate, loading the MALDI matrix solution, and performing MALDI-TOF mass spectrometry Disease diagnosis system. 4. The method of claim 3, wherein the MALDI matrix solution is selected from the group consisting of acetonitrile, trifluoroacetic acid (TFA), and? -Cyano-4-hydroxycinnamic acid (HCCA) ) Of the disease diagnosis system. 2. The method of claim 1, wherein the matching score is displayed in a range of 0 to 3,
Wherein the diagnosis means judges that the object suspected to be a patient is highly homologous with a group having a matching score close to 3 out of the matching scores of the patient group or the control group. 2. The disease diagnosis system according to claim 1, wherein the patient is a cancer patient. The method of claim 6, wherein the cancer is selected from the group consisting of liver cancer, lung cancer, skin cancer, non-small cell lung cancer, colon cancer, bone cancer, pancreatic cancer, head or neck cancer, uterine cancer, ovarian cancer, rectal cancer, gastric cancer, Endometrioid cancer, thyroid cancer, pituitary cancer, adrenal cancer, soft tissue sarcoma, urethral cancer, penile cancer, prostate cancer, endometrial cancer, uterine cancer, endometrial carcinoma, cervical cancer, vaginal cancer, vulvar carcinoma, Hodgkin's disease, (CNS) tumors, primary central nervous system lymphoma, spinal cord tumor, brainstem glioma, and pituitary adenoma. The present invention also relates to a method of treating a patient suffering from a chronic or acute leukemia, lymphocytic lymphoma, bladder cancer, kidney or ureteral cancer, kidney cell carcinoma, renal pelvic carcinoma, Wherein the disease diagnosis system is selected from the group consisting of:

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