KR102224910B1 - Method for automated detection and quantification of AIMP2-DX2 signal through RNA silver in situ hybridization - Google Patents

Abstract

The present invention relates to an automated detection method of AIMP2-DX2 signal through the RNA in situ hybridization method, and more particularly, RNA is a splicing variant of AIMP2 in a biological sample obtained from a subject using an in situ hybridization method. It relates to a method of automatically detecting the ratio of the AIMP2-DX2 signal.
According to the method of the present invention, the ratio of AIMP2-DX2 in the same sample can be quantitatively detected and automated, and the accuracy of the diagnosis result is excellent, as well as clinical application using FFPE samples. It can be very useful for classification diagnosis and prediction of prognosis.

Description

RNA silver in situ hybridization method for AIMP2-DX2 signal automation detection method {Method for automated detection and quantification of AIMP2-DX2 signal through RNA silver in situ hybridization}

The present invention relates to an automated detection method of AIMP2-DX2 signal through an in situ hybridization method for RNA, and more particularly, a splicing variant of AIMP2 in a biological sample obtained from a subject using an RNA in situ hybridization method. It relates to a method of automatically detecting the ratio of the AIMP2-DX2 signal.

“AminoacyltRNAsynthetase complex-interacting multifunctional protein 2 (AIMP2)” is a protein composed of four exons and performs the function of inhibiting cancer in cells. (Kim et. al., Nature Reviews Cancer, 11:708, 2011).

When a cell recognizes the tumor necrosis factor-a, the cell contains'tumor necrosis factor receptor associated factor 2 (TRAF2)' and'apoptosis inhibitory protein 2 (cellular inhibitor). of apoptosis protein 2, hereinafter referred to as cIAP2)' binds, and this binding causes chain signaling to command apoptosis to abnormal cells or cancer cells. At this time, AIMP2 plays a role in mediating the binding of these two proteins. However, AIMP2 (hereinafter referred to as'AIMP2-DX2'), which is deficient in exon 2, does not mediate the binding of these two proteins, so it interferes with the transmission of apoptosis commands of abnormal cells or cancer cells, which in turn causes tumor cell death. Inhibition (Choi et. al., PLoS Genetics, 7:e1001351, 2011).

In addition, there is a protein called p53 in the cell, which checks the condition of the cell to proceed with the cell cycle or command the repair or death of abnormal cells, which is ubiquitinated and decomposed by mouse double minute 2 (MDM2). By doing so, the intracellular concentration is regulated. AIMP2 protects p53 from MDM2 in this process and inhibits ubiquitination, thereby preventing its degradation in the cell. However, since AIMP2-DX2 is a mutant that has lost the function of protecting p53, it cannot prevent the cell cycle of abnormal cells or cancer cells from progressing, and as a result, it cannot prevent the growth and differentiation of cancer cells.

Because of these characteristics, AIMP2-DX2 is considered to be an important indicator for determining whether cells have tumorigenesis and cancer prognosis, and it is known that AIMP2-DX2 expression levels are high in patients with poor cancer prognosis, such as lung cancer. (J Cancer. 2017 May 12;8(8):1347-1354. doi: 10.7150/jca.18450. eCollection 2017.). Therefore, detecting the presence and amount of AIMP2-DX2 is a very useful path in diagnosing cancer or predicting the prognosis of cancer. However, until now, a technology that can detect AIMP2-DX2 quickly, accurately and sensitively has not been developed.

Accordingly, as a result of the inventor's dedication to developing an automated method that can detect AIMP2-DX2 quickly and accurately, a probe capable of detecting AIMP2-DX2 using RNA silver in situ hybridization. Was developed, and based on this, the present invention was completed by developing an automated method for calculating the expression level of AIMP2-DX2 in a biological sample obtained from a subject.

Accordingly, an object of the present invention is to provide a method for detecting AIMP2-DX2 comprising the following steps in order to provide information necessary for cancer diagnosis or prognosis:

(a) providing a sample from the subject;

(b) performing silver in situ hybridization of AIMP2-DX2 in cells isolated from the sample using a probe set, and generating a signal to image and image it;

(c) separating an image of a cancer cell region from the image by applying a pre-determined threshold value 1;

(d) setting and applying a predetermined threshold value 2 to select an AIMP2-DX2 signal in the isolated cancer cell region; And

(e) calculating the relative ratio of the AIMP2-DX2 signal to the cancer cell signal according to the following formula.

%AIMP2-DX2 = (AIMP2-DX2 signal / cancer cell signal) × 100

Another object of the present invention is to provide an automated method for calculating a predicted cancer prognosis, including the following steps, in order to provide information necessary for predicting the prognosis of a cancer patient:

(a) providing a sample from the subject;

(b) performing silver in situ hybridization of AIMP2-DX2 in cells isolated from the sample using a probe set, and generating a signal to image and image it;

(c) separating an image of a cancer cell region from the image by applying a pre-determined threshold value 1;

(d) setting and applying a predetermined threshold value 2 to select an AIMP2-DX2 signal in the isolated cancer cell region; And

(e) calculating the relative ratio of the AIMP2-DX2 signal to the cancer cell signal according to the following formula.

%AIMP2-DX2 = (AIMP2-DX2 signal / cancer cell signal) × 100

(f) calculating a predicted cancer prognosis value as the higher the value of %AIMP2-DX2 is, the worse the prognosis of cancer is.

In order to achieve the above object of the present invention, the present invention provides an AIMP2-DX2 detection method comprising the following steps to provide information necessary for cancer diagnosis or prognosis:

(a) providing a sample from the subject;

(b) performing silver in situ hybridization of AIMP2-DX2 in cells isolated from the sample using a probe set, and generating a signal to image and image it;

(c) separating an image of a cancer cell region from the image by applying a pre-determined threshold value 1;

(d) setting and applying a predetermined threshold value 2 to select an AIMP2-DX2 signal in the isolated cancer cell region; And

(e) calculating the relative ratio of the AIMP2-DX2 signal to the cancer cell signal according to the following formula.

%AIMP2-DX2 = (AIMP2-DX2 signal / cancer cell signal) × 100

In order to achieve another object of the present invention, the present invention provides an automated method for calculating a predicted cancer prognosis, including the following steps, in order to provide information necessary for predicting the prognosis of a cancer patient:

(a) providing a sample from the subject;

(b) performing silver in situ hybridization of AIMP2-DX2 in cells isolated from the sample using a probe set, and generating a signal to image and image it;

(c) separating an image of a cancer cell region from the image by applying a pre-determined threshold value 1;

(d) setting and applying a predetermined threshold value 2 to select an AIMP2-DX2 signal in the isolated cancer cell region; And

(e) calculating the relative ratio of the AIMP2-DX2 signal to the cancer cell signal according to the following formula.

%AIMP2-DX2 = (AIMP2-DX2 signal / cancer cell signal) × 100

(f) calculating a predicted cancer prognosis value as the higher the value of %AIMP2-DX2 is, the worse the prognosis of cancer is.

Hereinafter, the present invention will be described in more detail.

The present invention provides an AIMP2-DX2 detection method comprising the following steps to provide information necessary for cancer diagnosis or prognosis:

(a) providing a sample from the subject;

(b) performing silver in situ hybridization of AIMP2-DX2 in cells isolated from the sample using a probe set, and generating a signal to image and image it;

(c) separating an image of a cancer cell region from the image by applying a pre-determined threshold value 1;

(d) setting and applying a predetermined threshold value 2 to select an AIMP2-DX2 signal in the isolated cancer cell region; And

(e) calculating the relative ratio of the AIMP2-DX2 signal to the cancer cell signal according to the following formula.

%AIMP2-DX2 = (AIMP2-DX2 signal / cancer cell signal) × 100

Cancers subject to diagnosis of the present invention are not limited thereto, but colon cancer, cervical cancer, lung cancer, pancreatic cancer, non-small cell lung cancer, liver cancer, colon cancer, bone cancer, skin cancer, head or neck cancer, skin or intraocular melanoma, uterine cancer , Ovarian cancer, rectal cancer, gastric cancer, anal muscle cancer, colon cancer, breast cancer, fallopian tube carcinoma, endometrial carcinoma, vaginal carcinoma, vulvar carcinoma, esophageal cancer, small intestine cancer, endocrine cancer, thyroid cancer, parathyroid cancer, adrenal cancer, soft tissue sarcoma, urethral cancer , Penile cancer, prostate cancer, bladder cancer, kidney cancer or ureter cancer.

Hereinafter, each step is specifically described.

(a) providing a sample from the subject

The sample can be used without limitation as long as it is collected from a subject to be diagnosed with cancer.For example, cells or tissues obtained by biopsy, blood, whole blood, serum, plasma, saliva, cerebrospinal fluid, various secretions, urine, feces Etc. Preferably, it may be a tissue or cell obtained from an organ suspected of being a tumor, but is not limited thereto. The sample of the subject obtained in step (a) may be subjected to an additional treatment process such as formalin fixation and paraffin embedding.

In order to facilitate the morphological observation of cells or tissues, the process of fixing the specimen is the most important. This is because organelles of cells are well maintained in a well-fixed tissue, but insufficient fixation results in an incomplete morphological structure, and excessive fixation can result in loss of antigen and non-specific reactions. Typically, tissue fixatives are classified into coagulation-type fixatives and non-coagulant (denatured) fixatives, and the most commonly used tissue fixative is formalin, a non-coagulant fixative. Formalin penetrates the tissues by about 1mm per hour to achieve tissue fixation. It has the advantage of maintaining the shape of cells well because epitopes are less destroyed and proteins and peptides are cross-linked. In one embodiment of the present invention, the sample was fixed in formalin and embedded in paraffin.

Therefore, the sample of the present invention may preferably be a formalin-fixed paraffin-embedded section (FFPE, paraffin-embedded tissue).

(b) performing silver in situ hybridization of AIMP2-DX2 in cells isolated from the sample using a probe set, and generating a signal to image and image it;

The term "cells isolated from a sample" means cells isolated from the sample provided in step (a) according to a conventional method, or, if the sample is a tissue or a section thereof, a cell included in the tissue or its section itself. can do.

The AIMP2 transcript contains four exons (Fig. 1), and AIMP2-DX2, a splicing variant in which a second exon is deleted by this selective splicing mechanism, is specifically human cancer. It is found in many cells and tissues of cancer patients, and induces tumor formation by competitively binding to p53 and interfering with the normal apoptosis-inducing activity of AIMP2.

In the present invention, the AIMP2 transcript consists of a 1222 bp base, which is shown in SEQ ID NO: 1.

AIMP2-DX2 of the present invention is a variant in which the region of exon 2 in the AIMP2 nucleotide sequence has been deleted.

AIMP2-DX2 protein is a variant in which the region of exon 2 of the AIMP2 sequence is deleted, and the sequence of the AIMP2 protein (312aa version: AAC50391.1 or GI:1215669; 320aa version: AAH13630.1, GI:15489023, BC013630.1) is 312aa version: Nicolaides, NC, Kinzler, KW and Vogelstein, B. Analysis of the 5'region of PMS2 reveals heterogeneous transcripts and a novel overlapping gene, Genomics 29 (2), 329-334 (1995)// 320aa version : Generation and initial analysis of more than 15,000 full-length human and mouse cDNA sequences, Proc. Natl. Acad. Sci. USA 99 (26), 16899-16903 (2002)), which corresponds to exon 2 It is a protein that has a region that has been deleted.

In the present invention, “probe” refers to a nucleic acid fragment such as RNA or DNA corresponding to a few bases or hundreds of bases that can achieve specific binding with the mRNA, and is labeled so that the presence or absence of a specific mRNA can be confirmed. . The probe may be manufactured in the form of an oligonucleotide probe, a single stranded DNA probe, a double stranded DNA probe, an RNA probe, or the like. In an embodiment of the present invention, an oligonucleotide probe was used.

The probe of the present invention may be modified within a range that does not affect the detection of AIMP2-DX2. That is, a sequence having homology with the oligonucleotide of the present invention should be interpreted as being included in the probe of the present invention, and the sequence having the homology refers to, for example, 80% or more homology to a specific base sequence. A sequence having is preferred. The homology is, for example, preferably 85% or more, more preferably 90% or more, more preferably 95% or more, most preferably 96% or more, 97% or more, 98% or more, 99% or more. . That is, in the present invention, the sequence having homology may be a sequence consisting of the specific nucleotide sequence (100% homology), and with respect to the specific nucleotide sequence, one or more bases are substituted, deleted, inserted and/ Alternatively, it may be an added sequence (eg, 80% or more and less than 100% homology).

In the present invention, the probe set is an oligonucleotide complementary to bases 227 to 248 of the base sequence of SEQ ID NO: 1 (specifically hybridizes to the exon 1 region in the transcript of AIMP2) and 456 to 465 of the base sequence of SEQ ID NO: 1 A probe for detection of AIMP2-DX2 that is composed of an oligonucleotide complementary to the first base (specifically hybridizes to exon 3), and is preferably complementary to the base represented by SEQ ID NO: 2, which specifically hybridizes with AIMP2-DX2. It is characterized by that.

In general, in the case of hybridization probes, the longer the length of the target site in the genome, the greater the number of probes can bind, so the signal-to-noise ratio and specificity are high. In other words, it is possible to detect the target site more accurately and with high intensity. On the other hand, the probe of the present invention is designed to accurately and quickly detect each exon in an AIMP2 transcript having a short nucleic acid sequence of about 200 bp and has very high specificity.

“Hybridization” of the present invention means that complementary single-stranded nucleic acids form double-stranded nucleic acids. Hybridization can occur when the complementarity between the two nucleic acid strands is perfect match or even in the presence of some mismatched bases. The degree of complementarity required for hybridization may vary according to hybridization conditions, and in particular, may be controlled by temperature.

The probe set of the present invention can be used for RNA in situ hybridization analysis. In situ hybridization (ISH) includes, but is not limited to, for example, fluorescence in situ hybridization (FISH), color in situ hybridization (CISH) or silver in situ hybridization (SISH)), and is preferably used for in situ hybridization analysis. I can.

Silver in situ hybridization (SISH) includes a metallographic detection scheme for identification and localization of hybridized genomic target nucleic acid sequences. The metallographic detection method involves using an enzyme such as alkaline phosphatase in combination with a water-soluble metal ion and a redox-inert substrate of the enzyme. The substrate is enzymatically converted to a redox-active agent, and the redox-active agent reduces metal ions, causing a detectable precipitate to form (e.g., U.S. Patent Application Publication No. 2005/0100976, PCT Publication No. 2005/ 003777 and US Patent Application Publication No. 2004/0265922). The metallographic detection scheme also includes using oxidoreductases with water-soluble metal ions, oxidizing agents and reducing agents to re-form detectable precipitates (see, for example, US Pat. No. 6,670,113).

A nucleic acid molecule can be detected by labeling the probe used in the method of the present invention with any labeling or labeling molecule capable of binding to DNA. Examples of labels for labeling include, but are not limited to, radioactive isotopes, enzyme substrates, cofactors, ligands, chemiluminescent substances, fluorophores, haptens, enzymes, and combinations thereof. Methods of labeling, and guidelines for selecting suitable labels for different purposes, are described, for example, in Sambrook et al., Molecular Cloning: A Laboratory Manual, Cold Spring Harbor, New York, 1989 and Ausubel et al. , In Current Protocols in Molecular Biology, John Wiley and Sons, New York, 1998).

Examples of labels used for SISH in the present invention, for example, peroxidase, β-D-galactosidase, microperoxidase, horseradish peroxidase (HRP), fluorescein isothiocya Fluorescein isothiocyanate (FITC), rhodamine isothiocyanate (RITC), alkaline phosphatase, biotin and radioactive substances. In addition, in addition to a method of directly detecting a target protein using an antibody bound to a labeled substance, a method of indirectly detecting a target protein using a protein G, protein A, or a secondary antibody bound to a labeled substance may also be used. have. In an embodiment of the present invention, silver ions were reduced by binding HRP chromogenase to the probe.

Additionally, additional details relating to specific detection methods as used in the SISH procedure can be found in [Bourne, The Handbook of Immunoperoxidase Staining Methods, published by Dako Corporation, Santa Barbara, CA].

The step (b) is a step of hybridizing by treating a sample of the subject provided in step (a) with the probe set of the present invention including (i) oligonucleotides and (ii) oligonucleotides. In the step (b), a known method generally used in the art may be used to further amplify the signal, preferably RNAscope (The Journal of Molecular Diagnostics, Vol. 14, No. 1, January 2012). Technology may be used, but is not limited thereto.

In situ hybridization using an RNA probe can display intracellular mRNA by the strong avidity of RNA-RNA hybridization. RNA in situ hybridization is a very useful technique for observing the expression of mRNA directly in tissue sections, but it is a situation that is not easily used because there are differences in methods for each laboratory and the reliability of the results is weak. Northern blot can distinguish the size of mRNA through electrophoresis, whereas RNA in situ hybridization can identify the distribution of mRNA in tissues, especially expression in specific cells. And while Northern blot concentrates all mRNAs through the RNA separation process and concentrates on the membrane through blots, it is easy to confirm the presence of specific mRNAs, whereas in RNA in situ hybridization, the amount of mRNA currently expressed in a specific cell is observed. When only a small amount of mRNA is produced, it becomes difficult to observe its expression. However, since the probe of the present invention can hybridize with very high specificity with each exon in the mRNA of AIMP2-DX2, it is used for RNA in situ hybridization analysis to quickly and accurately determine the expression and expression level of AIMP2-DX2 in a specific cell. Can be detected.

That is, in the present invention, AIMP2-DX2 can be detected very quickly and accurately, as well as quantitatively grasping the ratio by performing the silver in situ hybridization method using a probe set and classifying the detected signal using a software program. .

In the present invention, after the hybridization process is completed, an image may be captured and acquired with an appropriate microscope. In the present invention, the imaging may be used without limitation as long as it is a general optical microscope used in the art.

Meanwhile, in the present invention, the step of reacting the cells isolated from the sample with a cell staining solution may be further included. This procedure is to stain the cells according to the cell staining method commonly performed in the art in order to be able to visually observe the cells isolated from the sample, and in the subsequent procedure, normal cells and cancer cells can be smoothly separated. It is to make it possible.

In the present invention, the cell staining solution may be a material used for staining a cell nucleus, cytoplasm, or mitochondria generally used for cell staining. DAPI (4',6-diamidino-2-phenylindole), methylene blue, acetate carmine, toluidine blue, hematoxylin or Hoechst, etc., which preferably stain the cell nucleus, Eosin, crystal violet or orange G, which stains the cytoplasm, or rhodamine 123, which stains mitochondria, MitoTracker, Janus green B ), tetrazolium salt, DASPMI(Dimethylaminostyrylmethylpyridiniumiodine), DASPEI(2-(4-(dimethylamino)styryl)-N-Ethylpyridinium Iodide), DiOC6(3,3'-dihexyloxacarbocyanine iodide), DiOC7(3,3 '-Diheptyloxacarbocyanine Iodide) or JC-1 (5,5,6,6-tetrachloro-1,1,3,3-tetraethyl-benzimidazolylcarbocyanine chloride), and the like, but is not limited thereto. Preferably, it may be ematoxylin staining the cell nucleus and eosin staining the cytoplasm.

In the step (b) of the present invention, the sequence of performing a silver in situ hybridization method for cells isolated from a sample using the probe set and staining the cells with a cell staining solution is not particularly limited. That is, after performing the silver in situ hybridization method using a probe set, the cells may be stained with a cell staining solution, or may be in the reverse order, or they may be performed simultaneously.

(c) separating an image of a cancer cell region from the image by applying a pre-determined threshold value 1;

In the present invention, step (c) is a step of separating an image of a cancer cell region that satisfies a specific threshold value 1 from the image captured and obtained in step (b) using a software program.

The software program used in the present invention is not limited as long as it is capable of separating cancer cell signals and normal cells from an imaged image and distinguishing between cancer cell signals and AIMP2-DX2 signals. It can be performed using Vision Development, Matlab, or a combination of LabView and MATLAB, and preferably MATLAB software.

In particular, in the step (c) of the present invention, after extracting the RGB image from the image using a software program, the cancer cell region may be separated from the extracted RGB image.

The threshold value 1 of the present invention refers to a value capable of most clearly distinguishing between normal cells and cancer cells by analyzing an image obtained by imaging in step (b) according to an RGB profile.

Accordingly, in step (c), the cancer cell region may be separated by separating an image that satisfies the threshold value by setting a threshold value 1 from a predetermined RGB range through a preliminary experiment.

According to an embodiment of the present invention, a full range of RGB (R: 0 to 255, G: 0 to 255, B: 0 to 255) images are extracted from the image obtained in step (b), and as a result, cancer cells A total of three criteria representing the greatest difference in the RGB profile result values of and normal cells were set. In other words, Red: 170-230, Green: 145-215, Blue: 160-220 have RGB values, and the image area where the value of Red-Green is greater than 6 and the value of Blue-Green is greater than 6 is classified as cancer cells. Can be, and a threshold value of 1 is set to detect this.

In the present invention, the threshold value 1 may vary depending on the type of labeling material attached to the probe hybridizing with AIMP2-DX2 and the type of cell staining solution. It is possible to set the range of the threshold value 1 that can most accurately classify.

Thereafter, the image region satisfying the threshold value 1 may be automatically separated using the software program, thereby automatically separating the cancer cell image region from the normal cell image region.

(d) setting a predetermined threshold 2 in order to select the AIMP2-DX2 signal from the isolated cancer cells

The step (d) of the present invention is a step of detecting the signal of AIMP2-DX2 from the cancer cell region image extracted from the step (c).

In the present invention, the threshold 2 refers to a value capable of most clearly distinguishing the two types of signals by analyzing the RGB profile of the extracted cancer cell region image in order to distinguish between the cancer cell signal and the AIMP2-DX2 signal. .

Accordingly, step (d) may include separating the AIMP2-DX2 signal by setting a predetermined threshold 2 from the RGB profile.

In one embodiment of the present invention, a criterion indicating the greatest difference as a result of the RGB profile of the cancer cell signal and the AIMP2-DX2 signal on an image in which normal cells and cancer cells are distinguished by a threshold value of 1 is set. That is, the image area that satisfies all of Red <181, Green <181, Blue <181, Red-Green> 30, and Blue-Green> 7 can be classified as AIMP2-DX2 signals, and this is set as the threshold value 2. And the region satisfying this was detected from the cancer cell region image.

In the present invention, the threshold value 2 may vary depending on the type of labeling material attached to the probe hybridizing with AIMP2-DX2 and the type of cell staining solution. You can set a range of threshold 2 that will most accurately distinguish the DX2 signal.

Thereafter, the AIMP2-DX2 image region can be automatically separated from the cancer cell image region by automatically detecting the image region satisfying the threshold value 2 using the software program.

(e) calculating the relative ratio of the AIMP2-DX2 signal to the total cancer cell signal according to the following formula.

%AIMP2-DX2 = (AIMP2-DX2 signal / total cancer cell signal) × 100

More specifically, the number of pixels determined as cancer cells in the image according to steps (c) and (d) is reported as a cancer cell signal, and the number of pixels determined as an AIMP2-DX2 signal is reported as an AIMP2-DX2 signal, and AIMP2- Calculate the DX2 degree.

% AIMP2-DX2 = (AIMP2-DX2 signal/cancer cell signal) × 100

Specific processes and descriptions for the steps (a) to (e) are described in detail in the embodiments of the present invention.

AIMP2-DX2, a splicing variant of AIMP2, is specifically found in human cancer cells and tissues of cancer patients, so by analyzing the expression level of AIMP2-DX2, cancer diagnosis, progress analysis, prognosis prediction, and anticancer treatment effects It can provide useful information for judgment, etc. That is, as a result of quantifying the ratio of AIMP2-DX2 by analyzing the signals detected in each step, if the ratio of the AIMP2-DX2 signal to the cancer cell signal is higher, it is diagnosed as cancer, or it is determined that there is a high possibility of progressing to cancer. Or, you can predict the prognosis of cancer patients.

When the method of the present invention is used, since AIMP2-DX2 can be classified and detected in the same sample obtained from a specific subject, it is possible to quantify the ratio of AIMP2-DX2 according to the above calculation formula. In one comparative example of the present invention, as a result of analyzing the FFPE sample of colorectal cancer tissue by the FISH method, it was confirmed that fluorescence was overexpressed and thus it was difficult to distinguish between cancer cells and AIMP2-DX2 signals, and accordingly, it was confirmed that it was difficult to apply in the clinic. However, as a result of analyzing the FFPE sample by the SISH method, it was confirmed that the AIMP2-DX2 signal can be clearly distinguished from cancer cells according to the method of the present invention, so that the AIMP2-DX2 signal can be easily and conveniently applied in the clinic. .

Thus, by combining the ratio of AIMP2-DX2 to the total cancer cell signal calculated according to the method of the present invention with clinical information such as the severity of the patient, the prognosis of the patient, and the effect of the anticancer regimen, information useful for diagnosis and prognosis of cancer, etc. It became possible to provide.

In particular, since the expression of AIMP2 exhibits different expression patterns for each tissue and according to the genetic characteristics of the subject, it is not possible to accurately diagnose cancer simply by absolutely quantifying the expression level of AIMP2-DX2. However, by using the method of the present invention, since the expression level of AIMP2-DX2 compared to all cancer cells can be quantitatively confirmed in the same sample, the expression level of AIMP2-DX2 in cancer tissues is checked more accurately and quickly, and the classification diagnosis is performed accordingly. Can provide useful information.

On the other hand, as described above, detecting the presence and amount of AIMP2-DX2 is not only very useful in diagnosing cancer or predicting the prognosis of cancer, but also to guide the treatment direction of cancer patients (selection of treatment drugs, etc.). It can be a very important factor in setting.

If the treatment response to the treatment and its side effects can be predicted in advance, it is possible to select a drug suitable for the patient in advance, reduce the dropout rate caused by incorrect drug selection, and increase drug compliance. It also avoids the time it takes for the drug to take effect and the risk of side effects the patient may experience.

Under this concept, a search for markers related to various drug responsiveness or a commercial in vitro diagnostic or companion diagnostic kit using the same are being developed, and some of them have already been commercialized and used in clinical practice. However, these methods are generally less reliable in results unless highly skilled experimenters are used. Accordingly, in some cases, relatively meaningful results can be obtained only when the patient's sample is provided to the service provider according to the prescribed process in the central lab method.Inter-laboratory variation and inter-experimental error (inter-laboratory variation) -observer variation) and day-to-day variation may raise questions about the reliability of the overall system.

As described above, in the conventional method, there is a possibility that an error may occur in the diagnosis result depending on the location, time, and experimenter, and thus a method or an automated process that excludes the involvement of the experimenter as much as possible to obtain a stable result is required. Since the expression rate of AIMP2-DX2 from one sample can be quantitatively detected with an automated system, it can be very useful in the field of companion diagnostics (CDx) for the treatment of cancer patients.

On the other hand, according to a previous report, it was confirmed that the higher the expression rate of AIMP2-DX2 in cancer patients, the worse the cancer prognosis, especially overall survival and progression-free survival (Journal of Cancer 2017, Vol. 8).

Accordingly, the present invention provides an automated method for calculating a predicted cancer prognosis, including the following steps, in order to provide information necessary for predicting the prognosis of a cancer patient:

(a) providing a sample from the subject;

(b) performing silver in situ hybridization of AIMP2-DX2 in cells isolated from the sample using a probe set, and generating a signal to image and image it;

(c) separating an image of a cancer cell region from the image by applying a pre-determined threshold value 1;

(d) setting and applying a predetermined threshold value 2 to select an AIMP2-DX2 signal in the isolated cancer cell region; And

(e) calculating the relative ratio of the AIMP2-DX2 signal to the cancer cell signal according to the following formula.

%AIMP2-DX2 = (AIMP2-DX2 signal / cancer cell signal) × 100

(f) calculating a predicted cancer prognosis value as the higher the value of %AIMP2-DX2 is, the worse the prognosis of cancer is.

In the present invention, the "prognosis" refers to a prospect for a future symptom or course determined by diagnosing a disease. For cancer patients, the prognosis usually refers to whether or not metastasis or survival within a certain period of time after the onset of cancer or surgical procedure, and the predictive diagnosis (prognosis or predictive diagnosis) is the direction of future treatment, including whether or not chemotherapy is especially for cancer patients. It is a very important clinical task because it provides clues about the problem. The prognosis prediction also includes predictions of the patient's response to the disease treatment and the course of treatment.

Steps (a) to (e) in the method of the present invention are as described above.

On the other hand, step (f) is a step of calculating the predicted prognosis of the cancer patient according to the %AIMP2-DX2 value calculated in step (e), and it is determined that the greater the value of %AIMP2-DX2 is, the poorer the prognosis of cancer is. Means to do.

In the present invention, the prognosis predicted value means to distinguish between a high-risk group with a very poor prognosis of cancer, a medium-risk group with a poor prognosis of cancer, and a low-risk group with a poor prognosis of cancer. It means that the probability of overall survival, progression-free survival, disease-free survival is low, and the probability of metastasis, recurrence or metastatic recurrence of cancer is high. This means that the probability of overall survival, progression-free survival, and disease-free survival is low.

According to the method of the present invention, the ratio of AIMP2-DX2 in the same sample can be quantitatively detected and automated, and the accuracy of the diagnosis result is excellent, as well as clinical application using FFPE samples. It can be very useful for classification diagnosis and prediction of prognosis.

1 is a schematic diagram showing the process of generating a transcript of AIMP2-DX2 and a process in which AIMP2-DX2 mRNA is specifically targeted and spliced.
Fig. 2 is a result of observing a silver precipitation signal that appears after hybridization by treating a sample (FFPE) obtained from a subject with the probe set of the present invention with a microscope. (The dark red dot in the image represents AIMP2-DX2. )
3 is a flow chart for calculating the degree of AIMP2-DX2 by analyzing SISH result data.
4 is a photograph of loading AIMP2-DX2 SISH images on MATLAB.
5 is a photograph of setting a threshold to leave a region of cancer cells by distinguishing cancer cells from normal cells on MATLAB.
6 is a photograph of calculating the number of pixels by setting a threshold for measuring an AIMP2-DX2 signal in MATLAB.
7 is a photograph of calculating %AIMP2-DX2 using “total area” determined as a cancer cell in MATLAB and “signal” determined as an AIMP2-DX2 signal.
8 is a result of diagnosis of cancer risk of AIMP2-DX2.
9 is a result of performing a fluorescence in situ hybridization method (FISH) using probe set 1 and a result of applying it to MATLAB.
10 is a result of performing the AIMP2-DX2 fluorescence in situ hybridization method (FISH) with low laser intensity.

Hereinafter, the present invention will be described in detail.

However, the following examples are merely illustrative of the present invention, and the contents of the present invention are not limited to the following examples.

<Example 1>

Fabrication of the probe set

The sequence information of the AIMP2 transcript was obtained from Genebank RefSeq Accession number NM_006303.3 transcript, and a probe complementary to the nucleotide sequence of the desired exon among exons 1 to 4 of AIMP2 and AIMP2-DX2 was produced by requesting ACD cell diagnostics.

The fabricated probes were divided into the following configurations:

(Set 1) (i) an oligonucleotide complementary to the 17th to 111th bases of the nucleotide sequence of SEQ ID NO: 1 labeled with the first fluorescent material, and the oligonucleotides complementary to the 456th to 1182th bases, and (ii) a second A probe set for detection of AIMP2 and AIMP2-DX2 comprising oligonucleotides complementary to bases 222 to 453 of the nucleotide sequence of SEQ ID NO: 1 labeled with a fluorescent substance,

In the Set 1 probe set, Atto 550 indicating red as the first fluorescent material and Alexa 488 indicating green color as the second fluorescent material were used.

(Set 2) A probe set for detection of AIMP2-DX2 comprising an oligonucleotide complementary to a base represented by SEQ ID NO: 2.

<Example 2>

AIMP2-DX2 detection in cancer cells using silver in situ hybridization (SISH)

Using the probe Set 2 prepared in Example 1, it was confirmed whether AIMP2-DX2 can be detected by Silver In situ hybridization in actual cancer cells.

First, tissues of 25 patients with formalin fixed paraffin embedded colorectal cancer were sectioned with 4 μl thick.

SISH was performed using a Basescope system (ACD, Inc., Hayward, California, US). The experiment was carried out within 1 week after making the section. Immediately before entering the experiment, it was heated in a dry oven at 60° C. for 1 hour, washed with xylene and 100% EtOH, and deparaffinized. All experiments conducted after that were put in a humidification chamber to minimize temperature change and prevent drying. As a pretreatment process, hydrogen peroxide was treated at room temperature for 15 minutes, and then added to a target retrieval reagent and then stopped at 98-102°C for 14 minutes. After that, the pretreatment was performed at 40° C. for 45 minutes by treatment with proteinase III.

The AIMP2-DX2 probe was hybridized to the proteinase-treated tissue for 2 hours at 40°C. After hybridization, amplification agents 1-6 were sequentially treated according to the protocol provided by the manufacturer. The final reaction product of SISH is a silver precipitate. As red A and red B are added continuously, the enzymatic chemical reaction of the coloring agent is carried out, where silver ions (Ag+) are reduced and red color is formed in the multi-linked amplifying agent. Was visualized. Afterwards, Gill's hematoxylin I contrast staining was performed for analysis in a general optical microscope. After control dyeing, it was dried in a drying oven at 60° C. for 15 minutes, and after applying a solution of Vector Labs (Peterbourough, UK), it was covered with a coverslip and examined with an optical microscope.

The fragments subjected to silver in situ hybridization using probe Set 2 were first read pathology at the Department of Pathology at Seoul National University Hospital, and the staining status and results of the tumor site were confirmed with an optical microscope (DMi8, Leica biosystems, Nussloch, Germany) and multiple macroscopic examinations. .

The confirmed SISH section was digitally scanned for the entire X20 magnification with a tissue slide scanner (SCN400, Leica biosystems, Nussloch, Germany) and stored as an electronic file. As a result of SISH, it was confirmed that hybridization with the target transcript within the cell normally occurred, and the signal was visualized as a red dot, and a representative image was extracted and analyzed (FIG. 2).

<Example 4>

Analysis of silver in situ hybridization (SISH) results

In order to automatically analyze the SISH result data according to the flow chart sequence of FIG. 3, the present inventors have partitioned the AIMP2-DX2 signal detection automation method using MATLAB software.

First, after running MATLAB software (version R2012a; The MathWorks, Inc.), the AIMP2-DX2 SISH image obtained in Example 3 was loaded by pressing the load image button as shown in FIG. 4. Images before and after the operation of the image loaded in Fig. 4. Since the signal is naturally stronger as more cancer cells are captured in the image, the following steps were performed to distinguish normal cells from cancer cells in the imported image to correct this. .

As shown in FIG. 5, a threshold value 1 for classifying cancer cells in the entire picture can be set by using a slide bar or numeric input, and after setting a threshold value 1, only the cancer cells are left by pressing the push button. Threshold 1 The default value is 50, and the lower the threshold, the more rigorous the image is judged, confirming that the cancer cell area is relatively reduced compared to the higher one. The threshold value 1 was designated using RGB data of normal cells and cancer cells. The RGB profiles of cancer cells and normal cells were found and the range in which the difference appeared was set. As a result, in this embodiment, red: 170-230, green: 145-215, blue: 160-220, red-green >6, blue-green >6 conditions were met, and threshold values satisfying these conditions ) It was confirmed that 1 is 50. Accordingly, the inventors set a threshold value of 50 to distinguish normal cells from cancer cells, and smooth the border. When pressing the push button after inputting the threshold value 1, as can be seen in FIG. 6, the normal cell area is divided into black and the cancer cell area is divided into red, so that only the cancer cell area is separated and extracted. . Thereafter, the number of pixels of the determined cancer cells was calculated and expressed as a total area, and the AIMP2-DX2 SISH signal was measured below among the pixels determined to be cancer cells.

As shown in FIG. 6, threshold 2 for measuring AIMP2-DX2 signal from an image extracted by separating only cancer cells can be set by using a slide bar or an input, and the signal can be measured by pressing a push button. AIMP2-DX2 signal also analyzed the range in which the RGB profile of the cancer cell signal and AIMP2-DX2 signal differs the most, and a threshold value of 2 was specified. Red <181, Green <151, Blue <181, Red-Green >30, Blue-Green> 7 conditions were met, and it was confirmed that the threshold 2 satisfying these conditions was 19. Accordingly, the inventors set a threshold value of 19 to distinguish cancer cell signals from AIMP2-DX2 signals, and smooth the boundary (FIG. 7). Similarly, it was confirmed that the lower the threshold, the more severe the signal was, and the signal area was relatively reduced compared to the higher one.

The number of pixels determined by the determined AIMP2-DX2 signal was calculated and set as “signal”, and the number of pixels determined as cancer cells was defined as “Total”.

As shown in FIG. 7, the %AIMP2-DX2 value was calculated as (Signal / total) X 100 (%).

That is, the calculated %AIMP2-DX2 value is a value representing the expression ratio of AIMP2-DX2 in the cancer cell region.

Next, in a total of 25 colon cancer tissue samples, the results of self-diagnosis by a doctor at the Department of Pathology at Seoul National University Hospital (Example 2) and %AIMP2-DX2 (signal/total) were automatically calculated according to the method of Example 4 above. The calculated results are shown in Table 1 and FIG. 8 below.

As can be seen in Table 1 and FIG. 8, it was confirmed that the automated %AIMP2-DX2 value according to the method of the present invention shows a certain tendency, similar to the result of visual diagnosis by the clinician. According to the method, it was confirmed that the expression ratio of AIMP2-DX2 in cancer cells can be detected more accurately and quickly and automatically.

<Comparative Example 1>

Detection of AIMP2 and AIMP2-DX2 in cancer cells using fluorescence in situ hybridization method (FISH)

Using the probe Set 1 prepared in Example 1, it was confirmed whether AIMP2 and AIMP2-DX2 in the same colorectal cancer tissue sample as in Example 2 could be automatically detected by fluorescence in situ hybridization. PPIB and POLR2A detection probes with high fluorescence expression levels were used as positive controls.

Specifically, the colorectal cancer tissue sample was washed with PBS and then fixed with 4% paraformaldehyde at room temperature for 1 hour. Paraformaldehyde was washed off with PBS and the cells were stored at 4° C. for at least one day in 70% EtOH. The cells were sprinkled on a microscope slide, dried at room temperature for 40 minutes, and attached. Cells on the slide were sequentially conditioned in 50% EtOH, 70% EtOH, and 100% EtOH, followed by treatment with Proteinase K at 40° C. for 30 minutes. Proteinase-treated cells were hybridized with the AIMP2 probe at 40°C for 2 hours. After washing the cells with Wash buffer, AMP1, AMP2, AMP3, and AMP4 were sequentially treated to attach a fluorescent substance. Finally, the cell nuclei were marked with DAPI and the cells were imaged with a Zeiss confocal microscope.

The results are shown in Figs. 9 and 10.

As shown in FIG. 9, as a result of performing the fluorescence in situ hybridization method (FISH) using probe Set 1, high autofluorescence was exhibited, and analysis was not possible. Although the fluorescence expression level was high and compared with the PPIB and POLR2A probes used as positive controls, strong fluorescence was shown in the entire tissue, and it was difficult to find a specific fluorescence pattern for AIMP2-DX2.

In addition, according to the above results, AIMP2-DX2 RNA FISH was attempted with low laser intensity, but it was impossible to obtain meaningful images in most tissues (FIG. 10).

Due to this problem, the RNA FISH method is not a suitable method for detecting AIMP2-DX2 in colon cancer tissue using FFPE samples, and accordingly, it has been confirmed that the FISH method is difficult to apply to clinical diagnosis.

According to the method of the present invention, since the ratio of AIMP2-DX2 in the same sample can be quantitatively detected and automated, the accuracy of the diagnosis result is excellent, and it can be applied to clinical use using FFPE samples. It can be very useful for classification diagnosis and prognosis prediction, so it has excellent industrial applicability.

<110> Medicinal Bioconvergence Research Center SEOUL NATIONAL UNIVERSITY HOSPITAL <120> Method for automated detection and quantification of AIMP2-DX2 signal through RNA silver in situ hybridization <130> NP18-0010 <160> 2 <170> KoPatentIn 3.0 <210> 1 <211> 1236 <212> DNA <213> Artificial Sequence <220> <223> human aminoacyl tRNA synthetase complex interacting multifunctional protein 2 (AIMP2), transcript variant 1 <400> 1 ccgaacgccc gcagcagggt cagaagggag gtggccggtc tccgtcgtga cctctgacgg 60 tttctgagcg ttggcctttg gcacgcgcta cccccttttg ctttggttct gccatgccga 120 tgtaccaggt aaagccctat cacgggggcg gcgcgcctct ccgtgtggag cttcccacct 180 gcatgtaccg gctccccaac gtgcacggca ggagctacgg cccagcgccg ggcgctggcc 240 acgtgcagga agagtctaac ctgtctctgc aagctcttga gtcccgccaa gatgatattt 300 taaaacgtct gtatgagttg aaagctgcag ttgatggcct ctccaagatg attcaaacac 360 cagatgcaga cttggatgta accaacataa tccaagcgga tgagcccacg actttaacca 420 ccaatgcgct ggacttgaat tcagtgcttg ggaaggatta cggggcgctg aaagacatcg 480 tgatcaacgc aaacccggcc tcccctcccc tctccctgct tgtgctgcac aggctgctct 540 gtgagcactt cagggtcctg tccacggtgc acacgcactc ctcggtcaag agcgtgcctg 600 aaaaccttct caagtgcttt ggagaacaga ataaaaaaca gccccgccaa gactatcagc 660 tgggattcac tttaatttgg aagaatgtgc cgaagacgca gatgaaattc agcatccaga 720 cgatgtgccc catcgaaggc gaagggaaca ttgcacgttt cttgttctct ctgtttggcc 780 agaagcataa tgctgtcaac gcaaccctta tagatagctg ggtagatatt gcgatttttc 840 agttaaaaga gggaagcagt aaagaaaaag ccgctgtttt ccgctccatg aactctgctc 900 ttgggaagag cccttggctc gctgggaatg aactcaccgt agcagacgtg gtgctgtggt 960 ctgtactcca gcagatcgga ggctgcagtg tgacagtgcc agccaatgtg cagaggtgga 1020 tgaggtcttg tgaaaacctg gctcctttta acacggccct caagctcctt aagtgaattg 1080 ccgtaactga ttttaaaggg tttagatttt aagaatggtg ctctttcatg cctattatca 1140 gtaaggggac ttgtattaga gtcagagtct ttttatttag gccagttgtc aagtgtcaat 1200 aaaagcatca tgtaatttaa aaaaaaaaaa aaaaaa 1236 <210> 2 <211> 32 <212> DNA <213> Artificial Sequence <220> <223> AIMP2-DX2 exon1-exon3 <400> 2 gccgggcgct ggccacgtgc aggattacgg gg 32

Claims (10)

In order to provide information necessary for cancer diagnosis or prognosis prediction, an AIMP2-DX2 signal detection method comprising the following steps:
(a) performing silver in situ hybridization of AIMP2-DX2 in cells isolated from the sample provided from the subject using a probe set, and imaging by inducing a signal to image it;
(b) extracting an RGB image from the obtained image;
(c) separating an image of a cancer cell region from the image by applying a pre-determined threshold value 1;
[Threshold 1]
Red: 170-230, Green: 145-215, Blue: 160-220, Red-Green >6 and Blue-Green >6
(d) setting and applying a predetermined threshold value 2 to select an AIMP2-DX2 signal in the isolated cancer cell region; And
[Threshold 2]
Red <181, Green <151, Blue <181, Red-Green >30 and Blue-Green >7
(e) calculating the relative ratio of the AIMP2-DX2 signal to the cancer cell signal according to the following formula.
%AIMP2-DX2 = (AIMP2-DX2 signal / total cancer cell signal) × 100
The method of claim 1, further comprising reacting the cells isolated from the sample in step (a) with a cell staining solution.
The method of claim 2, wherein the cell staining solution is DAPI (4',6-diamidino-2-phenylindole) for staining the cell nucleus, methylene blue, carmine acetate (acetocarmine), toluidine blue, and Hematoxylin or Hoechst; Eosin, crystal violet or orange G to stain the cytoplasm; And rhodamine 123 for staining mitochondria, mitotracker, janus green B, tetrazolium salt, dimethylaminostyrylmethylpyridiniumiodine (DASPMI), DASPEI (2-(4-(dimethylamino)) ) styryl)-N-Ethylpyridinium Iodide), DiOC6(3,3'-dihexyloxacarbocyanine iodide), DiOC7(3,3'-Diheptyloxacarbocyanine Iodide) or JC-1(5,5,6,6-tetrachloro-1,1, 3,3-tetraethyl-benzimidazolylcarbocyanine chloride), characterized in that at least one selected from the group consisting of.
The method of claim 1, wherein the probe set of step (a) is a probe set for detection of AIMP2-DX2 comprising an oligonucleotide complementary to a base represented by SEQ ID NO: 2.
delete The method of claim 1, wherein the sample is a formalin-fixed paraffin-embedded section (FFPE).
The method of claim 1, wherein the cancer is colon cancer, cervical cancer, lung cancer, pancreatic cancer, non-small cell lung cancer, liver cancer, colon cancer, bone cancer, skin cancer, head or neck cancer, skin or intraocular melanoma, uterine cancer, ovarian cancer, rectal cancer , Stomach cancer, anal muscle cancer, colon cancer, breast cancer, fallopian tube carcinoma, endometrial carcinoma, vaginal carcinoma, vulvar carcinoma, esophageal cancer, small intestine cancer, endocrine cancer, thyroid cancer, parathyroid cancer, adrenal cancer, soft tissue sarcoma, urethral cancer, penile cancer, prostate A method, characterized in that it is selected from the group consisting of cancer, bladder cancer, kidney cancer, or ureteral cancer.
In order to provide information necessary for predicting the prognosis of a cancer patient, an automated method of calculating a predicted cancer prognosis comprising the following steps:
(a) performing silver in situ hybridization of AIMP2-DX2 in cells isolated from the sample provided from the subject using a probe set, and imaging by inducing a signal to image it;
(b) extracting an RGB image from the obtained image;
(c) separating an image of a cancer cell region from the image by applying a pre-determined threshold value 1;
[Threshold 1]
Red: 170-230, Green: 145-215, Blue: 160-220, Red-Green >6 and Blue-Green >6
(d) setting and applying a predetermined threshold value 2 to select an AIMP2-DX2 signal in the isolated cancer cell region;
[Threshold 2]
Red <181, Green <151, Blue <181, Red-Green >30 and Blue-Green >7
(e) calculating the relative ratio of the AIMP2-DX2 signal to the cancer cell signal according to the following formula; And
%AIMP2-DX2 = (AIMP2-DX2 signal / total cancer cell signal) × 100
(f) calculating a predicted cancer prognosis value as the higher the value of %AIMP2-DX2, the worse the prognosis of cancer is.
The method of claim 8, wherein the prognosis is selected from the group consisting of overall survival, progression-free survival, and disease-free survival.
The method of claim 8, wherein the cancer is colon cancer, cervical cancer, lung cancer, pancreatic cancer, non-small cell lung cancer, liver cancer, colon cancer, bone cancer, skin cancer, head or neck cancer, skin or intraocular melanoma, uterine cancer, ovarian cancer, rectal cancer , Gastric cancer, anal muscle cancer, colon cancer, breast cancer, fallopian tube carcinoma, endometrial carcinoma, vaginal carcinoma, vulvar carcinoma, esophageal cancer, small intestine cancer, endocrine cancer, thyroid cancer, parathyroid cancer, adrenal cancer, soft tissue sarcoma, urethral cancer, penile cancer, prostate A method, characterized in that it is selected from the group consisting of cancer, bladder cancer, kidney cancer, or ureteral cancer.

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