KR101378919B1 - System biological method of biomarker selection for diagnosis of lung cancer, subtype of lung cancer, and biomarker selected by the same - Google Patents

Abstract

The present invention relates to a system for selecting a marker capable of diagnosing lung cancer and lung cancer directly from blood, and a marker capable of diagnosing lung cancer and lung cancer subtype directly from selected blood.

Description

System for screening markers capable of diagnosing lung cancer and subtypes of lung cancer directly from blood Biological method and markers for directly diagnosing lung cancer and subtypes of lung cancer from selected blood , SUBTYPE OF LUNG CANCER, AND BIOMARKER SELECTED BY THE SAME}

The present invention relates to a system for screening markers capable of diagnosing lung and lung cancer directly from blood, and to a marker capable of diagnosing lung cancer and lung cancer directly from selected blood.

Lung cancer is the most lethal cancer with the second highest incidence, the highest mortality rate, and the highest mortality rate (2002, 2005 National Cancer Center Report). Lung cancer is divided into small cell lung cancer and non-small cell lung cancer. Non-small cell lung cancer is the most representative cancer, accounting for about 80% of lung cancers (Society, AC Cancer Facts and Figures 2001, 2001). Non-small cell lung cancer is classified into adenocarcinoma, squamous cell carcinoma, and large cell carcinoma.

In the case of the non-small cell carcinoma, despite the recent development of multi-modality therapy, the overall 10-year survival rate is as low as 10% (Fry, WA, Phillips, JL, and Menck, HR Ten-year survey of lung). cancer treatment and survival in hospitals in the United States: a national cancer data base report, Cancer. 86: 1867-76., 1999). The main reason is that the diagnosis of NSCLC is mostly late, but it is not accurate in distinguishing between adenocarcinoma and squamous cell carcinoma, which is a subtype of non-small cell lung cancer, in lung cancer patients. to be.

Therefore, there is a need for the development of markers that can increase the survival rate of patients by increasing the accuracy of subtype diagnosis of lung cancer. Many studies have focused on finding the genetic predisposition of NSCLC related genes (EGFR, RAS, etc.). These genetic variations have been reported to predict metastasis and treatment prognosis. However, the useful information for the diagnosis of subtypes of non-small cell lung cancer has not been validated in large-scale experiments (Mitsudomi et al., Clin Cancer Res 6: 4055-63 (2000); Niklinski et al., Lung Cancer. 34 Suppl 2: S53-8 (2001); Watine, Bmj 320: 379-80 (2000).

Recently, in order to identify genes or protein biomarkers for more advanced diagnosis and treatment of lung cancer, a number of studies using genomics and proteomics analyzes comparing normal tissues with diseased tissues themselves have been conducted. However, this approach has difficulty in producing excess molecular information and systematically organizing and integrating them to organize useful tissue and blood marker candidates from large amounts of information. Recently, systemic biological methods for systematically integrating such information into network models and selecting high reliability biomarkers through analysis of models have emerged.

In addition, most of these genomics and proteomics studies have focused on presenting large numbers of tissue diagnostic marker candidates. However, these tissue markers require obtaining invasive tissue through endoscopy and surgery to obtain specific tissue. This is difficult to increase the survival rate is applied to patients who came for health checkup. Therefore, it is necessary to develop a non-invasive biomarker targeting biofluids such as blood and urine.

Although 20-30 blood markers are currently used in the medical field, these blood biomarkers (CEA, CA antigens, etc.) have low diagnostic specificity for certain cancers or inflammatory diseases because of the common characteristics of cancer. This is because the information overlapped as proteins increased / decreased into the blood by similar cellular mechanism changes accompanying cancer, making it difficult to distinguish between specific cancers. Therefore, in order to increase the accuracy of lung cancer diagnosis in the blood, it is necessary to select blood markers only for the lung-specific genes.

Systematic integration of such a large amount of data to perform network modeling and analysis, and also to develop a system biological framework (frame work) incorporating a series of methods to select high-reliability blood markers from tissue data to reflect tissue specificity It is a required situation.

An object of the present invention is to provide a method for screening lung cancer diagnostic markers capable of diagnosing lung cancer and subtypes of lung cancer directly from blood, and a marker for diagnosing lung cancer capable of diagnosing selected subtypes of lung cancer and lung cancer.

According to an aspect of the present invention,

First step to obtain transcript data from the literature of adenocarcinoma (ACC) and squamous cell carcinoma (SCC), the major subtypes of non-small cell lung cancer (NSCLC) ;

Analysis of the transcript data obtained in the first step 1) the second step of selecting a gene having a difference in the expression amount in the sub-type ADC and SCC, and 2) the difference in the expression amount in NSCLC and normal lung cells ;

A third step of selecting a gene specifically expressed in the lung from the marker gene candidate group obtained in the second step;

A fourth step of selecting a protein capable of secreting into blood from a marker gene capable of diagnosing the lung specific ACC / SCC subtype obtained in the third step;

In the fourth step, a fifth step of selecting a lung specific blood functional marker candidate by building and analyzing a network model using the obtained protein: And

Provided is a system biological method for screening lung cancer and diagnosing a subtype of lung cancer directly from blood, comprising a sixth step of validating the candidate substance obtained in the fifth step by a bioplex and an ELISA system. .

In the present invention, the first step consists of obtaining data from the existing literature on ACC, SCC and normal lung cells, which are major subtypes of non-small cell lung cancer (NSCLC). The database used to obtain the data is not particularly limited, and mentions differences between non-small cell lung cancer and normal lung cells, or mentions differences between ACC and SCC, the major subtypes of non-small cell lung cancer. Any database that contains existing data would be available.

In the second step, in the database obtained in the first step, the expression levels of NSCLC cells and normal cells are changed statistically significantly in adenocarcinoma and squamous cell carcinoma. Statistical processing is performed to select for proteins that differ.

Statistical methods for screening genes with differences in ACC and SCC and non-small cell lung cancer (NSCLC) and normal cells of the second stage are T-test, median test, Wilcoxon rank test Integrative statistical testing can be used to apply the Wilcoxon rank sum test and to calculate the integrated p-value using the (p-value) meta-analysis (Stouffer's method).

In the third step, in the third step, a gene (lung-specific gene) having a higher expression level in lung tissue than other tissues is selected from the gene candidate group obtained in the second step. Comparison of expression levels in lung tissue and other tissues can be performed using the integrated statistical test described above. Using information from Novartis gene expression atlas, genes with higher expression levels in lung tissues compared to other tissues (lung-specific genes) can be used. Can be screened.

In the fourth step, the protein that can be measured in the blood by using gene ontology information (Gene Ontology Cellular Component: plasma membrane and extracellular space) and peptide atlas (DB of the protein measured in the blood) is Measurable proteins are selected from blood by screening genes secreted into the extracellular space or measured in the blood.

In the fifth step, the network model is constructed using the lung specific blood marker candidate group obtained in the fourth step, and the blood marker is determined from the constructed network model.

In the network model, the lung-specific blood marker candidate group obtained in the fourth step is a protein with a large amount of expression and an antibody present, which represents each functional module through functional modularization analysis, and is highly reliable lung cancer (ACC & SCC) that can be monitored in the blood. Determine relevant blood marker candidates.

In the sixth step, the selected marker candidate group is verified using bioplex and ELISA.

The present invention also provides a marker for diagnosing lung cancer or a marker composition comprising the marker, which can be directly diagnosed from blood selected according to the first to fourth steps of the method, and specifically, the marker for diagnosing lung cancer is SFN (ENTREZ). ID 2810), MUC1 (ENTREZ ID 4582), JUP (ENTREZ ID 3728), IGFBP3 (ENTREZ ID 3486), SLC9A3R1 (ENTREZ ID 9368), MST1R (ENTREZ ID 4486), MDK (ENTREZ ID 4192), ACTL6A (ENTREZ ID 4192) 86), EPHB3 (ENTREZ ID 2049), SLC2A1 (ENTREZ ID 6513), CASK (ENTREZ ID 8573), SERPINB5 (ENTREZ ID 5268), PERP (ENTREZ ID 64065), MMP9 (ENTREZ ID 4318), LAD1 (ENTREZ ID 3898). ), GOLM1 (ENTREZ ID 51280), CEACAM1 (ENTREZ ID 634), AGR2 (ENTREZ ID 10551), DSC2 (ENTREZ ID 1824), DSG2 (ENTREZ ID 1829), SERPINE2 (ENTREZ ID 5270), CLDN4 (ENTREZ ID 1364) , CLDN3 (ENTREZ ID 2810), CEACAM5 (ENTREZ ID 1048), CEACAM5 (ENTREZ ID 1048), WFDC2 (ENTREZ ID 10406), CFB (ENTREZ ID 629), CP (ENTREZ ID 1356), SLC7A5 (ENTREZ ID 8140), DPP4 (ENTREZ ID 1803), CEACAM6 (ENTREZ ID 4680), RAMP1 (ENTREZ ID 10267), MMP12 (ENTREZ ID 4321), SORD (ENTREZ ID 6652), SPINK1 (ENTREZ ID 6690), ST14 (ENTREZ ID 6768), KCNK5 (ENTREZ ID 8645), ABCC3 (ENTREZ ID 8714), GGH (ENTREZ ID 8836), GDF15 (ENTREZ ID 9518), GPR87 (ENTREZ ID 53836), FERMT1 (ENTREZ ID 55612), SLC38A1 (ENTREZ ID 81539).

The present invention also provides a highly reliable lung cancer (ACC & SCC) related blood marker composition which can be directly diagnosed in the blood selected according to the first to sixth steps of the method. The high reliability markers include MUC1 (ENTREZ ID 4582), IGFBP3 (ENTREZ ID 3486), MDK (ENTREZ ID 4192), DPP4 (ENTREZ ID 1803), SERPINE2 (ENTREZ ID 5270), SORD (ENTREZ ID 6652), GDF15 (ENTREZ). ID 9518) and one or more markers selected from the group consisting of:

According to the present invention, there is a difference in the amount of expression in non-small cell lung cancer compared to normal people and at the same time there is a difference in the amount of expression in adenocarcinoma and squamous cell carcinoma as well as secretion into the blood Because they provide good blood protein markers, they can lead to effective anti-cancer treatment by early diagnosis of lung cancer and accurate diagnosis of ACC and SCC.

In addition, the system biological blood marker screening method applied to lung cancer is widely applied to other diseases, and will greatly contribute to the screening of highly reliable blood diagnostic markers.

1 is a schematic representation of a system biological method of the present invention.
2 shows a protein network constructed in accordance with the present invention.
3A is a diagram showing the result of ELISA analysis for DPP4.
3B shows ELISA analysis results for MDK.

Hereinafter, the system biological method developed in the present invention will be described in detail based on Examples.

1 shows the system biological method according to the invention as a whole.
In this example, more than a thousand microarray data obtained from the following eight large-scale experiments were obtained for subtypes (ACC and SCC) of non-small cell lung cancer (NSCLC) and normal lung cells.

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1) Bhattacharjee meat al ., Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses., PNAS., 2001. (data are available at http://www.broadinstitute.org/mpr/lung/).

2) Bild meat al ., Oncogenic pathway signatures in human cancers as a guide to targeted therapies., Nature., 2006. (data are available at http://data.cgt.duke.edu/oncogene.php and on GEO)

3) Borczuk meat al ., Molecular Signatures in Biopsy Specimens of Lung Cancer., Am J Respir Crit Care Med., 2004. (data available online at http://hora.cpmc.columbia.edu/dept/pulmonary/5ResearchPages/Laboratories/Powell% 20Lab.htm)

4) Kim meat al ., Prediction of Recurrence-Free Survival in Postoperative Non-Small Cell Lung Cancer Patients by Using an Integrated Model of Clinical Information and Gene Expression., Clin Cancer Res., 2008. (data are available online at GEO; GSE8894)

5) Lu meat al . A Gene Expression Signature Predicts Survival of Patients with Stage I Non-Small Cell Lung Cancer., PLoS Medicine., 2006. (data available online at GEO; GSE3141)

6) Bhattacharjee meat al ., Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses., PNAS., 2001. (data are available at http://www.broadinstitute.org/mpr/lung/).

7) Su meat al ., Selection of DDX5 as a novel internal control for Q-RT-PCR from microarray data using a block bootstrap re-sampling scheme., BMC Genomics., 2007. (data available online at GEO; GSE7670)

8) Landi meat al ., Gene Expression Signature of Cigarette Smoking and Its Role in Lung Adenocarcinoma Development and Survival., PLoS One., 2008. (data available online at GEO; GSE10072)

In the second step, statistics were obtained to select proteins that showed statistically significant expression differences in adenocarcinoma and squamous cell carcinoma while showing changes in expression levels in NSCLC cells and normal cells in the obtained database. Treatment was carried out.

Specifically, gene selection methods that differ between ACC, SCC, and NSCLC and normal cells were subjected to a T'-test, a median test, a Wilcoxon rank sum test, and the results (p) Integrative statistical testing was used to calculate the integrated p-value using the meta-analysis (Stouffer's method).

In the integrated statistical test, p-value = 0.01 value was used to express 963 genes showing significant differences in the amount of expression of ACC and SCC in the comparison of non-small cell lung cancer (NSCLC) and normal cells. 166 genes showing positive differences were selected as candidate genes.

As a third step, among the 166 genes selected, in the third step, among the gene candidate groups obtained in the second step, 85 genes with higher expression levels in lung tissue compared to other tissues are obtained using information of Novartis gene expression atlas. Were selected (lung specific genes). Specifically, the comparison of expression levels in lung tissue with 40 different tissues was performed using the integrated statistical test described above, and the integrated p-value cutoff value was 0.01.

In the fourth step, the extracellular space is obtained using the Gene Ontology Cellular Component (GOCC) and the peptide atlas (DB of the protein measured in the blood) for the 85 proteins selected in the third step. The genes secreted or measured in the blood were selected to select measurable proteins in the blood. Specifically, 42 proteins containing the term plasma membrane or extracellular space or contained in peptide atlas serum proteome DB in GOCC were selected as a marker for diagnosing lung cancer specific blood. Selected markers were shown in Table 1 below.

In the next step 5, a network model was constructed using the 42 lung-specific blood marker candidate groups, and proteins representing each functional module were selected as highly reliable markers through functional modularization analysis in the constructed network model.

The protein network constructed in this example is shown in FIG. 2. In FIG. 2, a highly reliable lung cancer (ACC & SCC) -related blood marker that can be monitored in the blood with a protein having a large amount of expression and a protein presenting each functional module for a major network function module (inflammation, cell adhesion related function module) Candidates were determined. Selected high reliability markers were as shown in Table 2 below.

In the next six steps, seven markers of high reliability selected through the first to fifth steps were verified using ELISA.

As shown in Table 3, the serum of 26 lung cancer patients aged 50 or older collected from 2008 through 2009 was collected from the Keimyung Human Life Resource Bank. The serum samples were selected through the first to fifth steps. Seven highly reliable marker candidates were applied and the sensitivity as a marker was verified by ELISA analysis.

Figure 3A shows the ELISA verification results for DPP4, Figure 3B shows the ELISA verification results for MDK. Statistical analysis showed that DPP4 was p = 0.101, and MD = p = 0.085.

Claims (8)

First to obtain transcript data from a literature of adenocarcinoma (ACC) and squamous cell carcinoma (SCC), the major subtypes of non-small cell lung cancer (NSCLC) step;
By analyzing the transcript data obtained in the first step, i) there is a difference in the amount of expression in the subtypes of non-small cell lung cancer, ADC and SCC, and ii) the amount of expression in non-small cell lung cancer and normal lung cells. A second step of selecting genes with differences;
A third step of selecting a gene specifically expressed in the lung from the marker gene candidate group obtained in the second step;
A fourth step of selecting a protein capable of secreting into blood from a marker gene capable of diagnosing the lung specific ACC / SCC subtype obtained in the third step;
In the fourth step, a network model is constructed using the obtained protein and analyzed to select marker candidates capable of diagnosing lung cancer and subtypes of lung cancer directly from blood.
And a sixth step of validating the candidate substance obtained in the fifth step with an ELISA system.
The method according to claim 1,
In the second step, a student's t-test, a median test, or a Wilcoxon rank sum test are applied to the data obtained in the first step, and the result of applying the result is (p-value). By applying integrative statistical testing, which calculates the integrated p-value using the meta-analysis method (Stouffer's method), i) there are differences in expression levels in ADC and SCC, and ii) non-small cell lung cancer (NSCLC). ) And a system biological method for selecting a marker capable of diagnosing lung cancer and diagnosing lung subtypes directly from blood, characterized by selecting genes having a difference in expression amount from normal lung cells.
The method according to claim 1,
In the third step, lung specific genes with higher expression levels in lung tissues compared to other tissues are selected using Novartis gene expression atlas information to select genes specifically expressed in lungs among marker gene candidate groups for diagnosing lung cancer subtypes. A system biological method for screening markers capable of diagnosing lung cancer and diagnosing lung subtypes directly from blood, characterized in that the screening is performed.
The method according to claim 1,
In the fourth step, Gene Ontology Cellular Component (GOCC) and peptide atlas (in blood) are used to select proteins capable of secreting blood from marker genes capable of diagnosing lung cancer specific ACC / SCC subtypes. A system biological method for selecting a marker capable of diagnosing lung cancer and diagnosing lung cancer directly from blood, characterized by selecting a gene secreted into an extracellular space or measured in blood using a collected protein).
The method according to claim 1,
In the fifth step, a network model is constructed using the lung cancer specific blood marker candidate protein selected in the fourth step, the expression level is high for the network function module of the network model, the antibody is present, and the blood is monitored. A system biological method for selecting a marker capable of diagnosing lung cancer directly and subtypes of lung cancer directly from blood, characterized by selecting possible proteins as highly reliable lung cancer related blood markers.
A marker composition comprising a marker selected by any one of claims 1 to 5, wherein the marker composition comprises SFN (ENTREZ ID 2810), MUC1 (ENTREZ ID 4582), JUP (ENTREZ ID 3728), IGFBP3 (ENTREZ ID 3486). , SLC9A3R1 (ENTREZ ID 9368), MST1R (ENTREZ ID 4486), MDK (ENTREZ ID 4192), ACTL6A (ENTREZ ID 86), EPHB3 (ENTREZ ID 2049), SLC2A1 (ENTREZ ID 6513), CASK (ENTREZ ID 8573), SERPINB5 (ENTREZ ID 5268), PERP (ENTREZ ID 64065), MMP9 (ENTREZ ID 4318), LAD1 (ENTREZ ID 3898), GOLM1 (ENTREZ ID 51280), CEACAM1 (ENTREZ ID 634), AGR2 (ENTREZ ID 10551), DSC2 (ENTREZ ID 1824), DSG2 (ENTREZ ID 1829), SERPINE2 (ENTREZ ID 5270), CLDN4 (ENTREZ ID 1364), CLDN3 (ENTREZ ID 2810), CEACAM5 (ENTREZ ID 1048), CEACAM5 (ENTREZ ID 1048), WFDC2 ( ENTREZ ID 10406), CFB (ENTREZ ID 629), CP (ENTREZ ID 1356), SLC7A5 (ENTREZ ID 8140), DPP4 (ENTREZ ID 1803), CEACAM6 (ENTREZ ID 4680), RAMP1 (ENTREZ ID 10267), MMP12 (ENTREZ ID 4321), SORD (ENTREZ ID 6652), SPINK1 (ENTREZ ID 6690), ST14 (ENTREZ ID 6768), KCNK5 (ENTREZ ID 8645), ABCC3 (ENTREZ ID 8714), GGH (ENT Lung cancer directly from blood, consisting of one or more markers selected from the group consisting of REZ ID 8836), GDF15 (ENTREZ ID 9518), GPR87 (ENTREZ ID 53836), FERMT1 (ENTREZ ID 55612), SLC38A1 (ENTREZ ID 81539) Marker composition capable of diagnosis and diagnosis of subtypes of lung cancer.
A marker composition comprising a marker selected by any one of claims 1 to 5, wherein MUC1 (ENTREZ ID 4582), IGFBP3 (ENTREZ ID 3486), MDK (ENTREZ ID 4192), SERPINE2 (ENTREZ ID 5270) Lung cancer diagnosis and subtype diagnosis of lung cancer, which is composed of one or more markers selected from the group consisting of DPP4 (ENTREZ ID 1803), SORD (ENTREZ ID 6652), and GDF15 (ENTREZ ID 9518). Marker composition.
A marker composition comprising a marker selected by the method of any one of claims 1 to 5, comprising MDK (ENTREZ ID 4192) and DPP4 (ENTREZ ID 1803), which directly diagnoses lung cancer from blood and serves as a lung cancer. Marker composition capable of diagnosis of the type.

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