US20110106739A1 - Method for determining the presence of disease - Google Patents

Method for determining the presence of disease Download PDF

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Publication number
US20110106739A1
US20110106739A1 US12/915,981 US91598110A US2011106739A1 US 20110106739 A1 US20110106739 A1 US 20110106739A1 US 91598110 A US91598110 A US 91598110A US 2011106739 A1 US2011106739 A1 US 2011106739A1
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gene
disease
genes
expression
levels
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Yuichiro Yoshida
Masaki Kobayashi
Yasuhiro Otomo
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Sysmex Corp
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Sysmex Corp
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Publication of US20110106739A1 publication Critical patent/US20110106739A1/en
Priority to US14/298,386 priority Critical patent/US9898574B2/en
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/30—Unsupervised data analysis
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • FIG. 1 is a diagram showing an example of an apparatus for determining the presence of a target disease, which is operated using the program of the invention
  • FIG. 6A shows the result of determination using averages of z-scores calculated from data on the levels of expression of gene transcription products in healthy subjects and Crohn's disease patients with respect to each of Crohn's disease-determining gene families, wherein the data are the same as those used in the identification of the gene families;
  • FIG. 12A shows the result of determination using data on the levels of expression of gene transcription products in healthy subjects and Huntington's disease patients with respect to genes belonging to Huntington's disease-determining gene families, wherein the data are the same as those used in the identification of the gene families;
  • the resulting gene transcription product extract is measured for the levels of expression of transcription products of genes comprising at least one gene belonging to each of at least two disease-determining gene families whose relationship with the target disease is known.
  • Whether or not and how much the gene transcription products or cDNAs or cRNAs thereof hybridize with the nucleic acid probes can be detected using a fluorescent substance or a dye or based on a hybridization-induced change in the amount of current flowing on the nucleic acid chip.
  • the levels of expression of transcription products of the corresponding genes in a plurality of healthy subjects may be obtained by a process including: collecting biological samples from healthy subjects by the same method as that performed to collect the biological sample from the subject; and measuring the levels of expression of transcription products of the object genes using the biological samples.
  • a plurality of healthy subjects means a statistically sufficient number of healthy subjects, which may be 30 or more, preferably 40 or more healthy subjects.
  • the determination formula may be prepared using discriminant analysis methods known per se.
  • Discriminant analysis methods are statistical methods which can provide criteria for determining which of two different groups newly obtained data belongs to, provided that previously presented pieces of data are known to be classified into the two different groups. Examples of such discriminant analysis methods include a support vector machine (SVM), a linear discriminant analysis, a neural network, a k-neighborhood discriminator, a decision tree, a random forest, and so on.
  • SVM support vector machine
  • the term “patients having the target disease” refers to subjects that can be confirmed to have the target disease based on criteria other than those for the determination method of the invention.
  • the patients are humans that can be confirmed to have cancer (as the target disease) by tissue characterization, CT, MRI, tumor marker method, or the like, an autoimmune disease (ditto) by blood test or the like, an infectious disease (ditto) by blood test or the like, a psychiatric disease or a nervous system disease (ditto) by diagnostic brain imaging, genetic testing, inquiry, or the like, Crohn's disease (ditto) by endoscopy, digestive tract imaging, or the like, or endometriosis (ditto) by CT, MRI, endoscopy, or the like.
  • the levels of the expression in each of the plurality of healthy subjects are also standardized so that values representing deviations for each of the plurality of healthy subjects are obtained.
  • the genes, whose expression levels are measured are first classified into at least two gene families using the classification system.
  • the average for each classified gene family is then obtained with respect to each of the plurality of patients and the plurality of healthy subjects in the same manner as in the step of obtaining the average for the subject described above.
  • the gene family is identified as a disease-determining gene family related to the target disease.
  • the determination method of the invention is particularly suitable for use in determining the presence of such a disease as Crohn's disease, Huntington's disease, or endometriosis.
  • examples of the disease-determining gene family include a G protein-related gene family, a blood coagulation-related gene family, an oxidative stress-related gene family, a phagocytosis-related gene family, and a fat oxidation-related gene family.
  • Huntington's disease is a chronic progressive neurodegenerative disease whose main symptoms include involuntary movement (mainly choreic movement), mental manifestation, and dementia. When diagnosed, this disease must be discriminated from symptomatic chorea caused by cerebrovascular disorders such as cerebral bleeding, drug-induced chorea caused by antipsychotic drugs, and other diseases such as Wilson's disease. Therefore, the determination method of the invention may be performed on a subject suspected of having Huntington's disease, so that a reliable determination result can be obtained as an index of diagnosis.
  • the determination method of the invention can be implemented by the program of the invention in cooperation with the computer 2 including a central processing unit, a storage unit, a reader for a recording medium such as a compact disc or a Floppy® disc, an input unit such as a keyboard, and an output unit such as a display.
  • FIG. 2 shows a more specific example of the computer system for implementing the method.
  • the RAM 110 c includes an SRAM, DRAM or the like.
  • the RAM 110 c is used to read out the computer program stored in the RAM 110 c , ROM 110 b , and hard disk 110 d . When these computer programs are executed, the RAM 110 c is also used as a work area for the CPU 110 a.
  • the transcription product expression level-measuring device 1 outputs, to the computer 2 , data on the measured expression levels in the plurality of patients (hereinafter referred to as “measured patient expression level data”) and data on the measured expression levels in the plurality of healthy subjects (hereinafter referred to as “measured healthy subject expression level data”).
  • the CPU 110 a receives the output measured patient expression level data and the output measured healthy subject expression level data, and stores the data into the RAM 110 c (step S 21 ).
  • FIG. 5 shows the distribution of the average of the z-scores for the healthy subjects 1 and the Crohn's disease patients 1 with respect to each gene family selected as described above.
  • the result is shown in FIG. 7A .
  • the result shows that the conventional method identified the Crohn's disease patients and the healthy subjects at a sensitivity of 100% and a specificity of 100%.
  • the 8,370 genes were classified into gene families (GO Terms) based on the classification of Gene Ontology, and the average of the z-scores for the Huntington's disease patients 1 (6 samples) obtained in the section (1-2) was calculated with respect to the gene within each GO Term.
  • a t-test was performed using the averages obtained as described above for the healthy subjects and the Huntington's disease patients with respect to each GO Term, so that a significance probability (p-value) was obtained.
  • Hierarchical clustering was performed using the z-scores for all genes contained in the extracted GO Terms, and synchronously varying gene clusters were selected.
  • the clustering was performed using software Cluster 3.0 (available from http://bonsai.ims.u-tokyo.ac.jp/ ⁇ mdehoon/software/cluster/software.htm), and the result was displayed using Java Tree View (available from http://sourceforge.net/projects/jtreeview/files/).
  • the average of the z-scores for the gene contained in each cluster was used as a cluster score, when a t-test was performed on the healthy subjects 3 (7 samples) and the Huntington's disease patients 1 (6 samples). From the clusters for which the resulting p-value was 0.05 or less, the microtubule-related gene family, mitochondria-related gene family, and prostaglandin-related gene family were selected as Huntington's disease-determining gene families. Table 4 shows these gene families, genes belonging to each family, and the p value for each family.
  • the result is shown in FIG. 14A .
  • the result shows that the conventional method using genes other than those belonging to Huntington's disease-determining gene families identified the Huntington's disease patients and the healthy subjects at a sensitivity of 100% and a specificity of 100%.
  • the result is shown in FIG. 14B .
  • the result shows that for samples different from those used in the identification of Huntington's disease-determining gene families, the sensitivity of the conventional determination method was reduced to 50%, although the specificity was 100%. It is therefore apparent that the conventional determination method using genes other than those belonging to Huntington's disease-determining gene families is more likely to misidentify Huntington's disease patients as healthy subjects than the determination method of the invention.
  • the data on lesion tissues and normal tissues obtained from the GEO were produced by analysis using GeneChip® U133 plus2.0 (Affymetrix, Inc.), a DNA chip.
  • the DNA chip has 54,675 probe sets, which include probe sets for the same gene.
  • the 16,207 genes were classified into gene families (GO Terms) based on the classification of Gene Ontology, and the average of the z-scores for the lesion tissues 1 (9 samples) obtained in the section (1-2) was calculated with respect to the gene within each GO Term.
  • the expression levels in the normal tissues 1 (8 samples) and the lesion tissues 1 (9 samples) with respect to each of the 39 genes in Table 5 were input to the SVM.
  • the accuracy of determining whether each sample was positive or negative was evaluated using the SVM containing the input expression levels in the 17 samples.
  • FIG. 17B The result shows that for samples different from those used in the identification of endometriosis-determining gene families, the sensitivity of the conventional determination method was reduced to 65% or less, although the specificity was 100%. It is therefore apparent that the conventional determination method is more likely to misidentify endometriosis patients as healthy subjects than the determination method of the invention.
  • Genes other than those belonging to endometriosis-determining gene families were further identified so that an examination could be performed using such genes. Specifically, a t-test was performed to calculate the significance probability (p-value) between the expression levels in the normal tissues 1 (8 samples) and the lesion tissues 1 (9 samples), and the gene for which the resulting p-value was 0.05 or less with respect to the expression level was determined to be used for the determination. As a result, ten genes were identified. Table 7 shows these genes and the p-value for each gene. FIG. 18 also shows the distribution of the level of expression of the transcription product of each gene in the normal tissues 1 and the lesion tissues 1.

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JP2009251017A JP5503942B2 (ja) 2009-10-30 2009-10-30 疾患の罹患の判定方法
JP2009-251017 2009-10-30

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CN107025386A (zh) * 2017-03-22 2017-08-08 杭州电子科技大学 一种基于深度学习算法进行基因关联分析的方法

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CN108779496B (zh) * 2016-02-10 2022-07-05 公立大学法人福岛县立医科大学 罹患食管基底细胞样鳞状细胞癌的鉴别方法
US12006547B2 (en) * 2017-10-02 2024-06-11 Oxford BioDynamics PLC Detection of chromosome interactions as indicative of amyotrophic lateral sclerosis
US20210233615A1 (en) * 2018-04-22 2021-07-29 Viome, Inc. Systems and methods for inferring scores for health metrics
CN111383736A (zh) * 2018-12-28 2020-07-07 康多富国际有限公司 免疫系统疾病保健食品组合确定方法及其可读取储存媒体
KR102176721B1 (ko) * 2019-03-20 2020-11-09 한국과학기술원 기능 유사한 유전자들의 그룹 지표를 이용한 질병 판별 시스템 및 방법
CN113943798B (zh) * 2020-07-16 2023-10-27 中国农业大学 一种circ RNA作为肝细胞癌诊断标志物及治疗靶点的应用
CN112017732B (zh) * 2020-10-23 2021-02-05 平安科技（深圳）有限公司 一种终端设备、装置、疾病分类方法及可读存储介质

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EP2328105A2 (en) 2011-06-01
JP2011092137A (ja) 2011-05-12
CN102051412B (zh) 2014-06-18
CN102051412A (zh) 2011-05-11
EP2328105A3 (en) 2016-05-18
JP5503942B2 (ja) 2014-05-28
US20140287965A1 (en) 2014-09-25

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