WO2007026773A1 - Processeur de diagnostic médical - Google Patents

Processeur de diagnostic médical Download PDF

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Publication number
WO2007026773A1
WO2007026773A1 PCT/JP2006/317125 JP2006317125W WO2007026773A1 WO 2007026773 A1 WO2007026773 A1 WO 2007026773A1 JP 2006317125 W JP2006317125 W JP 2006317125W WO 2007026773 A1 WO2007026773 A1 WO 2007026773A1
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WO
WIPO (PCT)
Prior art keywords
antibody titer
antibody
peptide
peptides
medical diagnostic
Prior art date
Application number
PCT/JP2006/317125
Other languages
English (en)
Japanese (ja)
Inventor
Kyogo Itoh
Nobukazu Komatsu
Shigeki Shichijo
Takashi Yanagawa
Kenta Murotani
Original Assignee
Kurume University
Japan Science And Technology Agency
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Kurume University, Japan Science And Technology Agency filed Critical Kurume University
Priority to JP2007533299A priority Critical patent/JPWO2007026773A1/ja
Publication of WO2007026773A1 publication Critical patent/WO2007026773A1/fr

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57438—Specifically defined cancers of liver, pancreas or kidney

Definitions

  • Non-patent document 5 "S inex (registered trademark) system", Hitachi Software Engineering Co., Ltd. [Search on August 23, 2005] Internet URL: http: ⁇ bio.hitachi-sk.co. Jp / luminex / inaex2.html> 0
  • the flow measurement is a method of quantifying an anti-peptide antibody by a fluorescently labeled antibody
  • the antibody titer is a unit representing the amount of antibody.
  • a computer readable recording medium is characterized in that the medical diagnostic processing program is recorded. Effect of the invention
  • FIG. 2 is a flowchart showing a learning process performed by the medical diagnostic processing device 10 of FIG.
  • the discriminant function of an ADDABOOD to discriminate diagnosis results for antibody titers to each of the above-mentioned peptides is calculated.
  • antibody titers to the above-mentioned peptides of the patient to be diagnosed are measured, and based on the above-mentioned measured antibody titers, the above-mentioned discriminative function of the above-mentioned calculated boost is determined.
  • the number is used to diagnose whether the patient to be diagnosed is a non-cancer patient who is a patient with knee cancer.
  • each of the above-mentioned peptides is a peptide fragment which is at least a part of the peptide and which comprises a predetermined amino acid.
  • the medical diagnostic processing system of the present embodiment can be broadly divided as shown in FIG.
  • the medical diagnostic processing apparatus 10 is
  • a communication interface 51 connected to the communication interface 2 of the antibody titer measuring device 1 and transmitting / receiving data to / from the communication interface 2;
  • the CRT display 43 is connected to a mouse 42 for inputting an instruction command, receives data and an instruction command input from the mouse 42, performs interface processing such as predetermined signal conversion, and the like. With a mouse interface 32 to transmit to
  • circuits 20-24, 31-34 and 51 are connected via a bus 30.
  • an antigenic peptide set has been selectively developed which has not only individual patterns but also disease characteristic patterns and enables analysis of cancer patients.
  • the antibody titer specifically, the fluorescence intensity
  • disease susceptibility analysis can be performed.
  • Example 2 selection of peptides in diagnosis of spleen cancer and measurement of antibody titer against peptide by measurement of anti-peptide antibody in Example 1 will be described, and in Example 2, statistical processing after measurement of antibody titer against peptide The method will be described. Further, in the third embodiment, a method of extracting an optimal discriminant function will be described.
  • Example 2 statistical analysis after completion of the measurement of peptide antibody titers in Example 2 will be described below.
  • Example 2 first, peptide selection was performed using a two-sample t-test.
  • the training data is the data obtained by natural log conversion of the 58 peptide antibody titers derived from PSCA measured for each patient and used as the training sample data, and the peptides that show differences in expression between the spleen cancer group and the non-cancer group are select.
  • the training sample data follows normal distribution in the spleen cancer group and the non-cancer group, respectively, it was tested by the two-sample t-test method. At this time, the number of tests increases because the hypothesis test is performed by the number of peptides, which causes a problem of test multiplicity.
  • antibody titers against a first peptide be X 1, X 2,..., X, and be a pair with a second peptide
  • X Y is defined as a product variable.
  • the significance of introducing this product variable is
  • the objective data are to capture the structure that the value of the correlation coefficient differs between the spleen cancer group and the non-cancer group.
  • the correlation coefficient between P is defined as p e, non kj «
  • peptide selection method a flow chart is shown! /,! / !, and the medical diagnostic processing apparatus 10 executes a processing program using an algorithm of the two-sample t-test method. And peptides with significant difference between spleen cancer patients and non-cancer patients can be automatically selected.
  • a two-sample t-test method is used in the present embodiment, the present invention is not limited to this, and other significance test methods such as non-parametric method may be used.
  • +1 is a spleen cancer person and -1 is a non-cancer person.
  • (peptide name) represents the amount of peptide expression in the ith case (specifically, the fluorescence intensity according to Example 1).
  • the random boost method is one of learning algorithm called ensemble learning.
  • a machine learning method that obtains final discriminant function by adding weighting coefficients to discriminant functions called many learners and adding them linearly. It is.
  • f be the learner (learner), and treat what has a functional form such as the following equation as f (generally called a stamp learner (stump learner)).
  • R represents a whole real number set
  • R 5 represents a 5 dimensional whole vector set having real numbers as components.
  • sign is a sign function.
  • stamp learners are a, b, k and
  • ⁇ (f) is a discriminant Discriminant function f is an index that measures the performance when discriminant of training data with a weighting factor using function f. Therefore, the discriminant function that minimizes this value changes as the weight coefficient of the training data changes, and the procedure using the adaptive algorithm using these symbols is as follows.
  • FIG. 2 is a flowchart showing learning processing executed by the medical diagnostic processing device 10 of FIG.
  • step S2 the maximum number of times of learning M is input using the keyboard 41.
  • step S3, 0 is substituted into FO (X) (the initial value of the judgment function of the boost) for all X ER 5 to initialize.
  • step S8 the reliability c of the learning function value fm (') is calculated using the following equation (19).
  • step S 9 the right side of the following equation is calculated, and the calculation result is set as the updated value F of the discriminant function of the adder boost.
  • step SIO the parameter m is incremented by 1.
  • step S12 the discriminant function F of the following formula obtained by the above learning processing by the following equation is output to the data memory 23 and stored.
  • step S 21 of FIG. 3 fluorescence intensity data Z derived from the anti-peptide antibody of an undiagnosed patient is received from antibody titer measuring device 1 and stored in data memory 23.
  • step S22 the discriminant function F ( ⁇ ) of the adder boost by M times of learning is used to
  • step S24 If YES, the process proceeds to step S24. If NO, the process proceeds to step S25. In step S24, after diagnosing a spleen cancer patient, the process proceeds to step S26. On the other hand, in step S25, it is diagnosed that the patient is not a spleen cancer, and the process proceeds to step S26. In step S26, the above diagnosis result is displayed on the CRT display 43 and printed out on the printer 44, and the diagnosis processing is finished.
  • Discrimination is made based on the experimental data (fluorescence intensity) of the diagnosis, and the reliability c, c, ⁇ ⁇ ⁇ , C for the discrimination result is linearly combined and added, and the diagnosis is performed with the code.
  • the cross-validation method CV is obtained as follows. Now, in the present example, 40 patients with spleen cancer, non cancer patients 29 There is learning data consisting of people. We deduct only one case from this data, use the remaining data to determine the decision function of the adder boost, and predict the excluded cases. All this data
  • E represents the number of cases in which a spleen cancer patient is misidentified as a non-cancerous person, and E represents a non-cancerous person.
  • the discriminant function of an adder boost can be generated by the learning process of FIG. 2 for the antibody titer data (fluorescence intensity) for each peptide, and that the goodness can be measured by the cross validation method CV.
  • the value of the cross-validation method CV differs between the discriminant function created with all five peptides as explanatory variables and the discriminant function created with two specific peptides as explanatory variables, and It is possible that the cross-validation method CV will be smaller.
  • the discriminant function of each candidate is created by combining all of the explanatory variables, and the cross-validation method value CV is obtained for each, and the value is minimized
  • the discriminant function of each candidate is created by combining all of the explanatory variables, and the cross-validation method value CV is obtained for each, and the value is minimized
  • step S 31 of FIG. 4 the maximum number of times of learning M is input using keyboard 41.
  • step S32 parameters p and j are initialized to 1.
  • step S37 while the process proceeds to step S38 when YES, the process returns to step S35 when NO.
  • step S 38 the minimum value among the M cross validation method CVs is set as CV.
  • C is the total number of combinations when selecting d medium powers p
  • step S40 while the process proceeds to step S41 when YES, the process returns to step S33 when NO.
  • the number of times of learning k (p) at that time is stored in the data memory 23 as k *.
  • step S45 the combination of the explanatory variables selected in step S44 above is used, using all the learning data.
  • Adder boost discriminant function F ( ⁇ ) that is trained k * times using the above learning process and used for diagnosis
  • the combination of the optimal explanatory variables when the product variable is not taken into consideration is the explanatory variables P, P, P, and P that give the minimum value 0.159 of the cross validation method value CV.
  • the minimum value of the cross validation method CV when using three explanatory variables and when using four explanatory variables is the same, but in this case the explanatory variables It is assumed that the smaller the number of is the better combination. From Table 39, it can be seen that the optimum combination of explanatory variables when the product variable is selected is the explanatory variables P4, P3 X P4 and P4 X P5, for which the cross validation value CV is 0.130. Thus, by putting the product variable into the explanatory variable, the value of the optimal cross-validation method CV is the minimum value of the cross-validation method CV when the product variable is not inserted 0.10 force et al. 0.10 130 J / J , I was able to drill S.
  • the cross-validation method is performed using at least one of the product variable of the antibody titer against each peptide and the antibody titer against each peptide as an explanatory variable.
  • the cross validation method value CV becomes the minimum value, and the misclassification rate and misdiagnosis rate are significantly reduced. It can be done.
  • the processing program data in FIGS. 2 to 5 are loaded and executed into the program memory 24 when stored and executed in the CD-ROM 45a, but the present invention is not limited to this. Alternatively, it may be stored in various recording media such as a recording medium of an optical disk such as a CD-R, a CD-RW, a DVD, and an MO, or a magneto-optical disk, or a recording medium of a magnetic disk such as a flexible disk. These recording media are computer readable recording media. Further, data of the processing program of FIGS. 2 to 5 may be stored in advance in the program memory 24 to execute the processing.
  • the medical diagnostic processing apparatus 10 is used for diagnosis of spleen cancer, but the present invention is not limited to this, and may be used for diagnosis of other cancers other than spleen cancer. it can.
  • the antibody titer of the anti-peptide antibody is determined by flowmetry (or fluorescent antibody method) among the immunostaining methods, but the present invention is not limited to this, and the ELISA method is used.
  • Enzyme antibody method such as (enzyme immobilization method), metal colloid method, RIA method (two antibody method) or the like may be used.
  • each of the above-mentioned each is processed using learning data including antibody titers to a plurality of peptides and actual diagnostic results.
  • the discriminant function of AddaBoost which determines the diagnostic result for antibody titer against the peptide, is calculated, the antibody titer of the patient to be diagnosed against each of the above peptides is measured, and based on the antibody titer measured above Using the function above It is diagnosed whether the patient to be diagnosed is a splenic cancer patient or a non-cancerous person. Therefore, it is possible to diagnose spleen cancer with extremely low misclassification rate and extremely simply as compared with the prior art.

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  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Urology & Nephrology (AREA)
  • Immunology (AREA)
  • Engineering & Computer Science (AREA)
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  • Gastroenterology & Hepatology (AREA)
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  • Physics & Mathematics (AREA)
  • Analytical Chemistry (AREA)
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Abstract

Processeur de diagnostic médical (10) qui calcule une fonction de discrimination d'AdaBoost permettant de juger les résultats de diagnostic pour le titre d'anticorps pour chaque peptide à l’aide de données d’apprentissage comprenant le titre d'anticorps pour une pluralité de peptides et des résultats de diagnostic réels. Ensuite, le processeur de diagnostic médical (10) mesure le titre d'anticorps pour chaque peptide d’un patient objet du diagnostic et diagnostique si le patient objet du diagnostic souffre d’un cancer du pancréas ou non sur la base du titre d’anticorps ainsi mesuré et à l’aide d’une fonction de discrimination d’AdaBoost.
PCT/JP2006/317125 2005-08-31 2006-08-30 Processeur de diagnostic médical WO2007026773A1 (fr)

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JP2007533299A JPWO2007026773A1 (ja) 2005-08-31 2006-08-30 医用診断処理装置

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JP2005251985 2005-08-31

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2018200322A (ja) * 2011-04-29 2018-12-20 キャンサー・プリヴェンション・アンド・キュア，リミテッド 分類システムおよびそのキットを使用した肺疾患の同定および診断方法
US11474104B2 (en) 2009-03-12 2022-10-18 Cancer Prevention And Cure, Ltd. Methods of identification, assessment, prevention and therapy of lung diseases and kits thereof including gender-based disease identification, assessment, prevention and therapy
US11769596B2 (en) 2017-04-04 2023-09-26 Lung Cancer Proteomics Llc Plasma based protein profiling for early stage lung cancer diagnosis

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2004030511A2 (fr) * 2002-05-10 2004-04-15 Eastern Virginia Medical School Biomarqueurs du cancer de la prostate

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2004030511A2 (fr) * 2002-05-10 2004-04-15 Eastern Virginia Medical School Biomarqueurs du cancer de la prostate

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
XIA Q.: "Proteomics-based identification of DEAD-box protein 48 as a novel autoantigen, a prospective serum marker for pancreatic cancer", BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS, vol. 330, 6 May 2005 (2005-05-06), pages 526 - 532, XP004816927 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11474104B2 (en) 2009-03-12 2022-10-18 Cancer Prevention And Cure, Ltd. Methods of identification, assessment, prevention and therapy of lung diseases and kits thereof including gender-based disease identification, assessment, prevention and therapy
JP2018200322A (ja) * 2011-04-29 2018-12-20 キャンサー・プリヴェンション・アンド・キュア，リミテッド 分類システムおよびそのキットを使用した肺疾患の同定および診断方法
JP2020064078A (ja) * 2011-04-29 2020-04-23 キャンサー・プリヴェンション・アンド・キュア，リミテッド 分類システムおよびそのキットを使用した肺疾患の同定および診断方法
US11769596B2 (en) 2017-04-04 2023-09-26 Lung Cancer Proteomics Llc Plasma based protein profiling for early stage lung cancer diagnosis

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