WO2018101450A1 - Procédé de surveillance de cancer, procédé de calcul, dispositif d'évaluation, dispositif de calcul, programme d'évaluation, programme de calcul, système d'évaluation et dispositif terminal - Google Patents

Procédé de surveillance de cancer, procédé de calcul, dispositif d'évaluation, dispositif de calcul, programme d'évaluation, programme de calcul, système d'évaluation et dispositif terminal Download PDF

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WO2018101450A1
WO2018101450A1 PCT/JP2017/043208 JP2017043208W WO2018101450A1 WO 2018101450 A1 WO2018101450 A1 WO 2018101450A1 JP 2017043208 W JP2017043208 W JP 2017043208W WO 2018101450 A1 WO2018101450 A1 WO 2018101450A1
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evaluation
value
trp
cancer
concentration
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PCT/JP2017/043208
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English (en)
Japanese (ja)
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山本　浩史
信矢 菊池
今泉　明
温子 池田
貴嗣 穴山
聖彦 東山
次郎 岡見
富雄 中山
文生 今村
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味の素株式会社
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Priority to JP2018554270A priority Critical patent/JP7336850B2/ja
Priority to KR1020197015532A priority patent/KR102477319B1/ko
Publication of WO2018101450A1 publication Critical patent/WO2018101450A1/fr
Priority to JP2022071128A priority patent/JP2022090070A/ja

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    • 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
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6806—Determination of free amino acids
    • G01N33/6812—Assays for specific amino acids
    • 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
    • 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
    • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
    • G01N33/57488—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites involving compounds identifable in body fluids
    • 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
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • 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
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • 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
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/54—Determining the risk of relapse

Definitions

  • the present invention relates to a cancer monitoring method, a calculation method, an evaluation device, a calculation device, an evaluation program, a calculation program, an evaluation system, and a terminal device.
  • Surgical therapy is a treatment that removes cancer lesions and is said to be the first choice of treatment for solid cancers except leukemia. Because cancer can be cured if it can be completely removed, surgical therapy is the most direct treatment.
  • Non-patent Document 1 it has been reported that in breast cancer patients, the concentration of serine and glutamic acid in blood is normalized by surgical operation (Non-patent Document 1).
  • Non-patent Document 2 blood concentrations of glutamine, tryptophan, alanine, glycine and arginine decreased immediately after chest surgery.
  • Non-patent Document 3 it has been reported that alanine and aspartic acid increase after surgery in gastric cancer patients and breast cancer patients.
  • Patent Documents 1, 2, and 3 relating to a method for associating an amino acid concentration with a biological state are disclosed as prior patents.
  • Patent Document 4 relating to a method for evaluating lung cancer status using amino acid concentration
  • Patent Literature 5 relating to a method for evaluating breast cancer status using amino acid concentration
  • Patent Document 7 related to a method for evaluating cancer status using amino acid concentration
  • Patent Document 8 related to a method for evaluating gastric cancer status using amino acid concentration
  • Patent Document 9 Patent Document 9
  • Patent Document 10 that evaluates the state of female genital cancer using amino acid concentration
  • Patent Document 11 that evaluates the state of prostate disease including prostate cancer using amino acid concentration
  • Patent Document 12 regarding a method for evaluating a state
  • Patent Document 14 relates to a method for evaluating the
  • Non-Patent Documents 1, 2, and 3 are short-term reports after surgical operation, and there are no reports of changes in blood amino acid concentration related to recurrence after cancer surgery.
  • the evaluation targets are different.
  • the present invention has been made in view of the above problems, and uses an amino acid concentration in blood to provide highly reliable information that can be used as a reference for knowing the possibility of cancer recurrence, an evaluation apparatus,
  • An object is to provide an evaluation program, an evaluation system, and a terminal device.
  • the evaluation method according to the present invention includes 12 kinds of amino acids (Glu, Ser, a-ABA) in the blood to be evaluated in which cancer has been found. , Val, Met, Lys, Ile, Leu, Trp, His, Orn and Pro) at least one or 21 kinds of amino acids (Glu, Arg, Orn, Cit, His, Val, Phe, Tyr, Met, Pro) , Trp, Asn, Leu, Lys, Thr, Ile, Gln, Ala, Ser, a-ABA, and Gly), and an evaluation for evaluating the likelihood of cancer recurrence for the evaluation target Including a step.
  • 12 kinds of amino acids Glu, Ser, a-ABA
  • the evaluation method in the evaluation method, in the evaluation step, at least one of the 12 kinds of amino acids or at least two concentration values of the 21 kinds of amino acids are substituted. And calculating the value of the expression by further using an expression including the expression, and evaluating the possibility of cancer recurrence for the evaluation object.
  • the evaluation apparatus is an evaluation apparatus including a control unit, and the control unit is at least one of the 12 types of amino acids in the blood of the evaluation target in which cancer has been found.
  • An evaluation method is an evaluation method executed in an information processing apparatus including a control unit, and is executed in the control unit, in blood of an evaluation target in which cancer has been found. Including an evaluation step of evaluating the likelihood of cancer recurrence for the evaluation object using concentration values of at least one of the 12 amino acids or at least two of the 21 amino acids. To do.
  • An evaluation program is an evaluation program for execution in an information processing apparatus including a control unit, and is an evaluation target blood for which cancer has been found for execution in the control unit.
  • a recording medium is a non-transitory computer-readable recording medium, and includes a programmed instruction for causing an information processing apparatus to execute the evaluation method.
  • an evaluation system includes an evaluation device including a control unit, and a control unit, and at least one of the 12 types of amino acids in blood of an evaluation target in which cancer has been found or
  • An evaluation system configured by connecting a terminal device that provides concentration data regarding at least two concentration values of the 21 types of amino acids so as to be communicable via a network, the control unit of the terminal device Comprises: concentration data transmitting means for transmitting the concentration data to the evaluation device; and result receiving means for receiving an evaluation result regarding the possibility of recurrence of cancer transmitted from the evaluation device;
  • the control unit includes density data receiving means for receiving the density data transmitted from the terminal device, and the density data received by the density data receiving means.
  • the evaluation means for evaluating the likelihood of cancer recurrence for the evaluation object, And a result transmitting means for transmitting the evaluation result obtained by the evaluating means to the terminal device.
  • the terminal device is a terminal device including a control unit, and the control unit includes a result acquisition unit that acquires an evaluation result related to the likelihood of cancer recurrence, Using the concentration values of at least one of the 12 amino acids or at least two of the 21 amino acids in the blood of the evaluation target that has been discovered, the possibility of recurrence of cancer for the evaluation target It is the result of having evaluated.
  • the terminal device is configured to be communicably connected to an evaluation device that evaluates the likelihood of cancer recurrence via the network in the terminal device, and the control unit Concentration data transmitting means for transmitting concentration data relating to concentration values of at least one of the 12 kinds of amino acids in blood or at least two of the 21 kinds of amino acids to the evaluation device, the result obtaining means Receives the evaluation result transmitted from the evaluation device.
  • the evaluation apparatus relates to a concentration value of at least one of the 12 amino acids or at least two of the 21 amino acids in blood of an evaluation object in which cancer has been found.
  • An evaluation apparatus including a control unit that is communicably connected to a terminal device that provides density data via a network, wherein the control unit receives the density data transmitted from the terminal device.
  • the control unit receives the density data transmitted from the terminal device.
  • the blood amino acid concentration is used to provide highly reliable information that can be used as a reference for knowing the possibility of cancer recurrence.
  • FIG. 1 is a principle configuration diagram showing the basic principle of the first embodiment.
  • FIG. 2 is a principle configuration diagram showing the basic principle of the second embodiment.
  • FIG. 3 is a diagram illustrating an example of the overall configuration of the present system.
  • FIG. 4 is a diagram showing another example of the overall configuration of the present system.
  • FIG. 5 is a block diagram showing an example of the configuration of the evaluation apparatus 100 of this system.
  • FIG. 6 is a diagram showing an example of information stored in the density data file 106a.
  • FIG. 7 is a diagram illustrating an example of information stored in the index state information file 106b.
  • FIG. 8 is a diagram illustrating an example of information stored in the designated index state information file 106c.
  • FIG. 9 is a diagram illustrating an example of information stored in the expression file 106d1.
  • FIG. 10 is a diagram illustrating an example of information stored in the evaluation result file 106e.
  • FIG. 11 is a block diagram illustrating a configuration of the evaluation unit 102d.
  • FIG. 12 is a block diagram illustrating an example of the configuration of the client device 200 of the present system.
  • FIG. 13 is a block diagram showing an example of the configuration of the database apparatus 400 of this system.
  • FIG. 14 is a diagram showing the amino acid concentration data of 21 kinds of recurrence-free cases.
  • FIG. 15 is a diagram showing 21 amino acid concentration data of recurrence cases.
  • FIG. 16 is a radar chart showing “distribution of each amino acid after surgery when each amino acid before surgery is taken as 100%” for a non-recurrence example.
  • FIG. 16 is a radar chart showing “distribution of each amino acid after surgery when each amino acid before surgery is taken as 100%” for a non-recurrence example.
  • FIG. 17 is a diagram showing a radar chart showing “distribution of each amino acid after surgery when each amino acid before surgery is 100%” for a recurrence example.
  • FIG. 18 is a diagram illustrating a result of logistic regression analysis.
  • FIG. 19 is a diagram showing combinations of two variables and ROC_AUC for each combination.
  • FIG. 20 is a diagram showing combinations of three variables and ROC_AUC for each combination.
  • FIG. 21 is a diagram showing combinations of four variables and ROC_AUC for each combination.
  • FIG. 22 is a diagram illustrating combinations of two variables and ROC_AUC for each combination.
  • FIG. 23 is a diagram illustrating combinations of three variables and ROC_AUC for each combination.
  • FIG. 24 is a diagram illustrating combinations of four variables and ROC_AUC for each combination.
  • Embodiments of an evaluation method according to the present invention (first embodiment) and embodiments of an evaluation apparatus, an evaluation method, an evaluation program, a recording medium, an evaluation system, and a terminal device according to the present invention (second embodiment) Will be described in detail with reference to the drawings. Note that the present invention is not limited to these embodiments.
  • FIG. 1 is a principle configuration diagram showing the basic principle of the first embodiment.
  • At least one of the 12 amino acids in the blood (including plasma, serum, etc.) of an evaluation target for example, an individual such as an animal or a human
  • Concentration data relating to concentration values of at least two of the amino acids is acquired (step S11).
  • cancer was found includes, for example, a definitive diagnosis of cancer.
  • step S11 for example, before the cancer treatment (for example, treatment by surgery, chemotherapy, radiation therapy, or cancer immunotherapy) is started from an evaluation object in which cancer has been discovered.
  • concentration data derived from collected blood concentration data before the start of treatment
  • concentration data derived from blood collected after the treatment was started concentration data after the start of treatment
  • the treatment is started”, for example, after the first narrowly defined treatment in the broadly defined treatment over a certain period of time and before the final narrowly defined treatment is performed (for example, the general treatment Or “after treatment” in a broad sense over a certain period of time (for example, “post-treatment” generally referred to) .
  • step S11 density data measured by a company or the like that performs density value measurement may be acquired.
  • concentration data may be acquired by measuring concentration values from blood collected from an evaluation object by, for example, the following measurement method (A), (B), or (C).
  • the unit of the concentration value may be, for example, a molar concentration, a weight concentration, or an enzyme activity, and may be obtained by adding / subtracting / dividing an arbitrary constant to / from these concentrations.
  • A Plasma is separated from blood by centrifuging the collected blood sample. All plasma samples are stored frozen at ⁇ 80 ° C. until the concentration value is measured.
  • concentration value measurement acetonitrile was added to remove protein, followed by precolumn derivatization using a labeling reagent (3-aminopyridyl-N-hydroxysuccinimidyl carbamate), and liquid chromatograph mass spectrometry The concentration value is analyzed by a meter (LC / MS) (see International Publication No. 2003/069328, International Publication No. 2005/116629).
  • LC / MS liquid chromatograph mass spectrometry
  • sulfosalicylic acid is added to remove the protein, and then the concentration value is analyzed by an amino acid analyzer based on the post-column derivatization method using a ninhydrin reagent.
  • C The collected blood sample is subjected to blood cell separation using a membrane, MEMS technology, or the principle of centrifugation to separate plasma or serum from the blood. Plasma or serum samples that are not measured immediately after plasma or serum are obtained are stored frozen at ⁇ 80 ° C. until the concentration is measured.
  • a molecule that reacts or binds to a target amino acid such as an enzyme or an aptamer, is used, and the concentration value is analyzed by quantifying a substance or spectroscopic value that increases or decreases by substrate recognition.
  • step S12 the possibility of cancer recurrence is evaluated for the evaluation target using the concentration value included in the concentration data acquired in step S11 (step S12).
  • data such as missing values and outliers may be removed from the density data acquired in step S11.
  • “Relapse” includes, for example, local recurrence, regional recurrence, and distant recurrence (metastasis).
  • “Evaluating the likelihood of cancer recurrence” includes, for example, evaluating the degree of possibility that the cancer will recur.
  • the concentration data before the start of treatment and the concentration data after the start of treatment are used in step S12, for example, the ratio or difference between the concentration value before the start of treatment and the concentration value after the start of treatment is calculated. The evaluation may be performed using the calculated ratio or difference value.
  • the concentration data of the evaluation target is acquired in step S11, and the concentration data included in the concentration data of the evaluation target acquired in step S11 is used in step S12.
  • Evaluate the likelihood of recurrence (in short, obtain information to assess the likelihood of cancer recurrence or reliable information that can be helpful in knowing the likelihood of cancer recurrence). This makes it possible to accurately evaluate the likelihood of cancer recurrence using the blood amino acid concentration (in short, it is useful for knowing the information for evaluating the possibility of cancer recurrence or the possibility of cancer recurrence). Can provide reliable information).
  • the evaluation result obtained in the present embodiment can be used as reference information for determining the treatment method.
  • concentration data after the start of treatment or after treatment is used in step S12
  • the evaluation result obtained in this embodiment can be used for monitoring of recurrence, or a further treatment method is determined. It can be used as reference information at the time of use, or it can be used as reference information when selecting the period and means of follow-up after surgery.
  • At least one of the 12 amino acids or at least two concentration values of the 21 amino acids may be the ratio or difference value described above reflected the possibility of cancer recurrence in the evaluation target. Or may be determined to be an index of the likelihood of cancer recurrence in the evaluation target, and further, the concentration value (which may be the ratio or difference value described above) is converted by, for example, the following methods Then, it may be determined that the value after conversion reflects the possibility of recurrence of cancer in the evaluation object or an index of the possibility of recurrence of cancer in the evaluation object. In other words, the concentration value or the converted value itself may be treated as an evaluation result regarding the possibility of recurrence of cancer in the evaluation target.
  • the possible range of the density value is a predetermined range (for example, a range from 0.0 to 1.0, a range from 0.0 to 10.0, a range from 0.0 to 100.0, or -10.0 to
  • a predetermined range for example, exponential conversion, logarithmic conversion, Conversion by angle conversion, square root conversion, probit conversion, reciprocal conversion, Box-Cox conversion, power conversion, etc., and by combining these calculations for density values, the density values are converted. May be.
  • the value of the exponential function with the concentration value as the index and the Napier number as the base (specifically, the natural logarithm ln (p / (1-p)) when the probability p of cancer recurrence is defined as the concentration value.
  • the value of p / (1-p) in the case of being equal may be further calculated, and the calculated exponential function value divided by the sum of 1 and the value (specifically, probability) The value of p) may be further calculated.
  • the density value may be converted so that the value after conversion under a specific condition becomes a specific value. For example, the density value may be converted so that the value after conversion when the specificity is 80% is 5.0 and the value after conversion when the specificity is 95% is 8.0.
  • the concentration distribution may be converted into a normal distribution and then converted into a deviation value so that the average is 50 and the standard deviation is 10. These conversions may be performed by gender or age.
  • the density value in the present specification may be the density value itself or a value after the density value is converted.
  • position information regarding the position of a predetermined mark on a predetermined ruler that is visibly displayed on a display device such as a monitor or a physical medium such as paper is obtained by using at least one of the 12 amino acids or the 21 types of amino acids. If at least two concentration values of amino acids (the ratio or difference value described above) or the concentration value is converted, it is generated using the converted value, and the generated positional information is cancer in the evaluation target. It may be determined that it reflects the possibility of recurrence or is an index of the possibility of recurrence of cancer in the evaluation target.
  • the predetermined ruler is for evaluating the possibility of cancer recurrence.
  • the ruler is a ruler with a scale, and the “concentration value or a range that can be taken after conversion, or That is, at least a scale corresponding to the upper limit value and the lower limit value in “part of range” is shown.
  • the predetermined mark corresponds to the density value or the value after conversion, and is, for example, a circle mark or a star mark.
  • At least one of the 12 kinds of amino acids or at least two concentration values of the 21 kinds of amino acids is a predetermined value (average value ⁇ 1SD, 2SD, (3SD, N quantile, N percentile, or cut-off value with clinical significance, etc.) lower than or below a predetermined value, or above or above a predetermined value May be evaluated.
  • a concentration deviation value (a value obtained by normalizing the concentration distribution by gender for each metabolite and each amino acid and then making the deviation value so that the average is 50 and the standard deviation is 10) It may be used.
  • the concentration deviation value is less than the average value ⁇ 2SD (when the concentration deviation value ⁇ 30) or when the concentration deviation value is higher than the average value + 2SD (when the concentration deviation value> 70), the recurrence of cancer in the evaluation target Possibilities may be evaluated.
  • At least one of the 12 kinds of amino acids or at least two of the 21 kinds of amino acids may be the ratio or difference value described above) and the concentration value (the ratio or difference value described above).
  • the possibility of cancer recurrence in the evaluation target may be evaluated by calculating the value of the expression using an expression including a variable to which is substituted.
  • the value of the calculated formula reflects the possibility of recurrence of cancer in the evaluation target or is an index of the possibility of recurrence of cancer in the evaluation target.
  • the value may be converted by the method described below, and the value after conversion may be determined to reflect the possibility of cancer recurrence in the evaluation target or to be an index of the possibility of cancer recurrence in the evaluation target.
  • the value of the expression or the converted value itself may be treated as an evaluation result regarding the possibility of recurrence of cancer in the evaluation target.
  • the possible range of the value of the expression is a predetermined range (for example, a range from 0.0 to 1.0, a range from 0.0 to 10.0, a range from 0.0 to 100.0, or -10.0
  • a predetermined range for example, a range from 0.0 to 1.0, a range from 0.0 to 10.0, a range from 0.0 to 100.0, or -10.0
  • an arbitrary value is added / subtracted / divided / divided from / to the value of the expression, or the value of the expression is converted into a predetermined conversion method (for example, exponential conversion, Logarithmic transformation, angular transformation, square root transformation, probit transformation, reciprocal transformation, Box-Cox transformation, or power transformation), or by combining these calculations on the value of the expression,
  • the value of the expression may be converted.
  • the value of the exponential function with the value of the formula as the index and the Napier number as the base (specifically, the natural logarithm ln (p / (1-p)) when defining the probability p of cancer recurrence is (P / (1-p) value in the case where the value is equal to the value) may be further calculated, and the calculated exponential function value divided by the sum of 1 and the value (specifically, , The value of probability p) may be further calculated.
  • the value of the expression may be converted so that the value after conversion under a specific condition becomes a specific value.
  • the value of the equation may be converted so that the value after conversion when the specificity is 80% is 5.0 and the value after conversion when the specificity is 95% is 8.0.
  • the values of the equations may be converted into deviation values so that the average is 50 and the standard deviation is 10. These conversions may be performed by gender or age. Note that the value of the expression in this specification may be the value of the expression itself, or may be a value after converting the value of the expression.
  • the position information generated using the later value may be determined to reflect the possibility of recurrence of cancer in the evaluation object or to be an index of the possibility of cancer recurrence in the evaluation object.
  • the predetermined ruler is for evaluating the possibility of recurrence of cancer, for example, a ruler with a scale, and “the range of the value of the formula or the value after conversion, or That is, at least a scale corresponding to the upper limit value and the lower limit value in “part of the range” is shown.
  • the predetermined mark corresponds to the value of the expression or the value after conversion, and is, for example, a circle mark or a star mark.
  • the degree of possibility of cancer recurrence in the evaluation target may be qualitatively evaluated.
  • a category for assigning subjects with a high likelihood of cancer recurrence for example, subjects considered to have cancer recurrence
  • a subject with a low likelihood of cancer recurrence For example, there may be included a classification for belonging to a subject that is considered to be a cancer that does not recur, and a classification for belonging to a subject that is moderately likely to recur.
  • the plurality of categories include a category for belonging to a subject having a high possibility of recurrence of cancer and a category for assigning a subject having a low possibility of recurrence of cancer. Also good.
  • the density value (which may be the ratio or difference value described above) or the value of the expression is converted by a predetermined method, and the evaluation target is classified into one of a plurality of categories using the converted value. May be.
  • the form used for the evaluation is not particularly limited, but for example, the following form may be used.
  • Linear models such as multiple regression, linear discriminant, principal component analysis, canonical discriminant analysis based on least square method
  • Generalized linear model such as logistic regression based on maximum likelihood method, Cox regression
  • Generalized linear mixed models that take into account random effects such as inter-individual differences, inter-facility differences, formulas created by cluster analysis such as K-means method, hierarchical cluster analysis, MCMC (Markov chain Monte Carlo method), Bayesian network, Formulas created based on Bayesian statistics such as Hierarchical Bayes method, formulas created by class classification such as support vector machines and decision trees, formulas created by methods not belonging to the above categories such as fractional formulas, sums of formulas of different formats Formula as shown in
  • the formula used in the evaluation is described in, for example, the method described in International Publication No. 2004/052191 which is an international application by the present applicant or International Publication No. 2006/098192 which is an international application by the present applicant. You may create by the method.
  • the formula can be suitably used for evaluating the possibility of recurrence of cancer regardless of the unit of the amino acid concentration value in the concentration data as input data.
  • a coefficient and a constant term are added to each variable.
  • the coefficient and the constant term are preferably real numbers, and more preferably May be any value belonging to the range of 99% confidence interval of the coefficient and constant term obtained for performing the various classifications from the data, and more preferably, the value obtained for performing the various classifications from the data Any value belonging to the range of the 95% confidence interval of the coefficient and the constant term may be used. Further, the value of each coefficient and its confidence interval may be obtained by multiplying it by a real number, and the value of the constant term and its confidence interval may be obtained by adding or subtracting any real constant to it.
  • the fractional expression means that the numerator of the fractional expression is represented by the sum of the variables A, B, C,... And / or the denominator of the fractional expression is the sum of the variables a, b, c,. It is represented by
  • the fractional expression includes a sum of fractional expressions ⁇ , ⁇ , ⁇ ,.
  • the fractional expression also includes a divided fractional expression. Note that each variable used in the numerator and denominator may have an appropriate coefficient. The variables used for the numerator and denominator may overlap. Further, an appropriate coefficient may be attached to each fractional expression. Further, the value of the coefficient of each variable and the value of the constant term may be real numbers.
  • the fractional expression includes one in which the numerator variable and the denominator variable are interchanged.
  • biological information other than amino acids eg, blood metabolites other than amino acids (amino acid metabolites, sugars, lipids, etc.), concentrations of proteins, peptides, minerals, hormones, etc.
  • Tumor marker values albumin, total protein, triglyceride, HbA1c, LDL cholesterol, HDL cholesterol, amylase, total bilirubin or uric acid blood test values, blood cytokines, number of immunocompetent cells, immunocompetent intracellular cytokines or delayed type Immune related test values such as excessive reaction (DTH), ultrasound echo, upper / lower endoscope, X-ray, CT or MRI image information, or age, height, weight, BMI, blood pressure, sex, smoking information , Meal information, drinking information, exercise information, stress information, sleep information, family history information or disease Gravel Information (diabetes, pancreatitis, etc.) biomarkers such, etc.) may be further used.
  • DTH excessive reaction
  • ultrasound echo ultrasound echo
  • upper / lower endoscope X-ray
  • CT or MRI image information or age, height, weight, BMI, blood pressure, sex, smoking information , Meal information, drinking information, exercise information, stress information
  • variables in formulas used for evaluation for example, multivariate discriminants
  • biological information other than amino acids for example, blood metabolites other than amino acids (amino acid metabolites, saccharides, lipids, etc.), proteins, Peptide, mineral or hormone concentration, tumor marker value, albumin, total protein, triglyceride, HbA1c, LDL cholesterol, HDL cholesterol, amylase, total bilirubin or uric acid blood test value, blood cytokine, number of immunocompetent cells, Immunity-related intracellular cytokines or immune-related test values such as delayed hyperfractionation (DTH), ultrasound echo, upper / lower endoscope, X-ray, CT or MRI image information, or age, height, weight, BMI, blood pressure, sex, smoking information, meal information, drinking information, exercise information, stress information, sleep Distribution, history information or disease history information family (diabetes, pancreatitis, etc.), such as biological indicators, etc.)
  • DTH delayed hyperfractionation
  • FIG. 2 is a principle configuration diagram showing the basic principle of the second embodiment.
  • the description overlapping the first embodiment described above may be omitted.
  • the case of using the value of the formula or the value after conversion is described as an example.
  • the concentration value, the ratio of the concentration values, or the concentration value A difference or a value after conversion may be used.
  • the control unit includes at least one of the 12 kinds of amino acids or at least two of the 21 kinds of amino acids in the blood of an evaluation target (for example, an individual such as an animal or a human) in which cancer has been found.
  • an evaluation target for example, an individual such as an animal or a human
  • the possibility of cancer recurrence is evaluated for the evaluation target (step S21).
  • control unit for example, the ratio of the concentration value before the start of treatment and the concentration value after the start of treatment or The possibility of recurrence of cancer may be evaluated for the evaluation target by calculating the difference and substituting the calculated ratio or difference value into a variable to calculate the value of the equation.
  • step S21 may be created based on formula creation processing (step 1 to step 4) described below.
  • formula creation processing step 1 to step 4
  • an overview of the formula creation process will be described. Note that the processing described here is merely an example, and the method of creating an expression is not limited to this.
  • the indicator status information includes concentration data (for example, preoperative data on amino acid concentration, postoperative data on amino acid concentration, or data on changes in amino acid concentration before and after surgery) and recurrence of cancer. Index data (for example, binary data relating to the presence or absence of recurrence).
  • step 1 multiple different formula creation methods (principal component analysis and discriminant analysis, support vector machine, multiple regression analysis, Cox regression analysis, logistic regression analysis, k-means method, cluster analysis, determination from index state information
  • a plurality of candidate expressions may be created using a combination of multivariate analysis such as trees).
  • index status information that is multivariate data composed of concentration data and index data obtained by analyzing blood obtained from a large number of relapsed groups and non-relapsed groups before and / or after surgery.
  • a plurality of groups of candidate expressions may be created simultaneously using a plurality of different algorithms.
  • discriminant analysis and logistic regression analysis may be performed simultaneously using different algorithms to create two different candidate formulas.
  • the candidate formulas may be created by converting index state information using candidate formulas created by performing principal component analysis and performing discriminant analysis on the converted index status information. As a result, it is possible to finally create an optimum expression for evaluation.
  • the candidate formula created using the principal component analysis is a linear formula including each variable that maximizes the variance of all density data.
  • Candidate formulas created using discriminant analysis are high-order formulas (including exponents and logarithms) that contain variables that minimize the ratio of the sum of variances within each group to the variance of all concentration data. is there.
  • the candidate formula created using the support vector machine is a high-order formula (including a kernel function) including variables that maximize the boundary between groups.
  • the candidate formula created using the multiple regression analysis is a high-order formula including each variable that minimizes the sum of the distances from all density data.
  • the candidate formula created using Cox regression analysis is a linear model including a log hazard ratio, and is a linear expression including each variable and its coefficient that maximize the likelihood of the model.
  • the candidate formula created using logistic regression analysis is a linear model that represents log odds of probability, and is a linear formula that includes each variable that maximizes the likelihood of the probability.
  • k-means method k neighborhoods of each density data are searched, the largest group among the groups to which the neighboring points belong is defined as the group to which the data belongs, and the group to which the input density data belongs. This is a method for selecting a variable that best matches the group defined as.
  • Cluster analysis is a technique for clustering (grouping) points that are closest to each other in all density data. Further, the decision tree is a technique for predicting a group of density data from patterns that can be taken by variables with higher ranks by adding ranks to the variables.
  • the control unit verifies (mutually verifies) the candidate formula created in step 1 based on a predetermined verification method (step 2).
  • Candidate expressions are verified for each candidate expression created in step 1.
  • the discrimination rate, sensitivity, specificity, information criterion, ROC_AUC (candidate expression of candidate formulas are determined based on at least one of the bootstrap method, holdout method, N-fold method, leave one-out method, and the like. It may be verified with respect to at least one of the area under the receiver characteristic curve).
  • the discrimination rate is an evaluation method according to the present embodiment, in which an evaluation object whose true state is negative (for example, a relapse-free evaluation object) is correctly evaluated as negative, and the true state is positive.
  • This is the rate at which subjects (for example, recurrence assessment targets) are correctly evaluated as positive.
  • Sensitivity is the rate at which an evaluation object whose true state is positive is correctly evaluated as positive in the evaluation method according to the present embodiment.
  • the specificity is a rate at which an evaluation object whose true state is negative is correctly evaluated as negative in the evaluation method according to the present embodiment.
  • the Akaike Information Criterion is a standard that indicates how much the observed data matches the statistical model in the case of regression analysis, etc., and is “ ⁇ 2 ⁇ (maximum log likelihood of statistical model) + 2 ⁇ (statistic The model having the smallest value defined by “the number of free parameters of the model)” is determined to be the best.
  • the predictability is an average of the discrimination rate, sensitivity, and specificity obtained by repeating the verification of candidate formulas.
  • Robustness is the variance of discrimination rate, sensitivity, and specificity obtained by repeating verification of candidate formulas.
  • the control unit selects a combination of density data included in the index state information used when creating a candidate formula by selecting a variable of the candidate formula based on a predetermined variable selection method.
  • the selection of variables may be performed for each candidate formula created in step 1. Thereby, the variable of a candidate formula can be selected appropriately.
  • Step 1 is executed again using the index state information including the density data selected in Step 3.
  • the candidate expression variable may be selected based on at least one of the stepwise method, the best path method, the neighborhood search method, and the genetic algorithm from the verification result in step 2.
  • the best path method is a method of selecting variables by sequentially reducing the variables included in the candidate formula one by one and optimizing the evaluation index given by the candidate formula.
  • the control unit repeatedly executes the above-described step 1, step 2, and step 3, and based on the verification results accumulated thereby, candidates to be used for evaluation from a plurality of candidate formulas By selecting an expression, an expression used for evaluation is created (step 4).
  • the selection of candidate formulas includes, for example, selecting an optimal formula from candidate formulas created by the same formula creation method and selecting an optimal formula from all candidate formulas.
  • FIG. 3 is a diagram showing an example of the overall configuration of the present system.
  • FIG. 4 is a diagram showing another example of the overall configuration of the present system.
  • the present system includes an evaluation apparatus 100 that evaluates the likelihood of cancer recurrence for an individual to be evaluated, and at least one of the 12 amino acids in the blood or the 21 amino acids.
  • the client apparatus 200 (corresponding to the terminal apparatus of the present invention) that provides individual density data regarding at least two density values is connected via the network 300 so as to be communicable.
  • the client apparatus 200 that is a provider of data used for evaluation and the client apparatus 200 that is a provider of evaluation results may be different.
  • this system stores a database device that stores index state information used when creating formulas used in evaluation, formulas used during evaluation, and the like in addition to the evaluation device 100 and the client device 200. 400 may be configured to be communicably connected via the network 300.
  • FIG. 5 is a block diagram showing an example of the configuration of the evaluation apparatus 100 of the present system, and conceptually shows only the portion related to the present invention in the configuration.
  • the evaluation device 100 includes a control unit 102 such as a CPU (Central Processing Unit) that controls the evaluation device in an integrated manner, a communication device such as a router, and a wired or wireless communication line such as a dedicated line.
  • the communication interface unit 104 that is communicably connected to the network 300, the storage unit 106 that stores various databases, tables, and files, and the input / output interface unit 108 that is connected to the input device 112 and the output device 114 are configured. These units are communicably connected via an arbitrary communication path.
  • the evaluation apparatus 100 may be configured in the same housing as various analysis apparatuses (for example, an amino acid analysis apparatus).
  • a small analyzer having a configuration may further include an evaluation unit 102d to be described later, and a result obtained by the evaluation unit 102d may be output using the configuration. .
  • the communication interface unit 104 mediates communication between the evaluation device 100 and the network 300 (or a communication device such as a router). That is, the communication interface unit 104 has a function of communicating data with other terminals via a communication line.
  • the input / output interface unit 108 is connected to the input device 112 and the output device 114.
  • a monitor including a home television
  • a speaker or a printer can be used as the output device 114 (hereinafter, the output device 114 may be described as the monitor 114).
  • the input device 112 a monitor that realizes a pointing device function in cooperation with a mouse can be used in addition to a keyboard, a mouse, and a microphone.
  • the storage unit 106 is a storage unit, and for example, a memory device such as a RAM (Random Access Memory) or a ROM (Read Only Memory), a fixed disk device such as a hard disk, a flexible disk, an optical disk, or the like can be used.
  • the storage unit 106 stores a computer program for giving instructions to the CPU and performing various processes in cooperation with an OS (Operating System). As illustrated, the storage unit 106 stores a density data file 106a, an index state information file 106b, a designated index state information file 106c, an expression related information database 106d, and an evaluation result file 106e.
  • the concentration data file 106a includes concentration data relating to concentration values of at least one of the 12 amino acids in the blood or at least two of the 21 amino acids (for example, concentration data before treatment start and after treatment start). Store one or both of the density data).
  • FIG. 6 is a diagram showing an example of information stored in the density data file 106a. As shown in FIG. 6, the information stored in the density data file 106a is configured by associating an individual number for uniquely identifying an individual (sample) to be evaluated with density data.
  • the density data is handled as a numerical value, that is, a continuous scale, but the density data may be a nominal scale or an order scale. In the case of a nominal scale or an order scale, analysis may be performed by giving an arbitrary numerical value to each state. Moreover, you may combine the value regarding the biometric information mentioned above other than an amino acid with density
  • concentration data may be combined.
  • the index state information file 106b stores the index state information used when creating the formula.
  • FIG. 7 is a diagram illustrating an example of information stored in the index state information file 106b.
  • the information stored in the index state information file 106b is configured by associating an individual number, index data regarding recurrence of cancer, and concentration data.
  • the index data and the density data are handled as numerical values (that is, continuous scales), but the index data and the density data may be nominal scales or order scales. In the case of a nominal scale or an order scale, analysis may be performed by giving an arbitrary numerical value to each state.
  • the designated index state information file 106c stores the index state information designated by the designation unit 102b described later.
  • FIG. 8 is a diagram illustrating an example of information stored in the designated index state information file 106c. As shown in FIG. 8, the information stored in the designated index state information file 106c is configured by associating an individual number, designated index data, and designated density data with each other.
  • the formula related information database 106d includes a formula file 106d1 that stores formulas created by a formula creation unit 102c described later.
  • the expression file 106d1 stores expressions used for evaluation.
  • FIG. 9 is a diagram illustrating an example of information stored in the expression file 106d1. As shown in FIG. 9, the information stored in the expression file 106d1 includes the rank, the expression (in FIG. 9, Fp (Phe,%), Fp (Gly, Leu, Phe), Fk (Gly, Leu, Phe,...)), A threshold value corresponding to each formula creation method, and a verification result of each formula (for example, the value of each formula) are associated with each other.
  • FIG. 10 is a diagram illustrating an example of information stored in the evaluation result file 106d.
  • Information stored in the evaluation result file 106d includes an individual number for uniquely identifying an individual (sample) to be evaluated, concentration data of the individual acquired in advance, and an evaluation result regarding the possibility of cancer recurrence (for example, The value of the formula calculated by the calculation unit 102d1 described later, the value after converting the value of the formula by the conversion unit 102d2 described later, the position information generated by the generation unit 102d3 described later, or the classification unit 102d4 described later Classification results, etc.) and the like.
  • control unit 102 has an internal memory for storing a control program such as an OS, a program that defines various processing procedures, and necessary data, and various information processing based on these programs. Execute. As shown in the figure, the control unit 102 is roughly divided into a reception unit 102a, a specification unit 102b, an expression creation unit 102d, an evaluation unit 102d, a result output unit 102e, and a transmission unit 102f.
  • the control unit 102 removes data with missing values, removes data with many outliers, and has data with missing values from the index state information sent from the database device 400 and the density data sent from the client device 200. Data processing such as removal of many variables is also performed.
  • the receiving unit 102a may receive information (specifically, concentration data, index state information, formulas, etc.) transmitted from the client device 200 or the database device 400 via the network 300 or the like.
  • the receiving unit 102a may receive data used for evaluation transmitted from a client device 200 different from the client device 200 that is the transmission destination of the evaluation result.
  • the designation unit 102b designates index data and density data that are targeted when creating an expression.
  • the formula creating unit 102c creates a formula based on the index state information received by the receiving unit 102a and the index state information specified by the specifying unit 102b. Note that if the formula is stored in a predetermined storage area of the storage unit 106 in advance, the formula creation unit 102 c may create the formula by selecting a desired formula from the storage unit 106. The formula creation unit 102c may create a formula by selecting and downloading a desired formula from another computer device (for example, the database device 400) that stores the formula in advance.
  • another computer device for example, the database device 400
  • the evaluation unit 102d uses a formula obtained in advance (for example, a formula created by the formula creation unit 102c or a formula received by the reception unit 102a) and a concentration value included in the concentration data of the individual received by the reception unit 102a. By calculating the value of the equation, the likelihood of cancer recurrence is assessed for the individual. Note that the evaluation unit 102d calculates the concentration value of at least one of the 12 amino acids or at least two of the 21 amino acids, the ratio of the concentration values, the difference of the concentration values, or the value after conversion ( For example, the possibility of recurrence of cancer may be evaluated for an individual using a concentration deviation value).
  • FIG. 11 is a block diagram showing a configuration of the evaluation unit 102d, and conceptually shows only a portion related to the present invention.
  • the evaluation unit 102d further includes a calculation unit 102d1, a conversion unit 102d2, a generation unit 102d3, and a classification unit 102d4.
  • the calculation unit 102d1 is a variable into which at least one of the 12 types of amino acids or at least two of the 21 types of amino acids (the ratio or difference value described above) may be substituted.
  • the value of the expression is calculated using an expression including at least.
  • the evaluation unit 102d may store the value of the expression calculated by the calculation unit 102d1 as an evaluation result in a predetermined storage area of the evaluation result file 106e.
  • the conversion unit 102d2 converts the value of the formula calculated by the calculation unit 102d1 using, for example, the conversion method described above.
  • the evaluation unit 102d may store the value after the conversion by the conversion unit 102d2 as an evaluation result in a predetermined storage area of the evaluation result file 106e.
  • the conversion unit 102d2 may convert the density value included in the density data, or the ratio or difference between the density values, for example, using the above-described conversion method.
  • the generation unit 102d3 uses the value of the expression calculated by the calculation unit 102d1 or the conversion unit 102d2 for the position information related to the position of the predetermined mark on the predetermined ruler that is visibly displayed on a display device such as a monitor or a physical medium such as paper. Are generated using the values after conversion in (1) (which may be density values, density value ratios or density value differences, or values after these conversions).
  • the evaluation unit 102d may store the position information generated by the generation unit 102d3 as an evaluation result in a predetermined storage area of the evaluation result file 106e.
  • the classification unit 102d4 uses the value of the formula calculated by the calculation unit 102d1 or the value after conversion by the conversion unit 102d2 (which may be a density value, a density value ratio or a difference between density values, or a value after conversion).
  • the individual is classified into any one of a plurality of categories defined taking into account at least the degree of likelihood that the cancer will recur.
  • the result output unit 102e outputs the processing result (including the evaluation result obtained by the evaluation unit 102d) in each processing unit of the control unit 102 to the output device 114.
  • the transmission unit 102f transmits the evaluation result to the client device 200 that is the transmission source of the individual concentration data, or transmits the formula or evaluation result created by the evaluation device 100 to the database device 400. Note that the transmission unit 102f may transmit the evaluation result to a client device 200 different from the client device 200 that is a transmission source of data used for evaluation.
  • FIG. 12 is a block diagram showing an example of the configuration of the client apparatus 200 of the present system, and conceptually shows only the portion related to the present invention in the configuration.
  • the client device 200 includes a control unit 210, a ROM 220, an HD (Hard Disk) 230, a RAM 240, an input device 250, an output device 260, an input / output IF 270, and a communication IF 280. These units are connected via an arbitrary communication path. Are connected to communicate.
  • the client device 200 is an information processing device in which peripheral devices such as a printer, a monitor, and an image scanner are connected as necessary (for example, a known personal computer, workstation, home game device, Internet TV, PHS (Personal Handyphone System) It may be based on a terminal, a portable terminal, a mobile communication terminal, an information processing terminal such as PDA (Personal Digital Assistant), or the like.
  • the input device 250 is a keyboard, a mouse, a microphone, or the like.
  • a monitor 261 which will be described later, also realizes a pointing device function in cooperation with the mouse.
  • the output device 260 is an output unit that outputs information received via the communication IF 280, and includes a monitor (including a home television) 261 and a printer 262. In addition, the output device 260 may be provided with a speaker or the like.
  • the input / output IF 270 is connected to the input device 250 and the output device 260.
  • the communication IF 280 connects the client device 200 and the network 300 (or a communication device such as a router) so that they can communicate with each other.
  • the client device 200 is connected to the network 300 via a communication device such as a modem, a TA (Terminal Adapter), or a router, and a telephone line, or via a dedicated line.
  • the client apparatus 200 can access the evaluation apparatus 100 according to a predetermined communication protocol.
  • the control unit 210 includes a reception unit 211 and a transmission unit 212.
  • the receiving unit 211 receives various types of information such as an evaluation result transmitted from the evaluation device 100 via the communication IF 280.
  • the transmission unit 212 transmits various types of information such as individual concentration data to the evaluation apparatus 100 via the communication IF 280.
  • the control unit 210 may be realized by a CPU and a program that is interpreted and executed by the CPU and all or any part of the processing performed by the control unit.
  • the ROM 220 or the HD 230 stores computer programs for giving instructions to the CPU in cooperation with the OS and performing various processes.
  • the computer program is executed by being loaded into the RAM 240, and constitutes the control unit 210 in cooperation with the CPU.
  • the computer program may be recorded in an application program server connected to the client apparatus 200 via an arbitrary network, and the client apparatus 200 may download all or a part thereof as necessary.
  • all or any part of the processing performed by the control unit 210 may be realized by hardware such as wired logic.
  • control unit 210 includes an evaluation unit 210a (a calculation unit 210a1, a conversion unit 210a2, a generation unit 210a3, and a classification unit 210a4) having the same functions as those of the evaluation unit 102d provided in the evaluation apparatus 100. ) May be provided.
  • evaluation unit 210a a calculation unit 210a1, a conversion unit 210a2, a generation unit 210a3, and a classification unit 210a4 having the same functions as those of the evaluation unit 102d provided in the evaluation apparatus 100.
  • the evaluation part 210a is based on the information contained in the evaluation result transmitted from the evaluation apparatus 100, and the value of a formula (in the conversion part 210a2)
  • the density value, the density value ratio, or the density value difference may be converted, or the value generated by the generation unit 210a3 or the converted value (the density value, the density value ratio or the density value difference, or after these conversions)
  • Position information corresponding to the value or the converted value (the density value, the ratio of the density values, or the difference between the density values, or the value after these conversions may be used).
  • the individual may be classified into any one of a plurality of categories using.
  • the network 300 has a function of connecting the evaluation device 100, the client device 200, and the database device 400 so that they can communicate with each other.
  • the Internet for example, the Internet, an intranet, a LAN (Local Area Network) (including both wired and wireless), and the like It is.
  • LAN Local Area Network
  • the network 300 includes a VAN (Value-Added Network), a personal computer communication network, a public telephone network (including both analog / digital), a dedicated line network (including both analog / digital), CATV ( Community Antenna Television (PD) network, mobile circuit switching network or mobile packet switching network (IMT (International Mobile Telecommunication) 2000 system, GSM (Registered Trademark) Mobile Communications-PDC (PDC)) System), wireless paging networks, and local wireless networks such as Bluetooth (registered trademark) , Or PHS network, satellite communication network (CS (Communication Satellite), BS (Broadcasting Satellite) or ISDB (including Integrated Services Digital Broadcasting), etc.) may be like.
  • VAN Value-Added Network
  • a personal computer communication network including both analog / digital
  • a public telephone network including both analog / digital
  • a dedicated line network including both analog / digital
  • CATV Community Antenna Television (PD) network
  • IMT International Mobile Telecommunication 2000 system
  • GSM Registered Trademark
  • FIG. 13 is a block diagram showing an example of the configuration of the database apparatus 400 of this system, and conceptually shows only the portion related to the present invention in the configuration.
  • the database apparatus 400 has a function of storing index state information used when creating an expression in the evaluation apparatus 100 or the database apparatus, an expression created in the evaluation apparatus 100, an evaluation result in the evaluation apparatus 100, and the like.
  • the database apparatus 400 includes a control unit 402 such as a CPU that controls the database apparatus in an integrated manner, a communication apparatus such as a router, and a wired or wireless communication circuit such as a dedicated line.
  • a communication interface unit 404 that connects the apparatus to the network 300 to be communicable, a storage unit 406 that stores various databases, tables, and files (for example, files for Web pages), and an input unit that connects to the input unit 412 and the output unit 414.
  • the output interface unit 408 is configured to be communicable via an arbitrary communication path.
  • the storage unit 406 is a storage means, and for example, a memory device such as a RAM / ROM, a fixed disk device such as a hard disk, a flexible disk, an optical disk, or the like can be used.
  • the storage unit 406 stores various programs used for various processes.
  • the communication interface unit 404 mediates communication between the database device 400 and the network 300 (or a communication device such as a router). That is, the communication interface unit 404 has a function of communicating data with other terminals via a communication line.
  • the input / output interface unit 408 is connected to the input device 412 and the output device 414.
  • a monitor including a home television
  • a speaker or a printer can be used as the output device 414.
  • the input device 412 can be a monitor that realizes a pointing device function in cooperation with the mouse.
  • the control unit 402 has an internal memory for storing a control program such as an OS, a program defining various processing procedures, required data, and the like, and executes various information processing based on these programs. As shown in the figure, the control unit 402 is roughly divided into a transmission unit 402a and a reception unit 402b.
  • the transmission unit 402a transmits various types of information such as index state information and formulas to the evaluation apparatus 100.
  • the receiving unit 402b receives various types of information such as expressions and evaluation results transmitted from the evaluation device 100.
  • the evaluation apparatus 100 executes from the reception of the concentration data to the calculation of the value of the expression, the classification into the individual categories, and the transmission of the evaluation result, and the client apparatus 200 receives the evaluation result.
  • the client device 200 includes the evaluation unit 210a
  • conversion of the value of the expression, position information The generation and the classification into individual sections may be appropriately shared by the evaluation apparatus 100 and the client apparatus 200.
  • the evaluation unit 210a converts the value of the expression in the conversion unit 210a2, or the value of the expression or the value after conversion in the generation unit 210a3.
  • the classification unit 210a4 may classify the individual into one of a plurality of categories using the value of the expression or the value after conversion. Further, when the client device 200 receives the converted value from the evaluation device 100, the evaluation unit 210a generates position information corresponding to the converted value in the generation unit 210a3, or converts it in the classification unit 210a4. An individual may be classified into any one of a plurality of divisions using a later value. When the client device 200 receives the value of the expression or the value after conversion and the position information from the evaluation device 100, the evaluation unit 210a uses the value of the expression or the value after conversion in the classification unit 210a4. The individual may be classified into any one of a plurality of sections.
  • the evaluation device, the evaluation method, the evaluation program, the evaluation system, and the terminal device according to the present invention are not limited to the second embodiment described above, but various different embodiments within the scope of the technical idea described in the claims. May be implemented.
  • each illustrated component is functionally conceptual and does not necessarily need to be physically configured as illustrated.
  • all or some of the processing functions provided in the evaluation apparatus 100 may be realized by the CPU and a program interpreted and executed by the CPU. Alternatively, it may be realized as hardware by wired logic.
  • the program is recorded on a non-transitory computer-readable recording medium including programmed instructions for causing the information processing apparatus to execute the evaluation method according to the present invention, and is stored in the evaluation apparatus 100 as necessary. Read mechanically. That is, a computer program for giving instructions to the CPU in cooperation with the OS and performing various processes is recorded in the storage unit 106 such as a ROM or HDD (Hard Disk Drive). This computer program is executed by being loaded into the RAM, and constitutes a control unit in cooperation with the CPU.
  • this computer program may be stored in an application program server connected to the evaluation apparatus 100 via an arbitrary network, and the whole or a part of the computer program can be downloaded as necessary.
  • the evaluation program according to the present invention may be stored in a computer-readable recording medium that is not temporary, and may be configured as a program product.
  • the “recording medium” refers to a memory card, USB (Universal Serial Bus) memory, SD (Secure Digital) card, flexible disk, magneto-optical disk, ROM, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electric Electric). Erasable and Programmable Read Only Memory (registered trademark), CD-ROM (Compact Disc Only Memory), MO (Magneto-Optical disk), DVD (Digital Versatile Register, etc.) Any “possible It is intended to include physical medium "of use.
  • the “program” is a data processing method described in an arbitrary language or description method, and may be in the form of source code or binary code. Note that the “program” is not necessarily limited to a single configuration, and functions are achieved in cooperation with a separate configuration such as a plurality of modules and libraries or a separate program represented by the OS. Including things. In addition, a well-known structure and procedure can be used about the specific structure and reading procedure for reading a recording medium in each apparatus shown to embodiment, the installation procedure after reading, etc.
  • Various databases and the like stored in the storage unit 106 are storage devices such as a memory device such as a RAM and a ROM, a fixed disk device such as a hard disk, a flexible disk, and an optical disk. Programs, tables, databases, web page files, and the like.
  • the evaluation apparatus 100 may be configured as an information processing apparatus such as a known personal computer or workstation, or may be configured as the information processing apparatus connected to an arbitrary peripheral device.
  • the evaluation apparatus 100 may be realized by installing software (including a program or data) that causes the information processing apparatus to realize the evaluation method of the present invention.
  • the specific form of distribution / integration of the devices is not limited to that shown in the figure, and all or a part of them may be functionally or physically in arbitrary units according to various additions or according to functional loads. It can be configured to be distributed and integrated. That is, the above-described embodiments may be arbitrarily combined and may be selectively implemented.
  • the amino acid concentration measurement of blood samples before and after 72 lung cancer patients who underwent surgical therapy was performed by the measurement method (A) described in the above-described embodiment. Of 72 cases, 14 cases relapsed.
  • FIG. 14 shows the concentration data of the non-recurrent cases.
  • the horizontal axis represents the preoperative value (pre) and postoperative value (post) of each amino acid concentration
  • the vertical axis represents the average value of each amino acid concentration.
  • the concentration data of the recurrence example for the 21 amino acids is shown in FIG.
  • the horizontal axis represents the preoperative value (pre) and postoperative value (post) of each amino acid concentration
  • the vertical axis represents the average value of each amino acid concentration.
  • FIG. 16 shows a radar chart showing “distribution of each amino acid after surgery when each amino acid before surgery is 100%” for a non-recurrent case.
  • FIG. 17 shows a radar chart showing “distribution of each amino acid after surgery when each amino acid before surgery is 100%” for a recurrence example.
  • the change in amino acid profile in relapse-free cases was different from that in relapse cases. Thus, it has been clarified that the plasma amino acid profile changes when relapse can occur.
  • Logistic regression analysis was performed using the same plasma amino acid concentration data as in Example 1 with the objective variable as the presence or absence of recurrence (binary variable) (FIG. 18).
  • the logistic regression analysis is performed by using a preoperative value (pre) as an explanatory variable, a postoperative value (post) as an explanatory variable, and a value obtained by subtracting the preoperative value from the postoperative value (post-pre). ) For each explanatory variable.
  • preoperative value (pre) of the plasma amino acid concentration data As in Example 1, it is effective for evaluating the possibility of cancer recurrence, and is used to determine the possibility of cancer recurrence including the plasma amino acid concentration as a variable.
  • a variable discriminant (multivariate function) was obtained.
  • the explanatory variable was the preoperative value (pre)
  • the objective variable was the presence or absence of recurrence (binary variable).
  • a combination of 2 to 4 variables obtained by round robin was evaluated by ROC_AUC, and a combination of 2 to 4 variables having ROC_AUC greater than 0.7 was extracted.
  • FIG. 19 shows a list of combinations of two variables where ROC_AUC is greater than 0.7.
  • Specific amino acid combinations are (Arg, Trp), (Orn, Trp), (Cit, Trp), (His, Cit), (His, Orn), (His, Arg), (Val, Orn), (Tyr, Trp), (Gln, His), (Met, Orn), (Ala, Orn), (Asn, His), (Cit, Met), (Orn, Lys), (His, Thr), (Glu , Trp), (His, Trp), (Thr, Trp), (Orn, Phe), (Orn, Leu), (Phe, Trp), (Gln, Met), (Orn, Ile), (Cit, Val ), (Glu, His), (Asn, Trp), (Ser, Orn), (Val, Ile), (Ile, Trp), (Leu, Trp), (Ala, Trp), (a_ABA, rp), (Asn
  • FIG. 20 shows a list of combinations of three variables in which ROC_AUC is larger than the maximum value of ROC_AUC in the combination of two variables.
  • Specific amino acid combinations are (Arg, Orn, Trp), (Glu, Met, Orn), (Cit, Orn, Trp), (Orn, Lys, Trp), (His, Cit, Orn), (Cit , Arg, Trp), (Glu, Orn, Trp), (Arg, Lys, Trp), (His, Arg, Trp), (Cit, Val, Orn), (Orn, Leu, Trp), (His, Cit) , Trp), (Arg, Tyr, Trp), (Gln, His, Cit), (Val, Orn, Trp), (Ser, Cit, Trp), (Met, Orn, Trp), (Arg, Met, Trp) ), (Arg, Met, Orn), (Glu, Arg, Trp), (Asn,
  • FIG. 21 shows a list of combinations of four variables in which ROC_AUC is larger than the maximum value of ROC_AUC in the combination of three variables.
  • Specific amino acid combinations are (Arg, Orn, Lys, Trp), (Arg, Met, Orn, Trp), (Ser, Arg, Orn, Trp), (Arg, a_ABA, Orn, Trp), (His).
  • Arg, Orn, Trp Arg, Val, Orn, Trp), (Arg, Orn, Ile, Trp), (Glu, Arg, Met, Orn), (Gly, Arg, Orn, Trp), (Cit , Orn, Leu, Trp), (Thr, Arg, Orn, Trp), (Arg, Orn, Phe, Trp), (Arg, Tyr, Orn, Trp) and (Ala, Arg, Orn, Trp).
  • Example 2 Based on the same plasma amino acid concentration data as in Example 1, a value obtained by subtracting the preoperative value from the postoperative value (post-pre) was calculated to obtain change amount data regarding the difference. Using the obtained variation data, a multivariate discriminant (multivariate function) for determining the possibility of cancer recurrence including the amino acid concentration in plasma as a variable was obtained, which was effective in evaluating the possibility of cancer recurrence. .
  • the explanatory variable was a value obtained by subtracting the preoperative value from the postoperative value (post-pre), and the objective variable was the presence or absence of recurrence (binary variable).
  • a combination of 2 to 4 variables obtained by round robin was evaluated by ROC_AUC, and a combination of 2 to 4 variables having ROC_AUC greater than 0.7 was extracted.
  • FIG. 22 shows a list of combinations of two variables in which ROC_AUC is greater than 0.7.
  • Specific amino acid combinations are (Ala, Trp), (Pro, Trp), (Cit, Trp), (Glu, Leu), (Arg, Trp), (Phe, Trp), (Glu, Trp), (Cit, Val), (Glu, Pro), (Orn, Trp), (Glu, Val), (Lys, Trp), (Glu, Ala), (Tyr, Trp), (His, Trp), (Asn) , Trp) and (Leu, Trp).
  • FIG. 23 shows a list of combinations of three variables in which ROC_AUC is larger than the maximum value of ROC_AUC in the combination of two variables.
  • Specific amino acid combinations are (Pro, Tyr, Trp), (Glu, Pro, Leu), (Tyr, Phe, Trp), (Arg, Pro, Lys), (Arg, Pro, Trp), (Ala).
  • FIG. 24 shows a list of combinations of four variables in which ROC_AUC is larger than the maximum value of ROC_AUC in the combination of three variables.
  • Specific amino acid combinations are (Glu, Ala, Ile, Leu), (Thr, Pro, Tyr, Met), (Pro, Tyr, Phe, Trp), (Thr, Pro, Tyr, Trp), (Thr).
  • the present invention can be widely implemented in many industrial fields, in particular, in fields such as pharmaceuticals, foods, and medical care, and is particularly useful in the bioinformatics field for evaluating recurrence of cancer.

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Abstract

Le problème décrit par la présente invention est de fournir un procédé d'évaluation grâce auquel des données hautement fiables pouvant être utilisées en tant que référence pour prédire le risque de récurrence d'un cancer peuvent être fournies, etc. Selon le présent mode de réalisation, le risque de récurrence de cancer pour un sujet d'évaluation, ledit sujet d'évaluation ayant été diagnostiqué comme atteint d'un cancer, est évalué à l'aide de la concentration d'au moins un élément choisi parmi Glu, Ser, a-ABA, Val, Met, Lys, Ile, Leu, Trp, His, Orn et Pro ou les concentrations d'au moins deux éléments choisis parmi Glu, Arg, Orn, Cit, His, Val, Phe, Tyr, Met, Pro, Trp, Asn, Leu, Lys, Thr, Ile, Gln, Ala, Ser, a-ABA et Gly dans le sang du sujet d'évaluation.
PCT/JP2017/043208 2016-12-01 2017-11-30 Procédé de surveillance de cancer, procédé de calcul, dispositif d'évaluation, dispositif de calcul, programme d'évaluation, programme de calcul, système d'évaluation et dispositif terminal WO2018101450A1 (fr)

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