WO2013168550A1 - Procédé d'évaluation d'immunothérapie anticancéreuse, dispositif d'évaluation d'immunothérapie anticancéreuse, programme d'évaluation d'immunothérapie anticancéreuse, système d'évaluation d'immunothérapie anticancéreuse et dispositif de terminal de communication d'informations - Google Patents

Procédé d'évaluation d'immunothérapie anticancéreuse, dispositif d'évaluation d'immunothérapie anticancéreuse, programme d'évaluation d'immunothérapie anticancéreuse, système d'évaluation d'immunothérapie anticancéreuse et dispositif de terminal de communication d'informations Download PDF

Info

Publication number: WO2013168550A1
Authority: WO; WIPO (PCT)
Prior art keywords: amino acid; treatment; evaluation; discriminant; value
Prior art date: 2012-05-11
Legal status (The legal status 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 status listed.): Ceased

Application number

PCT/JP2013/061807

Other languages

English (en)

Japanese (ja)

Inventor

信矢菊池

純也米田

祐子道端

小林　幹

Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)

Ajinomoto Co Inc

Original Assignee

Ajinomoto Co Inc

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.)

2012-05-11

Filing date

2013-04-22

Publication date

2013-11-14

2013-04-22 Application filed by Ajinomoto Co Inc filed Critical Ajinomoto Co Inc

2013-04-22 Priority to JP2014514434A priority Critical patent/JP6375947B2/ja

2013-11-14 Publication of WO2013168550A1 publication Critical patent/WO2013168550A1/fr

2014-11-11 Anticipated expiration legal-status Critical

Status Ceased legal-status Critical Current

Images

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
- G01N33/68—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
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2800/00—Detection or diagnosis of diseases
- G01N2800/70—Mechanisms involved in disease identification
- G01N2800/7023—(Hyper)proliferation
- G01N2800/7028—Cancer

Definitions

the present invention relates to a cancer immunotherapy evaluation method, a cancer immunotherapy evaluation device, a cancer immunotherapy evaluation method, a cancer immunotherapy evaluation program, a cancer immunotherapy evaluation system, and an information communication terminal device.
Immunotherapy is a treatment that uses the substance that activates immunocompetent cells, cytokines, antibodies, etc. to direct the immune function of the living body in the intended direction, and immunotherapy for various cancers It is known.
cancer immunotherapy can exert certain effects, there are responders and non-responders to treatment, and the effects vary among individuals.
the responder includes an individual whose cancer has been eradicated, reduced or improved (mixed responder or partial responder), an individual whose cancer has not progressed, and the like.
an individual in which cancer has not progressed is an increase in cancer, quality of life (QOL) is improved or maintained, and / or average compared to an individual who has not been treated. An individual whose life expectancy has increased.
QOL quality of life
a method of evaluating the effectiveness of a treatment method for cancer As a method of evaluating the effectiveness of a treatment method for cancer, a method of evaluating by image is basically proposed, and a method of evaluating by extending the survival period is usually used.
many of the conventional methods for evaluating the effectiveness of therapeutic methods for cancer are used in the evaluation and re-evaluation of new drugs. For this reason, it is difficult to apply the conventional method to the evaluation of normal cancer treatment.
Non-Patent Documents 1 and 2 There are also attempts to directly measure immune responses in tumor tissues and regional lymph nodes. For example, it has been reported that detection of antigen-specific activated T cells in a local cancer region is highly involved in clinical outcome (Non-patent Document 3). However, the burden on the patient side is large, labor is required, and clinical specimens are often not obtained.
Non-patent Document 4 blood serum serine and glutamic acid concentrations are normalized by surgery in breast cancer patients.
Non-Patent Documents 5 and 6 a decrease in non-essential amino acids in blood immediately after surgery for cancer patients.
Non-patent Document 7 a decrease in blood proline, taurine, and glutamic acid concentrations increase and alanine concentration decreases by Ukrain treatment of breast cancer patients.
Non-patent Document 8 17 types of blood amino acid concentrations were increased in the first cycle in patients who were effective with chemotherapy including cisplatin compared to patients who were not effective.
Patent Literature 1, Patent Literature 2, and Patent Literature 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 6 relating to a method for evaluating colon 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 are disclosed.
Non-Patent Documents 4 to 8 are reports on surgical operation and chemotherapy, and there are no reports related to changes in blood amino acid concentration in cancer immunotherapy. Further, even if the index formula groups disclosed in Patent Document 1 to Patent Document 11 are used for evaluating the therapeutic effect of cancer immunotherapy, the evaluation target is different, so that sufficient accuracy can be obtained for evaluating the therapeutic effect. Can not.
the present invention has been made in view of the above problems, and an cancer immunotherapy evaluation method, a cancer immunotherapy evaluation apparatus, which can accurately evaluate the therapeutic effect of cancer immunotherapy using the blood amino acid concentration,
An object is to provide a cancer immunotherapy evaluation method, a cancer immunotherapy evaluation program, a cancer immunotherapy evaluation system, and an information communication terminal device.
the present inventors have accurately determined the therapeutic effect of cancer immunotherapy using amino acids in blood (specifically, the anti-tumor immune effect in cancer immunotherapy). The present inventors have found that this can be done and have completed the present invention.
the method for evaluating cancer immunotherapy includes blood collected before the treatment is started from an evaluation subject receiving treatment by cancer immunotherapy.
An acquisition step of acquiring amino acid concentration data before starting treatment regarding the concentration value of amino acids in the blood, and amino acid concentration data after starting treatment regarding the concentration value of amino acids in blood collected after the treatment is started from the evaluation target;
the post-treatment amino acid concentration data may be data (after treatment amino acid concentration data) corresponding to “after treatment” amino acid concentration data described later.
cancer immunotherapy includes immune cell therapy, peptide / vaccine therapy, BMR (Biological Response Modifiers) therapy, cytokine therapy, antibody therapy, and induction of antitumor effect by release of immunosuppressive mechanism, Etc. are included.
BMR Bio Response Modifiers
before treatment is started may be referred to as “before treatment” or “before treatment start”
after treatment is started may be referred to as “after treatment start”.
before treatment start includes, for example, before the first narrow treatment in a broad sense over a certain period of time.
after the start of treatment includes, for example, after the first narrow treatment in the broad sense treatment for a certain period of time and before the final narrow treatment (for example, After being treated in a broad sense over a certain period of time (for example, after being treated generally), etc. included.
the concentration value reference evaluation step is included in the amino acid concentration data before starting treatment and the amino acid concentration data after starting treatment. Based on the concentration value of at least one of Val, Ile, Leu, His, Phe, Trp, Gln, Glu, Asp, Orn, Ala, Ser, Thr, Met, Lys, Arg, Gly, Cys2, and Pro. It is characterized by evaluating the effect of the said treatment with respect to an evaluation object.
the cancer immunotherapy evaluation method according to the present invention is the above-described cancer immunotherapy evaluation method, wherein the concentration value reference evaluation step is based on the concentration value and the treatment is effective for the evaluation object. (E.g., further comprising a concentration value criterion determining step for determining whether or not the treatment is effective for the evaluation object based on the concentration value).
the cancer immunotherapy evaluation method is the above-described cancer immunotherapy evaluation method, wherein the concentration value reference evaluation step includes the pre-treatment amino acid concentration data, the post-treatment amino acid concentration data, and the amino acid Based on a preset multivariate discriminant that includes the concentration of as a variable, a discriminant value calculation that calculates a discriminant value that is a value of the multivariate discriminant and that corresponds to an evaluation result regarding the effect of the treatment on the evaluation target
the method further includes a step.
the discriminant value calculating step is based on the amino acid concentration data before starting treatment and the amino acid concentration data after starting treatment, and the amino acid concentration value before starting treatment and the amino acid after starting treatment.
the discriminant value may be calculated by calculating the ratio or difference with the concentration value and substituting the calculated concentration value ratio or difference of each amino acid into each variable included in the multivariate discriminant.
the amino acid concentration data after the start of treatment may be data (after treatment amino acid concentration data) corresponding to the above-mentioned “after treatment” amino acid concentration data.
the cancer immunotherapy evaluation method is the above-described cancer immunotherapy evaluation method, wherein the multivariate discriminant is a logistic regression equation, a fractional equation, a linear discriminant, a multiple regression equation, or a support vector machine. It is any one of a created formula, a formula created by Mahalanobis distance method, a formula created by canonical discriminant analysis, and a formula created by a decision tree.
the cancer immunotherapy evaluation method is the above-described cancer immunotherapy evaluation method, wherein the discriminant value calculating step includes Val included in the pre-treatment amino acid concentration data and the post-treatment amino acid concentration data. , Ile, Leu, His, Phe, Trp, Gln, Glu, Asp, Orn, Ala, Ser, Thr, Met, Lys, Arg, Gly, Cys2, Pro, as well as Val, Ile, Based on the multivariate discriminant including at least one of Leu, His, Phe, Trp, Gln, Glu, Asp, Orn, Ala, Ser, Thr, Met, Lys, Arg, Gly, Cys2, and Pro as the variables. The discriminant value is calculated.
the cancer immunotherapy evaluation method is the above-described cancer immunotherapy evaluation method, wherein the multivariate discriminant is Glu, Cys2, Trp, Asp, Orn, Phe, Val, Ile, Gly, His.
the fractional expression including at least one of the variables as the variable, or including at least one of Trp, Thr, His, Arg, Ile, Pro, Phe, Met, Ala, Lys, Asp, Ser, Leu as the variable
the cancer immunotherapy evaluation method is the above-described cancer immunotherapy evaluation method, wherein the concentration value reference evaluation step is based on the discriminant value calculated in the discriminant value calculation step.
the method further comprises a discriminant value criterion evaluation step for evaluating the effect of the treatment on.
the cancer immunotherapy evaluation method is the above-described cancer immunotherapy evaluation method, wherein the discriminant value criterion evaluation step is based on the discriminant value and the treatment is effective for the evaluation object. (E.g., further including a discrimination value criterion discrimination step for discriminating whether or not the treatment is effective for the evaluation object based on the discrimination value).
the cancer immunotherapy evaluation apparatus is a cancer immunotherapy evaluation apparatus including a control unit and a storage unit, and the control unit is configured to perform the treatment before treatment by cancer immunotherapy is started.
the control unit is configured to perform the treatment before treatment by cancer immunotherapy is started.
a discriminant value calculating means for calculating a discriminant value that is a value of the multivariate discriminant and corresponds to an evaluation result relating to the effect of the treatment on the evaluation target. It is characterized by this.
the discriminant value calculating means is based on the amino acid concentration data before starting treatment and the amino acid concentration data after starting treatment, and the amino acid concentration value before starting the treatment and the amino acid after starting the treatment.
the discriminant value may be calculated by calculating the ratio or difference with the concentration value and substituting the calculated concentration value ratio or difference of each amino acid into each variable included in the multivariate discriminant.
the amino acid concentration data after the start of treatment may be data (after treatment amino acid concentration data) corresponding to the above-mentioned “after treatment” amino acid concentration data.
the cancer immunotherapy evaluation apparatus is the cancer immunotherapy evaluation apparatus according to the present invention, wherein the control unit has an effect of the treatment on the evaluation target based on the discriminant value calculated by the discriminant value calculation means. And a discriminant value criterion evaluating means for evaluating the above.
the cancer immunotherapy evaluation apparatus is the above-described cancer immunotherapy evaluation apparatus, wherein (i) the control unit includes amino acid concentration data and cancer state index data relating to an index representing a cancer state.
a multivariate discriminant creating unit that creates the multivariate discriminant stored in the storage unit based on the cancer state information stored in the unit; and (ii) the multivariate discriminant creating unit includes the cancer Candidate multivariate discriminant creating means for creating a candidate multivariate discriminant that is a candidate for the multivariate discriminant based on a predetermined formula creating method from state information, and the candidate multivariate discriminant creating means created by the candidate multivariate discriminant creating means
the candidate multivariate discriminant verification means for verifying the candidate multivariate discriminant based on a predetermined verification method, and selecting the candidate multivariate discriminant variable based on the predetermined variable selection method,
Variable size Variable selection means for selecting a combination of the amino acid concentration data included in the cancer state information used when creating an expression, the candidate multivariate
the cancer immunotherapy evaluation method is a cancer immunotherapy evaluation method executed in an information processing apparatus including a control unit and a storage unit, and is based on cancer immunotherapy executed in the control unit.
Amino acid concentration data before the start of treatment regarding the concentration value of the evaluation target amino acid before the start of treatment, and an amino acid concentration data after the start of treatment regarding the concentration value of the amino acid of the evaluation target after the start of the treatment Based on the multivariate discriminant stored in the storage unit including the amino acid concentration as a variable, the discriminant value corresponding to the evaluation result relating to the effect of the treatment on the evaluation target is a value of the multivariate discriminant And a discriminant value calculating step for calculating.
the cancer immunotherapy evaluation method according to the present invention is the cancer immunotherapy evaluation method according to the present invention, based on the discriminant value calculated in the discriminant value calculation step executed in the control unit, with respect to the evaluation object.
the method further includes a discriminant value criterion evaluation step for evaluating the effect of treatment.
a cancer immunotherapy evaluation program is a cancer immunotherapy evaluation program to be executed in an information processing apparatus including a control unit and a storage unit, and the cancer immunity evaluation program to be executed in the control unit.
Amino acid concentration data before the start of treatment regarding the concentration value of the evaluation target amino acid before the treatment by the therapy, and the amino acid after the start of treatment regarding the concentration value of the evaluation target amino acid after the start of the treatment Based on the concentration data and the multivariate discriminant stored in the storage unit including the amino acid concentration as a variable, the value of the multivariate discriminant corresponds to the evaluation result regarding the effect of the treatment on the evaluation target.
a discriminant value calculating step for calculating a discriminant value.
the cancer immunotherapy evaluation program according to the present invention is based on the discriminant value calculated in the discriminant value calculation step for the control unit to execute in the cancer immunotherapy evaluation program.
the method further comprises a discriminant value criterion evaluation step for evaluating the effect of the treatment.
a recording medium according to the present invention is a non-transitory computer-readable recording medium, and includes a programmed instruction for causing an information processing apparatus to execute the cancer immunotherapy evaluation method. To do.
the cancer immunotherapy evaluation system includes a cancer immunotherapy evaluation apparatus including a control unit and a storage unit, and an amino acid related to a concentration value of an amino acid to be evaluated that includes a control unit and receives treatment by cancer immunotherapy.
a cancer immunotherapy evaluation system configured to connect an information communication terminal device that provides concentration data to be communicable via a network, wherein the control unit of the information communication terminal device starts the treatment Pre-treatment amino acid concentration data relating to the concentration value of the evaluation target amino acid before treatment, and post-treatment amino acid concentration data relating to the concentration value of the evaluation target amino acid after initiation of the treatment, the cancer immunotherapy evaluation
a result receiving means for receiving a discriminant value corresponding to an evaluation result relating to the effect of the treatment on the evaluation target, wherein the control unit of the cancer immunotherapy evaluation device receives the information from the information communication terminal device.
Amino acid concentration data receiving means for receiving the transmitted pre-treatment amino acid concentration data and the post-treatment amino acid concentration data, and the pre-treatment amino acid concentration data and the post-treatment amino acid received by the amino acid concentration data receiving means Based on the density data and the multivariate discriminant stored in the storage unit, the discriminant value calculating means for calculating the discriminant value, and the discriminant value calculated by the discriminant value calculating means to the information communication terminal device And a result transmitting means for transmitting.
the cancer immunotherapy evaluation system is the cancer immunotherapy evaluation system, wherein the control unit of the cancer immunotherapy evaluation device is based on the discriminant value calculated by the discriminant value calculation means.
the apparatus further comprises discriminant value reference evaluation means for evaluating the effect of the treatment on the evaluation object, and the result transmitting means transmits the evaluation result of the evaluation object obtained by the discriminant value reference evaluation means to the information communication terminal device.
the result receiving means receives the evaluation result of the evaluation target transmitted from the cancer immunotherapy evaluation apparatus.
the information communication terminal device is an information communication terminal device that includes a control unit, and provides amino acid concentration data related to the concentration value of the amino acid to be evaluated that receives treatment by cancer immunotherapy, wherein the control unit includes: , Comprising a result acquisition means for acquiring a discriminant value corresponding to an evaluation result relating to the effect of the treatment on the evaluation target, which is a value of a multivariate discriminant including an amino acid concentration as a variable.
Amino acid concentration data before starting treatment regarding the concentration value of the amino acid to be evaluated before starting amino acid concentration data after starting treatment regarding the concentration value of the amino acid to be evaluated after starting the treatment, and multivariate discrimination It is calculated based on a formula.
the result acquisition unit acquires the evaluation result regarding the effect of the treatment on the evaluation target, and the evaluation result is the discriminant value. This is a result obtained by evaluating the effect of the treatment on the evaluation object.
the information communication terminal device is connected to the cancer immunotherapy evaluation device that stores the multivariate discriminant and calculates the discriminant value via the network in the information communication terminal device.
the control unit further comprises amino acid concentration data transmitting means for transmitting the pre-treatment amino acid concentration data and the post-treatment amino acid concentration data to the cancer immunotherapy evaluation device, and the result acquisition means includes the The discriminant value transmitted from the cancer immunotherapy evaluation device is received.
the information communication terminal device is the information communication terminal device, wherein the cancer immunotherapy evaluation device further evaluates the effect of the treatment, and the result acquisition means includes the cancer immunotherapy evaluation device.
the transmitted evaluation result relating to the effect of the treatment on the evaluation target is transmitted.
the cancer immunotherapy evaluation apparatus is communicably connected via an information communication terminal device that provides amino acid concentration data related to the concentration value of an amino acid to be evaluated that receives treatment by cancer immunotherapy,
a cancer immunotherapy evaluation device including a control unit and a storage unit, wherein the control unit relates to a concentration value of the evaluation target amino acid transmitted from the information communication terminal device before the treatment is started.
Amino acid concentration data receiving means for receiving amino acid concentration data before starting treatment and amino acid concentration data after starting treatment related to the concentration value of the amino acid to be evaluated after the treatment is started, and received by the amino acid concentration data receiving means
the discriminant value calculating means calculates the discriminant value corresponding to the evaluation result relating to the effect of the treatment on the evaluation object, which is the value of the multivariate discriminant.
a result transmitting means for transmitting the discriminant value calculated by the discriminant value calculating means to the information communication terminal device.
the cancer immunotherapy evaluation apparatus is the cancer immunotherapy evaluation apparatus according to the present invention, wherein the control unit has an effect of the treatment on the evaluation target based on the discriminant value calculated by the discriminant value calculation means. Further comprising: a discriminant value criterion-evaluating unit, wherein the result transmitting unit transmits the evaluation result of the evaluation target obtained by the discriminant value criterion-evaluating unit to the information communication terminal device. .
amino acid concentration data before starting treatment related to the concentration value of amino acids in blood collected before starting treatment from an evaluation subject receiving treatment by cancer immunotherapy, and treatment was started from the evaluation subject.
the amino acid concentration data after the start of treatment may be data (after treatment amino acid concentration data) corresponding to the above-mentioned “after treatment” amino acid concentration data.
Val, Ile, Leu, His, Phe, Trp, Gln, Glu, Asp, Orn, Ala, Ser, Thr which are included in the pre-treatment amino acid concentration data and the post-treatment amino acid concentration data.
the effect of treatment on the evaluation target is evaluated. This produces an effect that the therapeutic effect can be accurately evaluated using the amino acid concentration useful for evaluating the therapeutic effect of cancer immunotherapy.
the treatment is evaluated whether the treatment is effective for the evaluation object based on the concentration value (for example, whether the treatment is effective for the evaluation object based on the concentration value). To determine).
the value of the multivariate discriminant is calculated. For example, based on the amino acid concentration data before starting treatment and the amino acid concentration data after starting treatment, the ratio or difference between the concentration value of the amino acid before starting treatment and the concentration value of the amino acid after starting treatment is calculated.
the discriminant value may be calculated by substituting the ratio or difference of the calculated concentration values of each amino acid into each variable included in the multivariate discriminant.
the multivariate discriminant including the amino acid concentration as a variable, it is possible to obtain a discriminant value corresponding to the evaluation result regarding the therapeutic effect on the evaluation target.
the amino acid concentration data after the start of treatment may correspond to the above-mentioned “after treatment” amino acid concentration data (amino acid concentration data after treatment).
the multivariate discriminant is a logistic regression equation, a fractional equation, a linear discriminant equation, a multiple regression equation, an equation created by a support vector machine, an equation created by the Mahalanobis distance method, a canonical discriminant.
Val, Ile, Leu, His, Phe, Trp, Gln, Glu, Asp, Orn, Ala, Ser, Thr which are included in the pre-treatment amino acid concentration data and the post-treatment amino acid concentration data.
a discriminant value is calculated based on a multivariate discriminant including at least one of Arg, Gly, Cys2, and Pro as a variable.
a discriminant value corresponding to the evaluation result regarding the therapeutic effect on the evaluation target is obtained using a multivariate discriminant useful for the evaluation including the concentration of amino acid useful for evaluating the therapeutic effect of cancer immunotherapy as a variable. There is an effect that can be.
the multivariate discriminant is a fractional expression including at least one of Glu, Cys2, Trp, Asp, Orn, Phe, Val, Ile, Gly, His as a variable, or Trp, Thr,
This is a logistic regression equation including at least one of His, Arg, Ile, Pro, Phe, Met, Ala, Lys, Asp, Ser, and Leu as a variable.
This makes it possible to use a multivariate discriminant that is particularly useful for the evaluation including the concentration of amino acids that are particularly useful for evaluating the therapeutic effect of cancer immunotherapy as a variable. There is an effect that can be obtained.
the effect of treatment on the evaluation target is evaluated based on the discriminant value. Accordingly, the therapeutic effect can be accurately evaluated by using the discriminant value obtained by the multivariate discriminant including the amino acid concentration as a variable.
the treatment is evaluated whether the treatment is effective for the evaluation target based on the discriminant value (for example, whether the treatment is effective for the evaluation target based on the discriminant value). To determine).
the discriminant value obtained by the multivariate discriminant useful for the evaluation for example, discrimination
the concentration of amino acid useful for evaluation for example, discrimination
the effect can be performed with high accuracy.
the multivariate discriminant stored in the storage unit is created based on the cancer state information stored in the storage unit including the amino acid concentration data and the cancer state index data relating to the index representing the cancer state. May be. Specifically, (i) creating a candidate multivariate discriminant based on a predetermined formula creation method from cancer state information, (ii) verifying the created candidate multivariate discriminant based on a predetermined verification method, (Iii) by selecting a variable of the candidate multivariate discriminant based on a predetermined variable selection method, selecting a combination of amino acid concentration data included in the cancer state information used when creating the candidate multivariate discriminant, (Iv) A candidate multivariate discriminant to be adopted as a multivariate discriminant from among a plurality of candidate multivariate discriminants based on the verification results accumulated by repeatedly executing (i), (ii) and (iii) A multivariate discriminant may be created by selection. Thereby, there exists an effect that the multivariate discriminant most suitable for evaluation
FIG. 1 is a principle configuration diagram showing the basic principle of the first embodiment.
FIG. 2 is a flowchart showing an example of the cancer immunotherapy evaluation method according to the first embodiment.
FIG. 3 is a principle configuration diagram showing the basic principle of the second embodiment.
FIG. 4 is a diagram illustrating an example of the overall configuration of the present system.
FIG. 5 is a diagram showing another example of the overall configuration of the present system.
FIG. 6 is a block diagram showing an example of the configuration of the cancer immunotherapy evaluation apparatus 100 of the present system.
FIG. 7 is a diagram illustrating an example of information stored in the user information file 106a.
FIG. 8 is a diagram showing an example of information stored in the amino acid concentration data file 106b.
FIG. 9 is a diagram illustrating an example of information stored in the cancer state information file 106c.
FIG. 10 is a diagram illustrating an example of information stored in the designated cancer state information file 106d.
FIG. 11 is a diagram illustrating an example of information stored in the candidate multivariate discriminant file 106e1.
FIG. 12 is a diagram illustrating an example of information stored in the verification result file 106e2.
FIG. 13 is a diagram illustrating an example of information stored in the selected cancer state information file 106e3.
FIG. 14 is a diagram illustrating an example of information stored in the multivariate discriminant file 106e4.
FIG. 15 is a diagram illustrating an example of information stored in the discrimination value file 106f.
FIG. 16 is a diagram illustrating an example of information stored in the evaluation result file 106g.
FIG. 17 is a block diagram showing a configuration of the multivariate discriminant-preparing part 102h.
FIG. 18 is a block diagram illustrating a configuration of the discriminant value criterion-evaluating unit 102j.
FIG. 19 is a block diagram illustrating an example of the configuration of the client apparatus 200 of the present system.
FIG. 20 is a block diagram showing an example of the configuration of the database apparatus 400 of this system.
FIG. 21 is a flowchart showing an example of a cancer immunotherapy evaluation service process performed in the present system.
FIG. 22 is a flowchart showing an example of multivariate discriminant creation processing performed by the cancer immunotherapy evaluation apparatus 100 of the present system.
FIG. 23 is a diagram showing an experimental protocol of Example 1.
FIG. 24 shows changes in responder tumor growth relative to immunotherapy.
FIG. 25 shows changes in tumor growth of non-responders with respect to immunotherapy.
FIG. 26 is a diagram showing the distribution of amino acid variables between two groups before and after the start of treatment in the responder.
FIG. 27 is a diagram showing the distribution of amino acid variables between the two groups before and after the start of treatment in the responder.
FIG. 28 is a diagram showing the distribution of amino acid variables between two groups before and after the start of treatment in a non-responder.
FIG. 29 is a diagram showing the distribution of amino acid variables between two groups before and after the start of treatment in a non-responder.
FIG. 24 shows changes in responder tumor growth relative to immunotherapy.
FIG. 25 shows changes in tumor growth of non-responders with respect to immunotherapy.
FIG. 26 is a diagram showing the distribution of amino acid variables between two groups before and after the start of treatment in the responder.
FIG. 30 is a radar chart showing the distribution of amino acid variables after the start of treatment in the responder.
FIG. 31 is a radar chart showing the distribution of amino acid variables after the start of treatment in a non-responder.
FIG. 32 is a diagram showing the AUC of the ROC curve of each amino acid variable.
FIG. 33 is a diagram showing the AUC of the ROC curve of each amino acid variable.
FIG. 34 is a diagram showing a list of fractional expressions.
FIG. 35 is a diagram showing the appearance frequency of amino acid variables included in the fractional expression given in FIG.
FIG. 36 is a diagram showing a list of logistic regression equations.
FIG. 37 is a diagram showing a list of logistic regression equations.
FIG. 38 is a diagram showing a list of logistic regression equations.
FIG. 39 is a diagram showing the appearance frequency of amino acid variables included in the logistic regression equations listed in FIGS. 36, 37 and 38.
FIG. 40 is a diagram showing a list of logistic regression equations.
FIG. 41 is a diagram showing a list of logistic regression equations.
FIG. 42 is a diagram showing a list of logistic regression equations.
FIG. 43 is a diagram showing the appearance frequency of amino acid variables included in the logistic regression equations shown in FIGS. 40, 41, and 42.
FIG. 44 is a diagram showing a list of logistic regression equations.
FIG. 45 is a diagram showing a list of logistic regression equations.
FIG. 46 is a diagram showing a list of logistic regression equations.
FIG. 47 is a diagram showing a list of logistic regression equations.
FIG. 48 is a diagram showing a list of logistic regression equations.
FIG. 49 is a diagram showing the appearance frequency of amino acid variables included in the logistic regression equations shown in FIGS. 44-48.
FIG. 50 is a diagram showing a list of logistic regression equations.
FIG. 51 is a diagram showing a list of logistic regression equations.
FIG. 52 is a diagram showing a list of logistic regression equations.
FIG. 53 is a diagram showing a list of logistic regression equations.
FIG. 54 is a diagram showing a list of logistic regression equations.
FIG. 55 is a diagram showing the appearance frequency of amino acid variables included in the logistic regression equations shown in FIGS.
FIG. 56 is a diagram showing the experimental protocol of Example 6.
FIG. 57 is a diagram showing a section image of a tumor tissue.
FIG. 58 is a diagram showing the distribution of amino acid variables between the two groups before and after the start of treatment in the responder.
FIG. 59 is a diagram showing the distribution of amino acid variables between two groups before and after the start of treatment in a responder.
FIG. 60 is a diagram showing the distribution of amino acid variables between two groups before and after the start of treatment in a non-responder.
FIG. 61 is a diagram showing the distribution of amino acid variables between two groups before and after the start of treatment in a non-responder.
FIG. 62 is a radar chart showing the distribution of amino acid variables after the start of treatment in the responder.
FIG. 63 is a radar chart showing the distribution of amino acid variables after the start of treatment in a non-responder.
FIG. 64 is a diagram showing the AUC of the ROC curve of each amino acid variable.
FIG. 65 is a diagram showing the AUC of the ROC curve of each amino acid variable.
FIG. 66 is a diagram showing a list of fractional expressions.
FIG. 67 is a diagram showing the appearance frequency of amino acid variables included in the fractional expression given in FIG.
FIG. 68 is a diagram showing a list of logistic regression equations.
FIG. 69 is a diagram showing a list of logistic regression equations.
FIG. 70 is a diagram showing a list of logistic regression equations.
71 is a diagram showing the appearance frequency of amino acid variables included in the logistic regression equations listed in FIGS. 68, 69, and 70.
FIG. 72 is a diagram showing a list of logistic regression equations.
FIG. 73 is a diagram showing a list of logistic regression equations.
FIG. 74 is a diagram showing a list of logistic regression equations.
FIG. 75 is a diagram showing the appearance frequency of amino acid variables included in the logistic regression equations given in FIGS. 72, 73 and 74.
FIG. 76 is a diagram showing a list of logistic regression equations.
FIG. 77 is a diagram showing a list of logistic regression equations.
FIG. 78 is a diagram showing a list of logistic regression equations.
FIG. 79 is a diagram showing a list of logistic regression equations.
FIG. 80 is a diagram showing a list of logistic regression equations.
FIG. 80 is a diagram showing a list of logistic regression equations.
FIG. 81 is a diagram showing a list of logistic regression equations.
FIG. 82 is a diagram showing a list of logistic regression equations.
FIG. 83 is a diagram showing a list of logistic regression equations.
FIG. 84 is a diagram showing a list of logistic regression equations.
FIG. 85 is a diagram showing a list of logistic regression equations.
Embodiments of cancer immunotherapy evaluation method (first embodiment), cancer immunotherapy evaluation apparatus, cancer immunotherapy evaluation method, cancer immunotherapy evaluation program, recording medium, cancer immunotherapy evaluation system, and information communication terminal
An apparatus embodiment (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.
the amino acid concentration data after the start of treatment regarding the concentration value of the amino acid in the blood collected after the start of the treatment from the evaluation target is acquired (step S11).
amino acid concentration data measured by a company or the like that performs amino acid concentration measurement may be acquired.
the following (A) or (B) may be obtained from blood collected from an evaluation target.
Amino acid concentration data may be obtained by measuring amino acid concentration data by a measurement method.
the unit of amino acid concentration may be obtained by, for example, molar concentration, weight concentration, or by adding / subtracting / subtracting an arbitrary constant to / from these concentrations.
Plasma was separated from blood by centrifuging the collected blood sample. All plasma samples were stored frozen at ⁇ 80 ° C. until the measurement of amino acid concentration.
acetonitrile was added to remove protein, followed by precolumn derivatization using a labeling reagent (3-aminopyridyl-N-hydroxysuccinimidyl carbamate), and liquid chromatography mass spectrometry The amino acid concentration was analyzed by a total (LC / MS) (see International Publication No.
step S12 based on the pre-treatment amino acid concentration data and the post-treatment amino acid concentration data acquired in step S11, the effect of treatment by cancer immunotherapy on the evaluation target is evaluated (step S12).
the pre-treatment amino acid concentration data and the post-treatment amino acid concentration data to be evaluated are acquired, and evaluation is performed based on the acquired pre-treatment amino acid concentration data and post-treatment amino acid concentration data.
Evaluate the effect of treatment on the subject in short, provide information to assess the effect of treatment on the subject to be evaluated).
the amino acid concentration data after the start of treatment may be data (after treatment amino acid concentration data) corresponding to the above-mentioned “after treatment” amino acid concentration data.
step S12 data such as missing values and outliers may be removed from the pre-treatment amino acid concentration data and post-treatment amino acid concentration data acquired in step S11. Thereby, the therapeutic effect of cancer immunotherapy can be accurately evaluated.
the pre-treatment amino acid concentration data acquired in step S11 and the post-treatment amino acid concentration data include Val, Ile, Leu, His, Phe, Trp, Gln, Glu, Asp, Orn, Ala, Ser. , Thr, Met, Lys, Arg, Gly, Cys2, and Pro
the effect of treatment on the evaluation target may be evaluated based on at least one concentration value.
the therapeutic effect can be accurately evaluated using the amino acid concentration useful for evaluating the therapeutic effect of cancer immunotherapy.
the pre-treatment amino acid concentration data acquired in step S11 and the post-treatment amino acid concentration data include Val, Ile, Leu, His, Phe, Trp, Gln, Glu, Asp, Orn, Ala, Ser. , Thr, Met, Lys, Arg, Gly, Cys2, and Pro, whether or not the treatment is effective may be evaluated based on the concentration value. For example, it is determined whether or not the treatment is effective, and the evaluation target is assigned to any one of a plurality of categories (ranks) defined in consideration of the possibility of the treatment being effective. It may be classified. Thereby, the said evaluation (for example, discrimination, classification, etc.) can be accurately performed using the amino acid concentration useful for evaluation (for example, discrimination, classification, etc.) regarding the effectiveness of the therapeutic effect of cancer immunotherapy.
the range that the density value can take 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.
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.
an arbitrary value is added / subtracted / multiplied / divided with respect to the density value so as to fall within a range from 0 to 10.0, or the density value is converted into a predetermined conversion method (for example, exponential conversion, logarithmic conversion,
the density value may be converted by performing conversion by angle conversion, square root conversion, probit conversion, reciprocal conversion, or the like, or by combining these calculations with respect to the density value.
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 defining the probability p that the treatment is effective is the concentration value.
the value of p / (1-p) in the case where it is equal to), or a value obtained by dividing the calculated exponential function value by the sum of 1 and the value (specifically, The value of the probability 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 sensitivity is 80% is 5.0 and the value after conversion when the sensitivity is 95% is 8.0.
step S12 based on the pre-treatment amino acid concentration data and post-treatment amino acid concentration data obtained in step S11, and a preset multivariate discriminant including the amino acid concentration as a variable, the multivariate discriminant And the discriminant value corresponding to the evaluation result relating to the effect of the treatment on the evaluation target may be calculated, and the effect of the treatment on the evaluation target may be evaluated based on the calculated discriminant value. For example, based on the amino acid concentration data before starting treatment and the amino acid concentration data after starting treatment, the ratio or difference between the concentration value of the amino acid before starting treatment and the concentration value of the amino acid after starting treatment is calculated.
the discriminant value may be calculated by substituting the ratio or difference of the calculated concentration values of each amino acid into each variable included in the multivariate discriminant.
a multivariate discriminant including the amino acid concentration as a variable can be used to obtain a discriminant value corresponding to the evaluation result regarding the therapeutic effect on the evaluation target, or the obtained discriminant value can be used to obtain cancer. It is possible to accurately evaluate the therapeutic effect of immunotherapy.
the amino acid concentration data after the start of treatment may correspond to the above-mentioned “after treatment” amino acid concentration data (amino acid concentration data after treatment). Thereby, the said therapeutic effect can be continuously monitored even after completion
Multivariate discriminants are logistic regression formula, fractional formula, linear discriminant formula, multiple regression formula, formula created by support vector machine, formula created by Mahalanobis distance method, formula created by canonical discriminant analysis. Any one of the expressions created by the decision tree may be used. This makes it possible to obtain a discriminant value corresponding to the evaluation result related to the therapeutic effect on the evaluation target using the multivariate discriminant useful for evaluating the therapeutic effect of cancer immunotherapy, or to use the obtained discriminant value. Thus, the therapeutic effect can be accurately evaluated.
the pre-treatment amino acid concentration data acquired in step S11 and the post-treatment amino acid concentration data include Val, Ile, Leu, His, Phe, Trp, Gln, Glu, Asp, Orn, Ala, Ser. , Thr, Met, Lys, Arg, Gly, Cys2, Pro, as well as Val, Ile, Leu, His, Phe, Trp, Gln, Glu, Asp, Orn, Ala, Ser, Thr, Met , Lys, Arg, Gly, Cys2, and Pro, the discriminant value may be calculated based on a multivariate discriminant including at least one as a variable, and the treatment for the evaluation target is further performed based on the calculated discriminant value. You may evaluate the effect.
a discriminant value corresponding to the evaluation result regarding the therapeutic effect on the evaluation target is obtained using a multivariate discriminant useful for the evaluation including the concentration of amino acid useful for evaluating the therapeutic effect of cancer immunotherapy as a variable. Or the obtained discrimination value can be used to accurately evaluate the therapeutic effect.
the pre-treatment amino acid concentration data acquired in step S11 and the post-treatment amino acid concentration data include Val, Ile, Leu, His, Phe, Trp, Gln, Glu, Asp, Orn, Ala, Ser. , Thr, Met, Lys, Arg, Gly, Cys2, Pro, as well as Val, Ile, Leu, His, Phe, Trp, Gln, Glu, Asp, Orn, Ala, Ser, Thr, Met , Lys, Arg, Gly, Cys2, and Pro, the discriminant value may be calculated based on a multivariate discriminant including at least one as a variable, and further, based on the calculated discriminant value, To assess whether the treatment is effective.
the treatment is effective for the evaluation target, and any one of a plurality of categories (ranks) defined in consideration of the possibility of the treatment being effective. It is also possible to classify the evaluation target.
This makes it possible to use a multivariate discriminant useful for the evaluation (for example, discrimination, classification, etc.) including the concentration of amino acids useful for evaluation (for example, discrimination, classification, etc.) as a variable regarding the effectiveness of the therapeutic effect of cancer immunotherapy.
a discriminant value corresponding to the evaluation result regarding the effectiveness of the treatment for the evaluation target, or to accurately perform the evaluation (for example, discrimination, classification, etc.) using the obtained discriminant value.
the multivariate discriminant used in the evaluation is a fractional expression including at least one of Glu, Cys2, Trp, Asp, Orn, Phe, Val, Ile, Gly, His as a variable. Or a logistic regression equation including at least one of Trp, Thr, His, Arg, Ile, Pro, Phe, Met, Ala, Lys, Asp, Ser, and Leu as a variable.
This makes it possible to use a multivariate discriminant that is particularly useful for evaluating the effectiveness of the therapeutic effect of cancer immunotherapy (for example, discrimination, classification, etc.), and to obtain a discriminant value corresponding to the evaluation result regarding the effectiveness of the treatment for the evaluation target.
the evaluation (for example, discrimination, classification, etc.) can be performed with higher accuracy by using the obtained discrimination value.
the discriminant value can have 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.
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.
an arbitrary value is added / subtracted / multiplied / divided with respect to the discriminant value so that the discriminant value falls within a range from 0 to 10.0, etc.
the discriminant value may be converted by performing conversion by angle conversion, square root conversion, probit conversion, or reciprocal conversion, or by combining these calculations with respect to the discriminant value.
the value of the exponential function with the discriminant value as the exponent and the Napier number as the base is the discriminant value.
the value of p / (1-p) in the case where it is equal to), or a value obtained by dividing the calculated exponential function value by the sum of 1 and the value may be further calculated.
the discriminant value may be converted so that the value after conversion under a specific condition becomes a specific value.
the discriminant value may be converted so that the value after conversion when the sensitivity is 80% is 5.0 and the value after conversion when the sensitivity is 95% is 8.0.
the discriminant value in this specification may be the value of the multivariate discriminant itself, or may be a value after converting the value of the multivariate discriminant.
the treatment method to be evaluated can be selected before treatment.
a treatment method to be received by the evaluation object may be predicted before treatment.
each multivariate discriminant described above 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 of description. If the multivariate discriminant obtained by these methods is used, the multivariate discriminant is suitable for evaluating the therapeutic effect of cancer immunotherapy regardless of the unit of amino acid concentration in the amino acid concentration data as input data. Can be used.
the multivariate discriminant means a formula format generally used in multivariate analysis. For example, a fractional equation, multiple regression equation, multiple logistic regression equation, linear discriminant, Mahalanobis distance, canonical discriminant function, support vector Includes machines, decision trees, etc. Also included are expressions as indicated by the sum of different forms of multivariate discriminants. In addition, in multiple regression equations, multiple logistic regression equations, canonical discriminant functions, etc., coefficients and constant terms are added to each variable.
the coefficients and constant terms are preferably real numbers, more preferably Values belonging to the 99% confidence interval range of the coefficient and constant term obtained for discrimination from the data, more preferably within the 95% confidence interval range of the coefficient and constant term obtained from the data It doesn't matter if it belongs. 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 / subtracting / multiplying / dividing an arbitrary real constant thereto.
linear transformation addition of constants, constant multiplication
monotonic increase (decrease) conversion eg logit transformation
the fractional expression means that the numerator of the fractional expression is represented by the sum of amino acids A, B, C,... And / or the denominator of the fractional expression is the sum of amino acids 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.
An appropriate coefficient may be added to each amino acid used in the numerator and denominator.
amino acids used in the numerator and denominator may overlap.
an appropriate coefficient may be attached to each fractional expression.
the value of the coefficient of each variable and the value of the constant term may be real numbers.
the combination of the numerator variable and the denominator variable is generally reversed in the sign of the correlation with the target variable, but since the correlation is maintained, it can be considered equivalent in discriminability. Combinations of swapping numerator and denominator variables are also included.
the first embodiment when evaluating the therapeutic effect of cancer immunotherapy, in addition to the concentration of amino acids, other biological information (eg, tumor marker, blood cytokine, number of immunocompetent cells, immunocompetent intracellular cytokine, A delayed excessive reaction (DTH) or the like may be further used.
other biological information eg, tumor marker, blood cytokine, immunocompetent cell
Number immunocompetent intracellular cytokines, delayed hyperfractionation (DTH), etc.
DTH delayed hyperfractionation
FIG. 2 is a flowchart showing an example of the cancer immunotherapy evaluation method according to the first embodiment.
amino acid concentration data before starting treatment and amino acid concentration data after starting treatment of an individual (for example, an animal or a human) who are treated by cancer immunotherapy are acquired (step SA11).
amino acid concentration data measured by a company or the like that measures amino acid concentration may be acquired, and a measuring method such as (A) or (B) described above from blood collected from an individual.
the amino acid concentration data may be obtained by measuring the amino acid concentration data.
step SA12 data such as missing values and outliers are removed from the pre-treatment amino acid concentration data and post-treatment amino acid concentration data obtained in step SA11 (step SA12).
Val, Ile, Leu, His, Phe, Trp, Gln Val, Ile, Leu, His, Phe, Trp, Gln included in the pre-treatment amino acid concentration data and post-treatment amino acid concentration data of the individual from which data such as missing values and outliers have been removed in step SA12.
treatment by cancer immunotherapy is effective for an individual based on the concentration value of at least one of Glu, Asp, Orn, Ala, Ser, Thr, Met, Lys, Arg, Gly, Cys2, Pro Is determined (step SA13).
FIG. 3 is a principle configuration diagram showing the basic principle of the second embodiment.
control unit determines the amino acid concentration data before the start of treatment of the evaluation target (for example, an individual such as an animal or a human) to be treated by cancer immunotherapy, the amino acid concentration data before the start of treatment, and the amino acid after the start of the treatment of the evaluation target. Based on the amino acid concentration data after the start of treatment related to the concentration value of the substance and the multivariate discriminant stored in the storage unit including the amino acid concentration as a variable, the value of the multivariate discriminant and the effect of the treatment on the evaluation target A discriminant value corresponding to the evaluation result is calculated (step S21).
the evaluation target for example, an individual such as an animal or a human
control unit evaluates the effect of treatment by cancer immunotherapy on the evaluation target based on the discriminant value calculated in step S21 (step S22).
the discriminant value corresponding to the evaluation result regarding the effect of the treatment on the evaluation target. And the effect of the treatment on the evaluation object is evaluated based on the calculated discriminant value (in short, information for evaluating the effect of the treatment on the evaluation object is provided). For example, based on the amino acid concentration data before starting treatment and the amino acid concentration data after starting treatment, the ratio or difference between the concentration value of the amino acid before starting treatment and the concentration value of the amino acid after starting treatment is calculated.
the discriminant value may be calculated by substituting the ratio or difference of the calculated concentration values of each amino acid into each variable included in the multivariate discriminant.
a multivariate discriminant including the amino acid concentration as a variable can be used to obtain a discriminant value corresponding to the evaluation result regarding the therapeutic effect on the evaluation target, or the obtained discriminant value can be used to obtain cancer. It is possible to accurately evaluate the therapeutic effect of immunotherapy. Further, the amino acid concentration data after the start of treatment may correspond to the above-mentioned “after treatment” amino acid concentration data (amino acid concentration data after treatment). Thereby, the said therapeutic effect can be continuously monitored even after completion
Multivariate discriminants are logistic regression formula, fractional formula, linear discriminant formula, multiple regression formula, formula created by support vector machine, formula created by Mahalanobis distance method, formula created by canonical discriminant analysis. Any one of the expressions created by the decision tree may be used. This makes it possible to obtain a discriminant value corresponding to the evaluation result related to the therapeutic effect on the evaluation target using the multivariate discriminant useful for evaluating the therapeutic effect of cancer immunotherapy, or to use the obtained discriminant value. Thus, the therapeutic effect can be accurately evaluated.
step S21 Val, Ile, Leu, His, Phe, Trp, Gln, Glu, Asp, Orn, Ala, Ser, Thr, Met, and the amino acid concentration data before and after the treatment start are included. At least one concentration value of Lys, Arg, Gly, Cys2, Pro, and Val, Ile, Leu, His, Phe, Trp, Gln, Glu, Asp, Orn, Ala, Ser, Thr, Met, Lys, Arg, A discriminant value is calculated based on a multivariate discriminant including at least one of Gly, Cys2, and Pro as a variable.
step S22 the effect of treatment on the evaluation target is calculated based on the discriminant value calculated in step S21. You may evaluate.
a discriminant value corresponding to the evaluation result regarding the therapeutic effect on the evaluation target is obtained using a multivariate discriminant useful for the evaluation including the concentration of amino acid useful for evaluating the therapeutic effect of cancer immunotherapy as a variable. Or the obtained discrimination value can be used to accurately evaluate the therapeutic effect.
step S21 Val, Ile, Leu, His, Phe, Trp, Gln, Glu, Asp, Orn, Ala, Ser, Thr, Met, and the amino acid concentration data before and after the treatment start are included. At least one concentration value of Lys, Arg, Gly, Cys2, Pro, and Val, Ile, Leu, His, Phe, Trp, Gln, Glu, Asp, Orn, Ala, Ser, Thr, Met, Lys, Arg, A discriminant value is calculated based on a multivariate discriminant including at least one of Gly, Cys2, and Pro as a variable.
step S22 treatment is performed on the evaluation target based on the discriminant value calculated in step S21. You may evaluate whether it is effective.
the treatment is effective for the evaluation target, and any one of a plurality of categories (ranks) defined in consideration of the possibility of the treatment being effective. It is also possible to classify the evaluation target.
This makes it possible to use a multivariate discriminant useful for the evaluation (for example, discrimination, classification, etc.) including the concentration of amino acids useful for evaluation (for example, discrimination, classification, etc.) as a variable regarding the effectiveness of the therapeutic effect of cancer immunotherapy.
a discriminant value corresponding to the evaluation result regarding the effectiveness of the treatment for the evaluation target, or to accurately perform the evaluation (for example, discrimination, classification, etc.) using the obtained discriminant value.
the multivariate discriminant used in the evaluation is a fractional expression including at least one of Glu, Cys2, Trp, Asp, Orn, Phe, Val, Ile, Gly, His as a variable. Or a logistic regression equation including at least one of Trp, Thr, His, Arg, Ile, Pro, Phe, Met, Ala, Lys, Asp, Ser, and Leu as a variable.
This makes it possible to use a multivariate discriminant that is particularly useful for evaluating the effectiveness of the therapeutic effect of cancer immunotherapy (for example, discrimination, classification, etc.), and to obtain a discriminant value corresponding to the evaluation result regarding the effectiveness of the treatment for the evaluation target.
the evaluation (for example, discrimination, classification, etc.) can be performed with higher accuracy by using the obtained discrimination value.
the discriminant value can have 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.
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.
an arbitrary value is added / subtracted / multiplied / divided with respect to the discriminant value so that the discriminant value falls within a range from 0 to 10.0, etc.
the discriminant value may be converted by performing conversion by angle conversion, square root conversion, probit conversion, or reciprocal conversion, or by combining these calculations with respect to the discriminant value.
the value of the exponential function with the discriminant value as the exponent and the Napier number as the base is the discriminant value.
the value of p / (1-p) in the case where it is equal to), or a value obtained by dividing the calculated exponential function value by the sum of 1 and the value may be further calculated.
the discriminant value may be converted so that the value after conversion under a specific condition becomes a specific value.
the discriminant value may be converted so that the value after conversion when the sensitivity is 80% is 5.0 and the value after conversion when the sensitivity is 95% is 8.0.
the discriminant value in this specification may be the value of the multivariate discriminant itself, or may be a value after converting the value of the multivariate discriminant.
the treatment method to be evaluated can be selected before treatment.
a treatment method to be received by the evaluation object may be predicted before treatment.
each multivariate discriminant described above 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 of description. If the multivariate discriminant obtained by these methods is used, the multivariate discriminant is suitable for evaluating the therapeutic effect of cancer immunotherapy regardless of the unit of amino acid concentration in the amino acid concentration data as input data. Can be used.
the multivariate discriminant means a formula format generally used in multivariate analysis. For example, a fractional equation, multiple regression equation, multiple logistic regression equation, linear discriminant, Mahalanobis distance, canonical discriminant function, support vector Includes machines, decision trees, etc. Also included are expressions as indicated by the sum of different forms of multivariate discriminants. In addition, in multiple regression equations, multiple logistic regression equations, canonical discriminant functions, etc., coefficients and constant terms are added to each variable.
the coefficients and constant terms are preferably real numbers, more preferably Values belonging to the 99% confidence interval range of the coefficient and constant term obtained for discrimination from the data, more preferably within the 95% confidence interval range of the coefficient and constant term obtained from the data It doesn't matter if it belongs. 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 / subtracting / multiplying / dividing an arbitrary real constant thereto.
linear transformation addition of constants, constant multiplication
monotonic increase (decrease) conversion eg logit transformation
the fractional expression means that the numerator of the fractional expression is represented by the sum of amino acids A, B, C,... And / or the denominator of the fractional expression is the sum of amino acids 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.
An appropriate coefficient may be added to each amino acid used in the numerator and denominator.
amino acids used in the numerator and denominator may overlap.
an appropriate coefficient may be attached to each fractional expression.
the value of the coefficient of each variable and the value of the constant term may be real numbers.
the combination of the numerator variable and the denominator variable is generally reversed in the sign of the correlation with the target variable, but since the correlation is maintained, it can be considered equivalent in discriminability. Combinations of swapping numerator and denominator variables are also included.
the second embodiment when evaluating the therapeutic effect of cancer immunotherapy, in addition to the concentration of amino acids, other biological information (eg, tumor marker, blood cytokine, number of immunocompetent cells, immunocompetent intracellular cytokine, A delayed excessive reaction (DTH) or the like may be further used.
other biological information eg, tumor marker, blood cytokine, immunocompetent cell
Number immunocompetent intracellular cytokines, delayed hyperfractionation (DTH), etc.
DTH delayed hyperfractionation
step 1 to step 4 the outline of the multivariate discriminant creation process (step 1 to step 4) will be described in detail. Note that the processing described here is merely an example, and the method of creating the multivariate discriminant is not limited to this.
the control unit determines amino acid concentration data (for example, data relating to amino acid concentration or data relating to the amount of change in amino acid concentration) and cancer state index data relating to an index (for example, tumor size) indicating a cancer state.
amino acid concentration data for example, data relating to amino acid concentration or data relating to the amount of change in amino acid concentration
cancer state index data relating to an index (for example, tumor size) indicating a cancer state.
an index for example, tumor size
y cancer state index data
x i amino acid concentration data
Step 1 a plurality of different formula creation methods (principal component analysis, discriminant analysis, support vector machine, multiple regression analysis, logistic regression analysis, k-means method, cluster analysis, decision tree, etc.) are obtained from cancer status information.
a plurality of candidate multivariate discriminants may be created by using the above in combination. Specifically, for cancer status information that is multivariate data composed of amino acid concentration data and cancer status index data obtained by analyzing blood obtained from a large number of pre-treatment groups and a large number of post-treatment groups.
a plurality of groups of candidate multivariate discriminants may be created concurrently using a plurality of different algorithms. For example, two different candidate multivariate discriminants may be created by performing discriminant analysis and logistic regression analysis simultaneously using different algorithms.
the candidate multivariate discriminant is created by converting the cancer state information using the candidate multivariate discriminant created by performing the principal component analysis and performing the discriminant analysis on the converted cancer state information Good. Thereby, finally, an appropriate multivariate discriminant suitable for the evaluation condition can be created.
the candidate multivariate discriminant prepared using principal component analysis is a linear expression including each amino acid variable that maximizes the variance of all amino acid concentration data.
the candidate multivariate discriminant created using discriminant analysis is a high-order formula (exponential or exponential) including each amino acid variable that minimizes the ratio of the sum of variances within each group to the variance of all amino acid concentration data. Including logarithm).
the candidate multivariate discriminant created using the support vector machine is a higher-order formula (including a kernel function) including each amino acid variable that maximizes the boundary between groups.
the candidate multivariate discriminant created using multiple regression analysis is a high-order expression including each amino acid variable that minimizes the sum of distances from all amino acid concentration data.
a candidate multivariate discriminant created using logistic regression analysis is a linear model representing the log odds of probability, and is a linear expression including each amino acid variable that maximizes the likelihood of the probability.
the k-means method searches k neighborhoods of each amino acid concentration data, defines the largest group among the groups to which the neighboring points belong as the group to which the data belongs, This is a method of selecting an amino acid variable that best matches the group to which the group belongs.
Cluster analysis is a method of clustering (grouping) points that are closest to each other in all amino acid concentration data.
the decision tree is a technique for predicting a group of amino acid concentration data based on patterns that can be taken by amino acid variables having higher ranks by adding ranks to amino acid variables.
control unit verifies (mutually verifies) the candidate multivariate discriminant created in step 1 based on a predetermined verification method (step 2).
the candidate multivariate discriminant is verified for each candidate multivariate discriminant created in step 1.
step 2 the discrimination rate, sensitivity, specificity, etc. of the candidate multivariate discriminant based on at least one of random sampling method, bootstrap method, holdout method, N-fold method, leave one out method, etc.
the verification may be performed with respect to at least one of the information criterion, ROC_AUC (area under the curve of the receiver characteristic curve), and the like.
the discrimination rate is a ratio in which the result of the therapeutic effect evaluated in the present embodiment is negative as a true state and is evaluated as negative correctly, and a positive result as a true state is correctly evaluated as positive. is there.
Sensitivity is the ratio at which positive results are positively evaluated as positive as the result of the therapeutic effect evaluated in this embodiment.
specificity is a ratio at which negative results are correctly evaluated as negative as the true result of the therapeutic effect evaluated in the present embodiment.
the information criterion is the difference between the number of amino acid variables of the candidate multivariate discriminant created in step 1, the result of the treatment effect evaluated in this embodiment and the result of the treatment effect described in the input data, Are added together.
ROC_AUC area under the curve of the receiver characteristic curve
ROC receiver characteristic curve
the value of ROC_AUC is 1 in complete discrimination, and the closer this value is to 1, the higher the discriminability.
the predictability is an average of the discrimination rate, sensitivity, and specificity obtained by repeating the verification of the candidate multivariate discriminant.
Robustness is the variance of discrimination rate, sensitivity, and specificity obtained by repeating verification of candidate multivariate discriminants.
the control unit selects cancer candidate variable variables based on a predetermined variable selection method, and thus cancer state information used when creating a candidate multivariate discriminant A combination of amino acid concentration data contained in is selected (step 3).
the selection of amino acid variables may be performed for each candidate multivariate discriminant created in step 1. Thereby, the amino acid variable of a candidate multivariate discriminant can be selected appropriately.
Step 1 is executed again using the cancer state information including the amino acid concentration data selected in Step 3.
step 3 the amino acid variable of the candidate multivariate discriminant may be selected from the verification result in step 2 based on at least one of stepwise method, best path method, neighborhood search method, and genetic algorithm. .
the best path method is a method of selecting amino acid variables by sequentially reducing amino acid variables included in the candidate multivariate discriminant one by one and optimizing the evaluation index given by the candidate multivariate discriminant. is there.
the control unit repeatedly executes the above-described step 1, step 2 and step 3, and based on the verification results accumulated thereby, the control unit can select from a plurality of candidate multivariate discriminants.
a multivariate discriminant is created by selecting candidate multivariate discriminants to be adopted as the multivariate discriminant (step 4).
candidate multivariate discriminants for example, selecting the optimal one from among candidate multivariate discriminants created by the same formula creation method, and selecting the optimum from all candidate multivariate discriminants Sometimes there is a choice.
the multivariate discriminant creation process processing related to the creation of a candidate multivariate discriminant, verification of the candidate multivariate discriminant, and selection of a variable of the candidate multivariate discriminant based on the cancer state information.
systematization systematization
the amino acid concentration is used for multivariate statistical analysis, and the variable selection method and cross-validation are combined in order to select the optimal and robust variable set. Extract the variable discriminant.
logistic regression, linear discrimination, support vector machine, Mahalanobis distance method, multiple regression analysis, cluster analysis, decision tree, and the like can be used.
FIG. 4 is a diagram showing an example of the overall configuration of the present system.
FIG. 5 is a diagram showing another example of the overall configuration of the present system.
the present system includes a cancer immunotherapy evaluation apparatus 100 that evaluates the effect of treatment on an evaluation target that receives treatment by cancer immunotherapy, and a client that provides amino acid concentration data relating to the concentration value of the amino acid to be evaluated.
the apparatus 200 (corresponding to the information communication terminal apparatus of the present invention) is configured to be communicably connected via the network 300.
this system uses cancer status information used when creating a multivariate discriminant in the cancer immunotherapy evaluation apparatus 100
a database apparatus 400 storing a multivariate discriminant used for evaluating the therapeutic effect of immunotherapy may be configured to be communicably connected via the network 300. Accordingly, cancer in the treatment by cancer immunotherapy from the cancer immunotherapy evaluation apparatus 100 to the client apparatus 200 or the database apparatus 400, or from the client apparatus 200 or database apparatus 400 to the cancer immunotherapy evaluation apparatus 100 via the network 300. Information about the state of the is provided.
the information on the cancer state in the treatment with cancer immunotherapy is information on the value measured for a specific item related to the cancer state of an organism including humans in the treatment with cancer immunotherapy (for example, presence or absence of therapeutic effect) Or a difference value of tumor size).
information related to cancer status in cancer immunotherapy treatment is generated by the cancer immunotherapy evaluation apparatus 100, the client apparatus 200, and other apparatuses (for example, various measurement apparatuses), and is mainly stored in the database apparatus 400.
FIG. 6 is a block diagram showing an example of the configuration of the cancer immunotherapy evaluation apparatus 100 of the present system, and conceptually shows only the portion related to the present invention in the configuration.
the cancer immunotherapy evaluation apparatus 100 includes a control unit 102 such as a CPU that comprehensively controls the cancer immunotherapy evaluation apparatus, a communication device such as a router, and a wired or wireless communication line such as a dedicated line.
a communication interface unit 104 that connects the therapy evaluation device to the network 300 so as to be communicable, a storage unit 106 that stores various databases, tables, files, and the like, and an input / output interface unit 108 that connects to the input device 112 and the output device 114 These parts are connected to be communicable via an arbitrary communication path.
the cancer immunotherapy evaluation apparatus 100 may be configured in the same housing as various analysis apparatuses (for example, an amino acid analyzer or the like).
the storage unit 106 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 106 stores a computer program for giving instructions to the CPU and performing various processes in cooperation with an OS (Operating System).
the storage unit 106 includes a user information file 106a, an amino acid concentration data file 106b, a cancer state information file 106c, a designated cancer state information file 106d, a multivariate discriminant-related information database 106e, and a discriminant value.
a file 106f and an evaluation result file 106g are stored.
the user information file 106a stores user information related to users.
FIG. 7 is a diagram illustrating an example of information stored in the user information file 106a.
the information stored in the user information file 106a includes a user ID for uniquely identifying a user and authentication for whether or not the user is a valid person.
the amino acid concentration data file 106b stores amino acid concentration data before starting treatment and amino acid concentration data after starting treatment.
FIG. 8 is a diagram showing an example of information stored in the amino acid concentration data file 106b.
the information stored in the amino acid concentration data file 106b includes an individual number for uniquely identifying an individual (sample) to be evaluated, amino acid concentration data before starting treatment, and amino acid concentration after starting treatment. It is configured to correlate with data.
the amino acid concentration data is treated as a numerical value, that is, a continuous scale, but the amino acid concentration 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.
amino acid concentration data may be combined with other biological information (eg, tumor marker, blood cytokine, number of immunocompetent cells, cytokine in immunocompetent cell, delayed excessive reaction (DTH), etc.).
other biological information eg, tumor marker, blood cytokine, number of immuno
the cancer state information file 106 c stores cancer state information used when creating a multivariate discriminant.
FIG. 9 is a diagram illustrating an example of information stored in the cancer state information file 106c.
the information stored in the cancer state information file 106c includes cancer state index data relating to individual numbers and indices (index T 1 , index T 2 , index T 3 ...) Representing the cancer state. (T) and amino acid concentration data are associated with each other.
the cancer state index data and the amino acid concentration data are treated as numerical values (that is, a continuous scale), but the cancer state index data and the amino acid concentration 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.
the cancer state index data may be a known single state index serving as a marker of cancer state, or numerical data may be used.
the designated cancer state information file 106d stores the cancer state information designated by the cancer state information designation unit 102g described later.
FIG. 10 is a diagram illustrating an example of information stored in the designated cancer state information file 106d. As shown in FIG. 10, the information stored in the designated cancer state information file 106d is configured by associating individual numbers, designated cancer state index data, and designated amino acid concentration data with each other.
the multivariate discriminant-related information database 106e includes a candidate multivariate discriminant file 106e1 for storing the candidate multivariate discriminant created by the candidate multivariate discriminant-preparing part 102h1, which will be described later, and a candidate multivariate discriminant described later.
a verification result file 106e2 for storing a verification result in the discriminant verification unit 102h2
a selected cancer state information file 106e3 for storing cancer state information including a combination of amino acid concentration data selected by a variable selection unit 102h3 described later, and a later-described
a multivariate discriminant file 106e4 that stores the multivariate discriminant created by the multivariate discriminant creation unit 102h.
the candidate multivariate discriminant file 106e1 stores the candidate multivariate discriminant created by the candidate multivariate discriminant creation unit 102h1 described later.
FIG. 11 is a diagram illustrating an example of information stored in the candidate multivariate discriminant file 106e1.
information stored in the candidate multivariate discriminant file 106e1 includes a rank, a candidate multivariate discriminant (in FIG. 11, F 1 (Gly, Leu, Phe,%)) And F 2. (Gly, Leu, Phe,%), F 3 (Gly, Leu, Phe,...)) Are associated with each other.
FIG. 12 is a diagram illustrating an example of information stored in the verification result file 106e2.
the information stored in the verification result file 106e2 includes rank, candidate multivariate discriminant (in FIG. 12, F k (Gly, Leu, Phe,%) And F m (Gly, Le, Phe,%), Fl (Gly, Leu, Phe, etc) And the verification results of each candidate multivariate discriminant (for example, the evaluation value of each candidate multivariate discriminant). They are related to each other.
the selected cancer state information file 106e3 stores cancer state information including a combination of amino acid concentration data corresponding to variables selected by the variable selection unit 102h3 described later.
FIG. 13 is a diagram illustrating an example of information stored in the selected cancer state information file 106e3. As shown in FIG. 13, the information stored in the selected cancer state information file 106e3 is selected by an individual number, cancer state index data designated by a cancer state information designation unit 102g described later, and a variable selection unit 102h3 described later. The amino acid concentration data is associated with each other.
the multivariate discriminant file 106e4 stores the multivariate discriminant created by the multivariate discriminant-preparing part 102h described later.
FIG. 14 is a diagram illustrating an example of information stored in the multivariate discriminant file 106e4.
the information stored in the multivariate discriminant file 106e4 includes the rank, the multivariate discriminant (in FIG. 14, F p (Phe,%) And F p (Gly, Leu, Phe). ), F k (Gly, Leu, Phe,...)), A threshold corresponding to each formula creation method, a verification result of each multivariate discriminant (for example, an evaluation value of each multivariate discriminant), Are related to each other.
the discriminant value file 106f stores the discriminant value calculated by the discriminant value calculator 102i described later.
FIG. 15 is a diagram illustrating an example of information stored in the discrimination value file 106f. As shown in FIG. 15, information stored in the discriminant value file 106f includes an individual number for uniquely identifying an individual (sample) to be evaluated and a rank (for uniquely identifying a multivariate discriminant). Number) and the discriminant value are associated with each other.
the evaluation result file 106g stores the evaluation result in the discriminant value criterion-evaluating unit 102j described later (specifically, the discrimination result / classification result in the discriminant value criterion-discriminating unit 102j1 described later).
FIG. 16 is a diagram illustrating an example of information stored in the evaluation result file 106g.
Information stored in the evaluation result file 106g includes an individual number for uniquely identifying an individual (sample) to be evaluated, and a plurality of evaluation target amino acid concentration data (amino acid concentration data before treatment start and amino acid after treatment start).
Concentration data one or more discriminant values calculated by a multivariate discriminant (for example, a discriminant value before treatment start, a discriminant value after treatment start or a discriminant value before and after treatment start), and the therapeutic effect of cancer immunotherapy
a multivariate discriminant for example, a discriminant value before treatment start, a discriminant value after treatment start or a discriminant value before and after treatment start
the evaluation results related to the evaluation are associated with each other.
the storage unit 106 stores various types of Web data for providing the Web site to the client device 200, CGI programs, and the like as other information in addition to the information described above.
the Web data includes data for displaying various Web pages to be described later, and these data are formed as text files described in HTML or XML, for example.
a part file, a work file, and other temporary files for creating Web data are also stored in the storage unit 106.
the storage unit 106 stores audio for transmission to the client device 200 as an audio file such as WAVE format or AIFF format, and stores still images or moving images as image files such as JPEG format or MPEG2 format as necessary. Can be stored.
the communication interface unit 104 mediates communication between the cancer immunotherapy 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 control unit 102 has an internal memory for storing a control program such as an OS (Operating System), a program defining various processing procedures, and necessary data, and performs various information processing based on these programs. Execute. As shown in the figure, the control unit 102 is roughly divided into a request interpretation unit 102a, a browsing processing unit 102b, an authentication processing unit 102c, an email generation unit 102d, a Web page generation unit 102e, a reception unit 102f, and a cancer state information designation unit 102g.
a multivariate discriminant creation unit 102h, a discriminant value calculation unit 102i, a discriminant value criterion evaluation unit 102j, a result output unit 102k, and a transmission unit 102m are provided.
the control unit 102 removes data with missing values, removes data with many outliers, and has missing values with respect to the cancer state information transmitted from the database device 400 and the amino acid concentration data transmitted from the client device 200. Data processing such as removal of variables with a lot of data is also performed.
the request interpretation unit 102a interprets the request content from the client device 200 or the database device 400, and passes the processing to each unit of the control unit 102 according to the interpretation result.
the browsing processing unit 102b Upon receiving browsing requests for various screens from the client device 200, the browsing processing unit 102b generates and transmits Web data for these screens.
the authentication processing unit 102c makes an authentication determination.
the e-mail generation unit 102d generates an e-mail including various types of information.
the web page generation unit 102e generates a web page that the user browses on the client device 200.
the receiving unit 102 f receives information (specifically, amino acid concentration data, cancer state information, multivariate discriminant, etc.) transmitted from the client device 200 or the database device 400 via the network 300.
information specifically, amino acid concentration data, cancer state information, multivariate discriminant, etc.
the cancer state information specifying unit 102g specifies target cancer state index data and amino acid concentration data.
the multivariate discriminant creating unit 102h creates a multivariate discriminant based on the cancer state information received by the receiving unit 102f and the cancer state information designated by the cancer state information designating unit 102g. Specifically, the multivariate discriminant-preparing part 102h is accumulated by repeatedly executing the candidate multivariate discriminant-preparing part 102h1, the candidate multivariate discriminant-verifying part 102h2, and the variable selecting part 102h3 from the cancer state information. A multivariate discriminant is created by selecting a candidate multivariate discriminant to be adopted as a multivariate discriminant from a plurality of candidate multivariate discriminants based on the verification result.
the multivariate discriminant-preparing unit 102h selects a desired multivariate discriminant from the storage unit 106, A multivariate discriminant may be created.
the multivariate discriminant creation unit 102h creates a multivariate discriminant by selecting and downloading a desired multivariate discriminant from another computer device (for example, the database device 400) that stores the multivariate discriminant in advance. May be.
FIG. 17 is a block diagram showing the configuration of the multivariate discriminant-preparing part 102h, and conceptually shows only the part related to the present invention.
the multivariate discriminant creation unit 102h further includes a candidate multivariate discriminant creation unit 102h1, a candidate multivariate discriminant verification unit 102h2, and a variable selection unit 102h3.
the candidate multivariate discriminant creation unit 102h1 creates a candidate multivariate discriminant that is a candidate for the multivariate discriminant based on a predetermined formula creation method from the cancer state information.
the candidate multivariate discriminant-preparing part 102h1 may create a plurality of candidate multivariate discriminants from the cancer state information by using a plurality of different formula creation methods.
the candidate multivariate discriminant verification unit 102h2 verifies the candidate multivariate discriminant created by the candidate multivariate discriminant creation unit 102h1 based on a predetermined verification method.
the candidate multivariate discriminant verification unit 102h2 determines the discrimination rate of the candidate multivariate discriminant based on at least one of a random sampling method, a bootstrap method, a holdout method, an N-fold method, or a leave one out method.
the variable selection unit 102h3 selects a variable of the candidate multivariate discriminant based on a predetermined variable selection method, so that a combination of amino acid concentration data included in the cancer state information used when creating the candidate multivariate discriminant is selected. select. Note that the variable selection unit 102h3 may select a variable of the candidate multivariate discriminant from the verification result based on at least one of the stepwise method, the best path method, the neighborhood search method, and the genetic algorithm.
the discriminant value calculation unit 102 i includes the multivariate discriminant created by the multivariate discriminant creation unit 102 h and the evaluation target amino acid concentration data received by the receiving unit 102 f (specifically, the amino acid concentration before starting treatment). Based on the concentration data, amino acid concentration data after treatment start, etc., one or more discriminant values that are the values of the multivariate discriminant (specifically, discriminant value before treatment start, discriminant value after treatment start, treatment Calculate the discriminant value before and after the start.
Multivariate discriminants are logistic regression formula, fractional formula, linear discriminant formula, multiple regression formula, formula created by support vector machine, formula created by Mahalanobis distance method, formula created by canonical discriminant analysis. Any one of the expressions created by the decision tree may be used.
the discriminant value calculation unit 102i is configured to include the Val, Ile, Leu, His, Phe, Trp, Gln, Glu, Asp, Orn, Ala, Ser, and the like included in the pre-treatment amino acid concentration data and the post-treatment amino acid concentration data. At least one concentration value among Thr, Met, Lys, Arg, Gly, Cys2, Pro, and Val, Ile, Leu, His, Phe, Trp, Gln, Glu, Asp, Orn, Ala, Ser, Thr, Met, One or more discriminant values may be calculated based on a multivariate discriminant including at least one of Lys, Arg, Gly, Cys2, and Pro as a variable.
the discriminant value criterion discriminating unit 102j1 to be described later discriminates whether or not the cancer immunotherapy treatment is effective for the evaluation target, and considers the degree of possibility that the treatment is effective.
the discriminant value calculation unit 102i uses the amino acid concentration data before starting treatment and the amino acid concentration data after starting treatment.
the multivariate discriminant is a fractional expression including at least one of Glu, Cys2, Trp, Asp, Orn, Phe, Val, Ile, Gly, His as a variable, or Trp, Thr, His, Arg, Ile,
a logistic regression equation including at least one of Pro, Phe, Met, Ala, Lys, Asp, Ser, and Leu as a variable may be used.
the discriminant value criterion-evaluating unit 102j evaluates the effect of treatment by cancer immunotherapy on the evaluation target based on one or more discriminant values calculated by the discriminant value calculating unit 102i.
the discriminant value criterion-evaluating unit 102j evaluates, for example, whether the treatment is effective for the evaluation target.
the discrimination value criterion evaluation unit 102j further includes a discrimination value criterion discrimination unit 102j1.
FIG. 18 is a block diagram showing the configuration of the discriminant value criterion-evaluating unit 102j, and conceptually shows only the portion related to the present invention.
the discriminant value criterion discriminating unit 102j1 discriminates whether or not the cancer immunotherapy treatment is effective for the evaluation target based on the one or more discriminant values calculated by the discriminant value calculating unit 102i.
the evaluation target is classified into any one of a plurality of categories (ranks) defined in consideration of the possibility of being effective.
the discriminant value criterion discriminating unit 102j1 compares the one or more discriminant values calculated by the discriminant value calculating unit 102i with one or more threshold values (cut-off values) to be evaluated. In contrast, whether cancer immunotherapy treatment is effective or not, and evaluate to any one of multiple categories (ranks) defined taking into consideration the degree of possibility that the treatment is effective Or classify objects.
the result output unit 102k outputs the processing results in each processing unit of the control unit 102 (evaluation results in the discrimination value criterion evaluation unit 102j (specifically, discrimination results or classification in the discrimination value criterion discrimination unit 102j1). Including the result) is output to the output device 114.
the transmission unit 102m transmits, for example, a discrimination value, an evaluation result (eg, a discrimination result, a classification result, etc.) to the client device 200 that is a transmission source of the amino acid concentration data to be evaluated, or a database device 400.
a multivariate discriminant created by the cancer immunotherapy evaluation apparatus 100, an evaluation result (for example, a discrimination result, a classification result, etc.), etc. are transmitted.
FIG. 19 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 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 communicably connected via an arbitrary communication path. Has been.
the control unit 210 includes a web browser 211, an electronic mailer 212, a reception unit 213, and a transmission unit 214.
the web browser 211 performs browse processing for interpreting the web data and displaying the interpreted web data on a monitor 261 described later.
the Web browser 211 may be plugged in with various software such as a stream player having a function of receiving, displaying, and feeding back a stream video.
the electronic mailer 212 transmits and receives electronic mail according to a predetermined communication protocol (for example, SMTP (Simple Mail Transfer Protocol), POP3 (Post Office Protocol version 3), etc.).
SMTP Simple Mail Transfer Protocol
POP3 Post Office Protocol version 3
the receiving unit 213 (corresponding to an example of the result acquisition unit of the present invention), such as a discrimination value and an evaluation result (for example, a discrimination result, a classification result, etc.) transmitted from the cancer immunotherapy evaluation apparatus 100 via the communication IF 280 Receive various information.
the client device has a function of acquiring various information such as a discrimination value and an evaluation result.
the transmission unit 214 sends various information such as amino acid concentration data to be evaluated (specifically, pre-treatment amino acid concentration data, post-treatment amino acid concentration data, etc.) to the cancer immunotherapy evaluation apparatus 100 via the communication IF 280. Send.
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, TA, or router and a telephone line, or via a dedicated line.
the client apparatus 200 can access the cancer immunotherapy evaluation apparatus 100 according to a predetermined communication protocol.
an information processing device for example, a known personal computer, workstation, home game device, Internet TV, PHS terminal, portable terminal, mobile object
peripheral devices such as a printer, a monitor, and an image scanner as necessary.
the client device 200 may be realized by installing software (including programs, data, and the like) that realizes a Web data browsing function and an e-mail function in a communication terminal / information processing terminal such as a PDA).
control unit 210 of the client device 200 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 210.
the ROM 220 or the HD 230 stores computer programs for giving instructions to the CPU and performing various processes in cooperation with an OS (Operating System).
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.
the network 300 has a function of connecting the cancer immunotherapy evaluation apparatus 100, the client apparatus 200, and the database apparatus 400 so that they can communicate with each other, such as the Internet, an intranet, or a LAN (including both wired and wireless).
the network 300 includes a VAN, a personal computer communication network, a public telephone network (including both analog / digital), a dedicated line network (including both analog / digital), a CATV network, and a mobile line switching network.
mobile packet switching network including IMT2000 system, GSM (registered trademark) system or PDC / PDC-P system
wireless paging network including local wireless network such as Bluetooth (registered trademark)
PHS network including CS, BS or ISDB
satellite A communication network including CS, BS or ISDB
FIG. 20 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 device 400 is a cancer immunotherapy evaluation device 100 or cancer state information used when creating a multivariate discriminant in the database device, a multivariate discriminant created in the cancer immunotherapy evaluation device 100, and a cancer immunotherapy evaluation device.
100 has a function of storing an evaluation result (specifically, a discrimination result) obtained in 100.
the database device 400 includes a control unit 402 such as a CPU that comprehensively controls the database device, a communication device 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. And an output interface unit 408. These units are communicably connected 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.
the output device 414 in addition to a monitor (including a home TV), a speaker or a printer can be used as the output device 414 (hereinafter, the output device 414 may be described as the monitor 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 (Operating System), a program that defines various processing procedures, and necessary data, and performs various information processing based on these programs. Execute. As shown in the figure, the control unit 402 is roughly divided into a request interpreting unit 402a, a browsing processing unit 402b, an authentication processing unit 402c, an e-mail generating unit 402d, a Web page generating unit 402e, and a transmitting unit 402f.
a control program such as an OS (Operating System)
OS Operating System
the request interpretation unit 402a interprets the request content from the cancer immunotherapy evaluation apparatus 100, and passes the processing to each unit of the control unit 402 according to the interpretation result.
the browsing processing unit 402b Upon receiving browsing requests for various screens from the cancer immunotherapy evaluation apparatus 100, the browsing processing unit 402b generates and transmits Web data for these screens.
the authentication processing unit 402c receives an authentication request from the cancer immunotherapy evaluation device 100 and makes an authentication determination.
the e-mail generation unit 402d generates an e-mail including various types of information.
the web page generation unit 402e generates a web page that the user browses on the client device 200.
the transmission unit 402f transmits various types of information such as cancer state information and multivariate discriminants to the cancer immunotherapy evaluation apparatus 100.
FIG. 21 is a flowchart illustrating an example of a cancer immunotherapy evaluation service process according to the second embodiment.
the amino acid concentration data used in this processing is a specialist who uses blood (including plasma, serum, etc.) collected in advance from an individual such as an animal or human, for example, using a measurement method such as (A) or (B) below. Is related to the concentration value of amino acids obtained by analysis or independent analysis.
the unit of amino acid concentration may be obtained by, for example, molar concentration, weight concentration, or by adding / subtracting / subtracting an arbitrary constant to / from these concentrations.
Plasma was separated from blood by centrifuging the collected blood sample. All plasma samples were stored frozen at ⁇ 80 ° C. until the measurement of amino acid concentration.
amino acid concentration measurement For amino acid concentration measurement, acetonitrile was added to remove protein, followed by precolumn derivatization using a labeling reagent (3-aminopyridyl-N-hydroxysuccinimidyl carbamate), and liquid chromatography mass spectrometry The amino acid concentration was analyzed by a total (LC / MS) (see International Publication No. 2003/069328 and International Publication No. 2005/116629).
Plasma was separated from blood by centrifuging the collected blood sample. All plasma samples were stored frozen at ⁇ 80 ° C. until the measurement of amino acid concentration.
amino acid concentration When measuring the amino acid concentration, sulfosalicylic acid was added to remove the protein, and then the amino acid concentration was analyzed by an amino acid analyzer based on the post-column derivatization method using a ninhydrin reagent.
the client apparatus 200 causes the cancer immunotherapy evaluation apparatus to be displayed. 100 is accessed. Specifically, when the user instructs to update the screen of the Web browser 211 of the client device 200, the Web browser 211 uses the predetermined communication protocol to evaluate the cancer immunotherapy evaluation according to a predetermined communication protocol. By transmitting to the apparatus 100, a transmission request for a Web page corresponding to the amino acid concentration data transmission screen is made to the cancer immunotherapy evaluation apparatus 100 by routing based on the address.
an address such as URL
the cancer immunotherapy evaluation device 100 receives the transmission from the client device 200 at the request interpretation unit 102a, analyzes the content of the transmission, and moves the processing to each unit of the control unit 102 according to the analysis result.
the cancer immunotherapy evaluation apparatus 100 is a predetermined storage area of the storage unit 106 mainly in the browsing processing unit 102b. Web data for displaying the Web page stored in is acquired, and the acquired Web data is transmitted to the client device 200.
the cancer immunotherapy evaluation device 100 when there is a transmission request for a Web page corresponding to the amino acid concentration data transmission screen from the user, the cancer immunotherapy evaluation device 100 first uses the control unit 102 to check the user ID and the user password. Ask the user for input. Then, when the user ID and password are input, the cancer immunotherapy evaluation device 100 causes the authentication processing unit 102c to input the input user ID and password and the user ID stored in the user information file 106a. Make authentication with user password. The cancer immunotherapy evaluation apparatus 100 transmits Web data for displaying a Web page corresponding to the amino acid concentration data transmission screen to the client apparatus 200 by the browsing processing unit 102b only when authentication is possible. The client device 200 is identified by the IP address transmitted from the client device 200 together with the transmission request.
the client device 200 receives the Web data (for displaying a Web page corresponding to the amino acid concentration data transmission screen) transmitted from the cancer immunotherapy evaluation device 100 by the receiving unit 213, and receives the received Web data. Is interpreted by the Web browser 211, and the amino acid concentration data transmission screen is displayed on the monitor 261.
the client device 200 transmits input information and an identifier for specifying selection items to the cancer immunotherapy evaluation apparatus 100, so that the amino acid concentration data before starting treatment and the amino acid concentration data after starting treatment are evaluated for cancer immunotherapy. It transmits to the apparatus 100 (step SA21).
the transmission of amino acid concentration data in step SA21 may be realized by an existing file transfer technique such as FTP.
the cancer immunotherapy evaluation device 100 interprets the request contents of the client device 200 by interpreting the identifier transmitted from the client device 200 by the request interpretation unit 102a, and whether the cancer immunotherapy treatment is effective.
a request for transmission of a multivariate discriminant for determining whether or not is sent to the database apparatus 400.
the database apparatus 400 interprets the transmission request from the cancer immunotherapy evaluation apparatus 100 by the request interpretation unit 402a and stores the multivariate discriminant (for example, the updated latest data) stored in a predetermined storage area of the storage unit 406. Is transmitted to the cancer immunotherapy evaluation apparatus 100 (step SA22). Specifically, in step SA22, at least one of Val, Ile, Leu, His, Phe, Trp, Gln, Glu, Asp, Orn, Ala, Ser, Thr, Met, Lys, Arg, Gly, Cys2, and Pro. A multivariate discriminant including one as a variable is transmitted to the cancer immunotherapy evaluation apparatus 100.
the cancer immunotherapy evaluation apparatus 100 uses the receiving unit 102f to determine the pre-treatment amino acid concentration data and post-treatment amino acid concentration data transmitted from the client device 200, and the multivariate discrimination transmitted from the database device 400.
the received pre-treatment amino acid concentration data and post-treatment amino acid concentration data are stored in a predetermined storage area of the amino acid concentration data file 106b, and the received multivariate discriminant is stored in the multivariate discriminant file 106e4.
the data is stored in a predetermined storage area (step SA23).
the controller 102 removes data such as missing values and outliers from the pre-treatment amino acid concentration data and post-treatment amino acid concentration data of the individual received in step SA23 (step S23). SA24).
the cancer immunotherapy evaluation apparatus 100 includes the discriminant value calculation unit 102i in the pre-treatment amino acid concentration data and the post-treatment amino acid concentration data of the individual from which data such as a missing value and an outlier have been removed in step SA24.
At least one concentration value of Val, Ile, Leu, His, Phe, Trp, Gln, Glu, Asp, Orn, Ala, Ser, Thr, Met, Lys, Arg, Gly, Cys2, Pro and in step SA23 Multivariate including at least one of received Val, Ile, Leu, His, Phe, Trp, Gln, Glu, Asp, Orn, Ala, Ser, Thr, Met, Lys, Arg, Gly, Cys2, Pro as a variable
one or more discriminant values eg, pre-treatment start Discriminant value obtained by substituting noic acid concentration data into a multivariate discriminant (discriminant value before treatment start), discriminant value obtained by substituting amino acid concentration data after treatment start
the cancer immunotherapy evaluation apparatus 100 compares the discriminant value calculated in step SA25 with a preset threshold value in the discriminant value criterion discriminating unit 102j1, so that treatment by cancer immunotherapy is effective for the individual. And the determination result is stored in a predetermined storage area of the evaluation result file 106g (step SA26).
the cancer immunotherapy evaluation apparatus 100 uses the transmission unit 102m to send the determination result obtained in step SA26 (which may include the determination value calculated in step SA25) to the client apparatus 200 that is the transmission source of amino acid concentration data.
the data is transmitted to the database device 400 (step SA27).
the cancer immunotherapy evaluation apparatus 100 creates a web page for displaying the discrimination result in the web page generation unit 102e, and stores Web data corresponding to the created web page in the storage unit 106. Stored in the storage area.
the client device 200 issues a request for browsing the Web page to the cancer immunotherapy evaluation device 100. Send to.
the browsing processing unit 102b interprets the browsing request transmitted from the client device 200, and stores Web data corresponding to the Web page for displaying the determination result in the storage unit 106. Read from storage area. Then, the cancer immunotherapy evaluation device 100 transmits the read Web data to the client device 200 and transmits the Web data or the determination result to the database device 400 by the transmission unit 102m.
the cancer immunotherapy evaluation apparatus 100 may notify the user client apparatus 200 of the determination result by e-mail at the control unit 102. Specifically, the cancer immunotherapy evaluation apparatus 100 first refers to the user information stored in the user information file 106a based on the user ID or the like in the e-mail generation unit 102d according to the transmission timing, Get the user's email address. Next, the cancer immunotherapy evaluation apparatus 100 uses the e-mail generation unit 102d to generate data related to the e-mail including the name and discrimination result of the user with the acquired e-mail address as the destination. Next, the cancer immunotherapy evaluation apparatus 100 transmits the generated data to the user client apparatus 200 by the transmission unit 102m.
the cancer immunotherapy evaluation apparatus 100 may transmit the determination result to the user client apparatus 200 using an existing file transfer technique such as FTP.
control unit 402 receives the discrimination result or Web data transmitted from the cancer immunotherapy evaluation device 100, and stores the received discrimination result or Web data in a predetermined unit of the storage unit 406. Save (accumulate) in the storage area (step SA28).
the client device 200 receives the Web data transmitted from the cancer immunotherapy evaluation device 100 by the receiving unit 213, interprets the received Web data by the Web browser 211, and the Web page on which the individual determination result is written. Is displayed on the monitor 261 (step SA29).
the client apparatus 200 arbitrarily selects the e-mail transmitted from the cancer immunotherapy evaluation apparatus 100 by a known function of the e-mailer 212.
the received e-mail is displayed on the monitor 261.
the user can check the determination result by browsing the Web page displayed on the monitor 261.
the user may print the display content of the Web page displayed on the monitor 261 with the printer 262.
the user can check the discrimination result by browsing the e-mail displayed on the monitor 261.
the user may print the content of the e-mail displayed on the monitor 261 with the printer 262.
a cancer immunotherapy evaluation device a cancer immunotherapy evaluation method, a cancer immunotherapy evaluation program, a recording medium, a cancer immunotherapy evaluation system, and an information communication terminal device according to the present invention are claimed in addition to the second embodiment described above.
the present invention may be implemented in various different embodiments within the scope of the technical idea described in the above.
each illustrated component is functionally conceptual and does not necessarily need to be physically configured as illustrated.
the processing functions provided in the cancer immunotherapy evaluation apparatus 100 is interpreted and executed by a CPU (Central Processing Unit) and the CPU. It may be realized by a program or hardware based on 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 cancer immunotherapy evaluation method according to the present invention. It is mechanically read by the immunotherapy evaluation apparatus 100. That is, in the storage unit 106 such as a ROM or an HDD, computer programs for performing various processes by giving instructions to the CPU in cooperation with an OS (Operating System) are recorded. This computer program is executed by being loaded into the RAM, and constitutes a control unit in cooperation with the CPU.
OS Operating System
the computer program may be stored in an application program server connected to the cancer immunotherapy evaluation apparatus 100 via an arbitrary network, and may be downloaded in whole or in part as necessary. Is possible.
the cancer immunotherapy evaluation program according to the present invention may be stored in a non-temporary computer-readable recording medium, or may be configured as a program product.
the “recording medium” means a memory card, USB memory, SD card, flexible disk, magneto-optical disk, ROM, EPROM, EEPROM (registered trademark), CD-ROM, MO, DVD, and Blu-ray. (Registered trademark) It shall include any “portable physical medium” such as Disc.
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, but is distributed in the form of a plurality of modules and libraries, or in cooperation with a separate program typified by an OS (Operating System). Including those that achieve the function. 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 cancer immunotherapy 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 cancer immunotherapy evaluation apparatus 100 may be realized by installing software (including a program or data) that realizes the cancer immunotherapy evaluation method of the present invention in the information processing apparatus.
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.
FIG. 22 is a flowchart illustrating an example of multivariate discriminant creation processing.
the multivariate discriminant creation process may be performed by the database apparatus 400 that manages cancer state information.
the cancer immunotherapy evaluation device 100 stores cancer state information acquired in advance from the database device 400 in a predetermined storage area of the cancer state information file 106c.
the cancer immunotherapy evaluation apparatus 100 uses the cancer state index data specified in advance by the cancer state information specifying unit 102g (for example, data related to an index indicating a cancer state (for example, presence or absence of a therapeutic effect obtained based on the index). ), Or data relating to the amount of change in an indicator of cancer status (for example, a difference in tumor size) and amino acid concentration data (eg, data relating to amino acid concentration or data relating to the amount of change in amino acid concentration) , Etc.) is stored in a predetermined storage area of the designated cancer state information file 106d.
the cancer state index data specified in advance by the cancer state information specifying unit 102g for example, data related to an index indicating a cancer state (for example, presence or absence of a therapeutic effect obtained based on the index).
data relating to the amount of change in an indicator of cancer status for
the multivariate discriminant-preparing part 102h is a candidate multivariate discriminant-preparing part 102h1 based on a predetermined formula creation method from cancer state information stored in a predetermined storage area of the designated cancer state information file 106d. A multivariate discriminant is created, and the created candidate multivariate discriminant is stored in a predetermined storage area of the candidate multivariate discriminant file 106e1 (step SB21).
the multivariate discriminant-preparing part 102h is a candidate multivariate discriminant-preparing part 102h1, and a plurality of different formula creation methods (principal component analysis, discriminant analysis, support vector machine, multiple regression analysis, logistic regression) Analysis, k-means method, cluster analysis, decision tree, etc.
the multivariate discriminant-preparing part 102h is a candidate multivariate discriminant-preparing part 102h1, and executes various calculations (for example, average and variance) corresponding to the selected formula selection method based on the cancer state information. .
the multivariate discriminant-preparing part 102h determines the calculation result and parameters of the determined candidate multivariate discriminant-expression in the candidate multivariate discriminant-preparing part 102h1. Thereby, a candidate multivariate discriminant is created based on the selected formula creation method.
a candidate multivariate discriminant when created in parallel and in parallel by using a plurality of different formula creation methods, the above-described processing may be executed in parallel for each selected formula creation method.
the candidate multivariate discriminant when creating a candidate multivariate discriminant serially using a combination of multiple different formula creation methods, for example, transform cancer status information using a candidate multivariate discriminant created by performing principal component analysis. Then, the candidate multivariate discriminant may be created by performing discriminant analysis on the converted cancer state information.
the multivariate discriminant-preparing part 102h verifies (mutually verifies) the candidate multivariate discriminant created in step SB21 with the candidate multivariate discriminant-verifying part 102h2, and verifies the verification result.
the result is stored in a predetermined storage area of the verification result file 106e2 (step SB22).
the multivariate discriminant-preparing part 102h is a candidate multivariate discriminant-verifying part 102h2, based on the cancer state information stored in a predetermined storage area of the designated cancer state information file 106d.
the verification data used when verifying the formula is created, and the candidate multivariate discriminant is verified based on the created verification data.
the multivariate discriminant creation unit 102h creates each formula in the candidate multivariate discriminant verification unit 102h2.
Each candidate multivariate discriminant corresponding to the method is verified based on a predetermined verification method.
the discrimination rate, sensitivity, and specificity of the candidate multivariate discriminant based on at least one of the random sampling method, the bootstrap method, the holdout method, the N-fold method, the leave one-out method, etc. , Information criteria, ROC_AUC (area under the receiver characteristic curve), etc.
the multivariate discriminant-preparing part 102h creates a candidate multivariate discriminant by selecting a variable of the candidate multivariate discriminant based on a predetermined variable selection method in the variable selector 102h3.
a combination of amino acid concentration data included in the cancer state information to be used is selected, and cancer state information including the selected combination of amino acid concentration data is stored in a predetermined storage area of the selected cancer state information file 106e3 (step SB23).
step SB21 a plurality of candidate multivariate discriminants are created in combination with a plurality of different formula creation methods, and in step SB22, each candidate multivariate discriminant corresponding to each formula creation method is verified based on a predetermined verification method.
the multivariate discriminant-preparing part 102h selects a variable of the candidate multivariate discriminant based on a predetermined variable selection method for each candidate multivariate discriminant in the variable selector 102h3. Also good.
the variable of the candidate multivariate discriminant 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.
the best path method is a method of selecting variables by sequentially reducing the variables included in the candidate multivariate discriminant one by one and optimizing the evaluation index given by the candidate multivariate discriminant.
the multivariate discriminant-preparing part 102h selects a combination of amino acid concentration data based on the cancer state information stored in the predetermined storage area of the designated cancer state information file 106d by the variable selection part 102h3. May be.
the multivariate discriminant-preparing part 102h determines whether or not all combinations of amino acid concentration data included in the cancer state information stored in the predetermined storage area of the designated cancer state information file 106d have been completed. If the determination result is “end” (step SB24: Yes), the process proceeds to the next step (step SB25). If the determination result is not “end” (step SB24: No), the process proceeds to step SB21. Return. The multivariate discriminant-preparing part 102h determines whether or not the preset number of times has ended, and if the determination result is “end” (step SB24: Yes), the next step (step SB25). If the determination result is not “end” (step SB24: No), the process may return to step SB21.
the multivariate discriminant-preparing part 102h has a combination of amino acid concentration data included in the cancer state information stored in the predetermined storage area of the designated cancer state information file 106d as the combination of the amino acid concentration data selected in step SB23.
the multivariate discriminant creation unit 102h compares the evaluation value with a predetermined threshold corresponding to each formula creation method. Based on the result, it may be determined whether to proceed to step SB25 or to return to step SB21.
the multivariate discriminant-preparing part 102h selects a multivariate discriminant by selecting a candidate multivariate discriminant to be adopted as a multivariate discriminant from a plurality of candidate multivariate discriminants based on the verification result.
the determined multivariate discriminant (selected candidate multivariate discriminant) is stored in a predetermined storage area of the multivariate discriminant file 106e4 (step SB25).
step SB25 for example, when the optimum one is selected from candidate multivariate discriminants created by the same formula creation method, and when the optimum one is selected from all candidate multivariate discriminants There is.
FIGS. The horizontal axis of the graphs shown in FIG. 24 and FIG. 25 represents the number of days when the day of 5FU administration is day 0, and the vertical axis represents the average value of tumor size (mm 2 ).
FIG. 24 shows WT (responder) data and
FIG. 25 shows Nude (non-responder) data. 24 and 25 show a group with treatment and a group without treatment, respectively.
Measurement of amino acid concentration in plasma was performed by the measurement method (A) described in the above embodiment.
FIG. 26 and FIG. 27 show data on amino acid concentrations in plasma of WT mice. 26 and 27, the horizontal axis represents the pre-treatment start (pre) and the post-treatment start (post), and the vertical axis represents the average value of each amino acid concentration ( ⁇ M).
Pre pre-treatment start
post post
⁇ M average value of each amino acid concentration
FIG. 28 and FIG. 29 show data on amino acid concentrations in plasma of Nude mice. 28 and 29, the horizontal axis represents the pre-treatment start (pre) and the post-treatment start (post), and the vertical axis represents the average value of each amino acid concentration ( ⁇ M).
pre pre-treatment start
post post
⁇ M average value of each amino acid concentration
FIG. 30 shows a radar chart showing the distribution of each amino acid after the start of treatment, assuming that each amino acid before the start of treatment is 100% for WT (responder).
FIG. 31 shows a radar chart showing the distribution of each amino acid after the start of treatment when Nude (non-responder) is 100% of each amino acid before the start of treatment.
the amino acid profile change in WT and the amino acid profile change in Nude were different, and a characteristic amino acid profile change in plasma was clarified when an antitumor effect was obtained.
the amino acid variables Val, Leu, Ile, His, Phe, Trp, Gln, Asp, and Orn that were significantly different before and after the start of WT treatment were found to have the ability to discriminate the effects of cancer immunotherapy. It was also found that the amino acid variables Ala, Thr, Lys, and Pro that had a significant difference before and after the start of Nude treatment also had the ability to discriminate cancer immunotherapy effects.
the discrimination performance of 2-group discrimination before and after the start of treatment with each amino acid variable in WT mice was evaluated by the area under the ROC curve (ROC_AUC).
the AUC was greater than 0.7 for the amino acid variables Val, Leu, Ile, Lys, His, Phe, Trp, Gln, Asp, and Orn (FIGS. 32 and 33).
the amino acid variables Val, Leu, Ile, Lys, His, Phe, Trp, Gln, Asp, and Orn were found to have the ability to discriminate the effects of cancer immunotherapy.
a ratio (post / pre) obtained by dividing each plasma amino acid concentration after the start of treatment by each plasma amino acid concentration before the start of treatment was calculated, and changes related to the ratio Quantity data was obtained.
a multivariate discriminant (multivariate function) for determining the effect of cancer immunotherapy with the amino acid concentration in the plasma as a variable was determined, which was effective in determining the cancer immunotherapy effect. .
a combination of variables (4 or less) to be included in a fractional expression as a multivariate discriminant is searched from the following 22 types of amino acids, and the bootstrap method is used as cross validation Was used to search for a fractional expression that maximizes the ability to discriminate the effects of cancer immunotherapy.
22 kinds of amino acids are Ala, Arg, Asn, Asp, Cit, Cys2, Gln, Glu, Gly, His, Ile, Leu, Lys, Met, Orn, Phe, Pro, Ser, Thr, Trp, Tyr. , Val.
FIG. 34 shows a list of fractional expressions having a good discrimination ability that the value of ROC_AUC is 1.
FIG. 35 shows the frequency of appearance of variables in the formula included in FIG. The amino acid variables up to position 10 in the order of appearance frequency were Glu, Cys2, Trp, Asp, Orn, Phe, Val, Ile, Gly, and His. This revealed that these amino acid variables have the ability to discriminate the effects of cancer immunotherapy.
FIG. 36, 37, and 38 show a list of logistic regression equations with equally good discrimination ability evaluated by the value of ROC_AUC.
FIG. 36, FIG. 37, and FIG. 38 show the logistic regression equation (including variable combinations, coefficients, and constants) and the value of ROC_AUC with cross validation.
FIG. 39 shows the appearance frequency of variables in the expressions included in FIGS. 36, 37, and 38.
the amino acid variables up to position 10 in the order of appearance frequency were Trp, Thr, His, Arg, Ile, Pro, Phe, Met, Ala, and Lys. This revealed that these amino acid variables have the ability to discriminate the effects of cancer immunotherapy.
a difference (post-pre) was calculated by subtracting each plasma amino acid concentration after the start of treatment from each plasma amino acid concentration before the start of the treatment, and a change related to the difference. Quantity data was obtained. Using the obtained variation data, a multivariate discriminant (multivariate function) for determining the effect of cancer immunotherapy with the amino acid concentration in the plasma as a variable was determined, which was effective in determining the cancer immunotherapy effect. .
FIG. 40, FIG. 41 and FIG. 42 show a list of logistic regression equations with equally good discrimination ability evaluated by the value of ROC_AUC.
FIG. 40, FIG. 41, and FIG. 42 show the logistic regression equation (including variable combinations, coefficients, and constants) and the value of ROC_AUC with cross validation.
FIG. 43 shows the appearance frequency of variables in the expressions included in FIGS. 40, 41, and 42.
the amino acid variables up to position 10 in the order of appearance frequency were Trp, Asp, Ile, Thr, Arg, Pro, Ser, Leu, Met, and Lys. This revealed that these amino acid variables have the ability to discriminate the effects of cancer immunotherapy.
a ratio (post / pre) obtained by dividing each plasma amino acid concentration after the start of treatment by each plasma amino acid concentration before the start of treatment was calculated, and changes related to the ratio Quantity data was obtained.
a multivariate discriminant (multivariate function) for determining the effect of cancer immunotherapy with the amino acid concentration in the plasma as a variable was determined, which was effective in determining the cancer immunotherapy effect. .
FIGS. 44-48 shows a list of logistic regression equations with equally good discriminating ability evaluated by the value of ROC_AUC.
FIGS. 44 to 48 show the logistic regression equation (including variable combinations, coefficients, and constants) and the value of ROC_AUC with cross validation.
FIG. 49 shows the frequency of occurrence of variables in the expressions included in FIGS.
the amino acid variables up to position 10 in the order of appearance frequency were His, Thr, Lys, Phe, Arg, Ile, Met, Ser, Val, Asn. This revealed that these amino acid variables have the ability to discriminate the effects of cancer immunotherapy.
a difference (post-pre) was calculated by subtracting each plasma amino acid concentration after the start of treatment from each plasma amino acid concentration before the start of the treatment, and a change related to the difference. Quantity data was obtained. Using the obtained variation data, a multivariate discriminant (multivariate function) for determining the effect of cancer immunotherapy with the amino acid concentration in the plasma as a variable was determined, which was effective in determining the cancer immunotherapy effect. .
Figure 50-54 shows a list of logistic regression equations with equally good discriminating ability evaluated by the value of ROC_AUC.
50-54 show the logistic regression equation (including variable combinations, coefficients, and constants) and the value of ROC_AUC with cross validation.
FIG. 55 shows the appearance frequency of variables in the expressions included in FIGS.
the amino acid variables up to position 10 in the order of appearance frequency were Lys, Gln, Thr, Phe, Met, Pro, Ser, Ala, Asn, and Val. This revealed that these amino acid variables have the ability to discriminate the effects of cancer immunotherapy.
T cells were isolated from the spleen of 4T1 tumor-bearing mice, and anti-4T1 activated T cells were prepared by activating and proliferating in the presence of anti-CD3 antibody, anti-CD28 antibody and IL-2.
4T1 cells or CT26 cells were subcutaneously transplanted (1 ⁇ 10 6 cells / 100 ⁇ l) into 10 Balb / c mice, respectively, and the above-mentioned anti-4T1 activated T cells were transplanted from the tail vein 10 days after the transplantation.
tumor tissues of 4T1 tumor-bearing mice and CT26 tumor-bearing mice were excised and frozen sections were prepared, and then H / E staining, anti-CD4 antibody, anti-CD8 antibody and anti-F4 / 80 antibody (macrophage marker) ) Fluorescence immunostaining was performed (FIG. 57).
Measurement of amino acid concentration in plasma was performed by the measurement method (A) described in the above embodiment.
FIG. 58 and FIG. 58 and 59 Data of amino acid concentrations in plasma of 4T1 tumor-bearing mice are shown in FIG. 58 and FIG. 58 and 59, the horizontal axis represents the pre-treatment start (pre) and the post-treatment start (post), and the vertical axis represents the average value of each amino acid concentration ( ⁇ M).
pre pre-treatment start
post post
⁇ M average value of each amino acid concentration
FIGS. radar charts showing the distribution of each amino acid after the start of treatment when each amino acid before the start of treatment is taken as 100% are shown in FIGS.
the pattern of amino acid profiles was different.
the pattern of amino acid profile change in plasma differs depending on the presence or absence of antitumor effects.
Arg, His, Met, and Cys2 have the ability to discriminate the effects of cancer immunotherapy.
Ala, Thr, Pro among the amino acid variables that had a significant difference before and after the start of treatment of CT26 tumor-bearing mice also had the ability to discriminate cancer immunotherapy effects.
FIG. 66 shows a list of fractional expressions having good discrimination ability that the value of ROC_AUC is 1.
FIG. 67 shows the frequency of occurrence of variables in the formula included in FIG.
the amino acid variables up to position 10 in the order of appearance frequency were Pro, Glu, Orn, Arg, Gly, Ile, Thr, Trp, Ser, Tyr. This revealed that these amino acid variables have the ability to discriminate the effects of cancer immunotherapy.
FIG. 68, FIG. 69, and FIG. 70 show a list of logistic regression equations with equally good discrimination ability evaluated by the value of ROC_AUC.
FIG. 68, FIG. 69, and FIG. 70 show the logistic regression equation (including variable combinations, coefficients, and constants) and the value of ROC_AUC with cross validation.
FIG. 71 shows the frequency of occurrence of variables in the expressions included in FIGS.
the amino acid variables up to position 10 in the order of appearance frequency were Trp, His, Thr, Ile, Pro, Phe, Asn, Arg, Cys2, and Lys. This revealed that these amino acid variables have the ability to discriminate the effects of cancer immunotherapy.
Example 6 Based on the same plasma amino acid concentration data as in Example 6, a ratio (post-pre) obtained by dividing each plasma amino acid concentration after the start of treatment by each plasma amino acid concentration before the start of treatment was calculated, and a change related to the ratio Quantity data was obtained. Using the obtained variation data, a multivariate discriminant (multivariate function) for determining the effect of cancer immunotherapy with the amino acid concentration in the plasma as a variable was determined, which was effective in determining the cancer immunotherapy effect. .
FIG. 72, 73, and 74 show a list of logistic regression equations with equally good discrimination ability evaluated by the value of ROC_AUC.
FIG. 72, FIG. 73, and FIG. 74 show the logistic regression equation (including variable combinations, coefficients, and constants) and the value of ROC_AUC with cross validation.
FIG. 75 shows the appearance frequency of variables in the expressions included in FIGS. 72, 73, and 74.
the amino acid variables up to position 10 in the order of appearance frequency were Trp, His, Thr, Ile, Pro, Phe, Asn, Arg, Cys2, and Lys. This revealed that these amino acid variables have the ability to discriminate the effects of cancer immunotherapy.
FIG. 76-80 shows a logistic regression equation (including variable combinations, coefficients, and constants) and a value of ROC_AUC with cross validation.
Example 6 Based on the same plasma amino acid concentration data as in Example 6, a ratio (post-pre) obtained by dividing each plasma amino acid concentration after the start of treatment by each plasma amino acid concentration before the start of treatment was calculated, and a change related to the ratio Quantity data was obtained. Using the obtained variation data, a multivariate discriminant (multivariate function) for determining the effect of cancer immunotherapy with the amino acid concentration in the plasma as a variable was determined, which was effective in determining the cancer immunotherapy effect. .
FIGS. 81 to 85 show a list of logistic regression equations with discriminability evaluated by ROC_AUC value of 0.7 or more.
FIGS. 81 to 85 show the logistic regression equation (including variable combinations, coefficients, and constants) and the value of ROC_AUC with cross validation.
the cancer immunotherapy evaluation method and the like according to the present invention can be widely implemented in many industrial fields, particularly in the fields of pharmaceuticals, foods, and medicine, and in particular, the therapeutic effect of cancer immunotherapy. It is extremely useful in the field of bioinformatics for evaluating the above.

Landscapes

Life Sciences & Earth Sciences (AREA)
Health & Medical Sciences (AREA)
Engineering & Computer Science (AREA)
Molecular Biology (AREA)
Hematology (AREA)
Chemical & Material Sciences (AREA)
Urology & Nephrology (AREA)
Physics & Mathematics (AREA)
Immunology (AREA)
Biomedical Technology (AREA)
Biophysics (AREA)
Food Science & Technology (AREA)
Biotechnology (AREA)
Proteomics, Peptides & Aminoacids (AREA)
Microbiology (AREA)
Bioinformatics & Computational Biology (AREA)
Bioinformatics & Cheminformatics (AREA)
Cell Biology (AREA)
Medicinal Chemistry (AREA)
Analytical Chemistry (AREA)
Biochemistry (AREA)
General Health & Medical Sciences (AREA)
General Physics & Mathematics (AREA)
Pathology (AREA)
Investigating Or Analysing Biological Materials (AREA)

PCT/JP2013/061807 2012-05-11 2013-04-22 Procédé d'évaluation d'immunothérapie anticancéreuse, dispositif d'évaluation d'immunothérapie anticancéreuse, programme d'évaluation d'immunothérapie anticancéreuse, système d'évaluation d'immunothérapie anticancéreuse et dispositif de terminal de communication d'informations Ceased WO2013168550A1 (fr)

Priority Applications (1)

Application Number	Priority Date	Filing Date	Title
JP2014514434A JP6375947B2 (ja)	2012-05-11	2013-04-22	取得方法、評価装置、評価方法、評価プログラム、評価システムおよび端末装置

Applications Claiming Priority (2)

Application Number	Priority Date	Filing Date	Title
JP2012109920		2012-05-11
JP2012-109920		2012-05-11

Publications (1)

Publication Number	Publication Date
WO2013168550A1 true WO2013168550A1 (fr)	2013-11-14

Family

ID=49550601

Family Applications (1)

Application Number	Title	Priority Date	Filing Date
PCT/JP2013/061807 Ceased WO2013168550A1 (fr)	2012-05-11	2013-04-22	Procédé d'évaluation d'immunothérapie anticancéreuse, dispositif d'évaluation d'immunothérapie anticancéreuse, programme d'évaluation d'immunothérapie anticancéreuse, système d'évaluation d'immunothérapie anticancéreuse et dispositif de terminal de communication d'informations

Country Status (2)

Country	Link
JP (1)	JP6375947B2 (fr)
WO (1)	WO2013168550A1 (fr)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number	Priority date	Publication date	Assignee	Title
JPWO2018003638A1 (ja) *	2016-06-30	2019-04-18	味の素株式会社	分娩後ケトーシスの評価方法
KR20190089895A (ko)	2016-12-01	2019-07-31	아지노모토 가부시키가이샤	암 모니터링의 방법, 산출 방법, 평가 장치, 산출 장치, 평가 프로그램, 산출 프로그램, 평가 시스템, 및 단말 장치
CN110763806A (zh) *	2019-10-25	2020-02-07	三只松鼠股份有限公司	一种用于评定鸭脖辣味等级的方法
KR20220093122A (ko)	2019-11-08	2022-07-05	아지노모토 가부시키가이샤	면역 체크 포인트 저해제의 약리 작용의 평가 방법, 산출 방법, 평가 장치, 산출 장치, 평가 프로그램, 산출 프로그램, 기록 매체, 평가 시스템 및 단말 장치

Citations (4)

* Cited by examiner, † Cited by third party
Publication number	Priority date	Publication date	Assignee	Title
JP2007523346A (ja) *	2004-02-19	2007-08-16	イェールユニバーシティ	プロテオーム技術を使用した癌タンパク質バイオマーカーの同定
WO2008075664A1 (fr) *	2006-12-21	2008-06-26	Ajinomoto Co., Inc.	Procédé, appareil, procédé, système et logiciel d'évaluation du cancer, et support d'enregistrement
WO2010119003A1 (fr) *	2009-04-17	2010-10-21	Transgene Sa	Biomarqueur pour surveiller des patients
JP2011058949A (ja) *	2009-09-09	2011-03-24	Hitachi Ltd	粥状硬化性動脈硬化のマーカー因子と用途

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number	Priority date	Publication date	Assignee	Title
AU2003289263A1 (en) *	2002-12-09	2004-06-30	Ajinomoto Co., Inc.	Organism condition information processor, organism condition information processing method, organism condition information managing system, program, and recording medium

2013
- 2013-04-22 WO PCT/JP2013/061807 patent/WO2013168550A1/fr not_active Ceased
- 2013-04-22 JP JP2014514434A patent/JP6375947B2/ja active Active

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number	Priority date	Publication date	Assignee	Title
JP2007523346A (ja) *	2004-02-19	2007-08-16	イェールユニバーシティ	プロテオーム技術を使用した癌タンパク質バイオマーカーの同定
WO2008075664A1 (fr) *	2006-12-21	2008-06-26	Ajinomoto Co., Inc.	Procédé, appareil, procédé, système et logiciel d'évaluation du cancer, et support d'enregistrement
WO2010119003A1 (fr) *	2009-04-17	2010-10-21	Transgene Sa	Biomarqueur pour surveiller des patients
JP2011058949A (ja) *	2009-09-09	2011-03-24	Hitachi Ltd	粥状硬化性動脈硬化のマーカー因子と用途

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number	Priority date	Publication date	Assignee	Title
JPWO2018003638A1 (ja) *	2016-06-30	2019-04-18	味の素株式会社	分娩後ケトーシスの評価方法
JP7103220B2 (ja)	2016-06-30	2022-07-20	味の素株式会社	分娩後ケトーシスの評価方法
US12282024B2 (en)	2016-06-30	2025-04-22	Ajinomoto Co., Inc.	Evaluating method of ketosis in postpartum dairy cows
KR20190089895A (ko)	2016-12-01	2019-07-31	아지노모토 가부시키가이샤	암 모니터링의 방법, 산출 방법, 평가 장치, 산출 장치, 평가 프로그램, 산출 프로그램, 평가 시스템, 및 단말 장치
CN110763806A (zh) *	2019-10-25	2020-02-07	三只松鼠股份有限公司	一种用于评定鸭脖辣味等级的方法
KR20220093122A (ko)	2019-11-08	2022-07-05	아지노모토 가부시키가이샤	면역 체크 포인트 저해제의 약리 작용의 평가 방법, 산출 방법, 평가 장치, 산출 장치, 평가 프로그램, 산출 프로그램, 기록 매체, 평가 시스템 및 단말 장치
KR102875963B1 (ko)	2019-11-08	2025-10-24	아지노모토 가부시키가이샤	면역 체크 포인트 저해제의 약리 작용의 평가 방법, 산출 방법, 평가 장치, 산출 장치, 평가 프로그램, 산출 프로그램, 기록 매체, 평가 시스템 및 단말 장치

Also Published As

Publication number	Publication date
JPWO2013168550A1 (ja)	2016-01-07
JP6375947B2 (ja)	2018-08-22

Publication	Publication Date	Title
JP5746811B2 (ja)	2015-07-08	大腸癌の評価方法、ならびに大腸癌評価装置、大腸癌評価方法、大腸癌評価システム、大腸癌評価プログラムおよび記録媒体
JP6586980B2 (ja)	2019-10-09	取得方法、算出方法、女性生殖器癌評価装置、算出装置、女性生殖器癌評価プログラム、算出プログラム及び女性生殖器癌評価システム
JP6299063B2 (ja)	2018-03-28	膵臓癌リスク疾患の評価のための方法、膵臓癌リスク疾患評価装置、膵臓癌リスク疾患評価方法、膵臓癌リスク疾患評価プログラムおよび膵臓癌リスク疾患評価システム
JP2014025946A (ja)	2014-02-06	癌の評価方法、ならびに癌評価装置、癌評価方法、癌評価システム、癌評価プログラムおよび記録媒体
JP5746810B2 (ja)	2015-07-08	乳癌の評価方法、ならびに乳癌評価装置、乳癌評価方法、乳癌評価システム、乳癌評価プログラムおよび記録媒体
JP5817527B2 (ja)	2015-11-18	肥満の評価方法、肥満評価装置、肥満評価方法、肥満評価システム、肥満評価プログラム、記録媒体、および情報通信端末装置
US20210287802A1 (en)	2021-09-16	Method for evaluating pancreatic cancer, pancreatic cancer evaluating apparatus, pancreatic cancer evaluating method, pancreatic cancer evaluating program product, pancreatic cancer evaluating system and information communication terminal apparatus
WO2008016101A1 (fr)	2008-02-07	Procédé d'évaluation de stress, appareil d'évaluation de stress, système d'évaluation de stress, programme d'évaluation de stress et support d'enregistrement associé
JP6375947B2 (ja)	2018-08-22	取得方法、評価装置、評価方法、評価プログラム、評価システムおよび端末装置
JP6614215B2 (ja)	2019-12-04	取得方法、算出方法、脳卒中評価装置、算出装置、脳卒中評価プログラム、算出プログラムおよび脳卒中評価システム
JP2022066387A (ja)	2022-04-28	取得方法、算出方法、評価装置、算出装置、評価プログラム、算出プログラム、評価システム
JP2014044213A (ja)	2014-03-13	評価方法
JP7506646B2 (ja)	2024-06-26	取得方法、算出方法、評価装置、算出装置、評価プログラム、算出プログラム、及び評価システム
KR102477319B1 (ko)	2022-12-15	암 모니터링의 방법, 산출 방법, 평가 장치, 산출 장치, 평가 프로그램, 산출 프로그램, 평가 시스템, 및 단말 장치
WO2021090941A1 (fr)	2021-05-14	Procédé d'évaluation, procédé de calcul, dispositif d'évaluation, dispositif de calcul, programme d'évaluation, programme de calcul, support d'enregistrement, système d'évaluation et dispositif terminal pour une action pharmacologique d'inhibiteur de point de contrôle immunitaire
JP2011080957A (ja)	2011-04-21	線維筋痛症とうつ病の評価方法、線維筋痛症・うつ病評価装置および線維筋痛症・うつ病評価方法
JP7093162B2 (ja)	2022-06-29	取得方法、算出方法、評価装置、算出装置、評価プログラム、算出プログラム、および評価システム
JP2017122655A (ja)	2017-07-13	骨格筋面積の評価方法
JP2018100963A (ja)	2018-06-28	組織内アミノ酸プロファイルによるがん患者の予後評価方法、算出方法、評価装置、算出装置、評価プログラム、算出プログラム、評価システム、及び端末装置

Legal Events

Date	Code	Title	Description
2014-01-01	121	Ep: the epo has been informed by wipo that ep was designated in this application	Ref document number: 13787646 Country of ref document: EP Kind code of ref document: A1
2014-09-17	ENP	Entry into the national phase	Ref document number: 2014514434 Country of ref document: JP Kind code of ref document: A
2014-11-11	NENP	Non-entry into the national phase	Ref country code: DE
2015-06-03	122	Ep: pct application non-entry in european phase	Ref document number: 13787646 Country of ref document: EP Kind code of ref document: A1