WO2021241527A1 - 非小細胞肺がんの化学療法の効果を予測するための情報を提供する方法および情報提供用キット、非小細胞肺がんの化学療法の効果を予測する方法、非小細胞肺がんの化学療法の効果を予測する予測装置、予測装置のプログラムおよび記録媒体 - Google Patents

非小細胞肺がんの化学療法の効果を予測するための情報を提供する方法および情報提供用キット、非小細胞肺がんの化学療法の効果を予測する方法、非小細胞肺がんの化学療法の効果を予測する予測装置、予測装置のプログラムおよび記録媒体 Download PDF

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WO2021241527A1
WO2021241527A1 PCT/JP2021/019684 JP2021019684W WO2021241527A1 WO 2021241527 A1 WO2021241527 A1 WO 2021241527A1 JP 2021019684 W JP2021019684 W JP 2021019684W WO 2021241527 A1 WO2021241527 A1 WO 2021241527A1
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chemotherapy
abundance
protein
lung cancer
small cell
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English (en)
French (fr)
Japanese (ja)
Inventor
聖 柳澤
哲成 長谷
昌弘 中杤
好規 長谷川
征史 近藤
昌彦 安藤
隆 高橋
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Tokai National Higher Education and Research System NUC
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/575—Immunoassay; Biospecific binding assay; Materials therefor for cancer

Definitions

  • the disclosure in this application is a method and information kit for providing information for predicting the effect of chemotherapy for non-small cell lung cancer, a method for predicting the effect of chemotherapy for non-small cell lung cancer, and a method for predicting the effect of chemotherapy for non-small cell lung cancer. It relates to a predictor, a program of the predictor, and a recording medium for predicting the effect of chemotherapy.
  • Chemotherapy unlike molecular-targeted therapy, has no criteria for treatment selection such as gene mutation. Therefore, a method is known to measure the level of carcinogenic protein in the lysate of cancer cells isolated from a subject and predict the responsiveness of chemotherapy before chemotherapy (administration of anticancer drug). (See Patent Document 1).
  • Patent Document 1 measures the level or activation state of the carcinogenic fusion protein contained in the sample collected from the subject. Therefore, there is a problem that the measurement procedure is complicated. It is desired to identify a biomarker (index) that can predict the effect of chemotherapy for lung cancer, especially non-small cell lung cancer, which has a high proportion, by a simpler method.
  • the disclosure in this application is a method and information kit for providing information for predicting the effect of chemotherapy for non-small cell lung cancer, a method for predicting the effect of chemotherapy for non-small cell lung cancer, and non-small cell lung cancer.
  • the present invention relates to a predictor, a program of the predictor, and a recording medium for predicting the effect of chemotherapy for small cell lung cancer.
  • a method for providing information for predicting the effect of chemotherapy for non-small cell lung cancer is A step of measuring the abundance of at least Nucleolar protein 58 (Accession: Q9Y2X3) among the proteins in the biological sample of the subject. Including, how.
  • the step of measuring the abundance measures the abundance of protein1 and protein2.
  • the step of measuring the abundance is to measure the abundance of products1 to protein3.
  • the step of measuring the abundance is to measure the abundance of products 1 to 11.
  • the method according to (2) above. (6) The step of measuring the abundance measures the abundance of products1 to 36.
  • Chemotherapy (A) Carboplatin and pemetrexed, (B) Pemetrexed and cisplatin, It is one of the combination therapies selected from The method according to any one of (1) to (6) above.
  • (8) A method for predicting the effect of chemotherapy for non-small cell lung cancer. The method is In the biological sample of the subject, a prediction model constructed in advance based on the abundance of the protein according to any one of (1) to (7) above expressed in the biological sample of a non-small cell lung cancer patient was used.
  • the abundance of protein expressed in the biological sample of the subject (hereinafter, may be referred to as "the amount of protein of the subject") is determined.
  • Comprehensive protein abundance obtained by analyzing a sample by mass analysis or the like can be mentioned.
  • only the abundance of the protein used as an index when constructing the prediction model may be used as the protein amount of the subject.
  • subject includes those who do not have non-small cell lung cancer. In that case, when the protein of the subject is analyzed, it is assumed that the protein used as an index when constructing the prediction model does not exist. Therefore, in the present specification, when the description "abundance of protein expressed in the biological sample of the subject" is described, the case where the abundance is zero is also included.
  • the prediction model is not particularly limited as long as the effect of chemotherapy can be predicted by comparing it with the amount of protein in the subject.
  • it may be only the data showing the abundance of the protein as an index, or it may be a discriminant created by using statistical means.
  • a threshold value may be further created as necessary, the amount of protein of the subject may be applied to the discriminant formula to calculate a score, and the effect of chemotherapy may be predicted by comparing with the threshold value.
  • the embodiment of the information providing method is -A step of measuring the abundance of at least protein1 among the proteins in the biological sample of the subject, including.
  • the step of measuring the abundance is not particularly limited as long as the abundance of protein1 can be measured, and may be a combination of at least one proteinin and protein1 selected from the above-mentioned protein2 to 36.
  • the input unit 2 is not particularly limited as long as information regarding the amount of protein of the subject can be input to the prediction device 1, and examples thereof include a keyboard and USB. Further, the input unit 2 may use an internet line. For example, by transmitting and inputting information on the amount of subject protein measured at a remote hospital using an internet line to the prediction device 1 and sending the prediction result via the internet line, the subject at a remote hospital The effect of appropriate chemotherapy can be predicted.
  • ⁇ patient ⁇ The predictive model was created for patients who met the registration criteria shown below. As shown in FIG. 1, blood was collected from 249 patients prior to the start of chemotherapy. (1) Cases diagnosed as non-flat epithelial non-small cell lung cancer by histology or cytology (2) Cases of clinical stages IIIA, IIIB, IV that are not subject to radical irradiation or surgical resection (3) Chemotherapy Cases without therapy (However, patients with postoperative adjuvant chemotherapy can be registered if there is an interval of 6 months or more after the last administration).
  • Serum serum (sample) was separated by a routine method from blood collected before chemotherapy of patients for training data.
  • the expressed proteins were comprehensively analyzed from the separated sera using a mass spectrometer (5600 manufactured by Siex Co., Ltd.). Information on the abundance of the analyzed protein and the effect of chemotherapy was associated.
  • FIG. 6 is a graph showing an error rate when 1 to 483 proteins are selected.
  • Error rate means (the number of examples of the number of evaluation samples that were incorrect) / (the total number of evaluation samples), and the lower the Error rate, the more preferable.
  • selecting 1 to 483 proteins means that the proteins were selected in descending order of the number of selections. For example, selecting 1 to 36 proteins means a combination of the above M models, respectively.
  • the correct answer rate was relatively high even when only protein 1 was used, but for example, there are at least a combination of protein 1 to 11 shown in the 11 models in which the Error rate is 10% or less (correct answer rate is 90% or more).
  • the amount may be measured and the number of protein combinations may be increased as needed.
  • the Error rate showed the smallest value (about 4.58%) in the case of the 36 proteins (proteins 1 to 36) shown in the 36 model, so in the following examples, the 36 proteins shown in the 36 model A predictive model was created using proteins.
  • the created prediction model (discriminant) is shown below.
  • S2Ns1x (score of protein1-borders1) + S2Ns2x (score of protein2-borders2) + ...
  • S2Ns35x (score of protein35-borders35) + S2Ns36x (score of protein36-borders36)
  • the risk score of each sample was calculated.
  • the risk score can be calculated by performing the same calculation.
  • the threshold value may be appropriately set based on the calculated risk score. For example, in Example 2 described later, the threshold value is set to 0, the score is ⁇ 0: good, and the score is ⁇ 0: better, but other values may be used.
  • the correct answer rate (predicted to have a therapeutic effect) was 93.2% in the good group and 0% in the poor group, so the sensitivity was 93.2% and the specificity was 100%.
  • the overall classification accuracy (overall specification accuracy) was 95.8%.
  • FIG. 7 shows the ROC curve of the “good group patient” vs. the “poor group patient”, and the AUC (area under the curve: area under the concentration curve) was a very high value of 0.991.
  • Example 2 [Contrast with verification group] As described above, since the prediction model prepared with the 36 proteins shown in Table 16 had high sensitivity and specificity, a comparison with the validation cohort was performed.
  • the line graphs (Good and Poor) in FIG. 8 represent Kaplan-Meier curves. As is clear from FIG. 8, the overall survival time of the patients classified in the good group was longer than the overall survival time of the patients classified in the poor group. The median overall survival of 50% (0.5 on the vertical axis) was 25.7 months in the good group and 4.6 months in the poor group, which was about 5.6 times longer.
  • the patient information shown in Table 19 below more specifically, age (75 years old or older vs. less than 75 years old), gender (male vs female), general condition of the patient "Performance Status: PS" (0/1 vs 2),. Cox regression analysis was performed considering information on smoking (none vs past / present), EGFR (other vs positive), stage (III vs IV), and stage (III vs recurrence). R was used as the analysis software. As shown in Table 19, the hazard ratio was 0.16, the 95% confidence interval was 0.09-0.30, and the p value was 9.54 ⁇ 10 -9 .
  • the line graphs (Good and Poor) in FIG. 9 represent Kaplan-Meier curves. As is clear from FIG. 9, the progression-free survival of the patients classified in the good group was longer than the progression-free survival of the patients classified in the poor group. The median progression-free survival of 50% (0.5 on the vertical axis) was 6 months in the good group and 1.8 months in the poor group, which was about 3.3 times longer.
  • the patient information shown in Table 20 below more specifically, age (75 years old or older vs. less than 75 years old), gender (male vs female), general condition of the patient "Performance Status: PS" (0/1 vs 2),. Cox regression analysis was performed considering information on smoking (none vs past / present), EDFR (other vs positive), stage (III vs IV), and stage (III vs recurrence). R was used as the analysis software. As shown in Table 20, the hazard ratio was 0.13, the 95% confidence interval was 0.08 to 0.24, and the p value was 1.67 ⁇ 10 -11 .
  • the disclosure in this application can predict the effectiveness of chemotherapy for non-small cell lung cancer in advance. Therefore, it is useful for examination and research of lung cancer patients in research institutions such as medical institutions and university medical schools.

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PCT/JP2021/019684 2020-05-25 2021-05-24 非小細胞肺がんの化学療法の効果を予測するための情報を提供する方法および情報提供用キット、非小細胞肺がんの化学療法の効果を予測する方法、非小細胞肺がんの化学療法の効果を予測する予測装置、予測装置のプログラムおよび記録媒体 Ceased WO2021241527A1 (ja)

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