WO2018001295A1 - Marqueur moléculaire, gène de référence, son application et son kit de test, et procédé de construction du modèle de test - Google Patents

Marqueur moléculaire, gène de référence, son application et son kit de test, et procédé de construction du modèle de test Download PDF

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WO2018001295A1
WO2018001295A1 PCT/CN2017/090740 CN2017090740W WO2018001295A1 WO 2018001295 A1 WO2018001295 A1 WO 2018001295A1 CN 2017090740 W CN2017090740 W CN 2017090740W WO 2018001295 A1 WO2018001295 A1 WO 2018001295A1
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value
breast cancer
risk
internal reference
recurrence
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郭弘妍
孙义民
王亚辉
谢展
邢婉丽
程京
邓涛
张治位
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博奥生物集团有限公司
北京博奥医学检验所有限公司
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Priority to JP2018568674A priority Critical patent/JP2019527544A/ja
Priority to SG11201811263WA priority patent/SG11201811263WA/en
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N15/00—Mutation or genetic engineering; DNA or RNA concerning genetic engineering, vectors, e.g. plasmids, or their isolation, preparation or purification; Use of hosts therefor
    • C12N15/09—Recombinant DNA-technology
    • C12N15/11—DNA or RNA fragments; Modified forms thereof; Non-coding nucleic acids having a biological activity
    • 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
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids

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  • the invention relates to the field of biotechnology, in particular to a molecular marker, an internal reference gene and an application thereof, a detection kit and a construction method of the detection model.
  • Breast cancer is a kind of highly heterogeneous tumor with many prognostic factors. Breast cancer patients with the same clinical stage, histological grade and hormone receptor expression can receive the same treatment plan, and their prognosis may be different. How to accurately determine the prognosis of breast cancer patients and formulate corresponding individualized treatment programs to avoid the harm and burden caused by over-treatment and improper treatment is an urgent problem to be solved in clinical practice.
  • the present invention provides molecular markers and their applications, detection kits, and methods for constructing detection models.
  • the kit is superior to the clinical pathological evaluation results in the prognosis evaluation performance of breast cancer, which can reduce the over-treatment and improper treatment caused by pathological diagnosis errors to meet the needs of individualized and precise treatment of breast cancer patients. Further improved the technical methods for predicting the prognosis of breast cancer in China.
  • the present invention provides the following technical solutions:
  • the invention provides genetic compositions, including the molecular markers MAPT and/or MS4A1.
  • the present invention provides a genetic composition consisting of the molecular markers BCL2, PGR, SCUBE2, ESR1, MKi67, CCNB1, MYBL2, GRB7, ERBB2, MMP11, CD68, BAG1, MAPT and MS4A1.
  • the genetic composition further comprises the internal reference genes ACTB, GAPDH, GUSB, NUP214, VCAN.
  • the invention also provides the use of the genetic composition for the detection of a 3-10 year postoperative recurrence and/or mortality risk prediction for breast cancer.
  • the prognosis of breast cancer prognosis in the application is 3-10 years recurrence and/or death risk assessment is specifically: obtaining total RNA of the sample to be tested, obtaining cDNA by reverse transcription, using real-time PCR
  • the method obtains the Ct value of the molecular marker and the reference gene, averages the Ct value of the reference gene, obtains an average Ct value (Ct') of the internal reference gene combination, and then Ct the molecular marker
  • the values were subtracted from the internal reference gene combination Ct' value to normalize, and ⁇ Ct was obtained.
  • the ⁇ Ct value and the subject's age, pT value and LN value were reconstructed by random forest algorithm.
  • the annual recurrence or death risk prediction model was analyzed and the results were obtained. Among them, the pT value is the pathological stage, and the LN value is the number of lymph node metastasis.
  • the value obtained by the analysis is compared with a threshold value, and the result is obtained, the threshold value being 5.
  • the value obtained by the analysis ⁇ 5 is a good prognosis, and the value obtained by the analysis ⁇ 5 is a poor prognosis.
  • the method for constructing a 3-10 year recurrence or death risk assessment test model for breast cancer in the application is: constructing a ⁇ Ct value of the molecular marker of the sample to be tested and the age of the subject , pT value, LN value construct a mathematical matrix, randomly select 1/2 as the training set, 1/2 as the verification set, through the random forest
  • the algorithm establishes a prediction model with 10,000 decision trees. The total random sampling is ⁇ 1000 times, and ⁇ 1000 prediction models are established. From the ⁇ 1000 prediction model, ⁇ 39 preferred models with the highest coincidence rate with follow-up information are selected as the final model. The model was used with a median of ⁇ 39 submodels as the final prognostic risk predictor.
  • the random forest is composed of many decision trees.
  • the decision tree is constructed by a method of double randomness of attributes and samples, so it is also called random decision tree.
  • random forest In a random forest, there is no correlation between decision trees.
  • the test data enters the random forest it is classified by each decision tree.
  • the class with the most classification results in all decision trees is the final result, that is, the result of the decision tree "voting", in other words, the random forest is a inclusion.
  • a classifier for multiple decision trees, and the category of its output is determined by the mode of the category of the individual tree output.
  • a risk threshold threshold of 5
  • the test sample in the present invention is an FFPE sample of a newly diagnosed early or mid-term ER or PR positive breast cancer patient.
  • the present invention also provides a primer set for amplifying the gene composition, the sequence of which is shown in SEQ ID No. 1 to SEQ ID No. 28.
  • the present invention also provides a probe set for amplifying the gene composition, the sequence of which is shown in SEQ ID No. 29 to SEQ ID No. 42.
  • the present invention also provides a primer set for amplifying an internal reference gene of the gene composition, as shown in SEQ ID No. 43 to SEQ ID No. 47.
  • the present invention also provides a probe set for amplifying an internal reference gene of the gene composition, as shown in SEQ ID No. 48 to SEQ ID No. 52.
  • the invention also provides a test kit for predicting the risk of recurrence and/or mortality of breast cancer after 3-10 years, including the primer set and/or the probe set and reagents commonly used in the kit.
  • the invention also provides a method for constructing a risk assessment model for recurrence or death of breast cancer with a prognosis of 3-10 years, constructing a mathematical matrix of the ⁇ Ct value of the molecular marker of the sample to be tested and the age, pT value and LN value of the subject, randomized Select 1/2 as the training set and 1/2 as the verification set.
  • the prediction model with 10000 decision trees is established by the random forest algorithm.
  • the total random sampling is ⁇ 1000 times, and ⁇ 1000 prediction models are established.
  • the ⁇ 1000 prediction models are selected.
  • the ⁇ 39 preferred models with the highest rate of coincidence with follow-up information were used as sub-models of the final model, and the median of ⁇ 39 sub-models was used as the final prognostic risk predictor.
  • the invention also provides an evaluation method for the risk of recurrence or death of breast cancer with a prognosis of 3-10 years, obtaining total RNA of the sample to be tested, obtaining cDNA by reverse transcription, and obtaining the molecular marker and the reference gene by fluorescence quantitative PCR.
  • Ct value, the Ct value of the internal reference gene is averaged to obtain the average Ct value (Ct') of the internal reference gene combination, and then the Ct value of the molecular marker is respectively subtracted from the internal reference gene combination Ct' value to be normalized.
  • ⁇ Ct the ⁇ Ct value and the age, pT value and LN value of the subject were analyzed by the random forest algorithm for 3-10 years postoperative recurrence or death risk prediction model of breast cancer, and the result was obtained, that is, 3-10
  • the annual recurrence or death risk value is predicted to be a good prognosis or a poor prognosis based on the risk threshold (risk threshold of 5).
  • the sample to be tested in the present invention is an FFPE sample of a newly diagnosed early or mid-stage ER or PR positive breast cancer patient.
  • the technical solution to solve the problem of the present invention includes: (1) selecting 192 breast cancer related candidate genes (not limited to breast cancer prognosis related, including internal reference genes), and customizing TLDA gene expression detection chip (Applied Biosystems) through literature and database research. (2) systematically collect complete demographic data, clinical data and follow-up data (recurrence and metastasis time, survival time), and select untreated early and mid-term ER or PR positive breast cancer FFPE samples for newly diagnosed patients, using customized TLDA chip was used to detect 192 genes, and the molecular markers related to prognosis and breast cancer prognosis were screened. (3) The candidate molecular markers and reference genes were screened in independent samples and constructed by random forest algorithm.
  • the present invention provides a prognostic evaluation gene detection system for recurrence or death 3 to 10 years after surgery in a newly diagnosed early or mid-stage ER or PR positive breast cancer patient.
  • FFPE formalin-Fixed and Parrffin-Embedded
  • PCR detection of breast cancer prognosis Ct values were expressed for 14 molecular markers and 5 internal reference genes.
  • a predictive model of the risk of recurrence or death after 3-10 years of postoperative ER or PR-positive early-stage breast cancer patients with Ct values and subject age, pT value and LN number was determined by prognosis or poor prognosis. Compared with the follow-up information, the system achieved an accuracy of 70%. Except for patient age, pT stage, and LN number, there is no need to rely on other clinical pathological information.
  • the kit provided by the invention has a prediction accuracy of 81.1% for a newly diagnosed breast cancer patient with a low risk of recurrence or death of 3-10 years, and a pathological prediction accuracy of 71.9%, which is a risk of recurrence or death of 3-10 years.
  • the accuracy of the prediction accuracy of the newly diagnosed patients with high breast cancer was 54.4%, which was close to the accuracy of the corresponding pathological prediction detection of 56.8%.
  • the kit has a concordance rate of 70%. Except for patient age, pT stage, and LN number, there is no need to rely on other clinical pathological information.
  • the detection system and the kit are superior to the clinical pathological prediction results in the prognosis evaluation performance of breast cancer, and can reduce the excessive treatment and improper treatment caused by pathological diagnosis errors to meet the individualized precision treatment of breast cancer patients.
  • Figure 1 shows the results of correlation analysis between the internal reference gene and the test gene
  • Figure 2 shows the establishment of a risk assessment model for 3-10 years of recurrence or death in breast cancer.
  • the invention discloses a molecular marker, an internal reference gene and an application thereof, a detection kit and a construction method of the detection model, and those skilled in the art can learn from the contents of the paper and appropriately improve the process parameters. It is to be understood that all such alternatives and modifications are obvious to those skilled in the art and are considered to be included in the present invention.
  • the method and the application of the present invention have been described by the preferred embodiments, and it is obvious that the method and application described herein may be modified or appropriately modified and combined without departing from the scope of the present invention. The technique of the present invention is applied.
  • the technical solution to solve the problem of the present invention includes: (1) selecting 192 breast cancer related candidate genes (not limited to breast cancer prognosis related, including internal reference genes), and customizing TLDA gene expression detection chip (Applied Biosystems) through literature and database research. (2) systematically collect complete demographic data, clinical data and follow-up data (recurrence and metastasis time, survival time), and select untreated early and mid-term ER or PR positive breast cancer FFPE samples for newly diagnosed patients, using customized TLDA chip was used to detect 192 genes, and the molecular markers related to prognosis and breast cancer prognosis were screened. (3) The candidate molecular markers and reference genes were screened in independent samples and constructed by random forest algorithm.
  • LN has or (and) no transfer, and the number of LN transfers
  • Total RNA is subjected to reverse transcription reaction to obtain a cDNA sample
  • Total RNA is subjected to reverse transcription reaction to obtain a cDNA sample
  • the difference of internal reference gene and gene expression between 26 cases of breast cancer prognosis and 26 cases of breast cancer prognosis samples were determined, and the internal reference genes and differentially expressed genes were selected.
  • Candidate molecular markers were verified by large-sample quantities by reverse transcription fluorescent quantitative PCR.
  • the final screening of 14 genes and 5 internal reference genes for diagnosis of breast cancer prognosis (BCL2, PGR, SCUBE2, ESR1, MKi67, CCNB1, MYBL2, GRB7, ERBB2, MMP11, CD68, BAG1, MAPT, MS4A1 ;ACTB, GAPDH, GUSB, NUP214, VCAN).
  • Diagnostic kits include primers for these genes, probes, and other conventional reagents for qRT-PCR.
  • the kit further comprises a predictive model, wherein the expression levels of BCL2, PGR, SCUBE2, ESR1, MKi67, CCNB1, MYBL2, GRB7, ERBB2, MMP11, CD68, BAG1, MAPT and MS4A1 are ACTB, GAPDH, GUSB.
  • the mean values of NUP214 and VCAN were detected as reference genes.
  • the clinical information of age, PT and LN of breast cancer patients were comprehensively evaluated for the prognosis of recurrence or death after 3-10 years, and the prognosis of poor prognosis was poor.
  • the random forest algorithm in the machine learning method was used to evaluate the risk of recurrence or death after 3-10 years of postoperative detection, and a gene detection model for breast cancer prognosis evaluation was established.
  • the random forest is composed of many decision trees.
  • the decision tree is constructed by a method of double randomness of attributes and samples, so it is also called random decision tree.
  • random forest there is no correlation between decision trees.
  • the test data enters the random forest it is classified by each decision tree.
  • the class with the most classification results in all decision trees is the final result, that is, the result of the decision tree "voting", in other words, the random forest is a inclusion.
  • a classifier for multiple decision trees and the category of its output is the category output by the individual tree The majority depends on the number.
  • 192 candidate genes for breast cancer were detected by TLDA detection technology.
  • the gene expression differences of 26 breast cancer prognosis samples and 26 breast cancer prognosis samples were detected, and differentially expressed genes were screened.
  • the screening process of the internal reference gene using the genetic algorithm based on genorm, bestkeeper, normfinder, delta Ct and considering the biological function of the less fluctuating gene and its relationship with the tumor, screening candidate internal reference genes; calculating all candidate internal reference gene combinations Ct
  • the correlation between the mean and the mean Ct of 192 genes, the most relevant combination is the internal reference genes including: ACTB, GAPDH, GUSB, NUP214, VCAN.
  • Candidate gene screening criteria (1) overall analysis - good prognosis and poor prognosis, the difference between the two groups is 2 times or less, and the proportion of cases with Ct ⁇ 35 is 50%; (2) stratified analysis - no lymph nodes The prognosis of the metastatic group was better than that of the poor prognosis.
  • the present invention provides a prognostic evaluation gene for breast cancer in China: at present, foreign similar products are developed based on European and American populations, and different ethnic groups have different gene expressions. In the present invention, 19 genes are screened, wherein MAPT and MS4A1 are based on The genes related to the recurrence or death assessment of female breast cancer patients in China after 3-10 years postoperatively have been reported. Although this gene has been reported to be associated with breast cancer, no direct report related to the prognosis of breast cancer has been found.
  • the present invention establishes a new internal reference gene combination different from other inventions and products, and the gene combination is less affected by the RNA quality in the FFPE sample, so that the detection result of the molecular marker is more reliable.
  • the predictive model of the random forest algorithm was used for comprehensive analysis. The model predicts the risk of recurrence or death after 3-10 years of postoperative diagnosis of ER+ or PR+ breast cancer in the early and middle stages.
  • the present invention provides a prognostic evaluation gene detection system for relapse or death of patients with stage I and stage II ER or PR positive for untreated breast cancer who are 3-10 years postoperatively. Compared with the follow-up information, the system achieved an accuracy of 70%. Except for patient age, pT stage, and LN number, there is no need to rely on other clinical pathological information.
  • the materials and reagents used in the molecular markers, internal reference genes and their applications, detection kits, and detection methods for the detection models provided by the present invention are all commercially available.
  • TLDA Traqman Low Density Array
  • LN has or (and) no transfer, and the number of LN transfers
  • Example 2 TLDA chip screening for molecular markers and internal reference genes
  • RNA extraction from FFPE samples 4 samples of 20 ⁇ m slices per sample or 8 slices of 10 ⁇ m slices were taken, and RNA was extracted according to the instructions of High Pure FFPET RNA Isolation Kit (Roche). The extracted RNA was quantified by NanoDrop-2000. Downstream reverse transcription experiments were performed after control.
  • RNA is subjected to reverse transcription reaction to obtain cDNA sample: 1 ⁇ g of total RNA is taken according to VILO TM Master Mix kit (Invitrogen) instructions for reverse transcription.
  • 192 candidate genes derived from breast cancer were detected by TLDA detection technology.
  • the gene expression differences of 26 breast cancer prognosis samples and 26 breast cancer prognosis samples were detected, and differentially expressed genes were screened.
  • the screening process of the internal reference gene using the genetic algorithm based on genorm, bestkeeper, normfinder, delta Ct and considering the biological function of the less fluctuating gene and its relationship with the tumor, screening candidate internal reference genes; calculating all candidate internal reference gene combinations Ct
  • the correlation between the mean and the mean Ct of 192 genes, the most relevant combination is the internal reference genes including: ACTB, GAPDH, GUSB, NUP214, VCAN.
  • Candidate gene screening criteria (1) overall analysis - good prognosis and poor prognosis, the difference between the two groups is 2 times or less, and the proportion of cases with Ct ⁇ 35 is 50%; (2) stratified analysis - no lymph nodes The prognosis of the metastatic group was better than that of the poor prognosis.
  • the difference between the two groups was more than 2 times, and the statistical difference was ⁇ 0.05.
  • (3) The difference between the two groups was not significant, but it was reported in the prognosis of breast cancer, and Ct ⁇ The proportion of cases in 35 reached 90%.
  • the genes satisfying the above criteria include: BCL2, PGR, SCUBE2, ESR1, MKi67, CCNB1, MYBL2, GRB7, ERBB2, MMP11, CD68, BAG1, MAPT, MS4A1 and above.
  • RNA extraction of 289 FFPE samples 4 samples of 20 ⁇ m slices per sample or 8 slices of 10 ⁇ m slices, RNA was extracted according to the instructions of High Pure FFPET RNA Isolation Kit (Roche), and the extracted RNA was quantified by NanoDrop-2000. Downstream reverse transcription experiments were performed after quality control.
  • Table 4 housekeeping gene qRT-PCR primer sequence
  • Table 5 housekeeping gene qRT-PCR probe sequence
  • Example 4 Breast cancer prognosis 3-10 years recurrence or death risk prediction model establishment
  • the random forest algorithm in the machine learning method was used to evaluate the risk of recurrence or death after 3-10 years of postoperative detection, and a gene detection model for breast cancer prognosis evaluation was established.
  • the random forest is composed of many decision trees.
  • the decision tree is constructed by a method of double randomness of attributes and samples, so it is also called random decision tree. In a random forest, there is no correlation between decision trees.
  • the test data enters the random forest it is classified by each decision tree. Finally, the class with the most classification results in all decision trees is the final result, that is, the result of the decision tree "voting", in other words,
  • a random forest is a classifier that contains multiple decision trees, and the category of its output is determined by the mode of the category of the individual tree output.
  • PT staging was stage 1 and 2, of which patients were operated between 2004 and 2008, followed up from 2011 to 2015. In the year, the follow-up period was 3-10 years.
  • the high-purity FFPET RNA Isolation Kit (Roche) was used to extract the total RNA from the above 19 FFPE samples. After the quality control, the RNA was subjected to reverse transcription reaction to obtain cDNA samples. The cDNA products were subjected to qRT-PCR reaction to detect the internal reference genes ACTB, GAPDH, GUSB, NUP214. , VCAN, and BCL2, PGR, SCUBE2, ESR1, MKi67, CCNB1, MYBL2, GRB7, ERBB2, MMP11, CD68, BAG1, MAPT, MS4A1 genes.
  • the detection system used FFPE samples of 289 newly diagnosed breast cancer patients with known clinical follow-up data collected by Tianjin Medical University Cancer Hospital and Henan Cancer Hospital. Five internal reference genes and 14 molecular markers were detected.
  • the kit provided by the invention has an accuracy rate of 81.1% for a newly diagnosed breast cancer patient with a low risk of recurrence or death of 3-10 years, and a pathological detection accuracy of 71.8%, which has a high risk of recurrence or death of 3-10 years.
  • the accuracy rate of the newly diagnosed patients with breast cancer was 54.4%, which was close to the corresponding pathological detection accuracy of 56.8%.
  • the kit has a concordance rate of 70%. Except for patient age, pT stage, and LN number, there is no need to rely on other clinical pathological information.
  • the detection system and the kit are superior to the clinical pathological diagnosis result in the prognosis evaluation performance of breast cancer, and can reduce the excessive treatment and improper treatment caused by the pathological diagnosis error to meet the individualized precision of the breast cancer patient to a certain extent.
  • the need for treatment has further improved the technical methods for predicting the prognosis of breast cancer in China.

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Abstract

La présente invention concerne un marqueur moléculaire, un gène de référence, son application et son kit de test, et un procédé de construction d'un modèle de test. En utilisant l'information de suivi destinée à un procédé de comparaison, la précision du kit de test prévu dans la prédiction du risque, chez des patients ayant reçu initialement un diagnostic positif ER ou PR du cancer du sein, de récidive ou de mort 3 à 10 ans après chirurgie est de 70 %, et la précision de prédiction d'un groupe à faible risque et d'un groupe à haut risque est respectivement de 81,1 % et de 54,4 %. Les précisions correspondantes dans la prédiction des résultats de test de pathologie (FFPE) sont respectivement de 71,9 % et de 56,8 %. Le modèle de prédiction de risque soutenant le kit de test nécessite uniquement la valeur Ct du marqueur moléculaire, l'âge du patient, le stade pT, et la quantité LN, et n'a pas besoin de dépendre d'une autre information de pathologie clinique ; le modèle fournit une évaluation de prognostic de cancer qui est meilleure que le résultat de prédiction de pathologie seul, et réduit jusqu'à une certaine mesure l'apparition d'un traitement incorrect dû à une prédiction de pathologie erronée, améliorant ainsi de là le procédé technique d'évaluation du prognostic de cancer.
PCT/CN2017/090740 2016-06-30 2017-06-29 Marqueur moléculaire, gène de référence, son application et son kit de test, et procédé de construction du modèle de test WO2018001295A1 (fr)

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