CN109439753A - Detect application and the construction method of patient with breast cancer's NAC outcome prediction model of the reagent of gene expression dose - Google Patents
Detect application and the construction method of patient with breast cancer's NAC outcome prediction model of the reagent of gene expression dose Download PDFInfo
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Abstract
The invention discloses the application of the reagent of detection gene expression dose and the construction methods of patient with breast cancer's NAC outcome prediction model, it is related to breast cancer correlative technology field, specifically, the prediction model can predict the curative effect of anthracycline and purple sweater class drug new adjuvant chemotherapy, whether to receive Neoadjuvant Chemotherapy or the what kind of Neoadjuvant Chemotherapy offer foundation of selection to patient with breast cancer.Patient that is sensitive to chemotherapy regimen and being likely to be breached pCR, to carry out effective new adjuvant chemotherapy, improves by predicting that curative effect can catch the opportunity of new adjuvant chemotherapy and protects newborn rate or survival rate；And patient that is insensitive to chemotherapy regimen or may cause tumour progression, it can select to carry out other effective treatments in time.
Description
Technical field
The present invention relates to breast cancer correlative technology fields, in particular to answering for the reagent for detecting gene expression dose
With and patient with breast cancer's NAC outcome prediction model construction method.
Background technique
With the rapid development of diagnosis and treatment theory being constantly progressive with science and technology, the therapeutic effect of breast cancer has occurred significantly
It improves, but it is one of the most common malignant tumors in women.Nowadays, the treatment of breast cancer is using radical surgery as core, radiotherapy,
Chemotherapy is combined with targeted therapy.And new adjuvant chemotherapy (neoadjuvant chemotherapy, NAC) is even more to have obtained very
Extensive clinical application.
In addition to for locally advanced breast cancer patient, NAC can also detect chemotherapy regimen sensibility, strive for that the drop phase protects newborn machine
Can etc..Correlative study shows that the patient with breast cancer for receiving NAC treatment is able to reach pathology complete incidence graph (pathological
Complete remission, pCR) do not receive NAC patient probability it is higher.But there is also some patients for newly assisting
Chemotherapy does not have sensibility, more very occurs tumour progression during NAC.
Currently, not yet reaching for the indication of new adjuvant chemotherapy, specific chemotherapy regimen, chemotherapy time-histories, therapeutic evaluation etc.
Engineering affects benefited and new adjuvant chemotherapy the popularization and application of patient to a certain extent.Nowadays, the new auxiliary of breast cancer
There is also problems urgently further to study for treatment, such as the selection of new adjuvant chemotherapy object, predicts and monitor and assess curative effect.
Summary of the invention
The first object of the present invention is that the reagent for providing detection target gene expression level is preparing outcome prediction or commenting
Estimate the application in kit, can predict the curative effect of anthracycline and purple sweater class drug new adjuvant chemotherapy, breast cancer can be suffered from
Person provides the offer foundation for whether receiving Neoadjuvant Chemotherapy or the what kind of Neoadjuvant Chemotherapy of selection, makes breast cancer
Patient can timely and effectively be treated.
The second object of the present invention, which is to provide a kind of outcome prediction or assessment kit, the kit, can predict anthracene nucleus
The curative effect of class and purple sweater class drug new adjuvant chemotherapy, to provide whether receive Neoadjuvant Chemotherapy or selection to patient with breast cancer
The offer of what kind of Neoadjuvant Chemotherapy timely and effectively reference information, and the kit can be applied to detection paraffin
Histotomy sample, has wide range of applications.
The third object of the present invention is to provide a kind of construction method of patient with breast cancer NAC outcome prediction model, pass through
Patient with breast cancer can be effectively predicted to receiving anthracycline and purple sweater class drug newly assists in the prediction model that the construction method can be established
The curative effect of chemotherapy.
The present invention is implemented as follows:
The reagent of detection target gene expression level provided in an embodiment of the present invention is preparing outcome prediction or assessment reagent
Application in box, in this application, target gene select one group or several groups in following 3 groups of genes:
Genome A comprising the combination of one or more of following gene: TFF1, NAT1, AGR2, ESR1, FOXA1,
MLPH and GATA3.Preferably, genome A is the combination of 7 kinds of genes.
Genome B comprising the combination of one or more of following gene: CYAT1, IGLC1, IGHM, IGKC and
CXCL13.Preferably, genome B is the combination of 5 kinds of genes.
Genome C comprising the combination of one or more of following gene: SFRP1 and ELF5.Preferably, genome C
For the combination of 2 kinds of genes.
Further, above-mentioned target gene includes with reference to gene；It is the group of following one or more of genes with reference to gene
It closes: ACTB, GAPDH and RPLPO.Preferably, the target gene in the embodiment of the present invention for above-mentioned 3 groups of genes and refers to base
The combination of cause, i.e. 14 target genes and 3 refer to gene, totally 17 gene.
It should be noted that above-mentioned curative effect refers to that patient with breast cancer receives the curative effect of NAC treatment.In the embodiment of the present invention
Outcome prediction, which refers to, carries out curative effect quantization to the patient with breast cancer for receiving anthracycline and purple sweater class drug new adjuvant chemotherapy.
Specifically, anthracene nucleus medicament (Anthracyclines) is one of antitumor common drug, especially for mammary gland
Cancer.Anthracene nucleus medicament basic structure is that anthracene nucleus is connected with an amino sugar with glycosidic bond, chemically textural classification, anthracene nucleus medicament
Belong to antitumor antibiotics, is the chemical substance with anti-tumor activity generated by microorganism.On the basis of anthracene nucleus medicament
Benefit can be further increased after joint Taxane family, obtained in clinical practice with anthracycline and purple sweater class drug new adjuvant chemotherapy
To being widely applied.
But find that some patient with breast cancer is insensitive for new adjuvant chemotherapy in clinical test, for these trouble
Person, new adjuvant chemotherapy is not while increasing curative effect, it is also possible to can bring that chemical toxicity acts on and other local treatments are prolonged
Late.Therefore, the patient that the outcome prediction of new adjuvant chemotherapy facilitates that screening may benefit from new adjuvant chemotherapy carries out chemotherapy, and
The patient for avoiding chemotherapy invalid carries out unnecessary preoperative or postoperative chemotherapy, to improve the pCR rate of neoadjuvant chemotherapy in breast
And long-term survival rate.Meanwhile the meaning of Efficacy of Neoadjuvant Chemotherapy prediction also resides in it and can predict the curative effect of breast cancer adjuvant chemotherapy,
Foundation is provided for the Scheme Choice of Postoperative Adjuvant Chemotherapy in Breast Cancer.
Further, in some currently preferred embodiments of the present invention, the reagent for detecting target gene expression level is RT-
PCR detection method reagent, Northern-blot detection method reagent or in situ hybridization detection method reagent.
In some currently preferred embodiments of the present invention, detected in sample to be tested by the method for RT-PCR, above-mentioned 17 gene
Expression.
Specifically, RT-PCR detection method includes with reagent: RT-qPCR reaction solution, positive reference substance and feminine gender are right
According to product.Specifically, RT-qPCR reaction solution includes: RT-PCR buffer, dNTPs, RNase inhibitor, reverse transcriptase, DNA
Polymerase, DEPC water and following primer and probe:
Downstream primer shown in the upstream primer as shown in SEQ ID NO.1 and SEQ ID NO.2 for detecting gene
For detecting the probe of gene TFF1 shown in the first primer pair and SEQ ID NO.3 of TFF1；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.4 and SEQ ID NO.5 for detecting NAT1
The second primer pair and SEQ ID NO.6 shown in for detecting the probe of gene NAT1；
The for AGR2 of downstream primer shown in the upstream primer as shown in SEQ ID NO.7 and SEQ ID NO.8
For detecting the probe of Gene A/G R2 shown in three-primer pair and SEQ ID NO.9；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.10 and SEQ ID NO.11 for ESR1
For detecting the probe of gene ESR1 shown in 4th primer pair and SEQ ID NO.12；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.13 and SEQ ID NO.14 for detecting
For detecting the probe of gene FOXA1 shown in the 5th primer and SEQ ID NO.15 of FOXA1；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.16 and SEQ ID NO.17 for detecting base
For detecting the probe of gene M LPH shown in the 6th primer pair and SEQ ID NO.18 because of MLPH；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.19 and SEQ ID NO.20 for detecting
For detecting the probe of gene GATA3 shown in the 7th primer pair and SEQ ID NO.21 of GATA3；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.22 and SEQ ID NO.23 for CYAT1
For detecting the probe of gene C YAT1 shown in 8th primer pair and SEQ ID NO.24；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.25 and SEQ ID NO.26 for IGLC1
For detecting the probe of gene IGLC1 shown in 9th primer pair and SEQ ID NO.27；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.28 and SEQ ID NO.29 for detecting
For detecting the probe of gene IGHM shown in the tenth primer pair and SEQ ID NO.30 of IGHM；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.31 and SEQ ID NO.32 for detecting
For detecting the probe of gene IGKC shown in the 11st primer pair and SEQ ID NO.33 of IGKC；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.34 and SEQ ID NO.35 is used for CXCL13
The 12nd primer pair and SEQ ID NO.36 shown in for detecting the probe of gene C XCL13；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.37 and SEQ ID NO.38 for SFRP1
For detecting the probe of gene SFRP1 shown in tenth three-primer pair and SEQ ID NO.39；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.40 and SEQ ID NO.41 for detecting
For detecting the probe of gene ELF5 shown in the 14th primer pair and SEQ ID NO.42 of ELF5；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.43 and SEQ ID NO.44 for ACTB
For detecting the probe of Gene A CTB shown in 15th primer pair and SEQ ID NO.45；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.46 and SEQ ID NO.47 for GAPDH
For detecting the probe of gene GAPDH shown in 16th primer pair and SEQ ID NO.48；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.49 and SEQ ID NO.50 for detecting
For detecting the probe of gene RPLPO shown in the 17th primer pair and SEQ ID NO.51 of RPLPO.
Specifically, the sequence of primer and probe is as shown in table 1.
1 sequence table of table
In some currently preferred embodiments of the present invention, detected in sample to be tested by the method for Northern-blot detection
The expression of above-mentioned 17 gene.Specifically, Northern-blot detection method includes with reagent: radiolabeled spy
Needle, hybridization solution and detection liquid.
In some currently preferred embodiments of the present invention, detected in sample to be tested by situ hybridization detection method, above-mentioned 17
The expression of gene.Specifically, in situ hybridization detection method includes with reagent: hybridization probe, marker, hybridization solution with
And developing solution.
The embodiment of the present invention also provides a kind of outcome prediction or assessment kit comprising detection target gene expression level
Reagent, target gene is selected from one of following gene or a variety of:
Genome A comprising the combination of one or more of following gene: TFF1, NAT1, AGR2, ESR1, FOXA1,
MLPH and GATA3；
Genome B comprising the combination of one or more of following gene: CYAT1, IGLC1, IGHM, IGKC and
CXCL13；
Genome C comprising the combination of one or more of following gene: SFRP1 and ELF5；
Target gene includes with reference to gene；It is the combination of following one or more of genes: ACTB, GAPDH with reference to gene
And RPLPO.
Preferably, the target gene in the embodiment of the present invention is the combination of above-mentioned 3 groups of genes, i.e. 14 genes and 3 references
Gene, totally 17 gene.It should be noted that or genome A and genome B combination or genome B and genome C
Combination or genome A and genome C combination.Curative effect is the curative effect that patient with breast cancer receives NAC treatment.
In some currently preferred embodiments of the present invention, the reagent for detecting target gene expression level is RT-PCR detection method
With reagent, Northern-blot detection method reagent or in situ hybridization detection method reagent.
It further include above-mentioned primer and probe (SEQ ID NO.1 in the kit in some currently preferred embodiments of the present invention
~SEQ ID NO.51), ibid, repeat no more.
In addition, the embodiment of the present invention also provides a kind of construction method of patient with breast cancer NAC outcome prediction model, the building
Method includes:
Case in data set is divided into test by stratified random smapling by the sample size for determining training set and test set
Collection and training set；
Naive Bayes Classifier is trained using training set, table of the Naive Bayes Classifier based on target gene
Up to level to obtain patient with breast cancer's NAC outcome prediction result；
It is tested using the Naive Bayes Classifier that test set obtains training, in the standard of Naive Bayes Classifier
True rate determines that Naive Bayes Classifier is prediction model when reaching preset threshold.
Further, Naive Bayes Classifier is expression based on target gene and Clinical symptoms to obtain cream
Adenocarcinoma patients NAC outcome prediction is as a result, specifically, Clinical symptoms is HR state, HER2 state and tumor size.Preset threshold
Be arranged according to specific requirements, such as accuracy rate reaches 50% or more, such as 50%, 55%, 58%, 60%, 65%, 70%,
75%, 80%, 85%, 90%, 95%, 99% or 100%.
In embodiments of the present invention, inventor analyzes the gene expression data announced from microarray, and calculates and reach pCR
The difference of gene expression between the patient of non-pCR.Then using the microarray of 4 independent studies as a result, having chosen 14
A target gene: genome A, the combination of genome B and genome C and 3 refer to gene.It is polymerize using reverse transcriptase quantitative
Enzyme chain reaction (RT-qPCR) detects the data of 17 gene expression doses, establishes prediction model by machine learning techniques.The cream
Patient with breast cancer of the scoring of adenocarcinoma patients' NAC prediction model for anthracycline and purple sweater class drug new adjuvant chemotherapy treats
Effect quantization.It should be noted that in some embodiment of the invention, Northern-blot detection method or original can also be passed through
Position hybridization detection method) detection 17 genes expression.
Specifically, the prediction model which establishes is the curative effect for predicting anthracycline and purple sweater class drug NAC treatment.
Although based on prediction anthracycline and purple sweater class drug scheme in have different pharmaceutical compositions, it is reported that no matter drug
How to combine, several clinical pathologic characteristics are related with the sensibility of NAC, such as clinical tumor stage, hormone receptor status, HER2
State, Ki67 state and TILs (tumor infiltrating lymphocyte) etc..
It is some research by microarray gene expression analyze establishes prediction model, but in routine diagnosis feasibility compared with
It is low, because this method is at high cost, time-consuming more and very high to test sample requirement.And the prediction mould that the embodiment of the present invention is established
Type can meet the requirement of test using fixed paraffin tissue sections (FFPE) sample of formalin.
The invention has the following advantages:
The embodiment of the invention provides the reagents of detection target gene expression level to prepare outcome prediction or assessment reagent
Application in box, what which prepared is directed to neoadjuvant chemotherapy in patients of breast cancer outcome prediction or assessment kit, can predict
The curative effect of anthracycline and purple sweater class drug new adjuvant chemotherapy, whether to receive Neoadjuvant Chemotherapy or selection to patient with breast cancer
What kind of Neoadjuvant Chemotherapy provides foundation.Patient that is sensitive to chemotherapy regimen and being likely to be breached pCR is treated by prediction
Effect can catch the opportunity of new adjuvant chemotherapy, to carry out effective new adjuvant chemotherapy, improve and protect newborn rate or survival rate；And it is right
Chemotherapy regimen is insensitive or may cause the patient of tumour progression, can select to carry out other effective treatments in time.
The embodiment of the invention provides outcome predictions or assessment kit, the kit can predict anthracycline and purple sweater class
The curative effect of drug new adjuvant chemotherapy, for patient with breast cancer is provided whether receive Neoadjuvant Chemotherapy or selection it is what kind of
Neoadjuvant Chemotherapy provides foundation.
In addition, passing through the structure the embodiment of the invention also provides the construction method of patient with breast cancer's NAC outcome prediction model
Patient with breast cancer can be effectively predicted to receiving anthracycline and purple sweater class drug new adjuvant chemotherapy in the prediction model that construction method can be established
Curative effect.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be to needed in the embodiment attached
Figure is briefly described, it should be understood that the following drawings illustrates only certain embodiments of the present invention, therefore is not construed as pair
The restriction of range for those of ordinary skill in the art without creative efforts, can also be according to this
A little attached drawings obtain other relevant attached drawings.
Fig. 1 is the flow chart of the embodiment of the present invention 1；
Fig. 2 is the phase in the embodiment of the present invention 1 between the expression and pCR that 4 common datas concentrate candidate gene
Closing property result figure；
Fig. 3 is the correlation analysis of target gene mRNA expression in the embodiment of the present invention 1；
Fig. 4 is the expression and the correlation of Clinical symptoms of different genes group in the embodiment of the present invention 1；Wherein Fig. 4 A is
The correlation of genome A and ER and PR IHC expression, Fig. 4 B are the correlation of genome B and TILs level；
Fig. 5 is the flow chart that prediction model is constructed by algorithms of different；
When Fig. 6 is using 60~150 training set sample size, naive Bayesian calculation (Bayes) method establishes prediction model
Flow chart；
Fig. 7 A~7C is the predicted value for the different models that four kinds of algorithms are established in the embodiment of the present invention 1；Wherein, attached drawing 7A is
The comparison of AUC value between different models；The comparison of attached drawing 7B F1 value between different models；Attached drawing 7C is between different models
The comparison of Youden ' s index；
Fig. 8 is the matched curve for the model predication value that NB Algorithm is established in the embodiment of the present invention 1；
Fig. 9 is that the ROC for the prediction model established in the embodiment of the present invention 1 using Clinical symptoms and gene expression data is bent
Line；
Figure 10 is that SE group and INS group are concentrated in training set, close beta collection and external testing in the embodiment of the present invention 1
PCR rate；
Figure 11 is in embodiment 1 in the SE group of different phase and the pCR rate of INS group, wherein 11A is the pCR rate of II phase,
11B is the pCR rate of III phase；
Figure 12 is the pCR rate in embodiment 1 in difference NAC therapeutic scheme, wherein 12A is the pCR rate of TEC scheme, 12B
For the pCR rate of PE scheme；
Figure 13 A~13C is the pCR rate of the SE group and INS group in embodiment 1 in different molecular subgroup, wherein 13A is Asia
The pCR rate of group HR+HER2-, 13B are HR- /+HER2+ pCR rate, and 13C is the pCR rate of HR-HER2-.
Specific embodiment
It in order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below will be in the embodiment of the present invention
Technical solution be clearly and completely described.The person that is not specified actual conditions in embodiment, according to normal conditions or manufacturer builds
The condition of view carries out.Reagents or instruments used without specified manufacturer is the conventional production that can be obtained by commercially available purchase
Product.
Feature and performance of the invention are described in further detail with reference to embodiments.
Embodiment 1
The present embodiment provides a kind of construction methods of patient with breast cancer NAC outcome prediction model comprising following steps:
Patient and model
This research has obtained the approval of the West China Hospital System Ethics committee (IRS number:2017-476), clinical
Test registers (http://www.chictr.org.cn/ in Chinese Clinical Trial registration office；Registration
Number:ChiCTR1800016763).Retrospective have collected from January, 2013 in December, 2017 of inventor receives healing
Property operation and after NAC is treated patient clinical pathology data.It should be noted that NAC treatment refers to 4 periods or 6 periods
The therapeutic scheme of anthracycline joint Taxane family.
The NAC therapeutic scheme includes TEC scheme (every three weeks: docetaxel 75mg/m2, epirubicin 50mg/m2And
500 mg/m of cyclophosphamide2) and PE scheme is (weekly: taxol 120mg/m2And epirubicin 50mg/m2, it is every 3 weeks one
The course for the treatment of).It does not include the patient for newly assisting anti-HER2 targeted therapy or endocrine hormone targeted therapy.
According to international cancer federation (UICC/AJCC)) tumor lympha carries down shifting (TNM) categorizing system (prp-17),
Patient in embodiment is in the II phase to III phase of clinical stage, and before NAC treatment, patient receives tumour under ultrasound guidance
Hollow needle biopsies carry out histological examination.The flow chart and result of the embodiment of the present invention are as shown in Fig. 1.
Attached drawing 1 is specifically please referred to, inventor receives in II~III primary breast cancer patient that NAC is treated from 508 and carries out
Select, remove 102 do not receive anthracycline and purple sweater class drug NAC treatment, 76 without complete clinical data and
29, without achieving tissue enough, pick the patient of 301 anthracyclines and purple sweater class drug NAC treatment.
In remaining 301, remove 46 NAC treatments without receiving 4 periods or 6 periods and 12 without leading to
Quality inspection is crossed, totally 243 patient with breast cancers.In satisfactory 243, the treatment of 4 periods NAC is received including 219
Patient and 24 receive the patient of 6 periods NAC treatment.
Patient characteristic
In 243 II~III primary breast cancer patients (table 2), the median age is 47 years old (26 years old~67 years old), there is 58.8%
Patient age in the right side of fifty.65.4% tumour is ER positive, and 63.8% tumour is PR positive, and 28% is HER2 sun
Character state.222 (91.4%) there is the case where lymph node involvement.
About NAC chemotherapy, it is new auxiliary that 4 period anthracyclines-Taxane family therapeutic scheme is received at 219 (90.1%)
It helps in the patient of chemotherapy, there are 131 (59.8%) examples to receive TEC scheme, there is 88 (40.2%) to receive the scheme of PE.?
In 219, there are 37 (16.9%) cases to reach pCR after NAC.In 243 patients, 24 (9.9%) receive 6
Period anthracycline-Taxane family therapeutic scheme patient, wherein there are 9 to reach pCR after NAC treatment.
NAC curative effect evaluation and tumor tissues histologic analysis are assessed
In embodiment, pCR is defined as ypT0/is by us, is referred in breast without remnants invasive disease.?
Before NAC treatment, using the paraffin section tissue that IHC detection formalin is fixed, to detect estrogen receptor (ER), progesterone by
Body (PR) and the case where human epidermal growth factor receptor 2 (HER2).
The lower limit value of ER and PR positive staining is the core for the dyeing that is positive being not less than in 1% tumor tissues.If
There is at least 1% tumour cell nuclear staining, then ER and PR test positive.If ER and PR receptor is negative receptors, HR is determined
(hormone receptor) is feminine gender, and if ER and/or PR is the positive, judge that HR is positive.
When IHC is judged as 3 (+) or fluorescence in situ hybridization technique (FISH) is judged as gene magnification, then HER2 is determined as
Positive.According to explanation (TIL guide), the present embodiment based in hollow biopsy hematoxylin and eosin TILS is commented
Estimate, specifically, in the present embodiment, we only evaluate lymphatic infiltration of the TILs in mesenchyma stroma of tumors region.
Selection target gene
It is analyzed by the Expression Console1.4.1 software to 932 lower rectal cancer Affymetrix data,
It is reached in every sets of data collection (GSE41998, GSE25065, GSE20271 and GSE20194,4 data sets are originated from NCBI GEO)
To pCR patient and non-pCR patient between gene difference expression by Affymetrix another money software
Transcriptome Analysis Console 3.1 carries out analytical calculation.
14 genes are selected to carry out follow-up study, each gene concentrates having differences property to express at least 3 sets of data.So
Afterwards, the normalization of destination gene expression is realized with reference to gene (ACTB, GAPDH and RPLPO) using 3.
Assess target gene
It analyzes between the expression and pCR of 14 genes selected in " selection target gene " in each data set
Correlation (attached drawing 2).
Attached drawing 2 is please referred to, at least three data set, TFF1, NAT1, AGR2, ESR1, FOXA1, MLPH and GATA3
Have significant negatively correlated (odds ratio: value < 1 OR) with pCR, and IGLC1, IGHM, IGKC, CXCL13, SFRP1 and ELF5 with
PCR is positively correlated (value > 1 OR).
Then, the expression in sample to be tested that these candidate genes are detected using RT-qPCR, using hierarchy clustering method
Candidate gene is divided into 3 main genomes according to mRNA expression, correlation analysis be shown in each genome two-by-two it
Between being positively correlated property (attached drawing 3).
Genome A includes 7 genes (TFF1, NAT1, AGR2, ESR1, FOXA1, MLPH and GATA3), is ER phase
Correlation gene, these genes expression in the positive disease of HR are high (attached drawing 4A).
Genome B includes gene (CYAT1, IGLC1, IGHM, IGKC and CXCL13), is gene involved in immunity, specifically
For the serial genes of B cell, a large amount expression (attached drawing 4B) in high TILs case of these genes.
Genome C includes gene SFRP1 and ELF5.
The extraction of RNA and reverse transcription
It is preoperative from NAC with RNeasy FFPE kit (Qiagen GmbH, Hilden, Germany) according to operation instructions
RNA is extracted in paraffin-embedded tissue.It is detected with quality and quantity of the ScanDrop (Jena, Germany) to RNA.It is close using light
Degree 260/280 is less than or equal to 1.8 and sample of the total amount less than or equal to 0.9 μ g carries out reverse transcription.Utilize OmniscriptTM
RT kit (Qiagen) and random hexamers, the carry out reverse transcription using T100 type grads PCR instrument (Bole, the U.S.) are anti-
It answers.
Specifically, RT-PCR primer includes:
Downstream primer shown in the upstream primer as shown in SEQ ID NO.1 and SEQ ID NO.2 for detecting gene
For detecting the probe of gene TFF1 shown in the first primer pair and SEQ ID NO.3 of TFF1；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.4 and SEQ ID NO.5 for detecting NAT1
The second primer pair and SEQ ID NO.6 shown in for detecting the probe of gene NAT1；
The for AGR2 of downstream primer shown in the upstream primer as shown in SEQ ID NO.7 and SEQ ID NO.8
For detecting the probe of Gene A/G R2 shown in three-primer pair and SEQ ID NO.9；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.10 and SEQ ID NO.11 for ESR1
For detecting the probe of gene ESR1 shown in 4th primer pair and SEQ ID NO.12；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.13 and SEQ ID NO.14 for detecting
For detecting the probe of gene FOXA1 shown in the 5th primer and SEQ ID NO.15 of FOXA1；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.16 and SEQ ID NO.17 for detecting base
For detecting the probe of gene M LPH shown in the 6th primer pair and SEQ ID NO.18 because of MLPH；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.19 and SEQ ID NO.20 for detecting
For detecting the probe of gene GATA3 shown in the 7th primer pair and SEQ ID NO.21 of GATA3；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.22 and SEQ ID NO.23 for CYAT1
For detecting the probe of gene C YAT1 shown in 8th primer pair and SEQ ID NO.24；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.25 and SEQ ID NO.26 for IGLC1
For detecting the probe of gene IGLC1 shown in 9th primer pair and SEQ ID NO.27；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.28 and SEQ ID NO.29 for detecting
For detecting the probe of gene IGHM shown in the tenth primer pair and SEQ ID NO.30 of IGHM；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.31 and SEQ ID NO.32 for detecting
For detecting the probe of gene IGKC shown in the 11st primer pair and SEQ ID NO.33 of IGKC；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.34 and SEQ ID NO.35 is used for CXCL13
The 12nd primer pair and SEQ ID NO.36 shown in for detecting the probe of gene C XCL13；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.37 and SEQ ID NO.38 for SFRP1
For detecting the probe of gene SFRP1 shown in tenth three-primer pair and SEQ ID NO.39；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.40 and SEQ ID NO.41 for detecting
For detecting the probe of gene ELF5 shown in the 14th primer pair and SEQ ID NO.42 of ELF5；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.43 and SEQ ID NO.44 for ACTB
For detecting the probe of Gene A CTB shown in 15th primer pair and SEQ ID NO.45；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.46 and SEQ ID NO.47 for GAPDH
For detecting the probe of gene GAPDH shown in 16th primer pair and SEQ ID NO.48；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.49 and SEQ ID NO.50 for detecting
For detecting the probe of gene RPLPO shown in the 17th primer pair and SEQ ID NO.51 of RPLPO.
Quantitative polyase chain reaction (qPCR)
Real-time fluorescence quantitative PCR instrument 96FX based on Bole carries out the inspection of three-step approach using above-mentioned probe to testing gene
It surveys, thermal cycle conditions are as follows: unwinding 10s, 95 DEG C；Recycle 20s, 95 DEG C, 40 circulations；Anneal 45s, 60 DEG C, then carries out fluorescence
Detection.
Then the calculating that cycle threshold (Ct value) is carried out using software BIO-RAD CFX Manager 3.1, utilizes 2-ΔCt
Method obtains the expression of related gene.
Statistical analysis
From random forest (Random forest), support vector machines (SVM), knn (k nearest neighbor algorithm) and simple shellfish
In 4 kinds of different algorithms of Ye Si etc. (Naive Bayes), NB Algorithm is selected, is established using gene expression data pre-
Survey model.
Specifically, 4 period anthracyclines and purple sweater class medicine are received using 219 that the method for stratified random smapling is picked out
The patient with breast cancer of object treatment is divided into training set (N=160) and test set (N=59).Between training set and test set carry out
Independent identically distributed stochastic variable test.
In the present embodiment, when being tested using predictive ability of the test set to prediction model, to the pre- of prediction model
The preset threshold for surveying accuracy rate is adjusted according to specific requirements, preset threshold 75%.It should be noted that preset threshold is also
May include the sensitivity of prediction model or specificity or preset threshold also can be set as 58%, 60%, 65%, 70%,
80% or 90% etc..
Every kind of algorithm respectively optimizes prediction model, utilizes cross-validation and automatic majorization function training mould
Type utilizes AUC, Youden according to corresponding test set ' s index and F1 score assess prediction model, please refer to attached
Fig. 5.
In order to determine the sample size of training set, the sample size of test set is set to 59 by inventor, by the quantity of training set
It is set between 60~150.Then the method for using stratified random smapling 50 times, is divided into test set (N=59) and difference for case
The training set of sample size.
AUC value is calculated according to test set, utilizes formula y=a+b × xcIt is fitted the scatter plot of AUC value, flow chart please refers to
Attached drawing 6.Using the logistic regression analysis of single argument and multivariable, to determine between clinical pathologic characteristic and prediction model and pCR
Relationship.Bilateral global significance level is set as P < 0.05.
The optimization of the sample size of prediction model and algorithm
Please refer to attached drawing 7.In algorithms of different, NB Algorithm AUC value (attached drawing 7A) with higher, F1 value
(attached drawing 7B) and Youden ' s index (attached drawing 7C, P < 0.05).
It using the training set of different sample sizes, is differed from 60~150, then uses the test set (59 of constant number
Example).As the sample size of training set carries out incremental with 10 for increment units, the average value of AUC is also increased accordingly, and in training
Stablize (attached drawing 8) when the quantity of collection is 130.
Therefore, 4 period NAC patients undergoing chemotherapies will be received and is randomly divided into training set (133/219,60.7%) and close beta collection
(86/219,39.3%).The patient of 6 period NAC chemotherapy of remaining 24 receiving is divided into external testing collection.Training set, inside
The case where test set and external testing collection, is as shown in table 2.
The case where 2 training set of table, close beta collection and external testing collection
An important factor for pCR is predicted in Clinical symptoms and prediction model
Analyze the relationship (table 3) between the Clinical symptoms and pCR of all patients.
The single argument and multivariable logistic regression of 3 Clinical symptoms of table are analyzed and the prediction model of prediction pCR probability
Remarks: multi-variables analysis a is the multiplicity of all Clinical symptoms in the present embodiment；Multi-variables analysis b is more
Variable analysis Clinical symptoms and hereditary feature model, genome A high expression, genome B in HR positive tumor are swollen in high TILs
High expression in tumor, therefore, HR state and TILs are not included into the multi-variables analysis.
As shown in table 3, HR state (OR, 3.378,95%confidence interval, confidence interval (CI), 1.734-
6.580, P < 0.001), HER2 state (OR, 0.317,95%CI, 0.163-0.614, P=0.001), TILs (OR, 0.412,
95%CI, 0.189-0.899, P=0.026) and tumor size (OR, 2.548,95%CI, 1.313-4.945, P=
0.006) there is significant relation with pCR in univariate analysis.
In the multi-variables analysis of Clinical symptoms, and HR state (OR, 2.375,95%CI, 1.155~5.033, P=
0.019), HER2 state (OR, 0.368,95%CI, 0.176~0.769, P=0.008) and tumor size (OR, 2.726,
95%CI, 1.320~5.630, P=0.007) it is related in pCR.
Then, inventor establishes Clinical symptoms (HE, HER2, TILs and tumor size) prediction model and 17 genes are pre-
Model is surveyed, and predictive ability of the model in training set is compared with the predictive ability of close beta collection.It needs to illustrate
It is that 17 predictive genes models are patient with breast cancer NAC outcome prediction model provided in an embodiment of the present invention.The ROC of these models
Curve is as shown in Fig. 9.
Attached drawing 9 is please referred to, Gene is 17 predictive genes models in figure, and HR+HER2+TILs is Clinical symptoms model, Gene+
HER2+T is multivariate predictive model.As shown in Figure 9, the AUC value of 17 predictive genes models is 0.78, is higher than Clinical symptoms model
(AUC, 0.61), borderline significance P value are 0.06.Relative to predicted characteristics model, which has very high standard
True rate (69.8%vs.61.6%, 17 gene vs hereditary features), sensitivity (78.6%vs.71.4%), and specificity
(68.1%vs.59.7%) specifically asks table 4.
The test performance of 4 prediction model of table
| Prediction model | Accuracy rate (%) | Sensitivity (%) | Specificity | PPV (%) | NPV (%) |
| Clinical symptoms prediction model | 61.6 | 71.4 | 59.7 | 25.6 | 91.5 |
| 17 predictive genes models | 69.8 | 78.6 | 68.1 | 32.4 | 94.2 |
| 17 gene multivariate predictive models | 72.1 | 85.7 | 69.4 | 35.3 | 96.2 |
In univariate analysis, 17 predictive genes models (OR, 10.569,95%CI, 4.488~24.889, P < 0.001,
Table 3) with pCR have significant relation.After 17 predictive genes models and Clinical symptoms are included in multi-variables analysis by inventor, 17 genes are pre-
It surveys model (OR, 7.772,95%CI, 3.161-19.110, P < 0.001), and HER2 state (OR, 11.654,95%CI, 4.598
~29.537, P < 0.001) with pCR have high correlation.Certainly, also there are other factors relevant to pCR, such as HER2 state
(OR, 0.410,95%CI, 0.190-0.882, P=0.023) and tumor size (OR, 3.927,95%CI, 1.790-
8.614, P=0.001) (table 3) related to pCR.
Optimize 17 predictive genes models
For optimal prediction model, inventor mutually ties Clinical symptoms HER2 state and T grades with 17 predictive genes models
It closes, to establish 17 gene multivariate predictive models (multivariable patient with breast cancer's NAC outcome prediction model).Although 17 genes are changeable
The AUC value for measuring AUC value and 17 predictive genes models of the prediction model in test set is not significantly different (P=0.89), but with
17 predictive genes models are compared, and the predictive ability of 17 gene multivariate predictive models is obviously improved.
In test set, for 17 gene multivariate predictive models for 17 predictive genes models, there have to be higher accurate
Rate (17 predictive genes model of 72.1%vs. 69.8%=17 gene multivariate predictive model vs), sensitivity (85.7%
Vs.78.6%) and specific (69.4%vs. 68.1%), specifically please refer to table 4.
In case, 17 gene multivariate predictive models in univariate analysis with pCR in high-positive correlation (OR,
17.041,95%CI, 6.427-45.182, P < 0.001, table 3), accuracy rate, sensitivity, specificity, positive predictive value
(PPV) and negative predictive value (NPV) is respectively 71.6%, 89.1%, 67.5%, 39.0% and 96.4%.
The estimated performance of 17 gene multivariate predictive models
Hereditary feature (17 gene) is established 17 gene multivariate predictive models of prediction by inventor in conjunction with Clinical symptoms.Benefit
With 17 gene multivariate predictive models, by this study in patient be divided into two groups.The patient that prediction is likely to be breached pCR is divided into
SE group (sensitive group), the patient that prediction can not reach pCR is INS group (insensitive group).
In training set, the pCR rate of SE group is 35.1%, significantly larger than the pCR rate (3.9%, P < 0.001) of INS group.It is quasi-
True rate, sensitivity, specificity, PPV and NPV are respectively 69.9%, 87.0%, 66.4%, 35.1% and 96.1%.
In test set, there are significant difference (35.3%vs.2.8%, P for the pCR rate of the pCR rate of SE group and INS group
< 0.001, Figure 10 B).Accuracy rate, sensitivity, specificity, PPV and the NPV of SE group are respectively 72.1%, 85.7%,
69.4%, 35.3% and 96.2%.
In then extension test set that the multivariate predictive model is used to only receive the patient of 6 NAC treatment cycles.
In extension test set, the pCR rate of SE group is that the pCR rate of 64.3%, INS group is 0% (P=0.002).It is accuracy rate, sensitive
Degree, specificity, PPV and NPV are respectively 0.792,100.0%, 66.7%, 64.3% and 100.0%.
NAC therapeutic effect of the 17 gene multivariate predictive models to II~III phase patient
Attached drawing 11 is please referred to, Figure 11 A is estimated performance (the SE group 51.7%vs 3.0%INS to II primary breast cancer patient
Group, P < 0.001), Figure 11 B be to the estimated performance of III primary breast cancer patient (34.2%SE group vs 3.8%INS group, P <
0.001), estimated performance is good as the result is shown.
All it is the curative effect for predicting anthracycline and purple sweater class drug new adjuvant chemotherapy, includes the TEC scheme and 6 in 4 periods
The patient of the TEC or PE scheme in period.NAC therapeutic effect please refers to Figure 12.For TEC scheme patient as illustrated in fig. 12,
For PE scheme as shown in Figure 12 B, it is known that although NAC baseline is not exactly the same, to SE group and to the pre- of INS group
Result is surveyed there are significant difference, prediction result is good.
Detection of the 17 gene multivariate predictive models in different molecular subgroup
Although 17 gene multivariate predictive models are shown in subgroup HR+HER2-, HR- /+HER2+, and HR-HER2-
Different predictive abilities, but it can predict NAC curative effect well in all subgroups, specifically please refer to Figure 13, Figure 13 A
In, HR+HER2- (26.1%vs 2.0%, P=0.001)；In Figure 13 B, HR- /+HER2+ (42.4%vs 8.8%, P=
0.001)；In Figure 13 C, HR-HER2- (43.5%vs 0%, P=0.264).As it can be seen that 17 gene multivariate predictive models are not
With in molecule subgroup SE group and INS group pCR rate there were significant differences, estimated performance is good, and therefore, the 17 gene multivariable is pre-
The NAC curative effect of different molecular subgroup can be predicted by surveying model.
Comparative example 1
Currently, there is also some other disclosed prediction models.30 probe groups drug gene prediction instruments (25065-6) with
Based on cDNA microarray, applied to the patient for receiving T-FEC chemotherapy.It is concentrated in its verifying, the accuracy rate of model is 76%,
Sensitivity is 92%, and specificity is 71%.
Gene rank index (GGI) is 97 genetic models attached most importance to histological grade.And the higher patient of GGI
The risk of recurrence is higher.Liedtke et al. attempts to use the model with the patient that NAC is treated, patient's tool of discovery GGI high
There is very high pCR probability.After combining GGI and Clinical symptoms, the accuracy rate of the model reaches 71%, and sensibility reaches
38%, specificity reaches 86%.Supervision Risk fallout predictor is the inherent hypotype of 50 predictive genes, and the high score and pCR of ROS-S is general
Rate is higher related, sensitivity 94%, and specificity is 57.6%.And in the present embodiment, 17 gene expression characteristics are mutually tied with Clinical symptoms
When conjunction (multivariate predictive model), accuracy rate, sensitivity, specificity, PPV and NPV are respectively 71.6%, 89.1%,
67.5%, 39.0% and 96.4%.
Therefore, with 30 probe groups drug gene prediction instruments, genome and index (GGI) and Supervision Risk fallout predictor phase
Than 17 gene multivariate predictive model provided in an embodiment of the present invention has relatively high estimated performance, AUC with higher
The parameters such as value, accuracy rate, sensitivity and specificity.Pervious model or it is microarray based on cDNA or is a large amount of
Gene expression, and not well combine Clinical symptoms.17 predictive genes model or 17 genes provided in an embodiment of the present invention
Multivariate predictive model has convenient test, and repeatability is strong, and to the of less demanding of sample, FFPE sample is feasible, and having can
Potentiality applied to clinical practice.
To sum up, the embodiment of the invention provides the reagents of detection target gene expression level to prepare outcome prediction or assessment
Application in kit, what which prepared is directed to neoadjuvant chemotherapy in patients of breast cancer outcome prediction or assessment kit, energy
The curative effect of anthracycline and purple sweater class drug new adjuvant chemotherapy is predicted, to provide whether receive new adjuvant chemotherapy side to patient with breast cancer
Case or the what kind of Neoadjuvant Chemotherapy of selection provide foundation.Patient that is sensitive to chemotherapy regimen and being likely to be breached pCR is led to
The opportunity that prediction curative effect makes it catch new adjuvant chemotherapy is spent, to carry out effective new adjuvant chemotherapy, improves and protects newborn rate or survival rate；
And patient that is insensitive to chemotherapy regimen or may cause tumour progression, it can select to carry out other in time effectively to control
It treats.
The embodiment of the invention provides outcome predictions or assessment kit, the kit can predict anthracycline and purple sweater class
The curative effect of drug new adjuvant chemotherapy, for patient with breast cancer is provided whether receive Neoadjuvant Chemotherapy or selection it is what kind of
Neoadjuvant Chemotherapy provides foundation.
In addition, passing through the structure the embodiment of the invention also provides the construction method of patient with breast cancer's ANC outcome prediction model
Patient with breast cancer can be effectively predicted to receiving anthracycline and purple sweater class drug new adjuvant chemotherapy in the prediction model that construction method can be established
Curative effect.
These are only the preferred embodiment of the present invention, is not intended to restrict the invention, for those skilled in the art
For member, the invention may be variously modified and varied.All within the spirits and principles of the present invention, it is made it is any modification,
Equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.
SEQUENCE LISTING
<110>Huaxi Hospital Attached to Sichuan Univ
<120>a kind of construction method of patient with breast cancer NAC outcome prediction model
<130> 250
<160> 51
<170> PatentIn version 3.5
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Claims (10)
1. the reagent for detecting target gene expression level is preparing outcome prediction or assessing the application in kit, feature exists
In the target gene selects one group or several groups in following 3 groups of genes:
Genome A comprising the combination of one or more of following gene: TFF1, NAT1, AGR2, ESR1, FOXA1, MLPH
And GATA3；
Genome B comprising the combination of one or more of following gene: CYAT1, IGLC1, IGHM, IGKC and
CXCL13；
Genome C comprising the combination of one or more of following gene: SFRP1 and ELF5；
The curative effect refers to that patient with breast cancer receives the curative effect of NAC treatment.
2. application according to claim 1, which is characterized in that the target gene includes with reference to gene；The reference
Gene is the combination of following one or more of genes: ACTB, GAPDH and RPLPO.
3. application according to claim 1, which is characterized in that the reagent of the detection target gene expression level is RT-
PCR detection method reagent, Northern-blot detection method reagent or in situ hybridization detection method reagent.
4. application according to claim 3, which is characterized in that the RT-PCR detection method includes with reagent: RT-
QPCR reaction solution, positive reference substance and negative controls.
5. application according to claim 3, which is characterized in that the Northern-blot detection method includes with reagent
Have: radiolabeled probe, hybridization solution and detection liquid.
6. application according to claim 3, which is characterized in that the reagent further includes one of following primer pair or more
Kind combination:
Downstream primer shown in the upstream primer as shown in SEQ ID NO.1 and SEQ ID NO.2 for detecting gene TFF1
The first primer pair；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.4 and SEQ ID NO.5 for detecting the of NAT1
Two primer pairs；
The third for AGR2 of downstream primer shown in the upstream primer as shown in SEQ ID NO.7 and SEQ ID NO.8 is drawn
Object pair；
The 4th for ESR1 of downstream primer shown in the upstream primer as shown in SEQ ID NO.10 and SEQ ID NO.11
Primer pair；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.13 and SEQ ID NO.14 for detecting FOXA1
5th primer pair；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.16 and SEQ ID NO.17 for detecting gene
The 6th primer pair of MLPH；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.19 and SEQ ID NO.20 for detecting GATA3
7th primer pair；
The 8th for CYAT1 of downstream primer shown in the upstream primer as shown in SEQ ID NO.22 and SEQ ID NO.23
Primer pair；
The 9th for IGLC1 of downstream primer shown in the upstream primer as shown in SEQ ID NO.25 and SEQ ID NO.26
Primer pair；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.28 and SEQ ID NO.29 for detecting IGHM
Tenth primer pair；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.31 and SEQ ID NO.32 for detecting IGKC
11st primer pair；
The for CXCL13 of downstream primer shown in the upstream primer as shown in SEQ ID NO.34 and SEQ ID NO.35
12 primer pairs；
The tenth for SFRP1 of downstream primer shown in the upstream primer as shown in SEQ ID NO.37 and SEQ ID NO.38
Three-primer pair；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.40 and SEQ ID NO.41 for detecting ELF5
14th primer pair；
The tenth for ACTB of downstream primer shown in the upstream primer as shown in SEQ ID NO.43 and SEQ ID NO.44
Five primer pairs；
The tenth for GAPDH of downstream primer shown in the upstream primer as shown in SEQ ID NO.46 and SEQ ID NO.47
Six primer pairs；
Downstream primer shown in the upstream primer as shown in SEQ ID NO.49 and SEQ ID NO.50 for detecting RPLPO
17th primer pair.
7. application according to claim 6, which is characterized in that the reagent includes the spy of following one or more combination
Needle:
For detecting the probe of gene TFF1 as shown in SEQ ID NO.3；For detecting gene as shown in SEQ ID NO.6
The probe of NAT1；For detecting the probe of Gene A/G R2 as shown in SEQ ID NO.9；It is used for as shown in SEQ ID NO.12
Detect the probe of gene ESR1；For detecting the probe of gene FOXA1 as shown in SEQ ID NO.15；Such as SEQ ID NO.18
Shown in for detecting the probe of gene M LPH；For detecting the probe of gene GATA3 as shown in SEQ ID NO.21；Such as
For detecting the probe of gene C YAT1 shown in SEQ ID NO.24；For detecting gene as shown in SEQ ID NO.27
The probe of IGLC1；For detecting the probe of gene IGHM as shown in SEQ ID NO.30；It is used as shown in SEQ ID NO.33
In the probe of detection gene IGKC；For detecting the probe of gene C XCL13 as shown in SEQ ID NO.36；Such as SEQ ID
For detecting the probe of gene SFRP1 shown in NO.39；For detecting the spy of gene ELF5 as shown in SEQ ID NO.42
Needle；For detecting the probe of Gene A CTB as shown in SEQ ID NO.45；For detecting base as shown in SEQ ID NO.48
Because of the probe of GAPDH；For detecting the probe of gene RPLPO as shown in SEQ ID NO.51.
8. a kind of outcome prediction or assessment kit, which is characterized in that it includes the reagent for detecting target gene expression level, institute
It states target gene and is selected from one of following gene or a variety of:
Genome A comprising the combination of one or more of following gene: TFF1, NAT1, AGR2, ESR1, FOXA1, MLPH
And GATA3；
Genome B comprising the combination of one or more of following gene: CYAT1, IGLC1, IGHM, IGKC and
CXCL13；
Genome C comprising the combination of one or more of following gene: SFRP1 and ELF5；
The curative effect refers to that patient with breast cancer receives the curative effect of NAC treatment.
9. kit according to claim 8, which is characterized in that it is described detection target gene expression level reagent be
RT-PCR detection method reagent, Northern-blot detection method reagent or in situ hybridization detection method reagent.
10. a kind of construction method of patient with breast cancer NAC outcome prediction model, characterized in that it comprises:
The sample size for determining training set and test set, by stratified random smapling by the case in data set be divided into test set and
Training set；
Naive Bayes Classifier is trained using the training set, the Naive Bayes Classifier is based on target gene
Expression obtain NAC outcome prediction result；
It is tested using the Naive Bayes Classifier that the test set obtains training, in the Naive Bayes Classifier
Accuracy rate determine that the Naive Bayes Classifier is prediction model when reaching preset threshold；
The target gene is selected from one of following gene or a variety of:
Genome A comprising the combination of one or more of following gene: TFF1, NAT1, AGR2, ESR1, FOXA1, MLPH
And GATA3；
Genome B comprising the combination of one or more of following gene: CYAT1, IGLC1, IGHM, IGKC and
CXCL13；
Genome C comprising the combination of one or more of following gene: SFRP1 and ELF5.
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