阅读说明：本技术 一种用于预测胃癌免疫治疗的肿瘤微环境评分系统的构建方法以及分子探针 (Construction method of tumor microenvironment scoring system for predicting gastric cancer immunotherapy and molecular probe ) 是由 廖旺军 曾东强 于 2021-06-01 设计创作，主要内容包括：本发明提供了一组用于检测评估肿瘤微环境的基因集的分子探针,包括44条序列如SEQ ID NO:1～44所示的核苷酸序列。本发明筛选出的探针,在同一孔中对目的基因的表达进行NanoString数字化表达检测,绘制数字基因表达谱,可用于免疫治疗效果的评估。本发明还提供了用于评价胃癌肿瘤微环境的多基因评分模型的构建方法,筛选出得到的44个可代表不同微环境分型的基因集,可用于建立评估患者的肿瘤微环境多基因评分模型。本发明模型通过有效地评估肿瘤微环境,不仅能作为良好的预后生物标志物,还能够预测多种肿瘤的检查点抑制剂免疫治疗反应,对于实现个体化医疗的同时节约医疗资源,具有重要的意义和临床应用价值。(The invention provides a group of molecular probes for detecting and evaluating a gene set of a tumor microenvironment, which comprise 44 nucleotide sequences with sequences shown as SEQ ID NO: 1-44. The probe screened by the invention carries out NanoString digital expression detection on the expression of a target gene in the same hole, draws a digital gene expression profile, and can be used for evaluating the immunotherapy effect. The invention also provides a construction method of the polygene scoring model for evaluating the gastric cancer tumor microenvironment, and the 44 screened gene sets which can represent different microenvironment typing can be used for establishing the polygene scoring model for evaluating the tumor microenvironment of patients. The model can be used as a good prognosis biomarker by effectively evaluating the tumor microenvironment, can also be used for predicting the immunotherapy response of checkpoint inhibitors of various tumors, and has important significance and clinical application value for realizing individual medical treatment and saving medical resources.)

1. The molecular probes are used for detecting and evaluating a gene set of a tumor microenvironment and are characterized in that the molecular probes are formed by connecting a report probe and a capture probe in series in a 5'-3' direction; the report probe is a fluorescent molecular bar code; the capture probe is a DNA molecule with biotin at the 3' end and can be specifically combined with mRNA obtained by transcription of a target gene.

2. The molecular probe of claim 1, wherein the probe comprises 44 nucleotide sequences as shown in SEQ ID NO 1-44.

3. A kit for assessing the microenvironment of a tumor, the kit comprising the molecular probe of claim 2.

4. Use of the kit of claim 2 in the preparation of a NanoString nCounter assay system for assessing a tumor microenvironment scoring model.

5. The use of claim 4, wherein the kit further comprises an internal reference gene.

6. The use of claim 5, wherein the reference genes comprise ACTB, ABCF1, B2M, G6PD, GAPDH, GUSB, PGK1, RPLPO, TFRC, TUBB, and molecular probes for detecting the reference genes, wherein the sequences of the probes are shown in SEQ ID NO 46-54.

7. A construction method of a multi-gene scoring model for evaluating a tumor microenvironment is characterized by comprising the following steps:

(1) collecting sequencing data of a tumor patient;

(2) performing component evaluation of immune cells of the tumor microenvironment on the sequencing data by using a CIBERSORT and MCP-counter algorithm;

(3) unsupervised clustering of immune cells in the tumor microenvironment is carried out through consensus 2, so that different tumor microenvironment types are obtained;

(4) finally determining a gene set capable of evaluating the tumor microenvironment score through pairwise difference analysis and random forest dimension reduction;

(5) immunotherapy cohort data was collected;

(6) evaluating the significance difference of each gene in the gene set obtained in the step (4) in the immunotherapy queue for predicting the immunotherapy response to obtain a corresponding P value, dividing the P value by the number of samples in the corresponding queue to obtain characteristic importance, adding the characteristic importance of each gene in all queues to obtain the sum of the characteristic importance of each gene, obtaining the positive distribution of the importance indexes through Shapiro-Wilk test, and taking the genes outside the 95% intervals on both sides as the important genes obtained by screening;

(7) transforming the gene expression data obtained in the step (6) by Z-score, and constructing a multigene scoring model for evaluating the tumor microenvironment by adopting a PCA (principal component analysis) method, wherein the model is TMEscore _ plus ═ TMEscorea A _ plus TMEscoreB _ plus; TMEscorea A _ plus is the immune score and TMEscorea B _ plus is the interstitial score.

8. A gene set for assessing a tumor microenvironment, comprising tumor microenvironment immune-related genes and tumor microenvironment stroma-related genes; the tumor microenvironment immune-related genes comprise: CDT1, PSAT1, IDO1, BRIP1, WARS, DTL, GBP5, KIF18A, UBD, GZMB, CCL4, CCL5, RCC1, HELLS, GNLY, TAP1, CD8A, CXCL11, GBP4, ETV7, ZNF367, CXCL10, KLRC2, CXCL9, and IFNG; the tumor microenvironment stroma-related genes comprise: MIR100HG, pricke 2, PAPLN, PLEKHH2, TNS1, MCC, MAMDC2, C14orf132, SYNPO, PID1, MCEMP1, zchcc 24, PROS1, JAM3, TGFB1I1, MXRA7, FILIP1, CRYAB, and ANTXR 2.

9. A multigene scoring model for assessing the microenvironment of a tumor, wherein the model is TMEscore _ plus, TMEscore _ plus is TMEscore a _ plus-TMEscore b _ plus; TMEscorea A _ plus is an immune score, and TMEscorea B _ plus is an interstitial score; the immune score and the interstitial score are calculated from the expression data of the gene set according to claim 4 by: the gene expression data were Z-score transformed and the immune score and the interstitial score were calculated separately using PCA analysis.

10. Use of the gene set of claim 8 in the preparation of a kit for assessing a tumor microenvironment.

Technical Field

The invention belongs to the field of biological information, and relates to a method for constructing a gene set and a scoring system for evaluating a tumor microenvironment and a molecular probe for detecting the expression level of each gene in the gene set.

Background

The gastric cancer is a malignant tumor with high incidence of residents in China, and according to data of GLOBOCAN in 2012, newly increased gastric cancer cases in China account for 42.5 percent of the total number of worldwide diseases, and death cases account for 45 percent of the worldwide number. Patients with advanced gastric cancer have poor prognosis, high recurrence rate or disease progression incidence rate after first-line chemotherapy, low survival rate of patients, and limited selectable rear-line treatment means. The biggest challenge of immunotherapy is that only a part of patients can benefit from it, and how to accurately screen out the patient population that can benefit from checkpoint inhibitors is the biggest challenge facing gastric cancer immunotherapy. To date, biomarkers for predicting the efficacy of PD-1/PD-L1 monoclonal antibodies include immunohistochemical expression levels (CPS), high microsatellite instability (MSI-H) and Tumor Mutational Burden (TMB) of PD-L1.

However, there are limitations to the use of PD-L1 as a biomarker for predicting checkpoint inhibitor efficacy. For example, the KEYNOTE-061 study conducted with Pabollizumab compared the efficacy of Pabollizumab to paclitaxel in the second-line treatment of advanced gastroesophageal junction adenocarcinoma with PD-L1CPS ≧ 1, unfortunately, in the general population, Pabollizumab did not achieve statistical differences compared to paclitaxel, PFS and OS, and subgroup analysis suggested that further population exploration on biomarkers may be required. This phase three clinical randomized controlled study showed that there was a limitation in the expression level of PD-L1: expression of PD-L1 in tumors has heterogeneity, and expression differences of PD-L1 can exist among different tumor species and different regions of the same tumor, tumor focuses and metastasis focuses; ② the lack of standardization of antibodies and staining, the expression level of PD-L1 was mainly evaluated by immunohistochemical staining. However, antibodies used for PD-L1 immunohistochemical staining in current clinical studies are different, and staining conditions are also subject to subjective judgment of evaluators; there is controversy over the definition of the standard for the effectiveness of PD-L1, as to what percentage of PD-L1 expression intensity, i.e., was judged as "positive". A correlation between PD-L1 expression levels (> 50%) and clinical benefit was demonstrated in the KEYNOTE-010 study. The PD-L128-8 pharm Dx (Dako) IHC assay was approved in advanced NSCLC and melanoma patients receiving nivolumab with a scoring system defining PD-L1 expression < 1% as negative, low expression at 1% -5%, intermediate expression at 5% -10%, and high expression at > 10%.

Microsatellites are tandem repeats of short DNA sequences and are widely found in the genome of eukaryotes. The human mismatch repair gene (MMR gene) can express corresponding mismatch repair protein after transcription and translation, if any MMR protein expression deletion can cause the mismatch repair function defect of cells, the accumulation is caused by the loss of the repair function of base mismatch in the DNA replication process, and the occurrence of microsatellite instability (MSI) is caused. In 2017, FDA approved PD-1 inhibitor pabolizumab to treat microsatellite highly unstable (MSI-H)/mismatch repair deficient (dMMR) solid tumor patients with tumors covering 15 different sites of malignancies such as colorectal cancer, gastric cancer, non-small cell lung cancer, etc. However, although these patients can benefit from immunotherapy, they account for only 6% of all gastric cancer patients, and have a low incidence in advanced gastric cancer and are not suitable for most gastric cancer patients; in addition, CheckMate-032 (phase I/II) studies showed that the therapeutic efficacy of Nivolumab was independent of MSI, and that its single drug exhibited clinical efficacy and overall survival benefit in both MSI-H and non-MSI-H.

Tumor Mutation Burden (TMB) is defined as the total number of mutations per coding region in the tumor genome, which ranges from a few to thousands of mutations. There is increasing evidence that checkpoint inhibitors are associated with high tumor mutational load in a variety of tumors. However, the first prospective clinical study CHECKMATE-227 demonstrating that TMB can be a predictive marker of immunotherapeutic effect updated follow-up data results at 3 months in 2019 showed that TMB levels were not correlated with final patient survival outcomes. In addition, the results of two clinical studies comparing the superiority and inferiority of first-line chemotherapy and immunotherapy for non-small cell lung cancer, KEYNOTE-021 and KEYNOTE-189, show that the curative effect of patients with different tumor mutation loads is consistent.

In conclusion, the stability of various biomarkers in predicting the curative effect of the PD-1/PD-L1 monoclonal antibody is poor at present, the biomarkers are concentrated on tumor cells, certain limitations exist, the survival environment of the tumor is neglected, the microenvironment condition of the tumor is evaluated, the action mechanism of immunotherapy is not comprehensive, and the reason that the response rate of gastric cancer to the checkpoint inhibitor is not high in clinical tests of the biomarkers is partially explained; therefore, there is a need for more accurate biomarkers for predicting the efficacy of the immune checkpoint inhibitor PD-1/PD-L1 mab.

With the great breakthrough of immunotherapy in solid tumors, the importance of Tumor Microenvironment (TME) is gradually recognized, and Tumor cells are not isolated individuals and the microenvironment in which the Tumor cells are located is an active participant in Tumor development. The infiltration of various immune cells and interstitial cells in the tumor microenvironment plays an important role in killing tumors and escaping of tumor immunity. Compared with single biomarkers PD-L1 and MSI-H, TMB, the infiltration mode and the intricate and complex interaction of various immune cells and interstitial cells in a tumor microenvironment can more comprehensively reflect the evolution rule of tumor immunotherapy. The prediction of the reactivity of the immune checkpoint inhibitor PD-L1/PD-1 monoclonal antibody through the characteristics of the tumor microenvironment is an important measure for improving the treatment success rate of the current gastric cancer immune checkpoint inhibitor and developing the next generation immunotherapy.

The NanoString digital gene detection technology is a new generation of multiple nucleic acid quantitative technology, and plays an increasingly important role in the verification, research and clinical application of gene expression profiles. The NanoString technology can perform hybridization reactions of over eight hundred color barcode probes and specific sequences in one system, and finally, directly read quantitative results in a digital output mode. The technology has high automation degree, does not need reverse transcription and amplification processes of enzyme in the reaction, avoids related deviation, and has detection sensitivity and accuracy equivalent to those of a real-time fluorescent quantitative PCR technology. Currently, the NanoString digital gene detection technology plays an important role in the aspects of high-throughput research result verification, gene regulation and control mechanism, clinical disease molecular typing and the like. The research relates to a plurality of fields of animal and plant development, inflammation, immunity, stem cells, tumor, drug resistance, signal transduction and the like.

Disclosure of Invention

The invention aims to provide a group of molecular probes for detecting and evaluating a gene set of a tumor microenvironment, which is characterized in that the molecular probes are formed by connecting a report probe and a capture probe in series in a 5'-3' direction; the report probe is a fluorescent molecular bar code; the capture probe is a DNA molecule with biotin at the 3' end and can be specifically combined with mRNA obtained by transcription of a target gene.

Preferably, the probe comprises 44 nucleotide sequences shown as SEQ ID NO 1-44. The expression level of the gene related to the 44 selected tumor microenvironment according to the present invention, the probe accurately recognizes the target gene and binds to the target gene. The expression level of the target gene can be accurately obtained by a NanoString nCounter analysis system. The sequence designed by the probe influences the expression level of the target gene detected by the system. Therefore, in order to accurately detect the expression levels of 44 genes, probes with sequences shown as SEQ ID NO. 1-44 are screened out through a large number of experiments.

The invention also provides a kit for assessing a tumor microenvironment, which comprises the molecular probe.

The invention also provides application of the kit in preparing a NanoString nCounter analysis system for evaluating a tumor microenvironment scoring model.

Preferably, the kit further comprises an internal reference gene and a molecular probe for detecting the internal reference gene, wherein the sequence of the probe is shown in SEQ ID NO. 46-54.

Preferably, the reference genes include ACTB, ABCF1, B2M, G6PD, GAPDH, GUSB, PGK1, RPLPO, TFRC, TUBB. The introduction of the reference gene in the NanoString nCounter assay system allows the expression level of the target gene to be detected more accurately.

The invention also provides a construction method of the multi-gene scoring model for evaluating the tumor microenvironment, which comprises the following steps:

(1) collecting sequencing data of a tumor patient;

(2) performing component evaluation of immune cells of the tumor microenvironment on the sequencing data by using a CIBERSORT and MCP-counter algorithm;

(3) unsupervised clustering of immune cells in the tumor microenvironment is carried out through consensus 2, so that different tumor microenvironment types are obtained;

(4) finally determining a gene set capable of evaluating the tumor microenvironment score through pairwise difference analysis and random forest dimension reduction;

(5) immunotherapy cohort data was collected;

(6) evaluating the significance difference of each gene in the gene set obtained in the step (4) in the immunotherapy queue for predicting the immunotherapy response to obtain a corresponding P value, dividing the P value by the number of samples in the corresponding queue to obtain characteristic importance, adding the characteristic importance of each gene in all queues to obtain the sum of the characteristic importance of each gene, obtaining the positive distribution of the importance indexes through Shapiro-Wilk test, and taking the genes outside the 95% intervals on both sides as the important genes obtained by screening;

(7) transforming the gene expression data obtained in the step (6) by Z-score, and constructing a multigene scoring model for evaluating the tumor microenvironment by adopting a PCA (principal component analysis) method, wherein the model is TMEscore _ plus ═ TMEscorea A _ plus-TMEscoreB _ plus; TMEscorea A _ plus is the immune score and TMEscorea B _ plus is the interstitial score.

According to the invention, gene transcriptome data of tumors is utilized, a tumor microenvironment infiltration mode of gastric cancer is discovered through a machine learning algorithm, related genes capable of evaluating a tumor microenvironment are obtained through gene difference analysis and a random forest algorithm, the tumor microenvironment cell infiltration mode of a patient is quantitatively evaluated by inputting the related tumor microenvironment genes and using a principal component analysis algorithm, and a polygene scoring model for evaluating the tumor microenvironment is established.

Different data types need to be standardized, if the data is Affymetrix chip data, the data can be converted into a file format of RMA, and if the data is RNAseq data, the data needs to be converted into a format of TPM/FPKM as an input file of the next step.

References 1 to 3 for CIBERSORT, MCP-counter and consensus 2 algorithms, respectively:

1.M.Newman et al.,Robust enumeration of cell subsets from tissue expression profiles.Nat Methods 12,453-457(2015)

2.E.Becht et al.,Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression.Genome Biol 17,218(2016)

3.S.Monti,P.Tamayo,J.Mesirov,T.Golub,Consensus Clustering:AResampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data.Machine Learning 52,91-118(2003)。

pairwise difference analysis and random forest dimension reduction references 4, 5:

4.M.E.Ritchie et al.,limma powers differential expression analyses for RNA-sequencing and microarray studies.Nucleic Acids Research 43,e47-e47(2015)

5.M.B.Kursa,W.R.Rudnicki,Feature Selection with the Boruta Package.Journal of Statistical Software 036,(2010)。

the required sequencing data comprises the mRNA expression level of the gene detected by the technologies of real-time fluorescent quantitative PCR, gene chip, second-generation high-throughput sequencing, Panomics or NanoString and the like; when the data detected by using the second generation sequencing platform or Affymetrix chip technology needs to be paid attention to: samples of FFPE due to RNA degradation issues, tissue samples of greater than 2 years have been suggested for detection by Affymetrix chips or NanoString technology.

And (3) filtering, standardizing, correcting in batches and annotating genes of the original data of each sample, and removing repeated genes according to the average expression quantity.

TMEscore _ plus was calculated from the screened gene sets representing different microenvironment genotypes by Z-score transformation of gene expression data and quantification using principal component analysis algorithms (PCA). The code associated with the TMEscore _ plus calculation is referenced as follows:

# install TMEscore R packet

devtools::install_github("DongqiangZeng0808/TMEscore")

# load R Package

library('TMEscore')

# input Gene expression matrix-and calculation of tumor microenvironment score

tmescore<-tmescore(eset＝eset_stad,classify＝T)

Link where detailed code and parameter references: https:// github. com/DongqiangZeng0808/TMEscore

The code core is a PCA algorithm, and the calculation principle of the PCA analysis is as follows:

1) forming n rows and m columns of matrix X by the original data according to columns;

2) zero-averaging each row of X (representing an attribute field), i.e. subtracting the average of this row;

3) solving a covariance matrix;

4) solving the eigenvalue of the covariance matrix and the corresponding eigenvector;

5) arranging the eigenvectors into a matrix from top to bottom according to the size of the corresponding eigenvalue, and taking the first k rows to form a matrix P;

6) and Y is PX which is the data from dimensionality reduction to dimensionality k.

The invention also provides a gene set for evaluating the tumor microenvironment, which comprises tumor microenvironment immune related genes and tumor microenvironment stroma related genes; the tumor microenvironment immune-related genes comprise: CDT1, PSAT1, IDO1, BRIP1, WARS, DTL, GBP5, KIF18A, UBD, GZMB, CCL4, CCL5, RCC1, HELLS, GNLY, TAP1, CD8A, CXCL11, GBP4, ETV7, ZNF367, CXCL10, KLRC2, CXCL9, and IFNG; the tumor microenvironment stroma-related genes comprise: MIR100HG, pricke 2, PAPLN, PLEKHH2, TNS1, MCC, MAMDC2, C14orf132, SYNPO, PID1, MCEMP1, zchcc 24, PROS1, JAM3, TGFB1I1, MXRA7, FILIP1, CRYAB, and ANTXR 2.

The data (data from GSE62254) standardized by Affymetrix chips of 300 patients with gastric cancer were screened and modeled, and 244 genes were obtained by co-screening in the above steps (1) to (4), and the above 44 important genes were obtained by reduction in the steps (5) and (6). Compared with the gene set before further screening, the gene set is more simplified, and after evaluation, the simplified gene set has consistent tumor microenvironment evaluation efficacy and is remarkably improved in the aspect of predicting immunotherapy.

The invention also provides a multigene scoring model for evaluating a tumor microenvironment, which comprises TMEscore _ plus, TMEscorea _ plus is TMEscorea _ plus-TMEscoreB _ plus; TMEscorea A _ plus is an immune score, and TMEscorea B _ plus is an interstitial score; the immune score and the interstitial score are calculated from the expression data of the gene set according to claim 4 by: the gene expression data were Z-score transformed and the immune score and the interstitial score were calculated separately using PCA analysis.

The invention also provides application of the gene set in preparation of a kit for evaluating a tumor microenvironment.

The invention has the beneficial effects that: the invention screens 44 genes with highest prognosis correlation with gastric cancer, designs a probe for detecting mRNA of each gene transcript, carries out NanoString digital expression detection on the expression of the marker genes in the same hole, draws a digital gene expression profile, and can be used for evaluating the immunotherapy effect. Compared with the prior art, the invention has at least the following advantages: the detection flux is high, the manual operation consumes short time, the used sample amount is small, the repeatability is good, the accuracy and the sensitivity are high, and the detection result is equivalent to the RT-PCR quantitative technology. The invention also provides a construction method of the polygene scoring model for evaluating the gastric cancer tumor microenvironment, and 44 obtained gene sets capable of representing different microenvironment typing are screened out, and the gene sets can be used for establishing the polygene scoring model for evaluating the tumor microenvironment of patients. The model can be used as a good prognosis biomarker by effectively evaluating the tumor microenvironment, can also be used for predicting the immunotherapy response of checkpoint inhibitors of various tumors, and has important significance and clinical application value for realizing individual medical treatment and saving medical resources.

Drawings

FIG. 1 is a schematic diagram of screening and evaluation of important genes in tumor microenvironment. A shows the importance distribution of the predicted immune curative effect of 244 tumor microenvironment genes; panel B is the importance ranking of the 44 genes selected; graph C shows that the scoring model after the lean model has consistency in assessing the tumor microenvironment with the scoring model before the lean model.

Figure 2 is a graphical representation of the accuracy of tumor microenvironment predictive gastric cancer immunotherapy scores validated in multicenter NanoString data. As shown in Panel A, the tumor-effective patients had significantly higher TMEscorea A _ plus (immune score) than non-responsive tumors (progressive disease, PD; stable disease, SD), and the statistical P values of TMEscorea B _ plus, which were negatively correlated with the immunotherapy effect of advanced gastric cancer, were 6.1E-6, 0.047, and 0.00046, respectively, indicating that stromal activation is a key mechanism for primary drug resistance in I immunotherapy, and also indicating that the combination of immune score and stromal activation score can improve prediction accuracy; FIG. B shows that the TMEscore _ plus prediction accuracy of the present invention is higher than other authoritative gene scores, ROC equals 0.877; panel C shows that the prediction accuracy of TMEscore _ plus is significantly higher than that of detection of a checkpoint inhibitor gene alone; figure DEFG shows that 19 patients received immunosurgle and 29 received immunoconjugates or other inhibitors of multicenter nanostring data for gastric cancer. We systematically evaluated the predictive accuracy of the above biomarkers in immunotherapy single and combination therapies. Most immune checkpoint inhibitor-related genes and immune activation-related gene scores were positively correlated with the immunotherapy response alone (fig. 2 DE). However, their predicted effect was significantly reduced in the combination treatment group (fig. 2F), especially the signature gene score associated with immune activation (fig. 2G). However, TMEscore _ plus has better prediction capability in both cases.

Detailed Description

In order to show technical solutions, purposes and advantages of the present invention more concisely and clearly, the technical solutions of the present invention are described in detail below with reference to specific embodiments. Unless otherwise specified, the reagents involved in the examples of the present invention are all commercially available products, and all of them are commercially available. The technical means used in the examples are conventional means well known to those skilled in the art.

Embodiment 1 the multi-gene scoring model of the tumor microenvironment and the construction method thereof

The construction method of the multi-gene scoring model for evaluating the tumor microenvironment comprises the following steps:

(1) affymetrix chip standardized data from 300 patients with gastric cancer; the data is sourced from GSE62254 (obtained from NCBI public database);

(2) carrying out component evaluation on immune cells in the tumor microenvironment on the sequencing data through a CIBERSORT algorithm and an MCP-counter algorithm to obtain component evaluation results of 23 immune cells;

(3) unsupervised clustering of immune cells in the tumor microenvironment is carried out through consensus 2, so that different tumor microenvironment types are obtained;

(4) finally determining 244 gene sets capable of evaluating the tumor microenvironment scores through pairwise difference analysis and a random forest machine learning algorithm;

(5) in order to further realize the clinical transformation and higher economic benefit of the scoring model, 6 immunotherapy cohort data are collected to screen important genes; references 6-11 for the 6 immunotherapy cohorts of data;

6.S.T.Kim et al.,Comprehensive molecular characterization of clinicalresponses to PD-1inhibition in metastatic gastric cancer.Nat Med 24,1449-1458(2018)

7.S.Mariathasan et al.,TGFbeta attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells.Nature 554,544-548(2018)

8.J.D.Minna et al.,Contribution of systemic and somatic factors to clinical response and resistance to PD-L1 blockade in urothelial cancer:An exploratory multi-omic analysis.PLOS Medicine 14(2017)

9.F.Ulloa-Montoya et al.,Predictive gene signature in MAGE-A3antigenspecific cancer immunotherapy.J Clin Oncol 31,2388-2395(2013)

10.W.Hugo et al.,Genomic and Transcriptomic Features of Response to Anti-PD-1Therapy in Metastatic Melanoma.Cell 165,35-44(2016)

11.P.L.Chen et al.,Analysis of Immune Signatures in Longitudinal Tumor Samples Yields Insight into Biomarkers of Response and Mechanisms of Resistance to Immune Checkpoint Blockade.Cancer Discov 6,827-837(2016)；

(6) firstly, evaluating the significance difference of each gene in each queue for predicting the immunotherapy response, obtaining the statistical difference P value (P value) of each gene in the corresponding queue, dividing the P value by the number of samples of the corresponding queue to obtain the characteristic importance, and then adding the characteristic importance data of each gene in 6 queues to obtain the characteristic importance sum of 244 genes; obtaining positive and negative distribution of importance indexes through Shapiro-Wilk test, and taking genes outside an index 95% interval, namely [ - ∞, -90] and [80, + ∞ ] as input genes of a simple model according to the distribution rule of the importance indexes to finally obtain 44 genes;

FIG. 1A shows the characteristic importance distribution of 244 genes, and FIG. 1B shows that the input genes include tumor microenvironment immune-related genes and tumor microenvironment stroma-related genes (44 genes in total); the tumor microenvironment immune-related genes comprise: CDT1, PSAT1, ID01, BRIP1, WARS, DTL, GBP5, KIF18A, UBD, GZMB, CCL4, CCL5, RCC1, HELLS, GNLY, TAP1, CD8A, CXCL11, GBP4, ETV7, ZNF367, CXCL10, KLRC2, CXCL9, and IFNG; the tumor microenvironment stroma-related genes comprise: MIR100HG, PRICKLE2, PAPLN, PLEKHH2, TNS1, MCC, MAMDC2, C14or f132, SYNPO, PID1, MCEMP1, zchc 24, PROS1, JAM3, TGFB1I1, MXRA7, FILIP1, CRYAB, and ANTXR 2; FIG. 1C shows the consistency of the scoring model after the compact model (named as TMEscore plus, 44 genes) and the scoring model before the compact model (named as TMEscore, 244 genes) in the evaluation of the tumor microenvironment, which indicates that the correlation between the two scores before and after the evaluation is still high, and indicates that the efficiency of the gene set for evaluating the tumor microenvironment is not changed although the genes are reduced;

(7) constructing a multigene scoring model for evaluating a tumor microenvironment based on the input genes obtained in the step (6), wherein the model is TMEscore _ plus (immune score) -TMEscorea B _ plus (interstitial score);

the calculation method is as follows: the dimensionality reduction of the PCA algorithm is carried out on the expression matrixes of the 25 immune genes in the group A, PC1 is taken as the score of an immune microenvironment (TMEscore A), the matrix dimensionality of the PCA algorithm is carried out on the expression matrixes of the 19 mesenchymal genes in the group B, PC1 is taken as the score of the mesenchymal microenvironment (TMEscore B), and the TMEscore _ plus, namely the score of the tumor microenvironment, is obtained by subtracting the mesenchymal score from the immune score.

The relevant codes in step (7) are referred to as follows:

# install TMEscore R packet

devtools::install_github(“DongqiangZeng0808/TMEscoreN”)

# load R Package

library('TMEscore')

# inputting Gene expression matrix and calculating tumor microenvironment score

tmescore<-tmescore(eset＝eset_stad，classify＝T)

In the data (GSE15459, GSE57303, GSE62254, GSE84437 and TGGA-STAD), the efficacy of both sets of genes (TMEscore, TMEscore plus) before and after the reduction to assess the tumor microenvironment was evaluated and the results are shown in FIG. 1C. From the evaluation results, although the number of genes was reduced, the correlation between the two scores was still high, indicating that the efficacy of the two gene sets for evaluating the tumor microenvironment was hardly changed.

TMEscore is a gene set obtained by the above construction method without performing steps (5) and (6), and a value obtained by calculation is expressed as TMEscore, and reference 12 may be specifically made.

TMEscore, GEPs scoring model references 12, 13:

12.D.Zeng et al.,Tumor Microenvironment Characterization in Gastric Cancer Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures.Cancer Immunology Research 7,737-750(2019)

13.R.Cristescu et al.,Pan-tumor genomic biomarkers for PD-1checkpoint blockade-based immunotherapy.Science 362(2018)。

example 2 example of examination of samples from gastric cancer patients

In this example, of 70 specimens collected from a total of 5 clinical centers (southern hospital of southern medical university, tumor center of zhongshan university, chinese academy of guangdong province, sixth subsidiary hospital of zhongshan university, first subsidiary hospital of zhongshan university), 48 specimens had better RNA quality for subsequent detection. Patients from multiple clinical centers were collected either formalin-fixed paraffin-embedded (FFPE) or freshly frozen tumor tissue prior to receiving checkpoint immunotherapy. Tumors were evaluated for immunotherapy response according to RECIST 1.1 criteria.

The method comprises the following steps:

1. extraction of RNA from tumor tissue of gastric cancer patient

Firstly, directly placing tissues of a tumor sample into a mortar, adding a small amount of liquid nitrogen, quickly grinding, adding a small amount of liquid nitrogen when the tissues become soft, grinding again, and repeating for three times;

② adding 50-100mg of tissue sample into 1ml of Trizol, and transferring into a centrifuge tube. In addition, the tissue volume cannot exceed 10 percent of the Trizol volume, and then an electric homogenizer is used for fully homogenizing for 1-2 min;

thirdly, centrifuging for five minutes at 12000r/min, removing the precipitate, adding 200 microliters of chloroform into each milliliter of Trizol, tightly covering a centrifugal tube, shaking and mixing uniformly by hand for 15 seconds, and standing for ten minutes at room temperature;

fourthly, centrifuging for fifteen minutes at 12000g at four ℃ to absorb the upper aqueous phase, transferring the upper aqueous phase into another new centrifugal tube, adding 0.6ml of isoamyl alcohol into each ml of Trizol, uniformly mixing and standing for 5-10 minutes at room temperature;

fifthly, centrifuging for ten minutes at 12000g at four ℃, removing supernatant, adding 1ml of 75% ethanol into each ml of Trizol, and performing mild oscillation to perform suspension precipitation;

sixthly, drying in the air or in vacuum for 5-10min at room temperature, and then measuring the absorbance at 260nm to quantify the RNA concentration;

finally, mRNA can be separated from RNA, or the RNA can be stored in 70% ethanol and stored at-70 ℃, and the homogenate before adding chloroform can be stored for more than one month;

or 1, extracting RNA of formalin-fixed wax-lump tissue (FFPE) specimen

Note that: for samples of FFPE, it is recommended that the storage time of the samples be less than 2 years;

cutting 5-8 paraffin sections (5-10 μm thick);

adding PBS, whirling, shaking, mixing uniformly, centrifuging at room temperature, discarding supernatant, and repeating for 3 times;

filling the sample into a sterile centrifuge tube, adding dimethylbenzene, and violently swirling;

(iv) High purity RNA was extracted using the High Pure FFPET RNA Isolation Kit from Roche.

Step two: quantitative detection of RNA

Quantification of RNA Using the nCounter platform from NanoString

Carrying out hybridization: the fluorescent barcode probe directly hybridizes to a target molecule in a liquid phase. Wherein the reporter probe carries a signal and the capture probe is used to immobilize the complex for data acquisition;

in the process of detecting the expression levels of immune genes and mesenchymal genes by using a NanoString method, the inventor also designs molecular probes aiming at the 44 genes, wherein the molecular probes consist of a report probe and a capture probe, the 5 'end of the report probe carries fluorescent molecular barcodes with different colors, and the 3' end of the capture probe carries biotin.

The 44 probes and the corresponding gene names in the invention are specifically shown in the following table 1:

table 1:

purifying and fixing: transferring the sample to nCounter Prep Station, eluting the redundant probe, and combining the probe and the target molecule compound, fixing and arranging on the sample plate;

thirdly, digital data acquisition: the sample plate was placed in a counter digital analyzer for data acquisition. Counting and presenting in tabular form the fluorescent bar codes of each target molecule;

analysis is carried out through nSolver software: inputting the 44 gene expression data into the nSolver, selecting 10 reference genes as references, clicking the normalization of menu bar, introducing the off-line RCC data into nSovler software, carrying out data normalization analysis together with a database, and calculating the expression value of each gene in the sample, wherein the obtained expression value is the expression level of each gene.

In the step, the 10 reference genes are respectively: ACTB, ABCF1, B2M, G6PD, GAPDH, GUSB, PGK1, RPLPO, TFRC, TUBB.

Step three: calculation of tumor microenvironment scores

The score for each tumor patient was calculated using the following method:

1. calculating the arithmetic mean of 25 tumor microenvironment a genes by immune score (TMEscoreA _ plus); 2. calculating the arithmetic mean of 19 tumor microenvironment B genes (TMEscoreB _ plus);

(arithmetic mean is a statistical indicator of the tendency in a data set, which is the sum of a set of data divided by the number of items in the set)

3. TMEscore _ plus ═ immune score-interstitial score, i.e. the patient's tumor microenvironment score;

the arithmetic mean is the sum of the values of each gene divided by the number of genes.

Step four: verifying the effectiveness of the prediction model

The tumor microenvironment scoring model of the invention is used for predicting the curative effect of the gastric cancer immunotherapy of the specimen. The results of evaluating the predicted immunotherapy effect of the tumor microenvironment score by using a non-parametric test (wilcoxon: rank sum test) and a ROC test, respectively, are shown in fig. 2, patients with high tumor microenvironment scores are significantly enriched in patients who can benefit from treatment, P is 6.1e-6, and the ROC value of the prediction accuracy of TMEscore _ plus is 0.877, which is higher than that of other models compared with other prediction indexes. The TMEscore _ plus scoring model disclosed by the invention has better prediction stability in a single-use immunotherapy group or a combination therapy group.

Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the protection scope of the present invention, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

SEQUENCE LISTING

<110> southern hospital of southern medical university

<120> construction method of tumor microenvironment scoring system for predicting gastric cancer immunotherapy curative effect and molecular probe

<130> 5.13

<160> 54

<170> PatentIn version 3.3

<210> 1

<211> 100

<212> DNA

<213> Synthesis

<400> 1

gaagcttcct cgcaactttg tggtagatta ctatgagacc agcagcctct gctcccagcc 60

agctgtggta ttccaaacca aaagaagcaa gcaagtctgt 100

<210> 2

<211> 100

<212> DNA

<213> Synthesis

<400> 2

agtgtgtgcc aacccagaga agaaatgggt tcgggagtac atcaactctt tggagatgag 60

ctaggatgga gagtccttga acctgaactt acacaaattt 100

<210> 3

<211> 100

<212> DNA

<213> Synthesis

<400> 3

gctcagggct ctttcctcca caccattcag gtctttcttt ccgaggcccc tgtctcaggg 60

tgaggtgctt gagtctccaa cggcaaggga acaagtactt 100

<210> 4

<211> 100

<212> DNA

<213> Synthesis

<400> 4

gcggagcgtc tttgtgtccg aacgcaagcc tgcgctcagc atggaggtgg cctgtgccag 60

gatggtgggc agctgttgta ctatcatgag ccctggggaa 100

<210> 5

<211> 100

<212> DNA

<213> Synthesis

<400> 5

gccataattg ttcttagttt gcagttacac taaaaggtga ccaatgatgg tcaccaaatc 60

agctgctact actcctgtag gaaggttaat gttcatcatc 100

<210> 6

<211> 100

<212> DNA

<213> Synthesis

<400> 6

ttcaaaagag gacgctgtct ttgcataggc cctggggtaa aagcagtgaa agtggcagat 60

attgagaaag cctccataat gtacccaagt aacaactgtg 100

<210> 7

<211> 100

<212> DNA

<213> Synthesis

<400> 7

caccatctcc catgaagaaa gggaacggtg aagtactaag cgctagagga agcagccaag 60

tcggttagtg gaagcatgat tggtgcccag ttagcctctg 100

<210> 8

<211> 100

<212> DNA

<213> Synthesis

<400> 8

gtatgagctg ctccagtaca tcaagaccca gcggcgagcc ctggtgtgtg ggcccttttt 60

tggagggatc ttcaggctga agacgcccac ccagcactct 100

<210> 9

<211> 100

<212> DNA

<213> Synthesis

<400> 9

ttctacaaga tatgccatgg gccttttcac aggggacaca ggcttcttaa aacaacccgg 60

cttcctcacc ctatgtcctt tatttacaaa gctgtgctcc 100

<210> 10

<211> 100

<212> DNA

<213> Synthesis

<400> 10

attacagact gaccaggctc tcacagagac ggaaaaaaag aagaaagagg cacaagtgaa 60

agcagaagct gaaaaggctg aagcgcaaag gttggcggcg 100

<210> 11

<211> 100

<212> DNA

<213> Synthesis

<400> 11

tgccggctcc tcgcttcctc gatccagaat ccactctcca gtctccctcc cctgactccc 60

tctgctgtcc tcccctctca cgagaataaa gtgtcaagca 100

<210> 12

<211> 100

<212> DNA

<213> Synthesis

<400> 12

acactacaag aggtgaagat gacagtgcag gaagatcgaa agtgcgaatc tgacttacgc 60

cattattacg acagtaccat tgagttgtgc gtgggggacc 100

<210> 13

<211> 100

<212> DNA

<213> Synthesis

<400> 13

gatgggtcca tgtcttactc agagagagaa aaaaacatgc acagcttcaa cacggatcca 60

gaggtgttta tcttcttagt gagtacacga gctggtggcc 100

<210> 14

<211> 100

<212> DNA

<213> Synthesis

<400> 14

atcaccatgg catatgtgtg gggcaaaggt catggagatg tccgtaaggt cttgccaaga 60

aatattgctg ttccttactg ccaactctcc aagaaactgg 100

<210> 15

<211> 100

<212> DNA

<213> Synthesis

<400> 15

atactatcca gttactgccg gtttgaaaat atgcctgcaa tctgagccag tgctttaatg 60

gcatgtcaga cagaacttga atgtgtcagg tgaccctgat 100

<210> 16

<211> 100

<212> DNA

<213> Synthesis

<400> 16

catagacttg caatgttgaa aactcgtcgc tcctacctgg agaaaaggag ggaggaggaa 60

ttgaagcaat ttgatgagaa tactaattgg ctccatcgtg 100

<210> 17

<211> 100

<212> DNA

<213> Synthesis

<400> 17

tatgtgagtc agcttatagg aagtaccaag aacagtcaaa cccatggaga cagaaagtag 60

aatagtggtt gccaatgtct cagggaggtt gaaataggag 100

<210> 18

<211> 100

<212> DNA

<213> Synthesis

<400> 18

tggggtctcc tgggttcagc ggctgttgat tcaaggtcaa cattgaccat tggaggagtg 60

gtttaagagt gccaggcgaa gggcaaactg tagatcgatc 100

<210> 19

<211> 100

<212> DNA

<213> Synthesis

<400> 19

atatttggct cagaacaggt gtccatggga caaaaaagaa cgatcctcca cttgaccaag 60

aaaaaagtga ttctcccaga agcacaaagc atactcttgc 100

<210> 20

<211> 100

<212> DNA

<213> Synthesis

<400> 20

gtggctgcag tgggacaaga gccacaggta tttggaagaa gtcttcaaga aaatattgcc 60

tatggcctga cccagaagcc aactatggag gaaatcacag 100

<210> 21

<211> 100

<212> DNA

<213> Synthesis

<400> 21

gcaaagccaa atctcaggga agtccttggt tgatgtatct gggtctcctc tggagcactc 60

tgccctcctg tcacccagta gagtaaataa acttccttgg 100

<210> 22

<211> 100

<212> DNA

<213> Synthesis

<400> 22

ggcccagagc agatactgtc cgcgatttaa taaatgaagg agagcattca tccagcagaa 60

tccgttgtaa catctgtaat agggtgtttc cacgggagaa 100

<210> 23

<211> 100

<212> DNA

<213> Synthesis

<400> 23

aacatgcaaa ggtcatcaat gcagcctcaa gtcagttgcc ttttctaagt ttgagaaagc 60

tgtattctgt acgggtggaa gagatggcaa cattatggtc 100

<210> 24

<211> 100

<212> DNA

<213> Synthesis

<400> 24

cctcctccag gtgcgaaggt ccagctcagt ggcacaagtg aaagcaatga tcgagactaa 60

gacgggtata atccctgaga cccagattgt gacttgcaat 100

<210> 25

<211> 100

<212> DNA

<213> Synthesis

<400> 25

gatgtgcaaa gcctgggata tagaagaact tgtcagcctg gggaagaaac taaaggcctg 60

tccatattac acagcccgag aactaataca agatgctgac 100

<210> 26

<211> 100

<212> DNA

<213> Synthesis

<400> 26

gaggcaaaga tatccaggtc acttggggct agtgtttatt gtgttggtgt ccttgatttt 60

gaacaagcac agcttgaaag aattgctgat tccaaggagc 100

<210> 27

<211> 100

<212> DNA

<213> Synthesis

<400> 27

cgacgccgtc ttgctatgga ttgccatcat agctacgctg gggaacatcg tggtggtggg 60

cgtggtgtat gccttcacct tctgaggacg gcacaccctg 100

<210> 28

<211> 100

<212> DNA

<213> Synthesis

<400> 28

taaaacaaga aagtttcccc accagtgaat gaaagtcttg tgactagtgc tgaagcttat 60

taatgctaag ggcaggccca aattatcaag ctaataaaat 100

<210> 29

<211> 100

<212> DNA

<213> Synthesis

<400> 29

ggctggatag tttttgagaa ctcgaatctg tcttgggctg tgagacaagc cactgggttc 60

ttcaaaggat aaactaccta catagaggac atacctttgg 100

<210> 30

<211> 100

<212> DNA

<213> Synthesis

<400> 30

aattattggg ggggttctgg ttgtccttgc tgtactggcc ctgatcacgt tgggcatctg 60

ctgtgcatac agacgtggct acttcatcaa caataaacag 100

<210> 31

<211> 100

<212> DNA

<213> Synthesis

<400> 31

agggaggtta tgttgccctg gatgatattt cattctctcc tgttcactgc cagaatcaga 60

cagaacttct gttcagtgcc gtggaagcca gctgcaattt 100

<210> 32

<211> 100

<212> DNA

<213> Synthesis

<400> 32

cctgagcaaa gtgggatgtg catggctctt ttttgtgatt aagcaggcat caaatgggca 60

gttttcattt tcactgacac agaaacatgt ggctgaagca 100

<210> 33

<211> 100

<212> DNA

<213> Synthesis

<400> 33

catcatcctg tcagccttca tcatggtgaa gaatgctgag atgtccaagg agctgctggg 60

ctttaaaagg gagctttgga atgtctcaaa ctccgtacaa 100

<210> 34

<211> 100

<212> DNA

<213> Synthesis

<400> 34

atttagagtg acttacagaa gatcgaactt tggagtgtgg cagagtaagg gatggaaacc 60

gggccctcca gttcactatc agtagctttt gcactggtct 100

<210> 35

<211> 100

<212> DNA

<213> Probe

<400> 35

gggcctgagg aggaggacgg agaaggcttc tccttcaaat acagccccgg gaagctgagg 60

ggaaaccagt acaagaagat gatgaccaaa gaggagctgg 100

<210> 36

<211> 100

<212> DNA

<213> Synthesis

<400> 36

cctactacag cgccccaaac aagtgtgaac tgaactgcat tcccaagggg gagaacttct 60

actacaagca cagggaggct gtggttgatg ggacgccctg 100

<210> 37

<211> 100

<212> DNA

<213> Synthesis

<400> 37

ataagtagca gccctttcct ggatgactca tctgggtcag aggaagaaga cagctccaga 60

tccagctccc ggacgtcaga gtcagactca cgcagtagga 100

<210> 38

<211> 100

<212> DNA

<213> Synthesis

<400> 38

tcagggcgct ttccttctgt catatagttg tgggatctct accaagtgtg aaggtgaatg 60

aggtaaggga gatcagaacc atgcttcctg gtttttcata 100

<210> 39

<211> 100

<212> DNA

<213> Synthesis

<400> 39

atagatgaat gctctgagaa catgtgtgct cagctttgtg tcaattaccc tggaggttac 60

acttgctatt gtgatgggaa gaaaggattc aaacttgccc 100

<210> 40

<211> 100

<212> DNA

<213> Synthesis

<400> 40

cacaatccgc cagccaccga tgtcaatcag aacccaccgg caactgttgt cccacagagc 60

ctgccacttt ctagcatcca acagaattcc tcagaggccc 100

<210> 41

<211> 100

<212> DNA

<213> Synthesis

<400> 41

gccaccgtgg gacatcaagt ggaagaactt gtttgcttga aagtatctca gacccaaggc 60

acctcaggtc tctttgctgt gcctccacta tattgtcgtg 100

<210> 42

<211> 100

<212> DNA

<213> Synthesis

<400> 42

tctcatttcc caatgcctct ctgtgggaga gctccatgcc agttttcacc acgctcaggc 60

aaatactctg cagctgttat tggatgggcc attccgatct 100

<210> 43

<211> 100

<212> DNA

<213> Synthesis

<400> 43

aatcatgtct cagttcccat ctagcaaggt ggcttcagga gagcagaagg aggaccagtc 60

tgaagataag aaaagaccca gcctcccttc cagcccgtct 100

<210> 44

<211> 100

<212> DNA

<213> Synthesis

<400> 44

agctttattt ggggagtttc acccagaatg gtgggagaaa cctcccaggt gccaggtacc 60

ccgcatcgtg acccttcact tggtgtctta ggaagtcaag 100

<210> 45

<211> 100

<212> DNA

<213> Synthesis

<400> 45

acaacatcgc ctgcgttatc ctcaccttca aggagccctt tggcactgag ggtcgcgggg 60

gctatttcga tgaatttggg atcatccggg acgtgatgca 100

<210> 46

<211> 100

<212> DNA

<213> Synthesis

<400> 46

gaacgggaag cttgtcatca atggaaatcc catcaccatc ttccaggagc gagatccctc 60

caaaatcaag tggggcgatg ctggcgctga gtacgtcgtg 100

<210> 47

<211> 100

<212> DNA

<213> Synthesis

<400> 47

tgcagaagga gatcactgcc ctggcaccca gcacaatgaa gatcaagatc attgctcctc 60

ctgagcgcaa gtactccgtg tggatcggcg gctccatcct 100

<210> 48

<211> 100

<212> DNA

<213> Synthesis

<400> 48

ccgatttcat gactgaacag tcaccgacga gagtgctggg gaataaaaag gggatcttca 60

ctcggcagag acaaccaaaa agtgcagcgt tccttttgcg 100

<210> 49

<211> 100

<212> DNA

<213> Synthesis

<400> 49

gcaagaagta tgctgaggct gtcactcggg ctaagcagat tgtgtggaat ggtcctgtgg 60

gggtatttga atgggaagct tttgcccggg gaaccaaagc 100

<210> 50

<211> 100

<212> DNA

<213> Synthesis

<400> 50

gatgtcctcc cgccaagcca tgttagaaaa tgcatctgac atcaagctgg agaagttcag 60

catctccgct catggcaagg agctgttcgt caatgcagac 100

<210> 51

<211> 100

<212> DNA

<213> Synthesis

<400> 51

tactgaagaa tggagagaga attgaaaaag tggagcattc agacttgtct ttcagcaagg 60

actggtcttt ctatctcttg tactacactg aattcacccc 100

<210> 52

<211> 100

<212> DNA

<213> Synthesis

<400> 52

cgaaatgttt cattgtggga gcagacaatg tgggctccaa gcagatgcag cagatccgca 60

tgtcccttcg cgggaaggct gtggtgctga tgggcaagaa 100

<210> 53

<211> 100

<212> DNA

<213> Synthesis

<400> 53

cagtttccac catctcggtc atcaggattg cctaatatac ctgtccagac aatctccaga 60

gctgctgcag aaaagctgtt tgggaatatg gaaggagact 100

<210> 54

<211> 100

<212> DNA

<213> Synthesis

<400> 54

ttctaagtat gtccatttcc catctcagct tcaagggagg tgtcagcagt attatctcca 60

ctttcaatct ccctccaagc tctactctgg aggagtctgt 100