CN113981078B - Biomarker for predicting curative effect of EGFR (epidermal growth factor receptor) -resistant targeted therapy of patients with advanced esophageal cancer and curative effect prediction kit - Google Patents
Biomarker for predicting curative effect of EGFR (epidermal growth factor receptor) -resistant targeted therapy of patients with advanced esophageal cancer and curative effect prediction kit Download PDFInfo
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Abstract
The invention provides a biomarker for predicting curative effect of EGFR (epidermal growth factor receptor) targeted therapy of patients with advanced esophageal cancer and a curative effect prediction kit, wherein the biomarker acquisition method comprises the following steps: extracting RNA of a sample to be detected, breaking the RNA of the sample to be detected, and carrying out reverse transcription to obtain cDNA; constructing a gene library based on cDNA through a terminal repair, linker ligation and library enrichment method; capturing and enriching target genes from the constructed gene library through specific hybridization of the capture probes and the target regions; sequencing by using a high-throughput sequencer to obtain RNA targeted sequencing data; quantitatively evaluating the expression quantity of genes ALK, NTRK2 and NTRK3 in the RNA targeting sequencing data by adopting an RPKM method; the biomarker is obtained based on the average of the expression amounts obtained by the evaluation. The method has the advantages of high detection sensitivity, low sequencing cost, probe saving and the like, and is more suitable for developing clinical kits.
Description
Technical Field
The invention relates to the technical field of biomedicine, in particular to a biomarker for predicting curative effect of EGFR (epidermal growth factor receptor) targeted therapy of patients with advanced esophageal cancer and a curative effect prediction kit.
Background
In recent years, the incidence of esophageal cancer diagnosed by more than one million people worldwide per year, especially in developing countries, rises year by year, and the incidence of young people is rising per year, undoubtedly becoming a huge health burden worldwide. In gastroesophageal cancer, up to 30% of patients carry human epidermal growth factor receptor 2 (ERBB 2/HER 2) amplification or overexpression. Trastuzumab, a HER 2-targeted mab, was combined with first-line chemotherapy to increase survival in these patients.
Although trastuzumab increases the survival of ERBB 2-amplified gastroesophageal cancer patients, patients still typically develop disease progression within one year, and thus new post-line treatment regimens are urgently needed for trastuzumab-resistant gastroesophageal cancer patients.
Meanwhile, afatinib (Afatinib) which is an anti-EGFR targeting therapeutic drug is a powerful and irreversible dual inhibitor of EGFR (EGFR) and HER2 (HER 2) tyrosine kinase, is suitable for local advanced or metastatic non-small cell lung cancer (NSCLC) with Epidermal Growth Factor Receptor (EGFR) gene sensitive mutation, local advanced or metastatic squamous cell lung cancer (NSCLC) which has not been treated by EGFR Tyrosine Kinase Inhibitor (TKI) before, platinum-containing chemotherapy or disease progression after chemotherapy, and even has potential to be used for posterior-line anti-EGFR targeting treatment (Afatinib) after resistance of EGFR IHC trastuzumab in patients with esophagus with strong positive late IHC staining. Screening for potential or predictive biomarkers in patients is therefore of clinical importance.
Disclosure of Invention
Aiming at the problems, the invention provides a biomarker for predicting the curative effect of EGFR targeted therapy of patients with advanced esophageal cancer and a curative effect prediction kit, which effectively improve the sensitivity and the like for predicting the curative effect of EGFR targeted therapy (afatinib).
The technical scheme provided by the invention is as follows:
a biomarker for predicting the efficacy of anti-EGFR targeted therapy in patients with advanced esophageal cancer, calculated by the biomarker acquisition method comprising:
extracting RNA of a sample to be detected, breaking the RNA of the sample to be detected, and carrying out reverse transcription to obtain cDNA;
constructing a gene library based on the obtained cDNA by a terminal repair, linker ligation and library enrichment method;
capturing and enriching target genes from the gene library by specific hybridization of capture probes to target regions;
sequencing by using a high-throughput sequencer to obtain RNA targeted sequencing data;
quantitatively evaluating the expression quantity of genes ALK, NTRK2 and NTRK3 in the RNA targeting sequencing data by adopting an RPKM method;
the biomarker is obtained based on the average of the expression amounts obtained by the evaluation.
Further preferably, the obtaining the biomarker based on the estimated average of the expression amounts includes:
normalizing the expression quantity of genes ALK, NTRK2 and NTRK 3;
calculating a biomarker score according to the standardized expression quantity:
biomarker score = mean [ lg (nrkm) ALK )+lg(nRPKM NTRK2 )+lg(nRPKM NTRK3 )]
Wherein nRPKM ALK Represents the standardized expression level of the ALK gene, and nRPKM ALK ＝RPKM ALK ×HK_coefficient ALK ，RPKM ALK Expression Gene ALKNormalized expression level of HK_coefficient ALK The expression level change coefficient of the gene ALK is expressed; nRPKM NTRK2 Represents the standardized expression level of the gene NTRK2 and nRPKM NTRK2 ＝RPKM NTRK2 ×HK_coefficient NTRK2 ，RPKM NTRK2 Represents the standardized expression level of the gene NTRK2, HK_coefficient NTRK2 The expression level change coefficient of the gene NTRK 2; nRPKM NTRK3 Represents the standardized expression level of the gene NTRK3 and nRPKM NTRK3 ＝RPKM NTRK3 ×HK_coefficient NTRK3 ，RPKM NTRK3 Represents the normalized expression level of the gene NTRK3, HK_coefficient NTRK3 The expression level change factor of the gene NTRK3 is shown.
Further preferably, after the RNA-targeted sequencing data is obtained by sequencing with a high-throughput sequencer, the method further comprises a quality control step, including:
filtering low-quality sequencing data and reads containing a linker sequence in the obtained RNA targeted sequencing data;
comparing the filtered RNA targeted sequencing data with a reference genome;
evaluating whether the comparison result accords with a preset index, wherein the preset index comprises the following steps: the sequence posting alignment rate is above a preset alignment rate threshold, the target area data volume is above a preset data volume threshold, and the expressed housekeeping gene number is above a preset gene number threshold;
the quantitative evaluation of the expression quantity of genes ALK, NTRK2 and NTRK3 in the RNA targeting sequencing data by adopting the RPKM method comprises the following steps: and quantitatively evaluating the expression amounts of genes ALK, NTRK2 and NTRK3 in the filtered RNA targeting sequencing data meeting preset indexes by adopting an RPKM method.
Further preferably, in the step of sequencing by using the high-throughput sequencer, in the step of obtaining the RNA-targeted sequencing data, the high-throughput sequencer is used for sequencing by adopting a double-ended or single-ended mode to obtain the RNA-targeted sequencing data.
The application of the biomarker in a chip or a kit for predicting the curative effect of EGFR-targeting treatment of patients with advanced esophageal cancer.
The biomarker and the efficacy prediction kit for predicting the efficacy of EGFR targeted therapy (afatinib) of an advanced esophageal cancer patient (EGFR IHC (++)), provided by the invention, are used for predicting the efficacy of EGFR targeted therapy (afatinib) by singly using scores of the biomarkers obtained by further calculating the expression quantity of genes ALK, NTRK2 and NTRK3 in a sample to be detected (a tissue sample of a cancer patient), and further have the advantages of high detection sensitivity, low sequencing cost, probe saving and the like, and are more suitable for development of clinical kits and have wide application prospects. Specifically, in the process of biomarker acquisition, the method for detecting the expression level of the RNA targeting sequencing (targeted RNA sequencing) technology can efficiently enrich RNA transcripts expressed by related genes and analyze the expression level of the genes in tumor tissues. Furthermore, RNApanel targets the target gene, compared with the detection of the whole transcriptome by using RNA-seq, the method has lower sequencing cost, can remarkably enrich the target region, has higher detection sensitivity, and is more suitable for the development of clinical kits.
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The above features, technical features, advantages and implementation thereof will be further described in the following detailed description of preferred embodiments with reference to the accompanying drawings in a clearly understandable manner.
FIG. 1 is a flowchart of a biomarker acquisition method of the present invention.
FIG. 2 is a study cohort of 35 patients with advanced esophageal squamous carcinoma (EGFR IHC (++ +): a) RNA marker score and efficacy of anti-EGFR targeted therapy (afatinib), tumor shrinkage, PFS relationship. A. The marker score predicts a subject work profile (ROC profile) of the prediction of the effectiveness of anti-EGFR targeted therapy (afatinib) in 35 late esophageal squamous carcinoma exploration cohort patients; the area under the curve, confidence interval and P value are shown in the figure; B. the patients with high/low marker scores in 35 patients with advanced esophageal squamous carcinoma have tumor regression, and patients with low marker scores mostly have tumor regression with different degrees; C. survival curves for patients with high/low marker scores among 35 patients with advanced esophageal squamous carcinoma who received anti-EGFR targeting therapy (afatinib) progression-free survival, and patients with low marker scores with progression-free survival higher than those with high scores, the statistics are shown in the figure; D. the efficacy of this high/low marker score patient on anti-EGFR targeted therapy (afatinib) in 35 patients with advanced esophageal squamous carcinoma was significantly higher in the low marker score patients than in the high score patients, and the statistics are shown in the figure.
FIG. 3 is a graph showing the relationship between the marker score and the anti-EGFR targeted therapy (afatinib), tumor shrinkage, and PFS in 11 patients with advanced esophageal squamous carcinoma (EGFR IHC (++)) in a validation cohort. A. The marker score predicts a subject's working profile (ROC curve) of the prediction of the effectiveness of anti-EGFR targeted therapy (afatinib) in 11 patients with advanced esophageal squamous carcinoma; the area under the curve, confidence interval and P value are shown in the figure; B. the patients with high/low marker scores showed different degrees of tumor regression among 11 patients with advanced esophageal squamous carcinoma; C. survival curves for patients with high/low marker scores among 11 patients with advanced esophageal squamous carcinoma who received anti-EGFR targeting therapy (afatinib) progression-free survival, and patients with low marker scores with progression-free survival higher than those with high marker scores, the statistics are shown in the figure; D. of the 11 patients with advanced esophageal squamous carcinoma, patients with high/low marker scores received anti-EGFR targeting therapy (afatinib) with a higher proportion of patients with low marker scores responding to anti-EGFR targeting therapy (afatinib) than patients with high marker scores, and the statistics are shown in the figure.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will explain the specific embodiments of the present invention with reference to the accompanying drawings. It is evident that the drawings in the following description are only examples of the invention, from which other drawings and other embodiments can be obtained by a person skilled in the art without inventive effort.
In one embodiment of the invention, a biomarker for predicting the efficacy of anti-EGFR targeted therapy (afatinib) in patients with advanced esophageal cancer (EGFR IHC (++), the biomarker is calculated by the following biomarker acquisition method, as shown in fig. 1, which comprises the following steps: s10, extracting RNA of a sample to be detected, breaking the RNA of the sample to be detected, and carrying out reverse transcription to obtain cDNA; s20, constructing a gene library based on the obtained cDNA through a terminal repair, joint connection and library enrichment method; s30, capturing and enriching target genes from the constructed gene library through specific hybridization of the capture probes and the target regions; s40, sequencing by using a high-throughput sequencer to obtain RNA targeted sequencing data; s50, quantitatively evaluating the expression quantity of genes ALK, NTRK2 and NTRK3 in the RNA targeted sequencing data by adopting an RPKM method; s60 obtaining the biomarker based on the average of the expression amounts obtained by the evaluation.
Specifically, in step S10, the sample to be detected is late esophagus cancer patients (EGFR IHC (+++)) samples after anti-EGFR targeted therapy (afatinib).
After sequencing by using a high-throughput sequencer to obtain RNA-targeted sequencing data, the method further comprises filtering out low-quality sequencing data and reads containing a linker sequence and performing quality control to obtain data meeting the standard, and analyzing the change of gene mutation and expression quantity in the RNA-targeted sequencing data, wherein the quality control step comprises the following steps: s41, filtering low-quality sequencing data and adapter sequence reads contained in the obtained RNA targeted sequencing data; s42, comparing the filtered RNA targeted sequencing data with a reference genome to obtain a sequence comparison result; s43, evaluating whether the comparison result meets the preset index (quality control evaluation is carried out on the comparison result), and carrying out subsequent analysis on the sequencing sequence meeting the preset index. The preset indexes comprise: the sequence posting alignment rate is above a preset alignment rate threshold (which can be set according to practical situations, such as 80 percent, etc.), the target area data volume is above a preset data volume threshold (which can be set according to practical situations, such as 2M, etc.), and the number of expressed housekeeping genes is above a preset gene number threshold (which can be set according to practical situations, such as 4, 5, etc.). Based on the above, the quantitative evaluation of the expression amounts of the genes ALK, NTRK2 and NTRK3 in the RNA targeting sequencing data by adopting the RPKM method comprises the following steps: and quantitatively evaluating the expression amounts of genes ALK, NTRK2 and NTRK3 in the filtered RNA targeting sequencing data meeting preset indexes by adopting an RPKM method.
In step S40, sequencing is performed by using a high-throughput sequencer, and in the process of obtaining RNA-targeted sequencing data, the high-throughput sequencer performs sequencing in a double-ended or single-ended mode to obtain RNA-targeted sequencing data. The step S50 of averaging the expression amounts obtained based on the evaluation includes: the expression levels of the genes ALK, NTRK2 and NTRK3 are standardized, and the standardized process is as follows: nRPKM Gene =rpkm×hk_coeffient, where hk_coeffient represents a coefficient of variation in expression calculated from the expression level of the housekeeping gene in the sample to be detected and the expression level of the housekeeping gene in the standard. Then, the biomarker score is calculated from the normalized expression level: biomarker score = mean [ lg (nrkm) ALK )+lg(nRPKM NTRK2 )+lg(nRPKM NTRK3 )]Wherein nRPKM ALK Represents the standardized expression level of the ALK gene, and nRPKM ALK ＝RPKM ALK ×HK_coefficient ALK ，RPKM ALK Represents the standardized expression level of the gene ALK, HK_coefficient ALK The expression level change coefficient of the gene ALK is expressed; nRPKM NTRK2 Represents the standardized expression level of the gene NTRK2 and nRPKM NTRK2 ＝RPKM NTRK2 ×HK_coefficient NTRK2 ，RPKM NTRK2 Represents the standardized expression level of the gene NTRK2, HK_coefficient NTRK2 The expression level change coefficient of the gene NTRK 2; nRPKM NTRK3 Represents the standardized expression level of the gene NTRK3 and nRPKM NTRK3 ＝RPKM NTRK3 ×HK._coefficient NTRK3 ，RPKM NTRK3 Represents the normalized expression level of the gene NTRK3, HK_coefficient NTRK3 The expression level change factor of the gene NTRK3 is shown.
After the biomarker is obtained based on the method, in the prediction of the curative effect of the EGFR targeting therapy, the biomarker of the sample to be detected is compared with the biomarker score median of the exploration queue, and the curative effect of the EGFR targeting therapy is predicted according to the comparison result. The therapeutic effect of anti-EGFR targeting therapy (afatinib) is predicted by the height of the marker score value, and the response of the sample to be detected (tumor patient) with the biomarker score smaller than the median of the biomarker scores of the exploration queue (the value can be set according to practical conditions, such as 3) is indicated.
Correspondingly, in the above embodiment, the biomarker may also be calculated by a biomarker acquisition device comprising: the cDNA acquisition module is used for extracting RNA of a sample to be detected, breaking the RNA of the sample to be detected, and carrying out reverse transcription to obtain cDNA; the gene library construction module is used for constructing a gene library by using a terminal repair, joint connection and library enrichment method based on the cDNA obtained by the cDNA acquisition module; the target gene enrichment module is used for capturing and enriching target genes from the gene library constructed by the gene library construction module through specific hybridization of the capture probes and the target regions; the target sequencing data acquisition module is used for sequencing by using a high-throughput sequencer to obtain RNA target sequencing data; the gene expression quantity evaluation module is used for quantitatively evaluating the expression quantity of genes ALK, NTRK2 and NTRK3 in the RNA targeting sequencing data by adopting an RPKM method; and the biomarker scoring module is used for obtaining the biomarker by averaging the expression quantities estimated by the gene expression quantity estimation module. The biomarker scoring module comprises a gene expression quantity standardization unit and a biomarker calculation unit, wherein the gene expression quantity standardization unit is used for standardizing the expression quantities of genes ALK, NTRK2 and NTRK 3; and the biomarker calculation unit is used for calculating a biomarker score according to the standardized expression quantity. The biomarker acquisition device also comprises a quality control module, which comprises: the filtering unit is used for filtering quality sequencing data and the sequence containing the linker sequence reads in the RNA targeted sequencing data obtained by the targeted sequencing data obtaining module; the sequence comparison unit is used for comparing the RNA targeted sequencing data filtered by the filtering unit with a reference genome; the comparison result quality evaluation unit is used for evaluating whether the comparison result of the sequence comparison unit accords with a preset index or not, and the preset index comprises: the sequence posting alignment rate is above a preset alignment rate threshold, the target area data volume is above a preset data volume threshold, and the expressed housekeeping gene number is above a preset gene number threshold; the gene expression quantity evaluation module is also used for quantitatively evaluating the expression quantity of genes ALK, NTRK2 and NTRK3 in the filtered RNA targeting sequencing data meeting preset indexes by adopting an RPKM method. In the target sequencing data acquisition module, the high-throughput sequencer is used for sequencing in a double-end or single-end mode to obtain RNA target sequencing data.
After the biomarker is obtained based on the device, the invention also provides an anti-EGFR targeted therapy curative effect prediction kit or chip, in the process of predicting the anti-EGFR targeted therapy curative effect, the biomarker of a sample to be detected is compared with the biomarker scoring median of an exploration queue, and further the anti-EGFR targeted therapy curative effect is predicted according to the comparison result of the comparison module. The therapeutic effect of anti-EGFR targeting therapy (afatinib) is predicted by the height of the marker score value, and the response of the sample to be detected (tumor patient) with the biomarker score smaller than the median of the biomarker scores of the exploration queue (the value can be set according to practical conditions, such as 3) is indicated.
The therapeutic efficacy of the above biomarkers is further illustrated by the following examples:
example 1 exploration queue 35 cases late esophageal squamous carcinoma patient (EGFR IHC (+++)) RNA expression marker score predicts therapeutic efficacy against EGFR (afatinib)
1. Experiment:
rna extraction:
total RNA extraction was performed using 35 paraffin-embedded pathological sections of patients with advanced esophageal squamous carcinoma using the RNeasy FFPE Kit of Qiagen (Cat No./ID: 73504). The content of RNA was determined using the Qubit RNAHS, and quality control was performed using Labchip detection.
2. Preparation of a nucleotide library before hybridization:
nucleotide library construction was performed using the mRNA-seq Lib Prep Module for illumina of ABclonal corporation: comprises the steps of eDNA reverse transcription, fragmentation, terminal repair, linker ligation, library enrichment and the like. After purification of the constructed library using Agencourt AMpure XP magnetic beads, qubit 3.0 and Agilent 2100 capillary electrophoresis were used for concentration detection and quality control.
3. Probe capture hybridization:
according to the 3 target genes (ALK, NTRK2 and NTRK 3), a non-overlapping tiling probe sequence is designed according to the transcript sequence, and the 5' -end of the probe is marked by biotin. 2ug of the prepared pre-hybridization library was mixed with 5uL of Human Cot DNA (IDT), 2uL xGen Universal Blockers-TS Mix, evaporated to dryness (60 ℃ C., about 20min-1 hr) using a vacuum centrifugal concentrator, re-dissolved in hybridization solution, incubated at room temperature for 10min, and transferred to a PCR instrument for hybridization at 65 ℃ C. For 16h. The hybridization product captured overnight was mixed with streptavidin magnetic beads, and after incubation in a PCR instrument for 45min, the magnetic beads were washed with a washing solution. The eluted product was subjected to the next PCR amplification experiment, followed by purification with AgencourtAMPure XP magnetic beads, concentration determination and quality control using Qubit 3.0 and Agilent 2100 capillary electrophoresis.
4. High throughput sequencing: sequencing was performed in double ended mode using Illumina Nextseq, novaseq, etc.
2. Sequencing data analysis:
performing on-machine sequencing according to the RNA panel capture reads to obtain an original sequencing off-machine sequence, and performing the following treatment on the sequence by using Trimmomatic-0.36 to obtain a high-quality sequencing sequence
a) Removing low quality sequencing sequences;
b) Reads containing the linker sequence were removed.
The high-quality sequencing sequences (standard adopts common standard in the field) are compared with a reference genome by using STAR to obtain sequence comparison results, and the comparison results are subjected to quality control evaluation, so that the indexes of the following table 1 are met for carrying out the next gene expression analysis.
TABLE 1 quality control Standard for RNA Panel
| Sequence posting comparison rate | Threshold value | ＞＝80％ |
| Target area data volume | Threshold value | ＞＝2M |
| Number of housekeeping genes expressed | Threshold value | ＞＝4 |
1. Analysis of Gene expression level:
according to the sequence comparison result and the annotation file of the reference genome, the gene expression quantity is quantitatively estimated by using an RPKM method, wherein the RPKM formula is as follows:
total exon reads: comparing the sequence numbers of all exons of the genes, and evaluating the sequence numbers according to the gene annotation file and the comparison result by using FeatureConts software;
mapped reads (millions): comparing the number of all sequences on the genome, and obtaining the statistical result according to the comparison result;
exon length (KB): the exon length of the gene was calculated from the annotation file of the genome.
2. Normalization of gene expression level:
and (3) carrying out the standardization of the expression quantity of the RPKM by using the expression quantitative result of the housekeeping gene and the sequence comparison statistical result to obtain the nRPKM value.
nRPKM Gene ＝RPKM×HK_coefficient
Hk_coeffient: and calculating an expression quantity change coefficient according to the expression quantity of the housekeeping gene in the sample to be detected and the expression quantity of the housekeeping gene in the standard substance.
3. Calculating RNA expression marker scores:
calculating ALK, NTRK2 and NTRK3, and taking average after the expression amount lg of 3 genes is converted to obtain the marker score. The formula is as follows:
marker score = mean [ lg (nrkm) ALK )+lg(nRPKM NTRK2 )+lg(nRPKM NTRK3 )]
3. Results part:
the exploration cohort of this study detected a total of 35 late stages esophageal squamous carcinoma patient (EGFRIHC) (+++) and (3) organizing the sample. The average values obtained after lg conversion using ALK, NTRK2 and NTRK3 genes nrprkm were used as marker scores for each patient. The area under the curve (AUC) was calculated to be 0.786 (95% ci:0.637-0.936, p=0.006) as shown in fig. 2A.
Patients with high/low scores among 35 patients with advanced esophageal squamous carcinoma were examined for their different degrees of tumor regression, and patients with low scores for markers were most likely to develop different degrees of tumor regression, as shown in fig. 2B.
Survival curves for patients with high/low marker scores among 35 patients with advanced esophageal squamous carcinoma who received anti-EGFR targeted therapy (afatinib) Progression Free Survival (PFS), with low marker scores, with median Progression Free Survival (PFS) higher than those with high scores, p=0.020, as shown in fig. 2C.
Of the 35 patients with advanced esophageal squamous carcinoma, patients with high/low marker scores received anti-EGFR targeting therapy (afatinib) with significantly higher response rate than patients with high marker scores, with an effective rate of 70%, P < 0.01, as shown in FIG. 2D.
Example 2 validation cohort 11 patients with advanced esophageal squamous carcinoma (EGFR IHC (+) ++) marker score predicts anti-EGFR (afatinib) therapeutic efficacy
1. Experiment:
rna extraction:
total RNA extraction was performed using paraffin-embedded pathological sections of 11 patients with advanced esophageal squamous carcinoma using the RNeasy FFPE Kit of Qiagen (Cat No./ID: 73504). The content of RNA was measured using the Qubit RNA HS, and quality control was performed using Labchip detection.
2. Preparation of a nucleotide library before hybridization:
nucleotide library construction was performed using the mRNA-seq Lib Prep Module for illumina of ABclonal corporation: comprises the steps of cDNA reverse transcription, fragmentation, terminal repair, linker ligation, library enrichment and the like. After purification of the constructed library using Agencourt AMpure XP magnetic beads, qubit 3.0 and Agilent 2100 capillary electrophoresis were used for concentration detection and quality control.
3. Probe capture hybridization:
according to the 3 target genes (ALK, NTRK2 and NTRK 3), a non-overlapping tiling probe sequence is designed according to the transcript sequence, and the 5' -end of the probe is marked by biotin. 2ug of the prepared pre-hybridization library was mixed with 5uL of Human Cot DNA (IDT), 2uL xGen Universal Blockers-TS Mix, evaporated to dryness (60 ℃ C., about 20min-1 hr) using a vacuum centrifugal concentrator, re-dissolved in hybridization solution, incubated at room temperature for 10min, and transferred to a PCR instrument for hybridization at 65 ℃ C. For 16h. The hybridization product captured overnight was mixed with streptavidin magnetic beads, and after incubation in a PCR instrument for 45min, the magnetic beads were washed with a washing solution. The eluted product was subjected to the next PCR amplification experiment, followed by purification with Agencourt AMPure XP magnetic beads, concentration determination and quality control using Qubit 3.0 and Agilent 2100 capillary electrophoresis.
4. High throughput sequencing: sequencing was performed in double ended mode using Illumina Nextseq, novaseq, etc.
2. Sequencing data analysis:
performing on-machine sequencing according to the RNA panel capture reads to obtain an original sequencing off-machine sequence, and performing the following treatment on the sequence by using Trimmomatic-0.36 to obtain a high-quality sequencing sequence
a) Removing low quality sequencing sequences;
b) Reads containing the linker sequence were removed.
The high-quality sequencing sequences (standard adopts common standard in the field) are compared with a reference genome by using STAR to obtain sequence comparison results, and the comparison results are subjected to quality control evaluation, so that the following indexes of table 2 are met, and the next gene expression analysis is performed.
TABLE 2 quality control Standard for RNA Panel
| Sequence posting comparison rate | Threshold value | ＞＝80％ |
| Target area data volume | Threshold value | ＞＝2M |
| Number of housekeeping genes expressed | Threshold value | ＞＝4 |
1. Analysis of Gene expression level:
according to the sequence comparison result and the annotation file of the reference genome, the gene expression quantity is quantitatively estimated by using an RPKM method, wherein the RPKM formula is as follows:
total exon reads: comparing the sequence numbers of all exons of the genes, and evaluating the sequence numbers according to the gene annotation file and the comparison result by using FeatureConts software;
mapped reads (millions): comparing the number of all sequences on the genome, and obtaining the statistical result according to the comparison result;
exon length (KB): the exon length of the gene was calculated from the annotation file of the genome.
2. Normalization of gene expression level:
and (3) carrying out the standardization of the expression quantity of the RPKM according to the expression quantitative result and the sequence comparison statistical result of the housekeeping gene to obtain an nRPKM value, wherein the specific formula is as follows:
nRPKM Gene ＝RPKM×HK_coefficient
hk_coeffient: and calculating an expression quantity change coefficient according to the expression quantity of the housekeeping gene in the sample to be detected and the expression quantity of the housekeeping gene in the standard substance.
3. Calculating RNA expression marker scores:
calculating ALK, NTRK2 and NTRK3, and taking average after the expression amount lg of 3 genes is converted to obtain the RNA expression marker score. The formula is as follows:
marker score = mean [ lg (nrkm) ALK )+lg(nRPKM NTRK2 )
+lg(nRPKM NTRK3 )]
3. Results part:
a total of 11 cases of advanced esophageal squamous carcinoma were detected patient (EGFRIHC (++) a) a tissue sample. The average values obtained after lg conversion using ALK, NTRK2 and NTRK3 genes nrprkm were used as marker scores for each patient. The area under the curve (AUC) was calculated to be 0.733 (95% ci:0.378-1.000, p=0.201) as shown in fig. 3A.
The patients with high/low marker scores among the 11 patients with advanced esophageal squamous carcinoma were examined for different degrees of tumor regression, and the patients with low marker scores all showed different degrees of tumor regression, as shown in fig. 3B.
Survival curves for patients with high/low marker scores among 11 patients with advanced esophageal squamous carcinoma who received anti-EGFR targeted therapy (afatinib) progression-free survival (PFS), with low marker scores, median progression-free survival (PFS) was higher than for patients with high marker scores, p=0.018, as shown in fig. 3C.
Of the 11 patients examined with advanced esophageal squamous carcinoma, patients with high/low marker scores received significantly higher response rate to anti-EGFR targeted therapy (afatinib) than patients with high marker scores, with 75% efficiency, p=0.242, as shown in fig. 3D.
Thus, the present invention verifies the effectiveness of the marker score predictive anti-EGFR targeted therapy (afatinib) in a separate 11 cases of advanced esophageal squamous carcinoma patients.
While the invention has been described in detail in the foregoing general description and with reference to specific embodiments thereof, it will be apparent to one skilled in the art that modifications and improvements can be made thereto. Accordingly, such modifications or improvements may be made without departing from the spirit of the invention and are intended to be within the scope of the invention as claimed.
Claims (3)
1. The application of biomarker ALK, NTRK2 and NTRK3 genes in preparing a chip or a kit for predicting the curative effect of EGFR-resistant targeted therapy of patients with advanced esophageal cancer is characterized in that,
the biomarker is obtained by the following acquisition method, which comprises the following steps:
extracting RNA of a sample to be detected, breaking the RNA of the sample to be detected, and carrying out reverse transcription to obtain cDNA;
constructing a gene library based on the obtained cDNA by a terminal repair, linker ligation and library enrichment method;
capturing and enriching target genes from the gene library by specific hybridization of capture probes to target regions;
sequencing by using a high-throughput sequencer to obtain RNA targeted sequencing data;
quantitatively evaluating the expression quantity of genes ALK, NTRK2 and NTRK3 in the RNA targeting sequencing data by adopting an RPKM method;
normalizing the expression quantity of genes ALK, NTRK2 and NTRK 3;
calculating a biomarker score according to the standardized expression quantity:
biomarker score = mean [ lg (nrkm) ALK )+lg(nRPKM NTRK2 )+lg(nRPKM NTRK3 )]，
Wherein nRPKM ALK Represents the standardized expression level of the ALK gene, and nRPKM ALK ＝RPKM ALK ×HK_coefficient，RPKM ALK Represents the standardized expression level of the gene ALK, and HK_coefficient represents the expression level variation coefficient of the gene ALK; nRPKM NTRK2 Represents the standardized expression level of the gene NTRK2 and nRPKM NTRK2 ＝RPM NTRK2 ×HK_coefficient，RPKM NTRK2 Represents the standardized expression level of the gene NTRK2, and HK_coeffient represents the variation coefficient of the expression level of the gene NTRK 2; nRPKM NTRK3 Represents the standardized expression level of the gene NTRK3 and nRPKM NTRK3 ＝RPKM NTRK3 ×HK_coefficient，RPKM NTRK3 Represents the standardized expression level of the gene NTRK3, and HK_coeffient represents the variation coefficient of the expression level of the gene NTRK 3;
the biomarker is applied to a chip or a kit for predicting the curative effect of EGFR-resistant targeted therapy of patients with advanced esophageal cancer; the anti-EGFR targeted therapy is afatinib, and the scores of the biomarkers obtained by further calculating the expression amounts of genes ALK, NTRK2 and NTRK3 in a tissue sample to be detected of a cancer patient are singly used for predicting the effect of afatinib;
the patient's score was ranked from low to high, with patients with low marker scores having higher progression free survival than patients with high scores, and patients with low marker scores had a higher proportion of responses to afatinib than patients with high marker scores.
2. The use of claim 1, wherein after obtaining RNA-targeted sequencing data by sequencing using a high throughput sequencer, the method further comprises a quality control step comprising:
filtering low-quality sequencing data and reads containing a linker sequence in the obtained RNA targeted sequencing data;
comparing the filtered RNA targeted sequencing data with a reference genome;
evaluating whether the comparison result accords with a preset index, wherein the preset index comprises the following steps: the sequence posting alignment rate is above a preset alignment rate threshold, the target area data volume is above a preset data volume threshold, and the expressed housekeeping gene number is above a preset gene number threshold;
the quantitative evaluation of the expression quantity of genes ALK, NTRK2 and NTRK3 in the RNA targeting sequencing data by adopting the RPKM method comprises the following steps: and quantitatively evaluating the expression amounts of genes ALK, NTRK2 and NTRK3 in the filtered RNA targeting sequencing data meeting preset indexes by adopting an RPKM method.
3. The use of claim 1, wherein in obtaining RNA-targeted sequencing data by sequencing with a high-throughput sequencer, sequencing with the high-throughput sequencer is performed in a double-ended or single-ended mode to obtain RNA-targeted sequencing data.
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