CN113981078A - Biomarker for predicting curative effect of anti-EGFR (epidermal growth factor receptor) targeted therapy of patient with advanced esophageal cancer and curative effect prediction test kit - Google Patents

Biomarker for predicting curative effect of anti-EGFR (epidermal growth factor receptor) targeted therapy of patient with advanced esophageal cancer and curative effect prediction test kit Download PDF

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CN113981078A
CN113981078A CN202111089720.6A CN202111089720A CN113981078A CN 113981078 A CN113981078 A CN 113981078A CN 202111089720 A CN202111089720 A CN 202111089720A CN 113981078 A CN113981078 A CN 113981078A
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鲁智豪
张恒辉
沈琳
陈欢
李健
毛蓓蓓
张小田
洪媛媛
彭智
胡莹
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Beijing Cancer Hospital
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Abstract

The invention provides a biomarker and a curative effect prediction kit for predicting the curative effect of anti-EGFR (epidermal growth factor receptor) targeted therapy of a patient with late esophageal cancer, 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 by a terminal repair, linker connection and library enrichment method; capturing and enriching target genes from the constructed gene library through specific hybridization of a capture probe and the target region; 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 RNA targeted sequencing data by adopting an RPKM method; biomarkers were 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 clinical kit development.

Description

Biomarker for predicting curative effect of anti-EGFR (epidermal growth factor receptor) targeted therapy of patient with advanced esophageal cancer and curative effect prediction test kit
Technical Field
The invention relates to the technical field of biomedicine, in particular to a biomarker and a curative effect prediction kit for predicting the curative effect of anti-EGFR (epidermal growth factor receptor) targeted therapy of a patient with late esophageal cancer.
Background
In recent years, over one million people worldwide are diagnosed with esophageal cancer each year, the incidence of disease, particularly in developing countries, is rising year by year, and the incidence of disease in young people is rising every year, undoubtedly becoming a huge health burden worldwide. In gastroesophageal cancer, up to 30% of patients carry human epidermal growth factor receptor 2(ERBB2/HER2) amplification or overexpression. For these patients, Trastuzumab (Trastuzumab, a HER2 targeted mab drug) combined with chemotherapy first line drug increased survival.
Although trastuzumab increases the survival of ERBB 2-expanded gastroesophageal cancer patients, patients still often develop disease progression within a year, and new back-line treatment regimens are urgently needed for trastuzumab-resistant gastroesophageal cancer patients.
Meanwhile, the anti-EGFR targeted therapeutic drug Afatinib (Afatinib) is a potent and irreversible dual inhibitor of Epidermal Growth Factor Receptor (EGFR) and human epidermal growth factor receptor 2(HER2) tyrosine kinase, is suitable for locally advanced or metastatic non-small cell lung cancer (NSCLC) with Epidermal Growth Factor Receptor (EGFR) gene sensitive mutation, non-small cell lung cancer (NSCLC) which has not been treated by EGFR Tyrosine Kinase Inhibitor (TKI) before, and locally advanced or metastatic squamous cell lung cancer (NSCLC) of squamous histological type with disease progression during or after platinum-containing chemotherapy, and even has the potential to be used for late-line anti-EGFR targeted therapy (Afatinib) after esophageal cancer patients (EGFR IHC (+ +++)) trastuzumab drug resistance with strong positive staining of late stage IHC. Screening for potential or patient predictive biomarkers is therefore of great clinical interest.
Disclosure of Invention
Aiming at the problems, the invention provides a biomarker and a curative effect prediction kit for predicting the curative effect of the EGFR-resistant targeted therapy of a patient with advanced esophageal cancer, and the sensitivity of predicting the effect of the EGFR-resistant targeted therapy (afatinib) is effectively improved.
The technical scheme provided by the invention is as follows:
a biomarker for predicting the efficacy of an anti-EGFR targeted therapy in a patient with advanced esophageal cancer, calculated by the following 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 method, a linker connection method and a library enrichment method;
capturing and enriching a target gene from the gene library by specific hybridization of a capture probe to the target region;
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 RNA targeted sequencing data by adopting an RPKM method;
biomarkers were obtained based on the average of the expression amounts obtained by the evaluation.
Further preferably, the obtaining of the biomarker based on the average of the expression obtained by the evaluation comprises:
standardizing the expression level of ALK, NTRK2 and NTRK 3;
calculating a biomarker score from the normalized expression levels:
biomarker score mean [ lg (nRPKM)ALK)+lg(nRPKMNTRK2)+lg(nRPKMNTRK3)]
Wherein, nRPKMALKExpressing the normalized expression level of ALK gene, and nrPSMALK＝RPKMALK×HK_coefficientALK，RPKMALKIndicating the normalized expression level of ALK gene, HK _ coefficientALKA coefficient of expression change of the gene ALK; nRPKMNTRK2Indicating the normalized expression level of gene NTRK2, and nRPKMNTRK2＝RPKMNTRK2×HK_coefficientNTRK2，RPKMNTRK2Indicating the normalized expression level of gene NTRK2, HK _ coefficientNTRK2A coefficient indicating the change in expression level of gene NTRK 2; nRPKMNTRK3Indicating the normalized expression level of gene NTRK3, and nRPKMNTRK3＝RPKMNTRK3×HK_coefficientNTRK3，RPKMNTRK3Indicating the normalized expression level of gene NTRK3, HK _ coefficientNTRK3The expression level change coefficient of gene NTRK3 is shown.
Further preferably, after the RNA target sequencing data is obtained by sequencing with the high-throughput sequencer, the method further comprises a quality control step, including:
filtering low-quality sequencing data and reads containing linker sequences in the obtained RNA targeted sequencing data;
comparing the filtered RNA targeted sequencing data with a reference genome;
evaluating whether the comparison result meets a preset index, wherein the preset index comprises: the sequence replying comparison rate is above a preset comparison rate threshold, the data volume of the target area is above a preset data volume threshold, and the number of expressed housekeeping genes is above a preset gene number threshold;
the quantitative evaluation of the expression quantity of the genes ALK, NTRK2 and NTRK3 in the RNA targeted sequencing data by adopting the RPKM method comprises the following steps: and quantitatively evaluating the expression quantity of the genes ALK, NTRK2 and NTRK3 in the RNA targeted sequencing data which meet the preset indexes after filtering by adopting an RPKM method.
Preferably, in the obtaining of the RNA target sequencing data by sequencing with the high-throughput sequencer, the sequencing with the high-throughput sequencer adopts a double-ended or single-ended mode to obtain the RNA target sequencing data.
The biomarker is applied to a chip or a kit for predicting the curative effect of the anti-EGFR targeted therapy of the patient with the advanced esophageal cancer.
The biomarker and the curative effect prediction kit for predicting the curative effect of the anti-EGFR targeted therapy (Afatinib) of the late esophageal cancer patient (EGFR IHC (+++)) provided by the invention can be used for further calculating the score of the biomarker by independently using the expression levels of the genes ALK, NTRK2 and NTRK3 in a sample to be detected (a tissue sample of the cancer patient), and further predicting the effect of the anti-EGFR targeted therapy (Afatinib). Specifically, in the process of obtaining the biomarker, the expression quantity detection method of the RNA targeted sequencing technology can efficiently enrich the RNA transcripts expressed by the related genes and analyze the expression quantity of the genes in the tumor tissue. Furthermore, compared with the method for detecting the complete transcriptome by using RNA-seq, the RNAPlanel target gene has the advantages of lower sequencing cost, capability of obviously enriching a target region, higher detection sensitivity and more suitability for clinical kit development.
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The foregoing features, technical features, advantages and embodiments are further described in the following detailed description of the preferred embodiments, which is to be read in connection with the accompanying drawings.
FIG. 1 is a flow chart of the biomarker obtaining method of the present invention.
Figure 2 is a graph of the search for a correlation between the RNA marker scores of 35 patients with advanced esophageal squamous carcinoma (EGFR IHC (+++)) and the efficacy of anti-EGFR targeted therapy (afatinib), tumor shrinkage, PFS in the cohort. A. The marker scored a subject working characteristic curve (ROC curve) that predicts the efficacy of anti-EGFR targeted therapy (afatinib) in 35 patients in the advanced esophageal squamous carcinoma exploration cohort; the area under the curve, confidence interval and P value are shown in the figure; B. the tumor regression degree of the patients with high/low marker score in 35 patients with advanced esophageal squamous carcinoma, and the patients with low marker score mostly have tumor regression with different degrees; C. the survival curves of the progression-free survival of patients with high/low marker score among 35 patients with advanced esophageal squamous carcinoma received anti-EGFR targeted therapy (afatinib), the progression-free survival among patients with low marker score was higher than that of patients with high score, and the statistical results are shown in the figure; D. the effective rate of the patients with high/low marker score receiving the anti-EGFR targeted therapy (afatinib) in 35 patients with advanced esophageal squamous carcinoma is higher than that of the patients with low marker score responding to the anti-EGFR targeted therapy (afatinib) obviously, and the statistical results are shown in the figure.
Figure 3 is a graph demonstrating the relationship of the marker score to anti-EGFR targeted therapy (afatinib) efficacy, tumor shrinkage, PFS in 11 patients with advanced esophageal squamous carcinoma in cohort (EGFR IHC (+++)). A. The marker score predicts a subject working characteristic curve (ROC curve) for the efficacy prediction 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 tumor regression degree of the patients with high/low marker score in 11 patients with advanced esophageal squamous carcinoma, and the patients with low marker score all have tumor regression with different degrees; C. the survival curves of the progression-free survival of patients with high/low marker score among 11 patients with advanced esophageal squamous carcinoma received anti-EGFR targeted therapy (Afatinib), the progression-free survival among patients with low marker score was higher than that of patients with high marker score, and the statistical results are shown in the figure; D. the effective rate of receiving anti-EGFR targeted therapy (afatinib) in patients with high/low marker score among 11 patients with advanced esophageal squamous carcinoma was higher in patients with low marker score than in patients with high marker score, and the statistical results 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 be made with reference to the accompanying drawings. It is obvious that the drawings in the following description are only some examples of the invention, and that for a person skilled in the art, other drawings and embodiments can be derived from them without inventive effort.
In one embodiment of the present invention, a biomarker for predicting the efficacy of anti-EGFR targeted therapy (afatinib) in a patient with advanced esophageal cancer (EGFR IHC (+++)) is calculated by the following biomarker acquisition method, as shown in fig. 1, the biomarker acquisition method comprising: 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 by a terminal repair method, a linker connection method and a 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 RNA targeted sequencing data by an RPKM method; s60 biomarkers were obtained based on the average of the expression amounts obtained by the evaluation.
Specifically, in step S10, the sample to be tested is a sample after advanced esophageal cancer patient (EGFR IHC (+++)) anti-EGFR targeted therapy (afatinib).
In step S40, after obtaining RNA target sequencing data by sequencing with a high-throughput sequencer, filtering low-quality sequencing data and reads containing linker sequences and performing quality control to obtain data meeting the standard, and analyzing gene mutation and expression level change in the RNA target sequencing data, wherein the quality control step includes: s41, filtering low-quality sequencing data and reads containing linker sequences in the obtained RNA targeted sequencing data; s42, comparing the filtered RNA target sequencing data with a reference genome to obtain a sequence comparison result; s43, evaluating whether the comparison result meets the preset index (comparing and evaluating the quality control of the result), and carrying out subsequent analysis on the sequencing sequence meeting the preset index. The preset indexes comprise: the sequence replying comparison rate is above a preset comparison rate threshold (which can be set according to actual conditions, such as 80 percent) and the target region data volume is above a preset data volume threshold (which can be set according to actual conditions, such as 2M) and the number of expressed housekeeping genes is above a preset gene number threshold (which can be set according to actual conditions, such as 4 or 5). Based on the above, the quantitative evaluation of the expression levels of the genes ALK, NTRK2 and NTRK3 in the RNA targeted sequencing data by the RPKM method comprises the following steps: and quantitatively evaluating the expression quantity of the genes ALK, NTRK2 and NTRK3 in the RNA targeted sequencing data which meet the preset indexes after filtering by adopting an RPKM method.
In the step S40, in obtaining the RNA target sequencing data by sequencing with the high-throughput sequencer, the high-throughput sequencer performs sequencing in a double-ended or single-ended mode to obtain the RNA target sequencing data. Obtaining biomarkers based on the evaluated average of the expression amounts in step S50 includes: the expression levels of the ALK, NTRK2 and NTRK3 genes are standardized by the following steps: nRPKMGeneRPKM × HK _ Coeffient, wherein HK _ Coeffient represents calculation of an expression amount change coefficient from the expression amount of the housekeeping gene in the sample to be detected and the expression amount of the housekeeping gene in the standard. Thereafter, biomarker scores were calculated from the normalized expression levels: biomarker score mean [ lg (nRPKM)ALK)+lg(nRPKMNTRK2)+lg(nRPKMNTRK3)]Wherein nRPKMALKExpressing the normalized expression level of ALK gene, and nrPSMALK＝RPKMALK×HK_coefficientALK，RPKMALKIndicating the normalized expression level of ALK gene, HK _ coefficientALKA coefficient of expression change of the gene ALK; nRPKMNTRK2Indicating the normalized expression level of gene NTRK2, and nRPKMNTRK2＝RPKMNTRK2×HK_coefficientNTRK2，RPKMNTRK2Indicating the normalized expression level of gene NTRK2, HK _ coefficientNTRK2A coefficient indicating the change in expression level of gene NTRK 2; nRPKMNTRK3Indicating the normalized expression level of gene NTRK3, and nRPKMNTRK3＝RPKMNTRK3×HK._coefficientNTRK3，RPKMNTRK3Indicating the normalized expression level of gene NTRK3, HK _ coefficientNTRK3Coefficient of variation in expression level of gene NTRK3。
After the biomarkers are obtained based on the method, in the prediction of the anti-EGFR targeted therapy curative effect, the biomarkers of the sample to be detected are compared with the median of the biomarker scores of the exploration queue, and then the anti-EGFR targeted therapy curative effect is predicted according to the comparison result. The therapeutic effect of the anti-EGFR targeted therapy (Afatinib) is predicted through the high and low marker score values, and the response to the anti-EGFR targeted therapy (Afatinib) is shown for the sample to be detected (tumor patient) of which the biomarker score is smaller than the median of the biomarker scores of the exploration queue (the value can be set according to actual conditions, for example, can be set to be 3).
Correspondingly, in the above embodiment, the biomarker may be further calculated by a biomarker obtaining apparatus 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 based on the cDNA obtained by the cDNA acquisition module by a terminal repair method, a joint connection method and a library enrichment method; 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 utilizing a high-throughput sequencer to sequence to obtain RNA target sequencing data; the gene expression quantity evaluation module is used for quantitatively evaluating the expression quantities of the genes ALK, NTRK2 and NTRK3 in the RNA targeted sequencing data by adopting an RPKM method; and the biomarker scoring module is used for averaging the expression quantity obtained by the evaluation of the gene expression quantity evaluation module to obtain the biomarker. The biomarker scoring module comprises a gene expression level standardization unit and a biomarker calculation unit, wherein the gene expression level standardization unit is used for standardizing the expression levels of the genes ALK, NTRK2 and NTRK 3; and a biomarker calculation unit for calculating a biomarker score based on the normalized expression level. The biomarker acquisition device further comprises a quality control module, and the quality control module comprises: the filtering unit is used for filtering the quality sequencing data and the reads containing the connector sequences in the RNA targeted sequencing data obtained by the targeted sequencing data acquisition module; the sequence comparison unit is used for comparing the RNA target 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 meets a preset index, and the preset index comprises the following steps: the sequence replying comparison rate is above a preset comparison rate threshold, the data volume of the target area is above a preset data volume threshold, and the number of expressed housekeeping genes is above a preset gene number threshold; and the gene expression quantity evaluation module is also used for quantitatively evaluating the expression quantities of the genes ALK, NTRK2 and NTRK3 in the RNA targeted sequencing data which are in line with the preset indexes after being filtered by adopting an RPKM method. In the target sequencing data acquisition module, a high-throughput sequencer carries out sequencing by adopting a double-end or single-end mode to obtain RNA target sequencing data.
After the biomarkers are obtained based on the device, the invention also provides an anti-EGFR targeted therapy curative effect prediction kit or chip, in the prediction of the anti-EGFR targeted therapy curative effect, the biomarkers of the sample to be detected are compared with the median of the biomarker scores of the exploration queue, and then the anti-EGFR targeted therapy curative effect is predicted according to the comparison result of the comparison module. The therapeutic effect of the anti-EGFR targeted therapy (Afatinib) is predicted through the high and low marker score values, and the response to the anti-EGFR targeted therapy (Afatinib) is shown for the sample to be detected (tumor patient) of which the biomarker score is smaller than the median of the biomarker scores of the exploration queue (the value can be set according to actual conditions, for example, can be set to be 3).
The therapeutic efficacy of the above biomarkers is further illustrated by the examples below:
example 1 exploration cohort 35 patients with advanced esophageal squamous carcinoma (EGFR IHC (++++)) RNA expression marker score predictive anti-EGFR (afatinib) treatment efficacy
Firstly, 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 from Qiagen (Cat number/ID: 73504). The RNA content was determined using the Qubit RNAHS and the RNA quality control was performed using the Labchip assay.
2. Preparation of a Pre-hybridization nucleotide library:
the nucleotide library construction was performed using mRNA-seq Lib Prep Module for illumina from ABClonal corporation: comprises the steps of eDNA reverse transcription, fragmentation, end repair, linker ligation, library enrichment and the like. The constructed library was purified using Agencour AMpure XP magnetic beads and then subjected to Qubit 3.0 and Agilent 2100 capillary electrophoresis for concentration detection and quality control.
3. And (3) probe capture hybridization:
according to the selected 3 target genes (ALK, NTRK2 and NTRK3), 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-hybrid library was mixed with 5uL of Human Cot DNA (IDT), 2uL of xGen Universal Blockers-TS Mix, evaporated to dryness (60 ℃ C., about 20min-1hr) using a vacuum centrifugal concentrator, redissolved in a hybridization solution, incubated at room temperature for 10min, and transferred to a PCR instrument for hybridization at 65 ℃ for 16 h. And mixing the hybridization products captured overnight with streptavidin magnetic beads, incubating for 45min in a PCR instrument, and washing the magnetic beads with a washing solution. And (3) carrying out a next PCR amplification experiment on the eluted product, subsequently purifying the eluted product by using AgencourtAmpure XP magnetic beads, and carrying out concentration measurement and quality control by using the Qubit 3.0 and Agilent 2100 capillary electrophoresis.
4. High-throughput sequencing: sequencing was performed in paired-end mode using Illumina Nextseq, Novaseq, etc.
Secondly, sequencing data analysis:
performing on-machine sequencing according to RNA panel capture reads to obtain an original sequencing off-machine sequence, and performing the following processing on the sequence by using Trimmomatic-0.36 to obtain a high-quality sequencing sequence
a) Removing low quality sequencing sequences;
b) reads containing linker sequences are removed.
And aligning the high-quality sequencing sequence (the standard adopts the standard commonly used in the field) to a reference genome by using STAR to obtain a sequence alignment result, and performing quality control evaluation on the result by comparison to perform the next gene expression quantity analysis according to the index shown in the following table 1.
TABLE 1 quality control Standard under RNA panel
Sequence comparison rate Threshold value ＞＝80％
Target area data volume Threshold value ＞＝2M
Number of expressed housekeeping genes Threshold value ＞＝4
1. Analysis of gene expression level:
based on the sequence alignment results and the annotation files of the reference genome, the gene expression was quantitatively evaluated using the RPKM method, which is the following formula:
Figure BDA0003264838270000081
total exon reads: comparing the number of sequences of all exons of the gene, and evaluating by using FeatureCounts software according to the gene annotation file and the comparison result;
mapped reads (millions): comparing the number of all sequences on the genome, and obtaining the number according to the statistical result of the comparison result;
exon length (KB): the exon length of the gene is calculated according to the annotation file of the genome.
2. Normalization of gene expression level:
and standardizing the expression quantity of the RPKM according to the expression quantitative result of the housekeeping gene and the sequence comparison statistical result to obtain an nRPKM value.
nRPKMGene＝RPKM×HK_coefficient
HK _ Coeffent: and calculating the 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 score:
calculating the expression quantity lg conversion of ALK, NTRK2 and NTRK3, and taking the average number to obtain the marker score. The formula is as follows:
marker score mean [ lg (nRPKM)ALK)+lg(nRPKMNTRK2)+lg(nRPKMNTRK3)]
Thirdly, a result part:
the search cohort of this study tested a total of 35 tissue samples from patients with advanced esophageal squamous carcinoma (EGFRIHC (+++)). The nrpkms for ALK, NTRK2 and NTRK3 genes were transformed by lg and averaged to give the marker score 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.
The patients with high/low scores among 35 patients with advanced esophageal squamous carcinoma were tested, and most patients with low marker scores showed different degrees of tumor regression, as shown in fig. 2B.
Survival curves for Progression Free Survival (PFS) with high/low marker score in 35 patients with advanced esophageal squamous carcinoma tested received anti-EGFR targeted therapy (afatinib), with Progression Free Survival (PFS) higher in patients with low marker score than in patients with high score, P ═ 0.020, as shown in figure 2C.
Among 35 patients with advanced esophageal squamous carcinoma, patients with high/low marker score received the effective rate of anti-EGFR targeted therapy (afatinib), patients with low marker score responded significantly more than patients with high marker score, the effective rate was 70%, and P < 0.01, as shown in fig. 2D.
Example 2 validation of cohort 11 patients with advanced esophageal squamous carcinoma (EGFR IHC (++++)) marker score predictive anti-EGFR (afatinib) treatment efficacy
Firstly, experiment:
RNA extraction:
total RNA extraction was performed using 11 paraffin-embedded pathological sections of patients with advanced esophageal squamous carcinoma, using the RNeasy FFPE Kit from Qiagen (Cat number/ID: 73504). The content of RNA is measured by using a Qubit RNA HS, and the quality of RNA is controlled by using Labchip detection.
2. Preparation of a Pre-hybridization nucleotide library:
the nucleotide library construction was performed using mRNA-seq Lib Prep Module for illumina from ABClonal corporation: comprises the steps of cDNA reverse transcription, fragmentation, end repair, linker connection, library enrichment and the like. The constructed library was purified using Agencour AMpure XP magnetic beads and then subjected to Qubit 3.0 and Agilent 2100 capillary electrophoresis for concentration detection and quality control.
3. And (3) probe capture hybridization:
according to the selected 3 target genes (ALK, NTRK2 and NTRK3), 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-hybrid library was mixed with 5uL of Human Cot DNA (IDT), 2uL of xGen Universal Blockers-TS Mix, evaporated to dryness (60 ℃ C., about 20min-1hr) using a vacuum centrifugal concentrator, redissolved in a hybridization solution, incubated at room temperature for 10min, and transferred to a PCR instrument for hybridization at 65 ℃ for 16 h. And mixing the hybridization products captured overnight with streptavidin magnetic beads, incubating for 45min in a PCR instrument, and washing the magnetic beads with a washing solution. And (3) carrying out the next PCR amplification experiment on the eluted product, purifying by using Agencour AMPure XP magnetic beads, and carrying out concentration determination and quality control by using the Qubit 3.0 and Agilent 2100 capillary electrophoresis.
4. High-throughput sequencing: sequencing was performed in paired-end mode using Illumina Nextseq, Novaseq, etc.
Secondly, sequencing data analysis:
performing on-machine sequencing according to RNA panel capture reads to obtain an original sequencing off-machine sequence, and performing the following processing on the sequence by using Trimmomatic-0.36 to obtain a high-quality sequencing sequence
a) Removing low quality sequencing sequences;
b) reads containing linker sequences are removed.
And aligning the high-quality sequencing sequence (the standard adopts the standard commonly used in the field) to a reference genome by using STAR to obtain a sequence alignment result, and performing quality control evaluation on the result by comparison to perform the next gene expression quantity analysis according to the indexes in the following table 2.
TABLE 2 quality control Standard under RNA panel
Sequence comparison rate Threshold value ＞＝80％
Target area data volume Threshold value ＞＝2M
Number of expressed housekeeping genes Threshold value ＞＝4
1. Analysis of gene expression level:
based on the sequence alignment results and the annotation files of the reference genome, the gene expression was quantitatively evaluated using the RPKM method, which is the following formula:
Figure BDA0003264838270000111
total exon reads: comparing the number of sequences of all exons of the gene, and evaluating by using FeatureCounts software according to the gene annotation file and the comparison result;
mapped reads (millions): comparing the number of all sequences on the genome, and obtaining the number according to the statistical result of the comparison result;
exon length (KB): the exon length of the gene is calculated according to the annotation file of the genome.
2. Normalization of gene expression level:
and (3) standardizing the expression quantity of the RPKM according to the expression quantitative result of the housekeeping gene and the sequence comparison statistical result to obtain an nRPKM value, wherein the specific formula is as follows:
nRPKMGene＝RPKM×HK_coefficient
HK _ Coeffent: and calculating the 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 score:
calculating the expression quantity lg conversion of ALK, NTRK2 and NTRK3, and taking the average number to obtain the RNA expression marker score. The formula is as follows:
marker score mean [ lg (nRPKM)ALK)+lg(nRPKMNTRK2)+lg(nRPKMNTRK3)]
Thirdly, a result part:
a total of 11 tissue samples from patients with advanced esophageal squamous carcinoma (EGFRIHC (+++)) were tested. The nrpkms for ALK, NTRK2 and NTRK3 genes were transformed by lg and averaged to give the marker score 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 score among the 11 patients with advanced esophageal squamous carcinoma tested showed different degrees of tumor regression, and the patients with low marker score all showed different degrees of tumor regression, as shown in fig. 3B.
Survival curves for Progression Free Survival (PFS) with high/low marker score in 11 patients with advanced esophageal squamous carcinoma tested received anti-EGFR targeted therapy (afatinib), with Progression Free Survival (PFS) higher in patients with low marker score than in patients with high marker score, P ═ 0.018, as shown in figure 3C.
The effective rate of patients with high/low marker score among the 11 patients with advanced esophageal squamous carcinoma tested received anti-EGFR targeted therapy (afatinib), the response rate of patients with low marker score to anti-EGFR targeted therapy (afatinib) was significantly higher than that of patients with high marker score, the effective rate was 75%, and P is 0.242, as shown in fig. 3D.
Therefore, the present invention demonstrated that the marker score predicts the effectiveness of anti-EGFR targeted therapy (afatinib) in 11 independent patients with advanced esophageal squamous carcinoma.
Although the invention has been described in detail hereinabove with respect to a general description and specific embodiments thereof, it will be apparent to those skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.

Claims (5)

1. A biomarker for predicting the efficacy of an anti-EGFR targeted therapy in a patient with advanced esophageal cancer, the biomarker being calculated by a 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 method, a linker connection method and a library enrichment method;
capturing and enriching a target gene from the gene library by specific hybridization of a capture probe to the target region;
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 RNA targeted sequencing data by adopting an RPKM method;
biomarkers were obtained based on the average of the expression amounts obtained by the evaluation.
2. The biomarker of claim 1, wherein obtaining the biomarker based on an average of the expression values obtained by the assessment comprises:
standardizing the expression level of ALK, NTRK2 and NTRK 3;
calculating a biomarker score from the normalized expression levels:
biomarker score mean [ lg (nRPKM)ALK)+lg(nRPKMNTRK2)+lg(nRPKMNTRK3)]
Wherein, nRPKMALKExpressing the normalized expression level of ALK gene, and nrPSMALK＝RPKMALK×HK_coefficientALK，RPKMALKIndicating the normalized expression level of ALK gene, HK _ coefficientALKA coefficient of expression change of the gene ALK; nRPKMNTRK2Indicating the normalized expression level of gene NTRK2, and nRPKMNTRK2＝RPMNTRK2×HK_coefficientNTRK2，RPKMNTRK2Indicating the normalized expression level of gene NTRK2, HK _ coefficientNTRK2A coefficient indicating the change in expression level of gene NTRK 2; nRPKMNTRK3Indicating the normalized expression level of gene NTRK3, and nRPKMNTRK3＝RPKMNTRK3×HK_coefficientNTRK3，RPKMNTRK3Indicating the normalized expression level of gene NTRK3, HK _ coefficientNTRK3The expression level change coefficient of gene NTRK3 is shown.
3. The biomarker of claim 1 or 2,
after the RNA target sequencing data is obtained by sequencing with a high-throughput sequencer, the method also comprises a quality control step, which comprises the following steps:
filtering low-quality sequencing data and reads containing linker sequences in the obtained RNA targeted sequencing data;
comparing the filtered RNA targeted sequencing data with a reference genome;
evaluating whether the comparison result meets a preset index, wherein the preset index comprises: the sequence replying comparison rate is above a preset comparison rate threshold, the data volume of the target area is above a preset data volume threshold, and the number of expressed housekeeping genes is above a preset gene number threshold;
the quantitative evaluation of the expression quantity of the genes ALK, NTRK2 and NTRK3 in the RNA targeted sequencing data by adopting the RPKM method comprises the following steps: and quantitatively evaluating the expression quantity of the genes ALK, NTRK2 and NTRK3 in the RNA targeted sequencing data which meet the preset indexes after filtering by adopting an RPKM method.
4. The biomarker of claim 1 or 2, wherein in obtaining the RNA targeted sequencing data by sequencing with a high-throughput sequencer, the RNA targeted sequencing data is obtained by sequencing with a double-ended or single-ended mode by the high-throughput sequencer.
5. Use of a biomarker according to any of claims 1 to 4 in a chip or kit for predicting the efficacy of an anti-EGFR targeted therapy in patients with advanced esophageal cancer.
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