Application of Gene Marker in Predicting Prognosis of Non-Small Cell Lung Cancer, Prediction Device and Computer Readable Medium

Abstract

The present invention relates to the application of gene markers in predicting the prognosis of non-small cell lung cancer, a prediction device, and a computer-readable medium, belonging to the technical field of medical molecular biology. By performing NGS detection and follow-up analysis of the benefit time on patients treated with afatinib, molecular markers for predicting the efficacy of afatinib treatment in EGFR positive non-small cell lung cancer patients were found: Mutation signature APOBEC, which can effectively distinguish patients with primary resistance to afatinib, timely adjust the clinical plan, so that patients can benefit better.

Description

Technical Field

[0001] The present invention relates to the application of gene markers in predicting the prognosis of non-small cell lung cancer, a prediction device, and a computer-readable medium, and mainly relates to a molecular marker for predicting the efficacy of afatinib treatment for positive non-small cell lung cancer, belonging to the technical field of medical molecular biology. EGFR Background Art

[0002] Lung cancer is one of the cancers with the highest incidence and mortality rates globally. Among them, non-small cell lung cancer (NSCLC) is the main pathological subtype, accounting for about 80%-85%. EGFR The gene is a common targetable gene in non-small cell lung cancer, and about 10-50% of patients carry EGFR gene activation mutations. In Chinese NSCLC patients EGFR the gene mutation frequency can be as high as 35-50%, commonly found in women, non-smokers, and adenocarcinoma patients. In normal cells, EGFR the receptor binds to the ligand, activating a series of downstream pathways, promoting cell growth and proliferation. Usually, EGFR the function is tightly controlled and turned off after its function is completed. However, when EGFR mutates, the formed receptor no longer requires other signals and will continuously send messages by itself. After the pathway is continuously activated, it affects cell proliferation, differentiation, signal transduction, blood vessel formation, inhibition of apoptosis, etc., ultimately leading to the occurrence of cancer.

[0003] In 2005, the first targeted drug, gefitinib, for EGFR the gene was approved in China. Subsequently, based on the IPASS study, its first-line treatment status was established, kicking off the prelude to targeted lung cancer treatment. Subsequently, a series of targeted drugs, such as erlotinib, icotinib, afatinib, dacomitinib, etc., have all obtained relevant indications. Among them, afatinib belongs to the second-generation EGFR -TKI, which is the first irreversible ErbB family blocker and can inhibit the tyrosine kinase of the epidermal growth factor receptor ( EGFR ). Afatinib can irreversibly block EGFR and other related members of the other ErbB family. For EGFR rare mutations, such as G719X, L861Q, and S768I, etc., afatinib shows better efficacy than other TKIs. In addition, there are also reports EGFR 20ins, EGFR -KDD, EGFR patients with complex mutations have good clinical benefits when using afatinib. However, inevitably, drug resistance will occur after using afatinib for a period of time. The corresponding drug resistance mechanisms include EGFRAutogenous mutations, with T790M being the main one, accounting for approximately 50%. Other drug resistance mechanisms include activation of downstream or bypass pathways, such as activation of the PI3K - AKT pathway, ERBB2 amplification, MET amplification and KRAS gene mutations, etc. Compared with patients with secondary drug resistance, patients with primary drug resistance usually progress within three months after medication. Currently, there are few studies on the primary drug resistance related to afatinib. Only by clarifying EGFR -the molecular mechanism of primary resistance to TKI can the treatment plan be adjusted in a timely manner to bring more clinical benefits to patients. Summary of the Invention

[0004] Through the screening of gene data, the present invention has found molecular markers that can be used to evaluate EGFR the therapeutic efficacy of afatinib in positive non - small cell lung cancer. When using the markers to evaluate the progression - free survival time (PFS) of patients, it has a good discrimination effect.

[0005] The use of a reagent for detecting the proportion of characteristic mutations of the TP53 gene or APOBEC in the preparation of a reagent for evaluating the drug resistance of afatinib in treating EGFR positive non - small cell lung cancer; the proportion of APOBEC - characteristic mutations refers to the proportion of APOBEC - characteristic mutations in all somatic single - base mutations.

[0006] The APOBEC - characteristic mutations refer to the sum of Mutation Signature 2 and Signature 13 in COSMIC Mutation Signature.

[0007] The evaluation of drug resistance is carried out by using the progression - free survival time (PFS) as an index.

[0008] The above - mentioned use also includes the step of determining the comparison between the proportion of APOBEC - characteristic mutations and a threshold.

[0009] The threshold is 0.36 - 0.38.

[0010] A device for evaluating the drug resistance of afatinib in treating EGFR positive non - small cell lung cancer, comprising:

[0011] A sequencing module for sequencing a sample to obtain somatic gene mutation information of the sample;

[0012] An analysis module for analyzing based on the somatic gene mutation information to obtain TP53Gene mutation status or proportion of APOBEC signature mutations; the proportion of APOBEC signature mutations refers to the proportion of APOBEC signature mutations among all somatic single-base mutations;

[0013] A determination module that determines whether a sample has resistance to afatinib treatment for positive non-small cell lung cancer based on the TP53 gene mutation status or the proportion of APOBEC signature mutations; EGFR If the TP53 gene is a variant or the proportion of APOBEC signature mutations is greater than the threshold, the patient is determined to have poor efficacy of afatinib treatment.

[0014] The threshold is 0.36 - 0.38.

[0015] A computer-readable medium that stores a computer program capable of running the following steps:

[0016] Sequencing a sample to obtain the gene mutation information of the sample;

[0017] Analyzing the TP53 gene mutation status or the proportion of APOBEC signature mutations based on the somatic gene mutation information; the proportion of APOBEC signature mutations refers to the proportion of APOBEC signature mutations among all somatic single-base mutations;

[0018] Determining whether the sample has resistance to afatinib treatment for positive non-small cell lung cancer based on the TP53 gene mutation status or the proportion of APOBEC signature mutations; EGFR If TP53 The patient with a variant gene or an APOBEC signature mutation greater than the threshold is determined to have poor efficacy of afatinib treatment. Description of the Drawings

[0019] Figure 1 : Proportion of different mutation signatures in patients;

[0020] Figure 2 : Influence of APOBEC proportion on PFS of patients treated with afatinib under different thresholds;

[0021] Figure 3 : Difference in PFS benefit of patients treated with afatinib between high and low APOBEC proportions;

[0022] Figure 4 : Difference in PFS benefit of patients treated with afatinib between TP53 variant and wild-type patients. Detailed Embodiments

[0023] In the research of the present invention, 36 EGFR Baseline tissue samples of positive non-small cell lung cancer patients treated with afatinib were subjected to Shihe-1® Targeted panel sequencing was performed, and the sequencing data was analyzed through the bioinformatics analysis process of Nanjing Shihe Gene Biotechnology Co., Ltd. The R package sigminer was used for mutation signature analysis. Thirty signatures were divided into 10 groups according to their biological significance. Among them, the APOBEC enzyme-related mutation signatures include Signature 2 and Signature 13, and their main feature is the C>G / T mutation, which is related to the APOBEC enzyme activity. The optimal thresholds that can significantly distinguish the progression-free survival (PFS) of afatinib treatment were screened respectively for the 10 groups of signatures through R software, satisfying P <0.05, and only the APOBEC signatures met the threshold conditions. Genes with a population variant frequency of ≥5 people, clinical characteristics, and APOBEC mutation signatures were combined with the progression-free survival (PFS) of afatinib treatment for analysis, and statistical tests were performed through Log-rank to screen P <0.05 features, and further multiple corrections were performed through the False Discovery Rate method. It was found that patients with TP53 mutations or APOBEC High had significantly worse afatinib treatment efficacy (corrected P <0.05), and among them, APOBEC is a newly discovered potential molecular marker for predicting the efficacy of afatinib treatment.

[0024] In the present invention, TP53 The "gene variation" mentioned for genes includes gene mutations, fusions, and copy number changes.

[0025] The proportion of APOBEC characteristic mutations in the present invention refers to the proportion of APOBEC family-related characteristic mutations in all single nucleotide mutations in the sample. APOBEC is the apolipoprotein B mRNA editing enzyme catalytic polypeptide family, such as APOBEC1, APOBEC3A, and APOBEC3B, etc. They can specifically catalyze cytosine -> uracil in the genome and may be involved in the immune response and antiviral response in the human body. According to the COSMIC Mutation signature V2 version, the single nucleotide mutation signatures in the patient samples were classified into 30 types, and they were divided into 10 groups according to their biological significance. Among them, the APOBEC enzyme-related mutation signatures are the sum of Signature 2 and Signature 13 mutation signatures, and their main feature is the C>G / T mutation, which is related to the APOBEC enzyme activity. The threshold division of the APOBEC mutation signature is based on the progression-free survival time of the patients treated with afatinib in the present invention. The value that makes the survival difference between the two groups of patients the most significant (when the P value is the smallest and the HR is the largest) is screened as the threshold, and the patients are divided into two groups: APOBEC high (High) and APOBEC low (Low).

[0026] Obtaining APOBEC characteristic mutations is a known technology in the field. Currently, the three-base single nucleotide mutation pattern of a sample can be analyzed for mutation signature by using software version R3.6.3 (https: / / cran.r-project.org / bin / windows / base / old / 3.6.3 / ). The R packages involved are the Maftools package (v2.12.0, https: / / bioconductor.org / packages / release / bioc / html / maftools.html) and the Sigminer package (v2.1.4, https: / / cran.r-project.org / web / packages / sigminer / index.html).

[0027] The basic clinical information of the patients is as follows:

[0028] For 36 EGFR positive non-small cell lung cancer patients, targeted panel testing was performed. The stages of all patients were stage IV. The median age was 61 years, and the age range was 33 - 83 years. Among them, the number of patients less than or equal to 65 years old accounted for 75% (27 cases), and the number of patients older than 65 years old accounted for 25% (9 cases). There were more female patients in the cohort, 61% (22 cases), and the male proportion was 39% (14 cases). Among the patients, 31 cases (86%) were mainly adenocarcinoma of the lung, and there were also 2 cases (6%) of squamous cell carcinoma of the lung, 2 cases (6%) of adenosquamous carcinoma, and 1 case (3%) with an unknown pathological subtype. The specific patient information is shown in Table 1.

[0029] Table 1 Clinical information of the analysis cohort samples

[0030]

[0031] The influence of clinical factors on EGFR the progression-free survival (PFS) of positive non-small cell lung cancer patients treated with afatinib is as follows:

[0032] Some clinicopathological features may be potential predictive indicators of the efficacy of tumor treatment. In this study, univariate Log-rank analysis was used to analyze the association between these possible clinical features and the PFS of patients after afatinib treatment. Among them, the progression-free survival (PFS) of patients was defined as the time from the start of taking afatinib by the patient to the progression or death of the disease or the last follow-up date. As shown in Table 2, the clinical features including gender, age, and pathological classification may not be predictive indicators of PFS ( P >0.05).

[0033] Table 2: Clinical factors related to the efficacy of afatinib screened by Log-rank

[0034]

[0035] The influence of genomic characteristics on EGFR the progression-free survival (PFS) of afatinib treatment in patients with positive non-small cell lung cancer is as follows:

[0036] In this study, the tumor tissue samples of patients before treatment were subjected to next-generation sequencing gene detection using the 425-gene panel of SeqHealth. This gene panel comprehensively covers genes related to important cancer-related signaling pathways. The specific gene list is shown in Table 3:

[0037] Table 3: Gene list of 425-panel

[0038]

[0039]

[0040] The processes of DNA extraction and sequencing library preparation are as follows:

[0041] Genomic DNA was extracted from formalin-fixed paraffin-embedded (FFPE) tissue samples using the QIAamp DNA FFPE Tissue Kit (Qiagen). All samples were confirmed by a pathologist to have a tumor content of at least 10%. The concentration and quality of the extracted DNA were evaluated using a Qubit 3.0 fluorometer and a NanoDrop 2000 (Thermo Fisher Scientific), respectively. Then, the genomic DNA was fragmented into 350-bp fragments using a Covaris M220 sonication system and purified using Agencourt AMPure XP beads (Beckman Coulter). A sequencing library was prepared using the KAPA HyperPrep Kit (KAPA Biosystems). Libraries with different molecular tags were pooled. The pooled library was subjected to targeted enrichment using the above-mentioned 425-gene panel and IDT xGen Lockdown Reagents. The enriched library was amplified using Illumina p5 (5'AAT GAT ACGGCG ACC ACC GA 3') and p7 (5'CAA GCA GAA GAC GGC ATA CGAGAT 3') primers in KAPA Hifi Hot Start Ready Mix (KAPA Biosystems), and then the sequencing library was quantified by qPCR using the KAPA Library Quantification kit (KAPA Biosystems). The final library was sequenced on an Illumina Hiseq 4000 platform with an average sequencing depth of at least 250×.

[0042] The sequencing results were analyzed as follows:

[0043] The sequencing data was analyzed through the validated bioinformatics analysis pipeline of Nanjing Shihe Gene Biotechnology Co., Ltd., and the main steps are described as follows. The data was split using bck2fastq, and then the FASTQ file quality was filtered (QC) using Trimmomatic. Low-quality bases (phred score below 15) or N bases were removed. The sequenced reads were aligned to the human reference genome hg19 using the Burrows-Wheeler Aligner (BWA-mem, v0.7.12; https: / / github.com / lh3 / bwa / tree / master / bwakit), and Picard was applied to remove the duplicate sequences caused by PCR. The Genome Analysis Toolkit (GATK 3.4.0) was used to perform local assembly correction alignment around insertions and deletions and recalibrate the base quality scores. The VarScan2 software was used to detect single nucleotide variants (SNVs) and insertion / deletion mutations with the following parameters: minimum sequencing depth = 20, minimum base quality = 25, minimum variant allele frequency (VAF) = 0.03, minimum variant supporting reads = 3, variants detected on both the positive and negative strands, and strand bias not greater than 10%. In the next filtering step, only COSMIC hotspot (recurrence >= 20) mutations with VAF higher than 1% and at least 3 mutant reads, or other mutations with at least 5 variant supporting reads would be called. The mutations were annotated by ANNOVAR against the following databases: dbSNP (v138), 1000Genome, ExAC, COSMIC (v70), ClinVAR, and SIFT. Mutations were removed if their population frequency in the 1000GenomesProject or 65000 exomes project (ExAC) was >1%. Then, the mutation list was filtered through a list of duplicate sequencing errors on the same sequencing platform collected internally, which was derived from the sequencing results summary of 53 normal samples with a minimum average sequencing depth of 700x. If a variant (such as >= 3 mutant reads and >1% VAF) was detected in >20% of the normal samples, it was considered likely to be a human error and removed. Mutations occurring in repetitive regions were also removed. In the next filtering step, only COSMIC mutations with VAF higher than 2% and at least 3 mutant reads, or non-COSMIC mutations with VAF higher than 3% and at least 5 mutant reads would be called.

[0044] For CNV analysis, we used the droplet digital polymerase chain reaction (ddPCR) results as the "gold standard" and comprehensively tested and verified our CNV program with 38 samples. We performed principal component analysis on 100 normal samples in the same batch to reduce the systematic noise in the copy number data. For copy number loss, the threshold was 0.65, and for copy number gain, the threshold was 2.0.

[0045] The R package sigminer was used to analyze the mutation signatures of the patient's somatic single-base mutations. There were a total of 30 different signature features, and then these 30 signature features were merged according to the COSMIC V2 legacy signatures. Finally, 10 different signatures were obtained, including Age, APOBEC, BRCA, Smoking, MMR deficiency, Ultraviolet, Immunoglobulin, POLE, Temozolomide, and Others. The proportion of APOBEC signature mutations included the sum of the proportions of signature 2 and signature 13.

[0046] The data analysis results are as follows:

[0047] The progression-free survival (PFS) of the patients was calculated from the start of the patients' afatinib treatment until the disease progressed or the patients died or the last follow-up date. The Kaplan-Meier method was used to estimate the PFS of different genomes, and the Log-rank test was used to analyze the differences between groups.

[0048] Thirty mutation signatures were merged into ten, and the proportion of each signature in each group is as Figure 1 shown. Four signature features with relatively high proportions, namely Age, APOBEC, MMR deficiency, and Others, were selected to screen the threshold for distinguishing the PFS of the patients' afatinib efficacy through the R software, and it was required to meet P <0.05. Among the four signatures, only APOBEC could find the corresponding threshold, which could significantly distinguish the PFS of the patients' afatinib treatment ( P <0.05). The results are shown in Table 4. The division of the high and low thresholds of the APOBEC proportion was determined by R software analysis. When the threshold was 0.377, P the value was the most significant and the HR value was the largest (see Figure 2), so APOBEC ≥ 0.377 was defined as APOBEC High, and vice versa as APOBEC Low. There was a significant difference in the PFS of afatinib treatment between patients with APOBEC High and APOBEC Low (mPFS: 2.8 months vs 7.2 months; HR = 3.59, 95%CI(1.37~9.36); P = 0.005), see Figure 3 . There has been no research on the possibility that the proportion of APOBEC may lead to primary resistance to afatinib. Therefore, APOBEC is a potentially new molecular marker for predicting the efficacy of afatinib treatment discovered in this patent.

[0049] Table 4: Influence of signature proportion on patient PFS at different thresholds

[0050]

[0051]

[0052] Next, the somatic mutations of the patients' tumors were analyzed. According to the distribution of the mutation profiles of non-small cell lung cancer patients, a preliminary screening was carried out, and genes with somatic mutations ≥ 5 people were used as candidate objects. Then, somatic mutations related to the efficacy of afatinib treatment in positive non-small cell lung cancer were screened out by Log-rank. The distribution of the Log-rank screening results of somatic mutations is shown in Table 5. According to the screening results, EGFR patients with gene mutations had significantly poorer efficacy of afatinib (mPFS: 4.0 months vs 8.3 months; HR = 2.51, 95%CI(1.21~5.18); TP53 = 0.010), see P = 0.010), see Figure 4 . Another research report (PMID: 32272775) found that TP53 mutant patients EGFR -TKIs (drugs included gefitinib, afatinib and erlotinib) had poorer efficacy than wild-type, and the two results were consistent.

[0053] Table 5: Log-rank screening EGFR Somatic mutations related to the efficacy of afatinib treatment in positive non-small cell lung cancer

[0054]

[0055] For further verification, this patent further performed multiple corrections on the molecular characteristics with gene mutation frequency ≥ 5 people (14%) and the single-factor results of related clinical characteristics, using the False Discovery Rate method. Each characteristic factor PAfter FDR correction, the value shows a high proportion of APOBEC and TP53 gene mutations remain significant ( P = 0.035). The specific results are shown in Table 6.

[0056] Table 6: Results of univariate P-value correction for clinical and molecular characteristics

[0057]

[0058] It can be seen that through the above method, we have discovered two ( TP53 genes and Mutation signature APOBEC) potential molecular markers that can be used to predict EGFR the efficacy of afatinib treatment in positive non-small cell lung cancer. Both of these markers can be detected by the GenePlus One ® large panel, and can evaluate the primary drug resistance of patients. Among them, Mutation signature APOBEC was first discovered and verified in this patent.

Claims

1. Use of a reagent for detecting the proportion of APOBEC signature mutations in the preparation of a reagent for evaluating the resistance to afatinib in the treatment of EGFR-positive non-small cell lung cancer; the proportion of APOBEC signature mutations refers to the proportion of APOBEC signature mutations among all somatic single-base mutations; The APOBEC signature mutations refer to the sum of the mutation signatures of Signature 2 and Signature 13 in COSMIC Mutation Signature; The resistance evaluation is carried out by using the progression-free survival time as an index; The above application further includes the step of comparing the proportion of APOBEC signature mutations with a threshold; The threshold is 0.36 - 0.

38.

2. A device for predicting the resistance to afatinib in the treatment of EGFR-positive non-small cell lung cancer, characterized in that, it includes: A sequencing module for sequencing a sample to obtain the somatic gene mutation information of the sample; An analysis module for analyzing the proportion of APOBEC signature mutations according to the somatic gene mutation information; the proportion of APOBEC signature mutations refers to the proportion of APOBEC signature mutations among all somatic single-base mutations; A determination module for determining whether the sample has resistance to afatinib in the treatment of EGFR-positive non-small cell lung cancer according to the proportion of APOBEC signature mutations; patients with APOBEC signature mutations greater than the threshold are determined to have poor efficacy in afatinib treatment; The threshold is 0.36 - 0.38; The APOBEC signature mutations refer to the sum of the mutation signatures of Signature 2 and Signature 13 in COSMIC Mutation Signature; The resistance evaluation is carried out by using the progression-free survival time as an index.

3. A computer-readable medium, characterized in that, it stores a computer program capable of running the following steps: Sequencing a sample to obtain the gene mutation information of the sample; Analyzing the proportion of APOBEC signature mutations according to the somatic gene mutation information; the proportion of APOBEC signature mutations refers to the proportion of APOBEC signature mutations among all somatic single-base mutations; Determining whether the sample has resistance to afatinib in the treatment of EGFR-positive non-small cell lung cancer according to the proportion of APOBEC signature mutations; patients with APOBEC signature mutations greater than the threshold are determined to have poor efficacy in afatinib treatment; The APOBEC signature mutations refer to the sum of the mutation signatures of Signature 2 and Signature 13 in COSMIC Mutation Signature; The resistance evaluation is carried out by using the progression-free survival time as an index; The threshold is 0.36 - 0.38.

Images