Mutation Profile of Resected EGFR‐Mutated Lung Adenocarcinoma by Next‐Generation Sequencing

The best treatment option for patients with operable lung cancer is debatable. In this study, a comprehensive mutation profiling was performed on resected EGFR‐mutated lung adenocarcinoma using next‐generation sequencing that targeted cancer‐relevant genes to identify potential candidates for adjuvant tyrosine kinase inihibitors treatment post‐operation.

Abstract

Background.

The efficacy of adjuvant targeted therapy for operable lung cancer is still under debate. Comprehensive genetic profiling is needed for detecting co‐mutations in resected epidermal growth factor receptor (EGFR)‐mutated lung adenocarcinoma (ADC), which may interfere the efficacy of adjuvant tyrosine kinase inhibitor (TKI) treatment.

Materials and Methods.

Mutation profiling of 416 cancer‐relevant genes was conducted for 139 resected stage I–IIIa lung ADCs with EGFR mutations using targeted next‐generation sequencing. Co‐mutation profiles were systematically analyzed.

Results.

Rare EGFR alterations other than exon 19 deletion and L858R, such as L861Q (∼3%) and G719A (∼2%), were identified at low frequencies. Approximately 10% of patients had mutations in EGFR exon 20 that could confer resistance to first‐generation TKIs. Ninety‐one percent of patients harbored at least one co‐mutation in addition to the major EGFR mutation. TP53 was the top mutated gene and was found more frequently mutated at later stage. Markedly, NF1 mutations were found only in stage II–III ADCs. Conversely, RB1 mutations were more frequent in stage I ADCs, whereas APC mutations were observed exclusively in this group. Thirty‐four percent of patients with EGFR TKI‐sensitizing mutations had genetic alterations involving EGFR downstream effectors or bypass pathways that could affect the response to EGFR TKIs, such as PIK3CA, BRCA1, and NOTCH1.

Conclusion.

Operable lung ADCs with EGFR TKI‐sensitizing mutations are associated with a high proportion of co‐mutations. Mutation profiling of these resected tumors could facilitate in determining the applicability and efficacy of adjuvant EGFR TKI therapeutic strategy.

Implications for Practice.

The efficacy of adjuvant epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) therapy for lung cancer harboring EGFR mutation after surgical resection is still under debate. Next‐generation sequencing of 416 cancer‐relevant genes in 139 resected lung cancers revealed the co‐mutational landscape with background EGFR mutation. Notably, the study identified potential EGFR TKI‐resistant mutations in 34.71% of patients with a drug‐sensitizing EGFR mutation and who were naive in terms of targeted therapy. A comprehensive mutation profiling of these resected tumors could facilitate in determining the applicability and efficacy of adjuvant EGFR TKI therapeutic strategy for these patients.

Introduction

The precise treatment for operable lung cancer (stage I to IIIa) is still under debate [1], and unfortunately, most patients with resected non‐small cell lung cancer (NSCLC) eventually experience disease recurrence. Adjuvant chemotherapy for resected NSCLC improves the 5‐year survival by approximately 5%, especially for node‐positive patients [2]. Compared with metastatic lung adenocarcinoma (ADC), in which molecular diagnosis‐based targeted treatment is in routine practice with higher efficacy and lower toxicity, relatively little is known regarding the utility and benefit of targeted therapy for localized disease. The value of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) in the adjuvant setting for EGFR‐positive resected ADC has been evaluated in a recent clinical trial (NCT01405079). The preliminary results indicate that patients given adjuvant gefitinib have a better disease‐free survival (DFS) than that of those receiving chemotherapy (28.7 vs. 18.0 months) [3].

However, knowing the EGFR mutation status is not enough to estimate the patient's response to TKIs because primary drug resistance caused by secondary EGFR mutations or downstream or bypass signal activations may present in the patient. In this study, a comprehensive mutation profiling was performed on resected EGFR‐mutated lung ADC using next‐generation sequencing (NGS) targeting 416 cancer‐relevant genes aiming to identify potential candidates for adjuvant TKI treatment after operation.

Materials and Methods

One hundred and thirty‐nine patients, who were diagnosed with pathological stage I–IIIa lung ADC (TNM Classification of Malignant Tumors, 7th edition) with EGFR mutation confirmed by Sanger sequencing or the amplification‐refractory mutation system (ARMS) at the Sun Yat‐Sen University Cancer Center (Guangzhou, China) and underwent radical resection (R0) from 2011 to 2015, were recruited for the study (see flowchart in supplemental online Fig. 1) [4]. Postoperative evaluations of recurrence using routine chest and upper abdominal computed tomography, with cranial magnetic resonance imaging or positron emission tomography, if applicable, was performed every 3 months for the first 2 years and semiannually afterward. The institutional review board approved the study protocol, and written consent for tissue analysis had been obtained from every patient preoperatively. The collected samples were sent to the core facility of Nanjing Geneseeq Technology Inc. (Nanjing, China) for genetic testing by targeted NGS.

DNA Extraction

Serial formalin‐fixed paraffin‐embedded (FFPE) sections were microdissected to ensure that each sample comprised at least 70% tumor content. Genomic DNA was extracted using the QIAamp DNA FFPE Tissue Kit (Qiagen, Hilden, Germany). DNA was qualified using NanoDrop 2000 (Thermo Fisher Scientific, Waltham, MA), and its quantity was measured using the dsDNA HS Assay Kit (Thermo Fisher Scientific) on Qubit 3.0 [5].

Library Preparation and Sequencing

Genomic DNA was fragmented into 300∼350 base pairs using the Covaris M220 (Covaris, Woburn, MA). A sequencing library was prepared using the Kapa Hyper Prep kit (Kapa Biosystems, Wilmington, MA). In brief, the fragmented DNA was subjected to end‐repair, A‐tailing, adapter ligation, and size selection. The library was then subjected to polymerase chain reaction (PCR) amplification and purification before targeted enrichment.

A customized xGen lockdown probe panel (Integrated DNA Technologies, Skokie, IL) was used for targeted enrichment of 416 predefined genes. The hybridization reaction was performed using the NimbleGen SeqCap EZ Hybridization and Wash Kit (Roche, Basel, Switzerland). Human cot‐1 DNA (Thermo Fisher Scientific) and xGen Universal blocking oligos (Integrated DNA Technologies) were added to block nonspecific binding. Dynabeads M‐270 (Thermo Fisher Scientific) were used to capture probe‐binding fragments, and enriched library was amplified using Illumina (San Diego, CA) primers p5 (5' AAT GAT ACG GCG ACC ACC GA 3') and p7 (5' CAA GCA GAA GAC GGC ATA CGA GAT 3') in Kapa HiFi HotStart ReadyMix (Kapa Biosystems), followed by library purification using Agencourt AMPure XP beads (Beckman Coulter, Brea, CA). The sequencing library was quantified using the Kapa Library Quantification kit (Kapa Biosystems). The size distribution of each library was measured using the Agilent Technologies 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA). The enriched libraries were sequenced on HiSeq 4000 NGS platforms (Illumina) to coverage depths of at least ×300 for FFPE samples after removing PCR duplicates.

Annotation and Interpretation of Sequencing Results

Trimmomatic [6] was used for quality control of the sequencing data, which were then mapped to the human reference genome version 19 (hg19) using the Burrows‐Wheeler aligner [7] with optimized parameters. The Genome Analysis Toolkit (GATK) [8] was used for local realignment of indels and base quality score recalibration. Single nucleotide polymorphisms (SNPs) and indels were identified using VarScan2 (minor allele frequency [MAF] <10%) (http://dkoboldt.github.io/varscan/) and HaplotypeCaller/UnifiedGenotyper in GATK (MAF >10%). SNPs were filtered out using the dbSNP and 1000 Genome datasets. All alterations were manually inspected on Integrative Genomics Viewer (Broad Institute, Cambridge, MA). Genomic fusions were identified using FACTERA [9] with the default parameters. Copy number variation was detected using ADTEx (http://adtex.sourceforge.net) with the default parameters.

Statistical Analysis

ADCs harboring EGFR alterations other than exon 20 insertion or T790 M mutation were defined as EGFR TKI‐sensitizing group. Categorical variables were analyzed using the chi‐square test or Fisher's exact test as appropriate. Continuous data were compared by Student's t test as appropriate. DFS was defined as the interval between surgery and the first recurrence. The Kaplan‐Meier curve and log‐rank test were used to compare unadjusted survival. Multivariable Cox proportional hazards models that included variables with a p value less than .05 on univariable analysis were used to decide which variables remained in the final model. All reported p values were two‐tailed, and those less than .05 were considered to be statistically significant. The statistical analyses were performed using SPSS version 21 (IBM Corporation, Armonk, NY).

Results

Patient Characteristics

A total of 139 EGFR‐mutated resected lung ADCs, confirmed by Sanger sequencing or ARMS method, were enrolled in the study, and the baseline information is summarized in Table 1. As expected, compared with patients with pathological stage I (n = 75), patients with stage II–III (n = 64) had greater frequencies of male sex, smoking history, elevated carcinoembryonic antigen levels, poor differentiation, and pleural or lymphovascular invasion (LVI), as well as larger tumor size. More patients with stage II–III than with stage I received adjuvant chemotherapy (47/64, 73.4%, vs. 28/75, 37.3%). None of the patients received EGFR TKI as adjuvant therapy in this cohort. Additionally, 29 patients (20.86%) received first‐generation EGFR TKI therapy after disease recurrence or progression after first‐line chemotherapy (supplemental online Fig. 2).

Table 1. Baseline characteristics.

Abbreviations: BMI, body mass index; CEA, carcinoembryonic antigen; LN, lymph node; NA, not applicable; RML, right middle lobe; SD, standard deviation.

Co‐mutations that Potentially Affect TKI Efficacy in the EGFR TKI‐Sensitive Group

Of the 125 patients with EGFR TKI‐sensitizing mutations, 42 (33.60%) had at least one genetic mutation that can potentially influence the outcome of EGFR TKI treatment (Fig. 1A). PIK3CA was the most frequently mutated gene (30.95%), followed by BRCA1 (21.43%) and NOTCH1 (19.05%). No difference in the distribution of mutations other than those in NF1 (present only in stage II–III ADCs) was found between the two pathological groups. The functions of these genes can be categorized as follows: (a) bypassing receptor tyrosine kinases, including ERBB2, MET, and FGFR; (b) activating downstream effectors of the RAS‐RAF‐ERK‐MAPK pathway; (c) activating the PI3K‐AKT‐mTOR pathway, correlated with mutations in PIK3CA, AKT, etc.; and (d) inactivating or changing the copy number of genes in other pathways, such as JAK, NOTCH1, etc. A trend of shorter progression‐free survival (PFS) of EGFR TKI treatment was observed in patients with such mutations than in those without (log‐rank p = .06, Fig. 1B).

Figure 1.

Co‐mutations that potentially affect TKI efficacy in drug‐sensitive EGFR‐mutated lung adenocarcinomas. (A): Distribution of the 15 genes. The asterisk indicates a statistical difference between stage I and II–III. (B): Progression‐free survival of EGFR TKI treatment in patients with such mutations (group 1, n = 12) was slightly worse than those without (group 2, n = 15; log‐rank p = .06).

Abbreviations: del, deletion; TKI, tyrosine kinase inhibitor.

EGFR Mutation Status

In addition to exon 19 deletion and L858R, other rare EGFR mutations were also identified, such as L861Q (n = 5, 3.60%) and G719A (n = 3, 2.16%; Fig. 2). Of the 139 patients, 14 (10.07%) had TKI‐resistant mutations, including 7 (5.04%) with the T790 M mutation. The exon 20 insertion was present in all patients with a single‐site mutation conferring resistance to first generation EGFR TKIs (n = 3), two of whom had M766delinsMASV and A767delinsASVD, respectively. Statistical analysis indicated that the EGFR mutational status distribution was not associated with the postoperative clinical staging (p > .05).

Figure 2.

Co‐mutation plot of top 20 genes in EGFR‐mutant lung adenocarcinomas. Asterisks indicate statistical differences of mutation rate in the given gene between stages I and II–III.

Abbreviations: del, deletion; TKI, tyrosine kinase inhibitor.

Investigation of Gene Mutations at Different Stages

Most patients (127/139, 91.37%) in the cohort had at least one genetic alteration other than an EGFR mutation (median, 3.70 mutations per patient; supplemental online Table 1). Figure 2 summarizes the distribution of the top 20 mutated genes identified. TP53 (74/139, 53.24%) was the most frequently mutated gene and was found to be more common in patients with stage II–III ADC. The NF1 mutation (8/64, 12.50%) was found exclusively in patients with stage II–III ADC. Conversely, RB1 mutations were present at a greater frequency (10/75, 13.33%) in stage I ADCs, and APC mutations were present exclusively (7/75, 9.33%) in this group.

Patient Follow‐Up and Survival Analysis

The median follow‐up interval for DFS evaluation was 23 (2–53) months, and 59 patients (42.45%) had a documented recurrence, including 31 with relapse in the thoracic cavity and 28 with distal relapse. Sixteen patients (11.51%) died during the follow‐up period. Clinical and pathological variables were subjected to univariable analysis (supplemental online Table 2). Female sex, less advanced pathological stage, negative LVI, moderate‐to‐well differentiation, drug‐sensitive EGFR mutation, wild‐type TP53, wild‐type MAPK pathway, and co‐mutation status were significantly associated with a longer DFS. However, in the multivariable analysis, only more advanced pathological stage (vs. stage I: hazard ratio [HR], 23.41; 95% confidence interval [CI], 2.92–187.78) and TKI‐resistant EGFR mutations (vs. sensitizing mutations: HR, 4.26; 95% CI, 1.04–17.48) remained significantly associated with DFS (Table 2).

Table 2. Multivariable analyses for disease‐free survival.

Abbreviations: CI, confidence interval; EGFR, epidermal growth factor receptor; HR, hazard ratio; LVI, lymphovascular invasion; TKI, tyrosine kinase inhibitor.

Discussion

This study found that EGFR‐mutated resected lung ADC is associated with significant rates of EGFR rare mutations, some of which could lead to primary TKI resistance. Several alternative or downstream activating mutations that may influence the outcome of TKI treatment were also identified in EGFR TKI‐sensitive ADCs. The strength of the current study was the examination of EGFR‐mutant lung ADCs naive in terms of targeted therapy, after complete resection, thereby identifying the molecular backgrounds of potential candidates for adjuvant EGFR TKI treatment. Although initial randomized trial had proved that adjuvant EGFR TKI could prolong DFS in node‐positive NSCLCs with EGFR mutation, routine use of TKI in adjuvant setting is currently under debate because of its insufficient evidence of overall survival benefit [3]. Nevertheless, the findings in this study could help determine the appropriate adjuvant TKI therapy for patients in future trials.

The T790 M mutation, which is the primary cause of acquired resistance to first‐generation TKIs, was found in 5.04% of the ADCs in this study. This mutation is thought to increase the affinity of EGFR for ATP to wild‐type levels, thus decreasing drug efficacy [10]. Osimertinib, the third‐generation TKI, demonstrated a tumor response rate at 71% in the T790 M‐positive population after acquired resistance and hence could be ideal for these patients [11]. Rare EGFR mutations are found in small numbers of patients, who exhibit various outcomes and should be assessed individually. Exon 20 insertions (M766delinsMASV and A767delinsASVD) add residues into essential regions, thereby activating the kinase domain [12]. Studies have shown an extremely poor efficacy of second generation irreversible TKI afatinib in patients with exon 20 insertions [13]. Alterations in exon 18, such as the G719 or E709 point mutations, do not appear to cause resistance to EGFR TKIs [14]. The ADCs with the L861Q mutation seem to be less sensitive to gefitinib than those with L858R, but there was no difference in the outcomes compared with the chemotherapy group [15]. By contrast, another study indicated that the L861Q or G719 mutation was associated with improved sensitivity to afatinib and better PFS compared with rare complex mutations [13]. In concordance with previous studies, we found that EGFR TKI‐sensitizing mutations in ADCs were an independent predictor for better survival.

In addition to intrinsic resistance caused by specific EGFR TKI‐resistant mutations, many other mechanisms might potentially affect the efficacy of TKI treatment, and combination therapies may be need in certain situations. According to the study by Labbé et al., dual TP53 and EGFR mutation was found in 41% of lung ADCs [16]. Moreover, these patients had marginally shorter PFS under EGFR TKI treatment. However, the objective response rate to TKIs was not significantly different. Interestingly, in the report from Barnet and colleagues, patients with co‐occurrence of EGFR mutation, especially PIK3CA mutation, had significantly shorter PFS (5.7 vs. 12.3 months) and inferior response rate (38% vs. 89%) to TKIs than those without [17].

In this study, we found 15 genes that may affect TKI efficacy, and, notably, we observed a trend of shorter PFS in patients with such mutations who underwent TKI treatment. Patients with coexisting MET amplification may experience a partial response to the c‐MET inhibitor crizotinib [18], [19]. Similarly, ERBB2‐activating mutations or amplification can function in parallel of EGFR. Covalent EGFR and ErbB2 inhibitors, such as afatinib and dacomitinib, might achieve better clinical response [20], [21]. Other pathways, such as activation of the FGF2‐FGFR1 autocrine pathway, which can affect TKI efficacy, have also been reported [22]. However, one should be very careful when interpreting this result, because many of these mutations have never been individually proven to clinically influence TKI efficacy in the metastatic setting, and the real impact needs to be validated in a larger patient cohort [23]. Moreover, a TKI combination strategy for patients with co‐occurring mutations would cause a serious concern of toxic complications compared with single‐agent TKI therapy.

It is also worth noting that NF1 mutations were present in stage II–III ADCs exclusively. NF1 is a negative regulator of RAS proteins, and its mutation may lead to EGFR TKI resistance [24]. In addition, many alterations in the downstream effector molecules of the EGFR signaling pathway have been found. PIK3CA mutations are reported to result in dramatically suppressed sensitivity to gefitinib in vitro via activation of the AKT‐mTOR pathway [25], [26]. BRAF mutations confer resistance to EGFR TKIs by activating pathways downstream of EGFR and may be counteracted by MEK inhibitors [14], [27]. Together, these mutations may contribute to the resistance observed during TKI treatment.

Studies have reported that other signaling pathways mediate potential resistance to EGFR TKIs in NSCLC models. For example, activation of a JAK2‐related signaling pathway upregulated ROR1 via NKX2‐1 [28], [29], causing overexpression of NOTCH1, which led to epithelial‐mesenchymal transition [30]. High levels of BRCA‐1 increase the capacity of DNA damage repair, causing resistance to EGFR TKIs in patients with the T790 M mutation [31]. Moreover, Bivona et al. found that changes in the NFkB signaling pathway conferred TKI resistance to EGFR‐mutant NSCLC cells [32]. Nevertheless, a recent study reported that the combination of a toll‐like receptor 9 agonist, which inactivates NFkB, with erlotinib did not increase PFS compared with erlotinib alone [33].

There are several limitations in the current study. First, the follow‐up period was too short to investigate the relationship between molecular changes and overall survival. Second, because no patient has received adjuvant TKIs in this cohort, the data presented here are insufficient to make a definitive conclusion or recommendation on the potential efficacy of adjuvant TKI treatment in the presence of co‐mutations. Moreover, the new classification of ADC, which could affect survival outcomes [34], was not taken into consideration. Finally, the functional effects of potential resistance alterations were not evaluated in vitro.

Conclusion

To our knowledge, the current study represents the largest case series of localized EGFR‐mutant ADCs with comprehensive genotyping. Our study identified potential EGFR TKI‐resistant mutations in 34.71% (42/121) of patients with a drug‐sensitizing EGFR mutation and who were naive in terms of targeted therapy. These findings may, to some extent, explain the approximately 70% overall response rate to first‐line EGFR inhibitors in patients harboring activating mutations [35]. In localized disease, a similar response rate may be achieved by adjuvant EGFR TKI treatment, although this needs to be evaluated in future trials.

See http://www.TheOncologist.com for supplemental material available online.

Author Contributions

Conception/design: Yao‐Bin Lin, Hao Long

Provision of study material or patients: Ze‐Rui Zhao, Yao‐Bin Lin

Collection and/or assembly of data: Yao‐Bin Lin, Rong Zhang

Data analysis and interpretation: Ze‐Rui Zhao, Yao‐Bin Lin

Manuscript writing: Ze‐Rui Zhao, Yao‐Bin Lin, Calvin S.H. Ng, Rong Zhang, Xue Wu, Qiuxiang Ou, Wendan Chen, Wen‐Jie Zhou, Yong‐Bin Lin, Xiao‐Dong Su, Yang W. Shao, Hao Long

Final approval of manuscript: Ze‐Rui Zhao, Yao‐Bin Lin, Calvin S.H. Ng, Rong Zhang, Xue Wu, Qiuxiang Ou, Wendan Chen, Wen‐Jie Zhou, Yong‐Bin Lin, Xiao‐Dong Su, Yang W. Shao, Hao Long

Disclosures

Yang W. Shao: Geneseeq Technology Inc. (E, OI). The other authors indicated no financial relationships.

(C/A) Consulting/advisory relationship; (RF) Research funding; (E) Employment; (ET) Expert testimony; (H) Honoraria received; (OI) Ownership interests; (IP) Intellectual property rights/inventor/patent holder; (SAB) Scientific advisory board