Abstract
Background
Afatinib, an irreversible ErbB family inhibitor, is widely used as first-line treatment in advanced lung adenocarcinoma patients harbouring mutant epidermal growth factor receptor (EGFR). With the advancements in next-generation sequencing (NGS), comprehensive research into the clinical impact of co-occurring genetic mutations and the molecular mechanisms of acquired resistance is required for afatinib users.
Materials
From January 2010 to December 2019, we enrolled patients with advanced lung adenocarcinoma with EGFR mutations using afatinib as first-line treatment, and we retrospectively collected pre- and post-afatinib treatment specimens from these patients for NGS testing.
Results
Of the 362 enrolled patients, 73 samples (68.9%) from 56 patients successfully returned complete NGS reports. In pre-afatinib treatment specimens, the most frequent co-occurring alterations were TP53, MUC16, USH2A, SNYE1, RECQL4 and FAT1; however, they were not related to progression-free survival. Small cell lung cancer transformation, EGFR p.T790M, amplification of MET, ERBB2, KRAS, EGFR, cell cycle-regulated genes and MDM2, and PTEN alterations were identified as acquired resistance mechanisms. EGFR p.T790M (p=0.0304) and APC alterations (p=0.0311) in post-afatinib specimens were significantly associated with longer overall survival, while MET amplification was significantly associated with poor overall survival (p=0.0324). The co-occurrence of TP53 alterations was significantly associated with shorter overall survival (p=0.0298).
Conclusions
Our results show that the frequent co-occurring alterations in advanced EGFR-mutated lung adenocarcinoma did not influence the effectiveness of afatinib. EGFR p.T790M is not only the major resistance mechanism to afatinib but also related to favourable survival outcomes. MET amplification and TP53 mutations were associated with poorer overall survival.
Shareable abstract
Co-occurring mutations do not hinder afatinib's effectiveness in EGFR-mutated NSCLC. EGFR p.T790M predicts better outcomes, while MET amplification and TP53 mutations indicate poorer OS. https://bit.ly/3vydDiy
Introduction
Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are effective in treating advanced non-small cell lung cancer (NSCLC) with mutant EGFR [1–4]. However, de novo resistance can occur in approximately 20–30% of patients, and other patients who initially respond well to TKIs may develop resistance later [4–6]. The EGFR p.T790M mutation remains the main acquired resistance mechanism after first-generation TKI treatment [7, 8]. Although osimertinib was developed to treat p.T790M, it has emerged as the first-line treatment for patients with common EGFR mutations [4, 9]. It is crucial that the more diverse and complex mechanisms of resistance to EGFR TKIs, including osimertinib and other TKIs, are identified for the benefit of these patients.
Next-generation sequencing (NGS) can more accurately detect de novo p.T790M mutations at low allele frequencies, as well as other EGFR mutations like p.L747S and p.L747P, which are responsible for intrinsic resistance to first-generation TKIs, than commercial real-time PCR kits for EGFR detection [10, 11]. Therefore, NGS is a powerful tool to obtain detailed genetic information and help us understand cancer evolution through pre- and post-treatment assessments, which could provide answers about intrinsic and acquired resistance to EGFR TKIs [12].
Aberrations in cell-surface tyrosine kinase receptors and their downstream signalling pathways contribute to carcinogenesis, proliferation and drug resistance in NSCLC [13–16]. Co-mutations in EGFR-mutated lung tumours can lead to heterogeneity in TKI responses and survival outcomes [15–17]. Common genetic alterations, including TP53, RB1, PIK3CA, ERBB2, CDK4/6 and CCNE1, co-occur in EGFR-mutated NSCLC and can affect therapeutic response and small cell transformation risk [6, 15, 17–19]. Amplification of ERBB2, MET, CDK4/6 and CCNE1 is associated with shorter progression-free survival (PFS) after TKI therapy [6, 20]. However, the impact of co-occurring mutations on TKI treatment effectiveness in EGFR-mutated NSCLC patients remains inconclusive.
Despite extensive research on resistance mechanisms associated with first- and third-generation TKIs, studies on afatinib are lacking [21, 22]. Afatinib, a second-generation EGFR TKI and ErbB family blocker, could effectively treat common, major uncommon and compound EGFR mutations [23]. Unlike other TKIs, afatinib irreversibly inhibits the tyrosine kinase domain of EGFR, and its biological and clinical functions differ from first- and third-generation TKIs [24]. Therefore, our study aims to determine co-occurring genetic alterations and molecular changes in pre- and post-afatinib-treated specimens of EGFR-mutated lung adenocarcinoma patients.
Materials and methods
Patients and data collection
We retrospectively obtained the electronic medical record data of patients with advanced EGFR-mutated lung adenocarcinoma who received first-line afatinib treatment at National Taiwan University Hospital (NTUH) between January 2010 and December 2019. The remaining formalin-fixed paraffin-embedded (FFPE) tissues were reassessed for NGS analysis. The study was approved by the Research Ethics Committee of NTUH (no. 202010107RINA) and conducted according to the Declaration of Helsinki principles and the International Conference on Harmonization Good Clinical Practice Guideline.
Patients’ EGFR mutation status was determined using cobas EGFR Mutation Test v2 (Roche Diagnositics), Sequenom MassARRAY genotyping (Agena Bioscience) or direct DNA sequencing [11, 25]. Exon 19 deletion and p.L858R were stratified as common mutations; other mutations, such as p.G719X, p.L861Q and p.T790M, were classified as uncommon mutations.
Response to afatinib was evaluated using the Response Evaluation Criteria in Solid Tumours (RECIST, version 1.1) [26]. PFS was defined as the time from treatment initiation to detection of disease progression or death. Post-progression survival (PPS) was calculated as the time from tumour progression in patients treated with afatinib to death. Overall survival (OS) was calculated as the time from confirmation of advanced-stage (stage IIIB–IVB) lung adenocarcinoma or recurrence after curative treatment to death.
Tissue sample preparation, NGS and genomic analysis
The genomic DNA was extracted from FFPE samples and sequenced using NGS-based targeted sequencing with ACTOnco (ACT Genomics) and an Ion Torrent sequencer (Thermo Fisher Scientific), targeting 440 cancer-related genes (supplementary table S1). The mean coverage was >×500 and the target base coverage was ×100 ≥85%.
Raw reads were mapped to the human reference genome (hg19) using the Ion Torrent Suite. Single nucleotide variants and short insertions/deletions (INDELs) were identified using the Torrent Variant Caller plug-in from the Clinvar, COSMIC and Genome Aggregation Databases and subsequently annotated using the Ensembl Variant Effect Predictor. Variants with coverage ≥20, allele frequency ≥5% and actionable hotspot variants with allele frequencies ≥2% were reported. Variants with >1% minor allele frequency in the Genome Aggregation database were considered single nucleotide polymorphisms (SNPs). Tumour mutation burden was defined as the total number of nonsynonymous mutations per megabase within tumour genes. Copy number alterations (CNAs) were analysed using ONCOCNV [27]. A CNA ≥6 was defined as an amplification and a CNA=0 was defined as a homozygous deletion.
Statistical analysis
Categorical and continuous variables are summarised as percentages and medians. Survival analyses were performed using the Kaplan–Meier method, and log-rank tests were used to compare PFS, PPS and OS among the patient subgroups. PFS, PPS and OS are presented as medians with 95% confidence intervals. Statistical significance was defined as a two-sided p-value <0.05. SPSS version 22.0 (SPSS Inc.) and GraphPad Prism version 8 (GraphPad Software Inc.) were used for statistical analyses and illustrations.
Results
Patient recruitment
A total of 448 patients with advanced NSCLC who were treated with afatinib were recruited; 86 patients were excluded due to wild-type or unknown EGFR (n=18), concurrent use of other drugs (n=5), switching to another TKI before disease progression (n=43), receiving afatinib as the second-line or adjuvant therapy (n=16) or using afatinib for <7 days (n=4) (supplementary figure S1). Therefore, 362 patients with advanced EGFR-mutated lung adenocarcinoma treated with afatinib as the first-line therapy were enrolled and followed up until December 2021. At the cut-off date, 62 patients had been effectively treated with afatinib and 300 patients had experienced disease progression due to afatinib failure (supplementary figure S1).
Of the 300 patients with afatinib failure, 149 (49.7% of the total population) underwent tissue re-biopsy to examine the resistance mechanisms associated with afatinib. Only 106 tissue samples from 68 patients were available for NGS analysis. 88 tissue samples (83.0%) passed quality control, and 73 specimens (68.9%) from 56 patients passed DNA and sequencing quality control (supplementary figure S1).
Specimen acquisition for the successful NGS
In relation to identifying crucial factors for obtaining successful results in NGS, the preservation time of tissue and the timepoint of acquisition did not have an impact on NGS outcomes. Acquiring specimens via bronchoscopic biopsy was significantly associated with failed NGS results (p=0.031) (table 1). Cell blocks obtained from pleural effusion (four out of four specimens, 100%) successfully produced NGS results; specimens from bone tissues (four out of five specimens, 80%) did not.
TABLE 1.
Factors associated with the successful obtainment of NGS from FFPE specimens
| Total specimens | Success for NGS analysis | Failure for NGS analysis | p-value | |
| Specimens, n | 106 | 73 | 33 | |
| Timepoint of taking tissues | 0.470 | |||
| Pre-treatment specimen | 44 (41.5) | 32 (43.8) | 12 (36.4) | |
| Post-treatment specimen | 62 (58.5) | 41 (56.2) | 21 (63.6) | |
| Biopsy sites | 0.274 | |||
| Lung | 52 (49.1) | 37 (50.7) | 15 (45.5) | |
| Pleura | 10 (9.4) | 6 (8.2) | 4 (12.1) | |
| Mediastinal lymph node | 8 (7.5) | 4 (5.5) | 4 (12.1) | |
| Neck lymph node | 5 (4.7) | 4 (5.5) | 1 (3.0) | |
| Brain | 8 (7.5) | 6 (8.2) | 2 (6.1) | |
| Soft tissue | 7 (6.6) | 6 (8.2) | 1 (3.0) | |
| Liver | 6 (5.7) | 4 (5.5) | 2 (6.1) | |
| Bone | 5 (4.7) | 1 (1.4) | 4 (12.1) | |
| Pleural effusion (cell block) | 4 (3.8) | 4 (5.5) | 0 | |
| Adrenal gland | 1 (0.9) | 1 (1.4) | 0 | |
| Modality for taking tissues | 0.031* | |||
| Surgery (VATS or excisional biopsy) | 51 (48.1) | 39 (53.4) | 12 (36.4) | |
| CT-guided biopsy | 21 (19.8) | 15 (20.5) | 6 (18.2) | |
| Echo-guided biopsy | 18 (17.0) | 13 (17.8) | 5 (15.2) | |
| Bronchoscopy (including EBUS-TBNA) | 16 (15.1) | 6 (8.2) | 10 (30.3) | |
| Tissue preservation time | 0.845 | |||
| ≤2 years | 40 (37.7) | 28 (38.4) | 12 (36.4) | |
| >2 years | 66 (62.3) | 45 (61.6) | 21 (63.6) |
Data presented as n (%), unless otherwise indicated. NGS: next-generation sequencing; FFPE: formalin-fixed paraffin-embedded; VATS: video-assisted thoracic surgery; CT: computed tomography; EBUS-TBNA: endobronchial ultrasound-guided transbronchial needle aspiration. *: p<0.05.
Characteristics of the enrolled patients
The median age of the 362 enrolled afatinib users was 61.8 years. 213 patients (58.8%) were women, and 257 (71.0%) had never smoked (table 2). 332 patients (91.7%) had a good performance status (Eastern Cooperative Oncology Group performance score of 0–1) and 22 (6.1%) had other malignant diseases. Bone (40.1%) was the most frequent metastatic site. Out of the total patients, 281 (77.6%) had a common EGFR mutation, 16 (4.4%) had both common and uncommon mutations, and 65 (18.0%) had uncommon mutations (table 2).
TABLE 2.
Characteristics of the enrolled patients
| Total population | NGS subgroup | |
| Patients, n | 362 | 56 |
| Median age (range), years | 61.8 (28–89) | 59.5 (28–89) |
| Female | 213 (58.8) | 33 (58.9) |
| Never-smokers | 257 (71.0) | 40 (71.4) |
| ECOG 0–1 | 332 (91.7) | 53 (94.6) |
| Co-existence with other malignancies | 22 (6.1) | 1 (1.8) |
| Tumour relapse (previous stage ≤IIIA) | 70 (19.3) | 12 (23.2) |
| Metastatic sites | ||
| Bone | 145 (40.1) | 20 (35.7) |
| Lung | 144 (39.8) | 17 (30.4) |
| Brain | 110 (30.4) | 16 (28.6) |
| Pleural seeding or effusion | 101 (27.9) | 15 (26.8) |
| Liver | 26 (7.2) | 2 (3.6) |
| Adrenal gland | 16 (4.4) | 3 (5.4) |
| EGFR mutation type | ||
| Common mutations | 281 (77.6) | 42 (75.0) |
| Exon 19 deletion | 194 (53.6) | 26 (46.4) |
| p.L858R | 85 (23.5) | 16 (28.6) |
| p.L858R+exon 19 deletion | 3 (0.8) | 0 |
| Common/uncommon mutations | 16 (4.4) | 4 (7.1) |
| Uncommon mutations | 65 (18.0) | 10 (17.9) |
| Tumour response to afatinib | ||
| Partial response: | 265 (73.2) | 44 (78.6) |
| for common mutations | 217/281 (77.2) | 35/42 (83.3) |
| for common/uncommon mutations | 11/16 (68.8) | 3/4 (75.0) |
| for uncommon mutations | 37/65 (56.9) | 6/10 (60.0) |
| Stable disease | 64 (17.7) | 10 (17.9) |
| Progressive disease | 33 (9.1) | 2(3.6) |
Data presented as n (%) or n/N (%), unless otherwise indicated. NGS: next-generation sequencing; ECOG: Eastern Cooperative Oncology Group; EGFR: epidermal growth factor receptor.
Treatment responses were assessed based on imaging studies and a review of patients' medical records within the EGFR mutation subgroups of common mutation, co-occurring common and uncommon mutations (common/uncommon mutations) and uncommon mutations. In our cohort, the partial response rate was 73.2% (table 2). Specifically, partial response rates were 77.2% (217 of 281 patients) for the common mutation group, 68.8% (11 of 16 patients) for the common/uncommon mutations group and 56.9% (37 of 65 patients) for the uncommon mutations group (table 2).
EGFR status identified via non-NGS and NGS methods
Initially, five patients had common EGFR mutations in clinical practice, but NGS revealed that they also had other co-occurring EGFR mutations: two had exon 19 deletion and p.T790M, one had exon 19 deletion and p.A566T, one had p.L858R and p.E709G, and one had p.L858R and p.G810D (supplementary tables S2 and S3).
The median PFS, PPS and OS of the 56 patients in the NGS subgroup were 12.26 months, 25.54 months and 43.93 months, respectively (supplementary figure S2A–C). The median PFS of patients with common, common/uncommon and uncommon EGFR mutations was 14.62 months (95% CI 10.91–18.34 months), 10.82 months (95% CI 7.59–14.09 months) and 8.95 months (95% CI 0.21–17.69 months), respectively (p=0.299) (supplementary figure S2D). The median PPS of the three subgroups was 25.54 months (95% CI 15.73–35.36 months), 76.39 months (95% CI 0–166.76 months) and 16.53 months (95% CI 8.26–24.79 months), respectively (p=0.259) (supplementary figure S2E). The median OS of the three subgroups was 43.93 months (95% CI 30.12–57.75 months), 76.39 months (95% CI 2.57–150.21 months) and 24.00 months (95% CI 14.70–33.30 months), respectively (p=0.277) (supplementary figure S2F).
Tumour heterogeneity and response to afatinib
The most frequent co-occurring alterations in the 32 pre-afatinib treatment specimens were TP53 (n=21, 65.6%), MUC16 (n=14, 43.8%), USH2A (n=11, 34.4%), SYNE1 (n=11, 34.4%), RECQL4 (n=10, 31.3%) and FAT1 (n=9, 28.1%); other co-occurring alterations included EGFR amplification (n=6, 18.8%), ROS1 alterations (n=6, 18.8%), MDM2 amplification/mutations (n=4, 12.5%), MYC amplification (n=4, 12.5%) and RB1 alterations (n=3, 9.4%) (figure 1a and supplementary table S3). There was no correlation between EGFR mutation type and any co-occurring genetic alterations among the 32 patients (figure 1a). Although the co-occurrence of EGFR amplification was not significantly associated with poor PFS (p=0.0825), the Kaplan–Meier plot for afatinib PFS showed the two curves were separated without any crossover (supplementary figure S3A). Meanwhile, the post-afatinib therapy PFS was not associated with other frequently co-occurring genes such as TP53, FAT1 and SYNE1 (supplementary figure S3B–D).
FIGURE 1.
Oncoprints of concurrent genetic alterations detected in a) pre-afatinib treatment patients (n=32) and b) post-afatinib treatment patients (n=41). SCLC: small cell lung cancer; PFS: progression-free survival; OS: overall survival; PR: partial response; SD: stable disease; PD: progressive disease.
Tumour evolution and resistance to afatinib
Compared to pre-afatinib NGS results, patients who failed afatinib treatment showed genetic alterations in p.T790M, EGFR amplification, tumour suppressor and associated genes, proto-oncogenes, receptor tyrosine kinase, EGFR regulatory pathways and cell cycle-regulated genes. However, DNA repair, DNA damage response, angiogenesis, apoptosis, transcription factors and their regulation and immune-related mechanisms were not significantly affected by afatinib failure (figure 1a, b and supplementary table S3).
EGFR p.T790M (n=11, 26.8%); amplification of EGFR (n=8, 19.5%), MET (n=5, 12.2%), MDM2 (n=3, 7.3%), ERBB2 (n=1, 2.4%) and KRAS (n=1, 2.4%); PTEN mutations (n=3, 7.3%); small cell lung cancer (SCLC) transformation (n=2, 4.9%); and cell cycle-regulated gene amplification, including that of CCNE1/2, CDK4/6 and CCND1 (n=11, 26.8%), were identified in the post-afatinib treatment group (n=41) (figure 2 and supplementary table S3). The median tumour mutation burden was low (2.2 mutations·Mb−1) in the post-afatinib group, except for one patient (74.9 mutations·Mb−1) with the longest PFS among all enrolled patients. Among the 11 patients with EGFR p.T790M (26.8% of 41 patients), five (12.2%) had p.T790M alone and six (14.6%) had p.T790M co-occurring with other mechanisms. Finally, no resistance mechanisms were identified in 13 specimens (31.7%) from patients in whom afatinib therapy failed (figure 2 and supplementary figure S4).
FIGURE 2.
Oncoprints for resistance mechanisms in 41 patients after afatinib treatment. PFS: progression-free survival; OS: overall survival; TMB: tumour mutation burden; SCLC: small cell lung cancer; PR: partial response; SD: stable disease; PD: progressive disease; ND: not detected.
Post-afatinib failure-induced co-occurring genetic alterations associated with survival outcomes
EGFR p.T790M (p=0.0275) and APC alterations (p=0.0304) were associated with a longer PPS (figure 3a, b). Conversely, ALK mutations (p=0.0386), MET amplification (p=0.0124) and TP53 mutations (p=0.0189) were associated with significantly shorter PPS (figure 3c–e), but EGFR amplification was not (p=0.9499) (figure 3f).
FIGURE 3.
Kaplan–Meier curves of post-progression survival (PPS) in 41 patients with (+) or without (−) a) EGFR p.T790M, b) APC alterations, c) ALK mutations, d) MET amplification, e) TP53 mutations and f) EGFR amplification.
Patients with EGFR p.T790M (p=0.0304) and APC alterations (p=0.0311) in the post-afatinib specimens had significantly longer OS than those without p.T790M or APC alterations (figure 4a, b). ALK mutations were associated with poor OS, but this association was not statistically significant (p=0.1044) (figure 4c). MET amplification after afatinib failure was significantly associated with poor OS (p=0.0324) (figure 4d). Patients with TP53 mutations had significantly shorter OS than those without (p=0.0498) (figure 4e), but EGFR amplification was not associated with OS (p=0.7957) (figure 4f).
FIGURE 4.
Kaplan–Meier curves of overall survival (OS) in 41 patients with or without a) EGFR p.T790M, b) APC alterations, c) ALK mutations, d) MET amplification, e) TP53 mutations and f) EGFR amplification.
In the 41 post-afatinib specimens, the presence of any resistance mechanism (including EGFR p.T790M; amplification of EGFR, MET, KRAS and cell cycle regulatory genes; or PTEN alteration) was associated with a better median PFS (p=0.002) and OS (p=0.047) (supplementary figure S5A, B).
Discussion
Targeted NGS can comprehensively explore genetic alterations in lung cancer and improve understanding of their interactions with driver genes. Our study found that afatinib could provide a consistent PFS in patients with advanced lung adenocarcinoma, regardless of the presence of uncommon EGFR mutations or co-occurring alterations by NGS results. Genomic profiling of post-treatment specimens revealed that EGFR p.T790M and APC alterations were associated with longer PPS and OS, while MET amplification and TP53 mutations were associated with shorter PPS and OS.
Some patients diagnosed with common EGFR mutations only during routine analysis were found to have uncommon EGFR mutations when evaluated by NGS. Afatinib is effective in treating patients with advanced NSCLC with both common and uncommon EGFR mutations [11, 23, 28], but first- and third-generation TKIs may not consistently be effective against co-occurring uncommon EGFR mutations or may be influenced by co-occurring mutations [11, 29, 30]. Co-occurring alterations, such as TP53, PIK3CA and PTEN, are reportedly associated with poorer outcomes, such as faster resistance to EGFR TKIs and shorter OS [18, 20, 31, 32]. In our study, the most frequent co-occurring alterations in the 32 pre-afatinib treatment specimens were TP53, MUC16, USH2A, SYNE1, RECQL4, FAT1 and EGFR amplification; however, they were not significantly associated with any EGFR type or PFS outcome in afatinib users.
TP53 alterations are the most frequently co-occurring alteration in EGFR-mutated NSCLC. Their impact on clinical outcomes may vary depending on cohort size, TKIs and NGS panels used [18, 33, 34]. Nearly 70% of patients with EGFR mutation-related lung adenocarcinoma in our study had co-occurring TP53 alterations. In this study, TP53 mutations in pre-afatinib treatment specimens were not related to PFS with afatinib, while mutations in post-afatinib treatment specimens were associated with shorter PPS and OS. Five of the six patients with MDM2 gene amplification did not have TP53 mutations, which is consistent with a previous study's finding that MDM2 overexpression and TP53 mutations are mutually exclusive in some human tumours [18, 35]. The tumour suppressor protein p53 can be degraded by E3 ligase mouse double minute 2 homolog (MDM2), which can be upregulated by abnormal MDM2 amplification, which could also lead to oncogenesis and primary resistance to TKI in wild-type TP53 cancers [6, 36]. Meanwhile, APC inactivation involving the Wnt pathway promotes EGFR-driven TP53-deficient lung adenocarcinoma growth in vivo [37], but our study showed that the presence of APC alterations after afatinib treatment was associated with favourable survival outcomes. The role of APC in EGFR-mutated lung adenocarcinomas treated with TKI is seldom investigated and requires further study.
We discovered that afatinib-resistance mechanisms share some common mechanisms with resistance to first- and third-generation TKIs [22, 38]. Co-occurrence of EGFR amplification in pre-afatinib specimens was associated with poor PFS in our study. Several studies have reported that EGFR amplification may be a resistance mechanism to TKI [22, 39]. Among the 41 patients with post-afatinib treatment specimens, five patients (12.2%) were identified to have MET amplification as the major acquired resistance mechanism. MET amplification-dependent resistance is also recognised as an acquired mechanism in certain NSCLC cases with driver oncogene mutations, such as EGFR and BRAF mutations, and ALK, RET, ROS1 and NTRK fusions [40]. Genetic alterations of cyclin-dependent kinases (CDKs) 4/6 and other cell cycle-regulated genes are involved in the development of TKI resistance [41]; CDK 4/6 inhibitors could overcome post-TKI acquired resistance [42]. DNA repair and damage response, transcription factors and their regulation, and immune-related mechanisms did not show molecular changes in our post-afatinib treatment specimens. Acquisition of the p.T790M mutation following EGFR TKI failure remains an important factor in extending patients' PPS and OS owing to the efficacy of osimertinib as a subsequent therapeutic option [9]. Notably, in clinical studies and in our patients, the presence of acquired MET amplification after afatinib failure was associated with poor survival outcomes [43]. Compared with first- and third-generation EGFR TKIs, afatinib could irreversibly bind the tyrosine kinase domain of pan-ErbB family members, and afatinib could simultaneously share some resistance mechanisms to first- and third-generation TKIs [22, 38, 44].
To successfully guide cancer treatment and predict patient response, high-quality DNA/RNA and adequate tissue specimens are essential for NGS studies. In our study, tissue re-biopsy was performed on approximately 50% of patients (149 out of 300) with afatinib failure, and 70% of stored tissue samples (73 samples) successfully yielded NGS results. Tissue acquisition is necessary for diagnosis and to ensure sufficient tumour DNA for NGS. The use of nitric acid-based agents to accelerate decalcification and diagnose metastatic bone specimens may affect molecular studies due to poor DNA quality [45]. Although EDTA is less destructive, it is not routinely used in our hospital, and the choice of decalcification agent could have contributed to the lower NGS success rate. Our study also found that a cell block specimen from pleural effusion can be a reliable alternative for NGS, which is consistent with a previous study [46].
Although our study identified important findings, it had limitations, including a small sample size, potential sampling bias and reliance on a commercialised targeted NGS panel. During the study period, gefitinib, erlotinib and afatinib were all reimbursed by the National Health Insurance and prescribed as first-line EGFR TKIs in Taiwan. The drug was chosen according to the physician's preference or experience as discussed in our previous studies [47]. Patients were retrospectively selected based on having received afatinib and undergone biopsies before and after treatment, which may have resulted in a biased sample. Additionally, the use of the ACTOnco panel for targeted DNA-based amplicon sequencing of 440 genes may have limited the detection of non-targeted genes, fusion genes and low-frequency somatic variations of SNPs and INDELs.
Conclusions
Afatinib as a first-line therapy could provide a consistent PFS outcome in patients with advanced EGFR-mutated lung adenocarcinoma, regardless of the presence of uncommon EGFR mutations or other co-occurring alterations. NGS of post-afatinib therapy specimens revealed that the mechanisms of resistance to afatinib are complex and heterogeneous. Acquired EGFR p.T790M mutation was the main resistance mechanism to afatinib and was associated with better survival outcomes. However, MET amplification and TP53 mutations poorly affected PPS and OS.
Supplementary material
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Supplementary material 00676-2023.SUPPLEMENT (1.3MB, pdf)
Acknowledgements
We would like to thank the National Taiwan University Hospital, College of Medicine, and Office of Research and Development at the National Taiwan University (Taipei, Taiwan) for their support.
Footnotes
Provenance: Submitted article, peer reviewed.
Ethics statement: The study was approved by the Research Ethics Committee of NTUH (number 202010107RINA), and conducted according to the Declaration of Helsinki principles and International Conference on Harmonization Good Clinical Practice Guideline.
Conflict of interest: S-K. Liang has received speaking honoraria from Boehringer Ingelheim, AstraZeneca, Pfizer and Merck Sharp & Dohme. J-Y. Shih has served as an advisory board member for Roche, Boehringer Ingelheim, Amgen, AstraZeneca, Eli Lilly, Merck Sharp & Dohme, Chugai Pharma, Pfizer, Takeda, CStone Pharmaceuticals, Novartis, Ono Pharmaceutical, Janssen and Bristol-Myers Squibb; has received speaking honoraria from Genconn Biotech, AstraZeneca, ACTgenomics, Amgen, Roche, Eli Lilly, Pfizer, Novartis, Bayer, Boehringer Ingelheim, Merck Sharp & Dohme, Chugai Pharma, CStone Pharmaceuticals, Janssen, Takeda, TTY Biopharm, MundiPharma, Ono Pharmaceutical, Orient EuroPharma and Bristol-Myers Squibb; has received support for attending meetings from Roche, Boehringer Ingelheim, AstraZeneca and Chugai Pharma; and has received a grant from Roche.
Conflict of interest: The other authors have no conflict of interest to declare.
Support statement: This study was supported by the Taipei Chest Disease Academic Research and Education Foundation. Funding information for this article has been deposited with the Crossref Funder Registry.
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