ABSTRACT
Epidermal growth factor receptor (EGFR) mutations are key oncogenic drivers in non‐small cell lung cancer (NSCLC). Rare and compound EGFR mutation‐mediated resistance to tyrosine‐kinase inhibitors (TKIs) is a major hurdle for NSCLC treatment. Here, we characterized compound EGFR mutations and elucidated mechanisms of acquired resistance to EGFR TKIs in Chinese NSCLC patients. Using next‐generation sequencing (NGS) data, the mutation spectrum of 10 lung cancer‐related genes was analyzed in 8849 patients. From this cohort, 7081 NSCLC tissue samples were assessed, revealing common EGFR mutations (19‐Del, exon 21 p.L858R), rare EGFR mutations, variants of uncertain significance (VUSs), and compound EGFR mutations. Acquired resistance mutations were further evaluated in 77 paired baseline and post‐progression samples. Among the 8849 patients, 4320 (48.82%) harbored common EGFR mutations, 973 (11.00%) carried Kirsten rat sarcoma viral oncogene (KRAS) hotspot mutations, 357 (4.03%) had phosphatidylinositol‐4,5‐bisphosphate 3‐kinase catalytic subunit alpha (PIK3CA) mutations, 246 (2.78%) had Erb‐B2 receptor tyrosine kinase 2 (ERBB2) mutations, 102 (1.15%) had B‐Raf proto‐oncogene, serine/threonine kinase (BRAF) V600E mutations, and 95 (1.07%) had mesenchymal epithelial transition factor (MET) mutations. Compound EGFR mutations were identified in 7.99% of NSCLC patients, comprising 7.50% dual mutations and 0.49% multiple (> 2) mutations. Compared to single EGFR mutations, compound EGFR mutations were more frequently associated with EGFR exon 21 p.L858R and EGFR rare mutations, but not with EGFR 19‐Del or VUSs (p < 0.001). Compared to patients with EGFR 19‐Del, patients with EGFR exon 21 p.L858R mutation had significantly more rare point mutations in EGFR exon 7, 18, and 21, and fewer EGFR exon 20 p.T790M mutations. Additionally, EGFR exon 20 p.T790M‐mediated acquired resistance was the most common mechanism (71.43%), followed by mutations in the RAS/RAF/MEK pathway (18.18%). These resistance‐associated mutations frequently co‐occurred with tumor protein p53 (TP53) variants (15.58%). Notably, the mean duration of disease progression following EGFR‐TKI initiation did not differ significantly between patients with EGFR exon 21 p.L858R mutation and those with EGFR 19‐Del. Our study reveals the heterogeneity of compound EGFR mutations, and characterizes the spectrum of acquired resistance mutations to EGFR TKIs.
Keywords: compound EGFR mutations, non‐small cell lung cancer, resistance mutations
What's new?
Resistance to tyrosine kinase inhibitors (TKIs) is inevitable in advanced epidermal growth factor receptor (EGFR)‐mutated non‐small cell lung (NSCLC). EGFR driver alterations are among the most prevalent in NSCLC, though they remain poorly understood. Here, the authors analyzed EGFR mutation patterns and resistance mechanisms in NSCLC patients in China. Nearly 8 percent of patients carried two or more EGFR mutations. Such compound alterations were associated with specific mutation profiles. The most frequent TKI resistance mechanism involved the EGFR T790M mutation. The findings cast light on the genetic heterogeneity of EGFR‐mutant NSCLC and provide insight for personalized treatment strategies.
Resistance to tyrosine kinase inhibitors (TKIs) is inevitable in advanced epidermal growth factor receptor (EGFR)‐mutated non‐small cell lung (NSCLC). EGFR driver alterations are among the most prevalent in NSCLC, though they remain poorly understood. Here, the authors analyzed EGFR mutation patterns and resistance mechanisms in NSCLC patients in China. Nearly 8 percent of patients carried two or more EGFR mutations. Such compound alterations were associated with specific mutation profiles. The most frequent TKI resistance mechanism involved the EGFR T790M mutation. The findings cast light on the genetic heterogeneity of EGFR‐mutant NSCLC and provide insight for personalized treatment strategies.
Abbreviations
- ALK
anaplastic lymphoma kinase
- BRAF
B‐Raf proto‐oncogene, serine/threonine kinase
- CNAs
copy number alterations
- EGFR
epidermal growth factor receptor
- ERBB2
Erb‐B2 receptor tyrosine kinase 2
- FFPE
formalin‐fixed paraffin‐embedded
- InDels
insertions/deletions
- KRAS
kirsten rat sarcoma viral oncogene homolog
- MAF
mutant allele frequency
- MET
mesenchymal epithelial transition factor
- NGS
next‐generation sequencing
- NSCLC
non‐small cell lung cancer
- PCR
polymerase chain reaction
- PIK3CA
phosphatidylinositol‐4,5‐bisphosphate 3‐kinase catalytic subunit alpha
- RET
RET proto‐oncogene, receptor tyrosine kinase
- ROS1
ROS proto‐oncogene 1, receptor tyrosine kinase
- SNVs
single‐nucleotide variations
- TKIs
tyrosine kinase inhibitors
- TP53
tumor protein p53
- VUSs
variants of uncertain significance
1. Introduction
Lung cancer is the most frequently diagnosed cancer and the leading cause of cancer‐related mortality worldwide [1]. While a minority of lung cancer cases are diagnosed at an early stage, most patients present with metastatic disease [2]. Histologically, lung cancer is broadly classified into two major subtypes, with non‐small cell lung cancer (NSCLC) accounting for approximately 80% of cases [3]. NSCLC is a molecularly heterogeneous disease. The widespread identification of numerous oncogenic “driver” genomic aberrations, amenable to targeted interventions, has profoundly advanced the NSCLC treatment [4, 5]. The increasing affordability and accessibility of next‐generation sequencing (NGS) have facilitated the comprehensive detection of these driver gene mutations through multi‐gene panel sequencing. Genetic testing has advanced the development of precision medicine, which is critical for confirming the appropriate targeted therapy in NSCLC patients [6]. Routine genetic testing, considered standard of care, includes key driver alterations such as mutations in epidermal growth factor receptor (EGFR) [7], KRAS mutation [8], BRAF mutation [9], ALK rearrangement [10], and ROS1 rearrangement [11]. Therefore, genetic testing is an essential tool for improving the diagnosis and management of NSCLC.
The most prevalent driver genes in lung cancer include EGFR, KRAS, and BRAF. EGFR mutations are detected in approximately 40%–60% of Asian patients with lung adenocarcinoma, compared to only 7%–10% in European populations [12]. EGFR plays a critical role in NSCLC progression, with clinically relevant mutations primarily occurring in exons 18–21 [7]. EGFR tyrosine kinase inhibitors (TKIs) are the standard of care for patients harboring EGFR‐sensitive mutations [13]. However, acquired resistance mutations (e.g., EGFR exon 20 p.T790M) frequently emerge during disease progression. Rare EGFR mutations, comprising 10%–15% of all EGFR mutations, can occur alone or as compound mutations [14], generally showing reduced sensitivity to TKIs [14, 15]. Beyond well‐characterized common and rare EGFR mutations, variants of uncertain significance (VUSs) have been identified in NSCLC, but their landscape and response to TKIs remain unclear. KRAS mutations, accounting for approximately 25% of NSCLC cases, contribute to primary resistance to EGFR targeted therapy, even among EGFR‐positive tumors, and are associated with poor prognosis [16]. In western countries, approximately 30% of NSCLC cases harbor KRAS mutations, a prevalence substantially higher than the 10% observed in Asian NSCLC patients [17]. KRAS mutations lead to constitutive activation of the RAS/RAF/MEK/ERK (RAS/MAPK) signaling pathway, which is also implicated in NSCLC development [18]. TP53 alterations promote resistance evolution through mechanism‐independent pathways in EGFR‐mutant NSCLC [19]. However, the mechanisms of resistance to EGFR TKIs in EGFR‐mutated NSCLC still need to be further explored.
In the present study, we retrospectively analyzed the NGS data from tumor samples of lung cancer patients, including 7081 NSCLC cases. We explored the molecular landscapes of driver genes, characterized the features of EGFR mutation subtypes (common, uncommon, VUS, and compound mutations), and further studied the mechanisms of acquired resistance to EGFR TKIs.
2. Materials and Methods
2.1. Patients and Sample Collection
This retrospective study included qualified NGS data from 8849 lung cancer patients, among whom 7081 had NSCLC. Additionally, matched baseline (tissue, n = 77) and post‐progression (plasma, n = 20; tissue, n = 67) samples were analyzed. Targeted NGS testing was performed in Shanghai Biotecan Medical Laboratory Co. Ltd. (certified ISO15189). Although patients were sequenced using different target panels, these panels covered the common variation within regions of 10 key lung cancer‐related genes (Table S1). Peripheral blood (5–10 mL) was collected in EDTA‐coated tubes. Plasma was separated within 2 h of collection and shipped to the laboratory within 48 h. Pathologists assessed tumor purity in fresh tumor specimens and formalin‐fixed paraffin‐embedded (FFPE) tissue blocks or sections.
2.2. DNA Extraction, Quantification, and Library Preparation
Our laboratory has previously described the protocol for DNA extraction, quantification, and library preparation [20]. Genomic DNA (gDNA) was extracted from fresh tissues, FFPE samples, and plasma using the QIAamp DNA Mini Kit (Qiagen, Germany). Quantification of gDNA was performed with a Qubit 3.0 Fluorometer (Invitrogen; Thermo Fisher Scientific Inc.), followed by additional purification using a NanoDrop ND‐1000 spectrophotometer (Thermo Scientific, Waltham, MA, USA). DNA fragmentation was assessed using the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) with the High‐Sensitivity DNA Reagent (Agilent Technologies) to determine the DNA Integrity Number (DIN). Additionally, quality control (QC) of FFPE DNA integrity was conducted via multiplex polymerase chain reaction (PCR). For each sample, 300 ng of gDNA (quantified by the Qubit assay) was mechanically sheared to a target size of 150–200 base pairs (bp) using an E220 focused ultrasonicator (Covaris, Woburn, MA, USA). Subsequently, 10–100 ng of fragmented DNA was utilized for library construction with the KAPA Library Preparation Kit (Kapa Biosystems Inc., Wilmington, MA, USA). This process included end repair, A‐tailing, and adapter ligation, conducted without additional fragmentation, following the manufacturer's instructions. The NGS libraries were enriched using the xGen Lockdown Probe pool (IDT Technologies) and amplified with 12–13 PCR cycles using 1× KAPA HiFi Hot Start Ready Mix. Finally, NGS libraries were sequenced on an Illumina NextSeq CN500 platform (Illumina Inc., San Diego, CA, USA) Medium Flux chip.
2.3. Bioinformatics Analysis
Sequencing data were processed. Generally, Trimmomatic was used for FASTQ file quality control to remove leading and trailing low‐quality (quality scores below 36) or N bases. Qualified reads were then aligned to the human reference genome hg19 using the Burrows‐Wheeler Aligner (BWA) v.0.7.12. Subsequently, duplicate removal, local realignment, and base quality score recalibration were performed using Picard and the Genome Analysis Toolkit (GATK v.3.2). VarDict was employed for the detection of single‐nucleotide variations (SNVs) and insertions/deletions (InDels) mutations. SNVs and InDels were excluded if the mutant allele frequency (MAF) was < 1% in tumor tissue samples or below 0.5% in plasma samples. Somatic variants were annotated using the ANNOVAR software tool. Candidate somatic variants were further filtered based on the following criteria: (i) exclusion of mutations with coverage depth < 10×; (ii) removal of variant sites with MAF > 0.001 in the 1000 Genomes Project database (https://www.internationalgenome.org/) or in the ExAC database (https://ncbiinsights.ncbi.nlm.nih.gov/tag/exac/); (iii) retention of variant sites with an MAF between 0.001 and 0.1 in the 1000 Genomes database if supported by COSMIC evidence; (iv) retention of variants located in exonic or splicing regions (within 10 bp upstream or downstream of splicing sites); (v) exclusion of synonymous mutations; (vi) removal of variants with unknown classification; and (vii) exclusion of functionally benign variants as predicted by PolyPhen‐2 and MutationTaster. Somatic copy number variants (CNVs) were identified by CNVkit. The fusion events were determined with both Manta (v1.6.0) and Delly (v0.7.9) software, and were further manually reviewed and confirmed in Integrative Genomics Viewer (IGV). Additionally, the sequencing coverage and quality statistics for each sample are summarized in Table S2.
2.4. Statistical Analysis
The chi‐square test or Fisher's exact test was used to test the significance of categorical variables. Statistical analyses were performed using the SPSS (IBM SPSS 20.0, SPSS Inc.), and a two‐sided p value < 0.05 was considered to be statistically significant.
3. Results
3.1. Patient Characteristics
Demographic data from 8849 Chinese patients with lung cancer are summarized in Table S1. The risk of lung cancer was higher among patients aged 50–79 years, with the majority occurring in the 60–69 age group (3125/8849, 35.31%). Most patients were male (5034, 56.89%). Histological subtypes included adenocarcinoma (55.88%), squamous cell carcinoma (22.10%), mixed histology (1.73%), and other subtypes (0.31%).
3.2. Somatic Mutation Spectrum of Lung Cancer
Mutational characteristics of tissue samples from 8849 lung cancer patients are presented in Table 1 and Figure 1, including driver mutations, gene rearrangements, and copy number alterations (CNAs). EGFR was the most frequently mutated gene (4320/8849, 48.82%), with common mutations including EGFR exon 21 p.L858R (23.36%) and EGFR 19‐Del (20.58%). Among atypical EGFR mutations, the most common were L861Q/S768I/G719X (3.11%), EGFR exon 20 p.T790M (2.10%), and EGFR exon 20 insertions (1.84%). Rare point mutations such as EGFR exon 19 p.L747P (0.12%) and EGFR exon 18 p.E709X (0.73%) were also observed. For off‐target resistance mutations, KRAS mutations accounted for 11.00% (973/8849), predominantly exon 2 p.G12C (3.66%), followed by exon 2 p.G12V (2.23%), exon 2 p.G12D (1.99%), and exon 2 p.G13X (0.93%). PIK3CA mutations were more frequent in exon 10 (2.96%), followed by exon 21 (1.04%) and exon 8 (0.08%). ERBB2 exon 20 insertions were detected in 1.79% of patients, while uncommon ERBB2 mutations occurred in exon 17 p.V659E (0.09%), exon 19 p.L755X (0.07%), and exon 8 p.S310X (0.38%). BRAF mutations included 1.15% exon 15 p. V600E and 1.02% non‐V600E variants. MET mutations (1.07%) represented the smallest proportion in common driver mutations. Additionally, gene rearrangements were observed in ALK (3.71%), RET (1.02%), and ROS1 (0.72%). The most common CNAs were EGFR amplification (amp) (1.36%), ERBB2 amp (0.45%), and MET amp (0.46%).
TABLE 1.
The proportion of gene mutant subtypes, stratified by gender, in all lung cancer patients.
| Mutation types | Total N (%) | Female N (%) | Male N (%) | Age range | Median age |
|---|---|---|---|---|---|
| EGFR wt | 4529 (51.18%) | 1165 (25.72%) | 3364 (74.28%) | 18–103 | 66 |
| All EGFR variants | 4320 (48.82%) | 2649 (61.32%) | 1671 (38.68%) | 16–97 | 64 |
| EGFR exon 19 deletion | 1821 (20.58%) | 1114 (61.18%) | 707 (38.82%) | 16–93 | 63 |
| EGFR exon 21 p.L858R | 2067 (23.36%) | 1306 (63.18%) | 761 (36.82%) | 31–97 | 65 |
| EGFR exon 20 p.C797S | 11 (0.12%) | 2 (18.18%) | 9 (81.82%) | 50–74 | 62 |
| EGFR exon 20ins | 163 (1.84%) | 83 (50.92%) | 80 (49.08%) | 27–90 | 64 |
| EGFR exon 21 p.L861Q | 91 (1.03%) | 52 (57.14%) | 39 (42.86%) | 49–85 | 66 |
| EGFR exon 20 p.S768I | 74 (0.84%) | 36 (48.65%) | 38 (51.35%) | 42–85 | 63 |
| EGFR exon 18 p.G719 A/C/D/S | 168 (1.90%) | 100 (59.52%) | 68 (40.48%) | 34–87 | 63 |
| EGFR exon 20 p.T790M | 186 (2.10%) | 110 (59.14%) | 76 (40.86%) | 32–96 | 62 |
| EGFR exon 19 p.L747P | 11 (0.12%) | 8 (72.73%) | 3 (27.27%) | 43–75 | 64 |
| EGFR exon 18 p.E709A/G/K/V | 65 (0.73%) | 45 (69.23%) | 20 (30.77%) | 41–87 | 59 |
| EGFR amplification | 120 (1.36%) | 68 (56.67%) | 52 (43.33%) | 37–86 | 65.5 |
| KRAS wt | 7876 (89.00%) | 3622 (45.99%) | 4254 (54.01%) | 16–103 | 65 |
| All KRAS mutation | 973 (11.00%) | 193 (19.84%) | 780 (80.16%) | 27–90 | 66 |
| KRAS exon 2 p.G12C | 324 (3.66%) | 39 (12.04%) | 285 (87.96%) | 37–90 | 65 |
| KRAS exon 2 p.G12V | 197 (2.23%) | 55 (27.92%) | 142 (72.08%) | 27–88 | 65 |
| KRAS exon 2 p.G12D | 176 (1.99%) | 57 (32.39%) | 119 (67.61%) | 27–85 | 66 |
| KRAS exon 2 p.G13C/D/V | 82 (0.93%) | 10 (12.20%) | 72 (87.80%) | 40–83 | 65 |
| KRAS exon 3 p.Q61H/K/L/R | 83 (0.94%) | 15 (18.07%) | 68 (81.93%) | 41–85 | 65 |
| KRAS exon 2 p.G12A | 82 (0.93%) | 12 (14.63%) | 70 (85.37%) | 38–82 | 65 |
| KRAS exon 2 p.G12 S/R/F/L/Y | 24 (0.27%) | 4 (16.67%) | 20 (83.33%) | 48–89 | 66 |
| KRAS exon 4 p.A146 P/T/V | 14 (0.16%) | 4 (28.57%) | 10 (71.43%) | 52–75 | 61.5 |
| PIK3CA wt | 8492 (95.97%) | 4820 (56.76%) | 3672 (43.24%) | 18–103 | 65 |
| All PIK3CA mutation | 357 (4.03%) | 143 (40.06%) | 214 (59.94%) | 16–90 | 68 |
| PIK3CA exon 8 p.C420R | 7 (0.08%) | 2 (28.57%) | 5 (71.43%) | 54–80 | 76 |
| PIK3CA exon 10 p. E542K/E545X/Q546X | 262 (2.96%) | 101 (38.55%) | 161 (61.45%) | 41–90 | 67 |
| PIK3CA exon 21 p. H1047X | 92 (1.04%) | 40 (43.48%) | 52 (56.52%) | 34–85 | 66 |
| ERBB2 wt | 8603 (97.22%) | 3703 (43.04%) | 4900 (56.96%) | 16–103 | 65 |
| All ERBB2 variants | 246 (2.78%) | 112 (45.53%) | 134 (54.47%) | 25–82 | 61 |
| ERBB2 exon 20 insertions | 158 (1.79%) | 83 (52.53%) | 75 (47.47%) | 25–82 | 61 |
| ERBB2 exon 19 p. L755X/FI767M | 6 (0.07%) | 2 (33.33%) | 4 (66.67%) | 48–71 | 58.5 |
| ERBB2 exon 17 p. V659E | 8 (0.09%) | 4 (50.00%) | 4 (50.00%) | 50–71 | 61 |
| ERBB2 exon 8 p. S310F/Y | 34 (0.38%) | 16 (47.06%) | 18 (52.94%) | 43–90 | 66.5 |
| ERBB2 amplification | 40 (0.45%) | 7 (17.50%) | 33 (82.50%) | 36–85 | 68 |
| BRAF wt | 8658 (97.84%) | 3734 (43.13%) | 4924 (56.87%) | 18–103 | 65 |
| All BRAF mutation | 191 (2.16%) | 81 (42.71%) | 110 (57.29%) | 34–88 | 67 |
| BRAF exon 15 p.V600E | 102 (1.15%) | 52 (50.98%) | 50 (49.02%) | 34–88 | 68 |
| other | 90 (1.02%) | 30 (33.33%) | 60 (66.67%) | 38–85 | 64 |
| MET wt | 8754 (98.93%) | 3776 (43.13%) | 4978 (56.87%) | 16–103 | 66 |
| All MET driver variant | 95 (1.07%) | 39 (41.05%) | 56 (58.95%) | 31–90 | 67 |
| MET exon14 p.D1010H/N/Y | 31 (0.35%) | 22 (70.97%) | 9 (29.03%) | 35–90 | 69 |
| MET exon 14 in‐frame deletions | 10 (0.11%) | 4 (40.00%) | 6 (60.00%) | 63–86 | 69 |
| MET exon 14 skipping | 9 (0.10%) | 1 (11.11%) | 7 (77.78%) | 59–83 | 73 |
| MET amplification | 40 (0.46%) | 10 (25.00%) | 30 (75.00%) | 31–86 | 66 |
| Other (Y1003, L1195F/V) | 5 (0.06%) | 2 (40.00%) | 3 (60.00%) | 54–76 | 64 |
| ALK Fusion | 328 (3.71%) | 191 (58.23%) | 137 (41.77%) | 21–83 | 57 |
| RET Fusion | 90 (1.02%) | 48 (53.33%) | 42 (46.67%) | 32–80 | 57.5 |
| ROS1 Fusion | 64 (0.72%) | 41 (64.06%) | 23 (35.94%) | 28–85 | 59 |
| Total | 8849 |
Abbreviations: EGFR exon 20ins: EGFR exon 20 insertion; wt: wild type.
FIGURE 1.
Hierarchical pie chart showing the distribution of somatic mutation in 8849 lung cancer patients. Frequencies are shown for variants in classical driver genes (e.g., EGFR, KRAS, BRAF, and HER2). We included gene mutations in tissue samples with a frequency ≥ 1%, copy numbers of ERBB2 and MET ≥ 4 and copy numbers of EGFR ≥ 6. The EGFR exon 18 p.G719X represents p. G719A/C/D/S; KRAS exon 3 p.Q61X indicates p.Q61H/K/L/R; KRAS exon 3 p.G13X shows p.G13C/D/V; KRAS exon 3 p.G12X represents p.G12F/L/R/S/Y; KRAS exon 4 p.A146X indicates p.A146P/T/V. EGFR 20ins: EGFR exon 20 insertion; EGFR 19‐Del: EGFR exon 19 deletion; Amp: Amplification. [Color figure can be viewed at wileyonlinelibrary.com]
The distribution of driver mutations by sex is shown in Table 1. Female patients had a higher proportion of EGFR mutations (61.32% vs. 38.68%), and ALK, RET, and ROS1 rearrangements compared to male patients (58.23% vs. 41.77%; 53.33% vs. 46.67%; 64.06% vs. 35.94%). Conversely, females had lower rates of KRAS, PIK3CA, ERBB2, BRAF, and MET mutation (19.84% vs. 80.16%; 40.06% vs. 59.94%; 45.53% vs. 54.47%; 42.71% vs. 57.29%; 41.05% vs. 58.95%). Notably, the MET exon14 p.D1010H/N/Y mutation was more common in females (70.97% vs. 29.03%). Notably, patients with ALK and RET rearrangements were slightly younger than those with ROS1 rearrangements (ALK: median 57 years, range 21–83; RET: median 57.5 years, range 32–80; ROS1: median 59 years, range 28–85).
3.3. Compound EGFR Mutation Subtypes
To analyze the EGFR mutational landscape in 7081 patients with NSCLC (Table 2), EGFR mutations were classified into common mutations (EGFR 19‐Del and exon 21 p.L858R), rare mutations (e.g., EGFR exon 21 p.L861Q, exon 20 p.S768I, exon 18 p.G719X, EGFR exon 20 insertions, and other), and variants of uncertain significance (VUSs) (Table 2). Among these patients, 3861 (54.53%) harbored a single EGFR mutation, 531 (7.50%) had dual mutations, and 35 (0.49%) had multiple (> 2) EGFR mutations. Overall, compound EGFR mutations were present in 7.99% of patients. Among dual EGFR mutations, the most frequent combination was common plus rare mutations (common + rare, 3.56%), followed by rare plus rare mutations (rare + rare, 1.75%) and common plus VUSs (common + VUSs, 1.65%). Combined common EGFR mutations (EGFR 19‐Del and exon 21 p.L858R) were rare, observed in only 0.04% of patients. When comparing patients with single versus compound EGFR mutations, those with EGFR 19‐Del had significantly lower rates of compound EGFR mutations than those without (18.29% vs. 36.18%, p < 0.001; Figure 2A). Conversely, EGFR exon 21 p.L858R was more common in compound mutations (51.87% vs. 42.61%, p < 0.001; Figure 2A). Rare mutations were more frequent in compound cases (28.60% vs. 16.08%, p < 0.001; Figure 2A), while VUSs were less common (1.24% vs. 5.13%, p < 0.001; Figure 2A). Further analysis of compound mutations revealed that EGFR exon 21 p.L858R was more often associated with uncommon point mutations in exons 7, 18, and 21 (4.11% vs. 0%, p < 0.05; 8.90% vs. 0%, p < 0.01; 11.30% vs. 1.94%, p < 0.05; Figure 2B). In contrast, EGFR 19‐Del was more frequently co‐mutated with EGFR exon 20 p.T790M (53.40% vs. 24.32%, p < 0.001; Figure 2B). No significant differences were observed between EGFR 19‐Del and EGFR exon 21 p.L858R in their co‐occurrence with rare mutations (S768I/G719X/L861Q) or VUSs (Figure 2B).
TABLE 2.
Various types of EGFR mutations in the 7081 NSCLC patients.
| EGFR mutation type | Number of patients (n) | Percentage (%) |
|---|---|---|
| Single mutation | 3861 | |
| Common | 3042 | 42.96% |
| Rare | 621 | 8.77% |
| VUSs | 198 | 2.80% |
| Dual mutations | 531 | |
| Common + common | 3 | 0.04% |
| Common + rare | 252 | 3.56% |
| Common + VUSs | 117 | 1.65% |
| Rare + Rare | 124 | 1.75% |
| Rare + VUSs | 30 | 0.42% |
| VUSs+ VUSs | 5 | 0.07% |
| > 2 EGFR mutations | 35 | |
| Common + rare + VUSs | 10 | 0.14% |
| Common + rare + rare | 12 | 0.17% |
| Common + VUSs + VUSs | 1 | 0.01% |
| Rare + rare + rare | 3 | 0.04% |
| Rare + rare + VUSs | 4 | 0.06% |
| VUSs + VUSs + VUSs | 2 | 0.03% |
| Common + rare + rare + rare | 2 | 0.03% |
| Common + VUSs + VUSs + VUSs | 1 | 0.01% |
FIGURE 2.
Distinct patterns of concomitant mutations observed between EGFR exon 21 p.L858R and EGFR 19‐Del. (A) Comparison of the proportion of patients with single EGFR mutations (n = 3861) versus compound EGFR mutations (n = 563), stratified by EGFR mutation subtype. Based on the dominant EGFR mutation, compound mutations were classified into three groups: Common (i.e., common + rare, or common + VUSs), rare (i.e., rare + rare or rare + VUSs), and VUSs (i.e., VUSs + VUSs). (B) Comparison of the percentage of compound EGFR mutation types between patients with EGFR exon 21 p.L858R mutation (n = 292) and those with EGFR 19‐Del (n = 103). Patients with common + common mutations (n = 3) were excluded from the analysis. p value of < 0.05 was considered to be statistically significant (*p < 0.05, **p < 0.01, ***p < 0.001). EGFR 19‐Del: EGFR exon 19 deletion. [Color figure can be viewed at wileyonlinelibrary.com]
3.4. Molecular Characterization of Paired Baseline and Post‐Progression Samples
To elucidate the mechanisms of resistance to EGFR TKIs, NGS data from 77 EGFR‐positive NSCLC patients were analyzed by comparing paired baseline and progressive disease samples. As shown in Table S4, in baseline tissue samples, 61.0% (47/77) harbored EGFR 19‐Del, 35.1% (27/77) had EGFR exon 21 p.L858R, and 3.9% (3/77) carried rare EGFR mutations (exon 21 p.L861Q/exon 20 p.S768I/exon 18 p.G719A). The most common concomitant mutation was TP53 (49.35%) in baseline, comprising 38.96% missense mutations and 7.79% nonsense mutations. EGFR exon 20 p.T790M was the most predominant resistance mutation, increasing from 2.6% at baseline to 71.43% at progression (Figure 3). Additional acquired EGFR alterations included exon 20 p.C797 S/G (3.90%), exon 18 p.L718Q/V (1.30%), EGFR amplification (1.30%), and EGFR VUSs (2.60%) (Figure 3). Off‐target resistance mutations were also identified and occurred most frequently in the RAS/RAF/MEK pathway (18.18%, 14/77), followed by TP53 variants (15.58%, 12/77) and MET amplifications (3.90%, 3/77) (Figure 3). Notably, patients harboring baseline TP53 or PIK3CA mutations did not develop secondary mutations in these genes at disease progression. In addition, the mean duration to progression following EGFR‐TKI initiation was 14 months (range 5–36), with no significant difference between patients with EGFR exon 21 p.L858R and those with EGFR 19‐Del (p = 0.258; data not shown; Figure 3). However, patients harboring EGFR 19‐Del had a slightly longer median progression duration (14 vs. 12 months) (Figure 3). Overall, acquired resistance mechanisms to EGFR TKIs can be EGFR dependent or independent in EGFR‐sensitive NSCLC patients, but “on‐target” resistance appears more prominent.
FIGURE 3.
Acquired mutations following treatment with EGFR tyrosine kinase inhibitors (TKIs) (n = 77). [Color figure can be viewed at wileyonlinelibrary.com]
4. Discussion
Recent advances in targeted therapy largely rely on the identification of abnormal driver gene mutations in tumors [21, 22]. Notably, lung cancer cases with similar clinical and histopathological features can exhibit distinct genetic alterations. Targeting these differences enables a more personalized and effective therapeutic approach. For patients lacking drug‐sensitive mutations, chemotherapy remains the only available option. Therefore, rather than focusing solely on specific oncogenic mutations, comprehensive profiling of tumor gene mutations may be more effective for guiding personalized treatment strategies. Given the heterogeneity of mutational landscapes, a single‐institution study was conducted to characterize driver gene mutation patterns in a real‐world setting.
The mutation frequencies observed in our cohort were generally consistent with those reported in other populations. EGFR mutations occur in approximately 40%–58% of Asian and 17%–21% of non‐Asian NSCLC patients [23, 24]. In addition to classical mutations, uncommon EGFR mutations may account for up to 14% of all EGFR mutations, though estimates vary across studies [25, 26]. In our cohort, EGFR mutations were identified in 48.82% of patients' tumors using different NGS panels, comprising 43.94% with common mutations (EGFR exon 21 p.L858R and EGFR 19‐Del), 3.11% with major uncommon mutations (exon 21 p.L861Q/exon 20 p.S768I/exon 18 p.G719X), and 1.84% with EGFR exon 20 insertions. KRAS mutation is one of the most common gene alterations, occurring in approximately 10% of Asian and 7.5% of Chinese patients [27, 28]. Here, we report that KRAS mutations were detected in 11.00% of cases, with the most frequent subtypes being exon 2 p.G12C (3.66%), exon 2 p.G12V (2.23%), and exon 2 p.G12D (1.99%). Previous studies have reported the prevalence of ERBB2 mutations ranging from 1% to 6% [27, 29]. Comparable rates were observed in two independent cohorts: 5.6% in 1281 Chinese NSCLC patients (Geneplus) and 5.2% in 1719 U.S. NSCLC patients (Guardant360) [30]. We found ERBB2 mutations were detected in 2.78% of patients, including 1.79% with ERBB2 exon 20 insertions. The frequency of BRAF mutations in our cohort was 2.16%, which was slightly higher than rates reported in Asian populations but lower than those observed in Caucasian populations [31, 32]. Therefore, the incidence of lung cancer‐related gene mutations differs across ethnicity and region.
Most EGFR compound mutations are combinations of the rare mutation and common mutations [33]. In our single‐institution study from China, the overall incidence of compound EGFR mutation (7.99%) is consistent with that reported in other studies from Asian populations (4%–26%), and lower than that reported in a relevant study conducted on 2146 NSCLC in Southwest China (43.6% in the rural Qujing area) [34, 35, 36]. The high heterogeneity in the incidence of compound mutations is dependent on the different testing methods adopted and the specific mutations considered. Notably, the incidence of the dual EGFR mutations in all patients is quite low: rare + rare subtype (1.75%), common + VUSs subtype (1.65%), and multiple (> 2) EGFR mutations subtype (0.49%), and has been associated with inherited susceptibility and resistance mechanisms to lung cancer therapies [37]. In addition, EGFR exon 21 p.L858R and EGFR 19‐Del showed distinct patterns of concomitant mutations. Inconsistent with previous research findings [38], our study indicated EGFR 19‐Del was more frequently co‐mutated with secondary EGFR exon 20 p.T790M compared to EGFR exon 21 p.L858R, which may reflect differences in the use of various generations of EGFR‐TKIs. Conversely, EGFR exon 21 p.L858R is more significantly abundant in patients with compound mutations (51.87% vs. 42.61%), which is consistent with previous studies [39, 40]. Moreover, EGFR exon 21 p.L858R was more frequently accompanied by extremely rare point mutations in exons 7, 18, and 21 than EGFR 19‐Del, implying that EGFR exon 21 p.L858R combined with uncommon mutations in these exons may be more resistant to EGFR TKIs [41]. Notably, no significant differences were identified between EGFR 19‐Del and EGFR exon 21 p.L858R with regard to co‐occurring rare mutations (exon 21 p.L861Q/exon 20 p.S768I/exon 18 p.G719X) or VUSs, indicating comparable patterns of concomitant EGFR mutations in the two subtypes.
Our study further characterizes the acquired resistance mechanisms to EGFR‐TKIs in patients with EGFR‐positive NSCLC. In our study, EGFR‐mutated samples in the baseline were identified co‐occurring mutations: 38.96% TP53 missense mutations and 7.79% TP53 nonsense mutations. Previous studies have demonstrated that TP53 co‐mutations serve as predictors of TKI efficacy and survival in EGFR‐positive NSCLC, regardless of other clinical parameters [38]. Consistently with data reported, EGFR exon 20 p.T790M was the most common acquired resistance mutation (71.43% of cases) [42]. The major uncommon resistance mutations (exon 18 p.L718Q/V and exon 20 p.C797S/G) account for 5.2% of the 77 patients with EGFR mutations, which is comparable to the data (1%–8%) by Santoni‐Rugiu et al. [43] Off‐target resistance mechanisms were also observed, most frequently involving alterations in the RAS/RAF/MEK pathway (18.18%) and MET amplifications (3.90%). The incidence of these off‐target mutations was lower than that reported in studies involving osimertinib treatment [44].
There were several limitations in our study. First, a large proportion of patients had limited clinical information on the PD‐L1 expression, disease stage, and therapies received. Second, inherent selection bias may exist since all patients were enrolled from a single hospital and were ethnically Chinese. Finally, only 77 patients with paired baseline and post‐progression samples were included in the resistance analyses, and future studies with larger sample sizes are required to elucidate the differential resistant mechanisms for EGFR mutations. Therefore, care should be taken when generalizing the results in the present study.
5. Conclusions
Our large cohort underscores substantial heterogeneity of the rare and compound EGFR mutations. Multiple resistance mechanisms were observed at post‐progression, with EGFR exon 20 p.T790M being the most frequent, followed by gene mutations in the RAS/RAF/MEK pathway and MET amplifications. These findings contribute to a better understanding of the molecular characteristics of compound EGFR mutations and the diverse mechanisms underlying resistance to EGFR‐TKIs.
Author Contributions
Min Huang: conceptualization, writing – original draft. Jun Yang: conceptualization, writing – original draft. Pei Peng: conceptualization, writing – review and editing. Yongpan Wang: formal analysis, investigation. Li Song: formal analysis, methodology. Yuqin Lin: methodology, software. Jiangman Zhao: conceptualization, methodology.
Funding
This work was supported by the Noncommunicable Chronic Diseases‐National Science and Technology Major Project, 2024ZD0529005.
Ethics Statement
The retrospective study was approved by the Human Research Ethics Committee of Yellow River Sanmenxia Hospital (approval no. 202401090006).
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Table S1: The list of targeted genes.
Table S3: Clinicopathologic and demographic characteristics of enrolled lung cancer patients.
Table S4: Clinicopathological characteristics of 77 patients with matched baseline and progressive disease samples.
Table S2: The sequencing coverage and quality statistics for each sample using the GRCh37.
Contributor Information
Pei Peng, Email: pengpei@biotecan.com.
Jiangman Zhao, Email: jiangzhao@biotecan.com.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1: The list of targeted genes.
Table S3: Clinicopathologic and demographic characteristics of enrolled lung cancer patients.
Table S4: Clinicopathological characteristics of 77 patients with matched baseline and progressive disease samples.
Table S2: The sequencing coverage and quality statistics for each sample using the GRCh37.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.