Abstract
Atypical L858R or other L858X mutations in the epidermal growth factor receptor (EGFR) gene, beyond the classical EGFR L858R mutation caused by c.2573 T > G, have been identified in non‐small cell lung cancer (NSCLC), yet their genomic features and survival benefits with EGFR tyrosine kinase inhibitor (TKI) treatment have not been fully explored. We retrospectively enrolled 489 NSCLC patients with baseline tumor tissue/plasma samples carrying uncommon EGFR L858R (N = 124), EGFR L858Q/M (N = 17), or classical EGFR L858R mutations (N = 348). The comparison of molecular features was performed using treatment‐naïve tumor tissues. Survival benefits and resistance mechanisms of first‐line EGFR TKI treatment were studied in an advanced disease subcohort. NSCLCs harboring uncommon EGFR L858R had lower TP53 mutation prevalence (p = 0.04) and chromosome instability scores (p = 0.02) than those with classical EGFR L858R. Concomitant EGFR L861Q mutations were enriched in NSCLCs with EGFR L858Q/M (p < 0.01), with cooccurrence in those carrying EGFR L858M. Patients with uncommon EGFR L858R experienced improved progression‐free survival (PFS) compared to those with classical EGFR L858R (median: 13.0 vs. 10.0 months, hazard ratio [HR]: 0.57, 95% confidence interval [CI]: 0.41–0.80). The association remained significant when adjusting for sex, age, histological subtype, TKI category, and anti‐vascular therapy (HR: 0.55, 95% CI: 0.39–0.77). Furthermore, EGFR L858Q/M patients showed enhanced first‐line PFS (vs. classical EGFR L858R, HR: 0.26, 95% CI: 0.10–0.67), potentially benefiting more from afatinib. Additionally, NSCLCs with uncommon EGFR L858R and classical EGFR L858R had similar resistance profiles to EGFR TKIs. In conclusion, NSCLCs carrying atypical EGFR L858 aberrations, which had fewer TP53 mutations and higher chromosome stability, exhibited improved PFS under first‐line EGFR TKIs than those with the classical EGFR L858R.
Keywords: EGFR, EGFR tyrosine kinase inhibitor, EGFR L858R , first‐line treatment, non‐small cell lung cancer
This study reveals that NSCLC patients with uncommon EGFR L858R mutations experience improved outcomes with EGFR TKIs compared to those with the classical EGFR L858R mutation, evidenced by longer PFS, fewer TP53 mutations, and higher chromosome stability. Furthermore, patients with EGFR L858Q/M showed enhanced first‐line PFS, potentially benefiting more from afatinib.
Abbreviations
- CI
confidence interval
- CIN
chromosome instability
- EGFR
epidermal growth factor receptor
- HR
hazard ratio
- NGS
next‐generation sequencing
- NSCLC
non‐small cell lung cancer
- PFS
progression‐free survival
- SNV
single nucleotide variant
- TKI
tyrosine kinase inhibitor
- VAF
variant allele frequency
1. INTRODUCTION
Epidermal growth factor receptor (EGFR) tyrosine kinase, which is encoded by the EGFR gene, has been targeted in the treatment of various cancer subtypes. Multiple EGFR tyrosine kinase inhibitors (TKIs), disrupting the activity of EGFR kinase via the interaction of the catalytic region of the kinase domain and binding the adenosine triphosphate pocket, 1 have been approved for lung cancer treatment based on several clinical trials. For instance, treatment‐naïve and EGFR‐mutated patients with advanced non‐small cell lung cancer (NSCLC) could achieve response rates of 70%–75% when receiving first‐ (gefitinib or erlotinib) and second‐generation (afatinib or dacomitinib) EGFR TKIs, with improved progression‐free survival (PFS) of 10–14 months observed when compared to patients allocated into the control arms who were treated with platinum‐based chemotherapy. 2 , 3 , 4 , 5 , 6 Additionally, osimertinib, which is a third‐generation EGFR TKI, showed superior PFS (median: 18.9 vs. 10.2 months; hazard ratio [HR]: 0.46; 95% confidence interval [CI]: 0.37–0.57), 7 improved overall survival (median: 38.6 vs. 31.8 months; HR: 0.80; 95% CI: 0.64–1.00), and less frequently observed adverse events of grade 3 or higher than first‐generation EGFR TKIs (gefitinib/erlotinib) in EGFR‐mutated advanced NSCLC. 8 Of note, dramatic therapeutic efficacy of EGFR TKIs is associated with the somatic activating mutations in the EGFR gene. 9 , 10
EGFR L858R mutations and EGFR exon 19 deletions are the most common EGFR somatic activating mutations, accounting for approximately 40%–50% and 30%–40% of various EGFR somatic mutations, respectively. 11 In addition to these two prevalent EGFR mutation subtypes, considerable rarely detected EGFR aberrations have been observed in tumor samples with a prevalence of approximately 5% or lower, such as EGFR exon 20 insertions, EGFR G719X, EGFR L861X, and EGFR S768I/V. 12 Further, EGFR T790M mutations, acting as the acquired secondary mutation associated with the resistance to first‐ and second‐generation EGFR TKIs, are approximately 6% of the EGFR mutations recorded in the COSMIC database. 11 A variety of substitutions have been identified even though they lead to the same EGFR L858R protein mutation, including single nucleotide variants (SNVs) of c. 2573 T > G, as well as compound substitutions of c.2572_2573inv, c.2573_2574delinsGA, and c.2573_2574delinsGT. 13 As the predominant subtype, c. 2573 T > G accounts for over 99% (2707 of 2721) of the substitutions when excluding those with unknown genomic mutations; however, the three types of uncommon compound substitutions leading to EGFR L858R are identified in 14 samples. 13 Owing to the low detection frequency, there is a lack of comprehensive investigations of the genomic and clinical features, as well as the response to EGFR TKIs of these uncommon EGFR genomic mutations.
Although most EGFR genomic mutations in EGFR gene codon L858 result in translating arginine instead of leucine, other uncommon amino acid mutations have been reported. For example, Skronski et al. identified EGFR L858M and EGFR L858P mutations in two NSCLC samples using a real‐time PCR‐based method. 14 Saxon et al. reported EGFR L858M mutations observed in one patient's lung adenocarcinoma tumor sample using next‐generation sequencing (NGS). 15 Other uncommon mutations in EGFR codon L858, such as EGFR L858Q, 16 EGFR L858A, 17 and EGFR L858G, 18 have also been identified in lung cancer samples. Moreover, Bejjanki et al. demonstrated a potential association between EGFR L858M combined with EGFR L861Q and primary resistance to erlotinib as well as the possible response to afatinib. 19 Despite few case reports revealing the treatment efficacy of EGFR TKIs in lung cancer with EGFR L858X mutations other than EGFR L858R, the potency of TKIs in such settings remains unclear.
Here, we conducted a retrospective study of Chinese patients with NSCLC whose pre‐systemic treatment tumor tissue or liquid biopsies were identified with uncommon genomic aberrations in EGFR codon L858 causing EGFR L858R (uncommon EGFR L858R) or non‐EGFR L858R mutations. We performed genomic profiling using an NGS panel targeting 437 cancer‐related genes (GeneseeqPrime), studied the first‐line EGFR TKI treatment efficacy in patients with advanced disease, and made comparisons to NSCLCs carrying classical EGFR L858R mutations caused by the prevalent c. 2573 T > G genomic mutation. Furthermore, the resistance‐related secondary genetic aberrations were revealed using patients’ matched baseline and progression samples.
2. MATERIALS AND METHODS
2.1. Study design and participants
We retrospectively enrolled Chinese patients with NSCLC between May 2016 and February 2023 at The Second Affiliated Hospital, Zhejiang University School of Medicine. Patients fulfilling the following inclusion criteria were enrolled: (a) adults aged 18 years or older; (b) pathologically confirmed NSCLC according to the 2016 World Health Organization classification; (c) with baseline tumor tissue or plasma circulating tumor DNA (ctDNA) samples before systemic treatment; and (d) baseline samples identified with de novo classical EGFR L858R mutations (c. 2573 T > G) or uncommon genomic aberrations in EGFR codon L858 causing EGFR L858R (uncommon EGFR L858R) or mutations other than EGFR L858R, using a targeted next‐generation sequencing panel covering 437 cancer‐related genes. NSCLCs who received EGFR TKIs as first‐line therapy and for whom survival data were available were then grouped to study the survival benefits, and resistance mechanisms were investigated using matched baseline and progression samples (Figure 1). This study was approved by the ethics committee of The Second Affiliated Hospital, Zhejiang University School of Medicine (No. 2023–0736) and performed in accordance with the tenets of the Declaration of Helsinki. All patients signed informed consent forms prior to enrollment and sample collection.
FIGURE 1.
Study design and patient inclusion. A total of 348 NSCLCs with classical EGFR L858R, 124 NSCLCs with uncommon EGFR L858R and 17 NSCLCs with EGFR L858Q/M mutations were included in this study. Baseline tumor tissue samples from 428 patients were used for the comparison of mutational landscape and molecular features. The efficacy of EGFR TKIs in first‐line therapy was then studied in 301 patients with advanced diseases, and secondary mutations related to drug resistance were identified by matched baseline and progression samples. CIN, chromosome instability; EGFR, epidermal growth factor receptor; NSCLC, non‐small cell lung cancer; TKI, tyrosine kinase inhibitor; TMB, tumor mutation burden.
2.2. DNA extraction, library preparation, next‐generation sequencing, and sequencing data processing
Genomic DNA of tumor tissue biopsies was extracted from formalin‐fixed, paraffin‐embedded samples and purified using a QIAamp DNA FFPE Tissue Kit (Qiagen, Dusseldorf, Germany). Cell‐free DNA and genomic DNA of white blood cells (as normal control) were collected from 10‐mL peripheral blood using a DNeasy Blood and Tissue Kit (Qiagen, Dusseldorf, Germany). Sequencing libraries were prepared using the KAPA Hyper Prep Kit (KAPA Biosystems, Wilmington, MA, USA) and captured by probe‐based hybridization targeting 437 cancer‐related genes (GeneseeqPrime, Nanjing Geneseeq Technology, Nanjing, China). Enriched libraries were sequenced on Illumina sequencing platforms (Illumina, San Diego, CA, USA).
Sequencing data were analyzed as previously described. 20 In brief, sequencing readings with good quality were mapped to the reference (hg19) with the Burrows–Wheeler Aligner (https://github.com/lh3/bwa/tree/master/bwakit). VarScan2 and Mutect2 were used to identify somatic SNVs and indels with variant allele frequency (VAF) over 0.5% and at least three mutant reads. Genomic fusions were identified using Fusion And Chromosomal Translocation Enumeration and Recovery Algorithm (FACTERA) with default parameters. Gene‐level copy number variations (CNVs) were detected using the CNVkit (https://cnvkit.readthedocs.io) with a cutoff of fold change ≥1.6 and ≤0.6 for amplifications and deletions, respectively. Chromosome instability (CIN) scores were analyzed as previously reported. 21 Whole‐genome doubling was identified using FACETS. 22
2.3. Statistical analysis
Fisher's exact tests were performed to compare the frequencies of independent subgroups, and Wilcoxon rank‐sum tests were conducted to test the differences in medians. In term of survival data, PFS was defined as the duration between the initiation of EGFR TKIs to disease progression, encompassing both local–regional recurrence and distant metastasis, or death from any cause. Kaplan–Meier curves were generated, and log‐rank tests were used to compare differences. Cox proportional hazards models were fitted to estimate HRs with 95% CIs, and the proportionality of hazards was assessed using log(−log) survival plots. Individuals with missing data were excluded from analyses. All quoted p‐values were two‐tailed, and p‐values <0.05 were considered statistically significant. Data were analyzed using R software (version 4.2.2) and the survival, survminer, and epiR packages.
3. RESULTS
3.1. Patient and cohort overview
A total of 489 eligible patients with NSCLC were retrospectively enrolled in our study (Figure 1), including 348 detected with classical EGFR L858R caused by genomic mutation c. 2573 T > G, 124 detected with EGFR L858R mutations caused by uncommon genomic aberrations in EGFR codon L858, and 17 detected with EGFR L858Q/M, using targeted next generation sequencing. The baseline clinical characteristics of patients are summarized in Table 1. The median age of included patients was 63 (range: 25–86), with 61% of patients over 60. Most of the study cohort were female and adenocarcinoma patients, accounting for approximately 61% and 87%, respectively. We detected seven kinds of uncommon genomic aberrations in EGFR codon L858 leading to EGFR L858R mutations (Table 2), whereas unbalance in clinical characteristics was not observed between NSCLCs harboring classical and uncommon EGFR L858R. Similar results were obtained when comparing patients with EGFR L858Q/M and with classical EGFR L858R.
TABLE 1.
Clinical characteristics of included patients with non‐small cell lung cancer.
| Characteristics | Classical EGFR L858R (N = 348) | Uncommon EGFR L858R (N = 124) | EGFR L858Q/M (N = 17) | Total (N = 489) | p‐value | |
|---|---|---|---|---|---|---|
| Classical EGFR L858R vs. uncommon EGFR L858R | Classical EGFR L858R vs. EGFR L858Q/M | |||||
| Age, median (range) | 63 (35–86) | 63 (25–84) | 66 (41–79) | 63 (25–86) | 0.56 | 0.69 |
| Age, No. (%) | ||||||
| ≤60 | 137 (39.4) | 44 (35.5) | 7 (58.8) | 188 (38.4) | 0.66 | >0.99 |
| >60 | 211 (60.6) | 76 (61.3) | 10 (41.2) | 297 (60.7) | ||
| Unknown | 0 (0.0) | 4 (3.2) | 0 (0.0) | 4 (0.8) | ||
| sex, no. (%) | ||||||
| Female | 215 (61.8) | 71 (57.3) | 12 (70.6) | 298 (60.9) | 0.44 | 0.64 |
| Male | 133 (38.2) | 53 (42.7) | 5 (29.4) | 191 (39.1) | ||
| Stage at diagnosis, no. (%) | ||||||
| I–III | 19 (5.5) | 6 (4.8) | 0 (0.0) | 25 (5.1) | 0.79 | >0.99 |
| IV | 156 (44.8) | 41 (33.1) | 2 (11.8) | 199 (40.7) | ||
| Unknown | 173 (49.7) | 77 (62.1) | 15 (88.2) | 265 (54.2) | ||
| Histology subtype, no. (%) | ||||||
| Adenocarcinoma | 308 (88.5) | 103 (83.1) | 14 (82.4) | 425 (86.9) | 0.61 | 0.52 |
| Squamous cell carcinoma | 8 (2.3) | 1 (0.8) | 1 (5.9) | 10 (2.0) | ||
| Adenosquamous Carcinoma | 7 (2.0) | 1 (0.8) | 0 (0.0) | 8 (1.6) | ||
| Unknown | 25 (7.2) | 19 (15.3) | 2 (11.8) | 46 (9.4) | ||
TABLE 2.
Summary of uncommon EGFR L8585R mutations.
| Genomic aberrations | No. (%) |
|---|---|
| c.2573_2574TG > GT | 70 (56.5) |
| c.2572_2573CT > AG | 40 (32.3) |
| c.2573_2574TG > GA | 6 (4.8) |
| c.2573_2574TG > GC | 4 (3.2) |
| c.2572_2574CTG > AGA | 2 (1.6) |
| c.2571_2573GCT > ACG | 1 (0.8) |
| c.2573_2577TGGCC > GGGCT | 1 (0.8) |
Of 489 included patients, 301 patients receiving EFGR TKIs in first‐line therapy had PFS data available, consisting of 233 patients with classical EGFR L858R, 58 patients with uncommon EGFR L858R, and 10 patients with EGFR L858Q/M. EGFR TKIs used included gefitinib (37.5%), icotinib (34.9%), osimertinib (11.3%), erlotinib (6.3%), afatinib (6.3%), dacomitinib (2.7%), and ametinib (1.0%) (Table 3). In comparison to patients with classical EGFR L858R, obvious unbalance in age, sex, histology subtype, or category of first‐line EGFR TKI was not observed, whereas patients harboring EGFR L858Q/M were more likely to receive afatinib as first‐line therapy (40% vs. 1.7%, Table 3). Additionally, nine patients with classical EGFR L858R and one patient with uncommon EGFR L858R received EGFR TKIs combined with anti‐vascular therapy.
TABLE 3.
Clinical characteristics of patients with advanced non‐small cell lung cancer using EGFR TKIs.
| Characteristics | Classical EGFR L858R (N = 233) | Uncommon EGFR L858R (N = 58) | EGFR L858Q/M (N = 10) | Total (N = 301) | p‐value | |
|---|---|---|---|---|---|---|
| Classical EGFR L858R vs. uncommon EGFR L858R | Classical EGFR L858R vs. EGFR L858Q/M | |||||
| Age, median (range) | 63 (35–84) | 62 (40–84) | 60 (47–68) | 63 (35–84) | 0.89 | 0.72 |
| Age, no. (%) | ||||||
| ≤60 | 99 (42.5) | 27 (46.6) | 3 (30.0) | 129 (42.9) | 0.68 | 0.65 |
| >60 | 134 (57.5) | 31 (53.4) | 7 (70.0) | 172 (57.1) | ||
| Sex, no. (%) | ||||||
| Female | 140 (60.1) | 29 (50.0) | 8 (80.0) | 177 (58.8) | 0.21 | 0.35 |
| Male | 93 (39.9) | 29 (50.0) | 2 (20.0) | 124 (41.2) | ||
| Histology subtype, no. (%) | ||||||
| Adenocarcinoma | 202 (86.7) | 51 (87.9) | 9 (90.0) | 262 (87.0) | 0.96 | 0.90 |
| Squamous cell carcinoma | 6 (2.6) | 1 (1.7) | 0 (0.0) | 7 (2.3) | ||
| Adenosquamous | 6 (2.6) | 1 (1.7) | 0 (0.0) | 7 (2.3) | ||
| Unknown | 19 (8.2) | 5 (8.6) | 1 (10.0) | 25 (8.3) | ||
| First‐line EGFR TKIs, no. (%) | ||||||
| Afatinib | 14 (6.0) | 1 (1.7) | 4 (40.0) | 19 (6.3) | 0.31 | <0.01* |
| Ametinib | 3 (1.3) | 0 (0.0) | 0 (0.0) | 3 (1.0) | ||
| Dacomitinib | 6 (2.6) | 2 (3.4) | 0 (0.0) | 8 (2.7) | ||
| Erlotinib | 14 (6.0) | 5 (8.6) | 0 (0.0) | 19 (6.3) | ||
| Gefitinib | 81 (34.8) | 28 (48.3) | 4 (40.0) | 113 (37.5) | ||
| Icotinib | 85 (36.5) | 18 (31.0) | 2 (20.0) | 105 (34.9) | ||
| Osimertinib | 30 (12.9) | 4 (6.9) | 0 (0.0) | 34 (11.3) | ||
| Anti‐VEGF therapy, no. (%) | ||||||
| With | 9 (3.9) | 1 (1.7) | 0 (0.0) | 10 (3.3) | 0.69 | >0.99 |
| Without | 224 (96.1) | 57 (98.3) | 10 (100.0) | 291 (96.7) | ||
Abbreviations: EGFR, epidermal growth factor receptor; TKI, tyrosine kinase inhibitor; VEGF, vascular endothelial growth factor.
Statistically significant.
3.2. Cooccurrence of EGFR L858Q /M with EGFR L861Q and TP53 mutations enriched in patients with classical EGFR L858R
The mutational landscapes of baseline tumor tissue samples are presented in Figure 2A, showing somatic mutations/CNVs with prevalence ≥3%. The details of all detectable EGFR somatic mutations are summarized in Figure S1. EGFR L861Q were exclusively identified in patients with EGFR L858Q/M, with a significantly higher prevalence than other two subcohorts (71% vs. 0%, p < 0.01, Figure 2B); however, EGFR CNV amplifications were not identified in patients with EGFR L858Q/M. Concomitant EGFR V834L mutations were observed in all three subcohorts, without obvious difference in prevalence. TP53 was less frequently altered in the uncommon EGFR L858R subcohort (59% vs. 72%, p = 0.04, Figure 2B) and patients with EGFR L858Q/M (43% vs. 72%, p = 0.20, Figure 2B) than in the classical EGFR L858R subcohort; however, prevalence of other mutated genes was comparable across three subgroups. Although no obvious differences in tumor mutation burden were detected (Figure 2C), NSCLCs with classical EGFR L858R exhibited the highest CIN scores, followed by patients with uncommon EGFR L858R (Figure 2D). Moreover, patients harboring EGFR L858Q/M might have the lowest detection rate of whole‐genome doubling, despite the limited sample size (Figure 2E).
FIGURE 2.
Genomic characteristics of patients with different EGFR L858X mutations. (A) The heatmap showing frequently mutated genes in NSCLC patients with classical EGFR L858R, uncommon EGFR L858R, and EGFR L858Q/M mutations. (B) Concurrent EGFR CNV amplifications were not detected in NSCLCs with EGFR L858Q/M mutations, whereas EGFR L861Q mutations were exclusively observed in these patients. NSCLCs with classical EGFR L858R mutations appeared to have the highest prevalence of TP53 mutations. (C) The TMB levels, (D) CIN scores, and (E) WGD status of NSCLC patients with classical EGFR L858R, uncommon EGFR L858R, and EGFR L858Q/M mutations. CIN, chromosome instability; CNV, copy number variant; EGFR, epidermal growth factor receptor; NSCLC, non‐small cell lung cancer; TMB, tumor mutation burden; WGD, whole‐genome doubling.
3.3. Improved first‐line progression‐free survival in non‐small cell lung cancer with uncommon EGFR L858R under epidermal growth factor receptor‐tyrosine kinase inhibitor treatment
Next, we investigated the efficacy of EGFR TKIs in patients with various genomic aberrations in EGFR codon L858. In comparison to 233 patients carrying classical EGFR L858R, 58 patients with uncommon EGFR L858R exhibited improved PFS under first‐line EGFR TKI treatment (median: 13.0 vs. 10.0 months, HR: 0.57, 95% CI: 0.41–0.80, Figure 3A). Despite the limited sample size of the EGFR L858Q/M subcohort (N = 10), these patients showed superior PFS when compared to the patients carrying classical EGFR L858R (median: 22.0 vs. 11.0 months, HR: 0.34, 95% CI: 0.14–0.82, Figure 3A). As approximately 80% of patients were treated with first‐generation EGFR‐TKIs, sensitivity analysis was performed to evaluate the PFS difference in patients using first‐generation EGFR‐TKIs. Similarly, we observed better PFS in patients with uncommon EGFR L858R than in patients with classical EGFR L858R (median: 12.0 vs. 10.0 months, HR: 0.64, 95% CI: 0.45–0.92, Figure 3B), whereas patients harboring EGFR L858Q/M did not show significantly better PFS, possibly due to the sample size (median: 18.0 vs. 10.0 months, HR: 0.52, 95% CI: 0.19–1.40, Figure 3B). The covariate coefficients estimated by the multivariable Cox regression model controlling for sex, patient age, histology subtype, EGFR TKI category, and anti‐vascular therapy are presented in Figure 3C. Our results revealed that the uncommon EGFR L858R mutation might serve as an independent molecular feature indicating better PFS (HR: 0.55, CI: 0.39–0.77).
FIGURE 3.
Improved survival benefits observed in NSCLCs with uncommon EGFR L858R mutations. (A) The Kaplan–Meier curves for patients with advanced NSCLC who were given EGFR TKIs as first‐line therapy. HRs with 95% CIs were estimated by univariate Cox regression models. (B) Similar results were obtained in the sensitivity analysis within patients using first‐generation EGFR TKIs. (C) The forest plot presenting the results of the multivariable Cox regression model including EGFR mutation subtypes, sex, age, histology subtypes, EGFR TKI category, and anti‐VEGF treatment. Asterisks indicate levels of statistical significance: **p < 0.01, ***p < 0.001. (D) The swimmer plot showing the treatment and disease progression details in 10 patients with EGFR L858Q/M mutations. (E) Patients with EGFR L858Q/M mutations might have better PFS under second‐generation EGFR TKIs treatment than first‐generation EGFR TKIs, while the difference in PFS was not statistically significant. CI, confidence interval; EGFR, epidermal growth factor receptor; HR, hazard ratio; NSCLC, non‐small cell lung cancer; PFS, progression‐free survival; TKI, tyrosine kinase inhibitor; VEGF, vascular endothelial growth factor.
3.4. Epidermal growth factor receptor‐tyrosine kinase treatment among patients with EGFR L858Q /M
We summarized the EGFR TKI treatment history of 10 patients with EGFR L858Q/M (Figure 3D). All patients with EGFR L858M were also identified with genomic mutation in EGFR codon L861, with five patients harboring EGFR L861Q (P83, P81, P87, P77, and P85) and one patient harboring EGFR L861R (P86). Four and two patients were treated with gefitinib and icotinib as the first‐line treatment, respectively, and afatinib was used in the remaining four patients. By the end of follow‐up, five patients had developed disease progression, including three receiving gefitinib, one receiving icotinib, and one receiving afatinib. Of note, all of them were diagnosed with radiological progression after using EGFR‐TKIs as first‐line treatment for over 12 months. When comparing the treatment efficacy between the first‐ and second‐generation EGFR TKIs, we observed a potentially improved PFS among patients using second‐generation EGFR TKIs despite the limited sample size (median: 22.0 vs. 18.0 months, HR: 0.27, 95% CI: 0.03–2.59, Figure 3E). Of these five patients with disease progression, P76 and P77 were then given osimertinib after resistance to gefitinib and afatinib, respectively, and P85 was treated with afatinib subsequently. After receiving osimertinib for approximately 11 months, P76 harboring EGFR L858Q mutation was diagnosed with disease progression, followed by afatinib treatment with third‐line PFS of 9.5 months. In contrast, P85, who harbored EGFR L858M mutation and received afatinib, had not developed radiological progression by the end of follow‐up (34.6 months). P86, who was another patient with EGFR L858M mutation and treated with afatinib, did not experience disease progression (30.7 months). Although P83 and P87 had been treated with afatinib for a relatively short period of time by the end of follow‐up, our data revealed that NSCLCs with combined EGFR L858M and EGFR L861X were likely to have more survival benefits from afatinib.
3.5. Similar resistance mechanisms in non‐small cell lung cancer with uncommon EGFR L858R
Matched samples collected at baseline and progression were available in 33 patients with classical EGFR L858R mutations. The EGFR T790M mutation (N = 9, 27%) was the predominant resistance mechanism (Figure 4A). Resistance‐related amplified MET (N = 5), NKX2‐1 (N = 4), 23 and EGFR (N = 3) genes, as well as EGFR V834L (N = 1) 24 and PIK3CA mutations (N = 3), were also detected, whereas the resistance mechanisms of the remaining eight patients were unclear. We then investigated the resistance mechanisms among patients with uncommon EGFR L858R mutations by comparing the mutational landscapes of baseline biopsies to the matched samples collected at progression diagnosis. Our data of 13 pairs of matched biopsies demonstrated that NSCLCs with uncommon EGFR L858R mutations were likely to have similar EGFR TKI resistance mechanisms to those with classical EGFR L858R mutations (Figure 4B). On‐target resistance mechanisms included acquired EGFR T790M/C797S mutations and primary EGFR amplifications. Primary KRAS amplification combined with amplified MYC, secondary FGFR1, and ERBB2 amplifications and secondary PIK3CA and MED12 mutation 25 acted as potential off‐target resistance mechanisms. However, we were not able to identify intrinsic resistance mechanisms or acquired genetic alterations in three patients.
FIGURE 4.
Resistance mechanisms in NSCLC patients with classical and uncommon EGFR L858R mutations. Secondary mutations acquired in samples collected at disease progression and intrinsic resistance‐related mutations in NSCLC patients with (A) classical and (B) uncommon EGFR L858R mutations. AMP, amplification; EGFR, epidermal growth factor receptor; NSCLC, non‐small cell lung cancer.
4. DISCUSSION
In our study, the comparison in molecular features revealed the co‐occurrence of EGFR L858Q/M and EGFR L861Q. NSCLCs with uncommon EGFR L858R, who had less prevalent TP53 mutations and lower CIN scores, exhibited superior PFS under first‐line EGFR TKI treatment compared to those with classical EGFR L858R. Moreover, resistance mechanisms to EGFR TKIs were comparable between patients with classical and uncommon EGFR L858R.
A total of 124 and 17 NSCLC patients whose tumor tissue or liquid biopsies before systemic treatment were detected with uncommon EGFR L858R and EGFR L858Q/M, respectively, were included in this study. In our database, the prevalence of EGFR L858Q/M in all genomic aberrations in EGFR codon L858 was approximately 0.14% (95% CI: 0.09%–0.20%), and uncommon EGFR L858R mutations accounted for approximately 1.65% (95% CI: 1.48%–1.84%) of any EGFR L858R mutation subtypes. Consistently, in the COSMIC database, infrequent genomic aberrations in EGFR codon L858 other than EGFR L858R (excluding EGFR L858) were observed in 0.18% (95% CI: 0.11%–0.28%) of samples, and 0.51% (95% CI: 0.28%–0.86%) of samples with EGFR L858R were detected with uncommon EGFR L858R mutations. 13 Despite the comparable prevalence, only samples with EGFR L858Q/M were identified in our database; however, we detected multiple uncommon EGFR L858R mutations that have not been included in the COSMIC database, such as c.2573_2574TG > GC (N = 2) and c.2572_2574CTG > AGA (N = 1).
In this study, we included baseline tumor tissue samples to perform a comprehensive investigation for distinctive molecular features, including concurrent genetic alterations, tumor mutation burden, CIN scores, and whole‐genome doubling. Although uncommon EGFR L858R mutations had been previously reported, few studies have described their clinical or genomic characteristics due to the extremely low prevalence. For instance, Sonobe et al. detected two uncommon EGFR L858R mutations in addition to 20 classical EGFR L858R mutations in patients with NSCLC; however, they were not able to explore the correlation between uncommon EGFR L858R mutations and clinico‐pathological characteristics. 26 Further, Bell's study, which demonstrated the association between the mutated EGFR gene and increased response to gefitinib in NSCLC, did not pay significant attention to one included patient with an uncommon EGFR L858R mutation (c.2573_2574delinsGT) and did not present the response, time to progression, or overall survival. 27 However, we systemically investigated the genomic features of NSCLCs with uncommon EGFR L858R mutations by subjecting treatment‐naïve tissue biopsies to a next‐generation sequencing panel covering 437 cancer‐related genes.
NSCLCs carrying EGFR L858Q/M were more likely to have concurrent EGFR L861Q mutations. Consistently, previous studies have reported the occurrence of EGFR L858Q/M and EGFR L861Q double mutations in cis configuration. For example, a case study by Hong et al. revealed that a Korean NSCLC case with the EGFR L858M/L861R double mutation was misidentified as EGFR L858Q/L861R double mutation at initial diagnosis. 28 This patient developed disease progression after administration with gefitinib for 3 months, demonstrating EGFR L858M/L861R as gefitinib insensitive. Similarly, another case study reported a white female patient who was diagnosed as lung adenocarcinoma with EGFR L858M/L861R double mutation showing resistance to erlotinib and sensitivity to afatinib. 15 Our study suggested potentially improved PFS among patients receiving afatinib as the first‐line therapy when compared to those treated with gefitinib or icotinib. In this retrospective study, no individuals with EGFR L858Q/M mutations were given osimertinib during their first‐line therapy. Thus, further investigation is required to fulfill the knowledge gap of the comprehensive analysis for osimertinib first‐line treatment efficacy within NSCLCs harboring EGFR L858Q/M.
Our survival data revealed that NSCLCs with uncommon EGFR L858R were likely to have better PFS than those with classical EGFR L858R when receiving EGFR TKIs as the first‐line therapy. We hypothesized that the differences in the mutational landscape might explain the improved PFS observed in NSCLCs with uncommon EGFR L858R mutations. Mutated TP53, which has been identified as a biomarker indicating unfavorable prognosis in NSCLC, 29 , 30 had relatively low prevalence in our patients with uncommon EGFR L858R when compared to those with classical EGFR L858R mutations. However, based on our literature review, there is a lack of studies directly elucidating the specific biological mechanisms underlying the less frequent co‐occurrence of uncommon EGFR L858R mutations with mutated TP53, and further molecular, cellular, or animal model studies targeting the potential biological explanation are required. We also supposed that the synonymous codon usage of EGFR L858R mutations might affect transcription, translation, and protein conformation, 31 possibly resulting in the efficacy differences of EGFR TKIs. For instance, Fu et al. revealed that human KRAS gene expression was regulated by codon usage, as uncommon codons could lead to decreased transcription efficiency, suppressed translation, and altered protein structure. 32 The frequency of codon usage of classical EGFR L858R (CGG) in Homo sapiens is 11.4 per thousand codons, whereas the frequency of codon usage of the most prevalent uncommon EGFR L858R (CGT) in our study is 4.5 per thousand codons. 33 As the reverse relationship between gene expression and uncommon codons might vary across different genes, 34 further pre‐clinical investigations are required to explain the mechanisms behind the improved PFS in NSCLCs harboring uncommon EGFR L858R mutations.
This study has a few limitations that need to be considered. First, in this retrospective study, the timing of follow‐up and radiological assessment were not standardized, which is likely to result in a specious association between PFS under first‐line EGFR TKI treatment and patients with various EGFR L858X mutations. Second, we were not able to comprehensively study the efficacy of EGFR TKIs in NSCLCs with genomic aberrations in EGFR codon L858 resulting into mutations other than EGFR L858R, as only patients with EGFR L858Q/M were included in this study. Additionally, samples harboring classical EGFR L858R mutations that had been sequenced by a large next‐generation sequencing panel were retrospectively included in this study, as we aimed to comprehensively investigate concurrent mutations, TMB, and CIN. This means considerable samples with classical EGFR L858R mutations detected by small panels were excluded from this study, resulting in a high ratio of samples with uncommon EGFR L858R mutations over those with classical EGFR L858R mutations. Moreover, owing to the extremely low prevalence of uncommon EGFR L858R mutations, our main findings in the superior PFS could hardly be validated in a publicly available external dataset. Finally, as a translational study, we were not able to systematically rationalize the reason for the potential improved PFS of patients with uncommon EGFR L858R mutations, which requires further investigation.
In conclusion, NSCLCs with uncommon EGFR L858R mutations, who were likely to have fewer TP53 mutations and more stable chromosomes, exhibited improved PFS under first‐line EGFR TKI treatment in comparison to those with classical EGFR L858R mutations. Comparing the matched samples collected at the baseline and radiological progression, our data demonstrated that EGFR TKI resistance mechanisms were similar between these two groups.
AUTHOR CONTRIBUTIONS
Youyou Shao: Conceptualization; writing – original draft. Jingying Zhang: Conceptualization; writing – original draft. Zhi Feng: Formal analysis; writing – review and editing. Wei Wu: Formal analysis; writing – review and editing. Xiaotian Zhao: Data curation; formal analysis; visualization; writing – review and editing. Minyi Zhu: Data curation; formal analysis; visualization; writing – review and editing. Yao Xiao: Data curation; formal analysis; visualization; writing – review and editing. Jiaohui Pang: Data curation; formal analysis; visualization; writing – review and editing. Junfei Zhu: Formal analysis; funding acquisition; writing – review and editing. Hao Qu: Formal analysis; writing – review and editing. Minchi Yuan: Formal analysis; writing – review and editing. Guojie Xia: Formal analysis; writing – review and editing. Meng Liu: Project administration; supervision; writing – review and editing. Hengyuan Li: Project administration; supervision; writing – review and editing.
FUNDING INFORMATION
This study was supported by the Zhejiang Provincial Traditional Chinese Medicine Science Research Foundation (2022ZA095), the Traditional Chinese Medical Science and Technology Plan of Zhejiang Province (2023ZR108), the Medical and Health Science and Technology Plan of the Department of Health of Zhejiang Province (2023RC171), the Zhejiang Provincial Medicine and Health Research Foundation (No. 2023KY407), and the Natural Science Foundation of Zhejiang Province (No. LTGD23C040001).
CONFLICT OF INTEREST STATEMENT
Xiaotian Zhao, Minyi Zhu, Yao Xiao, and Jiaohui Pang are employees of Nanjing Geneseeq Technology, China. The remaining authors have nothing to disclose.
ETHICS STATEMENTS
Approval of the research protocol by an Institutional Review Board: This study was approved by the ethics committee of The Second Affiliated Hospital, Zhejiang University School of Medicine (No. 2023–0736) and performed in accordance with the tenets of Declaration of Helsinki.
Informed Consent: All patients signed informed consent forms prior to enrollment and sample collection.
Registry and the Registration No. of the study/trial: N/A.
Animal Studies: N/A.
Supporting information
Figure S1.
ACKNOWLEDGMENTS
The authors thank all the patients who participated in this study.
Shao Y, Zhang J, Feng Z, et al. Outcomes in non‐small cell lung cancer with uncommon epidermal growth factor receptor L858 substitutions under first‐line epidermal growth factor receptor tyrosine kinase inhibitors: A large real‐world cohort study. Cancer Sci. 2024;115:2751‐2761. doi: 10.1111/cas.16250
Youyou Shao and Jingying Zhang contributed equally to this paper.
Contributor Information
Meng Liu, Email: liumeng80@zju.edu.cn.
Hengyuan Li, Email: hengyuanxiang@zju.edu.cn.
DATA AVAILABILITY STATEMENT
The datasets used and/or analyzed in the current study are available from the corresponding author on 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
Figure S1.
Data Availability Statement
The datasets used and/or analyzed in the current study are available from the corresponding author on reasonable request.