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Article

High Tumor Mutation Burden Is Associated with Poor Clinical Outcome in EGFR-Mutated Lung Adenocarcinomas Treated with Targeted Therapy

by
Ji-Youn Sung
1,
Dong-Won Park
2 and
Seung-Hyeun Lee
3,*
1
Department of Pathology, College of Medicine, Kyung Hee University, Seoul 02447, Korea
2
Division of Pulmonary Medicine and Allergy, Department Internal Medicine, College of Medicine, Hanyang University, Seoul 04763, Korea
3
Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Kyung Hee University, Seoul 02447, Korea
*
Author to whom correspondence should be addressed.
Biomedicines 2022, 10(9), 2109; https://doi.org/10.3390/biomedicines10092109
Submission received: 1 August 2022 / Revised: 19 August 2022 / Accepted: 26 August 2022 / Published: 29 August 2022
(This article belongs to the Topic Targeting Signaling Networks for Cancer Therapy)
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Abstract

:

Simple Summary

Tumor mutation burden (TMB) represents the mutational load in the tumor cell genome and serves as a surrogate marker for tumor neoantigen production and potential immunogenicity. TMB has been widely explored as a complementary or alternative biomarker for immune checkpoint inhibitors in various malignancies including lung cancer. However, its clinical implication in the targeted-therapy setting is scarcely investigated. This study demonstrates that high TMB is independently associated with poor progression-free and overall survivals, and a low frequency of secondary T790M mutation in patients with epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma that are treated with tyrosine kinase inhibitors (TKIs). To the best of our knowledge, this is the first study demonstrating the significant association between the TMB level and resistance pattern after TKI failure.

Abstract

This study aimed to determine the association between TMB and treatment outcomes in patients with epidermal growth factor receptor (EGFR)-mutated lung cancer that were treated with tyrosine kinase inhibitors (TKIs). The TMB was assessed using a 409-gene targeted next-generation sequencing panel. We compared the response rate (RR), progression-free survival (PFS), overall survival (OS), and frequency of secondary T790M mutations among the different TMB groups. The median TMB of the study population (n = 88) was 3.36/megabases. We divided 52 (59%) and 36 (41%) patients into the low and high TMB groups, respectively. A high TMB level was significantly associated with liver metastasis and more advanced stage (all p < 0.05). RR was significantly lower in the high TMB group than that of the low TMB group (50.0% vs. 80.7%, all p = 0.0384). In multivariate analysis, high TMB was independently associated with a shorter PFS (hazard ratio [HR] = 1.80, p = 0.0427) and shorter OS (HR = 2.05, p = 0.0397) than that of the low TMB group. Further, high TMB was independently associated with decreased T790M mutation development. These results suggest that high TMB may be a predictive biomarker for adverse treatment outcomes and represent a patients’ subgroup warranting tailored therapeutic approaches.

1. Introduction

Tyrosine kinase inhibitors (TKIs) targeting epidermal growth factor receptor (EGFR) have revolutionized the treatment landscape of the non-small cell lung cancers (NSCLCs) which harbor EGFR sensitizing mutations over the past decades. First-generation EGFR-TKIs including gefitinib and erlotinib have shown better efficacies over platinum-based doublet, and second-generation TKIs including afatinib and dacomitinib, subsequently proved their efficacies which are comparable or superior to those of the first-generation TKIs [1,2,3]. Nevertheless, a wide variation exists in the degree and duration of response to these TKIs among patients, and most of the EGFR-mutant tumors inevitably progress within 8 to 14 months due to the emergence of resistance [4,5]. Studies have revealed that acquired resistance can occur either in EGFR-dependent or EGFR-independent manner; the former includes the acquisition of EGFR T790M or C797X mutations, and the latter includes bypass track activation and other driving alterations such as MET amplification or histologic transformation [6]. Another breakthrough was made when novel third-generation TKIs were used to overcome the EGFR T790M-mediated resistance. In addition, osimertinib, a third-generation TKI, is now the standard of care for first-line treatment of patients with NSCLC harboring common EGFR mutations [7]. Hence, understanding the biology of resistance is critical for the development of new therapeutic strategies and improving the survival of the patients.
The advances in the molecular profiling techniques such as next-generation sequencing (NGS) have made a significant contribution toward current cancer precision medicine including the discovery of novel biomarkers and the identification of resistance mechanisms and potential therapeutic targets [8]. Tumor mutation burden (TMB), also called as tumor mutational load, denotes the number of acquired somatic mutations in the coding region of the cancer cell genome [9]. It has been widely explored as a complementary or alternative biomarker for immune checkpoint inhibitors (ICIs) in various malignancies including lung cancer [10,11,12,13]. High TMB refers to a high probability of tumor neoantigen production, and therefore, the likelihood of immune recognition and clearance by effector T-cells [14,15]. Meanwhile, in the context of targeted therapy, elevated TMB may be related to the increased possibility of resistance mutations or subclones harboring different molecular characteristics, which is related to resistance to drugs. However, the clinical implication of TMB in this treatment setting is largely unknown.
In this study, we aimed to determine the possible predictive or prognostic value of TMB in patients with EGFR-mutant lung adenocarcinoma that were treated with first-line TKIs. We also investigated the associations of TMB levels with the emergence of secondary T790M mutations after TKI failure.

2. Materials and Methods

2.1. Study Subjects and Data Collection

We retrospectively enrolled patients with locally advanced or metastatic EGFR mutation-positive lung adenocarcinoma who received EGFR-TKIs as the frontline therapy at Kyung Hee University Hospital and Hanyang University Hospital, referral hospitals in South Korea, from March 2016 to July 2020. Patients with insufficient survival data, history of other malignant tumors newly diagnosed within 5 years, or other oncogenic drivers, including anaplastic lymphoma kinase (ALK), and ROS proto-oncogene 1 (ROS1) fusions, and those with a prior history of radiation therapy or chemotherapy were excluded.
Chest computed tomography (CT) scan, brain magnetic resonance imaging, and 18F-fluorodeoxyglucose positron emission tomography-computed tomography were used for staging workup in all the patients. The eighth edition of the TNM staging system of lung cancer by the International Association for the Study of Lung Cancer (IASLC) were used for the clinical staging [16]. The treatment response was regularly assessed with chest CT scans after every two or three cycles of systemic treatment according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 [17]. Patients’ medical records were reviewed to collect medical or social history, demographics, and survival data. The study protocol was reviewed and approved by the Institutional Review Board of Kyung Hee University Hospital (KHUH 2019-06-030). Written informed consent from all the participants that were alive was obtained. This study was conducted in compliance with the Declaration of Helsinki.

2.2. Next-Generation Sequencing and Calculation of TMB

Formalin-fixed paraffin-embedded tumor tissues that were obtained before initial EGFR-TKI treatment were used for genomic profiling. The detailed methods of sample preparation, sequencing, quality assessment, and variant calling are described in the Supplementary Material. Briefly, DNA extraction from tissues was performed using RecoverAllTM Multi-Sample Isolation Kit (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s instructions. TMB was assessed using a 409-gene targeted NGS panel (OncomineTM Tumor Mutation Load assay, Thermo Fisher Scientific). For NGS library preparation, 5–40 ng of DNA was used depending on the availability of the input material. The libraries were purified using Agencourt AmpureXP beads (Beckman Coulter, Brea, CA, USA) and quantified by qPCR using the Ion Universal Quantitation Kit (Thermo Fisher Scientific).
TMB calculations were performed on Ion ReporterTM Analysis Software v5.10 (IR) using the OncomineTM Tumor Mutation Load w2.0 workflow. The TMB was calculated by dividing the number of nonsynonymous (missense and nonsense) somatic single nucleotide variants and coding indels by the number of exonic bases with at least 60X coverage and expressed as the number of mutations per megabase (Mb) of genome. The TMB values were rounded to two decimal places.

2.3. EGFR Mutation Testing

EGFR mutation tests were performed using tumor tissues. Genomic DNA was extracted from formalin-fixed, paraffin-embedded, 5-µm-thick tissue sections using the High Pure Template Preparation Kit (Roche Applied Science, Mannheim, Germany). The extracted DNA was stored at −20 °C until analysis. PANAMutyperTM (PANAGENE Inc., Daejeon, Korea), a PNA-Clamping-based EGFR mutation detection kit was used for the detection of EGFR mutations. The primer sets covered mutations or deletions spanning exons 18–21 of the genes encoding the tyrosine kinase domain of EGFR. The results were interpreted according to the manufacturer’s instructions.

2.4. Statistical Analyses

The cut-off value for differentiating between low and high TMB was defined as the point with the lowest p-value by the log-rank test for all the possible TMB values. The baseline characteristics of different groups were compared using the Chi-square test or Fisher’s exact test, as appropriate. The clinical outcomes, including the response rate (RR), progression-free survival (PFS), and overall survival (OS) were assessed. The RR was defined as the percentage of patients who achieved complete or partial response. PFS was defined as the period from the first day of treatment to disease progression or death. OS was defined as the interval from the first day of treatment to death from any cause. Data of patients without tumor recurrence or death were censored at the last follow-up. Correlations between the survival outcomes and clinicopathological parameters were estimated by univariate analysis using the log-rank test, followed by Cox proportional hazard regression analysis. Parameters with p values < 0.2 in the univariate analysis were included for the multivariate analysis. The Kaplan-Meier method was used to estimate the survival rates. p < 0.05 was considered as statistically significant. All analyses were performed using SPSS v.20.0 (IBM Corporation, Armonk, NY, USA).

3. Results

3.1. Clinicopathological Characteristics of Patients

During the study period, 735 patients were newly diagnosed with NSCLC, while 243 patients were diagnosed with advanced lung adenocarcinoma. Of these patients, 131 received frontline EGFR-TKIs for EGFR-positive diseases. A total of fifteen patients with insufficient survival data, ten with concomitant cancers, and three who received other cancer treatments before targeted therapy were excluded. There were fifteen patients that were further excluded because the sequencing quality of their samples did not meet the minimum requirements. Finally, 88 patients were included for the TMB calculation and subsequent analysis. The modalities that were used for tissue acquisition were bronchoscopic biopsy (n = 5), transbronchial lung biopsy (n = 10), endobronchial ultrasonography-guided transbronchial needle aspiration (n = 28), and percutaneous needle biopsy (n = 45).
Table 1 shows the clinical characteristics of the study population. All were Korean, with a median age of 67 years (range, 40–89 years). A total of 47 (53.4%) patients were aged ≥70 years, 44 (50.0%) were women, and 26 (29.5%) were current or former smokers. A total of 70 (79.5%) patients had an Eastern Cooperative Oncology Group performance status (ECOG PS) of 0 or 1. There were 15 (17.0%) and 73 (83.0%) patients that had Stage III and IV diseases, respectively. Further, 21 (23.9%) had metastases involving three or more organs, while 28 (31.8%) and 14 (15.9%) patients had the brain or liver metastases, respectively. A total of 52 (59.1%) patients had exon 19 deletion (19del), 31 (35.2%) had L858R point mutation, and 5 (5.7%) had uncommon or compound mutations. There were 68 (77.2%) patients that received afatinib, while 20 (22.9%) were treated with gefitinib or erlotinib as a first-line therapy.

3.2. TMB and Molecular Landscape

Figure 1 shows the TMB distribution among the study population. The median TMB of the study population was 3.36/Mb (range: 0.0–19.32). Among the 88 patients, 62 (70.4%) patients had at least one concomitant genetic alteration coexisting with mutant EGFR. The frequencies of the top 10 mutations are presented in Supplementary Figure S1. TP53 mutations were the most common occurring in 45.5% (40/88) of the patients, while PIK3CA (n = 17, 19.3%), CTNNB1 (n = 14, 15.9%), and SMARCA4 (n = 12, 13.6%) mutations also occurred frequently, which is consistent with previous reports [18,19].

3.3. Association between Clinicopathological Parameters and TMB

To investigate which clinicopathological parameters were associated with the TMB, we compared the median TMB levels between the groups within each parameter. The median TMB level showed a non-significant increasing trend in patients with more advanced stages (p = 0.0543) and was significantly higher in patients with liver metastasis (p = 0.0030) (Supplementary Table S1). We subsequently compared the distribution of patients according to the TMB group. The optimal cut-off value for low and high TMB levels was determined to be 2.53/Mb by the log rank test. Using this cut-off, 52 (59.1%) and 36 (40.9%) patients were classified into the low and high TMB groups, respectively. As shown in Table 1, TMB was not related to parameters such as age, sex, smoking history, ECOG PS, and EGFR subtype. However, a high TMB level was significantly associated with a more advanced stage (p = 0.0171), three or more organs involvement (p = 0.0249), and liver metastasis (p = 0.0210).

3.4. Distribution of Co-Mutations according to TMB Levels

To further examine whether any of the co-mutations were enriched in the high TMB group, we investigated the frequency of co-mutations at different TMB levels. TP53 mutations were significantly more common in the high TMB group (23/36, 63.8%) than in the low TMB group (17/52, 32.6%; p =0.0254; Supplementary Figure S2). PIK3CA (9/36, 25.0% vs. 8/52, 15.4%) and CTNNB1 (7/36, 19.4% vs. 7/52, 13.4%) mutations were more frequent in the high TMB group, but the differences were not statistically significant.

3.5. Response Rate and PFS According to TMB Level

The median follow-up period was 38.9 months (range: 3.3–57.8 months). Table 2 summarizes the treatment response according to the TMB levels. In the low TMB group, three (5.8%) and 40 (76.9%) patients showed a complete response (CR) and partial response (PR), respectively, while 18 (50%) patients showed PR in the high TMB group. The RR was significantly lower in the high TMB group than that of the low TMB group (50.0% versus 80.7%, respectively, p = 0.0384).
Table 3 shows the PFS analysis results according to clinicopathological parameters. A total of 73 patients (82.9%) progressed during the follow-up period. The median PFS of the study population was 17.7 months (range, 3.1–44.7 months). Univariate analysis showed that male sex, three or more organ involvement, presence of liver metastasis, and positive TP53 mutation were associated with poor PFS (all p < 0.05). In addition, a high TMB level was significantly associated with shorter PFS (p = 0.0205). Multivariate analysis showed that male sex (hazard ratio [HR] = 1.84, 95% confidence interval [CI]: 1.10–3.08), presence of liver metastasis (HR = 2.01, 95% CI: 1.06–5.77), and high TMB (HR = 1.80, 95% CI: 1.17–4.43) were independently associated with shorter PFS. Patients with high TMB levels were likely to have poor PFS compared to those with low TMB levels as shown in the Kaplan–Meier survival curves (Figure 2a).

3.6. OS According to TMB Level

Table 4 shows the results of the OS analysis. The median OS of the study population was 35.5 months (range, 3.1–54.8 months). Among 73 patients who progressed after first-line TKI therapy, 52 (71.2%) underwent T790M mutation testing, all of whom received second-line treatment. Overall, 22 patients (42.3%) harbored the T790M mutation. The 22 patients who progressed with T790M mutation after frontline EGFR-TKIs were treated with osimertinib, and 30 patients received other treatments, either pemetrexed alone (n = 5) or pemetrexed/platinum doublet (n = 23). Patients who underwent small cell lung cancer transformation received etoposide/platinum (n = 2). Detailed data on salvage treatments are provided in Supplementary Table S2. Univariate analysis showed that metastases involving three or more organs and the presence of liver metastasis were significantly associated with shorter OS (all p < 0.05). In addition, a high TMB level was also significantly associated with shorter OS (p = 0.0080). Multivariate analysis showed that the presence of liver metastasis (HR = 2.17, 95% CI: 1.14–4.57) and high TMB levels (HR = 2.05, 95% CI: 1.04–4.07) were independently associated with shorter OS. Patients with high TMB levels were likely to have poor OS compared to those with low TMB levels as shown in the Kaplan–Meier survival curves (Figure 2b).

3.7. Frequency of Acquired T790M Mutation According to TMB Levels

To identify whether the TMB level is associated with the development of a secondary T790M mutation, we evaluated the mutation rates according to different TMB groups. Among 73 patients who progressed after first-line TKI therapy, 52 (71.2%) underwent T790M mutation testing. Table 5 summarizes the frequencies of T790M mutation stratified by clinicopathological parameters of the 52 patients. The mutation rates were significantly lower in patients with the L858R mutation and those who received TKI for less than 12 months (all p < 0.05). In addition, patients with high TMB levels showed a significantly lower frequency of T790M mutation than that of those with low TMB levels (26.4.7% vs. 51.2%, respectively, p = 0.0377; Figure 3). Univariate analysis for the factors that were associated with the frequency of T790M mutation showed that the L858R mutation, TKI use for less than 12 months, and high TMB levels were significantly associated with a lower incidence of T790M mutation (all p < 0.05). Multivariate analysis showed that the L858R mutation (odds ratio [OR] = 0.46, 95% CI: 0.08–0.94), TKI use for less than 12 months (OR = 0.28, 95% CI: 0.14–0.85), and high TMB levels (OR = 0.42, 95% CI: 0.17–0.96) were independently associated with a low incidence of acquired T790M mutation.

4. Discussion

Our study demonstrated that a high TMB level was independently associated with poor PFS and OS in patients with EGFR-mutated lung adenocarcinoma that were treated with frontline targeted therapy. In addition, TMB was also associated with a lower rate of secondary T790M mutations after TKI use. To the best of our knowledge, this is the first study demonstrating the significant association between the TMB level and resistance pattern after TKI failure.
The TMB level that was observed in the present study (median 3.36/Mb) was much lower than that which was observed in previous studies on unselective NSCLC [20,21] but was comparable to those of previous studies that reported low TMB levels in EGFR-mutant tumors [19,22]. In a study analyzing an MSK-IMPACT cohort (n = 1668 including 410 EGFR-mutant patients) from The Cancer Genome Atlas (TCGA) database, the median TMB values of wild-type EGFR, non-sensitive EGFR mutations, exon 19 deletions, and L858R were 6.12, 5.66, 3.77, and 4.72, respectively [22]. In that study, the authors also reported a similar trend in TMB levels in the Chinese cohort in each patient group (6.10, 4.95, 4.10, and 3.10, respectively, n = 292). Another study that was conducted using MSK-IMPACT NGS platform (n = 783 including 153 EGFR-mutant patients) reported a median TMB of 3.77/Mb in an EGFR-mutant population, which is significantly lower than that of EGFR-wild type [19]. Although we did not compare the TMB levels according to EGFR mutational status, our study confirmed previous findings of relatively low TMB levels in EGFR-positive tumors. Evidence indicates that ICIs are not effective in the oncogene-driven lung cancers, and it is partly due to an immunosuppressive tumor microenvironment (TME) in such tumors, including the recruitment of tumor-associated macrophages and regulatory T-cells and the production of inhibitory cytokines that are induced by the activated EGFR signaling [23]. TMB represents the mutational load in the tumor cell genome and serves as a surrogate marker for tumor neoantigen production and potential immunogenicity. Thus, based on the previous and our data, the low TMB can be another mechanism to explain such poor efficacy of immunotherapeutic approaches in EGFR-positive lung cancer.
The current data showed that TP53 mutations were associated with short PFS in univariate analysis and were more enriched among those with high TMB. Mutations in TP53 can be found in 35–60% of NSCLC patients, more frequently in squamous cell carcinoma, and in smokers [24,25]. TP53 mutations are associated with a poor response and shorter survival in patients with lung cancer that were treated with chemotherapy or surgical resection [26,27]. Similarly, accumulating evidence suggests that these mutations are also negative prognostic factors in EGFR-positive NSCLC [28,29,30]. The mechanism by which TP53 alterations are associated with poor outcomes in these populations remains unclear. A recent study by Lee et al. demonstrated that mutant TP53-induced epithelial-to-mesenchymal transition (EMT)-mediated resistance, and TP53 silencing led to primary resistance to EGFR-TKIs through AXL induction, suggesting that these mutations can be associated with both primary and acquired resistance to EGFR-TKIs [31]. In our data, the association between high TMB and poor clinical outcomes remained significant, even after adjusting for TP53 mutational status. This suggests that high TMB is an independent predictive and prognostic factor in the EGFR-positive population, although the enrichment of certain co-mutations in patients with higher TMB could contribute to worse outcomes.
In the present study, we identified that TMB was associated with more advanced stage and liver metastasis. Cumulative evidence has demonstrated that liver metastasis is associated with poor clinical outcomes in patients that are receiving the first- and second-generation EGFR-TKIs [32,33,34]. In our study, patients with liver metastasis showed significantly shorter PFS and OS, consistent with those previous findings. A very recent real-world study comparing the efficacies of different EGFR-TKIs showed that osimertinib, a third-generation EGFR-TKI, was not superior over other TKIs in patients with liver metastasis, while it provided significant clinical benefits in patients with brain or bone metastasis [35]. The association between liver metastasis and poor prognosis has been consistently reported across different treatment settings, including immunotherapy [36]. The exact underlying mechanism of the dismal prognostic impact of such a type of metastasis in lung cancer has not been fully understood; however, it can be partly attributed to an activation of the bypass tracts and enhanced escape from immune surveillance [37,38,39]. It has been reported that insulin-like growth factor 1 (IGF-1), the ligand of the IGF-1 receptor (IGF-1R), is highly expressed in the TME of the liver metastasis, and the signaling from IGF-1R promotes the tolerance to osimertinib in EGFR-positive lung cancer [40]. In addition, the expression of vascular endothelial growth factor (VEGF) is increased in the liver metastasis compared with other metastatic sites, and VEGF and EGFR signaling share downstream pathways [37]. Thus, the up-regulated EGFR signaling in EGFR-mutant cells can activate VEGF signaling through hypoxia-independent mechanisms, which in turn results in the emergence of resistance to TKIs [37]. Notably, a clinical trial of the combinational effect of an EGFR-TKI and an anti-VEGF receptor antibody demonstrated superior efficacy over TKI alone in patients with liver metastases [38]. Moreover, lower CD8 + T-cell infiltration in both the primary tumors and extrahepatic metastatic lesions has been observed in patients with liver metastasis, and this suggest that liver metastasis can induce systemic immunosuppression [39,41]. There is no study evaluating the clinicopathological factors which might be related with TMB so far. The present data suggest the possible association between high TMB and liver metastasis, and the unfavorable prognosis in the high TMB patients may be partly attributed to the accelerated liver metastasis. Our findings should be validated by further investigations.
The relationship between high TMB and poor clinical outcome in EGFR-mutated NSCLC has been suggested in two independent studies [19,22]. In one study, TMB was significantly associated with a shorter time-to-treatment discontinuation and OS, and the TMB levels were increased at the time of disease progression when comparing the pretreatment and post-progression samples [19]. The other study also reported a negative predictive value of TMB using TCGA data and validated their results using Chinese cohort [22]. In the present study, TMB was associated with poor PFS and OS, which is consistent with those previous data, and the association was significant even after adjusting the effect of liver metastasis in the multivariate analysis. A growing number of studies have demonstrated that various genomic alterations, including the activation of oncogenes and inactivation of tumor suppressor genes, are related with organotropism in metastasis in lung cancer [42]. For example, MYC, YAP1, or MMP13 overexpression were shown to be associated with the incidence of brain metastasis [43]. However, data on such genomic association or metastatic driver for liver metastasis in lung cancer are very limited. A recent phylogenetic analysis using paired primary tumors and metastases of lung adenocarcinoma reported that the genetic profiles are highly similar between the primary lung lesions and liver metastasis, and tumor cells in liver metastasis are genetically diverged from those in the primary tumor at a relatively later stage compared with the brain metastasis [44]. Such findings indicate that the liver metastasis follows the linear progression model of metastasis rather than the parallel model. In that study, the TMB levels were comparable between the primary tumor and liver metastasis [44]. More recently, another group analyzed the relationship between the genomic features of metastatic cancers and their organ-specific patterns of metastasis (n = 21,546) and reported the significant associations between TMB and organ-specific patterns of metastasis in several tumor types [45]. In that study, high TMB was associated with (1) lung adenocarcinoma to the brain and adrenal gland, (2) pancreatic adenocarcinoma to the liver, and (3) head and neck squamous to head and neck cancer. Based on our and previous data, we hypothesize that the high burden of somatic mutations can be a metastatic driver of liver metastasis or can induce genetic instability to promote metastasis and tumor progression. Further investigations are needed to validate our hypothesis.
The identification of secondary T790M mutation is critical for the management of EGFR-mutant patients because T790M-positive resistance is readily responsive to third-generation EGFR-TKIs, including osimertinib and lazertinib [46,47]. Previous data have demonstrated that the association between the more frequent acquisition of T790M mutation in 19del and the long duration of treatment with EGFR-TKIs [48,49,50]. Our results confirmed these findings, and additionally suggest that high TMB level may be associated with less emergence of T790M after prior TKI failure. The association between TMB and the frequency of the T790M mutation has scarcely been evaluated. In a study, T790M-negative tumor at progression showed relatively high TMB compared with T790M-positive tumors [51]. Another study reported that the TMB level showed a non-significant trend of increase in T790M-negative resistance [19]. Similarly, we found a significant association between the TMB and the resistance pattern, and our data suggest that baseline high TMB may be a negative predictor of T790M-positive resistance. It is well known that T790M-positive resistance is associated with more favorable outcomes than T790M-negative resistance [5]. The low frequency of T790M mutations in the high-TMB group may explain the worse OS in these populations. The mechanism by which TMB is associated with less development of the resistance mutation is unclear; however, it may be explained by the different durations of treatment with EGFR-TKI according to different TMB levels. As suggested in the previous studies, T790M-positive cells can be enriched or newly emerged during TKI treatment [52,53,54]. In addition, substantial time is required for the development of T790M-dominant tumor because the cells harboring the mutation grow indolently compared to T790M-negative cells [55]. Taken together, we can hypothesize that the low frequency of T790M mutation can be attributed to the shorter duration of treatment in the patients with high TMB.
The present data suggest that high TMB confers a distinct aggressive phenotype that may require different treatment strategies. As TKI monotherapy could be less effective in patients with a high TMB level, other therapeutic strategies, such as a combinational approach using chemotherapy or immunotherapy, might be feasible. Notably, a previous study showed that gefitinib plus platinum doublet showed better clinical outcomes in terms of PFS and OS compared with gefitinib alone in EGFR-mutant NSCLC [56]. Very recently, amivantamab, a bispecific EGFR-MET antibody, in combination with lazertinib, a third-generation EGFR-TKI, showed promising results in patients who are previously treated with osimertinib and platinum-based chemotherapy [57]. Prospective investigations are required to determine the optimal treatment strategy for those with different TMB levels.
This study had several limitations. First, it was a relatively small retrospective study that was performed at only two institutes, and using a data-driven TMB cutoff value, which could have increased the risk of bias. Second, all the patients were treated with either the first-or second-generation EGFR-TKIs, and data on the use of frontline osimertinib were not available. Third, unlike the FoundationOne and MSK-IMPACT panel tests, which were approved by the FDA, the assay that was adopted in this study (Oncomine Tumor Mutational Load assay) has not yet been validated for clinical use. However, previous studies revealed good concordance with TMB assessment by whole exome sequencing [58,59], and a recent study that was conducted by the Onconetwork Immuno-Oncology Consortium showed that the platform that we used demonstrated robustness and reproducibility for TMB evaluation [60]. Fourth, data on TMB levels at the time of disease progression and blood TMB levels were not analyzed. The blood TMB, determined using circulating tumor DNA, is emerging as a useful biomarker in different anti-cancer treatment settings [61]. Thus, the dynamics of TMB levels in tumor tissues and in the blood during treatment could be an interesting topic for future studies. Finally, we did not simultaneously explore the immune phenotypes that could affect the TKI resistance. We are currently working on the possible impact of immunologic signatures on the prognosis of EGFR-mutant populations.

5. Conclusions

The present data demonstrate that a high TMB level was associated with dismal clinical outcomes, not only due to the poor response to frontline TKI treatment but also the less emergence of T790M-associated resistance. Although further studies are needed to verify our results, the current findings highlight that a high TMB level might confer an aggressive phenotype, requiring tailored therapeutic approaches among EGFR-mutant patients. Future investigations should focus on the determination of the optimal cutoff of TMB among different NSG platforms, the possible predictive value of TMB in other malignancies harboring driver oncogenes, and the optimal treatment strategy for the TMB-high patients. Large prospective studies may facilitate the clinical use of TMB for the prediction of prognosis and risk stratification.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biomedicines10092109/s1, Materials and methods, Figure S1: The list of top 10 somatic mutations detected and their frequencies; Figure S2: Frequency of TP53 mutation according to different TMB levels; Table S1: Median TMB according to different clinicopathological parameters; Table S2: Salvage treatments after frontline EGFR-TKI failure (n = 52).

Author Contributions

Conceptualization, S.-H.L.; data curation, J.-Y.S., D.-W.P. and S.-H.L.; formal analysis, J.-Y.S. and S.-H.L.; funding acquisition, S.-H.L.; investigation, D.-W.P. and S.-H.L.; methodology, J.-Y.S. and S.-H.L.; project administration, S.-H.L.; resources, J.-Y.S. and S.-H.L.; supervision, S.-H.L.; validation, S.-H.L.; writing—original draft, J.-Y.S. and S.-H.L.; writing—review & editing, J.-Y.S., D.-W.P. and S.-H.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Korea Institute of Oriental Medicine of the Republic of Korea, grant number KSN2022240, and the Basic Research Program through the National Research Foundation funded by the Ministry of Science and ICT of the Republic of Korea, grant number 2019R1F1A1041812.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board Kyung Hee University Hospital (KHUH 2019-06-030).

Informed Consent Statement

Informed consent was obtained from all subjects that were involved in the study.

Data Availability Statement

Data are available on request due to privacy and ethical restrictions.

Acknowledgments

The authors are grateful to all the patients whose data were used in this study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Johnson, M.; Garassino, M.C.; Mok, T.; Mitsudomi, T. Treatment strategies and outcomes for patients with EGFR-mutant non-small cell lung cancer resistant to EGFR tyrosine kinase inhibitors: Focus on novel therapies. Lung Cancer 2022, 170, 41–51. [Google Scholar] [CrossRef] [PubMed]
  2. Wu, Y.L.; Cheng, Y.; Zhou, X.; Lee, K.H.; Nakagawa, K.; Niho, S.; Tsuji, F.; Linke, R.; Rosell, R.; Corral, J.; et al. Dacomitinib versus gefitinib as first-line treatment for patients with EGFR-mutation-positive non-small-cell lung cancer (ARCHER 1050): A randomised, open-label, phase 3 trial. Lancet Oncol. 2017, 18, 1454–1466. [Google Scholar] [CrossRef] [PubMed]
  3. Rosell, R.; Carcereny, E.; Gervais, R.; Vergnenegre, A.; Massuti, B.; Felip, E.; Palmero, R.; Garcia-Gomez, R.; Pallares, C.; Sanchez, J.M.; et al. Erlotinib versus standard chemotherapy as first-line treatment for European patients with advanced EGFR mutation-positive non-small-cell lung cancer (EURTAC): A multicentre, open-label, randomised phase 3 trial. Lancet Oncol. 2012, 13, 239–246. [Google Scholar] [CrossRef] [PubMed]
  4. Chang, Y.S.; Choi, C.M.; Lee, J.C. Mechanisms of Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor Resistance and Strategies to Overcome Resistance in Lung Adenocarcinoma. Tuberc. Respir. Dis. 2016, 79, 248–256. [Google Scholar] [CrossRef]
  5. Nagano, T.; Tachihara, M.; Nishimura, Y. Mechanism of Resistance to Epidermal Growth Factor Receptor-Tyrosine Kinase Inhibitors and a Potential Treatment Strategy. Cells 2018, 7, 212. [Google Scholar] [CrossRef]
  6. Le, X.; Puri, S.; Negrao, M.V.; Nilsson, M.B.; Robichaux, J.; Boyle, T.; Hicks, J.K.; Lovinger, K.L.; Roarty, E.; Rinsurongkawong, W.; et al. Landscape of EGFR-Dependent and -Independent Resistance Mechanisms to Osimertinib and Continuation Therapy Beyond Progression in EGFR-Mutant NSCLC. Clin. Cancer Res. 2018, 24, 6195–6203. [Google Scholar] [CrossRef]
  7. Ramalingam, S.S.; Vansteenkiste, J.; Planchard, D.; Cho, B.C.; Gray, J.E.; Ohe, Y.; Zhou, C.; Reungwetwattana, T.; Cheng, Y.; Chewaskulyong, B.; et al. Overall Survival with Osimertinib in Untreated, EGFR-Mutated Advanced NSCLC. N. Engl. J. Med. 2020, 382, 41–50. [Google Scholar] [CrossRef]
  8. De Maglio, G.; Pasello, G.; Dono, M.; Fiorentino, M.; Follador, A.; Sciortino, M.; Malapelle, U.; Tiseo, M. The storm of NGS in NSCLC diagnostic-therapeutic pathway: How to sun the real clinical practice. Crit. Rev. Oncol. Hematol. 2022, 169, 103561. [Google Scholar] [CrossRef]
  9. Campbell, B.B.; Light, N.; Fabrizio, D.; Zatzman, M.; Fuligni, F.; de Borja, R.; Davidson, S.; Edwards, M.; Elvin, J.A.; Hodel, K.P.; et al. Comprehensive Analysis of Hypermutation in Human Cancer. Cell 2017, 171, 1042–1056.e10. [Google Scholar] [CrossRef]
  10. Carbone, D.P.; Reck, M.; Paz-Ares, L.; Creelan, B.; Horn, L.; Steins, M.; Felip, E.; van den Heuvel, M.M.; Ciuleanu, T.E.; Badin, F.; et al. First-Line Nivolumab in Stage IV or Recurrent Non-Small-Cell Lung Cancer. N. Engl. J. Med. 2017, 376, 2415–2426. [Google Scholar] [CrossRef]
  11. Rizvi, N.A.; Hellmann, M.D.; Snyder, A.; Kvistborg, P.; Makarov, V.; Havel, J.J.; Lee, W.; Yuan, J.; Wong, P.; Ho, T.S.; et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 2015, 348, 124–128. [Google Scholar] [CrossRef] [PubMed]
  12. Schumacher, T.N.; Schreiber, R.D. Neoantigens in cancer immunotherapy. Science 2015, 348, 69–74. [Google Scholar] [CrossRef] [PubMed]
  13. Snyder, A.; Makarov, V.; Merghoub, T.; Yuan, J.; Zaretsky, J.M.; Desrichard, A.; Walsh, L.A.; Postow, M.A.; Wong, P.; Ho, T.S.; et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 2014, 371, 2189–2199. [Google Scholar] [CrossRef]
  14. Goodman, A.M.; Kato, S.; Bazhenova, L.; Patel, S.P.; Frampton, G.M.; Miller, V.; Stephens, P.J.; Daniels, G.A.; Kurzrock, R. Tumor Mutational Burden as an Independent Predictor of Response to Immunotherapy in Diverse Cancers. Mol. Cancer Ther. 2017, 16, 2598–2608. [Google Scholar] [CrossRef] [PubMed]
  15. Shien, K.; Papadimitrakopoulou, V.A.; Wistuba, I.I. Predictive biomarkers of response to PD-1/PD-L1 immune checkpoint inhibitors in non-small cell lung cancer. Lung Cancer 2016, 99, 79–87. [Google Scholar] [CrossRef] [PubMed]
  16. Goldstraw, P.; Chansky, K.; Crowley, J.; Rami-Porta, R.; Asamura, H.; Eberhardt, W.E.; Nicholson, A.G.; Groome, P.; Mitchell, A.; Bolejack, V.; et al. The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer. J. Thorac. Oncol. 2016, 11, 39–51. [Google Scholar] [CrossRef] [PubMed]
  17. Eisenhauer, E.A.; Therasse, P.; Bogaerts, J.; Schwartz, L.H.; Sargent, D.; Ford, R.; Dancey, J.; Arbuck, S.; Gwyther, S.; Mooney, M.; et al. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur. J. Cancer 2009, 45, 228–247. [Google Scholar] [CrossRef]
  18. Zhang, Y.; Li, S.; Lyu, Z.; Cai, J.; Zheng, N.; Li, Y.; Xu, T.; Zeng, H. The co-mutation of EGFR and tumor-related genes leads to a worse prognosis and a higher level of tumor mutational burden in Chinese non-small cell lung cancer patients. J. Thorac. Dis. 2022, 14, 185–193. [Google Scholar] [CrossRef]
  19. Offin, M.; Rizvi, H.; Tenet, M.; Ni, A.; Sanchez-Vega, F.; Li, B.T.; Drilon, A.; Kris, M.G.; Rudin, C.M.; Schultz, N.; et al. Tumor Mutation Burden and Efficacy of EGFR-Tyrosine Kinase Inhibitors in Patients with EGFR-Mutant Lung Cancers. Clin. Cancer Res. 2019, 25, 1063–1069. [Google Scholar] [CrossRef]
  20. Hellmann, M.D.; Ciuleanu, T.E.; Pluzanski, A.; Lee, J.S.; Otterson, G.A.; Audigier-Valette, C.; Minenza, E.; Linardou, H.; Burgers, S.; Salman, P.; et al. Nivolumab plus Ipilimumab in Lung Cancer with a High Tumor Mutational Burden. N. Engl. J. Med. 2018, 378, 2093–2104. [Google Scholar] [CrossRef]
  21. Rizvi, H.; Sanchez-Vega, F.; La, K.; Chatila, W.; Jonsson, P.; Halpenny, D.; Plodkowski, A.; Long, N.; Sauter, J.L.; Rekhtman, N.; et al. Molecular Determinants of Response to Anti-Programmed Cell Death (PD)-1 and Anti-Programmed Death-Ligand 1 (PD-L1) Blockade in Patients With Non-Small-Cell Lung Cancer Profiled With Targeted Next-Generation Sequencing. J. Clin. Oncol. 2018, 36, 633–641. [Google Scholar] [CrossRef] [PubMed]
  22. Jiao, X.D.; He, X.; Qin, B.D.; Liu, K.; Wu, Y.; Liu, J.; Hou, T.; Zang, Y.S. The prognostic value of tumor mutation burden in EGFR-mutant advanced lung adenocarcinoma, an analysis based on cBioPortal data base. J. Thorac. Dis. 2019, 11, 4507–4515. [Google Scholar] [CrossRef] [PubMed]
  23. Madeddu, C.; Donisi, C.; Liscia, N.; Lai, E.; Scartozzi, M.; Maccio, A. EGFR-Mutated Non-Small Cell Lung Cancer and Resistance to Immunotherapy: Role of the Tumor Microenvironment. Int. J. Mol. Sci. 2022, 23, 6489. [Google Scholar] [CrossRef] [PubMed]
  24. Scoccianti, C.; Vesin, A.; Martel, G.; Olivier, M.; Brambilla, E.; Timsit, J.F.; Tavecchio, L.; Brambilla, C.; Field, J.K.; Hainaut, P.; et al. Prognostic value of TP53, KRAS and EGFR mutations in nonsmall cell lung cancer: The EUELC cohort. Eur. Respir. J. 2012, 40, 177–184. [Google Scholar] [CrossRef]
  25. Halvorsen, A.R.; Silwal-Pandit, L.; Meza-Zepeda, L.A.; Vodak, D.; Vu, P.; Sagerup, C.; Hovig, E.; Myklebost, O.; Borresen-Dale, A.L.; Brustugun, O.T.; et al. TP53 Mutation Spectrum in Smokers and Never Smoking Lung Cancer Patients. Front. Genet. 2016, 7, 85. [Google Scholar] [CrossRef]
  26. Kandioler, D.; Stamatis, G.; Eberhardt, W.; Kappel, S.; Zochbauer-Muller, S.; Kuhrer, I.; Mittlbock, M.; Zwrtek, R.; Aigner, C.; Bichler, C.; et al. Growing clinical evidence for the interaction of the p53 genotype and response to induction chemotherapy in advanced non-small cell lung cancer. J. Thorac. Cardiovasc. Surg. 2008, 135, 1036–1041. [Google Scholar] [CrossRef]
  27. Mitsudomi, T.; Hamajima, N.; Ogawa, M.; Takahashi, T. Prognostic significance of p53 alterations in patients with non-small cell lung cancer: A meta-analysis. Clin. Cancer Res. 2000, 6, 4055–4063. [Google Scholar]
  28. Canale, M.; Petracci, E.; Delmonte, A.; Chiadini, E.; Dazzi, C.; Papi, M.; Capelli, L.; Casanova, C.; De Luigi, N.; Mariotti, M.; et al. Impact of TP53 Mutations on Outcome in EGFR-Mutated Patients Treated with First-Line Tyrosine Kinase Inhibitors. Clin. Cancer Res. 2017, 23, 2195–2202. [Google Scholar] [CrossRef]
  29. Jiao, X.D.; Qin, B.D.; You, P.; Cai, J.; Zang, Y.S. The prognostic value of TP53 and its correlation with EGFR mutation in advanced non-small cell lung cancer, an analysis based on cBioPortal data base. Lung Cancer 2018, 123, 70–75. [Google Scholar] [CrossRef]
  30. Hou, H.; Qin, K.; Liang, Y.; Zhang, C.; Liu, D.; Jiang, H.; Liu, K.; Zhu, J.; Lv, H.; Li, T.; et al. Concurrent TP53 mutations predict poor outcomes of EGFR-TKI treatments in Chinese patients with advanced NSCLC. Cancer Manag. Res. 2019, 11, 5665–5675. [Google Scholar] [CrossRef]
  31. Jung, S.; Kim, D.H.; Choi, Y.J.; Kim, S.Y.; Park, H.; Lee, H.; Choi, C.M.; Sung, Y.H.; Lee, J.C.; Rho, J.K. Contribution of p53 in sensitivity to EGFR tyrosine kinase inhibitors in non-small cell lung cancer. Sci. Rep. 2021, 11, 19667. [Google Scholar] [CrossRef] [PubMed]
  32. Taniguchi, Y.; Tamiya, A.; Nakahama, K.; Naoki, Y.; Kanazu, M.; Omachi, N.; Okishio, K.; Kasai, T.; Atagi, S. Impact of metastatic status on the prognosis of EGFR mutation-positive non-small cell lung cancer patients treated with first-generation EGFR-tyrosine kinase inhibitors. Oncol. Lett. 2017, 14, 7589–7596. [Google Scholar] [CrossRef] [PubMed]
  33. Ulivi, P.; Chiadini, E.; Dazzi, C.; Dubini, A.; Costantini, M.; Medri, L.; Puccetti, M.; Capelli, L.; Calistri, D.; Verlicchi, A.; et al. Nonsquamous, Non-Small-Cell Lung Cancer Patients Who Carry a Double Mutation of EGFR, EML4-ALK or KRAS: Frequency, Clinical-Pathological Characteristics, and Response to Therapy. Clin. Lung Cancer 2016, 17, 384–390. [Google Scholar] [CrossRef] [PubMed]
  34. Mordant, P.; Arame, A.; De Dominicis, F.; Pricopi, C.; Foucault, C.; Dujon, A.; Le Pimpec-Barthes, F.; Riquet, M. Which metastasis management allows long-term survival of synchronous solitary M1b non-small cell lung cancer? Eur. J. Cardiothorac. Surg. 2012, 41, 617–622. [Google Scholar] [CrossRef]
  35. Gen, S.; Tanaka, I.; Morise, M.; Koyama, J.; Kodama, Y.; Matsui, A.; Miyazawa, A.; Hase, T.; Hibino, Y.; Yokoyama, T.; et al. Clinical efficacy of osimertinib in EGFR-mutant non-small cell lung cancer with distant metastasis. BMC Cancer 2022, 22, 654. [Google Scholar] [CrossRef]
  36. Lindblad, K.E.; Lujambio, A. Liver metastases inhibit immunotherapy efficacy. Nat. Med. 2021, 27, 25–27. [Google Scholar] [CrossRef]
  37. Le, X.; Nilsson, M.; Goldman, J.; Reck, M.; Nakagawa, K.; Kato, T.; Ares, L.P.; Frimodt-Moller, B.; Wolff, K.; Visseren-Grul, C.; et al. Dual EGFR-VEGF Pathway Inhibition: A Promising Strategy for Patients With EGFR-Mutant NSCLC. J. Thorac. Oncol. 2021, 16, 205–215. [Google Scholar] [CrossRef]
  38. Nakagawa, K.; Garon, E.B.; Seto, T.; Nishio, M.; Ponce Aix, S.; Paz-Ares, L.; Chiu, C.H.; Park, K.; Novello, S.; Nadal, E.; et al. Ramucirumab plus erlotinib in patients with untreated, EGFR-mutated, advanced non-small-cell lung cancer (RELAY): A randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol. 2019, 20, 1655–1669. [Google Scholar] [CrossRef]
  39. Tumeh, P.C.; Hellmann, M.D.; Hamid, O.; Tsai, K.K.; Loo, K.L.; Gubens, M.A.; Rosenblum, M.; Harview, C.L.; Taube, J.M.; Handley, N.; et al. Liver Metastasis and Treatment Outcome with Anti-PD-1 Monoclonal Antibody in Patients with Melanoma and NSCLC. Cancer Immunol. Res. 2017, 5, 417–424. [Google Scholar] [CrossRef]
  40. Taniguchi, H.; Yamada, T.; Wang, R.; Tanimura, K.; Adachi, Y.; Nishiyama, A.; Tanimoto, A.; Takeuchi, S.; Araujo, L.H.; Boroni, M.; et al. AXL confers intrinsic resistance to osimertinib and advances the emergence of tolerant cells. Nat. Commun. 2019, 10, 259. [Google Scholar] [CrossRef]
  41. Daud, A.I.; Loo, K.; Pauli, M.L.; Sanchez-Rodriguez, R.; Sandoval, P.M.; Taravati, K.; Tsai, K.; Nosrati, A.; Nardo, L.; Alvarado, M.D.; et al. Tumor immune profiling predicts response to anti-PD-1 therapy in human melanoma. J. Clin. Investig. 2016, 126, 3447–3452. [Google Scholar] [CrossRef] [PubMed]
  42. Xie, S.; Wu, Z.; Qi, Y.; Wu, B.; Zhu, X. The metastasizing mechanisms of lung cancer: Recent advances and therapeutic challenges. Biomed. Pharmacother. 2021, 138, 111450. [Google Scholar] [CrossRef] [PubMed]
  43. Shi, J.; Hua, X.; Zhu, B.; Ravichandran, S.; Wang, M.; Nguyen, C.; Brodie, S.A.; Palleschi, A.; Alloisio, M.; Pariscenti, G.; et al. Somatic Genomics and Clinical Features of Lung Adenocarcinoma: A Retrospective Study. PLoS Med. 2016, 13, e1002162. [Google Scholar] [CrossRef] [PubMed]
  44. Jiang, T.; Fang, Z.; Tang, S.; Cheng, R.; Li, Y.; Ren, S.; Su, C.; Min, W.; Guo, X.; Zhu, W.; et al. Mutational Landscape and Evolutionary Pattern of Liver and Brain Metastasis in Lung Adenocarcinoma. J. Thorac. Oncol. 2021, 16, 237–249. [Google Scholar] [CrossRef] [PubMed]
  45. Nguyen, B.; Fong, C.; Luthra, A.; Smith, S.A.; DiNatale, R.G.; Nandakumar, S.; Walch, H.; Chatila, W.K.; Madupuri, R.; Kundra, R.; et al. Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients. Cell 2022, 185, 563–575.e11. [Google Scholar] [CrossRef]
  46. Mok, T.S.; Wu, Y.L.; Ahn, M.J.; Garassino, M.C.; Kim, H.R.; Ramalingam, S.S.; Shepherd, F.A.; He, Y.; Akamatsu, H.; Theelen, W.S.; et al. Osimertinib or Platinum-Pemetrexed in EGFR T790M-Positive Lung Cancer. N. Engl. J. Med. 2017, 376, 629–640. [Google Scholar] [CrossRef]
  47. Ahn, M.J.; Han, J.Y.; Lee, K.H.; Kim, S.W.; Kim, D.W.; Lee, Y.G.; Cho, E.K.; Kim, J.H.; Lee, G.W.; Lee, J.S.; et al. Lazertinib in patients with EGFR mutation-positive advanced non-small-cell lung cancer: Results from the dose escalation and dose expansion parts of a first-in-human, open-label, multicentre, phase 1–2 study. Lancet Oncol. 2019, 20, 1681–1690. [Google Scholar] [CrossRef]
  48. Nosaki, K.; Satouchi, M.; Kurata, T.; Yoshida, T.; Okamoto, I.; Katakami, N.; Imamura, F.; Tanaka, K.; Yamane, Y.; Yamamoto, N.; et al. Re-biopsy status among non-small cell lung cancer patients in Japan: A retrospective study. Lung Cancer 2016, 101, 1–8. [Google Scholar] [CrossRef]
  49. Matsuo, N.; Azuma, K.; Sakai, K.; Hattori, S.; Kawahara, A.; Ishii, H.; Tokito, T.; Kinoshita, T.; Yamada, K.; Nishio, K.; et al. Association of EGFR Exon 19 Deletion and EGFR-TKI Treatment Duration with Frequency of T790M Mutation in EGFR-Mutant Lung Cancer Patients. Sci. Rep. 2016, 6, 36458. [Google Scholar] [CrossRef]
  50. Goag, E.K.; Lee, J.M.; Chung, K.S.; Kim, S.Y.; Leem, A.Y.; Song, J.H.; Jung, J.Y.; Park, M.S.; Chang, Y.S.; Kim, Y.S.; et al. Usefulness of Bronchoscopic Rebiopsy of Non-Small Cell Lung Cancer with Acquired Resistance to Epidermal Growth Factor Receptor-Tyrosine Kinase Inhibitor. J. Cancer 2018, 9, 1113–1120. [Google Scholar] [CrossRef]
  51. Lee, C.K.; Kim, S.; Lee, J.S.; Lee, J.E.; Kim, S.M.; Yang, I.S.; Kim, H.R.; Lee, J.H.; Kim, S.; Cho, B.C. Next-generation sequencing reveals novel resistance mechanisms and molecular heterogeneity in EGFR-mutant non-small cell lung cancer with acquired resistance to EGFR-TKIs. Lung Cancer 2017, 113, 106–114. [Google Scholar] [CrossRef]
  52. Chmielecki, J.; Foo, J.; Oxnard, G.R.; Hutchinson, K.; Ohashi, K.; Somwar, R.; Wang, L.; Amato, K.R.; Arcila, M.; Sos, M.L.; et al. Optimization of dosing for EGFR-mutant non-small cell lung cancer with evolutionary cancer modeling. Sci. Transl. Med. 2011, 3, 90ra59. [Google Scholar] [CrossRef]
  53. Iwama, E.; Takayama, K.; Harada, T.; Okamoto, I.; Ookubo, F.; Kishimoto, J.; Baba, E.; Oda, Y.; Nakanishi, Y. Highly sensitive and quantitative evaluation of the EGFR T790M mutation by nanofluidic digital PCR. Oncotarget 2015, 6, 20466–20473. [Google Scholar] [CrossRef]
  54. Zheng, D.; Ye, X.; Zhang, M.Z.; Sun, Y.; Wang, J.Y.; Ni, J.; Zhang, H.P.; Zhang, L.; Luo, J.; Zhang, J.; et al. Plasma EGFR T790M ctDNA status is associated with clinical outcome in advanced NSCLC patients with acquired EGFR-TKI resistance. Sci. Rep. 2016, 6, 20913. [Google Scholar] [CrossRef]
  55. Hata, A.; Katakami, N.; Yoshioka, H.; Takeshita, J.; Tanaka, K.; Nanjo, S.; Fujita, S.; Kaji, R.; Imai, Y.; Monden, K.; et al. Rebiopsy of non-small cell lung cancer patients with acquired resistance to epidermal growth factor receptor-tyrosine kinase inhibitor: Comparison between T790M mutation-positive and mutation-negative populations. Cancer 2013, 119, 4325–4332. [Google Scholar] [CrossRef]
  56. Hosomi, Y.; Morita, S.; Sugawara, S.; Kato, T.; Fukuhara, T.; Gemma, A.; Takahashi, K.; Fujita, Y.; Harada, T.; Minato, K.; et al. Gefitinib Alone Versus Gefitinib Plus Chemotherapy for Non-Small-Cell Lung Cancer With Mutated Epidermal Growth Factor Receptor: NEJ009 Study. J. Clin. Oncol. 2020, 38, 115–123. [Google Scholar] [CrossRef]
  57. Cho, B.C.; Felip, E.; Hayashi, H.; Thomas, M.; Lu, S.; Besse, B.; Sun, T.; Martinez, M.; Sethi, S.N.; Shreeve, S.M.; et al. MARIPOSA: Phase 3 study of first-line amivantamab + lazertinib versus osimertinib in EGFR-mutant non-small-cell lung cancer. Future Oncol. 2022, 18, 639–647. [Google Scholar] [CrossRef]
  58. Chaudhary, R.; Quagliata, L.; Martin, J.P.; Alborelli, I.; Cyanam, D.; Mittal, V.; Tom, W.; Au-Young, J.; Sadis, S.; Hyland, F. A scalable solution for tumor mutational burden from formalin-fixed, paraffin-embedded samples using the Oncomine Tumor Mutation Load Assay. Transl. Lung Cancer Res. 2018, 7, 616–630. [Google Scholar] [CrossRef]
  59. Heeke, S.; Benzaquen, J.; Hofman, V.; Long-Mira, E.; Lespinet, V.; Bordone, O.; Marquette, C.H.; Delingette, H.; Ilie, M.; Hofman, P. Comparison of Three Sequencing Panels Used for the Assessment of Tumor Mutational Burden in NSCLC Reveals Low Comparability. J. Thorac. Oncol. 2020, 15, 1535–1540. [Google Scholar] [CrossRef]
  60. Fenizia, F.; Alborelli, I.; Costa, J.L.; Vollbrecht, C.; Bellosillo, B.; Dinjens, W.; Endris, V.; Heydt, C.; Leonards, K.; Merkelback-Bruse, S.; et al. Validation of a Targeted Next-Generation Sequencing Panel for Tumor Mutation Burden Analysis: Results from the Onconetwork Immuno-Oncology Consortium. J. Mol. Diagn. 2021, 23, 882–893. [Google Scholar] [CrossRef]
  61. Sholl, L.M.; Hirsch, F.R.; Hwang, D.; Botling, J.; Lopez-Rios, F.; Bubendorf, L.; Mino-Kenudson, M.; Roden, A.C.; Beasley, M.B.; Borczuk, A.; et al. The Promises and Challenges of Tumor Mutation Burden as an Immunotherapy Biomarker: A Perspective from the International Association for the Study of Lung Cancer Pathology Committee. J. Thorac. Oncol. 2020, 15, 1409–1424. [Google Scholar] [CrossRef]
Biomedicines 10 02109 g001 550
Figure 1. Distribution of TMB among the study population. Median TMB was 3.36/Mb. Bars indicate the median and interquartile range.
Figure 1. Distribution of TMB among the study population. Median TMB was 3.36/Mb. Bars indicate the median and interquartile range.
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Figure 2. Kaplan-Meier curves of progression-free survival (PFS, (a)) and overall survival (OS, (b)) according to the different expression levels of TMB. p-values were determined using the log-rank test.
Figure 2. Kaplan-Meier curves of progression-free survival (PFS, (a)) and overall survival (OS, (b)) according to the different expression levels of TMB. p-values were determined using the log-rank test.
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Figure 3. Frequency of secondary T790M mutation after tyrosine kinase inhibitor failure according to different TMB level. The rate of T790M mutation is significantly lower in the high TMB group compared with the low TMB group (p = 0.0377).
Figure 3. Frequency of secondary T790M mutation after tyrosine kinase inhibitor failure according to different TMB level. The rate of T790M mutation is significantly lower in the high TMB group compared with the low TMB group (p = 0.0377).
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Table
Table 1. Characteristics of the 88 study patients stratified by TMB levels.
Table 1. Characteristics of the 88 study patients stratified by TMB levels.
No. of Patients (%)TMBp-Value
Low (≤2.53, n = 52)High (>2.53, n = 36)
Age 0.7370
  <7041 (46.6)25 (48.1)16 (44.4)
  ≥7047 (53.4)27 (51.9)20 (55.6)
Sex 0.1933
  Male44 (50.0)29 (55.8)15 (41.7)
  Female44 (50.0)23 (44.2)21 (58.3)
Smoking history 0.1840
  Never62 (70.5)35 (67.3)27 (75.0)
  Ever26 (29.5)17 (32.7)9 (25.0)
Smoking intensity 0.7591
  <30 pack-years72 (81.8)42 (80.8)30 (83.3)
  ≥30 pack-years16 (18.2)10 (19.2)6 (16.7)
ECOG PS 0.3791
  0, 170 (79.5)43 (82.7)27 (75.0)
  ≥218 (20.5)9 (17.3)9 (25.0)
Stage 0.0171
  III15 (17.0)13 (25.0)2 (5.6)
  IV73 (83.0)39 (75.0)34 (94.4)
Involved organ 0.0249
  <367 (76.1)44 (84.6)23 (63.9)
  ≥321 (23.9)8 (15.4)13 (36.1)
Brain metastasis 0.6532
  No60 (68.2)35 (67.3)25 (69.4)
  Yes28 (31.8)17 (32.7)11 (30.6)
Liver metastasis 0.0210
  No74 (84.1)47 (90.3)27 (75.0)
  Yes14 (15.9)5 (9.7)9 (25.0)
EGFR subtypes 0.1898
  19del52 (59.1)29 (52.7)22 (61.1)
  L858R31 (35.2)20 (38.4)12 (33.3)
  Others5 (5.7)3 (5.7)2 (5.5)
First-line TKI 0.2451
  Gefitinib14 (15.9)8 (15.3)6 (16.7)
  Erlotinib6 (6.8)3 (5.7)3 (8.3)
  Afatinib68 (77.2)41 (78.8)27 (75.0)
ECOG PS, Eastern Cooperative Oncology Group Performance Status; EGFR, epidermal growth factor receptor; 19del, deletion mutation at exon 19; TKI, tyrosine kinase inhibitor.
Table
Table 2. Treatment response according to the different TMB levels.
Table 2. Treatment response according to the different TMB levels.
No. of Patients (%)p-Value
Low TMB (n = 52)High TMB (n = 36)
Response rate (CR + PR)42 (80.7)18 (50.0)0.0384
  CR3 (5.8)0 (0)
  PR40 (76.9)18 (50.0)
  SD8 (15.4)12 (33.3)
  PD1 (1.9)6 (16.7)
CR, complete remission; PR, partial remission; SD, stable disease; PD, progressive disease.
Table
Table 3. Progression-free survival analyses results according to clinicopathological parameters of all the study subjects.
Table 3. Progression-free survival analyses results according to clinicopathological parameters of all the study subjects.
Median PFS (Months)UnivariateMultivariate
HR (95% CI)p-ValueHR (95% CI)p-Value
All17.7
Age 0.4534NA
  <7018.9 reference
  ≥7016.6 1.2 (0.75–1.93)
Sex 0.0323 0.0214
  Male15.1 1.68 (1.04–2.69) 1.83 (1.11–3.22)
  Female20.9 reference reference
Smoking history 0.6920NA
  Never18.6 reference
  Ever16.1 1.11 (0.66–1.87)
Smoking intensity 0.1567 0.3650
  <30 pack-years18.3 reference reference
  ≥30 pack-years13.8 1.55 (0.85–2.84) 1.26 (0.74–2.79)
ECOG PS 0.3045
  0, 119.3 reference
  ≥216.2 1.34 (0.77–2.36)
Stage 0.1771 0.8555
  III19.3 reference reference
  IV16.4 1.57 (0.82–3.04) 1.21 (0.56–2.37)
Involved organ 0.0172 0.3258
  <319.5 reference reference
  ≥315.4 1.97 (1.13–3.43) 1.50 (0.78–2.98)
Brain metastasis 0.6139NA
  No19.2 reference
  Yes15.5 1.14 (0.69–1.87)
Liver metastasis 0.0189 0.0371
  No19.1 reference reference
  Yes14.7 2.15 (1.46–5.19) 2.05 (1.09–5.86)
EGFR subtypes * 0.2630NA
  19del18.7 reference
  L858R17.1 1.23 (0.23–2.44)
First-line TKI 0.0735 0.1321
  Gefitinib/erlotinib17.31.66 (0.97–4.16) 1.52 (0.89–6.73)
  Afatinib20.6reference reference
TP53 mutation 0.0311 0.0894
  Negative19.1reference reference
  Positive14.51.88 (1.12–3.09) 1.56 (0.26–4.15)
TMB level 0.0205 0.0427
  Low (<2.53)20.1reference reference
  High (≥2.53)13.4 1.98 (1.09–2.89) 1.80 (1.17–4.43)
* Analysis for 83 patients excluding 5 patients with uncommon or compound mutations. ECOG PS, Eastern Cooperative Oncology Group Performance Status; EGFR, epidermal growth factor receptor; 19del, deletion mutation at exon 19; TKI, tyrosine kinase inhibitor; HR, hazard ratio; CI, confidence interval; NA, not analyzed.
Table
Table 4. Overall survival analyses results according to the clinicopathological parameters of all the study subjects.
Table 4. Overall survival analyses results according to the clinicopathological parameters of all the study subjects.
Median OS (Months)UnivariateMultivariate
HR (95% CI)p-ValueHR (95% CI)* p-Value
All35.5
Age 0.7403NA
  <7036.8 reference
  ≥7032.2 1.12 (0.47–1.99)
Sex 0.4781NA
  Male32.6 1.22 (0.70–2.13)
  Female36.9 reference
Smoking history 0.4489NA
  Never36.8 reference
  Ever32.2 1.26 (0.69–2.31)
Smoking intensity 0.5614NA
  <30 pack-years36.8 reference
  ≥30 pack-years32.6 1.24 (0.60–2.55)
ECOG PS 0.6378NA
  0, 137.7 reference
  ≥232.5 1.15 (0.73–2.22)
Stage 0.1443 0.5604
  III37.5reference reference
  IV32.2 2.01 (0.79–5.04) 1.35 (0.49–3.76)
Involved organ 0.0346 0.4893
  <336.9 reference reference
  ≥321.4 1.94 (1.05–3.57) 1.28 (0.64–2.56)
Brain metastasis 0.1690 0.3589
  No37.1 reference 1.35 (0.71–2.59)
  Yes30.4 1.53 (0.83–2.81) reference
Liver metastasis 0.0097 0.0314
  No36.9 reference Reference
  Yes21.9 2.34 (1.25–4.40) 2.17 (1.14–4.57)
EGFR subtypes * 0.2154NA
  19del36.9 reference
  L858R30.81.56 (0.36–6.73)
First-line TKIs 0.2019
  Gefitinib/erlotinib30.2 1.70 (0.95–3.57)0.0847 1.41 (0.69–3.27)
  Afatinib36.1 reference reference
TP53 mutation 0.3142NA
  Negative35.7reference
  Positive30.31.47 (0.89–5.23)
TMB level 0.0080 0.0397
  Low (<2.53)37.1reference reference
  High (≥2.53)21.92.65 (1.50–4.67) 2.05 (1.04–4.07)
* Analysis for 83 patients excluding 5 patients with uncommon or compound mutations. ECOG PS, Eastern Cooperative Oncology Group Performance Status; EGFR, epidermal growth factor receptor; 19del, deletion mutation at exon 19; TKI, tyrosine kinase inhibitor; HR, hazard ratio; CI, confidence interval; NA, not analyzed.
Table
Table 5. Analysis for the factors that were associated with the emergence of secondary T790M mutation (n = 52).
Table 5. Analysis for the factors that were associated with the emergence of secondary T790M mutation (n = 52).
T790Mp-Value *Univariate AnalysisMultivariate Analysis
Negative (n = 30)Positive (n- = 22)OR (95% CI)p-ValueOR (95% CI)p-Value
Age 0.3710 0.2513NA
  <7015 (55.5)12 (45.5) 0.62 (0.12–1.87)
  ≥7015 (40.0)10 (60.0) reference
Sex 0.5956 0.6947NA
  Male19 (65.5)10 (34.5) 0.82 (0.11–2.23)
  Female11 (47.8)12 (52.2) reference
Smoking history 0.1781 0.2413NA
  Never19 (54.2)16 (45.8) 0.86 (0.23–4.57)
  Ever11 (64.7)6 (35.3) reference
Smoking intensity 0.3124 0.2991NA
  <30 pack-years18 (51.4)17 (48.6) 0.75 (0.13–3.46)
  ≥30 pack-years12 (70.5)5 (29.6) reference
ECOG PS 0.4136 0.5660NA
  0, 116 (42.1)12 (57.9) reference
  ≥214 (58.3)10 (41.7) 0.41 (0.23–3.62)
Stage 0.5480 0.9608NA
  III4 (44.4)5 (55.6) reference
  IV26 (60.4)17 (39.6) 0.78 (0.37–4.95)
Involved organ 0.9618 0.8068NA
  <320 (58.8)14 (41.2) reference
  ≥310 (55.5)8 (44.5) 0.95 (0.32–3.57)
Brain metastasis 0.2176 0.1425 0.9816
  No20 (64.5)11 (35.5) reference reference
  Yes10 (47.6)11 (52.4) 0.66 (0.17–3.05) 0.98 (0.25–3.89)
Liver metastasis 0.6902 0.5346NA
  No22 (59.5)17 (40.5) reference
  Yes8 (61.5)5 (38.5) 0.70 (0.22–3.39)
EGFR subtypes 0.0471 0.0334 0.0406
  19del11 (50.0)11 (50.0) reference reference
  L858R19 (63.3)11 (36.7) 0.38 (0.17–0.99) 0.46 (0.08–0.94)
First-line TKIs * 0.8460 0.6291NA
  Gefitinib/erlotinib11 (55.0)8 (45.0) reference
  Afatinib19 (57.7)14 (42.3) 0.75 (0.51–2.78)
Duration of TKI use 0.0324 0.0397 0.0423
  <12 months15 (75.0)5 (25.0) 0.32 (0.10–0.91) 0.28 (0.14–0.85)
  ≥12months15 (46.8)17 (53.2) reference reference
TMB expression 0.0377 0.0416 0.0479
  Low (<2.53)16 (48.8)17 (51.2) reference reference
  High (≥2.52)14 (73.6)5 (26.4) 0.30 (0.07–0.88) 0.42 (0.17–0.96)
* Analysis after excluding 2 patients with uncommon or compound mutations. ECOG PS, Eastern Cooperative Oncology Group Performance Status; EGFR, epidermal growth factor receptor; 19del, deletion mutation at exon 19; TKI, tyrosine kinase inhibitor; OR, odds ratio; CI, confidence interval; NA, not analyzed.
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Sung, J.-Y.; Park, D.-W.; Lee, S.-H. High Tumor Mutation Burden Is Associated with Poor Clinical Outcome in EGFR-Mutated Lung Adenocarcinomas Treated with Targeted Therapy. Biomedicines 2022, 10, 2109. https://doi.org/10.3390/biomedicines10092109

AMA Style

Sung J-Y, Park D-W, Lee S-H. High Tumor Mutation Burden Is Associated with Poor Clinical Outcome in EGFR-Mutated Lung Adenocarcinomas Treated with Targeted Therapy. Biomedicines. 2022; 10(9):2109. https://doi.org/10.3390/biomedicines10092109

Chicago/Turabian Style

Sung, Ji-Youn, Dong-Won Park, and Seung-Hyeun Lee. 2022. "High Tumor Mutation Burden Is Associated with Poor Clinical Outcome in EGFR-Mutated Lung Adenocarcinomas Treated with Targeted Therapy" Biomedicines 10, no. 9: 2109. https://doi.org/10.3390/biomedicines10092109

APA Style

Sung, J. -Y., Park, D. -W., & Lee, S. -H. (2022). High Tumor Mutation Burden Is Associated with Poor Clinical Outcome in EGFR-Mutated Lung Adenocarcinomas Treated with Targeted Therapy. Biomedicines, 10(9), 2109. https://doi.org/10.3390/biomedicines10092109

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