Impact of clinical and molecular features on efficacy and outcome of patients with non-small cell lung cancer receiving second-line osimertinib
BMC Cancer volume 22, Article number: 586 (2022)
Although with the impressive efficacy, several patients showed intrinsic resistance or an unsatisfactory response to Osimertinib. We aim to explore the impact of clinical and molecular features on efficacy and outcome of patients with EGFR T790M-mutation non-small cell lung cancer (NSCLC) receiving second-line Osimertinib.
Patients with EGFR T790M-mutant NSCLC who had acquired resistance to the first-generation EGFR TKI and then received Osimertinib as second-line treatment were included. Patients’ demographic and clinical information, as well as molecular data were extracted from electronic medical records. The impact of clinical and molecular features on treatment response and patients’ outcome were assessed.
Among the 99 patients, 60 patients were tissue/pleural effusion T790M positive and 69 patients were plasma positive with a median PFS of 12.1 m and 9.9 m (P = 0.25), respectively. In addition, median PFS were similar between patients of plasma T790M + and patients of plasma T790M- (P = 0.94). The Pearson correlation test showed no significant relationship between plasma T790M abundance and PFS (r = 0.074, P = 0.546). In subgroup analyses, PFS was significantly improved in elder patients (P = 0.009) and patients with longer PFS to the first-generation EGFR TKI (P = 0.0008), while smokers tended to have worse PFS compared with non-smokers (P = 0.064). PARP1 mutant-type patients had a worse PFS compared with wild-type group (P = 0.0003). Patients with MYC amplification also had a worse PFS than MYC wild-type patients (P = 0.016). A significant PFS shrinkage was observed in TMB-High group as 6.77 m, compared with 19.10 m in TMB-Low group. The multivariate Cox analysis revealed that years ≥ 65 was an independent positive feature for PFS, while PARP1 mutation and TMB-H were negative features for PFS.
In conclusion, our findings in this study demonstrated that clinical and molecular features can be served as predictive biomarkers to stratify patients with EGFR T790M-mutant NSCLC receiving second-line Osimertinib.
The discovery of epidermal growth factor receptor (EGFR) driver mutation has led to a dramatic paradigm shift in the therapeutic strategies for advanced non-small-cell lung cancer (NSCLC). However, despite high initial responses to first-/second-generation EGFR tyrosine kinase inhibitors (TKIs) and a median progression-free survival (PFS) of 10 to 14 months, resistance eventually develops in the majority of patients [1,2,3,4,5,6]. The most common mechanism of resistance, which accounts for ~ 60% of cases, is the acquired mutation T790M, namely threonine-to-methionine substitution at codon 790 .
Osimertinib is an oral third-generation EGFR TKI that selectively inhibits both EGFR-sensitive and T790M-resistant mutations, which has been approved as a standard of care after disease progression against first- generation (1st-G)/second-generation(2nd-G) EGFR TKIs in patients with mutant T790M, based on the results of the AURA series studies [8,9,10]. In a tissue-based assay, the objective response rate (ORR) ranged from 62 to 71%, and the median PFS ranged from 9.6 to 12.3 months.
In clinical practice, tumor re-biopsies for detecting T790M mutation in advanced NSCLC are not always feasible for patients with late-stage disease. Plasma cfDNA mutation testing has unique advantages by reducing risks of complications and overcoming tumor heterogeneity . However, whether the efficacy of Osimertinib in patients with T790M mutation identified by the cfDNA is equal to those identified by the tissue samples is not fully addressed. In addition, the correlation between mutant allele frequency (MAF) and drug responsiveness is an unmet needs.
Although the impressive efficacy of osimertinib, 30–40% of patients have limited response [8, 10, 12]. Targeted therapy typically induces an incomplete tumor response due to the Intratumor heterogeneity. Recent studies have found co-occurring genomic alterations were common in EGFR-mutated lung cancers, especially in advanced-stage cancers ; however, the biological significance of these co-occurring events and their correlation with clinical features were largely unknown. Although T790M mutation is a defined mechanism of 1st and 2nd-G TKI resistance, co-occurring somatic alterations associated with the innate resistance and subgroups would show distinct clinical outcomes have not been systematically investigated. Elucidating the correlation between molecular characteristics and treatment response, as well as identifying subgroups with poor efficacy, are essential in enhancing therapeutic responses.
In this study, we have investigated the clinical and molecular features predicting the efficacy and outcome of patients with EGFR T790M-mutant NSCLC receiving second-line Osimertinib retrospectively.
Patients and methods
This study was performed in line with the principles of the Declaration of Helsinki. All methods were performed in accordance with the relevant guidelines and regulations. Patients with EGFR T790M-mutant NSCLC who had acquired resistance to the 1st-G EGFR TKI and then received Osimertinib as second-line treatment from June 1, 2015 to Dec 31, 2019 at the Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) were included. Targeted NGS of post-treatment tumor samples had been performed before Osimertinib treatment for these patients. The NGS platform was Nanjing Geneseeq Technology Inc. (Nanjing, Jiangsu, China). Patients’ demographic and clinical information, as well as molecular data were extracted from electronic medical records. The histological classification was based on the World Health Organization Criteria (2015 version) .
DNA extraction and quantification
6–10 of 6–10 μm FFPE sections from tumor samples were used for genomic DNA extraction with QIAamp DNA FFPE Tissue Kit (QIAGEN) following the manufacturer’s instructions. Plasma was extracted from 8–10 ml peripheral blood in EDTA-coated tubes within 2 h of blood withdrawing. Circulating cell free DNA (cfDNA) from plasma and pleural effusions (PE) supernatants was extracted using the Circulating Nucleic Acid Kit (QIAGEN). Genomic DNA from the white blood cells were extracted using the DNeasy Blood & Tissue Kit (QIAGEN) and used as the normal control to remove germline variations.. The DNA quality was assessed by a Nanodrop2000 (Thermo Fisher Scientific) and the quantity was measured by dsDNA HS Assay Kit on Qubit 3.0 (Life Technologies).
Library preparation and targeted NGS
Extracted tumor genomic DNA was fragmented into 300 ~ 350 bp using Covaris M220 instrument (Covaris). Sequencing libraries were prepared with KAPA Hyper Prep kit (KAPA Biosystems) with optimized protocols. In brief, cfDNA or sheared tissue DNA were experienced with end-repairing, A-tailing, adapter ligation and size selection using Agencourt AMPure XP beads (Beckman Coulter). Libraries were then subjected to PCR amplification and purification before targeted enrichment.
Indexed DNA libraries were pooled up to 2 µg together with Human cot-1 DNA (Life Technologies) and xGen Universal blocking oligos (Integrated DNA Technologies) as blocking reagents. Customized xGen lockdown probes panel (Integrated DNA Technologies) were used to selectively enrich for 425 or 139 predefined genes. The enriched libraries were sequenced on Hiseq 4000 NGS platforms (Illumina) with 2 × 150 bp pair-end reads to coverage depths of at least 100x, 600x, 5000 × for blood, FFPE, and cfDNA, respectively.
Sequencing data were demultiplexed by bcl2fastq (v2.19), analyzed by Trimmomatic (Bolger et.al., 2014) to remove low-quality (quality < 15) or N bases. Then the data were aligned to the reference human genome (build hg19) with the Burrows–Wheeler Aligner (bwa-mem)  and further processed using the Picard suite (http://picard.sourceforge.net/) and the Genome Analysis Toolkit (GATK). Common SNPs were removed using dbSNP and the 1000 Genome data sets. Germline mutations were filtered out by comparing to the whole blood controls. A mutation was called when the MAF cutoff was ≥ 0.5% for tissue samples, 0.1% for liquid biopsy samples, and a minimum of three unique mutant reads on different strands with good quality scores and manually inspected in Integrative Genomics Viewer Software (IGV, Broad Institute). Gene fusions were identified by FACTERA  and copy number variations (CNVs) were analyzed with ADTEx . The log2 ratio cut-off for copy number gain was defined as 2.0 for tissue samples and 1.6 for liquid biopsy samples. A log2 ratio cut-off of 0.6 was used for copy number loss detection in all sample types. Tumor mutation burden (TMB) was defined as the number of somatic, coding, base substitution, and indel mutations per megabase of genome examined, and was calculated as previously described . Briefly, all base substitutions, including non-synonymous and synonymous alterations, and indels in the coding region of targeted genes were considered with the exception of known hotspot mutations in oncogenic driver genes and truncations in tumor suppressors. Synonymous mutations were counted in order to reduce sampling noise and known driver mutations were excluded as they are over-represented in the Panel.
Assessment of efficacy and follow-up
All patients were radiologically evaluable according to Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1  through spiral Computed tomography (CT) or magnetic resonance imaging (MRI) scans. PFS was defined as the time from the first medication to the first objective progression of disease or the date of death from any causes. Outpatient or telephonic follow-up was adopted, and the last follow-up time of this study was on April 21, 2020.
Quantitative data were displayed as median (range) or number of patients (percentage). Comparisons of proportion between two groups were done using the Fisher’s exact test. The Pearson correlation coefficient was used to determine the association of PFS with the allele frequency (AF) of EGFR T790M detected in plasma cfDNA. Wilcoxon rank-sum test was performed to compare the TMB between different sub-groups of PFS of 1st-G EGFR TKI (PFS1). Differences in AF of EGFR T790M between groups of best overall response (BOR) of osimertinib were measured by the Kruskal–Wallis test. For survival analyses, Kaplan–Meier curves were compared using the log-rank test, and hazard ratios (HRs) and the 95% confidence intervals (CIs) were calculated by Cox proportional hazards model. Univariate and multivariate analyses were performed to study the association between different variables and PFS. A two-sided P value of less than 0.05 was considered significant for all tests unless indicated otherwise. All statistical analyses were done in R (v.3.6.0).
Patient cohort and clinical characteristics
A total of 99 EGFR T790M + patients who received second-line Osimertinib were included in the study. The demographics and clinical characteristics of 99 NSCLC patients are presented in Table 1, including age, gender, smoking status, pathological type, EGFR mutant subtype, metastatic sites, BOR and median PFS1. There were 62 men and 37 women with a median age of 61 years (range, 36–80 years). Sixty-two (62.6%) patients originally harbored EGFR exon19 deletion and 37 (37.4%) harbored L858R mutation. The objective response rate for 1st-G EGFR TKI was 75.8%. The median PFS was 12.43 months (range: 3.2–51.8 months).
Of the 99 patients, targeted sequencing of 425 cancer genes was performed in 77 patients, while 139 gene panel was performed in 22 patients on account of lower cost before Osimertinib treatment. For NGS data analyses, after excluding samples from three patients without negative control samples, samples from 96 patients were included into analyses. Among the 76 patients whose samples were performed by targeted sequencing of 425 cancer genes, 48 had both paired tissue/pleural effusion and plasma samples, 4 patients only had tissue/pleural effusion samples and 4 patients only had plasma samples. Among the 20 patients whose samples were performed by targeted sequencing of 139 cancer genes, 12 had both paired tissue/pleural effusion and plasma samples, 2 patients only had tissue samples and 6 patients only had plasma samples (Fig. S1).
Genomic landscape of tissue/pleural effusion and plasma samples
The genomic landscape of 66 patients with tissue (n = 43) or plural effusion (n = 23) performed by NGS are shown in Fig. 1. Among them, sixty (90.0%) patients were identified with EGFR T790M mutation, with 22/23 (95.7%) pleural effusion were T790M+, and 38/42 (88.4%) tissue were T790M+. Of the 66 patients, 59.1% were identified with Exon19 Del, 37.9% with L858R mutation and 12.1% with other sites mutations of EGFR. Widespread genetic alterations were co-occurring with EGFR mutations, with TP53 (60.6%) being the most frequently commutated genes. Several genes that have been reported in NSCLC [13, 20, 21] were found to be co-occurred with EGFR, including APC (10.6%), CTNNB1 (10.6%), PIK3CA (9.1%), MED12 (7.6%), ARID1A (6.1%), PARP1 (6.1%), SETD2 (6.1%), CDKN2A (4.5%). 22.7% of patients had EGFR amplification, which is the most frequent SCNA in our cohort, followed by MYC amplification (12.1%), CDK4 amplification (9.1%) and MDM2 amplification (9.1%). In addition, the genomic landscape of 90 patients with plasma performed by NGS are depicted in Figure S2.
The efficacy of Osimertinib based on tissue or plasma T790M mutation
Among the 99 patients who were detected with T790M mutation by tissue or plasma, the ORR of Osimertinib was 53.5% and the median PFS was 10.3 m (Fig. S3). Separately, 60 patients were tissue/pleural effusion T790M positive and 69 patients were plasma positive with a median PFS of 12.1 m and 9.9 m (P = 0.25), respectively (Fig. 2A). The ORR and DCR in the tissue/pleural effusion T790M + group were 58.3% and 98.3%, compared with 52.2% and 87% in the plasma T790M + group (Fig. 2B). In addition, median PFS were similar between patients of plasma T790M + and patients of plasma T790M- (P = 0.94; Fig. S4). We also explored the relationship between plasma T790M abundance and the efficacy of Osimertnib. The median T790M abundance was 1.9%, ranging from 0.2%-25.1% and the pearson correlation test showed no significant relationship between plasma AF and PFS (r = 0.074,P = 0.546) (Fig. 2C). When patients were divided into three groups according to the BOR to Osimertinib, the median plasma T790M mutation-allele frequency (MAF) was similar as 1.93% vs 1.21% vs 2.13% among PR, SD and PD groups (P = 0.654) (Fig. 2D).
Subgroup analyses of PFS to Osimertinib
In subgroup analyses, PFS was significantly improved in elder patients (HR = 0.47, 95%CI 0.26–0.84, Log-rank p = 0.009) and patients with longer PFS to the 1st -G EGFR TKI (HR = 0.44, 95%CI 0.27–0.72, Log-rank p = 0.0008), while smokers tended to have worse PFS compared with non-smokers (HR = 1.63, 95%CI 0.97–2.73, Log-rank p = 0.064). No significant difference of PFS was detected in other subgroups like gender, BOR to the 1st -G EGFR TKI, EGFR mutation subtype and metastatic sites (Fig. 3A-D; Fig. S5; Fig. S6).
Baseline genetic alterations and TMB affected the clinical response to Osimertinib
In order to explore the effects of co-occurring genetic alterations on the clinical response to Osimertinib, somatic mutations and SCNAs in more than 4 patients were identified and linked to PFS (Fig. 4A-B). The results demonstrated that PARP1 mutant-type patients had a worse PFS compared with wild-type group (3.77 m vs. 13.70 m, HR = 8.4, Log-rank p = 0.0003). Patients with MYC amplification also had a worse PFS than MYC wild-type patients (4.83 m vs 12.60 m, HR = 2.95, Log-rank p = 0.016) (Fig. 4C-D).
Of the 66 patients with tissue/plural effusion, 55 patients were performed by NGS of 425 panel. We then elucidate the correlation between TMB and PFS by consecutively setting TMB cut-points. When TMB high was defined as TMB ≥ 6, a significant PFS shrinkage was observed in TMB-High group as 6.77 m, compared with 19.10 m in TMB-Low group. The trend remained along with higher threshold value of TMB-High (Fig. 5).
Multivariate Cox analyses of PFS
The multivariate Cox analysis was conducted incorporating clinical and genetic features that were found significant in univariate analysis, which was displayed in Table 2. The results revealed that years ≥ 65 was an independent positive feature for PFS, while PARP1 mutation and TMB-H were negative features for PFS. Interestingly, PFS1 was no longer a prognostic factor in multivariate Cox analysis. We speculated whether it is possible that PFS1 influences PFS of Osimertinib through the number of coincidental mutations, therefore, correlation analysis was conducted between TMB and multiple variables. As a result, PFS1 did have a moderate negative correlation with TMB (Fig. 6A). By T-test, we found there were significantly fewer patients with TMB-H in patients with longer PFS1 (P = 0.041) (Fig. 6B).
Osimertinib is a third-generation EGFR TKI, which has been approved for the treatment of patients with EGFR T790M-mutant NSCLC whose disease progress after 1st- or 2nd-G EGFR TKIs. Although with the impressive efficacy, several patients showed intrinsic resistance or an unsatisfactory response to Osimertinib, similar to that observed with other TKIs . Here we explored potential biomarkers based on clinical and genetic features to predict the efficacy and outcome of EGFR T790M-mutant NSCLC patients treated with second-line Osimertinib.
In the AURA3 trial, the ORR and median PFS of patients treated with Osimertinib was 71% and 10.1 m, better than with the chemotherapy group (ORR:31%, PFS:4.4 m) . In our real-world study, the ORR of Osimertinib as the second-line therapy in patients with acquired EGFR T790M mutation was 53.5% and the median PFS was 10.3 m. A meta-analysis including 3086 advanced NSCLC patients from 11 studies reported that the aggregate efficacy parameters for advanced NSCLC harboring T790M mutations after earlier-generation EGFR-TKI therapy are as follows: ORR 58%, DCR 80%, PFS 10.58 m .
In this study, we demonstrated that the ORR and outcome were similar between patients with tissue/pleural effusion T790M + and those with cfDNA T790M + . In addition, median PFS were similar between patients of plasma T790M + and patients of plasma T790M-. We also explored the relationship between plasma T790M abundance and the efficacy of Osimertnib and the results showed no significant relationship between plasma MAF and PFS. Another research also reported that ORR and median PFS were similar in patients with T790M-positive plasma (ORR, 63%; PFS, 9.7 months) or T790M-positive tumor (ORR, 62%; PFS, 9.7 months) results . These results suggest that, cfDNA T790M assay is a validated surrogate to identify patients who will have benefit from Osimertinib that are similar to patients with T790M mutation tested by tissue samples. On the other hand, a negative T790M result by liquid biopsy should be validated by tissue testing, to determine the T790M status, if possible.
The main point of this study is to identify clinical and molecular features predicting outcome in NSCLC patients receiving Osimertinib. Firstly, we conducted the ultivariate analyses of PFS in different subgroups based on clinical features. The results revealed that PFS was significantly improved in elder patients and patients with longer PFS to the 1st -G EGFR TKI, while smokers tended to have worse PFS compared with non-smokers. In a PRISMA-compliant systematic review and meta-analysis including 47 studies evaluating the efficacy and safety of Osimertinib in treating NSCLC, risk factors associated with survival outcome were analyzed. The results also showed that patients who have a smoking history may have a higher risk of progression than never-smoker patients . The worse survival outcome and the lower response rate to the EGFR TKI in smokers with activating EGFR mutations might be attributed to several reasons. High number of SNVs and structural mutation might be associated with smoking, which may decrease the efficacy of EGFR TKI [26, 27]. In addition, downstream activation of AKT, ERK pathway and Src signaling pathways via nicotine exposure might induce tumor growth [28, 29]. In several other research exploring how clinical characteristics affected the efficacy of Osimertinib therapy, elder patients exhibited higher response rates and better PFS [30, 31]. One of the reasonable explaination is the higher prevalence of uncommon EGFR mutations in younger patients, that are known to be less sensitive to EGFR TKIs. In addition, younger patients were more likely to have brain metastasis at baseline and to be heavily pretreated [32, 33].
In this study, we observed extensive accompanying genomic alterations that co-occurred with T790M mutations after the 1st-G EGFR TKI resistance, both in the tissue samples and in the plasma samples. Although T790M mutation is a major resistance mechanism for 1st- and 2nd-G EGFR TKIs, growing evidence revealed that other co-acquired alterations may cooperate with T790M. In our previous study about the resistant mechanism of 1st-G EGFR TKIs, we also demonstrated that T790M was accompanied by other acquired oncogenic alterations, especially gene mutations . Recent studies have found co-occurring genomic alterations were common in EGFR-mutated lung cancers. These accompanying alterations can affect the response of the tumors to EGFR TKIs, which may serve as predictive biomarkers to stratify patients and explore the novel treatment paradigm .
We then explored the effects of co-occurring genetic alterations on the clinical response to Osimertinib. The results demonstrated that PARP1 mutation was associated with worse PFS. PARP1 is the most abundant enzyme mediating post-translational polyADP-ribosylation, which is involved in DNA repair, transcriptional control, genomic stability, and cell death . A research using cell experiments and GEO dataset analysis confirmed that PARP1 was expressed at higher levels in TKI-resistant cells than in TKI-sensitive cells. PARP1-mediated autophagy was a key pathway for TKI resistance in NSCLC cells that participated in the resistance to TKIs. Olaparib may serve as a novel method to overcome the resistance to TKIs . Another study revealed that acquired resistance of EGFR-mutated lung cancer to TKI treatment promoted PARP inhibitor sensitivity . Despite the fact that these results still need to be validated in larger cohorts, they are indeed with potential implications that cytotoxic anticancer drug or PARP inhibitors combining with current standard TKI therapy may improve the prognosis of this subgroup of patients.
Furthermore, in our study, TMB high (TMB ≥ 6) was identified to be an independent negative prognostic factor for PFS. TMB is an emerging positively predictive marker of immune checkpoint blockade response in several malignancies including lung cancer, which is hypothesized to be associated with the increased tumor-specific neoantigen burden . In contrast, its utility in targeted therapy is still not addressed. It is hypothesized that the elevated TMB may correlate with additional mutations which represent potential pathways for resistance to targeted therapies. Analogous results were found by previous research demonstrating that TMB was negatively associated with clinical outcomes in metastatic patients with EGFR-mutant lung cancer treated with first- or second- generation EGFR TKIs [13, 39].
In this study, several clinical and molecular characteristics were identified to be associated with treatment outcome in patients with EGFR T790M-mutant NSCLC receiving second-line Osimertinib. The multivariate Cox analysis revealed that years ≥ 65 was an independent positive feature, while PARP1 mutation and TMB-H were negative features for PFS. These findings demonstrated that clinical and molecular features can be served as predictive biomarkers to stratify patients treated with second-line Osimertinib.
Availability of data and materials
The datasets analysed during the current study are available from the corresponding author on reasonable request.
Epidermal growth factor receptor
Non-small-cell lung cancer
Tyrosine kinase inhibitors
Objective response rate
Cell free DNA
Mutant allele frequency
Copy number variations
Tumor mutation burden
Response Evaluation Criteria in Solid Tumors
Magnetic resonance imaging
PFS of 1st-G EGFR TKI
Best overall response
Mok TS, Wu YL, Thongprasert S, Yang CH, Chu DT, Saijo N, Sunpaweravong P, Han B, Margono B, Ichinose Y, et al. Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N Engl J Med. 2009;361(10):947–57.
Maemondo M, Inoue A, Kobayashi K, Sugawara S, Oizumi S, Isobe H, Gemma A, Harada M, Yoshizawa H, Kinoshita I, et al. Gefitinib or chemotherapy for non-small-cell lung cancer with mutated EGFR. N Engl J Med. 2010;362(25):2380–8.
Rosell R, Carcereny E, Gervais R, Vergnenegre A, Massuti B, Felip E, Palmero R, Garcia-Gomez R, Pallares C, Sanchez JM, 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(3):239–46.
Sequist LV, Yang JC, Yamamoto N, O’Byrne K, Hirsh V, Mok T, Geater SL, Orlov S, Tsai CM, Boyer M, et al. Phase III study of afatinib or cisplatin plus pemetrexed in patients with metastatic lung adenocarcinoma with EGFR mutations. J Clin Oncol. 2013;31(27):3327–34.
Wu YL, Zhou C, Hu CP, Feng J, Lu S, Huang Y, Li W, Hou M, Shi JH, Lee KY, et al. Afatinib versus cisplatin plus gemcitabine for first-line treatment of Asian patients with advanced non-small-cell lung cancer harbouring EGFR mutations (LUX-Lung 6): an open-label, randomised phase 3 trial. Lancet Oncol. 2014;15(2):213–22.
Zhou C, Wu YL, Chen G, Feng J, Liu XQ, Wang C, Zhang S, Wang J, Zhou S, Ren S, et al. Erlotinib versus chemotherapy as first-line treatment for patients with advanced EGFR mutation-positive non-small-cell lung cancer (OPTIMAL, CTONG-0802): a multicentre, open-label, randomised, phase 3 study. Lancet Oncol. 2011;12(8):735–42.
Yu HA, Arcila ME, Rekhtman N, Sima CS, Zakowski MF, Pao W, Kris MG, Miller VA, Ladanyi M, Riely GJ. Analysis of tumor specimens at the time of acquired resistance to EGFR-TKI therapy in 155 patients with EGFR-mutant lung cancers. Clin Cancer Res. 2013;19(8):2240–7.
Janne PA, Yang JC, Kim DW, Planchard D, Ohe Y, Ramalingam SS, Ahn MJ, Kim SW, Su WC, Horn L, et al. AZD9291 in EGFR inhibitor-resistant non-small-cell lung cancer. N Engl J Med. 2015;372(18):1689–99.
Yang JC, Ahn MJ, Kim DW, Ramalingam SS, Sequist LV, Su WC, Kim SW, Kim JH, Planchard D, Felip E, et al. Osimertinib in Pretreated T790M-Positive Advanced Non-Small-Cell Lung Cancer: AURA Study Phase II Extension Component. J Clin Oncol. 2017;35(12):1288–96.
Mok TS, Wu YL, Ahn MJ, Garassino MC, Kim HR, Ramalingam SS, Shepherd FA, He Y, Akamatsu H, Theelen WS, et al. Osimertinib or Platinum-Pemetrexed in EGFR T790M-Positive Lung Cancer. N Engl J Med. 2017;376(7):629–40.
Keller L, Belloum Y, Wikman H, Pantel K. Clinical relevance of blood-based ctDNA analysis: mutation detection and beyond. Br J Cancer. 2021;124(2):345–58.
Goss G, Tsai CM, Shepherd FA, Bazhenova L, Lee JS, Chang GC, Crino L, Satouchi M, Chu Q, Hida T, et al. Osimertinib for pretreated EGFR Thr790Met-positive advanced non-small-cell lung cancer (AURA2): a multicentre, open-label, single-arm, phase 2 study. Lancet Oncol. 2016;17(12):1643–52.
Blakely CM, Watkins TBK, Wu W, Gini B, Chabon JJ, McCoach CE, McGranahan N, Wilson GA, Birkbak NJ, Olivas VR, et al. Evolution and clinical impact of co-occurring genetic alterations in advanced-stage EGFR-mutant lung cancers. Nat Genet. 2017;49(12):1693–704.
Travis WD, Brambilla E, Nicholson AG, Yatabe Y, Austin JHM, Beasley MB, Chirieac LR, Dacic S, Duhig E, Flieder DB, et al. The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification. J Thorac Oncol. 2015;10(9):1243–60.
Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754–60.
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20.
Amarasinghe KC, Li J, Halgamuge SK. CoNVEX: copy number variation estimation in exome sequencing data using HMM. BMC Bioinformatics. 2013;14(Suppl 2):S2.
Fang W, Ma Y, Yin JC, Hong S, Zhou H, Wang A, Wang F, Bao H, Wu X, Yang Y, et al. Comprehensive Genomic Profiling Identifies Novel Genetic Predictors of Response to Anti-PD-(L)1 Therapies in Non-Small Cell Lung Cancer. Clin Cancer Res. 2019;25(16):5015–26.
Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, 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(2):228–247.
Ding L, Getz G, Wheeler DA, Mardis ER, McLellan MD, Cibulskis K, Sougnez C, Greulich H, Muzny DM, Morgan MB, et al. Somatic mutations affect key pathways in lung adenocarcinoma. Nature. 2008;455(7216):1069–75.
Xu M, Wang F, Li G, Wang X, Fang X, Jin H, Chen Z, Zhang J, Fu L. MED12 exerts an emerging role in actin-mediated cytokinesis via LIMK2/cofilin pathway in NSCLC. Mol Cancer. 2019;18(1):93.
Sun JM, Lee SH, Ahn JS, Park K, Ahn MJ. Osimertinib for the treatment of non-small cell lung cancer. Expert Opin Pharmacother. 2017;18(2):225–31.
Yi L, Fan J, Qian R, Luo P, Zhang J. Efficacy and safety of osimertinib in treating EGFR-mutated advanced NSCLC: A meta-analysis. Int J Cancer. 2019;145(1):284–94.
Oxnard GR, Thress KS, Alden RS, Lawrance R, Paweletz CP, Cantarini M, Yang JC, Barrett JC, Janne PA. Association Between Plasma Genotyping and Outcomes of Treatment With Osimertinib (AZD9291) in Advanced Non-Small-Cell Lung Cancer. J Clin Oncol. 2016;34(28):3375–82.
Liu J, Li X, Shao Y, Guo X, He J. The efficacy and safety of osimertinib in treating nonsmall cell lung cancer: A PRISMA-compliant systematic review and meta-analysis. Medicine (Baltimore). 2020;99(34): e21826.
Govindan R, Ding L, Griffith M, Subramanian J, Dees ND, Kanchi KL, Maher CA, Fulton R, Fulton L, Wallis J, et al. Genomic landscape of non-small cell lung cancer in smokers and never-smokers. Cell. 2012;150(6):1121–34.
Hecht SS. Lung carcinogenesis by tobacco smoke. Int J Cancer. 2012;131(12):2724–32.
Chen RJ, Chang LW, Lin P, Wang YJ. Epigenetic effects and molecular mechanisms of tumorigenesis induced by cigarette smoke: an overview. J Oncol. 2011;2011: 654931.
Li D, Zhang L, Zhou J, Chen H. Cigarette smoke extract exposure induces EGFR-TKI resistance in EGFR-mutated NSCLC via mediating Src activation and EMT. Lung Cancer. 2016;93:35–42.
Gil HI, Um SW. The impact of age and performance status on the efficacy of osimertinib in patients with EGFR T790M-positive non-small cell lung cancer. J Thorac Dis. 2020;12(3):153–5.
Kato Y, Hosomi Y, Watanabe K, Yomota M, Kawai S, Okuma Y, Kubota K, Seike M, Gemma A, Okamura T. Impact of clinical features on the efficacy of osimertinib therapy in patients with T790M-positive non-small cell lung cancer and acquired resistance to epidermal growth factor receptor tyrosine kinase inhibitors. J Thorac Dis. 2019;11(6):2350–60.
Wu SG, Chang YL, Yu CJ, Yang PC, Shih JY: Lung adenocarcinoma patients of young age have lower EGFR mutation rate and poorer efficacy of EGFR tyrosine kinase inhibitors. ERJ Open Res. 2017;3(3):00092–2016.
Hsu CL, Chen KY, Shih JY, Ho CC, Yang CH, Yu CJ, Yang PC. Advanced non-small cell lung cancer in patients aged 45 years or younger: outcomes and prognostic factors. BMC Cancer. 2012;12:241.
Jin Y, Bao H, Le X, Fan X, Tang M, Shi X, Zhao J, Yan J, Xu Y, Quek K, et al. Distinct co-acquired alterations and genomic evolution during TKI treatment in non-small-cell lung cancer patients with or without acquired T790M mutation. Oncogene. 2020;39(9):1846–59.
Kraus WL. PARPs and ADP-Ribosylation: 50 Years … and Counting. Mol Cell. 2015;58(6):902–10.
Zhang Z, Lian X, Xie W, Quan J, Liao M, Wu Y, Yang ZZ, Wang G. Role of PARP1-mediated autophagy in EGFR-TKI resistance in non-small cell lung cancer. Sci Rep. 2020;10(1):20924.
Marcar L, Bardhan K, Gheorghiu L, Dinkelborg P, Pfaffle H, Liu Q, Wang M, Piotrowska Z, Sequist LV, Borgmann K et al: Acquired Resistance of EGFR-Mutated Lung Cancer to Tyrosine Kinase Inhibitor Treatment Promotes PARP Inhibitor Sensitivity. Cell Rep 2019, 27(12):3422–3432 e3424.
Hellmann MD, Ciuleanu TE, Pluzanski A, Lee JS, Otterson GA, 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(22):2093–104.
Offin M, Rizvi H, Tenet M, Ni A, Sanchez-Vega F, Li BT, Drilon A, Kris MG, Rudin CM, 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(3):1063–9.
This study’s publication was supported by the National Natural Science Foundation of China (grant No.81702248).
Ethics approval and consent to participate
This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Institutional Review Board/Privacy Board at the Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) (Ethics approval No.: IRB-2021–361). We have to provide complete informed consent for every patient before he or she will undergo gene-test diagnosis and cancer treatment in our hospital. There was an appropriate waiver of IRB for this retrospective analysis. The IRB exempted us from the informed consent for patients’ participation in this study because this study was retrospective design for their clinical data.
Consent for publication
Junrong Yan are the employee of Nanjing Geneseeq Technology Inc.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Fig. S1. Flow chart of patients and samples exclusion.
Fig. S2. Effect of Osimertinib of the included cohort (n = 99).
Fig. S3. The genomic landscape of 90 patients with plasma performed by NGS.
Fig. S4. Comparison of PFS between patients of plasma T790M+ and patients of plasma T790M-.
Fig. S5. Subgroup analyses of PFS to Osimertinib. A Comparison of PFS between patients with Ex19 Del and patients with Ex 21L858R. B Comparison of PFS between patients with PR and patients with SD for 1st -G EGFR TKI. C Comparison of PFS between patients with and without CNS metastasis. D Comparison of PFS between patients with and without liver metastasis. E Comparison of PFS between patients with and without bone metastasis. F Comparison of PFS between patients with and without pleural effusion.
Fig. S6. Forest plot of hazard ratio (HR) and p value for ultivariate analyses of different PFS1 cut-off value.
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Cite this article
Jin, Y., Lin, C., Shi, X. et al. Impact of clinical and molecular features on efficacy and outcome of patients with non-small cell lung cancer receiving second-line osimertinib. BMC Cancer 22, 586 (2022). https://doi.org/10.1186/s12885-022-09683-1
- Clinical and molecular features
- Efficacy and outcome
- EGFR T790M mutation
- Non-small-cell lung cancer