{"paper_id":"115bb467-c4ad-4ce3-9cf5-0c804bc6c652","body_text":"Classification and regression tree for estimating predictive markers to detect T790M mutations after acquired resistance to first line EGFR-TKI: HOPE-002 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Classification and regression tree for estimating predictive markers to detect T790M mutations after acquired resistance to first line EGFR-TKI: HOPE-002 Motohiro Tamiya, Kei Fujikawa, Hidekazu Suzuki, Toshihide Yokoyama, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1079146/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jan, 2022 Read the published version in Investigational New Drugs → Version 1 posted 5 You are reading this latest preprint version Abstract Background and objective: Osimertinib as first-line treatment for patients with non-small cell lung cancer (NSCLC) harboring epidermal growth factor (EGFR) mutations remains controversial. Sequential EGFR-tyrosine kinase inhibitor (TKI) might be superior to the first line osimertinib in patients at risk of developing acquired T790M mutations. Methods: We enrolled consecutive patients with EGFR -mutated (deletion 19 or L858R) advanced NSCLC treated with first-line drugs and evaluated predictive markers using classification and regression tree (CART) for the detection of T790M mutations based on patient backgrounds prior to initial treatment. Results: Patients without acquired T790M mutations had worse outcomes than those with T790M mutations (median OS: 798 days vs. not reached; HR: 2.70; P<0.001). CART identified three distinct groups based on variables associated with acquired T790M mutations (age, CYF, WBC, liver metastasis, and LDH; AUROC: 0.77). Based on certain variables, CART identified three distinct groups in deletion 19 (albumin, LDH, bone metastasis, pleural effusion, and WBC; AUROC: 0.81) and two distinct groups in L858R (age, CEA, and ALP; AUROC: 0.80). The T790M detection frequencies after TKI resistance of afatinib and first-generation EGFR-TKIs were similar (35.3% vs. 37.4%, P=0.933). Afatinib demonstrated longer PFS (398 vs. 279 days; HR: 0.67; P=0.004) and OS (1053 vs. 956 days; HR: 0.68; P=0.051) than first-generation EGFR-TKIs. Conclusion: Identification of patients at risk of acquiring T790M mutations after EGFR-TKI failure may aid in choice of first-line EGFR-TKI. Furthermore, afatinib may be the more effective 1st-line EGFR-TKI treatment for patients at risk of developing T790M as initial EGFR-TKI resistance. Oncology Clinical Pharmacology Toxicology Non-small cell lung cancer EGFR tyrosine kinase inhibitors T790M predict marker Classification and regression tree Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction: Epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) are key drugs for patients with non-small cell lung cancer (NSCLC) harboring EGFR mutations. First -, second, and third generation (1st-, 2nd-, and 3rd-G) EGFR-TKIs have been developed and evaluated for toxicity and efficacy in three randomized trials (LUX-Lung 7, ARCHER 1050, and FLAURA). All three trials showed more clinical benefits in the 2nd- and 3rd-G than the 1st-G EGFR-TKIs. While all studies showed that the 2nd- and 3rd-G EGFR-TKIs improved progression-free survival (PFS), only osimertinib in FLAURA exhibited a statistically significant improvement in overall survival (OS). While OS improvement was also observed with dacomitinib in ARCHER1050, statistical analysis was not performed in this study due to adoption of the gatekeeping method [ 1 – 3 ]. Therefore, osimertinib is the most strongly recommended drug for patients with common EGFR mutations. Japanese guidelines have judged the quality of this evidence as B and the strength of this recommendation as 1 [ 4 ]. Osimertinib was developed as an anti-cancer drug due to its activity against Thr790Met (T790M) mutations through covalent binding [ 5 ]. T790M is the most common resistance mechanism that impairs the activity of TKIs detected in approximately 50% of patients with 1st and 2nd-G EGFR-TKI-refractory tumors [ 6 , 7 ]. Thus, osimertinib is effective against T790M-mediated acquired resistance (AR) [ 8 ], making it a reasonable option for several EGFR -mutant patients initially treated with 1st- or 2nd-G EGFR-TKIs. At present, there are two main first-line strategies for advanced EGFR -mutant NSCLC: first-line osimertinib and salvage osimertinib for T790M-positive AR initially treated with first-line 1st- or 2nd-G EGFR-TKIs. However, prevention of the development of AR must also be considered as a key therapeutic strategy. Importantly, first-line and salvage therapy have different mechanisms of the development of osimertinib AR [ 9 – 12 ] that are more complicated than those of 1st- or 2nd-G EGFR-TKIs. Treatment strategies for AR to first-line osimertinib, are currently still limited. On the other hand, salvage osimertinib therapy was found to be a simple and effective treatment for patients with T790M detected after the development of AR to 1st- or 2nd-G EGFR-TKIs. However, the occurrence of T790M after developing AR to first-line 1st- or 2nd-G EGFR-TKIs was found to have a low incidence (approximately 30%) in real-world FLAURA trial data [ 3 , 13 ]. To more effectively utilize salvage osimertinib therapy for these cases, the detection rate of T790M must be increased. In the present study, we aimed to increase rates of T790M detection after first-line 1st- or 2nd-G EGFR-TKI therapy by evaluating predictive markers for T790M mutation detection in patients prior to first-line EGFR-TKI treatment. Method: Study design We conducted a multicenter, retrospective cohort study across nine medical institutes belonging to the Hanshin Oncology Clinical Problem Evaluation group (HOPE) in Japan. The clinical data of the patients were retrospectively extracted from their medical charts and added to a database. Because this was a retrospective observational study, sample size calculation based on hypothesis testing was not performed. This study was approved by the ethical review board or institutional review board of each participating institute. Informed consent was not required owing to the retrospective nature of the study, and an opt-out method was utilized so that patients and their families could refuse to participate in the study. Patient selection Patients >20 years of age were consecutively enrolled if they had pathologically confirmed stage IV non-squamous NSCLC (excluding recurrent cases, such as those who had undergone post-operation or post-chemoradiation therapy) with sensitizing EGFR mutations (deletion 19 or L858R) and had received gefitinib, erlotinib, or afatinib as first-line therapies between January 1, 2015 and March 31, 2017. The patients were classified as either never-smokers (those reported to have never smoked), current smokers (those who had smoked within 1 year of diagnosis), or former smokers (the remaining). The clinical stages of all the patients were determined according to the eighth edition of the tumor, node, and metastasis classification of malignant tumors. Anti-tumor responses were assessed using the RECIST version 1.1. The intervals between dates of commencing EGFR-TKI therapy and disease progression or death (PFS) and overall survival (OS) of the patients were calculated. The cutoff date for data collection was set at August 31, 2018. Statistical analysis Data were analyzed by independent statisticians. We evaluated predictive markers for T790M mutation detection based on the patients’ backgrounds before first-line EGFR-TKI treatment using a classification and regression tree (CART). The Gini coefficient was used to determine the best split. The data collected included sex, age, type of EGFR mutation, smoking history, Eastern Cooperative Oncology Group (ECOG) performance status (PS), type of EGFR-TKI, site of metastasis (brain, bone, liver, adrenal, lung, and pleural effusion), and laboratory data. Fisher’s exact test was used for categorical comparisons of data. Kaplan-Meier curves were used to evaluate PFS and OS. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using the Cox proportional hazards model. All statistical analyses were conducted using R software (version 4.0.5; http://R-project.org ; The R Foundation for Statistical Computing, Vienna, Austria). The following R libraries were used: rpart (version 4.1.15), rpart.plot (version 3.0.9), pROC (version 1.17.0.1), survival (version 3.2.10), and survminer (version 0.4.9). Differences were considered statistically significant at P<0.05. Results: Patient demographics A total of 289 consecutive stage 4 NSCLC patients (287 eligible) were enrolled at nine medical institutes belonging to the HOPE in Japan from January 1, 2015 to March 31, 2017. Among them, two patients with de novo T790M mutations were excluded. A total of 225 patients experienced disease progression (PD) after first-line EGFR-TKI treatment, of which 166 underwent re-biopsy by tissue or plasma. Among these patients, 147 patients remained for CART method analysis after excluding patients with missing background data, such as programmed cell death-ligand 1 (PD-L1), thyroid transcription factor-1 (TTF-1), sialylated carbohydrate antigen KL-6 (KL-6), neuron specific enolase (NSE), pro-gastrin releasing peptide (Pro-GRP) patients missing ≥10% data (Figure 1 ). The characteristics of the 166 patients are shown in Table 1. The median age of the patients was 69 years. Of the 166 patients, 71.7% were male, 31.9% had histories of smoking, 48.8% had L858R mutations and exon 19 deletions, and 2.4% had common and other uncommon mutations. Approximately 30.7% were treated with afatinib, 25.9% with erlotinib, and 43.4% with gefitinib as first-line treatments. Development of T790M mutation after PD in first line EGFR-TKI therapy A total of 36.7% (61/166) of the patients acquired T790M mutations after first-line EGFR-TKI therapy. The frequencies of T790M after each first-line EGFR-TKI failure were as follows: gefitinib, 34.7% (25/72); erlotinib, 41.9% (18/43); and afatinib, 35.3% (18/51). The frequencies of T790M in the 1st-G EGFR-TKIs, including gefitinib and erlotinib (37.4%; 43/115) were similar to that of afatinib (P=0.862). On the other hand, the frequency of T790M in patients with deletion 19 (44.4%; 36/81) tended to be higher than that in patients with L858R (44.4%, 36/81 vs 30.9%, 25/81; P=0.104). Evaluation of predictive markers for detecting T790M through CART First, we evaluated predictive markers for the detection of T790M in the patients included by CART analysis (N=147). CART identified three distinct groups of patients based on variables strongly associated with acquired T790M mutations (age, cytokeratin 19 fragmen (CYF), white blood cell count (WBC), liver metastasis, and lactate dehydrogenase (LDH)), with an area under the receiver operating characteristic (AUROC) of 0.77 (95% CI: 0.69–0.84) (Figure 2 a). Although we classified the three groups according to the frequency of T790M, the AUROC was found to be low. Next, we analyzed predictive markers for detecting T790M mutations with each type of EGFR mutation since a previous analysis found that its frequency varies according to EGFR type [ 14 ]. The CART identified three distinct groups in deletion 19 based on certain variables (albumin (Alb), LDH, bone metastasis, pleural effusion, and WBC), with an AUROC of 0.81 (95% CI: 0.71–0.91) (Figure 2 b). Furthermore, CART identified two distinct groups in L858R based on certain variables (age, carcinoembryonic antigen (CEA), and ALP), with an AUROC of 0.80 (95% CI: 0.69–0.90) (Figure 2 c). The detection rates of the highest groups in deletions 19 and L858R were ≥80% and ≥60%, respectively. Effect of T790M on OS The median OS across all patients (N=289) was 1041 days (Figure 3 a). The T790M mutation was detected in 64 patients. Furthermore, more patients without acquired T790M mutations died compared with those with acquired T790M mutations throughout the observation period (116/225 vs. 18/64; P<0.01). Additionally, the patients without acquired T790M mutations had worse outcomes than patients with the T790M mutation (median OS: 798 days vs. not reached; HR: 2.70 [95% CI: 1.64–4.55]; P<0.001) (Figure 3 b). PFS and OS of first-line EGFR-TKIs As mentioned above, the T790M mutation affected OS, and the frequency of the development of T790M after developing EGFR-TKI resistance was similar between afatinib and 1st-G EGFR-TKIs. However, afatinib was previously reported to have better outcomes than gefitinib [ 1 ]. We evaluated which first-line EGFR-TKI had the highest PFS and OS based on real-world data. Compared with 1st-G EGFR-TKIs, afatinib had a longer PFS (median PFS: 398 vs. 279 days; HR: 0.67 [95% CI: 0.50–0.88]; P=0.004) (Figure 4 a) and tended to have a longer OS (median OS: 1053 vs. 956 days; HR: 0.68 [95% CI: 0.46–1.01]; P=0.051) (Figure 4 b). Discussion: This is the first analysis to identify predictive markers using CART for the detection of T790M mutations based on patient backgrounds prior to first-line EGFR-TKI treatment. This study also identified markers that distinguish between EGFR mutation types, leading to more accurate predictions of T790M detection. CART classified the groups according to the detection rate of T790M based on certain variables (age, CYF, WBC, liver metastasis and LDH). Furthermore, deletion 19 mutations were classified into three distinct groups based on certain variables (Alb, LDH, bone metastasis, pleural effusion, and WBC), and L858R mutations were classified into two distinct groups based on certain variables (age, CEA, and ALP). As first-line treatment, osimertinib has been found to be associated with longer PFS and OS than 1st-G EGFR-TKIs against advanced NSCLC harboring EGFR mutations (exon-19 deletion and L858R) [ 3 ]. However, in the Asian subset (especially in the Japanese subset) analysis of OS in the FLAURA study, osimertinib was not observed to be superior to 1st-G EGFR-TKIs [ 15 ]. Furthermore, no additional molecular targets for therapy are known due to the heterogeneity of resistance mechanisms that are not well understood [ 11 , 16 ]. As a result, in the clinical care of most patients following cancer progression after osimertinib treatment, chemotherapy is the only remaining option for second-line treatment. In contrast, the most common mechanism of the development of resistance to 1st- and 2nd-G EGFR-TKI treatment is the T790M mutation. Fortunately, osimertinib overcomes the T790M mutation and provides significantly longer PFS compared to standard platinum-based chemotherapy in advanced T790M positive NSCLC patients with AR to first line 1st or 2nd-G EGFR-TKIs (median PFS: 10.1 vs. 4.4 months; HR: 0.30 [95% CI: 0.23–0.41]) [ 8 ]. In particular, a non-interventional GioTag study demonstrated clinical benefit with sequential afatinib and osimertinib in patients with EGFR mutation-positive NSCLC with T790M-acquired resistance; this trend was more pronounced among the Asian population [ 17 ]. In this study, we showed that patients with acquired T790M mutations had better outcomes than patients without T790M mutations after AR to first-line 1st- or 2nd-G EGFR-TKIs. Therefore, the use of osimertinib as first-line treatment for NSCLC patients harboring EGFR mutations remains controversial in practice. Furthermore, sequential EGFR-TKI treatment may be superior to first-line osimertinib in patients who will likely develop acquired T790M mutations. When considering sequential therapy, the benefit is likely to be diminished if T790M is not detected since osimertinib remains an important and beneficial drug that is essential for patients with advanced NSCLC harboring EGFR mutations. However, the incidence of T790M after AR to first-line first line 1st- or 2nd-G EGFR-TKIs was found to be low based on real-world and FLAURA trial data [ 3 , 13 ]. Previous reports have shown the value of performing a re-biopsy since patients who initially present as T790M-negative exhibited T790M-positive conversion after repeated re-biopsy. In these studies, performing re-biopsies increased their T790M detection rates from 36–80% and 45–67% [ 14 , 18 ]. While this method increases the frequency of T790M detection, it also increases the number of invasive procedures done on patients. Liquid biopsies, a type of re-biopsy, are less invasive for patients but have lower T790M detection rates [ 19 ]. On the other hand, the droplet digital polymerase chain reaction (ddPCR) is another method that increases the sensitivity and rate of T790M [ 20 , 21 ]. These studies suggest that patients with low T790M allele frequency had longer PFS with osimertinib than those with high T790M allele frequency. Thus, and detection and measurement of T790M using ddPCR is an effective method for increasing T790M detection rates that guarantees the efficacy of osimertinib. However, the use of ddPCR is non-reimbursable and impractical for daily clinical practice. Therefore, the development of other methods to increase the rate of T790M detection after first-line 1st- or 2nd-G EGFR-TKI therapy remains important. We evaluated predictive markers for the detection of T790M mutations based on patient backgrounds prior to first-line EGFR-TKI treatment through CART. CART analysis is a prediction model constructed by recursively partitioning a dataset and fitting a simple mode with machine learning methods for constructing prediction models from data [ 22 ]. CART classified three distinct groups of patients based on variables that were strongly associated with detecting acquired T790M mutations (age, CYF, LDH, and liver metastasis); however, the AUROC was not satisfactory. In our study, the T790M detection rates between cases with deletion 19 and L858R mutations were different, in which is consistent with a previous report [ 14 , 23 , 24 ]. Accordingly, we decided to analyze predictive markers for the detection of T790M mutations for each type of EGFR mutation. We demonstrated that CART highly stratified the T790M detection markers according to EGFR type, with a more satisfactory AUROC than that of the total population. This may be due to the biological and clinical differences between deletion 19 and L858R mutations. The affinity between ATP and EGFR in cases with deletion 19 mutations is higher than that of L858R [ 25 ]. The phosphorylation of Akt and Erk in cases of deletion 19 mutations, along with downstream signals of EGFR , are inhibited in a concentration-dependent manner compared with in cases of L858R [ 26 ]. Furthermore, a difference in the mechanisms of EGFR activation has been reported [ 27 ], and differences in the efficacy of EGFR-TKI treatment have been reported in clinical practice [ 3 , 28 ]. The present study had some limitations. First, this was a retrospective study, in which only Japanese patients were eligible for the analysis. Thus, our findings may not be generalizable to other ethnic populations. Second, the patients were treated according to the physician’s choices, and the treatment and examinations may not have been standardized. Therefore, this study was with a little missing data within eligible group that would limit the applicability of results. Third, the patients underwent various assays for primary and secondary EGFR mutations in this study. The sensitivity of each assay may have differed. However, this cohort is representative of true real-world practice. In conclusion, identification of patients at risk of acquiring the T790M mutation after failure of EGFR-TKI may help in the selection of first-line EGFR-TKI treatment options. Furthermore, prediction of T790M mutations after initial EGFR-TKI resistance aids in recommending afatinib as the more effective first-line EGFR-TKI treatment compared with 1st-G EGFR-TKIs. Declarations Author Declarations O Ethics approval and consent to participate: This study was approved by the ethical review board or institutional review board of each participating institute, and main institutional review board (IRB) approval for this study was obtained from the Medical Research Ethics Committee of Osaka International Cancer Institute. O Consent for publication: All authors approved final manuscript. O Availability of data and materials: The data that support the findings of this study are openly available in University hospital Medical Information Network at https://www.umin.ac.jp, reference number UMIN000041474. O Competing interests: M.T. has grants from Ono Pharmaceutical, Bristol-Myers Squibb, and BoehringerIngelheim, and payment or honoraria from AstraZeneca, Ono Pharmaceutical, Bristol-Myers Squibb, Taiho Pharmaceutical, Chugai Pharmaceutical, MSD, BoehringerIngelheim, Eli Lilly, Kyowa Kirin, Pfizer, and Asahi Kasei Pharmaceutical. H.S. has payment or honoraria from Chugai Pharmaceutical and AstraZeneca. A.T. has grants from AstraZeneca, and payment or honoraria from AstraZeneca, Ono Pharmaceutical, Bristol-Myers Squibb, Taiho Pharmaceutical, Chugai Pharmaceutical, MSD, BoehringerIngelheim, Eli Lilly, Kyowa Kirin, Pfizer, and Kissei, and advisory board from AstraZeneca, Pfizer, amd Ono Pharmaceutical. Y.S. has payment or honoraria from Chugai Pharmaceutical and AstraZeneca. M.K. has payment or honoraria from AstraZeneca, Ono Pharmaceutical, Shionogi Pharmaceutical, Chugai Pharmaceutical, MSD, BoehringerIngelheim, and Eli Lilly. T.S. has payment or honoraria from Kyorin Pharmaceutical, Ono Pharmaceutical, Daiichi Sankyo, Chugai Pharmaceutical, AstraZeneca, and Novartis Pharmaceutical. F.D. has grants from AstraZeneca, and Boehringer Ingelheim, and payment or honoraria from AstraZeneca, Ono Pharmaceutical, Bristol-Myers Squibb, Taiho Pharmaceutical, Chugai Pharmaceutical, MSD, BoehringerIngelheim, and Eli Lilly. Other co-authors have no COI. O Funding: This study was funded by Boehringer Ingelheim. O Authors' contributions: (I) Conception and design: M.T., K.F., A.T., and S.T., (II) Administrative support: M.T., K.F., A.T., and S.T., (III) Provision of study materials or patients: All authors,, (IV) Collection and assembly of data: All authors, (V) Data analysis and interpretation: M.T., K.F., H.S., A.T., and S.T., (VI) Manuscript writing: All authors, O Acknowledgements: This study was funded by Boehringer Ingelheim. We thank the staff, data manager and other support staff at all investigational sites. • Compliance with Ethical Standards O Disclosure of potential conflicts of interest: This study was approved by the ethical review board or institutional review board of each participating institute. Informed consent was not required owing to the retrospective nature of the study, and an opt-out method was utilized so that patients and their families could refuse to participate in the study. 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Better survival with EGFR exon 19 than exon 21 mutations in gefitinib-treated non-small cell lung cancer patients is due to differential inhibition of downstream signals. Cancer Lett. 2008; 265: 307–317. Cho J, Chen L, Sangji N, Okabe T, Yonesaka K, Francis JM, et al. Cetuximab response of lung cancer-derived EGF receptor mutants is associated with asymmetric dimerization. Cancer Res . 2013; 73: 6770–6779. Li WQ, Cui JW. Non-small cell lung cancer patients with ex19del or exon 21 L858R mutation: distinct mechanisms, different efficacies to treatments. J Cancer Res Clin Oncol. 2020; 146: 2329–2338. Tables Table 1. Patient demographics. Data are N (%) or median (range). T790M (+) N=61 T790M (-) N=105 Total N=166 Gender Male 43 (36) 76 (64) 119 Female 18 (38) 29 (62) 47 Age 68 (37–85) 70 (35–91) 69 (35–91) EGFR Type Del19 36 (44) 45 (56) 81 L858R 25 (31) 56 (69) 81 Other 0 (0) 4 (100) 4 Smoking Non-smoker 43 (38) 70 (62) 113 Ex-smoker 11 (28) 29 (72) 40 Current 7 (54) 6 (46) 13 Histology Adeno 61 (38) 100 (62) 161 Non-adeno 0 (0) 5 (100) 5 Performance status 0 15 (44) 19 (56) 34 1 37 (34) 73 (66) 110 2 6 (55) 5 (45) 11 3 2 (22) 7 (78) 9 4 1 (50) 1 (50) 2 First-line EGFR TKI Afatinib 18 (35) 33 (65) 51 Erlotinib 18 (42) 25 (58) 43 Gefitinib 25 (35) 47 (65) 72 Brain meta Positive 19 (34) 37 (66) 56 Negative 42 (38) 68 (62) 110 Bone meta Positive 30 (42) 41 (58) 71 Negative 31 (33) 64 (67) 95 Liver meta Positive 11 (61) 7 (39) 18 Negative 50 (34) 98 (66) 148 Adrenal meta Positive 4 (33) 8 (67) 12 Negative 57 (37) 97 (63) 154 Lung meta Positive 25 (41) 36 (59) 61 Negative 36 (34) 69 (66) 105 Pleural effusion Positive 25 (40) 38 (60) 63 Negative 36 (35) 67 (65) 103 LDH (U/L) 202 (83–1115) 204 (124–525) 203 (83–1115) ALP (U/L) n=59 n=102 n=161 264 (114–1311) 282.5 (104–6519) 276 (104–6519) CRP (mg/dL) n=61 n=104 n=165 0.2 (0–7.4) 0.2 (0–18.7) 0.2 (0–18.7) ALB (g/dL) n=59 n=103 n=162 3.9 (2.7–4.6) 3.9 (2.1–5.1) 3.9 (2.1–5.1) WBC (×10 3 /μL) 6.5 (2.5-18.3) 6.7 (3.7-22.8) 6.5 (2.5-22.8) Neut (×10 3 /μL) n=61 n=104 n=165 4.7 (1.6-15.2) 4.3 (1.8-19.6) 4.4 (1.6-19.6) Lym (/μL) n=61 n=103 n=164 1400 (200–3000) 1300 (100–3700) 1400 (100–3700) CEA (ng/mL) 33.8 (1–2230) 14.5 (0.7–4747.3) 18.4 (0.7–4747.3) CYF (ng/mL) n=56 n=102 n=158 4 (0.6-144) 3.5 (0.5-78.9) 3.7 (0.5-144) PD-L1 Negative 9 (39) 14 (61) 23 1–49% 5 (45) 6 (55) 11 ≥50% 1 (14) 6 (86) 7 Table 2. Frequency of T790M mutations after first line EGFR-TKI failure (N=166). Type of EGFR-TKI Gefitinib Erlotinib Afatinib Total Frequency of T790M mutations 25/72 18/43 18/51 61/166 (%) 34.7% + 41.9% = 37.4% 35.3 % 36.7 % Cite Share Download PDF Status: Published Journal Publication published 27 Jan, 2022 Read the published version in Investigational New Drugs → Version 1 posted Reviews received at journal 27 Nov, 2021 Reviewers invited by journal 27 Nov, 2021 Editorial decision: Accept as is. 27 Nov, 2021 Editor assigned by journal 22 Nov, 2021 First submitted to journal 14 Nov, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-1079146\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":66320283,\"identity\":\"1262dd10-710c-49fd-a3ef-9aafec17aca6\",\"order_by\":0,\"name\":\"Motohiro 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Kobe Shiritsu Nishi Kobe Iryo Center\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Mitsunori\",\"middleName\":\"\",\"lastName\":\"Morita\",\"suffix\":\"\"},{\"id\":66320293,\"identity\":\"8d641ae6-a06b-478c-b028-986395c483d2\",\"order_by\":10,\"name\":\"Tomonori Hirashima\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Osaka Habikino Iryo Center\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Tomonori\",\"middleName\":\"\",\"lastName\":\"Hirashima\",\"suffix\":\"\"},{\"id\":66320294,\"identity\":\"e026d751-7840-4af3-8c68-184c52f1c116\",\"order_by\":11,\"name\":\"Yasushi Fukuda\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Kurashiki Central Hospital: Kurashiki Chuo Byoin\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yasushi\",\"middleName\":\"\",\"lastName\":\"Fukuda\",\"suffix\":\"\"},{\"id\":66320295,\"identity\":\"7c4ebc2d-6dc4-4e92-8cd0-5acc69c6fc5f\",\"order_by\":12,\"name\":\"Masashi Kanazu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Kokuritsu Byoin Kiko Osaka Toneyama Iryo Center\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Masashi\",\"middleName\":\"\",\"lastName\":\"Kanazu\",\"suffix\":\"\"},{\"id\":66320296,\"identity\":\"a79332a5-773e-42e8-bf6b-dceb1695a414\",\"order_by\":13,\"name\":\"Kazutaka Hosoya\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Kobe City Medical Center General Hospital: Kobe Shiritsu Iryo Center Chuo Shimin Byoin\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Kazutaka\",\"middleName\":\"\",\"lastName\":\"Hosoya\",\"suffix\":\"\"},{\"id\":66320297,\"identity\":\"12bfc43a-3a3a-4306-883b-df60b92e8172\",\"order_by\":14,\"name\":\"Takuji Suzuki\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Chiba University: Chiba Daigaku\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Takuji\",\"middleName\":\"\",\"lastName\":\"Suzuki\",\"suffix\":\"\"},{\"id\":66320298,\"identity\":\"9fc141f5-2c42-4a01-89f5-de1f74e80cd2\",\"order_by\":15,\"name\":\"Kiyonobu Ueno\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Osaka General Medical Center: Osaka Kyuseiki Sogo Iryo Center\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Kiyonobu\",\"middleName\":\"\",\"lastName\":\"Ueno\",\"suffix\":\"\"},{\"id\":66320299,\"identity\":\"d0251f89-423d-4594-b1a9-c2ef5f110d18\",\"order_by\":16,\"name\":\"Daichi Fujimoto\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Kobe City Medical Center General Hospital: Kobe Shiritsu Iryo Center Chuo Shimin Byoin\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Daichi\",\"middleName\":\"\",\"lastName\":\"Fujimoto\",\"suffix\":\"\"},{\"id\":66320300,\"identity\":\"6c716170-52eb-49aa-abc5-3ec99966b846\",\"order_by\":17,\"name\":\"Toru Kumagai\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Osaka International Cancer Institute: Osaka Kokusai Gan Center\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Toru\",\"middleName\":\"\",\"lastName\":\"Kumagai\",\"suffix\":\"\"},{\"id\":66320301,\"identity\":\"abbd7453-9d4d-40fd-b26b-ce8e859a1a52\",\"order_by\":18,\"name\":\"Satoshi Teramukai\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Kyoto Prefectural University of Medicine: Kyoto Furitsu Ika Daigaku\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Satoshi\",\"middleName\":\"\",\"lastName\":\"Teramukai\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2021-11-14 13:38:50\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-1079146/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-1079146/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1007/s10637-021-01203-5\",\"type\":\"published\",\"date\":\"2022-01-28T00:44:29+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":15976047,\"identity\":\"1b0693b8-29f4-43d4-860b-940d9f361433\",\"added_by\":\"auto\",\"created_at\":\"2021-11-29 16:52:55\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":101911,\"visible\":true,\"origin\":\"\",\"legend\":\"CONSORT diagram.\\nEGFR m+: epidermal growth factor receptor mutation positive, EGFR-TKI: epidermal growth factor receptor-tyrosine kinase inhibitor, PD: disease progression, PD-L1: programmed cell death-ligand 1, TTF-1: thyroid transcription factor-1, KL-6: sialylated carbohydrate antigen KL-6, NSE: neuron specific enolase, Pro-GRP: pro-gastrin releasing peptide. \\n\",\"description\":\"\",\"filename\":\"Figure1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-1079146/v1/1dbcbcf87a8a76cda0df378c.jpg\"},{\"id\":15976046,\"identity\":\"49edd015-39fc-4d99-81e6-b0b9b5293bd5\",\"added_by\":\"auto\",\"created_at\":\"2021-11-29 16:52:55\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":216514,\"visible\":true,\"origin\":\"\",\"legend\":\"The predictive markers for the detection of T790M by CART analysis. (a) in the patients included by CART analysis, (b) in deletion 19, (c) in L858R. \\nCYF: cytokeratin 19 fragmen, WBC: white blood cell count, Liver: liver metastasis , LDH: lactate dehydrogenase, AUROC: area under the receiver operating characteristic, Alb: albumin, Bone: bone metastasis, Pleural: pleural effusion, CEA: carcinoembryonic antigen, ALP: alkaline phosphatase, +: existence, -: nonexistence.\\n\",\"description\":\"\",\"filename\":\"Figure2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-1079146/v1/4cc2aba97f98318bd5f6067b.jpg\"},{\"id\":15976048,\"identity\":\"045e412c-5a07-4362-aefe-84154aaeb2c9\",\"added_by\":\"auto\",\"created_at\":\"2021-11-29 16:52:55\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":110018,\"visible\":true,\"origin\":\"\",\"legend\":\"Kaplan-Meier plots for (a) overall survival (OS) in eligible patients. The median OS was 1041 days. (b) OS between T790M negative and T790M positive in eligible patients. The median OS were T790M (-) :798 days, T790M (+) :Not reached [HR = 2.70 (95%CI: 1.64 – 4.55), P \\u003c 0.001].\",\"description\":\"\",\"filename\":\"Figure3.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-1079146/v1/2d2ff9637b119865193b2cb2.jpg\"},{\"id\":15976045,\"identity\":\"908a0813-0c27-45ea-b308-b6243f281196\",\"added_by\":\"auto\",\"created_at\":\"2021-11-29 16:52:54\",\"extension\":\"jpg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":122251,\"visible\":true,\"origin\":\"\",\"legend\":\"Kaplan-Meier plots for (a) progression-free survival (PFS) and (b) OS between afatinib and 1st- generation EGFR-TKIs (gefitinib or erlotinib). The median PFS: 398 vs. 279 days; HR: 0.67 [95% CI: 0.50–0.88]; P=0.004. The median OS: 1053 vs. 956 days; HR: 0.68 [95% CI: 0.46–1.01]; P=0.051.\",\"description\":\"\",\"filename\":\"Figure4.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-1079146/v1/8056d329fe9162a51b137c01.jpg\"},{\"id\":17719895,\"identity\":\"95dc10cb-e530-47f7-8751-6fe1d7be4244\",\"added_by\":\"auto\",\"created_at\":\"2022-01-28 00:44:36\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":708498,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-1079146/v1/111742e5-97b6-4e36-9f72-2a13be6a7d33.pdf\"}],\"financialInterests\":\"\",\"formattedTitle\":\"Classification and regression tree for estimating predictive markers to detect T790M mutations after acquired resistance to first line EGFR-TKI: HOPE-002\",\"fulltext\":[{\"header\":\"Introduction:\",\"content\":\"\\u003cp\\u003eEpidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) are key drugs for patients with non-small cell lung cancer (NSCLC) harboring \\u003cem\\u003eEGFR\\u003c/em\\u003e mutations. First -, second, and third generation (1st-, 2nd-, and 3rd-G) EGFR-TKIs have been developed and evaluated for toxicity and efficacy in three randomized trials (LUX-Lung 7, ARCHER 1050, and FLAURA). All three trials showed more clinical benefits in the 2nd- and 3rd-G than the 1st-G EGFR-TKIs. While all studies showed that the 2nd- and 3rd-G EGFR-TKIs improved progression-free survival (PFS), only osimertinib in FLAURA exhibited a statistically significant improvement in overall survival (OS). While OS improvement was also observed with dacomitinib in ARCHER1050, statistical analysis was not performed in this study due to adoption of the gatekeeping method [\\u003cspan additionalcitationids=\\\"CR2\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Therefore, osimertinib is the most strongly recommended drug for patients with common \\u003cem\\u003eEGFR\\u003c/em\\u003e mutations. Japanese guidelines have judged the quality of this evidence as B and the strength of this recommendation as 1 [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eOsimertinib was developed as an anti-cancer drug due to its activity against Thr790Met (T790M) mutations through covalent binding [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. T790M is the most common resistance mechanism that impairs the activity of TKIs detected in approximately 50% of patients with 1st and 2nd-G EGFR-TKI-refractory tumors [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. Thus, osimertinib is effective against T790M-mediated acquired resistance (AR) [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e], making it a reasonable option for several \\u003cem\\u003eEGFR\\u003c/em\\u003e-mutant patients initially treated with 1st- or 2nd-G EGFR-TKIs.\\u003c/p\\u003e \\u003cp\\u003eAt present, there are two main first-line strategies for advanced \\u003cem\\u003eEGFR\\u003c/em\\u003e-mutant NSCLC: first-line osimertinib and salvage osimertinib for T790M-positive AR initially treated with first-line 1st- or 2nd-G EGFR-TKIs. However, prevention of the development of AR must also be considered as a key therapeutic strategy. Importantly, first-line and salvage therapy have different mechanisms of the development of osimertinib AR [\\u003cspan additionalcitationids=\\\"CR10 CR11\\\" citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e] that are more complicated than those of 1st- or 2nd-G EGFR-TKIs.\\u003c/p\\u003e \\u003cp\\u003eTreatment strategies for AR to first-line osimertinib, are currently still limited. On the other hand, salvage osimertinib therapy was found to be a simple and effective treatment for patients with T790M detected after the development of AR to 1st- or 2nd-G EGFR-TKIs. However, the occurrence of T790M after developing AR to first-line 1st- or 2nd-G EGFR-TKIs was found to have a low incidence (approximately 30%) in real-world FLAURA trial data [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. To more effectively utilize salvage osimertinib therapy for these cases, the detection rate of T790M must be increased.\\u003c/p\\u003e \\u003cp\\u003eIn the present study, we aimed to increase rates of T790M detection after first-line 1st- or 2nd-G EGFR-TKI therapy by evaluating predictive markers for T790M mutation detection in patients prior to first-line EGFR-TKI treatment.\\u003c/p\\u003e\"},{\"header\":\"Method:\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy design\\u003c/h2\\u003e \\u003cp\\u003eWe conducted a multicenter, retrospective cohort study across nine medical institutes belonging to the Hanshin Oncology Clinical Problem Evaluation group (HOPE) in Japan. The clinical data of the patients were retrospectively extracted from their medical charts and added to a database. Because this was a retrospective observational study, sample size calculation based on hypothesis testing was not performed.\\u003c/p\\u003e \\u003cp\\u003eThis study was approved by the ethical review board or institutional review board of each participating institute. Informed consent was not required owing to the retrospective nature of the study, and an opt-out method was utilized so that patients and their families could refuse to participate in the study.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePatient selection\\u003c/h2\\u003e \\u003cp\\u003ePatients \\u0026gt;20 years of age were consecutively enrolled if they had pathologically confirmed stage IV non-squamous NSCLC (excluding recurrent cases, such as those who had undergone post-operation or post-chemoradiation therapy) with sensitizing \\u003cem\\u003eEGFR\\u003c/em\\u003e mutations (deletion 19 or L858R) and had received gefitinib, erlotinib, or afatinib as first-line therapies between January 1, 2015 and March 31, 2017.\\u003c/p\\u003e \\u003cp\\u003eThe patients were classified as either never-smokers (those reported to have never smoked), current smokers (those who had smoked within 1 year of diagnosis), or former smokers (the remaining). The clinical stages of all the patients were determined according to the eighth edition of the tumor, node, and metastasis classification of malignant tumors. Anti-tumor responses were assessed using the RECIST version 1.1. The intervals between dates of commencing EGFR-TKI therapy and disease progression or death (PFS) and overall survival (OS) of the patients were calculated. The cutoff date for data collection was set at August 31, 2018.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e \\u003cp\\u003eData were analyzed by independent statisticians. We evaluated predictive markers for T790M mutation detection based on the patients\\u0026rsquo; backgrounds before first-line EGFR-TKI treatment using a classification and regression tree (CART). The Gini coefficient was used to determine the best split. The data collected included sex, age, type of \\u003cem\\u003eEGFR\\u003c/em\\u003e mutation, smoking history, Eastern Cooperative Oncology Group (ECOG) performance status (PS), type of EGFR-TKI, site of metastasis (brain, bone, liver, adrenal, lung, and pleural effusion), and laboratory data.\\u003c/p\\u003e \\u003cp\\u003eFisher\\u0026rsquo;s exact test was used for categorical comparisons of data. Kaplan-Meier curves were used to evaluate PFS and OS. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using the Cox proportional hazards model. All statistical analyses were conducted using R software (version 4.0.5; \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://R-project.org\\u003c/span\\u003e\\u003c/span\\u003e; The R Foundation for Statistical Computing, Vienna, Austria). The following R libraries were used: rpart (version 4.1.15), rpart.plot (version 3.0.9), pROC (version 1.17.0.1), survival (version 3.2.10), and survminer (version 0.4.9). Differences were considered statistically significant at P\\u0026lt;0.05.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results:\",\"content\":\"\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePatient demographics\\u003c/h2\\u003e \\u003cp\\u003eA total of 289 consecutive stage 4 NSCLC patients (287 eligible) were enrolled at nine medical institutes belonging to the HOPE in Japan from January 1, 2015 to March 31, 2017. Among them, two patients with de novo T790M mutations were excluded. A total of 225 patients experienced disease progression (PD) after first-line EGFR-TKI treatment, of which 166 underwent re-biopsy by tissue or plasma. Among these patients, 147 patients remained for CART method analysis after excluding patients with missing background data, such as programmed cell death-ligand 1 (PD-L1), thyroid transcription factor-1 (TTF-1), sialylated carbohydrate antigen KL-6 (KL-6), neuron specific enolase (NSE), pro-gastrin releasing peptide (Pro-GRP) patients missing \\u0026ge;10% data (Figure \\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe characteristics of the 166 patients are shown in Table 1. The median age of the patients was 69 years. Of the 166 patients, 71.7% were male, 31.9% had histories of smoking, 48.8% had L858R mutations and exon 19 deletions, and 2.4% had common and other uncommon mutations. Approximately 30.7% were treated with afatinib, 25.9% with erlotinib, and 43.4% with gefitinib as first-line treatments.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDevelopment of T790M mutation after PD in first line EGFR-TKI therapy\\u003c/h2\\u003e \\u003cp\\u003eA total of 36.7% (61/166) of the patients acquired T790M mutations after first-line EGFR-TKI therapy. The frequencies of T790M after each first-line EGFR-TKI failure were as follows: gefitinib, 34.7% (25/72); erlotinib, 41.9% (18/43); and afatinib, 35.3% (18/51). The frequencies of T790M in the 1st-G EGFR-TKIs, including gefitinib and erlotinib (37.4%; 43/115) were similar to that of afatinib (P=0.862). On the other hand, the frequency of T790M in patients with deletion 19 (44.4%; 36/81) tended to be higher than that in patients with L858R (44.4%, 36/81 vs 30.9%, 25/81; P=0.104).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eEvaluation of predictive markers for detecting T790M through CART\\u003c/h2\\u003e \\u003cp\\u003eFirst, we evaluated predictive markers for the detection of T790M in the patients included by CART analysis (N=147). CART identified three distinct groups of patients based on variables strongly associated with acquired T790M mutations (age, cytokeratin 19 fragmen (CYF), white blood cell count (WBC), liver metastasis, and lactate dehydrogenase (LDH)), with an area under the receiver operating characteristic (AUROC) of 0.77 (95% CI: 0.69\\u0026ndash;0.84) (Figure \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ea). Although we classified the three groups according to the frequency of T790M, the AUROC was found to be low. Next, we analyzed predictive markers for detecting T790M mutations with each type of \\u003cem\\u003eEGFR\\u003c/em\\u003e mutation since a previous analysis found that its frequency varies according to \\u003cem\\u003eEGFR\\u003c/em\\u003e type [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. The CART identified three distinct groups in deletion 19 based on certain variables (albumin (Alb), LDH, bone metastasis, pleural effusion, and WBC), with an AUROC of 0.81 (95% CI: 0.71\\u0026ndash;0.91) (Figure \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eb). Furthermore, CART identified two distinct groups in L858R based on certain variables (age, carcinoembryonic antigen (CEA), and ALP), with an AUROC of 0.80 (95% CI: 0.69\\u0026ndash;0.90) (Figure \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ec). The detection rates of the highest groups in deletions 19 and L858R were \\u0026ge;80% and \\u0026ge;60%, respectively.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eEffect of T790M on OS\\u003c/h2\\u003e \\u003cp\\u003eThe median OS across all patients (N=289) was 1041 days (Figure \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ea). The T790M mutation was detected in 64 patients. Furthermore, more patients without acquired T790M mutations died compared with those with acquired T790M mutations throughout the observation period (116/225 vs. 18/64; P\\u0026lt;0.01). Additionally, the patients without acquired T790M mutations had worse outcomes than patients with the T790M mutation (median OS: 798 days vs. not reached; HR: 2.70 [95% CI: 1.64\\u0026ndash;4.55]; P\\u0026lt;0.001) (Figure \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eb).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePFS and OS of first-line EGFR-TKIs\\u003c/h2\\u003e \\u003cp\\u003eAs mentioned above, the T790M mutation affected OS, and the frequency of the development of T790M after developing EGFR-TKI resistance was similar between afatinib and 1st-G EGFR-TKIs. However, afatinib was previously reported to have better outcomes than gefitinib [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. We evaluated which first-line EGFR-TKI had the highest PFS and OS based on real-world data. Compared with 1st-G EGFR-TKIs, afatinib had a longer PFS (median PFS: 398 vs. 279 days; HR: 0.67 [95% CI: 0.50\\u0026ndash;0.88]; P=0.004) (Figure \\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ea) and tended to have a longer OS (median OS: 1053 vs. 956 days; HR: 0.68 [95% CI: 0.46\\u0026ndash;1.01]; P=0.051) (Figure \\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eb).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion:\",\"content\":\"\\u003cp\\u003eThis is the first analysis to identify predictive markers using CART for the detection of T790M mutations based on patient backgrounds prior to first-line EGFR-TKI treatment. This study also identified markers that distinguish between \\u003cem\\u003eEGFR\\u003c/em\\u003e mutation types, leading to more accurate predictions of T790M detection. CART classified the groups according to the detection rate of T790M based on certain variables (age, CYF, WBC, liver metastasis and LDH). Furthermore, deletion 19 mutations were classified into three distinct groups based on certain variables (Alb, LDH, bone metastasis, pleural effusion, and WBC), and L858R mutations were classified into two distinct groups based on certain variables (age, CEA, and ALP).\\u003c/p\\u003e \\u003cp\\u003eAs first-line treatment, osimertinib has been found to be associated with longer PFS and OS than 1st-G EGFR-TKIs against advanced NSCLC harboring \\u003cem\\u003eEGFR\\u003c/em\\u003e mutations (exon-19 deletion and L858R) [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. However, in the Asian subset (especially in the Japanese subset) analysis of OS in the FLAURA study, osimertinib was not observed to be superior to 1st-G EGFR-TKIs [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. Furthermore, no additional molecular targets for therapy are known due to the heterogeneity of resistance mechanisms that are not well understood [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. As a result, in the clinical care of most patients following cancer progression after osimertinib treatment, chemotherapy is the only remaining option for second-line treatment. In contrast, the most common mechanism of the development of resistance to 1st- and 2nd-G EGFR-TKI treatment is the T790M mutation. Fortunately, osimertinib overcomes the T790M mutation and provides significantly longer PFS compared to standard platinum-based chemotherapy in advanced T790M positive NSCLC patients with AR to first line 1st or 2nd-G EGFR-TKIs (median PFS: 10.1 vs. 4.4 months; HR: 0.30 [95% CI: 0.23\\u0026ndash;0.41]) [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. In particular, a non-interventional GioTag study demonstrated clinical benefit with sequential afatinib and osimertinib in patients with EGFR mutation-positive NSCLC with T790M-acquired resistance; this trend was more pronounced among the Asian population [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn this study, we showed that patients with acquired T790M mutations had better outcomes than patients without T790M mutations after AR to first-line 1st- or 2nd-G EGFR-TKIs. Therefore, the use of osimertinib as first-line treatment for NSCLC patients harboring EGFR mutations remains controversial in practice. Furthermore, sequential EGFR-TKI treatment may be superior to first-line osimertinib in patients who will likely develop acquired T790M mutations. When considering sequential therapy, the benefit is likely to be diminished if T790M is not detected since osimertinib remains an important and beneficial drug that is essential for patients with advanced NSCLC harboring EGFR mutations. However, the incidence of T790M after AR to first-line first line 1st- or 2nd-G EGFR-TKIs was found to be low based on real-world and FLAURA trial data [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003ePrevious reports have shown the value of performing a re-biopsy since patients who initially present as T790M-negative exhibited T790M-positive conversion after repeated re-biopsy. In these studies, performing re-biopsies increased their T790M detection rates from 36\\u0026ndash;80% and 45\\u0026ndash;67% [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. While this method increases the frequency of T790M detection, it also increases the number of invasive procedures done on patients. Liquid biopsies, a type of re-biopsy, are less invasive for patients but have lower T790M detection rates [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. On the other hand, the droplet digital polymerase chain reaction (ddPCR) is another method that increases the sensitivity and rate of T790M [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. These studies suggest that patients with low T790M allele frequency had longer PFS with osimertinib than those with high T790M allele frequency. Thus, and detection and measurement of T790M using ddPCR is an effective method for increasing T790M detection rates that guarantees the efficacy of osimertinib. However, the use of ddPCR is non-reimbursable and impractical for daily clinical practice. Therefore, the development of other methods to increase the rate of T790M detection after first-line 1st- or 2nd-G EGFR-TKI therapy remains important.\\u003c/p\\u003e \\u003cp\\u003eWe evaluated predictive markers for the detection of T790M mutations based on patient backgrounds prior to first-line EGFR-TKI treatment through CART. CART analysis is a prediction model constructed by recursively partitioning a dataset and fitting a simple mode with machine learning methods for constructing prediction models from data [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. CART classified three distinct groups of patients based on variables that were strongly associated with detecting acquired T790M mutations (age, CYF, LDH, and liver metastasis); however, the AUROC was not satisfactory. In our study, the T790M detection rates between cases with deletion 19 and L858R mutations were different, in which is consistent with a previous report [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. Accordingly, we decided to analyze predictive markers for the detection of T790M mutations for each type of EGFR mutation. We demonstrated that CART highly stratified the T790M detection markers according to EGFR type, with a more satisfactory AUROC than that of the total population. This may be due to the biological and clinical differences between deletion 19 and L858R mutations. The affinity between ATP and EGFR in cases with deletion 19 mutations is higher than that of L858R [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. The phosphorylation of Akt and Erk in cases of deletion 19 mutations, along with downstream signals of \\u003cem\\u003eEGFR\\u003c/em\\u003e, are inhibited in a concentration-dependent manner compared with in cases of L858R [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. Furthermore, a difference in the mechanisms of EGFR activation has been reported [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e], and differences in the efficacy of EGFR-TKI treatment have been reported in clinical practice [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe present study had some limitations. First, this was a retrospective study, in which only Japanese patients were eligible for the analysis. Thus, our findings may not be generalizable to other ethnic populations. Second, the patients were treated according to the physician\\u0026rsquo;s choices, and the treatment and examinations may not have been standardized. Therefore, this study was with a little missing data within eligible group that would limit the applicability of results. Third, the patients underwent various assays for primary and secondary EGFR mutations in this study. The sensitivity of each assay may have differed. However, this cohort is representative of true real-world practice.\\u003c/p\\u003e \\u003cp\\u003eIn conclusion, identification of patients at risk of acquiring the T790M mutation after failure of EGFR-TKI may help in the selection of first-line EGFR-TKI treatment options. Furthermore, prediction of T790M mutations after initial EGFR-TKI resistance aids in recommending afatinib as the more effective first-line EGFR-TKI treatment compared with 1st-G EGFR-TKIs.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAuthor Declarations\\u003cbr\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eO Ethics approval and consent to participate:\\u0026nbsp;\\u003c/strong\\u003eThis study was approved by the ethical review board or institutional review board of each participating institute, and main institutional review board (IRB) approval for this study was obtained from the Medical Research Ethics Committee of Osaka International Cancer Institute.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eO Consent for publication:\\u003c/strong\\u003e All authors approved final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eO Availability of data and materials:\\u003c/strong\\u003e The data that support the findings of this study are openly available in University hospital Medical Information Network at https://www.umin.ac.jp, reference number UMIN000041474.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eO Competing interests:\\u003c/strong\\u003e M.T. has grants from Ono Pharmaceutical, Bristol-Myers Squibb, and BoehringerIngelheim, and payment or honoraria from AstraZeneca, Ono Pharmaceutical, Bristol-Myers Squibb, Taiho Pharmaceutical, Chugai Pharmaceutical, MSD, BoehringerIngelheim, Eli Lilly, Kyowa Kirin, Pfizer, and Asahi Kasei Pharmaceutical. H.S. has payment or honoraria from Chugai Pharmaceutical and AstraZeneca. A.T. has grants from AstraZeneca, and payment or honoraria from AstraZeneca, Ono Pharmaceutical, Bristol-Myers Squibb, Taiho Pharmaceutical, Chugai Pharmaceutical, MSD, BoehringerIngelheim, Eli Lilly, Kyowa Kirin, Pfizer, and Kissei, and advisory board from AstraZeneca, Pfizer, amd Ono Pharmaceutical. Y.S. has payment or honoraria from Chugai Pharmaceutical and AstraZeneca. M.K. has payment or honoraria from AstraZeneca, Ono Pharmaceutical, Shionogi Pharmaceutical, Chugai Pharmaceutical, MSD, BoehringerIngelheim, and Eli Lilly. T.S. has payment or honoraria from Kyorin Pharmaceutical, Ono Pharmaceutical, Daiichi Sankyo, Chugai Pharmaceutical, AstraZeneca, and Novartis Pharmaceutical. F.D. has grants from AstraZeneca, and Boehringer Ingelheim, and payment or honoraria from AstraZeneca, Ono Pharmaceutical, Bristol-Myers Squibb, Taiho Pharmaceutical, Chugai Pharmaceutical, MSD, BoehringerIngelheim, and Eli Lilly. Other co-authors have no COI.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eO Funding:\\u003c/strong\\u003e This study was funded by Boehringer Ingelheim.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eO Authors\\u0026apos; contributions:\\u003c/strong\\u003e (I) Conception and design: M.T., K.F., A.T., and S.T., (II) Administrative support: M.T., K.F., A.T., and S.T., (III) Provision of study materials or patients: All authors,, (IV) Collection and assembly of data: All authors, (V) Data analysis and interpretation: M.T., K.F., H.S., A.T., and S.T., (VI) Manuscript writing: All authors,\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eO Acknowledgements:\\u003c/strong\\u003e This study was funded by Boehringer Ingelheim. We thank the staff, data manager and other support staff at all investigational sites.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u0026bull; Compliance with Ethical Standards\\u003cbr\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eO Disclosure of potential conflicts of interest:\\u003c/strong\\u003e This study was approved by the ethical review board or institutional review board of each participating institute. Informed consent was not required owing to the retrospective nature of the study, and an opt-out method was utilized so that patients and their families could refuse to participate in the study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eO Research involving Human Participants and/or Animals:\\u003c/strong\\u003e Research involving Human Participants\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eO Informed consent:\\u0026nbsp;\\u003c/strong\\u003eInformed consent was not required owing to the retrospective nature of the study, and an opt-out method was utilized so that patients and their families could refuse to participate in the study.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003ePark K, Tan EH, O\\u0026apos;Byrne K,\\u0026nbsp;Zhang L, Boyer M, Mok T,\\u0026nbsp;et al. Afatinib versus gefitinib as first-line treatment of patients with EGFR mutation-positive non-small-cell lung cancer (LUX-Lung 7): a phase 2B, open-label, randomised controlled trial. \\u003cem\\u003eLancet Oncol.\\u003c/em\\u003e 2016; 17: 577\\u0026ndash;589.\\u003c/li\\u003e\\n \\u003cli\\u003eWu YL, Cheng Y, Zhou X,\\u0026nbsp;Lee KH, Nakagawa K, Niho S,\\u0026nbsp;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. \\u003cem\\u003eLancet Oncol.\\u003c/em\\u003e 2017; 18: 1454\\u0026ndash;1466.\\u003c/li\\u003e\\n \\u003cli\\u003eSoria JC, Ohe Y, Vansteenkiste J,\\u0026nbsp;Reungwetwattana T, Chewaskulyong B, Lee KH,\\u0026nbsp;et al. Osimertinib in Untreated EGFR-Mutated Advanced Non-Small-Cell Lung Cancer. \\u003cem\\u003eN Engl J Med\\u003c/em\\u003e. 2018; 378: 113\\u0026ndash;125.\\u003c/li\\u003e\\n \\u003cli\\u003eAkamatsu H, Ninomiya K, Kenmotsu H,\\u0026nbsp;Morise M, Daga H, Goto Y,\\u0026nbsp;et al. The Japanese Lung Cancer Society Guideline for non-small cell lung cancer, stage IV. \\u003cem\\u003eInt J Clin Oncol\\u003c/em\\u003e. 2019; 24: 731\\u0026ndash;770.\\u003c/li\\u003e\\n \\u003cli\\u003eCross DA, Ashton SE, Ghiorghiu S,\\u0026nbsp;Eberlein C, Nebhan CA, Spitzler PJ,\\u0026nbsp;et al. AZD9291, an irreversible EGFR TKI, overcomes T790M-mediated resistance to EGFR inhibitors in lung cancer. \\u003cem\\u003eCancer Discov.\\u003c/em\\u003e 2014; 4: 1046\\u0026ndash;1061.\\u003c/li\\u003e\\n \\u003cli\\u003eArcila ME, Oxnard GR, Nafa K,\\u0026nbsp;Riely GJ, Solomon SB, Zakowski MF,\\u0026nbsp;et al. Rebiopsy of lung cancer patients with acquired resistance to EGFR inhibitors and enhanced detection of the T790M mutation using a locked nucleic acid-based assay. \\u003cem\\u003eClin Cancer Res.\\u0026nbsp;\\u003c/em\\u003e2011; 17: 1169\\u0026ndash;1180.\\u003c/li\\u003e\\n \\u003cli\\u003eOxnard GR, Arcila ME, Sima CS,\\u0026nbsp;Riely GJ, Chmielecki J, Kris MG,\\u0026nbsp;et al. Acquired resistance to EGFR tyrosine kinase inhibitors in EGFR-mutant lung cancer: distinct natural history of patients with tumors harboring the T790M mutation. \\u003cem\\u003eClin Cancer Res.\\u003c/em\\u003e 2011; 17: 1616\\u0026ndash;1622.\\u003c/li\\u003e\\n \\u003cli\\u003eMok TS, Wu Y-L, Ahn M-J,\\u0026nbsp;Garassino MC, Kim HR, Ramalingam SS,\\u0026nbsp;et al. Osimertinib or Platinum-Pemetrexed in EGFR T790M-Positive Lung Cancer. \\u003cem\\u003eN Engl J Med\\u003c/em\\u003e. 2017; 376: 629\\u0026ndash;640.\\u003c/li\\u003e\\n \\u003cli\\u003eSchoenfeld AJ, Chan JM, Kubota D,\\u0026nbsp;Sato H, Rizvi H, Daneshbod Y,\\u0026nbsp;et al. Tumor Analyses Reveal Squamous Transformation and Off-Target Alterations As Early Resistance Mechanisms to First-line Osimertinib in \\u003cem\\u003eEGFR\\u003c/em\\u003e-Mutant Lung Cancer. \\u003cem\\u003eClin Cancer Res.\\u003c/em\\u003e 2020; 26: 2654\\u0026ndash;2663.\\u003c/li\\u003e\\n \\u003cli\\u003eFernandes MGO, Sousa C, Jacob M,\\u0026nbsp;Almeida L, Santos V, Ara\\u0026uacute;jo D,\\u0026nbsp;et al. Resistance Profile of Osimertinib in Pre-treated Patients With EGFR T790M-Mutated Non-small Cell Lung Cancer. \\u003cem\\u003eFront Oncol.\\u003c/em\\u003e 2021; 11: 602924.\\u003c/li\\u003e\\n \\u003cli\\u003eOxnard GR, Hu Y, Mileham KF,\\u0026nbsp;Husain H, Costa DB, Tracy P,\\u0026nbsp;et al. Assessment of Resistance Mechanisms and Clinical Implications in Patients With EGFR T790M-Positive Lung Cancer and Acquired Resistance to Osimertinib. \\u003cem\\u003eJAMA Oncol\\u003c/em\\u003e 2018; 4: 1527\\u0026ndash;1534.\\u003c/li\\u003e\\n \\u003cli\\u003eGray JE, Okamoto I, Sriuranpong V,\\u0026nbsp;Vansteenkiste J, Imamura F, Lee JS,\\u0026nbsp;et al. Tissue and Plasma EGFR Mutation Analysis in the FLAURA Trial: Osimertinib versus Comparator EGFR Tyrosine Kinase Inhibitor as First-Line Treatment in Patients with EGFR-Mutated Advanced Non-Small Cell Lung Cancer. \\u003cem\\u003eClin Cancer Res.\\u003c/em\\u003e 2019; 25: 6644\\u0026ndash;6652.\\u003c/li\\u003e\\n \\u003cli\\u003eSeto T, Nogami N, Yamamoto N,\\u0026nbsp;Atagi S, Tashiro N, Yoshimura Y,\\u0026nbsp;et al. Real-World EGFR T790M Testing in Advanced Non-Small-Cell Lung Cancer: A Prospective Observational Study in Japan. \\u003cem\\u003eOncol Ther.\\u003c/em\\u003e 2018; 6: 203\\u0026ndash;215.\\u003c/li\\u003e\\n \\u003cli\\u003eNinomaru T, Hata A, Kokan C,\\u0026nbsp;Okada H, Tomimatsu H, Ishida J. Higher osimertinib introduction rate achieved by multiple repeated rebiopsy after acquired resistance to first/second generation EGFR-TKIs. \\u003cem\\u003eThorac Cancer.\\u003c/em\\u003e 2021; 12: 746\\u0026ndash;751.\\u003c/li\\u003e\\n \\u003cli\\u003eOhe Y, Imamura F, Nogami N,\\u0026nbsp;Okamoto I, Kurata T, Kato T,\\u0026nbsp;et al. Osimertinib versus standard-of-care EGFR-TKI as first-line treatment for EGFRm advanced NSCLC: FLAURA Japanese subset. \\u003cem\\u003eJpn J Clin Oncol.\\u003c/em\\u003e 2019; 49: 29\\u0026ndash;36.\\u003c/li\\u003e\\n \\u003cli\\u003eLeonetti A, Sharma S, Minari R,\\u0026nbsp;Perego P, Giovannetti E, Tiseo M. Resistance mechanisms to osimertinib in EGFR-mutated non-small cell lung cancer. \\u003cem\\u003eBr J Cancer.\\u003c/em\\u003e 2019; 121: 725\\u0026ndash;737.\\u003c/li\\u003e\\n \\u003cli\\u003eYamamoto N, Mera T,\\u0026nbsp;M\\u0026auml;rten\\u0026nbsp;A, Hochmair MJ. Observational Study of Sequential Afatinib and Osimertinib in EGFR Mutation-Positive NSCLC: Patients Treated with a 40-mg Starting Dose of Afatinib. \\u003cem\\u003eAdv Ther\\u003c/em\\u003e. 2020; 37: 759\\u0026ndash;769.\\u003c/li\\u003e\\n \\u003cli\\u003eIchihara E, Hotta K, Kubo T,\\u0026nbsp;Higashionna T, Ninomiya K, Ohashi K,\\u0026nbsp;et al. Clinical significance of repeat rebiopsy in detecting the EGFR T790M secondary mutation in patients with non-small cell lung cancer. \\u003cem\\u003eOncotarget.\\u003c/em\\u003e 2018; 9: 29525\\u0026ndash;29531.\\u003c/li\\u003e\\n \\u003cli\\u003eMountzios G, Koumarianou A, Bokas A,\\u0026nbsp;Mavroudis D, Samantas E, Fergadis EG,\\u0026nbsp;et al. A Real-World, Observational, Prospective Study to Assess the Molecular Epidemiology of Epidermal Growth Factor Receptor (EGFR) Mutations upon Progression on or after First-Line Therapy with a First- or Second-Generation EGFR Tyrosine Kinase Inhibitor in EGFR Mutation-Positive Locally Advanced or Metastatic Non-Small Cell Lung Cancer: The \\u0026apos;LUNGFUL\\u0026apos; Study. \\u003cem\\u003eCancers (Basel).\\u003c/em\\u003e 2021; 13: 3172.\\u003c/li\\u003e\\n \\u003cli\\u003eHochmair MJ, Buder A, Schwab S,\\u0026nbsp;Burghuber OC, Prosch H, Hilbe W,\\u0026nbsp;et al. Liquid-Biopsy-Based Identification of EGFR T790M Mutation-Mediated Resistance to Afatinib Treatment in Patients with Advanced EGFR Mutation-Positive NSCLC, and Subsequent Response to Osimertinib. \\u003cem\\u003eTarget Oncol.\\u003c/em\\u003e 2019; 14: 75\\u0026ndash;83.\\u003c/li\\u003e\\n \\u003cli\\u003eBuder A, Hochmair MJ, Schwab S,\\u0026nbsp;Bundalo T, Schenk P, Errhalt P,\\u0026nbsp;et al. Cell-Free Plasma DNA-Guided Treatment With Osimertinib in Patients With Advanced EGFR-Mutated NSCLC. \\u003cem\\u003eJ Thorac Oncol.\\u003c/em\\u003e 2018; 13: 821\\u0026ndash;830.\\u003c/li\\u003e\\n \\u003cli\\u003eLemon SC, Roy J, Clark MA,\\u0026nbsp;Friedmann PD, Rakowski W. Classification and regression tree analysis in public health: methodological review and comparison with logistic regression. \\u003cem\\u003eAnn Behav Med.\\u003c/em\\u003e 2003; 26: 172\\u0026ndash;181.\\u003c/li\\u003e\\n \\u003cli\\u003eMatsuo N, Azuma K, Sakai K,\\u0026nbsp;Hattori S, Kawahara A, Ishii H,\\u0026nbsp;et al. Association of EGFR Exon 19 Deletion and EGFR-TKI Treatment Duration with Frequency of T790M Mutation in EGFR-Mutant Lung Cancer Patients. \\u003cem\\u003eSci Rep.\\u003c/em\\u003e 2016; 6: 36458.\\u003c/li\\u003e\\n \\u003cli\\u003eNosaki K, Satouchi M, Kurata T,\\u0026nbsp;Yoshida T, Okamoto I, Katakami N,\\u0026nbsp;et al. Re-biopsy status among non-small cell lung cancer patients in Japan: A retrospective study. \\u003cem\\u003eLung Cancer.\\u003c/em\\u003e 2016; 101: 1\\u0026ndash;8.\\u003c/li\\u003e\\n \\u003cli\\u003eCarey KD, Garton AJ, Romero MS,\\u0026nbsp;Kahler J, Thomson S, Ross S,\\u0026nbsp;et al. Kinetic analysis of epidermal growth factor receptor somatic mutant proteins shows increased sensitivity to the epidermal growth factor receptor tyrosine kinase inhibitor, erlotinib.\\u003cem\\u003e\\u0026nbsp;Cancer Res.\\u003c/em\\u003e 2006; 66: 8163\\u0026ndash;8171.\\u003c/li\\u003e\\n \\u003cli\\u003eZhu JQ, Zhong WZ, Zhang GC,\\u0026nbsp;Li R, Zhang XC, Guo AL,\\u0026nbsp;et al. Better survival with EGFR exon 19 than exon 21 mutations in gefitinib-treated non-small cell lung cancer patients is due to differential inhibition of downstream signals. \\u003cem\\u003eCancer Lett.\\u003c/em\\u003e 2008; 265: 307\\u0026ndash;317.\\u003c/li\\u003e\\n \\u003cli\\u003eCho J, Chen L, Sangji N,\\u0026nbsp;Okabe T, Yonesaka K, Francis JM,\\u0026nbsp;et al. Cetuximab response of lung cancer-derived EGF receptor mutants is associated with asymmetric dimerization. \\u003cem\\u003eCancer Res\\u003c/em\\u003e. 2013; 73: 6770\\u0026ndash;6779.\\u003c/li\\u003e\\n \\u003cli\\u003eLi WQ, Cui JW. Non-small cell lung cancer patients with ex19del or exon 21 L858R mutation: distinct mechanisms, different efficacies to treatments. \\u003cem\\u003eJ Cancer Res Clin Oncol.\\u003c/em\\u003e 2020; 146: 2329\\u0026ndash;2338.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"},{\"header\":\"Tables\",\"content\":\"\\u003cp\\u003eTable 1. Patient demographics.\\u003c/p\\u003e\\n\\u003cp\\u003eData are N (%) or median (range).\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellpadding=\\\"0\\\" cellspacing=\\\"0\\\" width=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"3\\\" width=\\\"33.333333333333336%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e \\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eT790M (+)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eN=61\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.280423280423282%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eT790M (-)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eN=105\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTotal\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eN=166\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" rowspan=\\\"2\\\" width=\\\"19.929453262786595%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGender\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.403880070546737%\\\"\\u003e\\n \\u003cp\\u003eMale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e43 (36)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.280423280423282%\\\"\\u003e\\n \\u003cp\\u003e76 (64)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e119\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"16.740088105726873%\\\"\\u003e\\n \\u003cp\\u003eFemale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e18 (38)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"29.07488986784141%\\\"\\u003e\\n \\u003cp\\u003e29 (62)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e47\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" width=\\\"19.929453262786595%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eAge\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.403880070546737%\\\"\\u003e\\n \\u003cp\\u003e \\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e68 (37\\u0026ndash;85)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.280423280423282%\\\"\\u003e\\n \\u003cp\\u003e70 (35\\u0026ndash;91)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e69 (35\\u0026ndash;91)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" rowspan=\\\"3\\\" width=\\\"19.929453262786595%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEGFR Type\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.403880070546737%\\\"\\u003e\\n \\u003cp\\u003eDel19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e36 (44)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.280423280423282%\\\"\\u003e\\n \\u003cp\\u003e45 (56)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e81\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"16.740088105726873%\\\"\\u003e\\n \\u003cp\\u003eL858R\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e25 (31)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"29.07488986784141%\\\"\\u003e\\n \\u003cp\\u003e56 (69)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e81\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"16.740088105726873%\\\"\\u003e\\n \\u003cp\\u003eOther\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e0 (0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"29.07488986784141%\\\"\\u003e\\n \\u003cp\\u003e4 (100)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" rowspan=\\\"3\\\" width=\\\"19.929453262786595%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSmoking\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.403880070546737%\\\"\\u003e\\n \\u003cp\\u003eNon-smoker\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e43 (38)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.280423280423282%\\\"\\u003e\\n \\u003cp\\u003e70 (62)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e113\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"16.740088105726873%\\\"\\u003e\\n \\u003cp\\u003eEx-smoker\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e11 (28)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"29.07488986784141%\\\"\\u003e\\n \\u003cp\\u003e29 (72)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"16.740088105726873%\\\"\\u003e\\n \\u003cp\\u003eCurrent\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e7 (54)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"29.07488986784141%\\\"\\u003e\\n \\u003cp\\u003e6 (46)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" rowspan=\\\"2\\\" width=\\\"19.929453262786595%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHistology\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.403880070546737%\\\"\\u003e\\n \\u003cp\\u003eAdeno\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e61 (38)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.280423280423282%\\\"\\u003e\\n \\u003cp\\u003e100 (62)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e161\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"16.740088105726873%\\\"\\u003e\\n \\u003cp\\u003eNon-adeno\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e0 (0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"29.07488986784141%\\\"\\u003e\\n \\u003cp\\u003e5 (100)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" rowspan=\\\"5\\\" width=\\\"19.929453262786595%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePerformance status\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.403880070546737%\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e15 (44)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.280423280423282%\\\"\\u003e\\n \\u003cp\\u003e19 (56)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e34\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"16.740088105726873%\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e37 (34)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"29.07488986784141%\\\"\\u003e\\n \\u003cp\\u003e73 (66)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e110\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"16.740088105726873%\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e6 (55)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"29.07488986784141%\\\"\\u003e\\n \\u003cp\\u003e5 (45)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"16.740088105726873%\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e2 (22)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"29.07488986784141%\\\"\\u003e\\n \\u003cp\\u003e7 (78)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"16.740088105726873%\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e1 (50)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"29.07488986784141%\\\"\\u003e\\n \\u003cp\\u003e1 (50)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" rowspan=\\\"3\\\" width=\\\"19.929453262786595%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eFirst-line EGFR TKI\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.403880070546737%\\\"\\u003e\\n \\u003cp\\u003eAfatinib\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e18 (35)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.280423280423282%\\\"\\u003e\\n \\u003cp\\u003e33 (65)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e51\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"16.740088105726873%\\\"\\u003e\\n \\u003cp\\u003eErlotinib\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e18 (42)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"29.07488986784141%\\\"\\u003e\\n \\u003cp\\u003e25 (58)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e43\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"16.740088105726873%\\\"\\u003e\\n \\u003cp\\u003eGefitinib\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e25 (35)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"29.07488986784141%\\\"\\u003e\\n \\u003cp\\u003e47 (65)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e72\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" rowspan=\\\"2\\\" width=\\\"19.929453262786595%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eBrain meta\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.403880070546737%\\\"\\u003e\\n \\u003cp\\u003ePositive\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e19 (34)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.280423280423282%\\\"\\u003e\\n \\u003cp\\u003e37 (66)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e56\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"16.740088105726873%\\\"\\u003e\\n \\u003cp\\u003eNegative\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e42 (38)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"29.07488986784141%\\\"\\u003e\\n \\u003cp\\u003e68 (62)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e110\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" rowspan=\\\"2\\\" width=\\\"19.929453262786595%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eBone meta\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.403880070546737%\\\"\\u003e\\n \\u003cp\\u003ePositive\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e30 (42)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.280423280423282%\\\"\\u003e\\n \\u003cp\\u003e41 (58)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e71\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"16.740088105726873%\\\"\\u003e\\n \\u003cp\\u003eNegative\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e31 (33)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"29.07488986784141%\\\"\\u003e\\n \\u003cp\\u003e64 (67)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e95\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" rowspan=\\\"2\\\" width=\\\"19.929453262786595%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLiver meta\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.403880070546737%\\\"\\u003e\\n \\u003cp\\u003ePositive\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e11 (61)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.280423280423282%\\\"\\u003e\\n \\u003cp\\u003e7 (39)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"16.740088105726873%\\\"\\u003e\\n \\u003cp\\u003eNegative\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e50 (34)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"29.07488986784141%\\\"\\u003e\\n \\u003cp\\u003e98 (66)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e148\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" rowspan=\\\"2\\\" width=\\\"19.929453262786595%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eAdrenal meta\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.403880070546737%\\\"\\u003e\\n \\u003cp\\u003ePositive\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e4 (33)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.280423280423282%\\\"\\u003e\\n \\u003cp\\u003e8 (67)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"16.740088105726873%\\\"\\u003e\\n \\u003cp\\u003eNegative\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e57 (37)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"29.07488986784141%\\\"\\u003e\\n \\u003cp\\u003e97 (63)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e154\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" rowspan=\\\"2\\\" width=\\\"19.929453262786595%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLung meta\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.403880070546737%\\\"\\u003e\\n \\u003cp\\u003ePositive\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e25 (41)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.280423280423282%\\\"\\u003e\\n \\u003cp\\u003e36 (59)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e61\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"16.740088105726873%\\\"\\u003e\\n \\u003cp\\u003eNegative\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e36 (34)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"29.07488986784141%\\\"\\u003e\\n \\u003cp\\u003e69 (66)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e105\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" rowspan=\\\"2\\\" width=\\\"19.929453262786595%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePleural effusion\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.403880070546737%\\\"\\u003e\\n \\u003cp\\u003ePositive\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e25 (40)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.280423280423282%\\\"\\u003e\\n \\u003cp\\u003e38 (60)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.693121693121693%\\\"\\u003e\\n \\u003cp\\u003e63\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"16.740088105726873%\\\"\\u003e\\n \\u003cp\\u003eNegative\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e36 (35)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"29.07488986784141%\\\"\\u003e\\n \\u003cp\\u003e67 (65)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.09251101321586%\\\"\\u003e\\n \\u003cp\\u003e103\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"0.176056338028169%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"19.8943661971831%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLDH (U/L)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.380281690140846%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd width=\\\"21.654929577464788%\\\"\\u003e\\n \\u003cp\\u003e202 (83\\u0026ndash;1115)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.239436619718308%\\\"\\u003e\\n \\u003cp\\u003e204 (124\\u0026ndash;525)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.654929577464788%\\\"\\u003e\\n \\u003cp\\u003e203 (83\\u0026ndash;1115)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"0.176056338028169%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"19.8943661971831%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eALP (U/L)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"13.380281690140846%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd width=\\\"21.654929577464788%\\\"\\u003e\\n \\u003cp\\u003en=59\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.239436619718308%\\\"\\u003e\\n \\u003cp\\u003en=102\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.654929577464788%\\\"\\u003e\\n \\u003cp\\u003en=161\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"0.2638522427440633%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"32.45382585751979%\\\"\\u003e\\n \\u003cp\\u003e264 (114\\u0026ndash;1311)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"34.82849604221636%\\\"\\u003e\\n \\u003cp\\u003e282.5 (104\\u0026ndash;6519)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"32.45382585751979%\\\"\\u003e\\n \\u003cp\\u003e276 (104\\u0026ndash;6519)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"0.176056338028169%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"19.8943661971831%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCRP (mg/dL)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"13.380281690140846%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd width=\\\"21.654929577464788%\\\"\\u003e\\n \\u003cp\\u003en=61\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.239436619718308%\\\"\\u003e\\n \\u003cp\\u003en=104\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.654929577464788%\\\"\\u003e\\n \\u003cp\\u003en=165\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"0.2638522427440633%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"32.45382585751979%\\\"\\u003e\\n \\u003cp\\u003e0.2 (0\\u0026ndash;7.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"34.82849604221636%\\\"\\u003e\\n \\u003cp\\u003e0.2 (0\\u0026ndash;18.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"32.45382585751979%\\\"\\u003e\\n \\u003cp\\u003e0.2 (0\\u0026ndash;18.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"0.176056338028169%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"19.8943661971831%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eALB (g/dL)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"13.380281690140846%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd width=\\\"21.654929577464788%\\\"\\u003e\\n \\u003cp\\u003en=59\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.239436619718308%\\\"\\u003e\\n \\u003cp\\u003en=103\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.654929577464788%\\\"\\u003e\\n \\u003cp\\u003en=162\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"0.2638522427440633%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"32.45382585751979%\\\"\\u003e\\n \\u003cp\\u003e3.9 (2.7\\u0026ndash;4.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"34.82849604221636%\\\"\\u003e\\n \\u003cp\\u003e3.9 (2.1\\u0026ndash;5.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"32.45382585751979%\\\"\\u003e\\n \\u003cp\\u003e3.9 (2.1\\u0026ndash;5.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"0.176056338028169%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"19.8943661971831%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eWBC (\\u0026times;10\\u003csup\\u003e3\\u003c/sup\\u003e/\\u0026mu;L)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.380281690140846%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd width=\\\"21.654929577464788%\\\"\\u003e\\n \\u003cp\\u003e6.5 (2.5-18.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.239436619718308%\\\"\\u003e\\n \\u003cp\\u003e6.7 (3.7-22.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.654929577464788%\\\"\\u003e\\n \\u003cp\\u003e6.5 (2.5-22.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"0.176056338028169%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"19.8943661971831%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eNeut (\\u0026times;10\\u003csup\\u003e3\\u003c/sup\\u003e/\\u0026mu;L)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"13.380281690140846%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd width=\\\"21.654929577464788%\\\"\\u003e\\n \\u003cp\\u003en=61\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.239436619718308%\\\"\\u003e\\n \\u003cp\\u003en=104\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.654929577464788%\\\"\\u003e\\n \\u003cp\\u003en=165\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"0.2638522427440633%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"32.45382585751979%\\\"\\u003e\\n \\u003cp\\u003e4.7 (1.6-15.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"34.82849604221636%\\\"\\u003e\\n \\u003cp\\u003e4.3 (1.8-19.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"32.45382585751979%\\\"\\u003e\\n \\u003cp\\u003e4.4 (1.6-19.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"0.176056338028169%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"19.8943661971831%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLym (/\\u0026mu;L)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"13.380281690140846%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd width=\\\"21.654929577464788%\\\"\\u003e\\n \\u003cp\\u003en=61\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.239436619718308%\\\"\\u003e\\n \\u003cp\\u003en=103\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.654929577464788%\\\"\\u003e\\n \\u003cp\\u003en=164\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"0.2638522427440633%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"32.45382585751979%\\\"\\u003e\\n \\u003cp\\u003e1400 (200\\u0026ndash;3000)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"34.82849604221636%\\\"\\u003e\\n \\u003cp\\u003e1300 (100\\u0026ndash;3700)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"32.45382585751979%\\\"\\u003e\\n \\u003cp\\u003e1400 (100\\u0026ndash;3700)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"0.176056338028169%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"19.8943661971831%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCEA (ng/mL)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.380281690140846%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd width=\\\"21.654929577464788%\\\"\\u003e\\n \\u003cp\\u003e33.8 (1\\u0026ndash;2230)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.239436619718308%\\\"\\u003e\\n \\u003cp\\u003e14.5 (0.7\\u0026ndash;4747.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.654929577464788%\\\"\\u003e\\n \\u003cp\\u003e18.4 (0.7\\u0026ndash;4747.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"0.176056338028169%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"19.8943661971831%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCYF \\u0026nbsp;(ng/mL)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" width=\\\"13.380281690140846%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd width=\\\"21.654929577464788%\\\"\\u003e\\n \\u003cp\\u003en=56\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.239436619718308%\\\"\\u003e\\n \\u003cp\\u003en=102\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.654929577464788%\\\"\\u003e\\n \\u003cp\\u003en=158\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"0.2638522427440633%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"32.45382585751979%\\\"\\u003e\\n \\u003cp\\u003e4 (0.6-144)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"34.82849604221636%\\\"\\u003e\\n \\u003cp\\u003e3.5 (0.5-78.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"32.45382585751979%\\\"\\u003e\\n \\u003cp\\u003e3.7 (0.5-144)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"0.176056338028169%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"19.8943661971831%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd width=\\\"13.380281690140846%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd width=\\\"21.654929577464788%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd width=\\\"23.239436619718308%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd width=\\\"21.654929577464788%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"0.176056338028169%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" width=\\\"19.8943661971831%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePD-L1\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"13.380281690140846%\\\"\\u003e\\n \\u003cp\\u003eNegative\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.654929577464788%\\\"\\u003e\\n \\u003cp\\u003e9 (39)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"23.239436619718308%\\\"\\u003e\\n \\u003cp\\u003e14 (61)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"21.654929577464788%\\\"\\u003e\\n \\u003cp\\u003e23\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"0.21978021978021978%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"16.703296703296704%\\\"\\u003e\\n \\u003cp\\u003e1\\u0026ndash;49%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.032967032967033%\\\"\\u003e\\n \\u003cp\\u003e5 (45)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"29.01098901098901%\\\"\\u003e\\n \\u003cp\\u003e6 (55)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.032967032967033%\\\"\\u003e\\n \\u003cp\\u003e11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"0.21978021978021978%\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"16.703296703296704%\\\"\\u003e\\n \\u003cp\\u003e\\u0026ge;50%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.032967032967033%\\\"\\u003e\\n \\u003cp\\u003e1 (14)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"29.01098901098901%\\\"\\u003e\\n \\u003cp\\u003e6 (86)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd width=\\\"27.032967032967033%\\\"\\u003e\\n \\u003cp\\u003e7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"0.176056338028169%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd width=\\\"19.8943661971831%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd width=\\\"13.380281690140846%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd width=\\\"21.654929577464788%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd width=\\\"23.239436619718308%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd width=\\\"21.654929577464788%\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTable 2. Frequency of T790M mutations after first line EGFR-TKI failure (N=166).\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellpadding=\\\"0\\\" cellspacing=\\\"0\\\" width=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"28.395061728395063%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eType of EGFR-TKI\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"18.34215167548501%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGefitinib\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"16.75485008818342%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eErlotinib\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"18.34215167548501%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eAfatinib\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"18.1657848324515%\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTotal\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"28.395061728395063%\\\"\\u003e\\n \\u003cp\\u003eFrequency of T790M mutations\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"18.34215167548501%\\\"\\u003e\\n \\u003cp\\u003e25/72\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"16.75485008818342%\\\"\\u003e\\n \\u003cp\\u003e18/43\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"18.34215167548501%\\\"\\u003e\\n \\u003cp\\u003e18/51\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"18.1657848324515%\\\"\\u003e\\n \\u003cp\\u003e61/166\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"28.445229681978798%\\\"\\u003e\\n \\u003cp\\u003e(%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" width=\\\"34.98233215547703%\\\"\\u003e\\n \\u003cp\\u003e34.7% + 41.9% = 37.4%\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"18.374558303886925%\\\"\\u003e\\n \\u003cp\\u003e35.3 %\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" width=\\\"18.197879858657245%\\\"\\u003e\\n \\u003cp\\u003e36.7 %\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e \"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"investigational-new-drugs\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"drug\",\"sideBox\":\"Learn more about [Investigational New Drugs](https://www.springer.com/journal/10637)\",\"snPcode\":\"10637\",\"submissionUrl\":\"https://submission.nature.com/new-submission/10637/3\",\"title\":\"Investigational New Drugs\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Non-small cell lung cancer, EGFR, tyrosine kinase inhibitors, T790M, predict marker, Classification and regression tree\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-1079146/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-1079146/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eBackground and objective: Osimertinib as first-line treatment for patients with non-small cell lung cancer (NSCLC) harboring epidermal growth factor (EGFR) mutations remains controversial. Sequential EGFR-tyrosine kinase inhibitor (TKI) might be superior to the first line osimertinib in patients at risk of developing acquired T790M mutations.\\u003c/p\\u003e\\u003cp\\u003eMethods: We enrolled consecutive patients with \\u003cem\\u003eEGFR\\u003c/em\\u003e-mutated (deletion 19 or L858R) advanced NSCLC treated with first-line drugs and evaluated predictive markers using classification and regression tree (CART) for the detection of T790M mutations based on patient backgrounds prior to initial treatment.\\u003c/p\\u003e\\u003cp\\u003eResults: Patients without acquired T790M mutations had worse outcomes than those with T790M mutations (median OS: 798 days vs. not reached; HR: 2.70; P\\u0026lt;0.001). CART identified three distinct groups based on variables associated with acquired T790M mutations (age, CYF, WBC, liver metastasis, and LDH; AUROC: 0.77). Based on certain variables, CART identified three distinct groups in deletion 19 (albumin, LDH, bone metastasis, pleural effusion, and WBC; AUROC: 0.81) and two distinct groups in L858R (age, CEA, and ALP; AUROC: 0.80). The T790M detection frequencies after TKI resistance of afatinib and first-generation EGFR-TKIs were similar (35.3% vs. 37.4%, P=0.933). Afatinib demonstrated longer PFS (398 vs. 279 days; HR: 0.67; P=0.004) and OS (1053 vs. 956 days; HR: 0.68; P=0.051) than first-generation EGFR-TKIs.\\u003c/p\\u003e\\u003cp\\u003eConclusion: \\u0026nbsp;Identification of patients at risk of acquiring T790M mutations after EGFR-TKI failure may aid in choice of first-line EGFR-TKI. Furthermore, afatinib may be the more effective 1st-line EGFR-TKI treatment for patients at risk of developing T790M as initial EGFR-TKI resistance.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Classification and regression tree for estimating predictive markers to detect T790M mutations after acquired resistance to first line EGFR-TKI: HOPE-002\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2021-11-29 16:52:52\",\"doi\":\"10.21203/rs.3.rs-1079146/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2021-11-27T14:26:36+00:00\",\"index\":0,\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2021-11-27T13:57:36+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"decision\",\"content\":\"Accept as is.\",\"date\":\"2021-11-27T12:59:43+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2021-11-23T04:59:23+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Investigational New Drugs\",\"date\":\"2021-11-14T08:38:34+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"investigational-new-drugs\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"drug\",\"sideBox\":\"Learn more about [Investigational New Drugs](https://www.springer.com/journal/10637)\",\"snPcode\":\"10637\",\"submissionUrl\":\"https://submission.nature.com/new-submission/10637/3\",\"title\":\"Investigational New Drugs\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"3096790c-6238-47a5-be35-7571763bbdeb\",\"owner\":[],\"postedDate\":\"November 29th, 2021\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[{\"id\":8807187,\"name\":\"Oncology\"},{\"id\":8807188,\"name\":\"Clinical Pharmacology\"},{\"id\":8807189,\"name\":\"Toxicology\"}],\"tags\":[],\"updatedAt\":\"2022-01-28T00:44:29+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-1079146\",\"link\":\"https://doi.org/10.1007/s10637-021-01203-5\",\"journal\":{\"identity\":\"investigational-new-drugs\",\"isVorOnly\":false,\"title\":\"Investigational New Drugs\"},\"publishedOn\":\"2022-01-28 00:44:29\",\"publishedOnDateReadable\":\"January 28th, 2022\"},\"versionCreatedAt\":\"2021-11-29 16:52:52\",\"video\":\"\",\"vorDoi\":\"10.1007/s10637-021-01203-5\",\"vorDoiUrl\":\"https://doi.org/10.1007/s10637-021-01203-5\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-1079146\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-1079146\",\"identity\":\"rs-1079146\",\"version\":[\"v1\"]},\"buildId\":\"J0_U0BvcaRcwD8yVFaRlm\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}