Predictive Value of TTF-1 Expression for Stratifying Outcomes Between Dual ICI and Chemoimmunotherapy in Patients with PD-L1-Negative NSCLC

Predictive Value of TTF-1 Expression for Stratifying Outcomes Between Dual ICI and Chemoimmunotherapy in Patients with PD-L1-Negative NSCLC | 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 Predictive Value of TTF-1 Expression for Stratifying Outcomes Between Dual ICI and Chemoimmunotherapy in Patients with PD-L1-Negative NSCLC Naoya Nishioka, Daiki Murata, Koichi Azuma, Kazuhiro Ito, Takashi Nomizo, and 22 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9478780/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Thyroid transcription factor-1 has emerged as a novel predictive biomarker associated with the prognosis and treatment response of non-squamous non-small-cell lung cancer to cytotoxic chemotherapy. This study evaluated the clinical significance of thyroid transcription factor-1 expression in advanced non-small-cell lung cancer with programmed death-ligand 1 < 1%, with a specific focus on its utility in stratifying treatment outcomes between dual immune checkpoint inhibitor (IO-IO) therapy and chemoimmunotherapy. Methods We conducted a multicenter retrospective study across 22 Japanese institutions, evaluating 194 patients with advanced or recurrent non-squamous non-small-cell lung cancer and programmed death-ligand 1 < 1% treated with IO–IO therapy or chemoimmunotherapy between 2019 and 2022. Survival outcomes were analyzed using multivariable Cox regression. Results Thyroid transcription factor-1-positive patients had significantly longer progression-free survival and overall survival than thyroid transcription factor-1-negative patients (adjusted HR for progression-free survival: 0.65, p = 0.009; adjusted HR for overall survival: 0.69, p = 0.04). Among thyroid transcription factor-1-positive patients, no significant difference in survival was observed between IO–IO therapy and chemoimmunotherapy. By contrast, thyroid transcription factor-1-negative patients experienced significantly improved outcomes with chemoimmunotherapy than with IO–IO therapy (adjusted HR for overall survival: 0.39, p = 0.024; adjusted HR for progression-free survival: 0.44, p = 0.051). Conclusion Thyroid transcription factor-1 expression serves as a prognostic and potentially predictive biomarker in programmed death-ligand 1-negative non-small-cell lung cancer. Thyroid transcription factor-1-negative patients derived relatively greater benefit from the addition of chemotherapy, supporting the utility of thyroid transcription factor-1 in guiding treatment selection. TTF-1 PD-L1 non-small-cell lung cancer chemoimmunotherapy IO-IO therapy Figures Figure 1 Figure 2 Figure 3 Introduction Thyroid transcription factor 1 (TTF-1) is a lineage-defining marker widely used in the pathological diagnosis of lung adenocarcinoma and is associated with tumor differentiation, clinical outcomes, and therapeutic sensitivity in non-squamous non-small-cell lung cancer (NSCLC) [ 1 ]. Its predictive and prognostic significance in the context of cytotoxic chemotherapy has also been reported [ 2 , 3 ]. Particularly, several studies have shown that TTF-1-negative patients tend to have poorer responses and survival outcomes when treated with pemetrexed-containing regimens [ 2 , 3 ]. Given that TTF-1-negative patients have historically responded poorly to standard cytotoxic regimens, optimal treatment selection for these individuals remains a significant clinical challenge. In recent years, the emergence of immune checkpoint inhibitors (ICIs) has transformed the treatment landscape of advanced NSCLC, with immunotherapy or chemoimmunotherapy now constituting the standard first-line therapy for most patients without actionable driver mutations [ 4 , 5 ]. However, patients with programmed death ligand-1 (PD-L1)-negative tumors (PD-L1 < 1%) represent a subgroup in which the benefit of immunotherapy is generally considered limited. Although several immunotherapy-based strategies have been investigated for this population, the optimal treatment approach remains uncertain. The KEYNOTE-189 trial demonstrated that the addition of pembrolizumab to platinum–pemetrexed chemotherapy significantly improved survival compared with chemotherapy alone, regardless of PD-L1 expression, thereby establishing chemoimmunotherapy as a standard first-line treatment option [ 6 ]. Similarly, the CheckMate-227 trial demonstrated that dual immune checkpoint blockade with nivolumab plus ipilimumab significantly improved overall survival compared with chemotherapy, even in patients with PD-L1-negative tumors [ 7 ]. More recently, the CheckMate-9LA trial demonstrated that nivolumab plus ipilimumab combined with a limited course of chemotherapy improved survival across PD-L1 subgroups, although concerns remain regarding treatment-related serious adverse events[ 8 , 9 ]. We previously analyzed cohorts with PD-L1 expression ≥ 50% and 1–49% and found that the poor prognosis typically observed in TTF-1-negative patients treated with cytotoxic chemotherapy was mitigated by ICI monotherapy or chemoimmunotherapy [ 10 , 11 ]. Nonetheless, in the setting of PD-L1 < 1%, where the benefit from ICIs is known to be limited, it remains uncertain whether TTF-1-negative patients exhibit similarly improved outcomes or whether their poorer prognosis persists as observed with conventional chemotherapy. Furthermore, while both chemoimmunotherapy and dual ICI (IO–IO) therapy are currently available options for this population, optimal patient selection between these regimens remains elusive. In this study, we aimed to investigate the clinical relevance of TTF-1 expression in patients with advanced NSCLC and PD-L1 < 1%, focusing on its potential role in stratifying outcomes between IO–IO therapy and chemoimmunotherapy. Our analysis sought to clarify whether TTF-1 could help inform treatment selection in this therapeutically challenging subgroup. Patients and Methods Patients and study design We conducted a multicenter, retrospective cohort study of patients with advanced or recurrent NSCLC and PD-L1 expression < 1% who received either IO-IO therapy or chemoimmunotherapy at 22 centers in Japan between January 2019 and December 2022. This study was conducted in compliance with the Declaration of Helsinki. Ethical approval was obtained from Kurume University (Approval IRB No. 24116) and the requirement for written informed consent was waived owing to the retrospective nature of the study, with an opt-out opportunity provided. Inclusion criteria: (i) a pathological diagnosis of NSCLC; (ii) advanced disease corresponding to stage III–IV based on the TNM classification (American Joint Committee on Cancer, 8th edition) or documented postoperative recurrence; (iii) availability of tumor TTF-1 expression data; and (iv) PD-L1 tumor proportion score < 1%. Exclusion criteria: (i) a histological diagnosis of squamous cell carcinoma; (ii) a lack of evaluable TTF-1 expression in tumor tissue; and (iii) the presence of known actionable oncogenic driver alterations. Statistical analysis Categorical variables were compared using the chi-square test or Fisher’s exact test, as appropriate. Survival curves were estimated using the Kaplan–Meier method and compared with the log-rank test. Progression-free survival (PFS) was defined as the time from treatment initiation to documented disease progression based on the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1, or death from any cause, and overall survival (OS) was defined as the time from treatment initiation to death from any cause. To identify factors independently associated with PFS and OS, multivariable analyses were performed using the Cox proportional hazards model. All statistical tests were two-sided, and a p-value of < 0.05 was considered statistically significant. Statistical analyses were performed using EZR (Jichi Medical University, Saitama Medical Center, Saitama, Japan) and the R software environment (R Foundation for Statistical Computing) [ 12 ]. The cut-off date for analyses was Jun 30, 2024. Results Patients Baseline patient characteristics are presented in Table 1. The cohort had a median age of 69 years (range, 27–89), and approximately three-quarters were men (76.8%). Nearly all patients exhibited a favorable Eastern Cooperative Oncology Group (ECOG) performance status of 0–1 (95.4%). Most tumors were adenocarcinomas (87.1%), and three-quarters of the population had stage IV disease at diagnosis (75.8%). In this study, patients were categorized according to TTF-1 expression, and the TTF-1-positive group demonstrated a significantly higher prevalence of adenocarcinoma than the TTF-1-negative group (93% vs. 80.9%, p = 0.02). Aside from histology, the two groups showed broadly comparable baseline characteristics. Impact of TTF-1 expression on the efficacy of chemoimmunotherapy/ dual immune checkpoint inhibitors in the total cohort With a median follow-up period of 19.1 months, progressive disease was documented in 156 patients (80.4%), and 127 patients died (65.5%). Regarding PFS, the TTF-1-negative group had significantly a shorter median PFS than did the TTF-1-positive group (4.8 months [95% confidence interval (CI): 4.0−6.4] vs. 8.0 months [95% CI: 6.7−10.0], p = 0.016) (Fig. 1A). Multivariable analysis revealed that TTF-1 expression (adjusted hazard ratio [HR] = 0.65, [95% CI = 0.47–0.90], p = 0.009) was an independent predictive factor for PFS (Table 2a). OS was numerically shorter in the TTF-1-negative group than in the TTF-1-positive group, although this difference did not reach statistical significance (median OS: 13.9 months [95% CI: 9.4–21.5] vs. 20.4 months [95% CI: 17.8–29.0], log-rank p = 0.16) (Fig. 1B). However, multivariable Cox regression analysis identified both TTF-1 expression (adjusted HR = 0.69, [95% CI = 0.48– 0.98], p = 0.04) and ECOG performance status (adjusted HR = 0.32, [95% CI = 0.15– 0.68], p = 0.003) as variables independently associated with OS after adjustment for other clinical factors (Table 2b). Comparison of survival outcomes between chemoimmunotherapy and dual immune checkpoint inhibitors according to TTF-1 expression In the overall population, OS and PFS did not differ significantly between patients treated with chemoimmunotherapy and those receiving IO-IO therapy (median OS: 19.98 months vs. 9.95 months, p = 0.44; median PFS: 6.70 months vs. 4.96 months, p = 0.96) (Fig. 2A, 2B). When survival outcomes were examined according to TTF-1 expression, no significant differences were observed in the TTF-1-positive cohort (median OS: 20.30 months vs. not reached, p = 0.24; median PFS: 7.75 months vs. 9.40 months, p = 0.29) (Fig. 2C, 2D). In multivariable Cox regression analyses, treatment modality was not independently associated with PFS (adjusted HR 1.67, 95% CI 0.72–3.92, p = 0.23) or OS (adjusted HR 2.17, 95% CI 0.67–7.02, p = 0.20) in this cohort (Table 3). In the TTF-1-negative cohort, OS and PFS differed significantly between treatment groups (median OS: 17.15 months vs. 3.25 months, p = 0.006; median PFS: 5.29 months vs. 3.88 months, p = 0.04) (Fig. 2E, 2F). In multivariable analyses, chemoimmunotherapy was independently associated with improved OS (adjusted HR 0.392, 95% CI 0.174–0.886, p = 0.024) and demonstrated a trend toward improved PFS (adjusted HR 0.44, 95% CI 0.19–1.01, p = 0.051) compared with IO-IO therapy after adjustment for covariates (Table 4). Comparison of pemetrexed-based and non–pemetrexed-based chemoimmunotherapy regimens stratified by TTF-1 expression To assess chemoimmunotherapy regimens, patients who received IO-IO therapy were excluded from the analysis (n = 23). Survival outcomes were then compared between pemetrexed-based and non–pemetrexed-based chemoimmunotherapy regimens according to TTF-1 expression status. In the TTF-1-positive cohort, OS was significantly longer in the pemetrexed-based group than in the non–pemetrexed-based group (median OS: 21.82 vs. 15.24 months, p = 0.047), although no significant difference was observed in PFS (median PFS: 7.75 vs. 7.62 months, p = 0.95) (Fig.3A,3B). In the TTF-1-negative cohort, OS and PFS did not differ significantly between the two regimens (median OS: 16.00 months vs. 17.15 months, p = 0.67; median PFS: 5.61 months vs. 4.83 months, p = 0.95) (Fig.3C,3D). Discussion This study evaluated the clinical significance of TTF-1 expression in patients with PD-L1-negative (< 1%) advanced NSCLC treated with immunotherapy-based regimens. In the overall cohort, the TTF-1-negative group had significantly shorter PFS and numerically shorter OS compared to the TTF-1 = positive group. When treatment outcomes were analyzed based on regimen type, no clear differences were observed between IO–IO therapy and chemoimmunotherapy in the TTF-1-positive cohort. By contrast, in the TTF-1-negative cohort, patients receiving IO–IO therapy exhibited a significantly shorter OS and a trend toward shorter PFS compared with those treated with chemoimmunotherapy. These findings suggest that the addition of chemotherapy to immunotherapy may improve clinical outcomes in TTF-1-negative and PD-L1-negative patients with NSCLC. Across different treatment regimens, several previous studies have reported that TTF-1 expression is associated with treatment response and prognosis in patients with NSCLC [ 2 , 3 , 13 – 20 ]. Consistent with these reports, our analysis of patients with PD-L1-negative (< 1%) NSCLC demonstrated inferior PFS in the TTF-1-negative group compared with the TTF-1-positive group (Fig. 1 , Table 2 ). However, in our earlier study of NSCLC cohorts with higher PD-L1 expression (PD-L1 ≥ 50% and PD-L1 1–49%), treatment efficacy and survival were comparable regardless of TTF-1 expression [ 10 , 11 ]. We considered that this discrepancy may be explained by differences in PD-L1 expression distribution across study populations. In patients with PD-L1–high (≥ 50%) NSCLC treated with PD-1/PD-L1 monotherapy, as well as those with PD-L1 ≥ 50% or PD-L1 1–49% treated with chemoimmunotherapy, no significant differences in outcomes were observed according to TTF-1 status [ 11 ]. By contrast, in a study focusing on cytotoxic chemotherapy, the TTF-1-negative group was consistently associated with inferior treatment outcomes and survival [ 10 ]. Taken together, these findings suggest that in patient populations more likely to benefit from immunotherapy, the prognostic impact of TTF-1 expression may be attenuated, whereas in PD-L1-negative populations with limited immunotherapy benefit, TTF-1 negativity remains associated with poorer outcomes. However, several previous studies reported inferior outcomes in TTF-1-negative patients even among cohorts treated with immunotherapy or chemoimmunotherapy [ 13 , 15 , 16 , 19 , 20 ]. We considered that this finding might be largely explained by the close association between TTF-1 expression and PD-L1 expression. In our earlier study, TTF-1-negative NSCLC tended to show lower PD-L1 expression than TTF-1-positive NSCLC [ 17 ]. Moreover, experimental studies have suggested that TTF-1 can directly induce PD-L1 expression, supporting a mechanistic link between TTF-1 and PD-L1 [ 21 ]. Accordingly, the proportion of TTF-1-negative patients increased with decreasing PD-L1 expression, accounting for 27.7% in previously reported PD-L1 ≥ 50% cohorts, 33% in PD-L1 1–49% cohorts, and 48.5% in the PD-L1 < 1% cohort of the present study [ 10 , 11 ]. Therefore, when PD-L1 expression was not considered, differences in PD-L1 distribution between TTF-1-positive and TTF-1-negative groups may have influenced the apparent efficacy of immunotherapy. By contrast, when analyses were stratified by PD-L1 expression, treatment efficacy in immunotherapy-responsive populations (PD-L1 ≥ 50% and PD-L1 1–49%) was largely comparable regardless of TTF-1 status, whereas in PD-L1-negative populations, which derived limited benefit from immunotherapy, outcomes resembled those seen with cytotoxic chemotherapy. Our study showed that the efficacy of immunotherapy-based regimens differed according to TTF-1 expression. In the TTF-1-positive cohort, no clear differences were observed between IO–IO therapy and chemoimmunotherapy (Fig. 2 C, 2 D; Table 3 ). By contrast, in the TTF-1-negative cohort, IO–IO therapy was associated with poorer outcomes compared with chemoimmunotherapy (Fig. 2 E, 2 F; Table 4 ). A plausible explanation is the difference in Ki-67 expression according to TTF-1 status. Ki-67, a marker of cell proliferation, is typically expressed at higher levels in TTF-1-negative tumors, indicating a larger fraction of actively proliferating tumor cells [ 22 , 23 ]. In such tumors, IO–IO therapy alone may be insufficient to prevent early disease progression due to its delayed onset of action, whereas the addition of chemotherapy provides rapid cytoreduction may help suppress early tumor growth, thereby translating to superior treatment outcomes compared with IO–IO therapy. We compared pemetrexed-based and non–pemetrexed-based chemoimmunotherapy according to TTF-1 expression. Although pemetrexed has been reported to be less effective in TTF-1-negative tumors when used as cytotoxic chemotherapy [ 2 , 24 – 26 ], this historical disadvantage was not evident in the present chemoimmunotherapy cohort. The absence of a clear difference may reflect the limited sample size and the possibility that immunotherapy provided some clinical benefit even in this cohort. However, the underlying mechanism remains unclear. This study has a few limitations. Although this represents one of the largest available cohorts in this setting, the retrospective design and modest sample size—particularly within treatment subgroups—may limit statistical power. Additionally, TTF-1 immunohistochemistry was not fully standardized across institutions, and TTF-1 was assessed only as positive or negative, despite prior reports suggesting that staining intensity may carry prognostic information[ 27 ]. Furthermore, data on other potential confounding factors, such as tumor mutational burden or comprehensive genomic profiles, were not completely available for all patients. In conclusion, TTF-1 expression serves as a robust prognostic and predictive biomarker in advanced PD-L1-negative NSCLC. While treatment outcomes were comparable between IO–IO therapy and chemoimmunotherapy in TTF-1-positive patients, the addition of chemotherapy to immunotherapy provided a significant survival advantage in the TTF-1-negative population. Our findings suggest that TTF-1 expression may help optimize treatment selection and personalize therapeutic strategies for this clinically challenging subgroup. Declarations Conflicts of interest Tadaaki Yamada received research grants from Ono Pharmaceutical and Takeda Pharmaceutical, and has received speaking honoraria from Eli Lilly and Chugai-Roshe outside the purview of the submitted work. Koichi Azuma received personal fees from AstraZeneca, MSD, Bristol Myers Squibb, Ono Pharmaceutical, Takeda Pharmaceutical, Pfizer, and Chugai Pharmaceutical outside the purview of the submitted work. Asuka Okada received speaking honoraria from AstraZeneca, Nippon Kayaku, Bristol-Myers Squibb, Boehringer Ingelheim, Chugai-Roshe, Taiho Pharmaceutical, Kyowa Kirin, MSD, and Eli Lilly Japan outside the purview of the submitted work. Makoto Hibino received personal fees from Asahi Kasei Pharma Corporation, AstraZeneca, Boehringer Ingelheim, Bristol Myers, Chugai Pharmaceutical, and Eli Lilly outside the purview of the submitted work. Yosuke Tamura received speaking honoraria from AstraZeneca, Chugai- Pharmaceutical, MSD, Nippon Boehringer Ingelheim, and Ono Pharmaceutical outside the purview of the submitted work. Yasuhiro Goto received personal fees from AstraZeneca, Boehringer Ingelheim, Bristol Myers, Chugai Pharmaceutical, Kyowa Kirin International, Novartis, Ono Pharmaceutical, Pfizer, Takeda, and Taiho Pharmaceutical outside the purview of the submitted work. Toshiyuki Sumi received personal fees from AstraZeneca, Chugai Pharmaceutical, and Ono Pharmaceutical outside the purview of the submitted work. Hiroyasu Kaneda received speaking honoraria from AstraZeneca, Bristol-Myers Squibb, Chugai-Pharmaceutical, MSD, and Ono Pharmaceutical outside the purview of the submitted work. The remaining authors have no conflicts of interest to disclose. Ethics approval: This study complied with the Declaration of Helsinki and was approved by the ethics review Board of Kurume University (Approval IRB No. 24116), which served as the central ethics committee for all participating centers. Consent to participate: Informed consent for the use of personal medical data was obtained using the opt-out method as described in the disclosure document. Consent to publish Not applicable. Data Sharing Statement The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. Declaration of AI and AI-assisted technologies in the writing process ChatGPT (GPT-5.2, 2025 version, developed by OpenAI) was used to assist with English editing and language refinement during the abstract writing process. The authors reviewed and modified all content to ensure accuracy and originality. No AI-generated content was used as a primary source, and the authors confirm that there is no plagiarism in the AI-assisted material. Funding: This study did not receive specific grants from public, commercial, or not-for-profit funding agencies. Author Contributions Conceptualization : Nishioka N, Yamada T. Methodology : Nishioka N, Yamada T. Software : Nishioka N. Formal analysis : Nishioka N. Investigation : Nishioka N, Murata D, Ito K, Nomizo T, Yamada K, Imabayashi T, Iwanaga K, Chibana K, Ota T, Nishii Y, Nakao A, Okada A, Hamai K, Sakai T, Harada T, Tanimura K, Yoshimine K, Tamura Y, Takaki R, Goto Y, Hibino M, Oba T, Sumi T, Kaneda H. Data Curation : Nishioka N. Writing-Original Draft : Nishioka N. Writing-Review & Editing : Yamada T. Visualization : Nishioka N. Supervision : Takayama K. Project administration : Yamada T, Azuma K. Acknowledgment: None References Phelps CA, Lai SC, Mu D (2018) Roles of Thyroid Transcription Factor 1 in Lung Cancer Biology. Vitam Horm 106:517–544 Frost N, Zhamurashvili T, von Laffert M et al (2020) Pemetrexed-Based Chemotherapy Is Inferior to Pemetrexed-Free Regimens in Thyroid Transcription Factor 1 (TTF-1)-Negative, EGFR/ALK-Negative Lung Adenocarcinoma: A Propensity Score Matched Pairs Analysis. 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Tumour Biol 39:1010428317691186 Sun JM, Han J, Ahn JS et al (2011) Significance of thymidylate synthase and thyroid transcription factor 1 expression in patients with nonsquamous non-small cell lung cancer treated with pemetrexed-based chemotherapy. J Thorac Oncol 6:1392–1399 Terashima Y, Matsumoto M, Iida H et al (2023) Predictive Impact of Diffuse Positivity for TTF-1 Expression in Patients Treated With Platinum-Doublet Chemotherapy Plus Immune Checkpoint Inhibitors for Advanced Nonsquamous NSCLC. JTO Clin Res Rep 4:100578 Tables Table 1 Patients’ characteristics. Characteristic Total n=194 TTF-1-negative n=94 TTF-1-positive n=100 p -value Age, y Median (range) 69 [27–89] 68 [27–89] 70 [38–84] 0.36 Sex Male Female 149 (76.8) 45 (23.2) 71 (75.5) 23 (24.5) 78 (78.0) 22 (22.0) 0.74 ECOG PS (%) 0-1 ≥ 2 185 (95.4) 9 (4.6) 91 (96.8) 3 (3.2) 94 (94.0) 6 (6.0) 0.50 Stage (%) III IV Recurrence 13 (6.7) 147 (75.8) 34 (17.5) 3 (3.2) 71 (75.5) 20 (21.3) 10 (10.0) 76 (76.0) 14 (14.0) 0.09 Histology (%) Adeno Others 169 (87.1) 25 (12.9) 76 (80.9) 18 (19.1) 93 (93.0) 7 (7.0) 0.02 Smoking history (%) Never Current/past 30 (15.5) 164 (84.5) 17 (18.1) 77 (81.9) 13 (13.0) 87 (87.0) 0.43 Liver metastasis (%) 23 (11.9) 10 (10.6) 13 (13.0) 0.66 Brain metastasis (%) 41 (21.1) 18 (19.1) 23 (23.0) 0.54 Treatment regimen (%) CDDP or CBDCA/PEM/Pembro CBDCA/PTX or nab-PTX/Pembro CBDCA/nab-PTX/Atezo CDDP or CBDCA/PEM/Atezo CBDCA/PTX/BEV/Atezo Chemotherapy/Nivo/IPI Nivo/IPI 85 (43.8) 10 (5.2) 20 (10.3) 5 (2.6) 15 (7.7) 37 (19.1) 22 (11.3) 38 (40.4) 8 (8.5) 11 (11.7) 1 (1.1) 7 (7.4) 17 (18.1) 12 (12.8) 47 (47.0) 2 (2.0) 9 (9.0) 4 (4.0) 8 (8.0) 20 (20.0) 10 (10.0) TTF-1, thyroid transcription factor-1; ECOG PS, Eastern Cooperative Oncology Group Performance Status; CBDCA: carboplatin, CDDP: cisplatin, PTX: paclitaxel, nab-PTX: nab-paclitaxel, BEV: bevacizumab, PEM: pemetrexed, Atezo: atezolizumab, Pembro: pembrolizumab, Nivo: nivolumab, IPI: Ipilimumab Table 2 (a) Predictors of PFS for the total cohort Covariates Crude HR 95% CI P Adjusted HR a 95% CI P TTF-1 (positive vs. negative) 0.680 0.495–0.933 0.017 0.646 0.465–0.897 0.009 Age (≥65 vs. <65) 0.758 0.547–1.051 0.097 0.759 0.545–1.056 0.102 Smoke (never vs. current/former) 0.911 0.601–1.380 0.659 0.876 0.574–1.336 0.539 ECOG-PS (PS 0-1 vs. PS≥2) 0.835 0.391–1.786 0.642 0.710 0.328–1.537 0.384 Chemoimmunotherapy vs. IO-IO 0.986 0.569–1.711 0.961 0.858 0.489–1.506 0.594 Histology (adenocarcinoma vs. others) 1.085 0.682–1.724 0.732 1.235 0.766–1.990 0.387 (b) Predictors of OS for the total cohort Covariates Crude HR 95% CI P Adjusted HR a 95% CI P TTF-1 (positive vs. negative) 0.778 0.549–1.103 0.159 0.685 0.477–0.982 0.040 Age (≥65 vs. <65) 1.038 0.722–1.492 0.842 1.004 0.695–1.451 0.982 Smoke (never vs. current/former) 1.427 0.844–2.412 0.184 1.436 0.846–2.437 0.180 ECOG-PS (PS 0-1 vs. PS≥2) 0.371 0.180–0.770 0.007 0.321 0.152–0.676 0.003 Chemoimmunotherapy vs. IO-IO 0.789 0.434–1.434 0.436 0.763 0.416–1.398 0.381 Histology (adenocarcinoma vs. others) 1.202 0.700–2.063 0.506 1.465 0.840–2.554 0.178 TTF-1, thyroid transcription factor-1; HR: Hazard ratio a) ; ECOG PS: Eastern Cooperative Oncology Group Performance status; IO, immune-oncology therapy; CI, Confidence interval; PFS, progression-free survival; OS, overall survival a) Hazard Ratio for TTF-1 was adjusted for Age, Smoke history, ECOG-PS, Chemotherapy regimen and Histology Table 3 (a) Predictors of PFS for the TTF-1-positive cohort Covariates Crude HR 95% CI P Adjusted HR a 95% CI P Age (≥65 vs. <65) 0.716 0.449–1.142 0.161 0.742 0.462–1.192 0.217 Smoke (never vs. current or former) 1.421 0.748–2.700 0.284 1.523 0.789–2.941 0.210 ECOG-PS (PS 0-1 vs. PS≥2) 0.807 0.325–2.004 0.644 0.830 0.325–2.117 0.696 Chemoimmunotherapy vs. IO-IO 1.565 0.679–3.609 0.293 1.674 0.716–3.916 0.234 Histology (adenocarcinoma vs. others) 1.344 0.579–3.122 0.492 1.644 0.687–3.936 0.264 (b) Predictors of OS for the TTF-1-positive cohort Covariates Crude HR 95% CI P Adjusted HR a 95% CI P Age (≥65 vs. <65) 0.979 0.583–1.642 0.935 1.051 0.624–1.770 0.852 Smoke (never vs. current or former) 1.976 0.850–4.594 0.114 2.087 0.891–4.884 0.090 ECOG-PS (PS 0-1 vs. PS≥2) 0.443 0.175–1.119 0.085 0.424 0.161–1.118 0.083 Chemoimmunotherapy vs. IO-IO 1.993 0.622–6.383 0.245 2.171 0.671–7.023 0.196 Histology (adenocarcinoma vs. others) 1.495 0.467–4.790 0.498 2.153 0.644–7.201 0.213 TTF-1, thyroid transcription factor-1; HR: Hazard ratio a) ; ECOG PS: Eastern Cooperative Oncology Group Performance status; IO, immune-oncology therapy; CI, Confidence interval; PFS, progression-free survival; OS, overall survival a) Hazard Ratio for TTF-1 was adjusted for Age, Smoke history, ECOG-PS, Chemotherapy regimen and Histology Table 4 (a) Predictors of PFS for the TTF-1-negative cohort Covariates Crude HR 95% CI P Adjusted HR a 95% CI P Age (≥65 vs. <65) 0.827 0.522–1.311 0.419 0.784 0.484–1.270 0.323 Smoke (never vs. current or former) 0.584 0.332–1.028 0.062 0.622 0.350–1.104 0.105 ECOG-PS (PS 0-1 vs. PS≥2) 0.534 0.129–2.213 0.387 0.848 0.187–3.837 0.830 Chemoimmunotherapy vs. IO-IO 0.468 0.220–0.996 0.049 0.438 0.191–1.006 0.051 Histology (adenocarcinoma vs. others) 1.122 0.636–1.98 0.691 1.131 0.635–2.017 0.675 (b) Predictors of OS for the TTF-1-negative cohort Covariates Crude HR 95% CI P Adjusted HR a 95% CI P Age (≥65 vs. <65) 1.125 0.676–1.872 0.651 0.917 0.531–1.584 0.757 Smoke (never vs. current or former) 1.202 0.609–2.370 0.596 1.391 0.691–2.801 0.356 ECOG-PS (PS 0-1 vs. PS≥2) 0.156 0.045–0.539 0.003 0.240 0.065–0.887 0.032 Chemoimmunotherapy vs. IO-IO 0.381 0.186–0.780 0.008 0.392 0.174–0.886 0.024 Histology (adenocarcinoma vs. others) 1.202 0.640–2.258 0.567 1.258 0.657–2.209 0.488 TTF-1, thyroid transcription factor-1; HR: Hazard ratio a) ; ECOG PS: Eastern Cooperative Oncology Group Performance status; IO, immune-oncology therapy; CI, Confidence interval; PFS, progression-free survival; OS, overall survival a) Hazard Ratio for TTF-1 was adjusted for Age, Smoke history, ECOG-PS, Chemotherapy regimen and Histology Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 02 May, 2026 Reviewers invited by journal 01 May, 2026 Editor assigned by journal 27 Apr, 2026 First submitted to journal 20 Apr, 2026 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. 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1","display":"","copyAsset":false,"role":"figure","size":105576,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTreatment outcomes according to TTF-1 expression in the overall population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSurvival analyses of (A) progression-free survival and (B) overall survival across the overall population\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9478780/v1/bd22a8089f97c38cc799a989.jpg"},{"id":109118224,"identity":"8fdfdf20-283a-4372-a2ab-67de8de80f7f","added_by":"auto","created_at":"2026-05-12 16:51:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":97142,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTreatment outcomes according to treatment strategy and TTF-1 expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSurvival curves comparing chemoimmunotherapy and dual immune checkpoint inhibitor (IO–IO) therapy are shown.\u003c/p\u003e\n\u003cp\u003e(A) Progression-free survival and (B) overall survival in the overall population.\u003c/p\u003e\n\u003cp\u003e(C) Progression-free survival and (D) overall survival among TTF-1-positive patients.\u003c/p\u003e\n\u003cp\u003e(E) Progression-free survival and (F) overall survival among TTF-1-negative patients.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9478780/v1/d2792e0c348bfad2a0898107.jpg"},{"id":109118223,"identity":"c027cf40-0943-4093-b0dd-a91d1c95be63","added_by":"auto","created_at":"2026-05-12 16:51:22","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":139984,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTreatment outcomes according to pemetrexed use and TTF-1 expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSurvival curves comparing pemetrexed-based and non–pemetrexed-based CIT (chemoimmunotherapy) are shown.\u003c/p\u003e\n\u003cp\u003e(A) Progression-free survival and (B) Overall survival among TTF-1-positive patients.\u003c/p\u003e\n\u003cp\u003e(C) Progression-free survival and (D) Overall survival among TTF-1-negative patients.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9478780/v1/6161c7775bb8e4915a4cdb20.jpg"},{"id":109204860,"identity":"1b16921e-a3dc-488c-91c4-7843a823b251","added_by":"auto","created_at":"2026-05-13 15:02:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":702825,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9478780/v1/a993f4ac-7e33-474e-9771-feace697218a.pdf"}],"financialInterests":"","formattedTitle":"Predictive Value of TTF-1 Expression for Stratifying Outcomes Between Dual ICI and Chemoimmunotherapy in Patients with PD-L1-Negative NSCLC","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThyroid transcription factor 1 (TTF-1) is a lineage-defining marker widely used in the pathological diagnosis of lung adenocarcinoma and is associated with tumor differentiation, clinical outcomes, and therapeutic sensitivity in non-squamous non-small-cell lung cancer (NSCLC) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Its predictive and prognostic significance in the context of cytotoxic chemotherapy has also been reported [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Particularly, several studies have shown that TTF-1-negative patients tend to have poorer responses and survival outcomes when treated with pemetrexed-containing regimens [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Given that TTF-1-negative patients have historically responded poorly to standard cytotoxic regimens, optimal treatment selection for these individuals remains a significant clinical challenge.\u003c/p\u003e \u003cp\u003eIn recent years, the emergence of immune checkpoint inhibitors (ICIs) has transformed the treatment landscape of advanced NSCLC, with immunotherapy or chemoimmunotherapy now constituting the standard first-line therapy for most patients without actionable driver mutations [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, patients with programmed death ligand-1 (PD-L1)-negative tumors (PD-L1\u0026thinsp;\u0026lt;\u0026thinsp;1%) represent a subgroup in which the benefit of immunotherapy is generally considered limited. Although several immunotherapy-based strategies have been investigated for this population, the optimal treatment approach remains uncertain. The KEYNOTE-189 trial demonstrated that the addition of pembrolizumab to platinum\u0026ndash;pemetrexed chemotherapy significantly improved survival compared with chemotherapy alone, regardless of PD-L1 expression, thereby establishing chemoimmunotherapy as a standard first-line treatment option [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Similarly, the CheckMate-227 trial demonstrated that dual immune checkpoint blockade with nivolumab plus ipilimumab significantly improved overall survival compared with chemotherapy, even in patients with PD-L1-negative tumors [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. More recently, the CheckMate-9LA trial demonstrated that nivolumab plus ipilimumab combined with a limited course of chemotherapy improved survival across PD-L1 subgroups, although concerns remain regarding treatment-related serious adverse events[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe previously analyzed cohorts with PD-L1 expression\u0026thinsp;\u0026ge;\u0026thinsp;50% and 1\u0026ndash;49% and found that the poor prognosis typically observed in TTF-1-negative patients treated with cytotoxic chemotherapy was mitigated by ICI monotherapy or chemoimmunotherapy [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Nonetheless, in the setting of PD-L1\u0026thinsp;\u0026lt;\u0026thinsp;1%, where the benefit from ICIs is known to be limited, it remains uncertain whether TTF-1-negative patients exhibit similarly improved outcomes or whether their poorer prognosis persists as observed with conventional chemotherapy. Furthermore, while both chemoimmunotherapy and dual ICI (IO\u0026ndash;IO) therapy are currently available options for this population, optimal patient selection between these regimens remains elusive.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to investigate the clinical relevance of TTF-1 expression in patients with advanced NSCLC and PD-L1\u0026thinsp;\u0026lt;\u0026thinsp;1%, focusing on its potential role in stratifying outcomes between IO\u0026ndash;IO therapy and chemoimmunotherapy. Our analysis sought to clarify whether TTF-1 could help inform treatment selection in this therapeutically challenging subgroup.\u003c/p\u003e"},{"header":"Patients and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and study design\u003c/h2\u003e \u003cp\u003eWe conducted a multicenter, retrospective cohort study of patients with advanced or recurrent NSCLC and PD-L1 expression\u0026thinsp;\u0026lt;\u0026thinsp;1% who received either IO-IO therapy or chemoimmunotherapy at 22 centers in Japan between January 2019 and December 2022. This study was conducted in compliance with the Declaration of Helsinki. Ethical approval was obtained from Kurume University (Approval IRB No. 24116) and the requirement for written informed consent was waived owing to the retrospective nature of the study, with an opt-out opportunity provided. Inclusion criteria: (i) a pathological diagnosis of NSCLC; (ii) advanced disease corresponding to stage III\u0026ndash;IV based on the TNM classification (American Joint Committee on Cancer, 8th edition) or documented postoperative recurrence; (iii) availability of tumor TTF-1 expression data; and (iv) PD-L1 tumor proportion score\u0026thinsp;\u0026lt;\u0026thinsp;1%. Exclusion criteria: (i) a histological diagnosis of squamous cell carcinoma; (ii) a lack of evaluable TTF-1 expression in tumor tissue; and (iii) the presence of known actionable oncogenic driver alterations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eCategorical variables were compared using the chi-square test or Fisher\u0026rsquo;s exact test, as appropriate. Survival curves were estimated using the Kaplan\u0026ndash;Meier method and compared with the log-rank test. Progression-free survival (PFS) was defined as the time from treatment initiation to documented disease progression based on the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1, or death from any cause, and overall survival (OS) was defined as the time from treatment initiation to death from any cause.\u003c/p\u003e \u003cp\u003eTo identify factors independently associated with PFS and OS, multivariable analyses were performed using the Cox proportional hazards model. All statistical tests were two-sided, and a p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant. Statistical analyses were performed using EZR (Jichi Medical University, Saitama Medical Center, Saitama, Japan) and the R software environment (R Foundation for Statistical Computing) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The cut-off date for analyses was Jun 30, 2024.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePatients\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline patient characteristics are presented in Table 1. The cohort had a median age of 69 years (range, 27\u0026ndash;89), and approximately three-quarters were men (76.8%). Nearly all patients exhibited a favorable Eastern Cooperative Oncology Group (ECOG) performance status of 0\u0026ndash;1 (95.4%). Most tumors were adenocarcinomas (87.1%), and three-quarters of the population had stage IV disease at diagnosis (75.8%). In this study, patients were categorized according to TTF-1 expression, and the TTF-1-positive group demonstrated a significantly higher prevalence of adenocarcinoma than the TTF-1-negative group (93% vs. 80.9%, \u003cem\u003ep\u003c/em\u003e = 0.02). Aside from histology, the two groups showed broadly comparable baseline characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eImpact of TTF-1 expression on the efficacy of chemoimmunotherapy/\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003edual immune checkpoint inhibitors\u0026nbsp;in the total cohort\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith a median follow-up period of 19.1 months, progressive disease was documented in 156 patients (80.4%), and 127 patients died (65.5%). Regarding PFS, the TTF-1-negative group had significantly a shorter median PFS than did the TTF-1-positive group (4.8 months [95% confidence interval (CI): 4.0\u0026minus;6.4] vs. 8.0 months [95% CI: 6.7\u0026minus;10.0], \u003cem\u003ep\u003c/em\u003e = 0.016) (Fig. 1A). Multivariable analysis revealed that TTF-1 expression (adjusted hazard ratio [HR] = 0.65, [95% CI = 0.47\u0026ndash;0.90], \u003cem\u003ep\u003c/em\u003e = 0.009) was an independent predictive factor for PFS (Table 2a). OS was numerically shorter in the TTF-1-negative group than in the TTF-1-positive group, although this difference did not reach statistical significance (median OS: 13.9 months [95% CI: 9.4\u0026ndash;21.5] vs. 20.4 months [95% CI: 17.8\u0026ndash;29.0], log-rank \u003cem\u003ep\u003c/em\u003e = 0.16) (Fig. 1B). However, multivariable Cox regression analysis identified both TTF-1 expression (adjusted HR = 0.69, [95% CI = 0.48\u0026ndash; 0.98], \u003cem\u003ep\u003c/em\u003e = 0.04) and ECOG performance status (adjusted HR = 0.32, [95% CI = 0.15\u0026ndash; 0.68], \u003cem\u003ep\u003c/em\u003e = 0.003) as variables independently associated with OS after adjustment for other clinical factors (Table 2b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eComparison of survival outcomes between chemoimmunotherapy and dual immune checkpoint inhibitors according to TTF-1 expression\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the overall population, OS and PFS did not differ significantly between patients treated with chemoimmunotherapy and those receiving IO-IO therapy (median OS: 19.98 months vs. 9.95 months, \u003cem\u003ep\u003c/em\u003e = 0.44; median PFS: 6.70 months vs. 4.96 months, \u003cem\u003ep\u003c/em\u003e = 0.96) (Fig. 2A, 2B).\u003c/p\u003e\n\u003cp\u003eWhen survival outcomes were examined according to TTF-1 expression, no significant differences were observed in the TTF-1-positive cohort (median OS: 20.30 months vs. not reached, \u003cem\u003ep\u003c/em\u003e = 0.24; median PFS: 7.75 months vs. 9.40 months, \u003cem\u003ep\u003c/em\u003e = 0.29) (Fig. 2C, 2D). In multivariable Cox regression analyses, treatment modality was not independently associated with PFS (adjusted HR 1.67, 95% CI 0.72\u0026ndash;3.92, \u003cem\u003ep\u003c/em\u003e = 0.23) or OS (adjusted HR 2.17, 95% CI 0.67\u0026ndash;7.02, \u003cem\u003ep\u003c/em\u003e = 0.20) in this cohort (Table 3).\u003c/p\u003e\n\u003cp\u003eIn the TTF-1-negative cohort, OS and PFS differed significantly between treatment groups (median OS: 17.15 months vs. 3.25 months, \u003cem\u003ep\u003c/em\u003e = 0.006; median PFS: 5.29 months vs. 3.88 months, \u003cem\u003ep\u003c/em\u003e = 0.04) (Fig. 2E, 2F). In multivariable analyses, chemoimmunotherapy was independently associated with improved OS (adjusted HR 0.392, 95% CI 0.174\u0026ndash;0.886, \u003cem\u003ep\u003c/em\u003e = 0.024) and demonstrated a trend toward improved PFS (adjusted HR 0.44, 95% CI 0.19\u0026ndash;1.01, \u003cem\u003ep\u003c/em\u003e = 0.051) compared with IO-IO therapy after adjustment for covariates (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eComparison of pemetrexed-based and non\u0026ndash;pemetrexed-based chemoimmunotherapy regimens stratified by TTF-1 expression\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess chemoimmunotherapy regimens, patients who received IO-IO therapy were excluded from the analysis (n = 23). Survival outcomes were then compared between pemetrexed-based and non\u0026ndash;pemetrexed-based chemoimmunotherapy regimens according to TTF-1 expression status.\u003c/p\u003e\n\u003cp\u003eIn the TTF-1-positive cohort, OS was significantly longer in the pemetrexed-based group than in the non\u0026ndash;pemetrexed-based group (median OS: 21.82 vs. 15.24 months,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e = 0.047), although no significant difference was observed in PFS (median PFS: 7.75 vs. 7.62 months,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e = 0.95) (Fig.3A,3B). In the TTF-1-negative cohort, OS and PFS did not differ significantly between the two regimens (median OS: 16.00 months vs. 17.15 months, \u003cem\u003ep\u003c/em\u003e = 0.67; median PFS: 5.61 months vs. 4.83 months, \u003cem\u003ep\u003c/em\u003e = 0.95) (Fig.3C,3D).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated the clinical significance of TTF-1 expression in patients with PD-L1-negative (\u0026lt;\u0026thinsp;1%) advanced NSCLC treated with immunotherapy-based regimens. In the overall cohort, the TTF-1-negative group had significantly shorter PFS and numerically shorter OS compared to the TTF-1\u0026thinsp;=\u0026thinsp;positive group. When treatment outcomes were analyzed based on regimen type, no clear differences were observed between IO\u0026ndash;IO therapy and chemoimmunotherapy in the TTF-1-positive cohort. By contrast, in the TTF-1-negative cohort, patients receiving IO\u0026ndash;IO therapy exhibited a significantly shorter OS and a trend toward shorter PFS compared with those treated with chemoimmunotherapy. These findings suggest that the addition of chemotherapy to immunotherapy may improve clinical outcomes in TTF-1-negative and PD-L1-negative patients with NSCLC.\u003c/p\u003e \u003cp\u003eAcross different treatment regimens, several previous studies have reported that TTF-1 expression is associated with treatment response and prognosis in patients with NSCLC [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17 CR18 CR19\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Consistent with these reports, our analysis of patients with PD-L1-negative (\u0026lt;\u0026thinsp;1%) NSCLC demonstrated inferior PFS in the TTF-1-negative group compared with the TTF-1-positive group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, in our earlier study of NSCLC cohorts with higher PD-L1 expression (PD-L1\u0026thinsp;\u0026ge;\u0026thinsp;50% and PD-L1 1\u0026ndash;49%), treatment efficacy and survival were comparable regardless of TTF-1 expression [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. We considered that this discrepancy may be explained by differences in PD-L1 expression distribution across study populations. In patients with PD-L1\u0026ndash;high (\u0026ge;\u0026thinsp;50%) NSCLC treated with PD-1/PD-L1 monotherapy, as well as those with PD-L1\u0026thinsp;\u0026ge;\u0026thinsp;50% or PD-L1 1\u0026ndash;49% treated with chemoimmunotherapy, no significant differences in outcomes were observed according to TTF-1 status [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. By contrast, in a study focusing on cytotoxic chemotherapy, the TTF-1-negative group was consistently associated with inferior treatment outcomes and survival [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Taken together, these findings suggest that in patient populations more likely to benefit from immunotherapy, the prognostic impact of TTF-1 expression may be attenuated, whereas in PD-L1-negative populations with limited immunotherapy benefit, TTF-1 negativity remains associated with poorer outcomes.\u003c/p\u003e \u003cp\u003eHowever, several previous studies reported inferior outcomes in TTF-1-negative patients even among cohorts treated with immunotherapy or chemoimmunotherapy [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. We considered that this finding might be largely explained by the close association between TTF-1 expression and PD-L1 expression. In our earlier study, TTF-1-negative NSCLC tended to show lower PD-L1 expression than TTF-1-positive NSCLC [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Moreover, experimental studies have suggested that TTF-1 can directly induce PD-L1 expression, supporting a mechanistic link between TTF-1 and PD-L1 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Accordingly, the proportion of TTF-1-negative patients increased with decreasing PD-L1 expression, accounting for 27.7% in previously reported PD-L1\u0026thinsp;\u0026ge;\u0026thinsp;50% cohorts, 33% in PD-L1 1\u0026ndash;49% cohorts, and 48.5% in the PD-L1\u0026thinsp;\u0026lt;\u0026thinsp;1% cohort of the present study [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, when PD-L1 expression was not considered, differences in PD-L1 distribution between TTF-1-positive and TTF-1-negative groups may have influenced the apparent efficacy of immunotherapy. By contrast, when analyses were stratified by PD-L1 expression, treatment efficacy in immunotherapy-responsive populations (PD-L1\u0026thinsp;\u0026ge;\u0026thinsp;50% and PD-L1 1\u0026ndash;49%) was largely comparable regardless of TTF-1 status, whereas in PD-L1-negative populations, which derived limited benefit from immunotherapy, outcomes resembled those seen with cytotoxic chemotherapy.\u003c/p\u003e \u003cp\u003eOur study showed that the efficacy of immunotherapy-based regimens differed according to TTF-1 expression. In the TTF-1-positive cohort, no clear differences were observed between IO\u0026ndash;IO therapy and chemoimmunotherapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). By contrast, in the TTF-1-negative cohort, IO\u0026ndash;IO therapy was associated with poorer outcomes compared with chemoimmunotherapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A plausible explanation is the difference in Ki-67 expression according to TTF-1 status. Ki-67, a marker of cell proliferation, is typically expressed at higher levels in TTF-1-negative tumors, indicating a larger fraction of actively proliferating tumor cells [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In such tumors, IO\u0026ndash;IO therapy alone may be insufficient to prevent early disease progression due to its delayed onset of action, whereas the addition of chemotherapy provides rapid cytoreduction may help suppress early tumor growth, thereby translating to superior treatment outcomes compared with IO\u0026ndash;IO therapy.\u003c/p\u003e \u003cp\u003eWe compared pemetrexed-based and non\u0026ndash;pemetrexed-based chemoimmunotherapy according to TTF-1 expression. Although pemetrexed has been reported to be less effective in TTF-1-negative tumors when used as cytotoxic chemotherapy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], this historical disadvantage was not evident in the present chemoimmunotherapy cohort. The absence of a clear difference may reflect the limited sample size and the possibility that immunotherapy provided some clinical benefit even in this cohort. However, the underlying mechanism remains unclear.\u003c/p\u003e \u003cp\u003eThis study has a few limitations. Although this represents one of the largest available cohorts in this setting, the retrospective design and modest sample size\u0026mdash;particularly within treatment subgroups\u0026mdash;may limit statistical power. Additionally, TTF-1 immunohistochemistry was not fully standardized across institutions, and TTF-1 was assessed only as positive or negative, despite prior reports suggesting that staining intensity may carry prognostic information[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Furthermore, data on other potential confounding factors, such as tumor mutational burden or comprehensive genomic profiles, were not completely available for all patients.\u003c/p\u003e \u003cp\u003eIn conclusion, TTF-1 expression serves as a robust prognostic and predictive biomarker in advanced PD-L1-negative NSCLC. While treatment outcomes were comparable between IO\u0026ndash;IO therapy and chemoimmunotherapy in TTF-1-positive patients, the addition of chemotherapy to immunotherapy provided a significant survival advantage in the TTF-1-negative population. Our findings suggest that TTF-1 expression may help optimize treatment selection and personalize therapeutic strategies for this clinically challenging subgroup.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eConflicts of interest\u003c/strong\u003e \u003cp\u003eTadaaki Yamada received research grants from Ono Pharmaceutical and Takeda Pharmaceutical, and has received speaking honoraria from Eli Lilly and Chugai-Roshe outside the purview of the submitted work. Koichi Azuma received personal fees from AstraZeneca, MSD, Bristol Myers Squibb, Ono Pharmaceutical, Takeda Pharmaceutical, Pfizer, and Chugai Pharmaceutical outside the purview of the submitted work. Asuka Okada received speaking honoraria from AstraZeneca, Nippon Kayaku, Bristol-Myers Squibb, Boehringer Ingelheim, Chugai-Roshe, Taiho Pharmaceutical, Kyowa Kirin, MSD, and Eli Lilly Japan outside the purview of the submitted work. Makoto Hibino received personal fees from Asahi Kasei Pharma Corporation, AstraZeneca, Boehringer Ingelheim, Bristol Myers, Chugai Pharmaceutical, and Eli Lilly outside the purview of the submitted work. Yosuke Tamura received speaking honoraria from AstraZeneca, Chugai- Pharmaceutical, MSD, Nippon Boehringer Ingelheim, and Ono Pharmaceutical outside the purview of the submitted work. Yasuhiro Goto received personal fees from AstraZeneca, Boehringer Ingelheim, Bristol Myers, Chugai Pharmaceutical, Kyowa Kirin International, Novartis, Ono Pharmaceutical, Pfizer, Takeda, and Taiho Pharmaceutical outside the purview of the submitted work. Toshiyuki Sumi received personal fees from AstraZeneca, Chugai Pharmaceutical, and Ono Pharmaceutical outside the purview of the submitted work. Hiroyasu Kaneda received speaking honoraria from AstraZeneca, Bristol-Myers Squibb, Chugai-Pharmaceutical, MSD, and Ono Pharmaceutical outside the purview of the submitted work. The remaining authors have no conflicts of interest to disclose.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics approval:\u003c/strong\u003e \u003cp\u003e This study complied with the Declaration of Helsinki and was approved by the ethics review Board of Kurume University (Approval IRB No. 24116), which served as the central ethics committee for all participating centers.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate:\u003c/strong\u003e \u003cp\u003e Informed consent for the use of personal medical data was obtained using the opt-out method as described in the disclosure document.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to publish\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eData Sharing Statement\u003c/h2\u003e \u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eDeclaration of AI and AI-assisted technologies in the writing process\u003c/h2\u003e \u003cp\u003eChatGPT (GPT-5.2, 2025 version, developed by OpenAI) was used to assist with English editing and language refinement during the abstract writing process. The authors reviewed and modified all content to ensure accuracy and originality. No AI-generated content was used as a primary source, and the authors confirm that there is no plagiarism in the AI-assisted material.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis study did not receive specific grants from public, commercial, or not-for-profit funding agencies.\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003e \u003cb\u003eConceptualization\u003c/b\u003e: Nishioka N, Yamada T. \u003cb\u003eMethodology\u003c/b\u003e: Nishioka N, Yamada T. \u003cb\u003eSoftware\u003c/b\u003e: Nishioka N. \u003cb\u003eFormal analysis\u003c/b\u003e: Nishioka N. \u003cb\u003eInvestigation\u003c/b\u003e: Nishioka N, Murata D, Ito K, Nomizo T, Yamada K, Imabayashi T, Iwanaga K, Chibana K, Ota T, Nishii Y, Nakao A, Okada A, Hamai K, Sakai T, Harada T, Tanimura K, Yoshimine K, Tamura Y, Takaki R, Goto Y, Hibino M, Oba T, Sumi T, Kaneda H. \u003cb\u003eData Curation\u003c/b\u003e: Nishioka N. \u003cb\u003eWriting-Original Draft\u003c/b\u003e: Nishioka N. \u003cb\u003eWriting-Review \u0026amp; Editing\u003c/b\u003e: Yamada T. \u003cb\u003eVisualization\u003c/b\u003e: Nishioka N. \u003cb\u003eSupervision\u003c/b\u003e: Takayama K. \u003cb\u003eProject administration\u003c/b\u003e: Yamada T, Azuma K.\u003c/p\u003e\u003ch2\u003eAcknowledgment:\u003c/h2\u003e \u003cp\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePhelps CA, Lai SC, Mu D (2018) Roles of Thyroid Transcription Factor 1 in Lung Cancer Biology. Vitam Horm 106:517\u0026ndash;544\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrost N, Zhamurashvili T, von Laffert M et al (2020) Pemetrexed-Based Chemotherapy Is Inferior to Pemetrexed-Free Regimens in Thyroid Transcription Factor 1 (TTF-1)-Negative, EGFR/ALK-Negative Lung Adenocarcinoma: A Propensity Score Matched Pairs Analysis. Clin Lung Cancer 21:e607\u0026ndash;e621\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakeuchi A, Oguri T, Yamashita Y et al (2020) Value of TTF-1 expression in non-squamous non-small-cell lung cancer for assessing docetaxel monotherapy after chemotherapy failure. Mol Clin Oncol 13:9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGandhi L, Rodr\u0026iacute;guez-Abreu D, Gadgeel S et al (2018) Pembrolizumab plus Chemotherapy in Metastatic Non-Small-Cell Lung Cancer. N Engl J Med 378:2078\u0026ndash;2092\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWest H, McCleod M, Hussein M et al (2019) Atezolizumab in combination with carboplatin plus nab-paclitaxel chemotherapy compared with chemotherapy alone as first-line treatment for metastatic non-squamous non-small-cell lung cancer (IMpower130): a multicentre, randomised, open-label, phase 3 trial. Lancet Oncol 20:924\u0026ndash;937\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGadgeel S, Rodr\u0026iacute;guez-Abreu D, Speranza G et al (2020) Updated Analysis From KEYNOTE-189: Pembrolizumab or Placebo Plus Pemetrexed and Platinum for Previously Untreated Metastatic Nonsquamous Non-Small-Cell Lung Cancer. J Clin Oncol 38:1505\u0026ndash;1517\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrahmer JR, Lee JS, Ciuleanu TE et al (2023) Five-Year Survival Outcomes With Nivolumab Plus Ipilimumab Versus Chemotherapy as First-Line Treatment for Metastatic Non-Small-Cell Lung Cancer in CheckMate 227. J Clin Oncol 41:1200\u0026ndash;1212\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaz-Ares L, Ciuleanu TE, Cobo M et al (2021) First-line nivolumab plus ipilimumab combined with two cycles of chemotherapy in patients with non-small-cell lung cancer (CheckMate 9LA): an international, randomised, open-label, phase 3 trial. Lancet Oncol 22:198\u0026ndash;211\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShiraishi Y, Nomura S, Sugawara S et al (2024) Comparison of platinum combination chemotherapy plus pembrolizumab versus platinum combination chemotherapy plus nivolumab-ipilimumab for treatment-naive advanced non-small-cell lung cancer in Japan (JCOG2007): an open-label, multicentre, randomised, phase 3 trial. Lancet Respir Med 12:877\u0026ndash;887\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishioka N, Hata T, Yamada T et al (2025) Impact of TTF-1 Expression on the Prognostic Prediction of Patients with Non-Small Cell Lung Cancer with PD-L1 Expression Levels of 1% to 49%, Treated with Chemotherapy vs. Chemoimmunotherapy: A Multicenter, Retrospective Study. Cancer Res Treat 57:412\u0026ndash;421\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishioka N, Kawachi H, Yamada T et al (2024) Unraveling the influence of TTF-1 expression on immunotherapy outcomes in PD-L1-high non-squamous NSCLC: a retrospective multicenter study. Front Immunol 15:1399889\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanda Y (2013) Investigation of the freely available easy-to-use software 'EZR' for medical statistics. Bone Marrow Transpl 48:452\u0026ndash;458\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchilsky JB, Ni A, Ahn L et al (2017) Prognostic impact of TTF-1 expression in patients with stage IV lung adenocarcinomas. Lung Cancer 108:205\u0026ndash;211\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNakao A, Inoue H, Ikeuchi N et al (2022) Impact of Results of TTF-1 Immunostaining on Efficacy of Platinum-Doublet Chemotherapy in Japanese Patients with Nonsquamous Non-Small-Cell Lung Cancer. J Clin Med 12\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNakahama K, Kaneda H, Osawa M et al (2022) Association of thyroid transcription factor-1 with the efficacy of immune-checkpoint inhibitors in patients with advanced lung adenocarcinoma. Thorac Cancer 13:2309\u0026ndash;2317\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIso H, Hisakane K, Mikami E et al (2023) Thyroid transcription factor-1 (TTF-1) expression and the efficacy of combination therapy with immune checkpoint inhibitors and cytotoxic chemotherapy in non-squamous non-small cell lung cancer. Transl Lung Cancer Res 12:1850\u0026ndash;1861\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKatayama Y, Yamada T, Morimoto K et al (2023) TTF-1 Expression and Clinical Outcomes of Combined Chemoimmunotherapy in Patients With Advanced Lung Adenocarcinoma: A Prospective Observational Study. JTO Clin Res Rep 4:100494\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamada J, Jimbo N, Yamasaki N et al (2025) Diffuse and Strong TTF-1 Expression Predicts Response to Pemetrexed-Based Immunochemotherapy in Advanced Lung Adenocarcinoma. Cancer Manag Res 17:1599\u0026ndash;1611\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIbusuki R, Yoneshima Y, Hashisako M et al (2022) Association of thyroid transcription factor-1 (TTF-1) expression with efficacy of PD-1/PD-L1 inhibitors plus pemetrexed and platinum chemotherapy in advanced non-squamous non-small cell lung cancer. Transl Lung Cancer Res 11:2208\u0026ndash;2215\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUhlenbruch M, Kr\u0026uuml;ger S (2024) Effect of TTF-1 expression on progression free survival of immunotherapy and chemo-/immunotherapy in patients with non-small cell lung cancer. J Cancer Res Clin Oncol 150:394\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo M, Tomoshige K, Meister M et al (2017) Gene signature driving invasive mucinous adenocarcinoma of the lung. EMBO Mol Med 9:462\u0026ndash;481\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZu YF, Wang XC, Chen Y et al (2012) Thyroid transcription factor 1 represses the expression of Ki-67 and induces apoptosis in non-small cell lung cancer. Oncol Rep 28:1544\u0026ndash;1550\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMyong NH (2003) Thyroid transcription factor-1 (TTF-1) expression in human lung carcinomas: its prognostic implication and relationship with wxpressions of p53 and Ki-67 proteins. J Korean Med Sci 18:494\u0026ndash;500\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoherty MK, O'Connor E, Hannon D et al (2019) Absence of thyroid transcription factor-1 expression is associated with poor survival in patients with advanced pulmonary adenocarcinoma treated with pemetrexed-based chemotherapy. Ir J Med Sci 188:69\u0026ndash;74\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFiala O, Pesek M, Skrickova J et al (2017) Thyroid transcription factor 1 expression is associated with outcome of patients with non-squamous non-small cell lung cancer treated with pemetrexed-based chemotherapy. Tumour Biol 39:1010428317691186\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun JM, Han J, Ahn JS et al (2011) Significance of thymidylate synthase and thyroid transcription factor 1 expression in patients with nonsquamous non-small cell lung cancer treated with pemetrexed-based chemotherapy. J Thorac Oncol 6:1392\u0026ndash;1399\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTerashima Y, Matsumoto M, Iida H et al (2023) Predictive Impact of Diffuse Positivity for TTF-1 Expression in Patients Treated With Platinum-Doublet Chemotherapy Plus Immune Checkpoint Inhibitors for Advanced Nonsquamous NSCLC. JTO Clin Res Rep 4:100578\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatients\u0026rsquo; characteristics.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4231%;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003en=194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3077%;\"\u003e\n \u003cp\u003eTTF-1-negative\u003c/p\u003e\n \u003cp\u003en=94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3077%;\"\u003e\n \u003cp\u003eTTF-1-positive\u003c/p\u003e\n \u003cp\u003en=100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4231%;\"\u003e\n \u003cp\u003eAge, y\u003c/p\u003e\n \u003cp\u003eMedian (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e69 [27\u0026ndash;89]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3077%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e68 [27\u0026ndash;89]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3077%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e70 [38\u0026ndash;84]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4231%;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e149 (76.8)\u003c/p\u003e\n \u003cp\u003e45 (23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3077%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e71 (75.5)\u003c/p\u003e\n \u003cp\u003e23 (24.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3077%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e78\u0026nbsp;(78.0)\u003c/p\u003e\n \u003cp\u003e22\u0026nbsp;(22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4231%;\"\u003e\n \u003cp\u003eECOG PS (%)\u003c/p\u003e\n \u003cp\u003e0-1\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;\u0026nbsp;\u003c/strong\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e185 (95.4)\u003c/p\u003e\n \u003cp\u003e9 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3077%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e91 (96.8)\u003c/p\u003e\n \u003cp\u003e3 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3077%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e94 (94.0)\u003c/p\u003e\n \u003cp\u003e6 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4231%;\"\u003e\n \u003cp\u003eStage (%)\u003c/p\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003cp\u003eRecurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13 (6.7)\u003c/p\u003e\n \u003cp\u003e147 (75.8)\u003c/p\u003e\n \u003cp\u003e34 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3077%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3 (3.2)\u003c/p\u003e\n \u003cp\u003e71 (75.5)\u003c/p\u003e\n \u003cp\u003e20 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3077%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10 (10.0)\u003c/p\u003e\n \u003cp\u003e76 (76.0)\u003c/p\u003e\n \u003cp\u003e14 (14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4231%;\"\u003e\n \u003cp\u003eHistology (%)\u003c/p\u003e\n \u003cp\u003eAdeno\u003c/p\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e169 (87.1)\u003c/p\u003e\n \u003cp\u003e25 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3077%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e76 (80.9)\u003c/p\u003e\n \u003cp\u003e18 (19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3077%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e93 (93.0)\u003c/p\u003e\n \u003cp\u003e7 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4231%;\"\u003e\n \u003cp\u003eSmoking history (%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Never\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Current/past\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e30 (15.5)\u003c/p\u003e\n \u003cp\u003e164 (84.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3077%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17 (18.1)\u003c/p\u003e\n \u003cp\u003e77 (81.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3077%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13 (13.0)\u003c/p\u003e\n \u003cp\u003e87 (87.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4231%;\"\u003e\n \u003cp\u003eLiver metastasis (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003e23 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3077%;\"\u003e\n \u003cp\u003e10 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3077%;\"\u003e\n \u003cp\u003e13 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4231%;\"\u003e\n \u003cp\u003eBrain metastasis (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003e41 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3077%;\"\u003e\n \u003cp\u003e18 (19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3077%;\"\u003e\n \u003cp\u003e23 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4231%;\"\u003e\n \u003cp\u003eTreatment regimen (%)\u003c/p\u003e\n \u003cp\u003eCDDP or CBDCA/PEM/Pembro\u003c/p\u003e\n \u003cp\u003eCBDCA/PTX or nab-PTX/Pembro\u003c/p\u003e\n \u003cp\u003eCBDCA/nab-PTX/Atezo\u003c/p\u003e\n \u003cp\u003eCDDP or CBDCA/PEM/Atezo\u003c/p\u003e\n \u003cp\u003eCBDCA/PTX/BEV/Atezo\u003c/p\u003e\n \u003cp\u003eChemotherapy/Nivo/IPI\u003c/p\u003e\n \u003cp\u003eNivo/IPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3846%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e85 (43.8)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; 10 (5.2)\u003c/p\u003e\n \u003cp\u003e20 (10.3)\u003c/p\u003e\n \u003cp\u003e5 (2.6)\u003c/p\u003e\n \u003cp\u003e15 (7.7)\u003c/p\u003e\n \u003cp\u003e37 (19.1)\u003c/p\u003e\n \u003cp\u003e22 (11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3077%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e38 (40.4)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 8 (8.5)\u003c/p\u003e\n \u003cp\u003e11 (11.7)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 1 (1.1)\u003c/p\u003e\n \u003cp\u003e7 (7.4)\u003c/p\u003e\n \u003cp\u003e17 (18.1)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 12 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3077%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e47 (47.0)\u003c/p\u003e\n \u003cp\u003e2 (2.0)\u003c/p\u003e\n \u003cp\u003e9 (9.0)\u003c/p\u003e\n \u003cp\u003e4 (4.0)\u003c/p\u003e\n \u003cp\u003e8 (8.0)\u003c/p\u003e\n \u003cp\u003e20 (20.0)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 10 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTTF-1, thyroid transcription factor-1; ECOG PS, Eastern Cooperative Oncology Group Performance Status; CBDCA: carboplatin, CDDP: cisplatin, PTX: paclitaxel, nab-PTX: nab-paclitaxel, BEV: bevacizumab, PEM: pemetrexed, Atezo: atezolizumab, Pembro: pembrolizumab, Nivo: nivolumab, IPI: Ipilimumab\u003cbr clear=\"all\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a) Predictors of PFS for the total cohort\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"679\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.8424%;\"\u003e\n \u003cp\u003eCovariates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6038%;\"\u003e\n \u003cp\u003eCrude HR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2548%;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.06922%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1384%;\"\u003e\n \u003cp\u003eAdjusted HR \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2548%;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.83652%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.8424%;\"\u003e\n \u003cp\u003eTTF-1 (positive vs. negative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6038%;\"\u003e\n \u003cp\u003e0.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2548%;\"\u003e\n \u003cp\u003e0.495\u0026ndash;0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.06922%;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1384%;\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2548%;\"\u003e\n \u003cp\u003e0.465\u0026ndash;0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.83652%;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.8424%;\"\u003e\n \u003cp\u003eAge (\u0026ge;65 vs. \u0026lt;65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6038%;\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2548%;\"\u003e\n \u003cp\u003e0.547\u0026ndash;1.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.06922%;\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1384%;\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2548%;\"\u003e\n \u003cp\u003e0.545\u0026ndash;1.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.83652%;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.8424%;\"\u003e\n \u003cp\u003eSmoke (never vs. current/former)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6038%;\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2548%;\"\u003e\n \u003cp\u003e0.601\u0026ndash;1.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.06922%;\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1384%;\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2548%;\"\u003e\n \u003cp\u003e0.574\u0026ndash;1.336\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.83652%;\"\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.8424%;\"\u003e\n \u003cp\u003eECOG-PS (PS 0-1 vs. PS\u0026ge;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6038%;\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2548%;\"\u003e\n \u003cp\u003e0.391\u0026ndash;1.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.06922%;\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1384%;\"\u003e\n \u003cp\u003e0.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2548%;\"\u003e\n \u003cp\u003e0.328\u0026ndash;1.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.83652%;\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.8424%;\"\u003e\n \u003cp\u003eChemoimmunotherapy vs. IO-IO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6038%;\"\u003e\n \u003cp\u003e0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2548%;\"\u003e\n \u003cp\u003e0.569\u0026ndash;1.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.06922%;\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1384%;\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2548%;\"\u003e\n \u003cp\u003e0.489\u0026ndash;1.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.83652%;\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.8424%;\"\u003e\n \u003cp\u003eHistology (adenocarcinoma vs. others)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6038%;\"\u003e\n \u003cp\u003e1.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2548%;\"\u003e\n \u003cp\u003e0.682\u0026ndash;1.724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.06922%;\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1384%;\"\u003e\n \u003cp\u003e1.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2548%;\"\u003e\n \u003cp\u003e0.766\u0026ndash;1.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.83652%;\"\u003e\n \u003cp\u003e0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e(b) Predictors of OS for the total cohort\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"672\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.0357%;\"\u003e\n \u003cp\u003eCovariates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7143%;\"\u003e\n \u003cp\u003eCrude HR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3929%;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.03571%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4345%;\"\u003e\n \u003cp\u003eAdjusted HR \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7381%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.0357%;\"\u003e\n \u003cp\u003eTTF-1 (positive vs. negative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7143%;\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3929%;\"\u003e\n \u003cp\u003e0.549\u0026ndash;1.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.03571%;\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4345%;\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e0.477\u0026ndash;0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7381%;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.0357%;\"\u003e\n \u003cp\u003eAge (\u0026ge;65 vs. \u0026lt;65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7143%;\"\u003e\n \u003cp\u003e1.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3929%;\"\u003e\n \u003cp\u003e0.722\u0026ndash;1.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.03571%;\"\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4345%;\"\u003e\n \u003cp\u003e1.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e0.695\u0026ndash;1.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7381%;\"\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.0357%;\"\u003e\n \u003cp\u003eSmoke (never vs. current/former)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7143%;\"\u003e\n \u003cp\u003e1.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3929%;\"\u003e\n \u003cp\u003e0.844\u0026ndash;2.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.03571%;\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4345%;\"\u003e\n \u003cp\u003e1.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e0.846\u0026ndash;2.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7381%;\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.0357%;\"\u003e\n \u003cp\u003eECOG-PS (PS 0-1 vs. PS\u0026ge;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7143%;\"\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3929%;\"\u003e\n \u003cp\u003e0.180\u0026ndash;0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.03571%;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4345%;\"\u003e\n \u003cp\u003e0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e0.152\u0026ndash;0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7381%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.0357%;\"\u003e\n \u003cp\u003eChemoimmunotherapy vs. IO-IO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7143%;\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3929%;\"\u003e\n \u003cp\u003e0.434\u0026ndash;1.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.03571%;\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4345%;\"\u003e\n \u003cp\u003e0.763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e0.416\u0026ndash;1.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7381%;\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.0357%;\"\u003e\n \u003cp\u003eHistology (adenocarcinoma vs. others)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7143%;\"\u003e\n \u003cp\u003e1.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3929%;\"\u003e\n \u003cp\u003e0.700\u0026ndash;2.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.03571%;\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4345%;\"\u003e\n \u003cp\u003e1.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e0.840\u0026ndash;2.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.7381%;\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTTF-1, thyroid transcription factor-1; HR: Hazard ratio \u003csup\u003ea)\u003c/sup\u003e; ECOG PS: Eastern Cooperative Oncology Group Performance status; IO, immune-oncology therapy; CI, Confidence interval; PFS, progression-free survival; OS, overall survival\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea)\u0026nbsp;\u003c/sup\u003eHazard Ratio for TTF-1 was adjusted for Age, Smoke history, ECOG-PS, Chemotherapy regimen and Histology\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a) Predictors of PFS for the TTF-1-positive cohort\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"666\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eCovariates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8108%;\"\u003e\n \u003cp\u003eCrude HR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5135%;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4144%;\"\u003e\n \u003cp\u003eAdjusted HR \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6126%;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.20721%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eAge (\u0026ge;65 vs. \u0026lt;65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5135%;\"\u003e\n \u003cp\u003e0.449\u0026ndash;1.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4144%;\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6126%;\"\u003e\n \u003cp\u003e0.462\u0026ndash;1.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.20721%;\"\u003e\n \u003cp\u003e0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eSmoke (never vs. current or former)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e1.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5135%;\"\u003e\n \u003cp\u003e0.748\u0026ndash;2.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4144%;\"\u003e\n \u003cp\u003e1.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6126%;\"\u003e\n \u003cp\u003e0.789\u0026ndash;2.941\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.20721%;\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eECOG-PS (PS 0-1 vs. PS\u0026ge;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5135%;\"\u003e\n \u003cp\u003e0.325\u0026ndash;2.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4144%;\"\u003e\n \u003cp\u003e0.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6126%;\"\u003e\n \u003cp\u003e0.325\u0026ndash;2.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.20721%;\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eChemoimmunotherapy vs. IO-IO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e1.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5135%;\"\u003e\n \u003cp\u003e0.679\u0026ndash;3.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4144%;\"\u003e\n \u003cp\u003e1.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6126%;\"\u003e\n \u003cp\u003e0.716\u0026ndash;3.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.20721%;\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eHistology (adenocarcinoma vs. others)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e1.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5135%;\"\u003e\n \u003cp\u003e0.579\u0026ndash;3.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4144%;\"\u003e\n \u003cp\u003e1.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6126%;\"\u003e\n \u003cp\u003e0.687\u0026ndash;3.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.20721%;\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e(b) Predictors of OS for the TTF-1-positive cohort\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"672\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.9286%;\"\u003e\n \u003cp\u003eCovariates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7143%;\"\u003e\n \u003cp\u003eCrude HR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3929%;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.03571%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4345%;\"\u003e\n \u003cp\u003eAdjusted HR \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.84524%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.9286%;\"\u003e\n \u003cp\u003eAge (\u0026ge;65 vs. \u0026lt;65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7143%;\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3929%;\"\u003e\n \u003cp\u003e0.583\u0026ndash;1.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.03571%;\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4345%;\"\u003e\n \u003cp\u003e1.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e0.624\u0026ndash;1.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.84524%;\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.9286%;\"\u003e\n \u003cp\u003eSmoke (never vs. current or former)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7143%;\"\u003e\n \u003cp\u003e1.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3929%;\"\u003e\n \u003cp\u003e0.850\u0026ndash;4.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.03571%;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4345%;\"\u003e\n \u003cp\u003e2.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e0.891\u0026ndash;4.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.84524%;\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.9286%;\"\u003e\n \u003cp\u003eECOG-PS (PS 0-1 vs. PS\u0026ge;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7143%;\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3929%;\"\u003e\n \u003cp\u003e0.175\u0026ndash;1.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.03571%;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4345%;\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e0.161\u0026ndash;1.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.84524%;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.9286%;\"\u003e\n \u003cp\u003eChemoimmunotherapy vs. IO-IO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7143%;\"\u003e\n \u003cp\u003e1.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3929%;\"\u003e\n \u003cp\u003e0.622\u0026ndash;6.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.03571%;\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4345%;\"\u003e\n \u003cp\u003e2.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e0.671\u0026ndash;7.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.84524%;\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.9286%;\"\u003e\n \u003cp\u003eHistology (adenocarcinoma vs. others)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7143%;\"\u003e\n \u003cp\u003e1.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3929%;\"\u003e\n \u003cp\u003e0.467\u0026ndash;4.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.03571%;\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4345%;\"\u003e\n \u003cp\u003e2.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6488%;\"\u003e\n \u003cp\u003e0.644\u0026ndash;7.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.84524%;\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTTF-1, thyroid transcription factor-1; HR: Hazard ratio\u003csup\u003e\u0026nbsp;a)\u003c/sup\u003e; ECOG PS: Eastern Cooperative Oncology Group Performance status; IO, immune-oncology therapy; CI, Confidence interval; PFS, progression-free survival; OS, overall survival\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea)\u0026nbsp;\u003c/sup\u003eHazard Ratio for TTF-1 was adjusted for Age, Smoke history, ECOG-PS, Chemotherapy regimen and Histology\u0026nbsp;\u003cbr clear=\"all\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a) Predictors of PFS for the TTF-1-negative cohort\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"654\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.1101%;\"\u003e\n \u003cp\u003eCovariates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0092%;\"\u003e\n \u003cp\u003eCrude HR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.844%;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.25688%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6789%;\"\u003e\n \u003cp\u003eAdjusted HR \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.844%;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.25688%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.1101%;\"\u003e\n \u003cp\u003eAge (\u0026ge;65 vs. \u0026lt;65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0092%;\"\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.844%;\"\u003e\n \u003cp\u003e0.522\u0026ndash;1.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.25688%;\"\u003e\n \u003cp\u003e0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6789%;\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.844%;\"\u003e\n \u003cp\u003e0.484\u0026ndash;1.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.25688%;\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.1101%;\"\u003e\n \u003cp\u003eSmoke (never vs. current or former)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0092%;\"\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.844%;\"\u003e\n \u003cp\u003e0.332\u0026ndash;1.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.25688%;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6789%;\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.844%;\"\u003e\n \u003cp\u003e0.350\u0026ndash;1.104\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.25688%;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.1101%;\"\u003e\n \u003cp\u003eECOG-PS (PS 0-1 vs. PS\u0026ge;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0092%;\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.844%;\"\u003e\n \u003cp\u003e0.129\u0026ndash;2.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.25688%;\"\u003e\n \u003cp\u003e0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6789%;\"\u003e\n \u003cp\u003e0.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.844%;\"\u003e\n \u003cp\u003e0.187\u0026ndash;3.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.25688%;\"\u003e\n \u003cp\u003e0.830\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.1101%;\"\u003e\n \u003cp\u003eChemoimmunotherapy vs. IO-IO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0092%;\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.844%;\"\u003e\n \u003cp\u003e0.220\u0026ndash;0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.25688%;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6789%;\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.844%;\"\u003e\n \u003cp\u003e0.191\u0026ndash;1.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.25688%;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.1101%;\"\u003e\n \u003cp\u003eHistology (adenocarcinoma vs. others)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0092%;\"\u003e\n \u003cp\u003e1.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.844%;\"\u003e\n \u003cp\u003e0.636\u0026ndash;1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.25688%;\"\u003e\n \u003cp\u003e0.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6789%;\"\u003e\n \u003cp\u003e1.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.844%;\"\u003e\n \u003cp\u003e0.635\u0026ndash;2.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.25688%;\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e(b) Predictors of OS for the TTF-1-negative cohort\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"666\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eCovariates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8108%;\"\u003e\n \u003cp\u003eCrude HR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6126%;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4144%;\"\u003e\n \u003cp\u003eAdjusted HR \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5135%;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.20721%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eAge (\u0026ge;65 vs. \u0026lt;65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e1.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6126%;\"\u003e\n \u003cp\u003e0.676\u0026ndash;1.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4144%;\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5135%;\"\u003e\n \u003cp\u003e0.531\u0026ndash;1.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.20721%;\"\u003e\n \u003cp\u003e0.757\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eSmoke (never vs. current or former)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e1.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6126%;\"\u003e\n \u003cp\u003e0.609\u0026ndash;2.370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4144%;\"\u003e\n \u003cp\u003e1.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5135%;\"\u003e\n \u003cp\u003e0.691\u0026ndash;2.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.20721%;\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eECOG-PS (PS 0-1 vs. PS\u0026ge;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6126%;\"\u003e\n \u003cp\u003e0.045\u0026ndash;0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4144%;\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5135%;\"\u003e\n \u003cp\u003e0.065\u0026ndash;0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.20721%;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eChemoimmunotherapy vs. IO-IO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6126%;\"\u003e\n \u003cp\u003e0.186\u0026ndash;0.780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4144%;\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5135%;\"\u003e\n \u003cp\u003e0.174\u0026ndash;0.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.20721%;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eHistology (adenocarcinoma vs. others)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8108%;\"\u003e\n \u003cp\u003e1.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6126%;\"\u003e\n \u003cp\u003e0.640\u0026ndash;2.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.10811%;\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4144%;\"\u003e\n \u003cp\u003e1.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5135%;\"\u003e\n \u003cp\u003e0.657\u0026ndash;2.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.20721%;\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTTF-1, thyroid transcription factor-1; HR: Hazard ratio \u003csup\u003ea)\u003c/sup\u003e; ECOG PS: Eastern Cooperative Oncology Group Performance status; IO, immune-oncology therapy; CI, Confidence interval; PFS, progression-free survival; OS, overall survival\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea)\u0026nbsp;\u003c/sup\u003eHazard Ratio for TTF-1 was adjusted for Age, Smoke history, ECOG-PS, Chemotherapy regimen and Histology\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-clinical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijco","sideBox":"Learn more about [International Journal of Clinical Oncology](http://link.springer.com/journal/10147)","snPcode":"10147","submissionUrl":"https://www.editorialmanager.com/ijco/default2.aspx","title":"International Journal of Clinical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"TTF-1, PD-L1, non-small-cell lung cancer, chemoimmunotherapy, IO-IO therapy","lastPublishedDoi":"10.21203/rs.3.rs-9478780/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9478780/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThyroid transcription factor-1 has emerged as a novel predictive biomarker associated with the prognosis and treatment response of non-squamous non-small-cell lung cancer to cytotoxic chemotherapy. This study evaluated the clinical significance of thyroid transcription factor-1 expression in advanced non-small-cell lung cancer with programmed death-ligand 1\u0026thinsp;\u0026lt;\u0026thinsp;1%, with a specific focus on its utility in stratifying treatment outcomes between dual immune checkpoint inhibitor (IO-IO) therapy and chemoimmunotherapy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a multicenter retrospective study across 22 Japanese institutions, evaluating 194 patients with advanced or recurrent non-squamous non-small-cell lung cancer and programmed death-ligand 1\u0026thinsp;\u0026lt;\u0026thinsp;1% treated with IO\u0026ndash;IO therapy or chemoimmunotherapy between 2019 and 2022. Survival outcomes were analyzed using multivariable Cox regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThyroid transcription factor-1-positive patients had significantly longer progression-free survival and overall survival than thyroid transcription factor-1-negative patients (adjusted HR for progression-free survival: 0.65, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009; adjusted HR for overall survival: 0.69, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04). Among thyroid transcription factor-1-positive patients, no significant difference in survival was observed between IO\u0026ndash;IO therapy and chemoimmunotherapy. By contrast, thyroid transcription factor-1-negative patients experienced significantly improved outcomes with chemoimmunotherapy than with IO\u0026ndash;IO therapy (adjusted HR for overall survival: 0.39, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024; adjusted HR for progression-free survival: 0.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.051).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThyroid transcription factor-1 expression serves as a prognostic and potentially predictive biomarker in programmed death-ligand 1-negative non-small-cell lung cancer. Thyroid transcription factor-1-negative patients derived relatively greater benefit from the addition of chemotherapy, supporting the utility of thyroid transcription factor-1 in guiding treatment selection.\u003c/p\u003e","manuscriptTitle":"Predictive Value of TTF-1 Expression for Stratifying Outcomes Between Dual ICI and Chemoimmunotherapy in Patients with PD-L1-Negative NSCLC","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-12 16:51:17","doi":"10.21203/rs.3.rs-9478780/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-05-02T04:07:30+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-01T10:22:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-27T04:25:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Clinical Oncology","date":"2026-04-21T00:54:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-clinical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijco","sideBox":"Learn more about [International Journal of Clinical Oncology](http://link.springer.com/journal/10147)","snPcode":"10147","submissionUrl":"https://www.editorialmanager.com/ijco/default2.aspx","title":"International Journal of Clinical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7b1e9164-1576-41ca-934c-420723d4eaf3","owner":[],"postedDate":"May 12th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"","date":"2026-05-02T04:07:30+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-01T10:22:52+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T16:51:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-12 16:51:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9478780","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9478780","identity":"rs-9478780","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}