A validated nomogram integrating baseline peripheral T-lymphocyte subsets and NK cells for predicting survival in stage I-IIIA non-small cell lung cancer after resection.

Original Article Page 1 of 13 A validated nomogram integrating baseline peripheral T-lymphocyte subsets and NK cells for predicting survival in stage I–IIIA non-small cell lung cancer after resection Lili Xu#, Yingbin Luo#, Jianhui Tian, Zhihong Fang, Weikang Zhu, Bo Zhang, Jianchun Wu*, Yan Li* Department of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China Contributions: (I) Conception and design: L Xu, Y Luo, Y Li; (II) Administrative support: Y Luo, J Wu, Y Li; (III) Provision of study materials or patients: L Xu, J Tian, Z Fang, Y Li; (IV) Collection and assembly of data: L Xu, B Zhang; (V) Data analysis and interpretation: L Xu, W Zhu; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. # These authors contributed equally to this work. *These authors are corresponding authors and contributed equally to this work. Correspondence to: Jianchun Wu, MD; Yan Li, MD. Department of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200071, China. Email: ; . Background: Accurately predicting the risk of recurrence in stage I–IIIA non-small cell lung cancer (NSCLC) after resection is critical in the treatment process. This study aimed to establish a novel nomogram to identify patients with a risk of disease progression in stage I–IIIA lung cancer based on clinical characteristics, peripheral T-lymphocyte subsets, and CD16+56 natural killer (NK) cells. Methods: A total of 306 NSCLC patients from Shanghai Municipal Hospital of Traditional Chinese Medicine between 2010 and 2020 who met the inclusion and exclusion criteria between January 2011 and December 2020 were retrospectively reviewed. Patients were randomly assigned to the training cohort (206 patients) and the validation cohort (100 patients). A nomogram model was developed based on the results of multivariate Cox regression in the training cohort. The optimal cut-off values were determined by X-tile software. The bootstrap method was used to validate the nomogram. Receiver operating characteristics curves (ROC) and the area under the ROC curve (AUC) were used to compare prognostic factors. The concordance index (C-index) was calculated to determine the accuracy of the nomogram in predicting disease-free survival (DFS). Results: Gender, drinking history, TNM stage, and CD4+T/CD8+T were independent factors for DFS and were integrated into the model, while CD16+56 NK cells were not proven to be significant independent factors for DFS. The calibration curves for probability of 3- and 5-year DFS showed excellent agreement between predicted and actual survival. The C-index for the nomogram to predict DFS was 0.839 in the training cohort. The nomogram showed an excellent predictive performance in the training cohort (3-/5-year AUC: 0.860/0.847) and in the validation cohort (3-/5-year AUC: 0.726/0.748). Conclusions: We developed a prognostic model which provided individual prediction of DFS for stage I–IIIA NSCLC patients after resection. This practical prognostic tool may help oncologists in clinical treatment planning. Keywords: Non-small cell lung cancer (NSCLC); disease-free survival (DFS); T-lymphocyte subsets; CD16+56 NK cells; nomogram Submitted Nov 05, 2021. Accepted for publication Feb 22, 2022. doi: 10.21037/atm-21-6347 View this article at: https://dx.doi.org/10.21037/atm-21-6347 © Annals of Translational Medicine. All rights reserved. Ann Transl Med 2022;10(5):250 | https://dx.doi.org/10.21037/atm-21-6347 Page 2 of 13 Introduction Lung cancer remains the leading cause of cancer-related death worldwide, with non-small cell lung cancer (NSCLC) representing approximately 85% of all lung cancer cases (1,2). Advances in medical examination techniques have increased detection rates of early-stage lung cancer (3). Surgical resection is the first optimal treatment for nonadvanced lung cancer (4,5). In spite of progress in diagnosis and treatment, the 5-year recurrence-free survival (RFS) rate and the 5-year overall survival (OS) rate of clinical stage I NSCLC patients were 73.2% and 79.5%, respectively (6). For patients with stage II–IIIA NSCLC, the 5-year OS rate after resection is estimated to be between 41–65% (7). Although postoperative adjuvant therapy can improve the 5-year OS of NSCLC patients, it has also been recently confirmed that neoadjuvant chemotherapy may achieve similar outcomes as adjuvant chemotherapy (8). For postoperative patients with NSCLC, recurrence and metastasis remain the key factors related to long-term survival (9). The tumor-node-metastasis (TNM) staging system is used to determine prognosis and provide guidance for treatment of NSCLC (10). However, nomograms have been proposed as an alternative standard over the traditional TNM staging system as predicting prognosis via TNM staging appears to be insufficient (11,12). Nomograms are graphical calculating tools that have been widely applied in clinical practice to predict cancer outcomes by integrating patient factors and relevant hematological parameters (13). For many types of cancer, nomograms have been identified as a reliable approach for predicting a particular endpoint using statistical methods (14-17). There were nomograms which included immune genes as prognostic factors via The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) (18). Besides, numerous studies have explored the influence of T-lymphocyte subsets and natural killer (NK) cells on the prognosis of cancers (19-21). For lung cancer in particular, T-lymphocyte subsets and NK cells are considered to be independent factors related to clinical outcomes such as progression-free survival (PFS) and OS (22,23). The relationship between immunity and lung cancer is complex. The composition of immune cells may predict response and act as an indicator of the ability of the immune system to eliminate residual disease after therapy. It has been found that the absence of lung CD4 cells with an effectorlike phenotype (CD45RA +/CD27 −) is a predictor for a favorable outcome (24). NK cells are generally defined as © Annals of Translational Medicine. All rights reserved. Xu et al. Nomogram for DFS in patients with I-IIIA NSCLC the first line of defense in the fight against infection and circulating cancer cells which can accelerate metastasis (25,26). Functionally, NK cells can produce cytokines that support T helper polarization and T cell activation, as well as stimulate dendritic cell (DC) and B cell maturation to bridge and orchestrate innate and adaptive immune responses (27). Hence, NK cells play an important role in tumor surveillance and can be manipulated by artificial activation techniques to present a highly effective anticancer tool against malignancies, and dependent on successful further rearming and mobilization, against solid tumors in the fu (...truncated)