A population-based nomogram for prognostic assessment in advanced lung cancer following progression with immune checkpoint inhibitor

Introduction

Currently, in terms of incidence and mortality rates of malignant tumors, lung cancer (LC) is the leading cause of cancer-related deaths in China (1). Accelerated industrialization and increased access to tobacco have led to a steady increase in the incidence of LC worldwide, and its treatment has thus become a social health issue of great concern (2). Early LC symptoms are insidious, and as many as 61% of patients are diagnosed at an advanced stage, with a 5-year survival rate of 18% (3). Approximately 25–30% of non-small-cell lung cancer (NSCLC) patients are diagnosed when the disease is still resectable, although the risk of recurrence is significant (4). Less than 50% of NSCLC patients have common driver gene mutations, suitable for targeted therapy: KRAS (20–25%), EGFR (10–20%), ALK (5%) or ROS1 (1–2%) (5).

Breakthroughs in immune checkpoint inhibitors (ICIs) have kicked off the immune era. ICIs have proven efficacy and safety in single and combination therapy for NSCLC (6,7). The approval of programmed cell death protein 1 (PD-1) and programmed cell death ligand 1 (PD-L1) in ICIs for treating unresectable advanced or metastatic NSCLC has altered the treatment landscape for advanced NSCLC (8). The KEYNOTE 042 (9) and 024 (10) trials showed that patients treated with pembrolizumab survived longer than those who received platinum doublet chemotherapy as first-line treatment. The interaction of PD-1 and PD-L1 contributes largely to the maintenance of immune homeostasis and the prevention of immune dysregulation and deleterious immune responses, mainly by inhibiting the activity of effector T cells and enhancing the function of immunosuppressive regulatory T cells (Tregs). However, cancer cells utilize the PD-1/PD-L1 axis to evade immune surveillance, which is an important factor in cancer development and progression (11). Despite the long-term, potentially cure-like clinical benefits, treatment resistance remains a great challenge limiting the further application of PD-1/PD-L1. It is estimated that typically only a minority of patients (20–30%) have a positive response to PD-1/PD-L1 inhibitors (12,13), primary or acquired resistance may lead to cancer progression in patients with clinical response (14). However, there is a lack of a clear definition and consensus on immunoresistance, and the concept is largely a continuation of previous definitions of resistance to targeted therapies. The main modes of immune resistance are primary resistance, adaptive resistance, and acquired resistance. Due to the heterogeneity of tumors, the mechanism of PD-1/PD-L1 resistance is very complex, and may be related to mutations in tumor antigens and antigen presentation, dynamic changes in the immune microenvironment, interactions between multiple immune checkpoints, etc. (15-17).

Nomogram is superior to traditional tumor-node-metastasis (TNM) staging systems and allows for the reduction of statistical prediction models to single-number estimates of event probabilities based on individual patients (18), widely used for cancer prognosis and recurrence (19-21). This study aimed to identify independent prognostic factors and to develop a novel nomogram predicting the prognosis of LC patients with disease progression (PD) after immunotherapy, which is an attempt to provide new references for the post-progression diagnosis and treatment. We present this article in accordance with the TRIPOD reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-165/rc).

Methods

Patients

There were 465 LC patients treated with ICIs between January 2019 and February 2024 at Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University. The last follow-up date was April 29, 2024. The inclusion criteria were as follows: (I) age ≥18 years; (II) pathologically confirmed LC; (III) patients receiving immune monotherapy or combination therapy; (IV) PD after treatment with ICIs; and (5) Eastern Cooperative Oncology Group performance status (ECOG PS) no more than 3. The exclusion criteria were: (I) concurrent malignancies; (II) incomplete clinical data and a follow-up time of 0 months; (III) immunotherapy contraindications include active autoimmune disorders (e.g., systemic lupus erythematosus, rheumatoid arthritis), organ transplant recipients, or uncontrolled severe infections. After screening, a total of 245 patients were enrolled in the statistical analysis. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of the Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University (approval No. DZQH-KYLL-23-06), and patients gave informed consent. All methods were performed in accordance with relevant guidelines and regulations.

Clinical endpoints and data collection

Overall survival (OS) was measured from the time of first progression after ICIs treatment until death from any cause. Progression-free survival (PFS) was measured from the time of first progression after ICIs treatment until the time of re-progression, relapse, or death from any cause. Patients with a survival time of 0 months were excluded. Survival status was determined by patient retention telephone follow-up.

All clinical and pathologic information used for diagnostic purposes was obtained from the archived medical records of Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University. The following were collected and analyzed: demographic characteristics (age, gender, smoking history), previous treatment (surgery, chemotherapy, targeted therapy, and radiotherapy), histology type (adenocarcinoma, squamous carcinoma, small cell carcinoma and others), baseline metastasis (lungs, liver, bone, lymph nodes), immunotherapy drugs (camrelizumab, pembrolizumab, sintilimab, and trelizumab), immune-combination regimens, hematological biomarkers measured prior to ICIs treatment encompass the white blood cell (WBC) count, hemoglobin (HGB) level, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and carbohydrate antigen 199 (CA199).

Tumor staging was classified according to the pathological TNM staging system published in the 8th edition of the American Joint Committee on Cancer (AJCC). Response Evaluation Criteria in Solid Tumors (RECIST) (version 1.1) was used to assess the therapeutic efficacy of ICIs, including complete remission (CR), partial remission (PR), stable disease (SD), and PD. Safety assessments were performed on eligible patients, and adverse events (AEs) occurring during treatment were graded according to the National Cancer Institute Common Terminology Criteria for Adverse Events, version 5.0.

Statistical analysis

The statistical analysis was performed using R version 4.1.3. Categorical variables were compared using the Chi-squared test and Fisher’s exact test. Continuous variables were compared using the two-tailed unpaired t-test or Mann-Whitney U test. Kaplan-Meier curves and log-rank test were utilized to analyze OS and PFS. The endpoints of this study (OS and PFS) were separately evaluated by the least absolute shrinkage with selection operator (LASSO) analysis, and the variables with P<0.05 were entered into the multivariate analysis, hazard ratio (HR) and 95% confidence interval (CI) were calculated. The independent predictors were determined based on the multivariate analysis and were selected to build a nomogram model. The concordance index (C-index) and the time-dependent area under the receiver operating characteristic (ROC) curves (AUCs) were used to assess the predictive discrimination of the nomogram. Calibration curves and decision curves analysis (DCA) were utilized to evaluate the performance of nomogram. A two-sided P<0.05 was considered statistically significant.

Results

Patients’ clinical characteristics

Between January 2019 and February 2024, 245 patients who experienced PD after immunotherapy were recruited as previously described (Figure 1). At the final follow-up, 113 patients remained alive, 153 continued ICIs therapy post-progression, and 92 transitioned to alternative treatment regimens. One hundred and eighty-four (75.10%) of the patients were no more than 70 years of age (≤70 years), and 201 (82.04%) were male, much more than female. A history of smoking was present in close to 60% of the deceased population. One hundred and fifty-nine (64.90%) patients had a histology type of NSCLC. The highest number of metastatic sites at baseline was lung (37.55%), and the lowest was liver metastasis (11.43%). A minority of patients (27, 11.02%) presented with an ECOG performance status of ≥2. ICIs treatment as first line was in 140 patients (57.14%), the second line in 63 patients (25.71%), and more than the second line in 42 patients (17.14%). Far more patients were treated in combination with chemotherapy (164, 66.94%) than with ICI monotherapy (18, 7.35%). Immunologic efficacy varied significantly among patients, with 104 (42.45%) in PD, 106 (43.27%) in SD, and 35 (14.29%) in PR. Additional baseline demographic and clinical characteristics are summarized in Table 1.

Figure 1 Study flow chart. CT, computed tomography; ECOG PS, Eastern Cooperative Oncology Group performance status; ICIs, immune checkpoint inhibitors; MRI, magnetic resonance imaging.

Risk factors that influence OS and PFS

The results were analyzed by LASSO-Cox combined with multivariate regression analysis, which showed that PLR (HR =1.470, 95% CI: 1.012–2.137, P=0.04), smoking history (HR =1.631, 95% CI: 1.097–2.424, P=0.02), immunization combination regimen (monotherapy) (HR =0.338, 95% CI: 0.140–0.815, P=0.02), and liver metastasis (HR =2.039, 95% CI: 1.183–3.515, P=0.01) were independent factors affecting OS (Figure 2A, Table 2). Significantly associated with PFS were also PLR (HR =1.471, 95% CI: 1.049–2.062, P=0.03), smoking history (HR =1.693, 95% CI: 1.183–2.423, P=0.004), followed by tumor stage (HR =1.560, 95% CI: 1.025–2.374, P=0.04) (Figure 2B, Table 3). Among these five prognostic factors, only the immunization combination regimen was a protective factor (HR <1), and the rest were risk factors (HR >1), The prediction models of OS and PFS were constructed and validated by these independent risk factors, respectively.

Figure 2 Factor selection of OS and PFS. LASSO analysis with 10-fold cross validation of candidate variables influencing OS (A) and PFS (B). LASSO, least absolute shrinkage and selection operator; OS, overall survival; PFS, progression-free survival.

Construction and evaluation of nomogram model for OS

Based on the results of multivariate Cox analysis, four independent factors were utilized to construct a nomogram for predicting OS at 1–3 years. Figure 3A demonstrated that patients with PLR ≤205.99 exhibit improved clinical prognosis when receiving immune monotherapy, particularly among non-smoking individuals without liver metastasis. The calibration curve showed good agreement between predicted and actual results without significant deviation [C-index: 0.643 (95% CI: 0.592–0.695)] (Figure 3B). The area under the ROC curve reached 0.795 (95% CI: 0.581–1.000) at 36 months (Figure 3C). The DCA decision curve demonstrated a better net clinical benefit, demonstrating the important clinical application of the model in predicting survival (Figure 3D). Based on the optimal threshold, patients were stratified into high-risk (score >90.28) and low-risk groups (score <90.28) cohorts. Kaplan-Meier analysis demonstrated significantly prolonged OS in the low-risk group, with median OS of 20.9 versus 9.3 months in the high-risk group (P<0.001), demonstrating the model’s ability to stratify prognosis accurately (Figure 3E).

Figure 3 Nomogram for OS prediction in lung cancer patients with progressive disease. (A) The nomogram for the prediction OS at 12, 24 and 36 months. Calibration plots (B), ROC curves and AUC value (95% confidence interval) (C), and DCA curves for assessing the accuracy, discrimination and clinical usefulness of the nomogram (D). (E) Kaplan-Meier curves for the low-risk and high-risk group according to the cutoff value (90.28) of the OS nomogram. AUC, area under the curve; CI, confidence interval; DCA, decision curve analysis; ICIs, immune checkpoint inhibitors; OS, overall survival; PLR, platelet-to-lymphocyte ratio; ROC, receiver operating characteristic.

Construction and evaluation of nomogram model for PFS

Based on the results of multivariate Cox’s analysis, three independent factors were used to construct a nomogram for predicting patients’ 1–3-year PFS. Figure 4A clearly showed that advanced LC patients with higher PLR (>205.99) and the presence of smoking history were more prone to re-progression. The calibration curve showed good agreement between predicted and actual outcomes without significant deviation [C-index: 0.588 (95% CI: 0.541–0.636)] (Figure 4B). The AUC reached 0.694 (95% CI: 0.546–0.843) at 36 months (Figure 4C). Based on the optimal threshold, patients were classified into high-risk (score >172.16) and low-risk (score <172.16) cohorts. Kaplan-Meier analysis demonstrated significantly prolonged PFS in the low-risk group, with median PFS of 11.8 versus 5.8 months in the high-risk group (P=0.003), validating the prognostic stratification ability of the model (Figure 4D). Unfortunately, DCA did not present the desired results, and although we demonstrated the validity and reliability of the predictive model, refinements are still needed to promote its clinical application.

Figure 4 Nomogram for PFS prediction in lung cancer patients with progressive disease. (A) The nomogram for the prediction PFS at 12, 24 and 36 months. (B) Calibration plots. (C) ROC curves and AUC value (95% confidence interval) representing the time-correlated prediction performance of the nomogram. (D) Kaplan-Meier curves for the low-risk and high-risk group according to the cutoff value (172.16) of the PFS-nomogram. AUC, area under the curve; CI, confidence interval; PFS, progression-free survival; PLR, platelet-to-lymphocyte ratio; ROC, receiver operating characteristic.

Discussion

In 2022, LC is the leading cause of cancer morbidity and mortality, with nearly 2.5 million new cases and more than 1.8 million deaths globally, accounting for 12.4% of cancer diagnoses and 18.7% of cancer deaths worldwide (22). In the Global Burden of Disease (GBD), Injuries, and Risk Factors Study on LC, it was noted that population growth and aging are contributing to the increase in LC deaths globally (23). LC is expected to remain the leading cause of cancer-related deaths in both men and women in China in 2025. Although immunotherapy is currently the mainstay of LC treatment, the majority of advanced LC still experience PD. It is evident that there is still considerable variation in the design and implementation of follow-up treatment programs.

This retrospective study analysed 465 LC patients treated with immunotherapy, with 245 patients who underwent PD ultimately included according to strict inclusion criteria. In follow-up, 153 cases continued with ICIs, and we differed from previous studies in that there was no significant difference in patient OS with continuation of PD-1 monoclonal antibody after PD versus switching to other regimens (24,25). We comprehensively assessed various clinical parameters and laboratory indicators associated with patients’ survival after progression by LASSO and multivariate analysis, in which PLR level and smoking history were significantly associated with OS and PFS, whereas immune-combination regimen and liver metastasis were also important factors affecting OS, and tumour stage was an independent factor affecting PFS, highlighting their potential role as biomarkers. We then integrated these five key variables to build nomogram model to predict the prognosis and likelihood of re-progression in patients who had progressed. Model validation showed (ROC, calibration curves, C index and DCA curves) that it had good predictive accuracy, feasibility and clinical utility. The low-risk group classified according to the model had a significant survival benefit, with median OS and PFS of 20.9 and 11.8 months, respectively, further demonstrating the significant value of the predictive model in stratifying the prognostic population. It should be noted that the DCA curve of the nomogram for PFS suggests its limited clinical applicability, and that increasing the sample size of patients as well as lengthening the time of PFS assessment may be important measures to improve the application of this model.

Inflammation can provide biologically active molecules to the tumor microenvironment, so it is not only one of the hallmarks of cancer, but also an important factor in promoting tumor progression (26). PLR can reflect inflammation and host immune response (27), elevated platelets accelerate tumor progression by promoting neovascularization and the production of adhesion molecules (28). In a comprehensive analysis involving 2,312 patients with advanced LC receiving immunotherapy, elevated PLR was associated with poorer OS (HR =2.24; 95% CI: 1.87–2.68; I²=44%; P=0.01) and PFS (HR =1.66; 95% CI: 1.36–2.04; I²=64%; P<0.010) (29). This is in high agreement with the results of our study.

Smoking, as one of the common risk factors for LC, kills 80% of LC patients, and carcinogens in smoke can induce elevated levels of PD-L1, which causes tumor cells to escape recognition by immune cells, leading to reduced T-cell anticancer activity, decreased immunity, and consequent increase in tumor incidence (30). Research indicates that smoking is also associated with a poor prognosis in NSCLC patients after surgical procedures (31). Smoking is a risk factor for NSCLC progression in epidemiologic analyses. Our study further emphasizes this point, especially for LC patients who have progressed after immunotherapy, where smoking history is a key prognostic factor.

In all lines of NSCLC treatment, immunologic combination strategies have shown better clinical efficacy than immune monotherapy in a safe and controlled manner. However, some combination regimens did not improve clinical outcomes but accrued toxic side effects of the drugs. Chemotherapy-induced adverse effects and immune-related adverse events (irAEs) are very similar in clinical practice, yet the lack of biomarkers to distinguish between the two adds a new obstacle to patient management. The damage to normal tissues by immunotherapy is unknown, and some severe irAEs can progress rapidly in a short period of time, making the disease difficult to control. Immunologic monotherapy reduces to some extent the cumulative adverse effects of combination therapy and is one of the most important factors for influencing PD.

About 5.8% of LC patients have liver metastases at the time of initial diagnosis (32). In a study analyzing the correlation between different metastatic sites and prognosis in NSCLC, liver metastasis was the type of metastasis with the worst prognosis, and patients with liver metastasis had a 53% higher risk of death compared to central nervous system (brain and spinal cord) metastasis, which had a median OS of 5 months, and a median OS of only 3 months (33).

However, as a single-center retrospective analysis, our study inevitably has limitations, such as selection bias, lack of external validation, information bias, lack of uniform and standardized follow-up and assessment protocols, and incomplete or unavailability of important information, such as immunohistochemistry and gene mutations. Despite these limitations, our nomograms are based on a large number of samples, as well as with internal validation to ensure credibility. In the future, we need to focus on exploring specific medication regimens for prolonging OS and PFS after progression, and guiding the uniform and standardized use of clinical medications after PD. Besides, we should promote the collection of multicenter and cross-regional clinical data and use more comprehensive data validation to facilitate the practical application of the model.

Conclusions

In this study, we found that survival prognosis could be inferred based on patients’ pretreatment PLR level, smoking history, choice of immunotherapy regimen, presence of liver metastases, and tumor staging status, which provides a reference for post-progression therapy. We constructed and validated a nomogram for predicting OS and PFS after progression in LC patients, and confirmed its reliability and clinical applicability by ROC, calibration curve, and DCA. In addition, a clinically applicable risk stratification model was further developed, which can provide clinicians with an accurate prognostic assessment tool for individualized treatment decisions.

Acknowledgments

We appreciate the help from other teammates and the staff of the Biochemical Laboratory of Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University who provided various biochemical markers.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-165/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-165/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-165/prf

Funding: This work was supported by Nanjing Medical Science and Technology Development (No. ZKX22026), Jiangsu Provincial Graduate Student Practice Innovation Project (No. JX22013930), Internal Research Fund Project of Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University (No. 22LCZLXJS21), Internal Research Fund Project of Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University (No. 22LCYY-LH5), and the Postdoctoral Fellowship Fund of Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University (No. 96727).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-165/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Institute Research Ethics Committee of the Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University (approval No. DZQH-KYLL-23-06), and informed consent was taken from all individual participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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