Impact of Immune Checkpoint Inhibitors on Second Primary Cancer Risk in Patients With Metastatic Lung Cancer Using Real-World Data From the TriNetX Network: Retrospective Cohort Study
Bibliographic record
Abstract
Background: Survivors of metastatic lung cancer (MLC) face a heightened risk of developing second primary cancers (SPCs), which significantly impact long-term outcomes. Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, but their potential role in reducing SPC risk remains underexplored. This study investigates the association between ICI treatment and the incidence of SPCs in a large, real-world cohort of patients with MLC. Objective: This study aims to evaluate whether treatment with ICIs is associated with a reduced risk of developing SPCs in patients with metastatic or locally advanced lung cancer, using real-world data from the TriNetX global health research network. Methods: We conducted a retrospective cohort study using the TriNetX Global Collaborative Network, which aggregates deidentified electronic health records from more than 135 million patients. Adults diagnosed with MLC between February 2004 and February 2024 were included. Patients were divided into 2 cohorts based on ICI exposure. Propensity score matching was applied to balance baseline characteristics. Kaplan-Meier survival analysis and Cox proportional hazards models were used to assess the incidence of SPCs and the composite outcome of SPC or death. Results: Among 2844 eligible patients, 685 received ICIs and 2157 did not. After propensity score matching, both cohorts included 685 patients. The 5-year incidence of SPCs was lower in the ICI group (1.5%) compared to the non-ICI group (4.2%), with a hazard ratio of 0.49 (95% CI 0.24-1.01), suggesting a potential protective effect. Furthermore, ICI treatment was significantly associated with a reduced risk of the composite outcome of SPC or death (hazard ratio 0.74, 95% CI 0.62-0.89). Median follow-up was 20.2 (IQR 60-not reached) months for the ICI group and 68.4 (IQR 36-not reached) months for the non-ICI group. Conclusions: In this large real-world cohort, ICI treatment was associated with a lower risk of developing SPCs and improved overall outcomes in patients with MLC. These findings support the hypothesis that ICIs may offer a preventive benefit beyond their primary oncologic indications. While the retrospective nature and data limitations warrant cautious interpretation, this study underscores the value of real-world evidence in identifying novel therapeutic benefits and guiding future prospective research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".