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Cancer Survivorship and Care / Cancer Related Cognitive Impairment Studies · Journal article
PLOS One · September 9, 2026
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This cross-sectional study of 971 lung cancer immunotherapy patients identified three distinct psychosomatic symptom phenotypes using latent profile analysis and network methods, with computer-simulated intervention targets proposed for each. The findings are hypothesis-generating and do not provide evidence that the proposed interventions are effective; they require prospective validation.
Cross-sectional observational study with latent profile analysis and network analysis. Lung cancer patients undergoing immune checkpoint inhibitor immunotherapy; inclusion and exclusion criteria not specified. Intervention: No intervention; observational phenotyping and computational simulation. n = 971.
Three phenotypes identified: 'High Symptom Burden with Comorbid Distress' (C1, 25.1%), 'Emotional Distress Dominant' (C2, 29.2%), 'Mild Adaptive' (C3, 45.6%) Smoking history was a strong risk factor for C1 phenotype (OR=28.1, 95% CI: 5.2–151.8) Poor ECOG performance status associated with C1 (OR range: 5.9–8.4)
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These findings suggest that lung cancer patients receiving immunotherapy present heterogeneous psychosomatic symptom profiles that may benefit from phenotype-tailored interventions. However, the proposed intervention targets are computer-simulated hypotheses and require prospective testing before clinical implementation.
Cross-sectional observational study identifying symptom phenotypes and generating hypotheses about intervention targets through network analysis and computer simulation, without intervention trials or prospective validation.
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These findings suggest that lung cancer patients receiving immunotherapy present heterogeneous psychosomatic symptom profiles that may benefit from phenotype-tailored interventions. However, the proposed intervention targets are computer-simulated hypotheses and require prospective testing before clinical implementation.
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Background Immune checkpoint inhibitors improve non-small cell lung cancer prognosis but induce complex, heterogeneous psychosomatic symptoms. Traditional symptom-focused approaches fail to capture underlying patient heterogeneity and interaction mechanisms. Methods In this cross-sectional study of 971 lung cancer patients undergoing immunotherapy, symptom phenotypes were identified using Latent Profile Analysis. Influencing factors were analyzed via multivariate logistic regression. Multivariate logistic regression was used to analyze influencing factors, and symptom networks and optimal intervention targets were investigated using network analysis and computer-simulated interventions. Results The study identified three phenotypes: “High Symptom Burden with Comorbid Distress”(C1, 25.1%), “Emotional Distress Dominant”(C2, 29.2%), and “Mild Adaptive”(C3, 45.6%). Logistic regression (FDR‑adjusted q < 0.05) revealed that smoking history (OR=28.1, 95% CI: 5.2–151.8), clinical stage IV, and poor ECOG performance status (OR range: 5.9–8.4) were strong risk factors for the high‑burden phenotype (C1), while combination therapy (OR=0.22, 95% CI: 0.08–0.56), living with family (OR=0.15, 95% CI: 0.05–0.44), and absence of tumor metastasis were protective. Female sex (OR=3.3) was a risk factor for C2. Network analysis revealed core symptoms and bridging symptoms specific to each phenotype: Fatigue was the core symptom for C1, with interference in general activities serving as the bridging symptom; C2’s core symptom was sleep disturbance, with loss of interest serving as the bridging symptom; in C3, diminished enjoyment of life functioned as both core and bridging symptom. Based on cross‑sectional data, computer simulations further suggested potential intervention targets for each phenotype: fatigue for C1, enjoyment of life for C3, and interpersonal networks for C2. Network density was significantly higher in C1 and C2 than in C3 (both p < 0.001). Conclusions This study highlights the complex interplay between somatic symptoms and anxiety‑depression in lung cancer immunotherapy patients, offering hypothesis‑generating evidence for precise intervention pathways tailored to distinct symptom phenotypes. It provides preliminary insights toward advancing symptom management toward stratified, personalized intervention models.
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