ArticleCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology2025
Optimizing Biomarker Models for Biologically Heterogeneous Cancers: A Nested Model Approach for Lung Cancer.
Article in Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Leveraging Commercially Available Protein Assays as Biomarkers for Lung Cancer.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026Article
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12 authors.
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Abstract
backgroundThe heterogeneous biology of cancer subtypes, especially in lung cancer, poses significant challenges for biomarker development. Standard model building techniques often fall short in accurately incorporating various histologic subtypes because of their diverse biological characteristics. This study explores a nested biomarker model to address this issue, aiming to improve lung cancer early detection.
methodsThe study included 337 patients from two clinical sites. Blood biomarkers were analyzed and various statistical methods employed to develop a nested model. This model was designed to account for the biological heterogeneity across histologic subtypes, compared against traditional logistic regression models.
resultsThe patient cohort included a range of malignant and benign nodules and included different cancer subtypes reflecting lung cancer heterogeneity. The nested model had comparable performance overall with the Mayo Clinic model and a standard logistic regression model with an AUC of 77.6 (95% confidence interval, 72.2-83.0) in training and 77.3 (95% confidence interval, 65.8-88.9) in testing. The nested subtype versus benign model had the best performance in the training set overall and had a particular advantage for small cell subtype prediction.
conclusionsThis study highlights the challenges cancer heterogeneity present for biomarker development and the potential for nested biomarker models to improve early cancer detection. Validation of this approach in larger cohorts is essential to prove its predictive benefit in biologically diverse cancers. IMPACT: This work addresses the challenge of biological heterogeneity in biomarker development. A nested modeling approach may assist in developing more effective multicancer early detection strategies.
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