Synergy between imputed genetic pathway and clinical information for predicting recurrence in early stage non-small cell lung cancer

TIMILSINA, Mohan, Dirk FEY, Samuele BUOSI, Adrianna JANIK, Luca COSTABELLO, Enric CARCERENY, Delvys Rodrıguez ABREU, Manuel COBO, Rafael López CASTRO, Reyes BERNABÉ, Pasquale MINERVINI, Maria TORRENTE, Mariano PROVENCIO and Vít NOVÁČEK. Synergy between imputed genetic pathway and clinical information for predicting recurrence in early stage non-small cell lung cancer. Journal for Biomedical Informatics. Elsevier, 2023, vol. 144, No 104424, p. 1-12. ISSN 1532-0464. Available from: https://dx.doi.org/10.1016/j.jbi.2023.104424.

Basic information

• Original name: Synergy between imputed genetic pathway and clinical information for predicting recurrence in early stage non-small cell lung cancer
• Authors: TIMILSINA, Mohan, Dirk FEY, Samuele BUOSI, Adrianna JANIK, Luca COSTABELLO, Enric CARCERENY, Delvys Rodrıguez ABREU, Manuel COBO, Rafael López CASTRO, Reyes BERNABÉ, Pasquale MINERVINI, Maria TORRENTE, Mariano PROVENCIO and Vít NOVÁČEK (203 Czech Republic, guarantor, belonging to the institution).
• Edition: Journal for Biomedical Informatics, Elsevier, 2023, 1532-0464.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: Netherlands
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Impact factor: Impact factor: 4.500 in 2022
• RIV identification code: RIV/00216224:14330/23:00131336
• Organization unit: Faculty of Informatics
• Doi: http://dx.doi.org/10.1016/j.jbi.2023.104424
• UT WoS: 001030137400001
• Keywords in English: Regression; Classification; Imputation; Recurrence; Supervised; Explanation
• Tags: International impact, Reviewed

Abstract

Objective: Lung cancer exhibits unpredictable recurrence in low-stage tumors and variable responses to different therapeutic interventions. Predicting relapse in early-stage lung cancer can facilitate precision medicine and improve patient survivability. While existing machine learning models rely on clinical data, incorporating genomic information could enhance their efficiency. This study aims to impute and integrate specific types of genomic data with clinical data to improve the accuracy of machine learning models for predicting relapse in early-stage, non-small cell lung cancer patients. Methods: The study utilized a publicly available TCGA lung cancer cohort and imputed genetic pathway scores into the Spanish Lung Cancer Group (SLCG) data, specifically in 1348 early-stage patients. Initially, tumor recurrence was predicted without imputed pathway scores. Subsequently, the SLCG data were augmented with pathway scores imputed from TCGA. The integrative approach aimed to enhance relapse risk prediction performance. Results: The integrative approach achieved improved relapse risk prediction with the following evaluation metrics: an area under the precision–recall curve (PR-AUC) score of 0.75, an area under the ROC (ROC-AUC) score of 0.80, an F1 score of 0.61, and a Precision of 0.80. The prediction explanation model SHAP (SHapley Additive exPlanations) was employed to explain the machine learning model’s predictions. Conclusion: We conclude that our explainable predictive model is a promising tool for oncologists that addresses an unmet clinical need of post-treatment patient stratification based on the relapse risk while also improving the predictive power by incorporating proxy genomic data not available for specific patients.