1. Accounting for EGFR mutations in epidemiological analyses of non-small cell lung cancers: Examples based on the International Lung Cancer Consortium data
- Author
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Schmid, Sabine, Jiang, Mei, Brown, M Catherine, Fares, Aline, Garcia, Miguel, Soriano, Joelle, Dong, Mei, Thomas, Sera, Kohno, Takashi, Leal, Leticia Ferro, Diao, Nancy, Xie, Juntao, Wang, Zhichao, Zaridze, David, Holcatova, Ivana, Lissowska, Jolanta, Świątkowska, Beata, Mates, Dana, Savic, Milan, Wenzlaff, Angela S, Harris, Curtis C, Caporaso, Neil E, Ma, Hongxia, Fernandez-Tardon, Guillermo, Barnett, Matthew J, Goodman, Gary, Davies, Michael PA, Pérez-Ríos, Mónica, Taylor, Fiona, Duell, Eric J, Schoettker, Ben, Brenner, Hermann, Andrew, Angeline, Cox, Angela, Ruano-Ravina, Alberto, Field, John K, Marchand, Loic Le, Wang, Ying, Chen, Chu, Tardon, Adonina, Shete, Sanjay, Schabath, Matthew B, Shen, Hongbing, Landi, Maria Teresa, Ryan, Brid M, Schwartz, Ann G, Qi, Lihong, Sakoda, Lori C, Brennan, Paul, Yang, Ping, Zhang, Jie, Christiani, David C, Reis, Rui Manuel, Shiraishi, Kouya, Hung, Rayjean J, Xu, Wei, and Liu, Geoffrey
- Subjects
Lung ,Lung Cancer ,Cancer ,Prevention ,Carcinoma ,Non-Small-Cell Lung ,ErbB Receptors ,Humans ,Lung Neoplasms ,Mutation ,Survival Analysis ,Medical and Health Sciences ,Epidemiology - Abstract
BackgroundSomatic EGFR mutations define a subset of non-small cell lung cancers (NSCLC) that have clinical impact on NSCLC risk and outcome. However, EGFR-mutation-status is often missing in epidemiologic datasets. We developed and tested pragmatic approaches to account for EGFR-mutation-status based on variables commonly included in epidemiologic datasets and evaluated the clinical utility of these approaches.MethodsThrough analysis of the International Lung Cancer Consortium (ILCCO) epidemiologic datasets, we developed a regression model for EGFR-status; we then applied a clinical-restriction approach using the optimal cut-point, and a second epidemiologic, multiple imputation approach to ILCCO survival analyses that did and did not account for EGFR-status.ResultsOf 35,356 ILCCO patients with NSCLC, EGFR-mutation-status was available in 4,231 patients. A model regressing known EGFR-mutation-status on clinical and demographic variables achieved a concordance index of 0.75 (95% CI, 0.74-0.77) in the training and 0.77 (95% CI, 0.74-0.79) in the testing dataset. At an optimal cut-point of probability-score = 0.335, sensitivity = 69% and specificity = 72.5% for determining EGFR-wildtype status. In both restriction-based and imputation-based regression analyses of the individual roles of BMI on overall survival of patients with NSCLC, similar results were observed between overall and EGFR-mutation-negative cohort analyses of patients of all ancestries. However, our approach identified some differences: EGFR-mutated Asian patients did not incur a survival benefit from being obese, as observed in EGFR-wildtype Asian patients.ConclusionsWe introduce a pragmatic method to evaluate the potential impact of EGFR-status on epidemiological analyses of NSCLC.ImpactThe proposed method is generalizable in the common occurrence in which EGFR-status data are missing.
- Published
- 2022