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| Home > Publications database > Accounting for EGFR Mutations in Epidemiologic Analyses of Non-Small Cell Lung Cancers: Examples Based on the International Lung Cancer Consortium Data. > print |
| Home > Publications database > Accounting for EGFR Mutations in Epidemiologic Analyses of Non-Small Cell Lung Cancers: Examples Based on the International Lung Cancer Consortium Data. > print |
| 001 | 179129 | ||
| 005 | 20240229145533.0 | ||
| 024 | 7 | _ | |a 10.1158/1055-9965.EPI-21-0747 |2 doi |
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| 100 | 1 | _ | |a Schmid, Sabine |b 0 |
| 245 | _ | _ | |a Accounting for EGFR Mutations in Epidemiologic Analyses of Non-Small Cell Lung Cancers: Examples Based on the International Lung Cancer Consortium Data. |
| 260 | _ | _ | |a Philadelphia, Pa. |c 2022 |b AACR |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1647511668_5965 |2 PUB:(DE-HGF) |
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| 520 | _ | _ | |a Somatic 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.Through 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.Of 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.We introduce a pragmatic method to evaluate the potential impact of EGFR-status on epidemiological analyses of NSCLC.The proposed method is generalizable in the common occurrence in which EGFR-status data are missing. |
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| 700 | 1 | _ | |a Jiang, Mei |0 0000-0002-7393-2664 |b 1 |
| 700 | 1 | _ | |a Brown, M Catherine |b 2 |
| 700 | 1 | _ | |a Fares, Aline |b 3 |
| 700 | 1 | _ | |a Garcia, Miguel |b 4 |
| 700 | 1 | _ | |a Soriano, Joelle |b 5 |
| 700 | 1 | _ | |a Dong, Mei |0 0000-0003-1735-5361 |b 6 |
| 700 | 1 | _ | |a Thomas, Sera |b 7 |
| 700 | 1 | _ | |a Kohno, Takashi |0 0000-0002-5371-706X |b 8 |
| 700 | 1 | _ | |a Leal, Leticia Ferro |b 9 |
| 700 | 1 | _ | |a Diao, Nancy |0 0000-0003-1741-0244 |b 10 |
| 700 | 1 | _ | |a Xie, Juntao |b 11 |
| 700 | 1 | _ | |a Wang, Zhichao |0 0000-0002-0136-1911 |b 12 |
| 700 | 1 | _ | |a Zaridze, David |b 13 |
| 700 | 1 | _ | |a Holcatova, Ivana |b 14 |
| 700 | 1 | _ | |a Lissowska, Jolanta |b 15 |
| 700 | 1 | _ | |a Świątkowska, Beata |0 0000-0003-3757-3868 |b 16 |
| 700 | 1 | _ | |a Mates, Dana |0 0000-0002-6219-9807 |b 17 |
| 700 | 1 | _ | |a Savic, Milan |b 18 |
| 700 | 1 | _ | |a Wenzlaff, Angela S |0 0000-0001-8669-8240 |b 19 |
| 700 | 1 | _ | |a Harris, Curtis C |b 20 |
| 700 | 1 | _ | |a Caporaso, Neil E |b 21 |
| 700 | 1 | _ | |a Ma, Hongxia |b 22 |
| 700 | 1 | _ | |a Fernandez-Tardon, Guillermo |0 0000-0002-7680-158X |b 23 |
| 700 | 1 | _ | |a Barnett, Matthew J |0 0000-0002-1028-1091 |b 24 |
| 700 | 1 | _ | |a Goodman, Gary |b 25 |
| 700 | 1 | _ | |a Davies, Michael P A |0 0000-0002-7609-4977 |b 26 |
| 700 | 1 | _ | |a Pérez-Ríos, Mónica |b 27 |
| 700 | 1 | _ | |a Taylor, Fiona |b 28 |
| 700 | 1 | _ | |a Duell, Eric J |0 0000-0001-5256-0163 |b 29 |
| 700 | 1 | _ | |a Schoettker, Ben |0 P:(DE-HGF)0 |b 30 |
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| 700 | 1 | _ | |a Andrew, Angeline |b 32 |
| 700 | 1 | _ | |a Cox, Angela |0 0000-0002-5138-1099 |b 33 |
| 700 | 1 | _ | |a Ruano-Ravina, Alberto |0 0000-0001-9927-7453 |b 34 |
| 700 | 1 | _ | |a Field, John K |0 0000-0003-3951-6365 |b 35 |
| 700 | 1 | _ | |a Marchand, Loic Le |b 36 |
| 700 | 1 | _ | |a Wang, Ying |b 37 |
| 700 | 1 | _ | |a Chen, Chu |b 38 |
| 700 | 1 | _ | |a Tardon, Adonina |0 0000-0001-5150-1209 |b 39 |
| 700 | 1 | _ | |a Shete, Sanjay |b 40 |
| 700 | 1 | _ | |a Schabath, Matthew B |0 0000-0003-3241-3216 |b 41 |
| 700 | 1 | _ | |a Shen, Hongbing |b 42 |
| 700 | 1 | _ | |a Landi, Maria Teresa |b 43 |
| 700 | 1 | _ | |a Ryan, Brid M |b 44 |
| 700 | 1 | _ | |a Schwartz, Ann G |0 0000-0002-9525-1157 |b 45 |
| 700 | 1 | _ | |a Qi, Lihong |b 46 |
| 700 | 1 | _ | |a Sakoda, Lori C |0 0000-0002-0900-5735 |b 47 |
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| 700 | 1 | _ | |a Yang, Ping |0 0000-0002-8588-847X |b 49 |
| 700 | 1 | _ | |a Zhang, Jie |b 50 |
| 700 | 1 | _ | |a Christiani, David C |0 0000-0002-0301-0242 |b 51 |
| 700 | 1 | _ | |a Reis, Rui Manuel |0 0000-0002-9639-7940 |b 52 |
| 700 | 1 | _ | |a Shiraishi, Kouya |b 53 |
| 700 | 1 | _ | |a Hung, Rayjean J |0 0000-0002-4486-7496 |b 54 |
| 700 | 1 | _ | |a Xu, Wei |b 55 |
| 700 | 1 | _ | |a Liu, Geoffrey |0 0000-0002-2603-7296 |b 56 |
| 773 | _ | _ | |a 10.1158/1055-9965.EPI-21-0747 |g Vol. 31, no. 3, p. cebp.0747.2021 - |0 PERI:(DE-600)2036781-8 |n 3 |p cebp.0747.2021 - |t Cancer epidemiology, biomarkers & prevention |v 31 |y 2022 |x 1055-9965 |
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