Open AccessDevelopment of a prognosis‐prediction model incorporating genetic polymorphism with pathologic stage in stage I non‐small cell lung cancer: A multicenter study
Author(s) -
Lee Won Kee,
Lee Shin Yup,
Choi Jin Eun,
Seok Yangki,
Lee Eung Bae,
Lee Hyun Cheol,
Kang HyoGyoung,
Yoo Seung Soo,
Lee Myung Hoon,
Cho Sukki,
Jheon Sanghoon,
Kim Young Chul,
Oh In Jae,
Na Kook Joo,
Jung Chi Young,
Park ChangKwon,
Kim MiHyun,
Lee Min Ki,
Park Jae Yong
Publication year - 2017
Publication title -
thoracic cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.823
H-Index - 28
eISSN - 1759-7714
pISSN - 1759-7706
DOI - 10.1111/1759-7714.12434
Subject(s) - medicine , nomogram , oncology , stage (stratigraphy) , proportional hazards model , lung cancer , single nucleotide polymorphism , cohort , adjuvant therapy , chemotherapy , genotype , paleontology , biochemistry , chemistry , gene , biology
Background This multicenter study was performed to develop a prognosis‐prediction model incorporating genetic polymorphism with pathologic stage for surgically treated non‐small cell lung cancer ( NSCLC ) patients. Methods A replication study including 720 patients and a panel of eight single nucleotide polymorphisms ( SNP s), which predicted the prognosis of surgically treated NSCLC in our previous study, was conducted. Using the combined cohort of current and previous studies including 1534 patients, a nomogram for predicting overall survival was made using C ox proportional hazards regression. Results Among the eight SNP s, C3 rs2287845, GNB2L1 (alias RACK1 ), and rs3756585 were significantly associated with overall survival. A nomogram was constructed based on pathologic stage and the genotypes of the two SNP s, and the risk score was calculated for each patient in the combined cohort. Using the prognosis‐prediction model, we categorized patients into low, intermediate, and high‐risk groups, which had greater accuracy in predictive ability (log‐rank statistics = 54.66) than the conventional tumor node metastasis staging (log‐rank statistics = 39.56). Next, we generated a prognosis‐prediction model for stage I to identify a subgroup of potential candidates for adjuvant chemotherapy. Notably, 97 out of 499 stage IB patients were classified as high‐risk patients with a similar prognosis to stage II patients, suggesting the benefit of adjuvant chemotherapy. Conclusions This prognosis‐prediction model incorporating genetic polymorphism with pathologic stage may lead to more precise prognostication in surgically resected NSCLC patients. In particular, this model may be useful in selecting a subgroup of stage IB patients who may benefit from adjuvant chemotherapy.