A clinicogenetic model to predict lymph node invasion by use of genome-based biomarkers from exome arrays in prostate cancer patients
오종진, 박승현, 이상은, 홍성규, 이상철, 이학민, Lee Jeung-Keun, 효진녕, 윤성로, 변석수,
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오종진 ( Oh Jong-Jin )
Seoul National University Bundang Hospital Department of Urology
박승현 ( Park Seung-Hyun )
Seoul National University Department of Electrical and Computer Engineering
이상은 ( Lee Sang-Eun )
Seoul National University Bundang Hospital Department of Urology
홍성규 ( Hong Sung-Kyu )
Seoul National University Bundang Hospital Department of Urology
이상철 ( Lee Sang-Chul )
Seoul National University Bundang Hospital Department of Urology
이학민 ( Lee Hak-Min )
Seoul National University Bundang Hospital Department of Urology
( Lee Jeung-Keun )
Seoul National University Bundang Hospital Department of Urology
효진녕 ( Ho Jin-Nyoung )
Seoul National University Bundang Hospital Department of Urology
윤성로 ( Yoon Sung-Roh )
Seoul National University Department of Electrical and Computer Engineering
변석수 ( Byun Seok-Soo )
Seoul National University Bundang Hospital Department of Urology
KMID : 0358320150560020109
Abstract
Purpose: Genetic variations among prostate cancer (PCa) patients who underwent radical prostatectomy (RP) and pelvic lymph node dissection were evaluated to predict lymph node invasion (LNI). Exome arrays were used to develop a clinicogenetic model that combined clinical data related to PCa and individual genetic variations.
Materials and Methods: We genotyped 242,186 single-nucleotide polymorphisms (SNPs) by using a custom HumanExome BeadChip v1.0 (Illumina Inc.) from the blood DNA of 341 patients with PCa. The genetic data were analyzed to calculate an odds ratio as an estimate of the relative risk of LNI. We compared the accuracies of the multivariate logistic model incorporating clinical factors between the included and excluded selected SNPs. The Cox proportional hazard models with or without genetic factors for predicting biochemical recurrence (BCR) were analyzed.
Results: The genetic analysis indicated that five SNPs (rs75444444, rs8055236, rs2301277, rs9300039, and rs6908581) were significant for predicting LNI in patients with PCa. When a multivariate model incorporating clinical factors was devised to predict LNI, the predictive accuracy of the multivariate model was 80.7%. By adding genetic factors in the aforementioned multivariate model, the predictive accuracy increased to 93.2% (p=0.006). These genetic variations were significant factors for predicting BCR after adjustment for other variables and after adding the predictive gain to BCR.
Conclusions: Based on the results of the exome array, the selected SNPs were predictors for LNI. The addition of individualized genetic information effectively enhanced the predictive accuracy of LNI and BCR among patients with PCa who underwent RP.
키워드
Exome; Genotype; Lymph nodes; Predictive value of tests; Prostate neoplasms
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