de Charry Félicité, Colomban Olivier, You Benoit, Ruffion Alain, Paparel Philippe, Wilbaux Mélanie, Tod Michel, Freyer Gilles, Perrin Paul
Oncologie Médicale, Centre Hospitalier Lyon-Sud, Institut de cancérologie des Hospices Civils de Lyon (IC-HCL), Lyon, France; Service de santé des Armées, Service de médecine interne et oncologie, HIA Desgenettes, Lyon, France.
EMR UCBL/HCL 3738, Faculté de Médecine Lyon-Sud, Université Claude Bernard Lyon-1, Université de Lyon, Lyon, France.
Clin Genitourin Cancer. 2016 Jun;14(3):210-217.e1. doi: 10.1016/j.clgc.2015.12.006. Epub 2015 Dec 18.
Tools for differentiating aggressive and indolent prostate carcinoma (PCa) are needed. Mathematical modeling is a promising approach for longitudinal analysis of tumor marker kinetics.
The prostate-specific antigen (PSA) increases from patients with PCa and those with benign prostatic hyperplasia (BPH) were retrospectively analyzed using a mathematical model. Using the NONMEM program, individual PSA kinetics were fit to the following equation: [d(PSA)/dt = (PROD.K × exp [RHO1 × t]) × (1 - BPH) + PROD.NK × exp (RHO2 × t) - KELIM × (PSA)], where RHO1 is the PSA production increase rate by PCa cells (PROD.K), RHO2 is the PSA production increase rate by non-PCa cells (PROD.NK), and KELIM is the PSA elimination rate. The comparative value of the modeled kinetic parameters, estimated for each patient, for predicting the D'Amico score and relapse-free survival (RFS) were tested using logistic regression analysis and multivariate survival tests.
The PSA kinetics from 62 patients with BPH and 149 patients with PCa before radical prostatectomy were successfully modeled. We identified statistically significant relationships between the PSA growth rate related to cancer cells (RHO1) and the probability of D'Amico high-risk group (less than the median RHO1 vs. at the median or greater: odds ratio, 2.15; 95% confidence interval [CI], 1.00-4.77; P = .05). RHO1 was also a significant prognostic factor for RFS on univariate analysis and against other reported prognostic factors using multivariate Cox tests. Three independent prognostic factors of RFS were found: RHO1 (hazard ratio [HR], 2.71; 95% CI, 1.25-5.84; P = .01), Gleason score (HR, 8.54; 95% CI, 4.19-17.40; P < .01), and positive surgical margins (HR, 2.04; 95% CI, 1.05-3.97; P = .03).
Using a few PSA time points analyzed with a mathematical model (easily manageable in routine practice), it could be possible to determine before surgery whether a patient has presented with aggressive PCa.
需要用于区分侵袭性和惰性前列腺癌(PCa)的工具。数学建模是对肿瘤标志物动力学进行纵向分析的一种有前景的方法。
使用数学模型对前列腺癌患者和良性前列腺增生(BPH)患者的前列腺特异性抗原(PSA)升高情况进行回顾性分析。使用NONMEM程序,将个体PSA动力学拟合至以下方程:[d(PSA)/dt = (PROD.K × exp [RHO1 × t]) × (1 - BPH) + PROD.NK × exp (RHO2 × t) - KELIM × (PSA)],其中RHO1是癌细胞导致的PSA产生增加率(PROD.K),RHO2是非PCa细胞导致的PSA产生增加率(PROD.NK),KELIM是PSA清除率。使用逻辑回归分析和多变量生存检验对为每位患者估计的模型动力学参数预测达米科评分和无复发生存率(RFS)的比较值进行检验。
成功对62例BPH患者和149例前列腺癌根治术前的PCa患者的PSA动力学进行建模。我们确定了与癌细胞相关的PSA增长率(RHO1)和达米科高危组概率之间具有统计学意义的关系(RHO1低于中位数与中位数及以上相比:比值比,2.15;95%置信区间[CI],1.00 - 4.77;P = .05)。在单变量分析中,RHO1也是RFS的显著预后因素,并且在多变量Cox检验中与其他报告的预后因素相比也是如此。发现RFS的三个独立预后因素:RHO1(风险比[HR],2.71;95% CI,1.25 - 5.84;P = .01)、 Gleason评分(HR,8.54;95% CI,4.19 - 17.40;P < .01)和手术切缘阳性(HR,2.04;95% CI,1.05 - 3.97;P = .03)。
通过使用数学模型分析几个PSA时间点(在常规实践中易于管理),有可能在手术前确定患者是否患有侵袭性PCa。