O'Connell Shea P, Frantzi Maria, Latosinska Agnieszka, Webb Martyn, Mullen William, Pejchinovski Martin, Salji Mark, Mischak Harald, Cooper Colin S, Clark Jeremy, Brewer Daniel S
Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK.
Department of Biomarker Research, Mosaiques Diagnostics GmbH, 30659 Hannover, Germany.
Cancers (Basel). 2022 Apr 14;14(8):1995. doi: 10.3390/cancers14081995.
There is a clinical need to improve assessment of biopsy-naïve patients for the presence of clinically significant prostate cancer (PCa). In this study, we investigated whether the robust integration of expression data from urinary extracellular vesicle RNA (EV-RNA) with urine proteomic metabolites can accurately predict PCa biopsy outcome. Urine samples collected within the Movember GAP1 Urine Biomarker study (n = 192) were analysed by both mass spectrometry-based urine-proteomics and NanoString gene-expression analysis (167 gene-probes). Cross-validated LASSO penalised regression and Random Forests identified a combination of clinical and urinary biomarkers for predictive modelling of significant disease (Gleason Score (Gs) ≥ 3 + 4). Four predictive models were developed: ‘MassSpec’ (CE-MS proteomics), ‘EV-RNA’, and ‘SoC’ (standard of care) clinical data models, alongside a fully integrated omics-model, deemed ‘ExoSpec’. ExoSpec (incorporating four gene transcripts, six peptides, and two clinical variables) is the best model for predicting Gs ≥ 3 + 4 at initial biopsy (AUC = 0.83, 95% CI: 0.77−0.88) and is superior to a standard of care (SoC) model utilising clinical data alone (AUC = 0.71, p < 0.001, 1000 resamples). As the ExoSpec Risk Score increases, the likelihood of higher-grade PCa on biopsy is significantly greater (OR = 2.8, 95% CI: 2.1−3.7). The decision curve analyses reveals that ExoSpec provides a net benefit over SoC and could reduce unnecessary biopsies by 30%.
临床上需要改进对未经活检的患者进行具有临床意义的前列腺癌（PCa）检测的评估方法。在本研究中，我们调查了将尿细胞外囊泡RNA（EV-RNA）的表达数据与尿液蛋白质组代谢物进行稳健整合，是否能够准确预测PCa活检结果。对在“胡须月”GAP1尿液生物标志物研究中收集的尿液样本（n = 192）进行了基于质谱的尿液蛋白质组学分析和NanoString基因表达分析（167个基因探针）。通过交叉验证的套索罚回归和随机森林算法确定了一组临床和尿液生物标志物，用于对重大疾病（Gleason评分（Gs）≥ 3 + 4）进行预测建模。开发了四个预测模型：“质谱”（CE-MS蛋白质组学）、“EV-RNA”和“标准治疗”（SoC）临床数据模型，以及一个完全整合的组学模型，即“外泌体质谱”（ExoSpec）。ExoSpec（包含四种基因转录本、六种肽和两个临床变量）是预测初次活检时Gs≥ 3 + 4的最佳模型（AUC = 0.83，95% CI：0.77−0.88），优于仅使用临床数据的标准治疗（SoC）模型（AUC = 0.71，p < 0.001，1000次重采样）。随着ExoSpec风险评分的增加，活检时高级别PCa的可能性显著增加（OR = 2.8，95% CI：2.1−3.7）。决策曲线分析表明，ExoSpec比SoC具有净效益，可减少30%的不必要活检。
