Centre for Cancer Research & Cell Biology, Queens University Belfast, Belfast, Northern Ireland.
BMC Med. 2013 Jan 17;11:12. doi: 10.1186/1741-7015-11-12.
Ineffective risk stratification can delay diagnosis of serious disease in patients with hematuria. We applied a systems biology approach to analyze clinical, demographic and biomarker measurements (n = 29) collected from 157 hematuric patients: 80 urothelial cancer (UC) and 77 controls with confounding pathologies.
On the basis of biomarkers, we conducted agglomerative hierarchical clustering to identify patient and biomarker clusters. We then explored the relationship between the patient clusters and clinical characteristics using Chi-square analyses. We determined classification errors and areas under the receiver operating curve of Random Forest Classifiers (RFC) for patient subpopulations using the biomarker clusters to reduce the dimensionality of the data.
Agglomerative clustering identified five patient clusters and seven biomarker clusters. Final diagnoses categories were non-randomly distributed across the five patient clusters. In addition, two of the patient clusters were enriched with patients with 'low cancer-risk' characteristics. The biomarkers which contributed to the diagnostic classifiers for these two patient clusters were similar. In contrast, three of the patient clusters were significantly enriched with patients harboring 'high cancer-risk" characteristics including proteinuria, aggressive pathological stage and grade, and malignant cytology. Patients in these three clusters included controls, that is, patients with other serious disease and patients with cancers other than UC. Biomarkers which contributed to the diagnostic classifiers for the largest 'high cancer- risk' cluster were different than those contributing to the classifiers for the 'low cancer-risk' clusters. Biomarkers which contributed to subpopulations that were split according to smoking status, gender and medication were different.
The systems biology approach applied in this study allowed the hematuric patients to cluster naturally on the basis of the heterogeneity within their biomarker data, into five distinct risk subpopulations. Our findings highlight an approach with the promise to unlock the potential of biomarkers. This will be especially valuable in the field of diagnostic bladder cancer where biomarkers are urgently required. Clinicians could interpret risk classification scores in the context of clinical parameters at the time of triage. This could reduce cystoscopies and enable priority diagnosis of aggressive diseases, leading to improved patient outcomes at reduced costs.
血尿患者的风险分层效果不佳可能导致严重疾病的诊断延误。我们应用系统生物学方法分析了从 157 名血尿患者（80 名尿路上皮癌（UC）患者和 77 名伴有混杂性病变的对照组患者）收集的临床、人口统计学和生物标志物测量值（n=29）：基于生物标志物，我们进行了凝聚层次聚类，以确定患者和生物标志物聚类。然后，我们使用卡方分析探索了患者聚类与临床特征之间的关系。我们使用生物标志物聚类来降低数据的维度，确定随机森林分类器（RFC）对患者亚群的分类错误和接收者操作特征曲线下面积。
凝聚聚类确定了五个患者聚类和七个生物标志物聚类。最终诊断类别在五个患者聚类中不是随机分布的。此外，两个患者聚类富含具有“低癌症风险”特征的患者。对这两个患者聚类的诊断分类器有贡献的生物标志物是相似的。相比之下，三个患者聚类显著富含具有“高癌症风险”特征的患者，包括蛋白尿、侵袭性病理分期和分级以及恶性细胞学。这些三个聚类中的患者包括对照患者，即患有其他严重疾病的患者和患有非 UC 癌症的患者。对最大的“高癌症风险”聚类的诊断分类器有贡献的生物标志物与对“低癌症风险”聚类的分类器有贡献的生物标志物不同。根据吸烟状况、性别和用药情况进行亚群划分的生物标志物也不同。
本研究应用的系统生物学方法允许血尿患者根据其生物标志物数据的异质性自然聚类，分为五个不同的风险亚群。我们的研究结果强调了一种有前途的方法，可以挖掘生物标志物的潜力。这在急需生物标志物的诊断膀胱癌领域尤其有价值。临床医生可以根据分诊时的临床参数解释风险分类评分。这可以减少膀胱镜检查，并能够优先诊断侵袭性疾病，从而以降低的成本改善患者的预后。
