背景:前列腺癌(PCa)是全球最普遍的癌症之一。PCa的临床表现和分子特征是高度可变的。攻击性类型需要激进治疗,而惰性治疗可能适用于主动监测或器官保留局灶性治疗。按临床或病理风险类别进行的患者分层仍然缺乏足够的精确性。结合分子生物标志物,例如全转录组表达特征,改善了患者分层,但迄今排除了染色体重排.在这项研究中,我们研究了PCa中的基因融合,以潜在的新候选人为特征,并探讨了它们作为PCa进展预后标志物的作用。
方法:我们分析了四个队列中的630名患者,这些患者在测序方案方面具有不同的特征,样本保存,和PCa风险组。数据集包括全转录组表达和匹配的临床随访数据,以检测和表征PCa中的基因融合。有了融合呼叫软件Arriba,我们通过计算预测了基因融合。检测后,我们使用已发表的癌症基因融合数据库对基因融合进行了注释.将基因融合的发生与格里森分级组和疾病预后联系起来,我们使用Kaplan-Meier估计器进行了生存分析,对数秩检验,和Cox回归。
结果:我们的分析确定了两个潜在的新基因融合体,MBTTPS2、L0XNC01::SMS和AMACR::AMACR。在所有四个研究队列中都检测到了这些融合,为这些融合的有效性及其在PCa中的相关性提供了令人信服的证据。我们还发现,在四个队列中的两个队列中,患者样本中检测到的基因融合数量与生化复发时间显着相关(对数秩检验,两个队列的p值<0.05)。在调整格里森分级组的预后模型后也证实了这一点(Cox回归,p值<0.05)。
结论:我们的基因融合表征工作流程揭示了两种对PCa特异的潜在新型融合体。我们发现有证据表明基因融合的数量与PCa的预后有关。然而,由于数量相关性只有中等强度,在潜在应用前需要进一步验证和评估临床价值.
BACKGROUND: Prostate cancer (PCa) is one of the most prevalent cancers worldwide. The clinical manifestations and molecular characteristics of PCa are highly variable. Aggressive types require radical treatment, whereas indolent ones may be suitable for active surveillance or organ-preserving focal therapies. Patient stratification by clinical or pathological risk categories still lacks sufficient precision. Incorporating molecular biomarkers, such as transcriptome-wide expression signatures, improves patient stratification but so far excludes chromosomal rearrangements. In this study, we investigated gene fusions in PCa, characterized potential novel candidates, and explored their role as prognostic markers for PCa progression.
METHODS: We analyzed 630 patients in four cohorts with varying traits regarding sequencing protocols, sample conservation, and PCa risk group. The datasets included transcriptome-wide expression and matched clinical follow-up data to detect and characterize gene fusions in PCa. With the fusion calling software Arriba, we computationally predicted gene fusions. Following detection, we annotated the gene fusions using published databases for gene fusions in cancer. To relate the occurrence of gene fusions to Gleason Grading Groups and disease prognosis, we performed survival analyses using the Kaplan-Meier estimator, log-rank test, and Cox regression.
RESULTS: Our analyses identified two potential novel gene fusions, MBTTPS2,L0XNC01::SMS and AMACR::AMACR. These fusions were detected in all four studied cohorts, providing compelling evidence for the validity of these fusions and their relevance in PCa. We also found that the number of gene fusions detected in a patient sample was significantly associated with the time to biochemical recurrence in two of the four cohorts (log-rank test, p-value < 0.05 for both cohorts). This was also confirmed after adjusting the prognostic model for Gleason Grading Groups (Cox regression, p-values < 0.05).
CONCLUSIONS: Our gene fusion characterization workflow revealed two potential novel fusions specific for PCa. We found evidence that the number of gene fusions was associated with the prognosis of PCa. However, as the quantitative correlations were only moderately strong, further validation and assessment of clinical value is required before potential application.