Pham Vu V H, Liu Lin, Bracken Cameron P, Nguyen Thin, Goodall Gregory J, Li Jiuyong, Le Thuc D
UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia.
Centre for Cancer Biology, An alliance of SA Pathology and University of South Australia, Adelaide, SA 5000, Australia.
Bioinformatics. 2021 Oct 11;37(19):3285-3292. doi: 10.1093/bioinformatics/btab262.
Unravelling cancer driver genes is important in cancer research. Although computational methods have been developed to identify cancer drivers, most of them detect cancer drivers at population level. However, two patients who have the same cancer type and receive the same treatment may have different outcomes because each patient has a different genome and their disease might be driven by different driver genes. Therefore new methods are being developed for discovering cancer drivers at individual level, but existing personalized methods only focus on coding drivers while microRNAs (miRNAs) have been shown to drive cancer progression as well. Thus, novel methods are required to discover both coding and miRNA cancer drivers at individual level.
We propose the novel method, pDriver, to discover personalized cancer drivers. pDriver includes two stages: (i) constructing gene networks for each cancer patient and (ii) discovering cancer drivers for each patient based on the constructed gene networks. To demonstrate the effectiveness of pDriver, we have applied it to five TCGA cancer datasets and compared it with the state-of-the-art methods. The result indicates that pDriver is more effective than other methods. Furthermore, pDriver can also detect miRNA cancer drivers and most of them have been confirmed to be associated with cancer by literature. We further analyze the predicted personalized drivers for breast cancer patients and the result shows that they are significantly enriched in many GO processes and KEGG pathways involved in breast cancer.
pDriver is available at https://github.com/pvvhoang/pDriver.
Supplementary data are available at Bioinformatics online.
在癌症研究中，揭示癌症驱动基因至关重要。尽管已经开发了计算方法来识别癌症驱动基因，但大多数方法是在群体水平上检测癌症驱动基因。然而，两名患有相同癌症类型且接受相同治疗的患者可能会有不同的结果，因为每个患者拥有不同的基因组，其疾病可能由不同的驱动基因驱动。因此，正在开发新的方法来在个体水平上发现癌症驱动基因，但现有的个性化方法仅关注编码驱动基因，而微小RNA（miRNA）也已被证明可驱动癌症进展。因此，需要新的方法来在个体水平上发现编码和miRNA癌症驱动基因。
我们提出了一种新方法pDriver来发现个性化癌症驱动基因。pDriver包括两个阶段：（i）为每个癌症患者构建基因网络；（ii）基于构建的基因网络为每个患者发现癌症驱动基因。为了证明pDriver的有效性，我们将其应用于五个TCGA癌症数据集，并与最先进的方法进行比较。结果表明，pDriver比其他方法更有效。此外，pDriver还可以检测miRNA癌症驱动基因，其中大多数已被文献证实与癌症相关。我们进一步分析了乳腺癌患者预测出的个性化驱动基因，结果表明它们在许多与乳腺癌相关的GO过程和KEGG途径中显著富集。
pDriver可在https://github.com/pvvhoang/pDriver获取。
补充数据可在《生物信息学》在线获取。
