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HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology
Genome Research ( IF 6.2 ) Pub Date : 2017-09-01 , DOI: 10.1101/gr.221218.117
Raunak Shrestha , Ermin Hodzic , Thomas Sauerwald , Phuong Dao , Kendric Wang , Jake Yeung , Shawn Anderson , Fabio Vandin , Gholamreza Haffari , Colin C. Collins , S. Cenk Sahinalp

Prioritizing molecular alterations that act as drivers of cancer remains a crucial bottleneck in therapeutic development. Here we introduce HIT'nDRIVE, a computational method that integrates genomic and transcriptomic data to identify a set of patient-specific, sequence-altered genes, with sufficient collective influence over dysregulated transcripts. HIT'nDRIVE aims to solve the “random walk facility location” (RWFL) problem in a gene (or protein) interaction network, which differs from the standard facility location problem by its use of an alternative distance measure: “multihitting time,” the expected length of the shortest random walk from any one of the set of sequence-altered genes to an expression-altered target gene. When applied to 2200 tumors from four major cancer types, HIT'nDRIVE revealed many potentially clinically actionable driver genes. We also demonstrated that it is possible to perform accurate phenotype prediction for tumor samples by only using HIT'nDRIVE-seeded driver gene modules from gene interaction networks. In addition, we identified a number of breast cancer subtype-specific driver modules that are associated with patients’ survival outcome. Furthermore, HIT'nDRIVE, when applied to a large panel of pan-cancer cell lines, accurately predicted drug efficacy using the driver genes and their seeded gene modules. Overall, HIT'nDRIVE may help clinicians contextualize massive multiomics data in therapeutic decision making, enabling widespread implementation of precision oncology.



中文翻译:

HIT'nDRIVE:针对特定肿瘤的患者特异性多驱动基因优先级

优先考虑作为癌症驱动因素的分子改变仍然是治疗发展中的关键瓶颈。在这里,我们介绍HIT'nDRIVE,这是一种计算方法,它整合了基因组和转录组数据,以识别一组患者特异性,序列改变的基因,并对失调的转录本具有足够的集体影响力。HIT'nDRIVE旨在解决基因(或蛋白质)相互作用网络中的“随机步行设施位置”(RWFL)问题,该问题与标准设施位置问题不同,它使用了另一种距离度量方法:“多发时间”,从序列改变的基因组中的任何一个到表达改变的靶基因的最短随机游走的预期长度。当应用于来自四种主要癌症类型的2200种肿瘤时,HIT' nDRIVE揭示了许多潜在的可在临床上应用的驱动基因。我们还证明,仅通过使用来自基因相互作用网络的HIT'nDRIVE种子驱动基因模块,就可以对肿瘤样品进行准确的表型预测。此外,我们确定了许多与患者生存结果相关的乳腺癌亚型特异性驱动模块。此外,当将HIT'nDRIVE应用于大量泛癌细胞系时,可以使用驱动基因及其种子基因模块准确预测药物功效。总体而言,HIT'nDRIVE可以帮助临床医生在治疗决策过程中根据海量的多组学数据进行背景分析,从而可以广泛实施精密肿瘤学。我们还证明,仅通过使用来自基因相互作用网络的HIT'nDRIVE种子驱动基因模块,就可以对肿瘤样品进行准确的表型预测。此外,我们确定了许多与患者生存结果相关的乳腺癌亚型特异性驱动模块。此外,当将HIT'nDRIVE应用于大量泛癌细胞系时,可以使用驱动基因及其种子基因模块准确预测药物功效。总体而言,HIT'nDRIVE可以帮助临床医生在治疗决策过程中根据海量的多组学数据进行背景分析,从而实现精确肿瘤学的广泛实施。我们还证明,仅通过使用来自基因相互作用网络的HIT'nDRIVE种子驱动基因模块,就可以对肿瘤样品进行准确的表型预测。此外,我们确定了许多与患者生存结果相关的乳腺癌亚型特异性驱动模块。此外,当将HIT'nDRIVE应用于大量泛癌细胞系时,可以使用驱动基因及其种子基因模块准确预测药物功效。总体而言,HIT'nDRIVE可以帮助临床医生在治疗决策过程中根据海量的多组学数据进行背景分析,从而实现精确肿瘤学的广泛实施。我们确定了许多与患者生存结果相关的乳腺癌亚型特异性驱动模块。此外,当将HIT'nDRIVE应用于大量泛癌细胞系时,可以使用驱动基因及其种子基因模块准确预测药物功效。总体而言,HIT'nDRIVE可以帮助临床医生在治疗决策过程中根据海量的多组学数据进行背景分析,从而可以广泛实施精密肿瘤学。我们确定了许多与患者生存结果相关的乳腺癌亚型特异性驱动模块。此外,当将HIT'nDRIVE应用于大量泛癌细胞系时,可以使用驱动基因及其种子基因模块准确预测药物功效。总体而言,HIT'nDRIVE可以帮助临床医生在治疗决策过程中根据海量的多组学数据进行背景分析,从而可以广泛实施精密肿瘤学。

更新日期:2017-09-08
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