Altmann André, Sing Tobias, Vermeiren Hans, Winters Bart, Van Craenenbroeck Elke, Van der Borght Koen, Rhee Soo-Yon, Shafer Robert W, Schülter Eugen, Kaiser Rolf, Peres Yardena, Sönnerborg Anders, Fessel W Jeffrey, Incardona Francesca, Zazzi Maurizio, Bacheler Lee, Van Vlijmen Herman, Lengauer Thomas
Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbr product operatorcken, Germany.
Antivir Ther. 2009;14(2):273-83.
Inferring response to antiretroviral therapy from the viral genotype alone is challenging. The utility of an intermediate step of predicting in vitro drug susceptibility is currently controversial. Here, we provide a retrospective comparison of approaches using either genotype or predicted phenotypes alone, or in combination.
Treatment change episodes were extracted from two large databases from the USA (Stanford-California) and Europe (EuResistDB) comprising data from 6,706 and 13,811 patients, respectively. Response to antiretroviral treatment was dichotomized according to two definitions. Using the viral sequence and the treatment regimen as input, three expert algorithms (ANRS, Rega and HIVdb) were used to generate genotype-based encodings and VircoTYPE() 4.0 (Virco BVBA, Mechelen, Belgium) was used to generate a predicted -phenotype-based encoding. Single drug classifications were combined into a treatment score via simple summation and statistical learning using random forests. Classification performance was studied on Stanford-California data using cross-validation and, in addition, on the independent EuResistDB data.
In all experiments, predicted phenotype was among the most sensitive approaches. Combining single drug classifications by statistical learning was significantly superior to unweighted summation (P<2.2x10(-16)). Classification performance could be increased further by combining predicted phenotypes and expert encodings but not by combinations of expert encodings alone. These results were confirmed on an independent test set comprising data solely from EuResistDB.
This study demonstrates consistent performance advantages in utilizing predicted phenotype in most scenarios over methods based on genotype alone in inferring virological response. Moreover, all approaches under study benefit significantly from statistical learning for merging single drug classifications into treatment scores.
仅从病毒基因型推断对抗逆转录病毒疗法的反应具有挑战性。预测体外药物敏感性这一中间步骤的实用性目前存在争议。在此，我们对单独使用基因型或预测表型，或两者结合的方法进行了回顾性比较。
从美国（斯坦福 - 加利福尼亚）和欧洲（EuResistDB）的两个大型数据库中提取治疗变化事件，这两个数据库分别包含6706例和13811例患者的数据。根据两种定义将对抗逆转录病毒治疗的反应进行二分。以病毒序列和治疗方案作为输入，使用三种专家算法（ANRS、Rega和HIVdb）生成基于基因型的编码，并使用VircoTYPE() 4.0（Virco BVBA，比利时梅赫伦）生成基于预测表型的编码。通过简单求和和使用随机森林的统计学习将单一药物分类合并为治疗评分。使用交叉验证在斯坦福 - 加利福尼亚数据上研究分类性能，此外，还在独立的EuResistDB数据上进行研究。
在所有实验中，预测表型是最敏感的方法之一。通过统计学习合并单一药物分类明显优于未加权求和（P<2.2x10(-16)）。通过结合预测表型和专家编码可进一步提高分类性能，但仅结合专家编码则不能。这些结果在仅包含来自EuResistDB数据的独立测试集上得到了证实。
本研究表明，在推断病毒学反应方面，在大多数情况下，利用预测表型比仅基于基因型的方法具有一致的性能优势。此外，所研究的所有方法都从将单一药物分类合并为治疗评分的统计学习中受益匪浅。
