Amin S B, Yip W-K, Minvielle S, Broyl A, Li Y, Hanlon B, Swanson D, Shah P K, Moreau P, van der Holt B, van Duin M, Magrangeas F, Pieter Sonneveld P, Anderson K C, Li C, Avet-Loiseau H, Munshi N C
1] Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA [2] Department of Hematology/Oncology, Boston VA Healthcare System, Harvard Medical School, Boston, MA, USA [3] Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA.
Leukemia. 2014 Nov;28(11):2229-34. doi: 10.1038/leu.2014.140. Epub 2014 Apr 15.
With advent of several treatment options in multiple myeloma (MM), a selection of effective regimen has become an important issue. Use of gene expression profile (GEP) is considered an important tool in predicting outcome; however, it is unclear whether such genomic analysis alone can adequately predict therapeutic response. We evaluated the ability of GEP to predict complete response (CR) in MM. GEP from pretreatment MM cells from 136 uniformly treated MM patients with response data on an IFM, France led study were analyzed. To evaluate variability in predictive power due to microarray platform or treatment types, additional data sets from three different studies (n=511) were analyzed using same methods. We used several machine learning methods to derive a prediction model using training and test subsets of the original four data sets. Among all methods employed for GEP-based CR predictive capability, we got accuracy range of 56-78% in test data sets and no significant difference with regard to GEP platforms, treatment regimens or in newly diagnosed or relapsed patients. Importantly, permuted P-value showed no statistically significant CR predictive information in GEP data. This analysis suggests that GEP-based signature has limited power to predict CR in MM, highlighting the need to develop comprehensive predictive model using integrated genomics approach.
随着多发性骨髓瘤（MM）多种治疗方案的出现，选择有效的治疗方案已成为一个重要问题。基因表达谱（GEP）的应用被认为是预测预后的重要工具；然而，尚不清楚仅靠这种基因组分析能否充分预测治疗反应。我们评估了GEP预测MM完全缓解（CR）的能力。分析了来自136例接受统一治疗且有反应数据的MM患者的预处理MM细胞的GEP，这些数据来自法国IFM牵头的一项研究。为了评估由于微阵列平台或治疗类型导致的预测能力差异，使用相同方法分析了来自三项不同研究（n = 511）的额外数据集。我们使用几种机器学习方法，利用原始四个数据集的训练子集和测试子集推导预测模型。在所有用于基于GEP的CR预测能力的方法中，我们在测试数据集中的准确率范围为56 - 78%，在GEP平台、治疗方案方面，以及新诊断或复发患者之间均无显著差异。重要的是，置换P值显示GEP数据中没有统计学上显著的CR预测信息。该分析表明，基于GEP的特征在预测MM的CR方面能力有限，这突出了使用综合基因组学方法开发全面预测模型的必要性。
