Gene expression profile alone is inadequate in predicting complete response in multiple myeloma

• Published in: Leukemia Vol. 28; no. 11; pp. 2229 - 2234
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01-11-2014; Nature Publishing Group
• Subjects: 38/39; 45; 631/208/191/2018; 631/67/69; 692/699/67/1990/804; 692/700/1750; Cancer Research; Cancer therapies; Critical Care Medicine; Datasets; DNA microarrays; FDA approval; Gene expression; Genetic aspects; Genetic Testing; Genomic analysis; Genomics; Hematology; Humans; Intensive; Internal Medicine; Leukemia; Machine learning; Medical prognosis; Medicine; Medicine & Public Health; Microarray Analysis; Multiple myeloma; Multiple Myeloma - drug therapy; Multiple Myeloma - genetics; Oncology; original-article; Patients; Prediction models; Remission Induction; Secondary Prevention; Sensitivity and Specificity; Statistical analysis; Transcriptome; France; United States > US
• ISSN: 0887-6924; 1476-5551
• DOI: 10.1038/leu.2014.140

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.