Abstract
Gene expression microarrays had been used to classify known tumor types and various hematological malignancies (Yeoh et al, Cancer Cell 2002; Kohlmann et al, Genes Chromosomes Cancer 2003), enforcing the objective that microarray analysis could be introduced soon in the routine classification of cancer (Haferlach et al, Blood 2005). However, there’re still doubts about gene expression experiments performance in clinical laboratory diagnosis. For instance, the quality of starting material is a major concern in microarray technology and there are no data on the variation in gene expression profiles ensuing from different RNA extraction procedures. Here, as part of the internal multicenter MILE Study program, we assess the impact of different RNA preparation methods on gene expression data, analyzing 27 patients representative of nine different subtypes of pediatric acute leukemias. We compared the three currently most used protocols to isolate RNA for routine diagnosis (PCR assays) and microarray experiments. They are named as method A: lysis of mononuclear leukemia cells, followed by lysate homogeniziation, followed by total RNA isolation; method B: TRIzol RNA isolation, and method C: TRIzol RNA isolation followed by total RNA purification step. The methods were analyzed in triplicates for each sample (24) and additional three samples were performed in technical replicates of three data sets for each preparation (HG-U133 Plus 2.0). Method A results in better total RNA quality as demonstrated by 3′/5′ GAPD ratios and by RNA degradation plots. High comparability of gene expression data is found between samples in the same leukemia subclasses and collected with different RNA preparation methods thus demonstrating that sample preparation procedures do not impair the overall signal distribution. Unsupervised analyses showed clustering of samples first by each patient’s replicate conditions, then by leukemia type, and finally by leukemia lineage. In fact, B-ALL samples are clustered together, separately from T-ALL and AML, demonstrating that clustering reflects biological differences between leukemias and that the RNA isolation method is a secondary effect. Also, supervised cluster analyses highlight that samples are grouped depending on intra-lineage features (i.e. chromosomal aberrations) thus confirming the clustering organizations as reported in recent gene expression profiling studies of acute leukemias. Our study shows that biological features of pediatric acute leukemia classes largely exceed the variations between different total RNA sample preparation protocols. However, technical replicates analyses reveal that gene expression data from method A have the lowest degree of variation, are more reproducible and more precise as compared to the other two methods. Furthermore, compared to methods B and C, method A produces more differentially expressed probe sets between distinct leukemia classes and is therefore considered the more robust RNA isolation procedure for gene expression experiments using high-density microarray technology. We therefore conclude that method A (initial homogenization of the leukemia cell lysate followed by total RNA isolation) combined with a standardized microarray analysis protocol is highly reproducible and contributes to robustness of gene expression data and that this procedure is most practical for a routine laboratory use.
Improved Sparse Multi-Class SVM and Its Application for Gene Selection in Cancer Classification