ABSTRACT
PURPOSE: Predictive biomarkers are required to identify patients who may benefit from the use of BH3 mimetics such as ABT-263. This study investigated the efficacy of ABT-263 against a panel of patient-derived pediatric acute lymphoblastic leukemia (ALL) xenografts and utilized cell and molecular approaches to identify biomarkers that predict in vivo ABT-263 sensitivity. EXPERIMENTAL DESIGN: The in vivo efficacy of ABT-263 was tested against a panel of 31 patient-derived ALL xenografts composed of MLL-, BCP-, and T-ALL subtypes. Basal gene expression profiles of ALL xenografts were analyzed and confirmed by quantitative RT-PCR, protein expression and BH3 profiling. An in vitro coculture assay with immortalized human mesenchymal cells was utilized to build a predictive model of in vivo ABT-263 sensitivity. RESULTS: ABT-263 demonstrated impressive activity against pediatric ALL xenografts, with 19 of 31 achieving objective responses. Among BCL2 family members, in vivo ABT-263 sensitivity correlated best with low MCL1 mRNA expression levels. BH3 profiling revealed that resistance to ABT-263 correlated with mitochondrial priming by NOXA peptide, suggesting a functional role for MCL1 protein. Using an in vitro coculture assay, a predictive model of in vivo ABT-263 sensitivity was built. Testing this model against 11 xenografts predicted in vivo ABT-263 responses with high sensitivity (50%) and specificity (100%). CONCLUSION: These results highlight the in vivo efficacy of ABT-263 against a broad range of pediatric ALL subtypes and shows that a combination of in vitro functional assays can be used to predict its in vivo efficacy.
Subject(s)
Aniline Compounds/administration & dosage , Neoplasm Proteins/biosynthesis , Precursor Cell Lymphoblastic Leukemia-Lymphoma/drug therapy , Precursor Cell Lymphoblastic Leukemia-Lymphoma/genetics , Sulfonamides/administration & dosage , Apoptosis/drug effects , Child , Gene Expression Regulation, Neoplastic/drug effects , Humans , Myeloid Cell Leukemia Sequence 1 Protein/biosynthesis , Precursor Cell Lymphoblastic Leukemia-Lymphoma/pathology , Proto-Oncogene Proteins c-bcl-2/genetics , RNA, Messenger/biosynthesis , Xenograft Model Antitumor AssaysABSTRACT
Microarrays technology has been expanding remarkably since its launch about 15 years ago. With its advancement along with the increase of popularity, the technology affords the luxury that gene expressions can be measured in any of its multiple platforms. However, the generated results from the microarray platforms remain incomparable. In this direction, we earlier developed and tested an approach to address the incomparability of the expression measures of Affymetrix®- and cDNA-platforms. The method was an exploit involving transformation of Affymetrix data, which brought the gene expressions of both cDNA and Affymetrix platforms to a common and comparable level. The encouraging outcome of that investigation has subsequently acted as a motivator to focus attention on examining further in the direction of defining the association between the two platforms. Accordingly, this paper takes on a novel exploration towards determining a precise association using a wide range of statistical and machine learning approaches, specifically the various models are elaborately trailed using-regression (linear, cubic-polynomial, LOESS, bootstrap aggregating) and artificial neural networks (self-organizing maps and feedforward networks). After careful comparison, the existing relationship between the data from the two platforms is found to be non-linear where feedforward neural network captures the best delineation of the association.
