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 comprised 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 co-culture 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 co-culture 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.

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 in the end, the existing relationship between the data from the two platforms is found to be nonlinear where feedforward neural network captures the best delineation of the association.

No abstract

<p>Supplementary Tables S1-S4. Summary of the Objective Response Measure (ORM) scoring method; S2. Details of known chromosomal translocations in the xenograft panel; S3. Top 100 discriminating genes for each xenograft subtype identified by microarray analysis of gene expression; S4. Gene Set Enrichment Analysis (GSEA) of subtype specific genes</p>

<p>Supplementary Tables S5-S8. Complete summary of in vivo single-agent ABT-263 responses of individual mice; S7. In vivo efficacy of combining ABT-263 with vincristine (VCR), dexamethasone (DEX) or L-asparaginase (L-ASN) against pediatric ALL Xenografts; S8. Complete summary of in vivo responses of ABT-263 combined with vincristine (VCR), dexamethasone (DEX) or L-asparaginase (L-ASN) of individual mice</p>