Abstract 1675

Background:

While the use of tyrosine kinase inhibitors (TKI) for the treatment of chronic-phase chronic myeloid leukemia (CP-CML) has dramatically improved patient survival, clinical responses are heterogeneous. Approximately 28% of imatinib (IM), 55% of nilotinib and 46% of dasatinib treated patients achieve major molecular response (MMR) by 12 months. We have previously demonstrated that the functional activity of IM's major active transport protein, OCT-1 (OCT-1 activity, OA), performed on patient cells prior to IM commencement, provides a strong prognostic indicator of response. Importantly, very low OA (bottom 25%, poor risk cohort) is associated with CP-CML patients at significant risk for poor molecular response, mutation development and leukemic transformation. Furthermore, we have also demonstrated in the TIDEL II study that patients with very low OA frequently fail to achieve a MMR when switched to nilotinib. Identifying patients at diagnosis that are more likely to respond sub-optimally to TKI is critical to enable therapeutic intervention aimed at overcoming poor response. To date however, this assay is not widely used because of the requirement for live cells and 14-C IM.

Aim:

To determine the variation in CP-CML patient immunophenotype at diagnosis using the more widely transferable technique of flow cytometry and relate this to the patient OA characteristics at presentation.

Method:

Immunophenotyping of PB-MNCs from 27 newly diagnosed CP-CML patients [10 very low OA (poor risk), 17 higher OA (standard risk)] was undertaken using a 39-marker antibody panel. Cell surface antigen profiles were determined by multicolour flow cytometry with the Beckman Coulter FC500. Statistical analysis was performed using GraphPad Prism. Further analysis was performed using gene set enrichment analysis (GSEA) for comparison to publicly available microarray datasets.

Results:

Differential lineage involvement was identified between the poor risk and standard risk OA patients. A number of cell populations; CD45negGlyA+ (erythroid), CD14+ (monocytic), CD20+ (B-cell) and ITGB5+ (selective monocyte-associated expression and cell adhesion) displayed significant variation between these two patient groups. The CD45negGlyA+ population was significantly increased in poor risk patients (p=0.022), while the CD14+, CD20+ and ITGB5+ cell populations also displayed significantly increased mean fluorescence intensity (MFI) in the poor risk patients (p=0.029, p=0.029 and p=0.042 respectively), as shown in Table 1.

Table 1.

Summary of differential cell lineage involvement identified between poor and standard risk OA CP-CML patients. Results shown as percentage (%) positive or MFI shift, in relation to isotype control.

MarkerP-valueIncreased in:Median
Poor RiskStandard Risk
CD45negGlyA+ 0.022 Poor risk 4.25% 2.70% 
CD14 0.029 Poor risk 4.65 2.39 
CD20 0.029 Poor risk 1.62 1.15 
ITGB5 0.042 Poor risk 1.03 0.88 
MarkerP-valueIncreased in:Median
Poor RiskStandard Risk
CD45negGlyA+ 0.022 Poor risk 4.25% 2.70% 
CD14 0.029 Poor risk 4.65 2.39 
CD20 0.029 Poor risk 1.62 1.15 
ITGB5 0.042 Poor risk 1.03 0.88 
Discussion:

Our previous gene expression profiling of CP patients with poor or standard risk OA revealed significant lineage differences in these two groups based on GSEA. This preliminary data demonstrated enrichment of monocytic and erythroid cell populations in the poor risk patients, whereas the granulocytic cell population was enriched in the standard risk patients. Additionally the monocyte-associated gene ITGB5, which was initially identified from the microarray results, was also validated by the immunophenotyping. Thus, both the gene expression profiling and immunophenotyping results support differential lineage involvement in CP-CML patients.

We postulate that poor risk CP-CML is characterised by an increased erythroid and monocytic component, with possibly increased cellular adhesion characteristics. These differences in cellular characteristics may allow the development of a predictive classifier that will enable quick and accurate identification of these patient groups. Importantly this analysis may provide a valuable tool for dissecting underlying disease biology which likely contributes to the response heterogeneity observed in TKI-treated CP-CML patients. Ultimately, accurate identification of poor risk patients will enable tailored therapeutic intervention, improving the poor outcomes currently observed for this patient cohort.

Disclosures:

Hughes:Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Ariad: Honoraria, Membership on an entity's Board of Directors or advisory committees. Slader:Novartis Pharmaceuticals: Employment, Equity Ownership. White:Novartis Pharmaceuticals: Research Funding; BMS: Research Funding.

Author notes

*

Asterisk with author names denotes non-ASH members.

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