Deep phenotypic analysis of the malignant cells in chronic lymphocytic leukaemia reveals distinctive signatures of surface antigen expression that change in response to therapy

Abstract

Chronic lymphocytic leukaemia (CLL) is characterized by the clonal accumulation of CD5+ B cells that mature into apoptosis-resistant B cells. CLL cells are clonally heterogeneous, variation in surface antigen (Ag) expression on individual cells can provide important pathobiological information. One of the standard diagnostic techniques for CLL is surface marker expression analysis. Because of technological limitations, most phenotypic surface marker analyses in CLL have only considered whole CLL populations of cells as a monoclonal population, and the individual clone behavior study has been largely ignored (1-3). Mass cytometry is a novel technique that overcomes these limitations by measuring more than 40 different parameters on individual cells. Therefore, this study aimed to use this technique to characterize CLL cells at the single-cell level. Initially, we created a panel of 34 cluster of differentiation (CD) marker antibodies (Abs) based on their reported ability to characterise surface Ags on CLL cells. Simultaneously, we used cell barcoding of anti-CD45 Abs to enable sample multiplexing for single-cell analysis. We used the dimensionality reduction algorithm FlowSOM to analyse the resulting mass cytometry data. Further analysis of 10 individual patient samples showed that each case appeared to contain an exclusive phenotypic signature, reflecting overall differential Ag expression. I next attempted to differentiate the mutation statuses of CLL cases by exploring the expression of specific Ags. Our data suggest that mutated (M-CLL) and unmutated (UM-CLL) cases cannot be differentiated by their clonal phenotypic signature. A secondary aim of this project was to investigate whether the phenotypic signatures of CLL cells change during therapy. We hypothesized that mass cytometry could be used to track the clonal evolution of disease relapse to fludarabine, cyclophosphamide and rituximab (FCR) or ibrutinib therapy. Investigation of the effects of ibrutinib or FCR showed that CLL subclones could be identified according to their differential phenotypic responses. These data prove the interpatient and intraclonal heterogeneity of CLL. We also detected resistance to ibrutinib by analysing the CD62L+CD49d+ subclone. Specifically, this subclone was continuously present both before and during ibrutinib treatment, suggesting that the presence of this subclone may correlate with CLL patient relapse. This finding indicates a selection, or changing of cell phenotype, that is biased towards increased function for adhesion and migration (4). However, with the FCR, no consistency of specific clone is detected in my experiments. That suggests the FCR selection for gene clones is not necessarily selected for the phenotypic clone (5). Overall, these data reveal the strength of mass cytometry to classify the clonal phenotypes of CLL cells and identify therapeutic resistance in patient samples. Although the expansion of specific CLL subclones was detected and tracked in serial samples from relapsed patients, I was unable to identify a unique clonal evolution phenotype for CLL resistance.