Detection and monitoring of normal and leukemic cell populations with hierarchical clustering of flow cytometry data

Abstract

Flow cytometry is a valuable tool in research and diagnostics including minimal residual disease (MRD) monitoring of hematologic malignancies. However, its gradual advancement toward increasing numbers of fluorescent parameters leads to information rich datasets, which are challenging to analyze by standard gating and do not reflect the multidimensionality of the data. We have developed a novel method to analyze complex flow cytometry data, based on hierarchical clustering analysis (HCA) but with a new underlying algorithm, using Mahalanobis distance measure. HCA is scalable to analyze complex multiparameter datasets (here demonstrated on up to 12 color flow cytometry and on a 20-parameter synthetic dataset). We have validated this method by comparison with standard gating approaches when performed independently by expert cytometrists. Acute lymphoblastic leukemia blast populations were analyzed in diagnostic and follow-up datasets (n 5 123) from three centers. HCA results correlated very well (Passing-Bablok correlation coefficient 5 0.992, slope 5 1, intercept 5 20.01) with standard gating data obtained by the I-BFM FLOW-MRD study group. To further improve the performance in follow-up samples with low MRD levels and to automate MRD detection, we combined HCA with support vector machine (SVM) learning. HCA in combination with SVM provides a novel diagnostic tool that not only allows analysis of increasingly complex flow cytometry data but also is less observer-dependent compared with classical gating and has potential for automation. ' 2011 International Society for Advancement of Cytometry Key terms hierarchical clustering; minimal residual disease; acute lymphoblastic leukemia; support vector machines; multiparameter flow cytometry IN childhood acute lymphoblastic leukemia (ALL), response to therapy as measured by minimal residual disease (MRD) monitoring is an important biomarker for predicting relapse and stratifying treatment (1-5). MRD can be assessed by molecular analysis of B-and T-cell receptor gene rearrangements or by flow cytometric analysis of aberrant immunophenotypes. Flow cytometric MRD monitoring is a fast and sensitive method and has been incorporated in several large childhood ALL clinical trials (1,6,7). However, flow cytometry generates increasingly large and information-rich datasets, which provide new challenges for analysis. Modern multilaser flow cytometers are able to simultaneously measure up to 12 or more parameters and acquire such information from millions of single cells (8,9). Traditional gating of populations on two-parameter plots is tedious (e.g., 28 plots in six-color flow cytometry, 66 plots for 10-color analysis, 91 plots for 12-color analysis, etc.) and does not reflect the multidimensionality of the data. Moreover, both the setting of the gates and interpreta-

Immunophenotypic Modulation of the Blast Cells in Childhood Acute Lymphoblastic Leukemia Minimal Residual Disease Detection

“…10,11 Briefl y, FCM utilizes the aberrant phenotypic expression of leukemic cells to discriminate them from their normal counterparts. [12][13][14] The stability of the leukemia-associated immunophenotype (LAIP) during therapy infl uences the FCM sensitivity and specifi city. Such changes in the LAIP (also known as therapeutic immunophenotypic modulation, TIM) have continuously been described and include changes of the level of expression, acquisition or loss of lineage-associated antigens, or lineage switch.…”

Interactive Dendrograms: The R Packages idendro and idendr0

“…The code results in plots similar to Figure 3, in which, however, the clustering described in Fišer et al (2012) was used instead of hclust. Clusters of (blood) cells are shown both in the dendrogram and in traditional scatter plots.…”

“…While traditionally the data are analyzed manually by drawing regions of interest on two-parameter scatter plots, novel approaches are emerging. One of them is HCA (Fišer et al 2012).…”

Endogenous light scattering as an optical signature of circulating tumor cell clusters

“…To measure the mahalanobis distance MATLAB's function mahal.m in the Statistics and Machine Learning Toolbox was used. The mahalanobis distance is an alternative classification method, well-suited for identification of outliers, and has been shown to be a robust classifier in multi-parameter flow cytometry [26]. It is analogous to the Euclidian distance between the center of a reference population and an individual event, with the distance between them being normalized by that population's variance.…”