To the editor:

In their article, Yamazaki et al1  reported the identification of malignant stem cells in the Tax-transgenic (Tax-Tg) mouse model of adult T-cell leukemia/lymphoma (ATL). This statement was grounded partly on a limiting dilution transplantation assay (LDTA) reported in Table 2 of their paper,1  aimed at estimating the frequency of cancer stem cells (CSCs) among splenic lymphomatous cells (SLCs). From this assay, the authors reported that one CSC existed in 104 SLCs (0.01%, ie, 1 in 10 000), and this frequency estimate was found to be consistent with the frequency of the CD38/CD71/CD117+ cells estimated by flow cytometry (0.03%, ie, 1 in 3333). Based on these cell frequencies, the authors claimed that CD38/CD71/CD117+ cells can stand for the rare stem cell population among the whole SLC population, and this hypothesis was documented by the ability of sorted CD38/CD71/CD117+ cells to regenerate the original lymphoma in transplanted animals in a single dose assay.

The rarity of cancer-initiating cells is usually considered as a major attribute favoring the cancer stem cell hypothesis.2  This perception of cancer-initiating cells' rarity, and the related stem cell concept, mainly relate to the estimation of cancer-initiating cell frequencies by the “gold standard” limiting dilution transplantation assay in recipient animals.3  However, the hypothesis of rarity of cancer-initiating cells is deeply impeded by the observation that LDTA practitioners, including Yamazaki et al,1  usually do not use proper statistical analysis of LDTA data that may validate the reported cell frequency estimates. We are capable to demonstrate that appropriate statistical modeling of the LDTA performed with SLCs1  invalidate this experiment and the related “stem” cell frequency estimate provided by the authors. Mathematical modeling of limiting dilution experiments of cells injected into recipient animals traditionally refers to the well-known single-hit Poisson model (SHPM), which posits that a single cell is necessary and sufficient to form a detectable tissue in the host.3  In a previously published paper4  we advised a statistical test aimed at estimating the fit of the SHPM to the data and based on a generalized linear modeling approach.5  Briefly, a general linear model, termed GLMloglog, can be written as

Yi = α + βXi

with Yi = ln [−ln (πi)] and Xi = ln (xi), where πi is the proportion of recipient mice free of tumor, and xi is the cell dose, that is, the number of cells injected into each mouse at each group i of cell dose. On elementary rearrangement4  of the above equation, the GLMloglog reduces to the SHPM for the special case as the slope β = 1. Therefore, testing the null hypothesis β = 1 explores the SHPM hypothesis, and this can performed by a standard likelihood ratio test5,6  and by a standard Wald test.4  The results of our modeling study are presented in Table 1. The null hypothesis β = 1 is clearly rejected at a very significant level, indicating that the LDTA does not conform to the SHPM, and the consequence is that no frequency estimate can be rendered. The precise frequency of tumorigenic cells in the Tax-transgenic (Tax-Tg) model remains unknown, and the hypothesis that this frequency may be considerably higher than 0.01% must not be ruled out, ultimately challenging the stem cell concept.

Table 1

Rejection of the SHPM hypothesis by fitting a generalized log-log linear regression model (GLMloglog) to a limiting dilution transplantation assay (LDTA) performed with whole splenic lymphomatous cells (SLCs)

CharacteristicDefinition
Cell population SLCs 
Deviance dispersion statistic* 1.89 
β 0.473 
SE(β) 0.159 
Null hypothesis, β = 1  
    P(z) 0.0009 
    P2) 0.0116 
SHPM hypothesis Rejected 
CharacteristicDefinition
Cell population SLCs 
Deviance dispersion statistic* 1.89 
β 0.473 
SE(β) 0.159 
Null hypothesis, β = 1  
    P(z) 0.0009 
    P2) 0.0116 
SHPM hypothesis Rejected 
*

The deviance dispersion statistic can be considered as a summary measure of goodness of fit of the GLMloglog to the data and should be inferior to 1.5-2 when the model adequately fits the data.5 

P(z) associated to the Wald statistic Z.4 

P2) associated to the standard likelihood ratio test used to compare nested models.6  The GLMloglog holds, as demonstrated by the rejection of the null hypothesis β = 0 by both Wald and likelihood ratio test statistics with P < .005 (data not shown).

Acknowledgment: This research is financially supported by Association pour la Recherche sur le Cancer (ARC) grant 3218.

Contribution: T.B. performed the statistical analyses; and T.B. and M.C. cowrote the paper.

Conflict-of-interest disclosure: The authors declare no competing financial interests.

Correspondence: Thierry Bonnefoix, Inserm, U823, Oncogenic Pathways in Haematological Malignancies, Institut Albert Bonniot, Grenoble cedex 9, France; e-mail: thierry.bonnefoix@ujf-grenoble.fr.

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