Researchers Float New Way of Identifying CLLers Who Will Progress

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J Cell Physiol. 2004 Dec 16; [Epub ahead of print]

Signature of B-CLL with different prognosis by Shrunken centroids of

surface antigen expression profiling.

Zucchetto A, Sonego P, Degan M, Bomben R, Bo MD, Russo S, Attadia V,

Rupolo M, Buccisano F, Principe MI, Poeta GD, Pucillo C, Colombatti

A, Campanini R, Gattei V.

Clinical and Experimental Hematology Research Unit, Centro di

Riferimento Oncologico, I.R.C.C.S., Aviano (PN), Italy.

With the aim of identifying the immunophenotypic profile of B-cell

chronic lymphocytic leukemia (B-CLL) subsets with different

prognosis, we investigated by flow cytometry the expression of 36

surface antigens in 123 cases, all with survivals.

By analyzing results with unsupervised (hierarchical and K-means

clustering) algorithms, three distinct immunophenotypic groups (I,

II, and III) were identified, group I (51/123) with longer survivals,

as compared to the group II (36/123) and III (36/123).

The immunophenotypic signatures of these groups, as determined by

applying the nearest Shrunken centroids method* as class predictor,

were characterized by the coordinated and differential expression of

12 surface markers, that is, group I: above-average expression of

CD62L, CD54, CD49c, and CD25, below-average expression of CD38; group

II: above-average expression of CD38, CD49d, CD29, and CD49e; and

group III: below-average expression of the above markers,

overexpression of CD23, CD20, SmIg, and CD79b.

As opposed to groups II-III, group I B-CLLs lacked expression of ZAP-

70 and activation-induced cytidine deaminase in the majority of

cases, while more frequently had mutated IgV(H) genes and IgV(H)

mutations consistent with antigen-driven selection.

Our findings contribute to improve the immunophenotypical

identification of disease subsets with different prognosis and

suggest a set of surface antigens to be employed as prognosticators

in routine diagnostic/prognostic procedures. © 2004 Wiley-Liss, Inc.

* a method of identifying gene defects using microarrays

PMID: 15605425 [PubMed - as supplied by publisher]