The discovery of almost identical or ‘stereotyped’ B-cell receptor immunoglobulins (BcR IG) among unrelated patients with chronic lymphocytic leukemia (CLL) cemented the idea of antigen selection in disease ontogeny and evolution. The systematic analysis of the stereotypy phenomenon in CLL revealed that around one-third of CLL patients may be grouped into subsets based on shared sequence motifs within the variable heavy complementarity determining region 3. Stereotyped subsets display a strikingly similar biology of the leukemic clones, referring to many different levels, from the immunogenetic and genetic and extending to the epigenetic and functional levels. Even more importantly, the homogeneity of stereotyped subsets has clinical consequences as patients assigned to the same stereotyped subset generally exhibit an overall similar disease course and outcome. In other words, stereotypy-based patient classification of CLL has already provided a more compartmentalized view of this otherwise heterogeneous disease and can assist in refining prognostication models. While this is relevant only for the one-third of cases expressing stereotyped BcR IG; in principle, however, the findings from further analysis of the stereotyped subsets may also contribute towards improved understanding of the remaining non-stereotyped fraction of CLL patients.
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We thank present and past members of our groups for their commitment and enthusiasm. We would also like to acknowledge the trustful collaboration of all the members of the IMGT/CLL-DB initiative and, in particular, Professor Marie-Paule Lefranc and Dr Veronique Giudicelli, Laboratoire d’Immunogenetique Moleculaire, LIGM, Universite Montpellier II, Montpellier, France, and IMGT, the international ImMunoGeneTics information system, for their enormous support and help with IG gene sequence analysis. Finally, a special thanks to our friends and fellow members of the IgCLL Group (www.igcll.org), Drs Belessi, Darzentas and Davi, for many years of stimulating and fruitful collaboration. This work was supported in part by H2020 ‘AEGLE, An analytics framework for integrated and personalized healthcare services in Europe’ by the EU; ‘MEDGENET, Medical Genomics and Epigenomics Network’ (No.692298) by the EU; Associazione Italiana per la Ricerca sul Cancro AIRC (Investigator Grant #15189 and Special Program Molecular Clinical Oncology—5 per mille #9965), Milano, Italy and Ricerca Finalizzata 2010—Ministero della Salute, Roma (#2318823); The Swedish Cancer Society, the Swedish Research Council, Uppsala University, Uppsala University Hospital and the Lion’s Cancer Research Foundation, Uppsala.
The authors declare no conflict of interest.
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