Panagiotis Baliakas1, Anastasia Hadzidimitriou2, Lesley-Ann Sutton3, Eva Minga4, Andreas Agathangelidis5, Michele Nichelatti6, Athina Tsanousa7, Lydia Scarfò5, Zadie Davis8, Xiao-Jie Yan9, Tait Shanafelt10, Karla Plevova11, Yorick Sandberg12, Fie Juhl Vojdeman13, Myriam Boudjogra14, Tatiana Tzenou15, Maria Chatzouli16, Charles C Chu9, Silvio Veronese6, Anne Gardiner8, Larry Mansouri3, Karin E Smedby17, Lone Bredo Pedersen13, Kirsten van Lom18, Véronique Giudicelli19, Hana Skuhrova Francova11, Florence Nguyen-Khac14, Panagiotis Panagiotidis15, Gunnar Juliusson20, Lefteris Angelis7, Achilles Anagnostopoulos21, Marie-Paule Lefranc19, Monica Facco22, Livio Trentin22, Mark Catherwood23, Marco Montillo6, Christian H Geisler13, Anton W Langerak12, Sarka Pospisilova11, Nicholas Chiorazzi9, David Oscier8, Diane F Jelinek24, Nikos Darzentas25, Chrysoula Belessi16, Frederic Davi14, Richard Rosenquist3, Paolo Ghia26, Kostas Stamatopoulos27. 1. Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden; Hematology Department and HCT Unit, G Papanicolaou Hospital, Thessaloniki, Greece. 2. Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden; Institute of Applied Biosciences, CERTH, Thessaloniki, Greece. 3. Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden. 4. Institute of Applied Biosciences, CERTH, Thessaloniki, Greece. 5. Università Vita-Salute San Raffaele, Milan, Italy; Division of Molecular Oncology and Department of Onco-Hematology, IRCCS, San Raffaele Scientific Institute, Milan, Italy. 6. Molecular Pathology Unit and Haematology Department, Niguarda Cancer Center, Niguarda Ca' Granda Hospital, Milan, Italy. 7. Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece. 8. Department of Haematology, Royal Bournemouth Hospital, Bournemouth, UK. 9. The Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, NY, USA. 10. Division of Hematology, Department of Medicine, Mayo Clinic, Rochester, MN, USA. 11. Central European Institute of Technology, Masaryk University and University Hospital Brno, Brno, Czech Republic. 12. Department of Immunology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands. 13. Department of Hematology, Rigshospitalet, Copenhagen, Denmark. 14. Hôpital Pitié-Salpêtrière, Service d'Hématologie Biologique, Paris, France. 15. First Department of Propaedeutic Medicine, University of Athens, Athens, Greece. 16. Hematology Department, Nikea General Hospital, Piraeus, Greece. 17. Department of Medicine, Solna, Clinical Epidemiology Unit, Karolinska Institutet, Stockholm, Sweden. 18. Department of Hematology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands. 19. IMGT-the International ImMunoGeneTics Information System, University of Montpellier, LIGM, Institut de Génétique Humaine IGH, Montpellier, France. 20. Lund University and Hospital Department of Hematology, Lund Stem Cell Center, Lund, Sweden. 21. Hematology Department and HCT Unit, G Papanicolaou Hospital, Thessaloniki, Greece. 22. Department of Medicine, Hematology and Clinical Immunology Branch, Padua University School of Medicine, Italy; Venetian Institute of Molecular Medicine (VIMM), Padova, Italy. 23. Department of Haemato-Oncology, Belfast City Hospital, Belfast, UK. 24. Department of Immunology, Department of Medicine, Mayo Clinic, Rochester, MN, USA. 25. Central European Institute of Technology, Masaryk University, Brno, Czech Republic. 26. Università Vita-Salute San Raffaele, Milan, Italy; Division of Molecular Oncology and Department of Onco-Hematology, IRCCS, San Raffaele Scientific Institute, Milan, Italy. Electronic address: ghia.paolo@hsr.it. 27. Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden; Hematology Department and HCT Unit, G Papanicolaou Hospital, Thessaloniki, Greece; Institute of Applied Biosciences, CERTH, Thessaloniki, Greece.
Abstract
BACKGROUND: About 30% of cases of chronic lymphocytic leukaemia (CLL) carry quasi-identical B-cell receptor immunoglobulins and can be assigned to distinct stereotyped subsets. Although preliminary evidence suggests that B-cell receptor immunoglobulin stereotypy is relevant from a clinical viewpoint, this aspect has never been explored in a systematic manner or in a cohort of adequate size that would enable clinical conclusions to be drawn. METHODS: For this retrospective, multicentre study, we analysed 8593 patients with CLL for whom immunogenetic data were available. These patients were followed up in 15 academic institutions throughout Europe (in Czech Republic, Denmark, France, Greece, Italy, Netherlands, Sweden, and the UK) and the USA, and data were collected between June 1, 2012, and June 7, 2013. We retrospectively assessed the clinical implications of CLL B-cell receptor immunoglobulin stereotypy, with a particular focus on 14 major stereotyped subsets comprising cases expressing unmutated (U-CLL) or mutated (M-CLL) immunoglobulin heavy chain variable genes. The primary outcome of our analysis was time to first treatment, defined as the time between diagnosis and date of first treatment. FINDINGS: 2878 patients were assigned to a stereotyped subset, of which 1122 patients belonged to one of 14 major subsets. Stereotyped subsets showed significant differences in terms of age, sex, disease burden at diagnosis, CD38 expression, and cytogenetic aberrations of prognostic significance. Patients within a specific subset generally followed the same clinical course, whereas patients in different stereotyped subsets-despite having the same immunoglobulin heavy variable gene and displaying similar immunoglobulin mutational status-showed substantially different times to first treatment. By integrating B-cell receptor immunoglobulin stereotypy (for subsets 1, 2, and 4) into the well established Döhner cytogenetic prognostic model, we showed these, which collectively account for around 7% of all cases of CLL and represent both U-CLL and M-CLL, constituted separate clinical entities, ranging from very indolent (subset 4) to aggressive disease (subsets 1 and 2). INTERPRETATION: The molecular classification of chronic lymphocytic leukaemia based on B-cell receptor immunoglobulin stereotypy improves the Döhner hierarchical model and refines prognostication beyond immunoglobulin mutational status, with potential implications for clinical decision making, especially within prospective clinical trials. FUNDING: European Union; General Secretariat for Research and Technology of Greece; AIRC; Italian Ministry of Health; AIRC Regional Project with Fondazione CARIPARO and CARIVERONA; Regione Veneto on Chronic Lymphocytic Leukemia; Nordic Cancer Union; Swedish Cancer Society; Swedish Research Council; and National Cancer Institute (NIH).
BACKGROUND: About 30% of cases of chronic lymphocytic leukaemia (CLL) carry quasi-identical B-cell receptor immunoglobulins and can be assigned to distinct stereotyped subsets. Although preliminary evidence suggests that B-cell receptor immunoglobulin stereotypy is relevant from a clinical viewpoint, this aspect has never been explored in a systematic manner or in a cohort of adequate size that would enable clinical conclusions to be drawn. METHODS: For this retrospective, multicentre study, we analysed 8593 patients with CLL for whom immunogenetic data were available. These patients were followed up in 15 academic institutions throughout Europe (in Czech Republic, Denmark, France, Greece, Italy, Netherlands, Sweden, and the UK) and the USA, and data were collected between June 1, 2012, and June 7, 2013. We retrospectively assessed the clinical implications of CLL B-cell receptor immunoglobulin stereotypy, with a particular focus on 14 major stereotyped subsets comprising cases expressing unmutated (U-CLL) or mutated (M-CLL) immunoglobulin heavy chain variable genes. The primary outcome of our analysis was time to first treatment, defined as the time between diagnosis and date of first treatment. FINDINGS: 2878 patients were assigned to a stereotyped subset, of which 1122 patients belonged to one of 14 major subsets. Stereotyped subsets showed significant differences in terms of age, sex, disease burden at diagnosis, CD38 expression, and cytogenetic aberrations of prognostic significance. Patients within a specific subset generally followed the same clinical course, whereas patients in different stereotyped subsets-despite having the same immunoglobulin heavy variable gene and displaying similar immunoglobulin mutational status-showed substantially different times to first treatment. By integrating B-cell receptor immunoglobulin stereotypy (for subsets 1, 2, and 4) into the well established Döhner cytogenetic prognostic model, we showed these, which collectively account for around 7% of all cases of CLL and represent both U-CLL and M-CLL, constituted separate clinical entities, ranging from very indolent (subset 4) to aggressive disease (subsets 1 and 2). INTERPRETATION: The molecular classification of chronic lymphocytic leukaemia based on B-cell receptor immunoglobulin stereotypy improves the Döhner hierarchical model and refines prognostication beyond immunoglobulin mutational status, with potential implications for clinical decision making, especially within prospective clinical trials. FUNDING: European Union; General Secretariat for Research and Technology of Greece; AIRC; Italian Ministry of Health; AIRC Regional Project with Fondazione CARIPARO and CARIVERONA; Regione Veneto on Chronic Lymphocytic Leukemia; Nordic Cancer Union; Swedish Cancer Society; Swedish Research Council; and National Cancer Institute (NIH).
Authors: Elisa ten Hacken; Maria Gounari; Jaap Willem Back; Ekaterina Shimanovskaya; Lydia Scarfò; Ekaterina Kim; Jared Burks; Maurilio Ponzoni; Giuseppe Alvise Ramirez; William G Wierda; Zeev Estrov; Michael J Keating; Alessandra Ferrajoli; Kostas Stamatopoulos; Paolo Ghia; Jan A Burger Journal: Haematologica Date: 2017-07-27 Impact factor: 9.941
Authors: Rosa Catera; Yun Liu; Chao Gao; Xiao-Jie Yan; Amanda Magli; Steven L Allen; Jonathan E Kolitz; Kanti R Rai; Charles C Chu; Ten Feizi; Kostas Stamatopoulos; Nicholas Chiorazzi Journal: Mol Med Date: 2017-01-12 Impact factor: 6.354
Authors: A Vardi; E Vlachonikola; M Karypidou; E Stalika; V Bikos; K Gemenetzi; C Maramis; A Siorenta; A Anagnostopoulos; S Pospisilova; N Maglaveras; I Chouvarda; K Stamatopoulos; A Hadzidimitriou Journal: Leukemia Date: 2016-11-25 Impact factor: 11.528
Authors: Panagiotis Baliakas; Sabine Jeromin; Michalis Iskas; Anna Puiggros; Karla Plevova; Florence Nguyen-Khac; Zadie Davis; Gian Matteo Rigolin; Andrea Visentin; Aliki Xochelli; Julio Delgado; Fanny Baran-Marszak; Evangelia Stalika; Pau Abrisqueta; Kristina Durechova; George Papaioannou; Virginie Eclache; Maria Dimou; Theodoros Iliakis; Rosa Collado; Michael Doubek; M Jose Calasanz; Neus Ruiz-Xiville; Carolina Moreno; Marie Jarosova; Alexander C Leeksma; Panayiotis Panayiotidis; Helena Podgornik; Florence Cymbalista; Achilles Anagnostopoulos; Livio Trentin; Niki Stavroyianni; Fred Davi; Paolo Ghia; Arnon P Kater; Antonio Cuneo; Sarka Pospisilova; Blanca Espinet; Anastasia Athanasiadou; David Oscier; Claudia Haferlach; Kostas Stamatopoulos Journal: Blood Date: 2019-01-02 Impact factor: 22.113
Authors: Lesley-Ann Sutton; Emma Young; Panagiotis Baliakas; Anastasia Hadzidimitriou; Theodoros Moysiadis; Karla Plevova; Davide Rossi; Jana Kminkova; Evangelia Stalika; Lone Bredo Pedersen; Jitka Malcikova; Andreas Agathangelidis; Zadie Davis; Larry Mansouri; Lydia Scarfò; Myriam Boudjoghra; Alba Navarro; Alice F Muggen; Xiao-Jie Yan; Florence Nguyen-Khac; Marta Larrayoz; Panagiotis Panagiotidis; Nicholas Chiorazzi; Carsten Utoft Niemann; Chrysoula Belessi; Elias Campo; Jonathan C Strefford; Anton W Langerak; David Oscier; Gianluca Gaidano; Sarka Pospisilova; Frederic Davi; Paolo Ghia; Kostas Stamatopoulos; Richard Rosenquist Journal: Haematologica Date: 2016-05-19 Impact factor: 9.941