Article Dans Une Revue Blood Année : 2021

Higher-order connections between stereotyped subsets: implications for improved patient classification in CLL

Andreas Agathangelidis (1, 2) , Anastasia Chatzidimitriou (3, 4) , Katerina Gemenetzi (3, 5) , Veronique Giudicelli (6) , Maria Karypidou (7, 3) , Karla Plevova (8, 9, 10) , Zadie Davis , Xiao-Jie Yan (11) , Sabine Jeromin , Christof Schneider (12) , Lone Bredo Pedersen (13) , Renee Tschumper (14) , Lesley-Ann Sutton (4) , Panagiotis Baliakas (15) , Lydia Scarfò (2) , Ellen van Gastel (16) , Marine Armand (17, 18) , Eugen Tausch (19) , Bella Biderman , Constance Baer , Davide Bagnara (20) , Alba Navarro (21, 22) , Anne Langlois de Septenville , Valentina Guido , Gerlinde Mitterbauer-Hohendanner (23) , Aleksandar Dimovski (24) , Christian Brieghel (25) , Sarah Lawless (26) , Manja Meggendorfer , Kamila Brazdilova (9) , Matthias Ritgen (27) , Monica Facco (28) , Cristina Tresoldi (29) , Andrea Visentin (28) , Andrea Patriarca , Mark Catherwood , Lisa Bonello (30) , Andrey Sudarikov , Katrina Vanura (23) , Maria Roumelioti (31) , Hana Skuhrova Francova (10) , Theodoros Moysiadis (3) , Silvio Veronese , Krzysztof Giannopoulos (32) , Larry Mansouri (4) , Teodora Karan-Djurasevic (33) , Raphael Sandaltzopoulos (5) , Csaba Bödör (34) , Franco Fais (20) , Arnon Kater (35) , Irina Panovska (24) , Davide Rossi (36) , Salem Alshemmari (37) , Panagiotis Panagiotidis (31) , Paul Costeas (38) , Blanca Espinet , Darko Antic (39) , Letizia Foroni (40) , Marco Montillo , Livio Trentin (28) , Niki Stavroyianni (7) , Gianluca Gaidano , Paola Francia Di Celle (41) , Carsten Niemann (42) , Elias Campo (21) , Achilles Anagnostopoulos (40) , Christiane Pott (27) , Kirsten Fischer (43) , Michael Hallek (44) , David Oscier , Stephan Stilgenbauer (12) , Claudia Haferlach , Diane Jelinek (45) , Nicholas Chiorazzi , Sarka Pospisilova (9) , Marie-Paule Lefranc (6) , Sofia Kossida (6) , Anton Langerak , Chrysoula Belessi , Frederic Davi (46) , Richard Rosenquist (4, 47) , Paolo Ghia (2) , Kostas Stamatopoulos (4, 3)
1 IRCCS San Raffaele Pisana - Istituto di Ricovero e Cura a Carattere Scientifico San Raffaele [Roma]
2 UniSR - Universita Vita Salute San Raffaele = Vita-Salute San Raffaele University [Milan, Italie]
3 CERTH - Centre for Research and Technology Hellas
4 Karolinska Institutet = Karolinska Institute [Stockholm]
5 DUTH - Democritus University of Thrace
6 IGH - Institut de génétique humaine
7 General Hospital of Thessaloniki George Papanikolaou
8 CEITEC MU - Central European Institute of Technology [Brno]
9 MU / MUNI - Masaryk University [Brno] = Masarykova univerzita [Brno] = Université Masaryk [Brno]
10 University Hospital Brno
11 The Feinstein Institutes for Medical Research [Manhasset, NY, USA]
12 Universität Ulm - Ulm University [Ulm, Allemagne]
13 Rigshospitalet [Copenhagen, Denmark]
14 Mayo Clinic [Rochester]
15 Uppsala University
16 University Medical Center
17 CRC - UMR_S 1138 - Métabolisme, Cancer et Immunité
18 CRC (UMR_S_1138 / U1138) - Centre de Recherche des Cordeliers
19 Universitätsklinikum Ulm - University Hospital of Ulm
20 UniGe - Università degli studi di Genova = University of Genoa = Université de Gênes
21 CIBERONC - Centro de Investigación Biomédica en Red de Cáncer
22 IDIBAPS - Institut d'Investigacions Biomèdiques August Pi i Sunyer
23 Universität Wien = University of Vienna
24 UKIM - Ss. Cyril and Methodius University in Skopje / Универзитет „Св. Кирил и Методиј“ во Скопје [Skopje, Republic of North Macedonia]
25 UCPH - Københavns Universitet = University of Copenhagen = Université de Copenhague
26 Belfast City Hospital
27 CAU - Christian-Albrechts-Universität zu Kiel = Christian-Albrechts University of Kiel = Université Christian-Albrechts de Kiel
28 Unipd - Università degli Studi di Padova = University of Padua
29 IRCCS San Raffaele Scientific Institute [Milan, Italie]
30 AOUC - Azienda Ospedaliero Universitaria Careggi [Firenze] = Careggi University Hospital [Florence, Italy]
31 University of Athens Medical School [Athens]
32 Medical University of Lublin
33 University of Belgrade [Belgrade]
34 Semmelweis University [Budapest]
35 UvA - Universiteit van Amsterdam = University of Amsterdam
36 IOSI - Oncology Institute of Southern Switzerland
37 Kuwait University
38 UNIC - University of Nicosia
39 KCS - Clinical Center of Serbia
40 Hammersmith Hospital NHS Imperial College Healthcare
41 A.O.U - Città della Salute e della Scienza di Torino
42 KUH - Copenhagen University Hospital [Denmark] = Københavns Universitetshospital [Danmark]
43 University Hospital of Cologne [Cologne]
44 Universität zu Köln = University of Cologne
45 Mayo Clinic [Scottsdale]
46 CHU Pitié-Salpêtrière [AP-HP]
47 Karolinska University Hospital [Stockholm]
Zadie Davis
Sabine Jeromin
  • Fonction : Auteur
Bella Biderman
Constance Baer
  • Fonction : Auteur
Anne Langlois de Septenville
  • Fonction : Auteur
Valentina Guido
  • Fonction : Auteur
Manja Meggendorfer
  • Fonction : Auteur
Andrea Patriarca
Mark Catherwood
  • Fonction : Auteur
Andrey Sudarikov
Silvio Veronese
Salem Alshemmari
Blanca Espinet
Marco Montillo
  • Fonction : Auteur
Gianluca Gaidano
  • Fonction : Auteur
David Oscier
  • Fonction : Auteur
Claudia Haferlach
  • Fonction : Auteur
Nicholas Chiorazzi
  • Fonction : Auteur
Marie-Paule Lefranc
Sofia Kossida
Anton Langerak
Chrysoula Belessi
  • Fonction : Auteur
Frederic Davi

Résumé

Chronic lymphocytic leukemia (CLL) is characterized by the existence of subsets of patients with (quasi)identical, stereotyped B-cell receptor (BcR) immunoglobulins. Patients in certain major stereotyped subsets often display remarkably consistent clinicobiological profiles, suggesting that the study of BcR immunoglobulin stereotypy in CLL has important implications for understanding disease pathophysiology and refining clinical decision-making. Nevertheless, several issues remain open, especially pertaining to the actual frequency of BcR immunoglobulin stereotypy and major subsets, as well as the existence of higher-order connections between individual subsets. To address these issues, we investigated clonotypic IGHV-IGHD-IGHJ gene rearrangements in a series of 29 856 patients with CLL, by far the largest series worldwide. We report that the stereotyped fraction of CLL peaks at 41% of the entire cohort and that all 19 previously identified major subsets retained their relative size and ranking, while 10 new ones emerged; overall, major stereotyped subsets had a cumulative frequency of 13.5%. Higher-level relationships were evident between subsets, particularly for major stereotyped subsets with unmutated IGHV genes (U-CLL), for which close relations with other subsets, termed “satellites,” were identified. Satellite subsets accounted for 3% of the entire cohort. These results confirm our previous notion that major subsets can be robustly identified and are consistent in relative size, hence representing distinct disease variants amenable to compartmentalized research with the potential of overcoming the pronounced heterogeneity of CLL. Furthermore, the existence of satellite subsets reveals a novel aspect of repertoire restriction with implications for refined molecular classification of CLL.

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hal-04337928 , version 1 (14-12-2023)

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Andreas Agathangelidis, Anastasia Chatzidimitriou, Katerina Gemenetzi, Veronique Giudicelli, Maria Karypidou, et al.. Higher-order connections between stereotyped subsets: implications for improved patient classification in CLL. Blood, 2021, 137 (10), pp.1365-1376. ⟨10.1182/blood.2020007039⟩. ⟨hal-04337928⟩
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