Details

Autor(en) / Beteiligte

• Visentin, Andrea
• Bonaldi, Laura
• Rigolin, Gian Matteo
• Mauro, Francesca Romana
• Martines, Annalisa
• Frezzato, Federica
• Pravato, Stefano
• Gargarella, Leila Romano
• Bardi, Maria Antonella
• Cavallari, Maurizio
• Volta, Eleonora
• Cavazzini, Francesco
• Nanni, Mauro
• Facco, Monica
• Piazza, Francesco
• Guarini, Anna
• Foà, Robin
• Semenzato, Gianpietro
• Cuneo, Antonio
• Trentin, Livio

Titel

The complex karyotype landscape in chronic lymphocytic leukemia allows the refinement of the risk of Richter syndrome transformation

Ist Teil von

• Haematologica (Roma), 2022-04, Vol.107 (4), p.868-876

Ort / Verlag

Italy: Fondazione Ferrata Storti

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Elektronische Zeitschriftenbibliothek (Open access)

Beschreibungen/Notizen

• Complex karyotype (CK) at chronic lymphocytic leukemia (CLL) diagnosis is a negative biomarker of adverse outcome. Since the impact of CK and its subtypes, namely type-2 CK (CK with major structural abnormalities) or high-CK (CK with ≥5 chromosome abnormalities), on the risk of developing Richter syndrome (RS) is unknown, we carried out a multicenter real-life retrospective study to test its prognostic impact. Among 540 CLL patients, 107 harbored a CK at CLL diagnosis, 78 were classified as CK2 and 52 as high-CK. Twenty-eight patients developed RS during a median follow-up of 6.7 years. At the time of CLL diagnosis, CK2 and high-CK were more common and predicted the highest risk of RS transformation, together with advanced Binet stage, unmutated (U)-IGHV, 11q-, and TP53 abnormalities. We integrated these variables into a hierarchical model: high-CK and/or CK2 patients showed a 10-year time to RS (TTRS) of 31%; U-IGHV/11q- /TP53 abnormalities/Binet stage B-C patients had a 10-year TTRS of 12%; mutated (M)-IGHV without CK and TP53 disruption a 10-year TTRS of 3% (P<0.0001). We herein demonstrate that CK landscape at CLL diagnosis allows the risk of RS transformation to be refined and we recapitulated clinico-biological variables into a prognostic model.