• Media type: E-Article
  • Title: The complex karyotype landscape in chronic lymphocytic leukemia allows the refinement of the risk of Richter syndrome transformation
  • Contributor: 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
  • Published: Ferrata Storti Foundation (Haematologica), 2021
  • Published in: Haematologica, 107 (2021) 4, Seite 868-876
  • Language: Not determined
  • DOI: 10.3324/haematol.2021.278304
  • ISSN: 1592-8721; 0390-6078
  • Origination:
  • Footnote:
  • Description: 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.
  • Access State: Open Access