Peter Turcsanyi1, Eva Kriegova2, Milos Kudelka3, Martin Radvansky4, Lenka Kruzova5, Renata Urbanova6, Petra Schneiderova7, Helena Urbankova8, Tomas Papajik9. 1. Department of Hemato-Oncology, Faculty of Medicine and Dentistry, Palacky University Olomouc and University Hospital Olomouc, Olomouc, Olomouc, Czech Republic. Electronic address: Peter.Turcsanyi@fnol.cz. 2. Department of Immunology, Faculty of Medicine and Dentistry, Palacky University Olomouc and University Hospital Olomouc, Olomouc, Czech Republic, Olomouc, Czech Republic. Electronic address: eva.kriegova@email.cz. 3. Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, Ostrava, Czech Republic. Electronic address: Milos.Kudelka@vsb.cz. 4. Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, Ostrava, Czech Republic. Electronic address: Martin.Radvansky@vsb.cz. 5. Department of Hemato-Oncology, Faculty of Medicine and Dentistry, Palacky University Olomouc and University Hospital Olomouc, Olomouc, Olomouc, Czech Republic. Electronic address: Lenka.Kruzova@fnol.cz. 6. Department of Hemato-Oncology, Faculty of Medicine and Dentistry, Palacky University Olomouc and University Hospital Olomouc, Olomouc, Olomouc, Czech Republic. Electronic address: Renata.Urbanova@fnol.cz. 7. Department of Immunology, Faculty of Medicine and Dentistry, Palacky University Olomouc and University Hospital Olomouc, Olomouc, Czech Republic, Olomouc, Czech Republic. Electronic address: Petra.Schneiderova@fnol.cz. 8. Department of Hemato-Oncology, Faculty of Medicine and Dentistry, Palacky University Olomouc and University Hospital Olomouc, Olomouc, Olomouc, Czech Republic. Electronic address: Helena.Urbankova@fnol.cz. 9. Department of Hemato-Oncology, Faculty of Medicine and Dentistry, Palacky University Olomouc and University Hospital Olomouc, Olomouc, Olomouc, Czech Republic. Electronic address: Tomas.Papajik@fnol.cz.
Abstract
BACKGROUND: Better risk-stratification of patients with chronic lymphocytic leukemia (CLL) and identification of subsets of ultra-high-risk (HR)-CLL patients are crucial in the contemporary era of an expanded therapeutic armamentarium for CLL. METHODS: A multivariate patient similarity network and clustering was applied to assess the prognostic values of routine genetic, laboratory, and clinical factors and to identify subsets of ultra-HR-CLL patients. The study cohort consisted of 116 HR-CLL patients (F/M 36/80, median age 63 yrs) carrying del(11q), del(17p)/TP53 mutations and/or complex karyotype (CK) at the time of diagnosis. RESULTS: Three major subsets based on the presence of key prognostic variables as genetic aberrations, bulky lymphadenopathy, splenomegaly, and gender: profile (P)-I (n = 34, men/women with CK + no del(17p)/TP53 mutations), P-II (n = 47, predominantly men with del(11q) + no CK + no del(17p)/TP53 mutations), and P-III (n = 35, men/women with del(17p)/TP53 mutations, with/without del(11q) and CK) were revealed. Subanalysis of major subsets identified three ultra-HR-CLL groups: men with TP53 disruption with/without CK, women with TP53 disruption with CK and men/women with CK + del(11q) with poor short-term outcomes (25% deaths/12 mo). Besides confirming the combinations of known risk-factors, the used patient similarity network added further refinement of subsets of HR-CLL patients who may profit from different targeted drugs. CONCLUSIONS: This study showed for the first time in hemato-oncology the usefulness of the multivariate patient similarity networks for stratification of HR-CLL patients. This approach shows the potential for clinical implementation of precision medicine, which is especially important in view of an armamentarium of novel targeted drugs.
BACKGROUND: Better risk-stratification of patients with chronic lymphocytic leukemia (CLL) and identification of subsets of ultra-high-risk (HR)-CLL patients are crucial in the contemporary era of an expanded therapeutic armamentarium for CLL. METHODS: A multivariate patient similarity network and clustering was applied to assess the prognostic values of routine genetic, laboratory, and clinical factors and to identify subsets of ultra-HR-CLLpatients. The study cohort consisted of 116 HR-CLLpatients (F/M 36/80, median age 63 yrs) carrying del(11q), del(17p)/TP53 mutations and/or complex karyotype (CK) at the time of diagnosis. RESULTS: Three major subsets based on the presence of key prognostic variables as genetic aberrations, bulky lymphadenopathy, splenomegaly, and gender: profile (P)-I (n = 34, men/women with CK + no del(17p)/TP53 mutations), P-II (n = 47, predominantly men with del(11q) + no CK + no del(17p)/TP53 mutations), and P-III (n = 35, men/women with del(17p)/TP53 mutations, with/without del(11q) and CK) were revealed. Subanalysis of major subsets identified three ultra-HR-CLL groups: men with TP53 disruption with/without CK, women with TP53 disruption with CK and men/women with CK + del(11q) with poor short-term outcomes (25% deaths/12 mo). Besides confirming the combinations of known risk-factors, the used patient similarity network added further refinement of subsets of HR-CLLpatients who may profit from different targeted drugs. CONCLUSIONS: This study showed for the first time in hemato-oncology the usefulness of the multivariate patient similarity networks for stratification of HR-CLLpatients. This approach shows the potential for clinical implementation of precision medicine, which is especially important in view of an armamentarium of novel targeted drugs.
Authors: Francisco Martín-Rodríguez; Raúl López-Izquierdo; Ancor Sanz-García; Carlos Del Pozo Vegas; Miguel Ángel Castro Villamor; Agustín Mayo-Iscar; José L Martín-Conty; Guillermo José Ortega Journal: J Med Syst Date: 2022-05-21 Impact factor: 4.920