Improving risk-stratification of patients with chronic lymphocytic leukemia using multivariate patient similarity networks.


Journal

Leukemia research
ISSN: 1873-5835
Titre abrégé: Leuk Res
Pays: England
ID NLM: 7706787

Informations de publication

Date de publication:
04 2019
Historique:
received: 24 09 2018
revised: 04 02 2019
accepted: 18 02 2019
pubmed: 11 3 2019
medline: 18 12 2019
entrez: 11 3 2019
Statut: ppublish

Résumé

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. 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. 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. 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.

Sections du résumé

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.

Identifiants

pubmed: 30852300
pii: S0145-2126(19)30031-1
doi: 10.1016/j.leukres.2019.02.005
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

60-68

Informations de copyright

Copyright © 2019 Elsevier Ltd. All rights reserved.

Auteurs

Peter Turcsanyi (P)

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.

Eva Kriegova (E)

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.

Milos Kudelka (M)

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.

Martin Radvansky (M)

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.

Lenka Kruzova (L)

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.

Renata Urbanova (R)

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.

Petra Schneiderova (P)

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.

Helena Urbankova (H)

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.

Tomas Papajik (T)

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.

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