PhD Scientific Days 2022

Budapest, 6-7 July 2022

Pathology and Oncology I. (Poster discussion will take place in the Aula during the Coffee Break)

Novel Copy Number Aberration-based Classification Methods Refine Risk Assessment in Pediatric B-cell Precursor Acute Lymphoblastic Leukemia

Gábor Bedics1, Bálint Egyed1,2, Lili Kotmayer1, Anne Benard-Slagter3, Karel de Groot3, Anna Bekő1, Lajos László Hegyi1, Szilvia Krizsán1, Gergely Kriván4, Dániel J Erdélyi2, Gábor Kovács2, Irén Haltrich2, Béla Kajtár5, László Pajor5, Ágnes Vojcek6, Gábor Ottóffy6, Anikó Ujfalusi7, Csongor Kiss8, István Szegedi8, Katalin Bartyik9, György Péter10, Endre Sebestyén1, Zsuzsanna Jakab11, András Matolcsy1, Suvi Savola3, Csaba Bödör1, Donát Alpár1

1 HCEMM-SU Molecular Oncohematology Research Group, Department of Pathology and Experimental Cancer Research, Semmelweis University, Budapest
2 2nd Department of Pediatrics, Semmelweis University, Budapest
3 Department of Oncogenetics, MRC Holland, Amsterdam, The Netherlands
4 Central Hospital of Southern Pest - National Institute of Hematology and Infectious Diseases, Budapest
5 Department of Pathology, University of Pécs Clinical Centre, Pécs
6 Department of Pediatrics, University of Pécs Clinical Centre, Pécs
7 Department of Laboratory Medicine, Faculty of Medicine, University of Debrecen
8 Department of Pediatric Hematology-Oncology, Institute of Pediatrics, University of Debrecen, Debrecen
9 Department of Paediatrics and Paediatric Health Care Center, Faculty of Medicine, University of Szeged, Szeged
10 Hemato-Oncology Unit, Heim Pál Children's Hospital, Budapest
11 Hungarian Childhood Cancer Registry, Hungarian Pediatric Oncology Network, Budapest

Text of the abstract

Introduction
Acute lymphoblastic leukemia (ALL) is the most common malignancy of childhood. The genomic landscape of pediatric ALL is heterogeneous with distinct copy number aberrations (CNAs) being detectable in vast majority of the patients. A subset of these alterations provides prognostic and/or predictive information; therefore, various CNA-based patient classifiers were introduced in the past.
Aims
We applied a next-generation sequencing (NGS) based method to comprehensively screen for disease-relevant CNAs in a cohort of Hungarian patients, allowing us to establish novel patient risk stratification approaches.
Methods
Diagnostic bone marrow samples from 260 children with B-ALL were investigated by digital multiplex ligation-dependent probe amplification (digitalMLPA) using the ALL-specific D007 probemix. DigitalMLPA libraries were sequenced on an Illumina NGS platform. Whole chromosome gains and losses, as well as subchromosomal CNAs were simultaneously profiled. Survival rates were estimated using the Kaplan-Meier method and compared by log-rank tests in R version 4.1.2.
Results
In total, 1400 CNAs including numerical chromosomal aberrations and subchromosomal CNAs were detected in 93.5% of the samples. On average, 5.36 CNAs were observed per patient with a mean of 2.45 subchromosomal alterations. Numerous CNAs in disease-relevant genes responsible for cell cycle control, lymphoid development, signaling, or tumor suppression were identified. The combined genetic classification methods identified 3 and 4 patient subgroups, respectively, with significantly different 5-year progression-free survival rates.
Conclusions
Comprehensive and highly optimized CNA profiling with digitalMLPA revealed subtype-defining gross-chromosomal changes and disease-relevant subchromosomal CNAs in a large cohort of Hungarian patients. Two novel risk stratification approaches have been established by combining cytogenetic data with digitalMLPA-based copy number profiling, with one of those laying more emphasis on CNA data than seen in previously introduced classifications, and the other one combining cytogenetic data with IKAROS status for the first time. DigitalMLPA offers a fast and reliable DNA copy number analysis which is easily implementable in the diagnostic workflow of ALL.
Supporting grants: FK20_134253, K21_137948, H2020-739593, EFOP-3.6.3-VEKOP-16-2017-00009.