Enhanced Risk Stratification for Children and Young Adults with B-Cell Acute Lymphoblastic Leukemia: A Children's Oncology Group Report

N J. DelRocco, Department of Biostatistics, Colleges of Medicine, Public Health and Health Professions, University of Florida, Gainesville, FL, USA. delrocco@usc.edu.
M L. Loh, Department of Pediatrics and the Ben Towne Center for Childhood Cancer Research, Seattle Children's Hospital, University of Washington, Seattle, WA, USA.
M J. Borowitz, Department of Pathology, Johns Hopkins University, Baltimore, MD, USA.
S Gupta, Division of Haematology/Oncology, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada.
K R. Rabin, Division of Pediatric Hematology/Oncology, Texas Children's Cancer Center, Baylor College of Medicine, Houston, TX, USA.
P Zweidler-McKay, Immunogen, Inc, Waltham, MA, USA.
K W. Maloney, Department of Pediatrics, University of Colorado and Children's Hospital Colorado, Aurora, CO, USA.
L A. Mattano, HARP Pharma Consulting, Mystic, CT, USA.
E Larsen, Department of Pediatrics, Maine Children's Cancer Program, Scarborough, ME, USA.
A Angiolillo, Servier Pharmaceuticals, Boston, MA, USA.
R J. Schore, Division of Pediatric Oncology, Children's National Hospital, Washington, DC and the George Washington University School of Medicine and Health Sciences, Washington, DC, USA.
M J. Burke, Division of Pediatric Hematology-Oncology, Medical College of Wisconsin, Milwaukee, WI, USA.
W L. Salzer, Uniformed Services University, F. Edward Hebert School of Medicine, Bethesda, MD, USA.
B L. Wood, Children's Hospital Los Angeles, Pathology and Laboratory Medicine, Los Angeles, CA, USA.
A J. Carroll, Department of Genetics, University of Alabama at Birmingham, Birmingham, AL, USA.
N A. Heerema, Department of Pathology, The Ohio State University Wexner School of Medicine, Columbus, OH, USA.
S C. Reshmi, Department of Pathology and Laboratory Medicine, Nationwide Children's Hospital and Departments of Pathology and Pediatrics, Ohio State University College of Medicine, Columbus, OH, USA.
J M. Gastier-Foster, Department of Pathology, The Ohio State University Wexner School of Medicine, Columbus, OH, USA.
R Harvey, University of New Mexico Cancer Center, Albuquerque, NM, USA.
I M. Chen, University of New Mexico Cancer Center, Albuquerque, NM, USA.
K G. Roberts, Department of Pathology, St Jude Children's Research Hospital, Memphis, TN, USA.
C G. Mullighan, Department of Pathology, St Jude Children's Research Hospital, Memphis, TN, USA.
C Willman, Mayo Clinic, Cancer Center/Laboratory Medicine and Pathology, Rochester, NY, USA.
N Winick, UTSouthwestern, Simmons Cancer Center, Dallas, TX, USA.
W L. Carroll, Perlmutter Cancer Center and Department of Pediatrics, NYU Langone Health, New York, NY, USA.
R E. Rau, Department of Pediatrics and the Ben Towne Center for Childhood Cancer Research, Seattle Children's Hospital, University of Washington, Seattle, WA, USA.
D T. Teachey, Department of Pediatrics and The Center for Childhood Cancer Research, Children's Hospital of Philadelphia and the Perelman School of Medicine at The University of Pennsylvania, Philadelphia, PA, USA.
S P. Hunger, Department of Pediatrics and The Center for Childhood Cancer Research, Children's Hospital of Philadelphia and the Perelman School of Medicine at The University of Pennsylvania, Philadelphia, PA, USA.
E A. Raetz, Perlmutter Cancer Center and Department of Pediatrics, NYU Langone Health, New York, NY, USA.
M Devidas, Department of Global Pediatric Medicine, St. Jude Children's Research Hospital, Memphis, TN, USA.
J A. Kairalla, Department of Biostatistics, Colleges of Medicine, Public Health and Health Professions, University of Florida, Gainesville, FL, USA.

Document Type

Journal Article

Publication Date

2-15-2024

Journal

Leukemia

DOI

10.1038/s41375-024-02166-1

Abstract

Current strategies to treat pediatric acute lymphoblastic leukemia rely on risk stratification algorithms using categorical data. We investigated whether using continuous variables assigned different weights would improve risk stratification. We developed and validated a multivariable Cox model for relapse-free survival (RFS) using information from 21199 patients. We constructed risk groups by identifying cutoffs of the COG Prognostic Index (PI) that maximized discrimination of the predictive model. Patients with higher PI have higher predicted relapse risk. The PI reliably discriminates patients with low vs. high relapse risk. For those with moderate relapse risk using current COG risk classification, the PI identifies subgroups with varying 5-year RFS. Among current COG standard-risk average patients, PI identifies low and intermediate risk groups with 96% and 90% RFS, respectively. Similarly, amongst current COG high-risk patients, PI identifies four groups ranging from 96% to 66% RFS, providing additional discrimination for future treatment stratification. When coupled with traditional algorithms, the novel PI can more accurately risk stratify patients, identifying groups with better outcomes who may benefit from less intensive therapy, and those who have high relapse risk needing innovative approaches for cure.

Department

Pediatrics

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