A New Histology-Based Prognostic Index for Acute Myeloid Leukemia: Preliminary Results for the “AML Urayasu Classification”

<b>Background:</b> This study was aimed at elucidating the mechanisms underlying the development of treatment resistance in patients with acute myeloid leukemia (AML) other than M3 myeloid leukemia in order to devise ways to overcome treatment resistance and improve the treatment outcomes in these patients. <b>Methods</b>: For this study, we randomly selected 35 patients with AML who had received combined cytarabine plus idarubicin treatment for new-onset AML at our hospital. We performed immunohistochemical analysis of biopsy specimens obtained from the patients to investigate the expressions of 23 treatment-resistance-related proteins, and retrospectively analyzed the correlations between the expression profiles of the resistance proteins and the patient survival. <b>Results</b>: The following four proteins were identified as being particularly significant in relation to treatment resistance and patient prognosis: (1) p53; (2) multidrug resistance-associated protein 1 (MRP1; idarubicin extracellular efflux pump); (3) aldo-keto reductase family 1 member B10 (AKR1B10; idarubicin-inactivating enzyme); and (4) AKR1B1 (competitive inhibitor of AKR1B10). Based on our findings, we propose the following Urayasu classification for AML, which we believe would be very useful for accurately stratifying patients with AML according to the predicted prognosis: Group 1 (<i>n</i> = 22, 63%): p53(-)/MRP1(-) associated with AKR1B10(+)/AKR1B1(+) or AKR1B10(-)/AKR1B1(-); 5-year overall survival (OS), 82%-100%; Group 2 (<i>n</i> = 9, 26%): p53(-)/MRP1(-) associated with AKR1B10(+)/AKR1B1(-); 5-year OS, 68%; Group 3 (<i>n</i> = 4, 11%): p53(+) or MRP1(+); median survival, 12-14 months; 2-year OS, 0%. <b>Conclusions</b>: The Urayasu classification for AML is useful for predicting the prognosis of patients with AML. Group 1 in this classification included twice as many patients as that included in the Favorable prognosis group in the AML prognostic classification proposed by the European Leukemia Net. As the Urayasu classification for AML is based on the mechanisms of resistance to chemotherapy, it is not only useful for prognostic stratification of the patients, but also provides insights for developing more effective treatments for AML.

Authors

Institutions

Publication Details

Journal
Journal of Clinical Medicine
Published
2025-03-15
DOI
https://doi.org/10.3390/jcm14061989
Primary Topic
Acute Myeloid Leukemia Research
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A New Histology-Based Prognostic Index for Acute Myeloid Leukemia: Preliminary Results for the “AML Urayasu Classification”

Hiroko Iizuka, Takashi Fujishiro, Toru Mitsumori, Masaaki Noguchi et al.
Journal of Clinical Medicine
Acute Myeloid Leukemia Research
article

A New Histology-Based Prognostic Index for Acute Myeloid Leukemia: Preliminary Results for the “AML Urayasu Classification”

Hiroko Iizuka, Takashi Fujishiro, Toru Mitsumori, Masaaki Noguchi, Miki Ando, Hideaki Nitta, Chiho Furuya, Haruko Takizawa, S. Tomita, Tomohiro Sawada, Mitsuo Okubo, Akane Toriyama, Kazunori Miyake, Yasunobu Sekiguchi
article en

Abstract

<b>Background:</b> This study was aimed at elucidating the mechanisms underlying the development of treatment resistance in patients with acute myeloid leukemia (AML) other than M3 myeloid leukemia in order to devise ways to overcome treatment resistance and improve the treatment outcomes in these patients. <b>Methods</b>: For this study, we randomly selected 35 patients with AML who had received combined cytarabine plus idarubicin treatment for new-onset AML at our hospital. We performed immunohistochemical analysis of biopsy specimens obtained from the patients to investigate the expressions of 23 treatment-resistance-related proteins, and retrospectively analyzed the correlations between the expression profiles of the resistance proteins and the patient survival. <b>Results</b>: The following four proteins were identified as being particularly significant in relation to treatment resistance and patient prognosis: (1) p53; (2) multidrug resistance-associated protein 1 (MRP1; idarubicin extracellular efflux pump); (3) aldo-keto reductase family 1 member B10 (AKR1B10; idarubicin-inactivating enzyme); and (4) AKR1B1 (competitive inhibitor of AKR1B10). Based on our findings, we propose the following Urayasu classification for AML, which we believe would be very useful for accurately stratifying patients with AML according to the predicted prognosis: Group 1 (<i>n</i> = 22, 63%): p53(-)/MRP1(-) associated with AKR1B10(+)/AKR1B1(+) or AKR1B10(-)/AKR1B1(-); 5-year overall survival (OS), 82%-100%; Group 2 (<i>n</i> = 9, 26%): p53(-)/MRP1(-) associated with AKR1B10(+)/AKR1B1(-); 5-year OS, 68%; Group 3 (<i>n</i> = 4, 11%): p53(+) or MRP1(+); median survival, 12-14 months; 2-year OS, 0%. <b>Conclusions</b>: The Urayasu classification for AML is useful for predicting the prognosis of patients with AML. Group 1 in this classification included twice as many patients as that included in the Favorable prognosis group in the AML prognostic classification proposed by the European Leukemia Net. As the Urayasu classification for AML is based on the mechanisms of resistance to chemotherapy, it is not only useful for prognostic stratification of the patients, but also provides insights for developing more effective treatments for AML.

Journal of Clinical MedicineVol. 14(6)
Juntendo University (JP), Saitama Cancer Center (JP), Juntendo University Urayasu Hospital (JP), Juntendo University Hospital (JP)
Juntendo University
Good health and well-being
Openalex Percentile: Top 93%
Acute Myeloid Leukemia Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.