Cargando…
Effective Prognostic Model for Therapy Response Prediction in Acute Myeloid Leukemia Patients
SIMPLE SUMMARY: Acute myeloid leukemia (AML) is a hematopoietic disorder characterized by the malignant transformation and abnormal proliferation of bone marrow-derived myeloid progenitors. To select the optimal treatment regimens and predict the therapy response in AML patients, stratification into...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10455213/ https://www.ncbi.nlm.nih.gov/pubmed/37623484 http://dx.doi.org/10.3390/jpm13081234 |
_version_ | 1785096398716272640 |
---|---|
author | Kolesnikova, Maria A. Sen’kova, Aleksandra V. Pospelova, Tatiana I. Zenkova, Marina A. |
author_facet | Kolesnikova, Maria A. Sen’kova, Aleksandra V. Pospelova, Tatiana I. Zenkova, Marina A. |
author_sort | Kolesnikova, Maria A. |
collection | PubMed |
description | SIMPLE SUMMARY: Acute myeloid leukemia (AML) is a hematopoietic disorder characterized by the malignant transformation and abnormal proliferation of bone marrow-derived myeloid progenitors. To select the optimal treatment regimens and predict the therapy response in AML patients, stratification into risk groups based mostly on genetic factors is carried out. Despite this contemporary approach, tumor cell resistance to chemotherapeutic drugs represents one of the main obstacles for improving survival outcomes in AML. In the present study, a new prognostic scale for risk stratification of AML patients based on the drug responsiveness of tumor cells detected in vitro as well as MDR1 mRNA/P-glycoprotein expression, tumor origin (primary or secondary), cytogenetic abnormalities, and aberrant immunophenotype was developed. Using correlation, ROC, and Cox regression analyses, we demonstrated that the risk stratification of AML patients in accordance with the developed prognostic scale correlates well with the response to therapy and represents an independent predictive factor for the overall survival of patients with newly diagnosed AML. ABSTRACT: Acute myeloid leukemia (AML) is a hematopoietic disorder characterized by the malignant transformation of bone marrow-derived myeloid progenitor cells with extremely short survival. To select the optimal treatment options and predict the response to therapy, the stratification of AML patients into risk groups based on genetic factors along with clinical characteristics is carried out. Despite this thorough approach, the therapy response and disease outcome for a particular patient with AML depends on several patient- and tumor-associated factors. Among these, tumor cell resistance to chemotherapeutic agents represents one of the main obstacles for improving survival outcomes in AML patients. In our study, a new prognostic scale for the risk stratification of AML patients based on the detection of the sensitivity or resistance of tumor cells to chemotherapeutic drugs in vitro as well as MDR1 mRNA/P-glycoprotein expression, tumor origin (primary or secondary), cytogenetic abnormalities, and aberrant immunophenotype was developed. This study included 53 patients diagnosed with AML. Patients who received intensive or non-intensive induction therapy were analyzed separately. Using correlation, ROC, and Cox regression analyses, we show that the risk stratification of AML patients in accordance with the developed prognostic scale correlates well with the response to therapy and represents an independent predictive factor for the overall survival of patients with newly diagnosed AML. |
format | Online Article Text |
id | pubmed-10455213 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104552132023-08-26 Effective Prognostic Model for Therapy Response Prediction in Acute Myeloid Leukemia Patients Kolesnikova, Maria A. Sen’kova, Aleksandra V. Pospelova, Tatiana I. Zenkova, Marina A. J Pers Med Article SIMPLE SUMMARY: Acute myeloid leukemia (AML) is a hematopoietic disorder characterized by the malignant transformation and abnormal proliferation of bone marrow-derived myeloid progenitors. To select the optimal treatment regimens and predict the therapy response in AML patients, stratification into risk groups based mostly on genetic factors is carried out. Despite this contemporary approach, tumor cell resistance to chemotherapeutic drugs represents one of the main obstacles for improving survival outcomes in AML. In the present study, a new prognostic scale for risk stratification of AML patients based on the drug responsiveness of tumor cells detected in vitro as well as MDR1 mRNA/P-glycoprotein expression, tumor origin (primary or secondary), cytogenetic abnormalities, and aberrant immunophenotype was developed. Using correlation, ROC, and Cox regression analyses, we demonstrated that the risk stratification of AML patients in accordance with the developed prognostic scale correlates well with the response to therapy and represents an independent predictive factor for the overall survival of patients with newly diagnosed AML. ABSTRACT: Acute myeloid leukemia (AML) is a hematopoietic disorder characterized by the malignant transformation of bone marrow-derived myeloid progenitor cells with extremely short survival. To select the optimal treatment options and predict the response to therapy, the stratification of AML patients into risk groups based on genetic factors along with clinical characteristics is carried out. Despite this thorough approach, the therapy response and disease outcome for a particular patient with AML depends on several patient- and tumor-associated factors. Among these, tumor cell resistance to chemotherapeutic agents represents one of the main obstacles for improving survival outcomes in AML patients. In our study, a new prognostic scale for the risk stratification of AML patients based on the detection of the sensitivity or resistance of tumor cells to chemotherapeutic drugs in vitro as well as MDR1 mRNA/P-glycoprotein expression, tumor origin (primary or secondary), cytogenetic abnormalities, and aberrant immunophenotype was developed. This study included 53 patients diagnosed with AML. Patients who received intensive or non-intensive induction therapy were analyzed separately. Using correlation, ROC, and Cox regression analyses, we show that the risk stratification of AML patients in accordance with the developed prognostic scale correlates well with the response to therapy and represents an independent predictive factor for the overall survival of patients with newly diagnosed AML. MDPI 2023-08-07 /pmc/articles/PMC10455213/ /pubmed/37623484 http://dx.doi.org/10.3390/jpm13081234 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kolesnikova, Maria A. Sen’kova, Aleksandra V. Pospelova, Tatiana I. Zenkova, Marina A. Effective Prognostic Model for Therapy Response Prediction in Acute Myeloid Leukemia Patients |
title | Effective Prognostic Model for Therapy Response Prediction in Acute Myeloid Leukemia Patients |
title_full | Effective Prognostic Model for Therapy Response Prediction in Acute Myeloid Leukemia Patients |
title_fullStr | Effective Prognostic Model for Therapy Response Prediction in Acute Myeloid Leukemia Patients |
title_full_unstemmed | Effective Prognostic Model for Therapy Response Prediction in Acute Myeloid Leukemia Patients |
title_short | Effective Prognostic Model for Therapy Response Prediction in Acute Myeloid Leukemia Patients |
title_sort | effective prognostic model for therapy response prediction in acute myeloid leukemia patients |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10455213/ https://www.ncbi.nlm.nih.gov/pubmed/37623484 http://dx.doi.org/10.3390/jpm13081234 |
work_keys_str_mv | AT kolesnikovamariaa effectiveprognosticmodelfortherapyresponsepredictioninacutemyeloidleukemiapatients AT senkovaaleksandrav effectiveprognosticmodelfortherapyresponsepredictioninacutemyeloidleukemiapatients AT pospelovatatianai effectiveprognosticmodelfortherapyresponsepredictioninacutemyeloidleukemiapatients AT zenkovamarinaa effectiveprognosticmodelfortherapyresponsepredictioninacutemyeloidleukemiapatients |