关于急性髓性白血病的临床结果和特征的回顾性研究:同一风险组的不同结果
Fanqiao Meng1, Maoyuan Xiang1, Huan Xie1
1Department of Hematology, Daping Hospital, The Third Military Medical University, Chongqing, China.
PeerJ
|December 10, 2025
概括
2022年欧洲白血病网络 (ELN) 对急性髓性白血病 (AML) 的指导方针显示,预测存活率存在局限性. 为了更好的患者预后,需要采用包含KIT等额外突变的精细模型.
科学领域:
- 血液学 血液学 血液学
- 在瘤学瘤学.
- 遗传学 是一个遗传学.
背景情况:
- 尽管分子风险分层化,急性髓性白血病 (AML) 呈现出异质的预后.
- 2022年欧洲白血病网络 (ELN) 的指导方针旨在完善AML风险分类.
- 验证2022年ELN指南在预测各种遗传特征的生存率方面的准确性至关重要.
研究的目的:
- 评估2022年ELN风险分类在新诊断的AML患者队列中的预后准确性.
- 确定影响AML生存结果的关键遗传突变和临床因素.
- 探索当前风险分层的局限性,并为个性化预后提出改进建议.
主要方法:
- 对154名新诊断的AML患者进行了回顾性分析.
- 整合基于下一代测序 (NGS) 的基因分析.
- 遗传数据的相关性,2022年ELN风险分类和临床结果 (总体存活率和无进展存活率).
主要成果:
- 经常发生的突变包括FLT3,DNMT3A,NPM1,CEBPA和TET2.2.
- 血造干细胞移植 (HSCT),女性性别,年龄<60岁,和正常的型是有利的预后因素.
- TP53和KIT突变表明生存率较低,而NPM1和CEBPA突变预测了更长的生存时间. 在2022年ELN风险组内观察到显著的生存异质性,特别是FLT3,CEBPA或TET2突变.
结论:
- 2022年ELN风险分类在预后方面存在局限性,特别是对于具有特定突变 (FLT3,CEBPA,TET2) 的患者.
- 改进的风险模型,包括额外的分子标记 (例如,KIT) 和突变相互作用,是加强个性化预后的必要条件.
- 在确定患者的预后时,应考虑基因共存.
更多相关视频
09:01Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up
Published on: March 26, 2018
14.6K
08:31Murine Model of Leukemia Relapse to Induction Chemotherapy for Acute Lymphoblastic Leukemia
Published on: October 17, 2025
530
相关概念视频
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
380
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
380
01:20Comparing the Survival Analysis of Two or More Groups
533
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
533
01:21Cancer Survival Analysis
629
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
629
01:24Kaplan-Meier Approach
536
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
536
01:20Actuarial Approach
276
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
276
01:15Assumptions of Survival Analysis
382
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
382