Han Dong-Jin, Kim Sunmin, Lee Seo-Young, Kang Su Jung, Moon Youngbeen, Kim Hoon Seok, Kim Myungshin, Kim Tae-Min
Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Front Cell Dev Biol. 2024 Mar 7;12:1345660. doi: 10.3389/fcell.2024.1345660. eCollection 2024.
Previous studies have reported that genes highly expressed in leukemic stem cells (LSC) may dictate the survival probability of patients and expression-based cellular deconvolution may be informative in forecasting prognosis. However, whether the prognosis of acute myeloid leukemia (AML) can be predicted using gene expression and deconvoluted cellular abundances is debatable. Nine different cell-type abundances of a training set composed of the AML samples of 422 patients, were used to build a model for predicting prognosis by least absolute shrinkage and selection operator Cox regression. This model was validated in two different validation sets, TCGA-LAML and Beat AML ( = 179 and 451, respectively). We introduce a new prognosis predicting model for AML called the LSC activity (LSCA) score, which incorporates the abundance of 5 cell types, granulocyte-monocyte progenitors, common myeloid progenitors, CD45RA + cells, megakaryocyte-erythrocyte progenitors, and multipotent progenitors. Overall survival probabilities between the high and low LSCA score groups were significantly different in TCGA-LAML and Beat AML cohorts (log-rank -value = and , respectively). Also, multivariate Cox regression analysis on these two validation sets shows that LSCA score is independent prognostic factor when considering age, sex, and cytogenetic risk (hazard ratio, HR = 2.17; 95% CI 1.40-3.34; < 0.001 and HR = 1.20; 95% CI 1.02-1.43; < 0.03, respectively). The performance of the LSCA score was comparable to other prognostic models, LSC17, APS, and CTC scores, as indicated by the area under the curve. Gene set variation analysis with six LSC-related functional gene sets indicated that high and low LSCA scores are associated with upregulated and downregulated genes in LSCs. We have developed a new prognosis prediction scoring system for AML patients, the LSCA score, which uses deconvoluted cell-type abundance only.
先前的研究报告称，在白血病干细胞（LSC）中高表达的基因可能决定患者的生存概率，基于表达的细胞反卷积在预测预后方面可能具有参考价值。然而，能否使用基因表达和反卷积后的细胞丰度来预测急性髓系白血病（AML）的预后仍存在争议。利用由422例患者的AML样本组成的训练集的9种不同细胞类型丰度，通过最小绝对收缩和选择算子Cox回归建立了一个预测预后的模型。该模型在两个不同的验证集TCGA-LAML和Beat AML（分别为179例和451例）中进行了验证。我们引入了一种新的AML预后预测模型，称为LSC活性（LSCA）评分，该评分纳入了5种细胞类型的丰度，即粒细胞-单核细胞祖细胞、普通髓系祖细胞、CD45RA+细胞、巨核细胞-红细胞祖细胞和多能祖细胞。在TCGA-LAML和Beat AML队列中，高LSCA评分组和低LSCA评分组的总生存概率有显著差异（对数秩检验P值分别为 和 ）。此外，对这两个验证集进行的多变量Cox回归分析表明，在考虑年龄、性别和细胞遗传学风险时，LSCA评分是独立的预后因素（风险比，HR = 2.17；95%可信区间1.40-3.34；P < 0.001和HR = 1.20；95%可信区间1.02-1.43；P < 0.03）。曲线下面积表明，LSCA评分的性能与其他预后模型LSC17、APS和CTC评分相当。对六个与LSC相关的功能基因集进行基因集变异分析表明，高LSCA评分和低LSCA评分分别与LSC中上调和下调的基因相关。我们为AML患者开发了一种新的预后预测评分系统，即LSCA评分，该评分仅使用反卷积后的细胞类型丰度。
