白血病患者 H-ARS 严重程度预测基因的验证 - 种间比较、挑战和前景。

Daniel Schwanke, Marco Valente, Patrick Ostheim, Simone Schüle, Laure Bobyk, Michel Drouet, Diane Riccobono, Nicolas Magné, Elisabeth Daguenet, Samantha Jo Stewart, Razan Muhtadi, Matthias Port, Michael Abend
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引用次数: 0

摘要

目的:在之前的一项狒狒研究中,共发现了 29 个用于预测血液病、急性辐射综合征(H-ARS)严重程度的临床结果的基因。其中,有四个基因(FDXR、DDB2、POU2AF1、WNT3)很有希望,并在五名白血病患者中得到了验证。在本研究中,我们试图在更多的全身辐照患者中进一步进行体内验证:在对10名白血病患者进行2-12Gy的全身分次(2Gy/天)辐照(TBI)之前和辐照期间的3天内,抽取他们的外周血。在进行 RNA 分离后,采用 qRT-PCR 方法对广泛用于生物模拟和 H-ARS 预测的 31 个基因的基因表达(GE)进行了评估。定制的低密度阵列(LDA)可同时分析所有基因,96 孔格式进一步检查了四个最有希望的基因。计算了GE相对于辐照前的折叠变化(FC):结果:五名急性淋巴细胞白血病(ALL)患者和一名非霍奇金淋巴瘤(NHL)患者的RNA量以及相应的淋巴细胞和中性粒细胞计数足以进行qRT-PCR,而急性髓细胞白血病(AML)患者和一名骨髓纤维化患者无法提供足够的RNA。一般来说,分离到的 RNA 总量为 1-2 微克,而在暴露前和暴露后的样本中,发现 RNA 数量最多相差 10 倍(相关的抑制基因变化)。在 31 个基因中,23 个基因在至少一个暴露前样本中表达。与辐照前相比,辐照后 48 小时和 72 小时表达基因的数量可能减少一半。利用 LDA,13 个基因在人体样本中得到了验证。其中最有希望的四个基因(参见上图)要么尚未确定,要么与辐照前太过接近。不过,除了无法检测到的 WNT3 外,它们都是用灵敏度更高的 96 孔格式检测的。与之前的研究一样,白血病患者(上调)与狒狒(下调)的 FDXR 的 GE 调节相反,这一点再次得到证实。辐射诱导的 DDB2(上调)和 POU2AF1(下调)的 GE 变化在两个物种中表现相似。因此,在两个物种的23个基因中,16个基因的GE变化方向相同,人类研究中上调的FDXR得到了重新验证:结论:先前在狒狒中发现的用于预测H-ARS严重程度的基因在ALL患者中得到了验证,但在AML患者中未得到验证。与白血病类型有关的局限性、相关的 RNA 量减少、GE 变化受抑制以及方法学上的挑战必须被视为对验证基因总数产生负面影响的因素。在此基础上,我们建议进行更多的控制,包括血细胞计数和基于荧光的 RNA 数量测量,以选择有希望的样本,并使用更灵敏的 96 孔格式来检测基线拷贝数较低的候选基因。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Validation of genes for H-ARS severity prediction in leukemia patients - interspecies comparison, challenges, and promises.

Purpose: In a previous baboon-study, a total of 29 genes were identified for clinical outcome prediction of the hematologic, acute, radiation, syndrome (H-ARS) severity. Among them, four genes (FDXR, DDB2, POU2AF1, WNT3) appeared promising and were validated in five leukemia patients. Within this study, we sought further in-vivo validation in a larger number of whole-body irradiated patients.

Material and methods: Peripheral blood was drawn from 10 leukemia patients before and up to 3 days during a fractionated (2 Gy/day) total-body irradiation (TBI) with 2-12Gy. After RNA-isolation, gene expression (GE) was evaluated on 31 genes widely used in biodosimetry and H-ARS prediction employing qRT-PCR. A customized low-density-array (LDA) allowed simultanously analyzing all genes, the 96-well format further examined the four most promising genes. Fold-changes (FC) in GE relative to pre-irradiation were calculated.

Results: Five patients suffering from acute-lymphoblastic-leukemia (ALL) respectively non-Hodgkin-lymphoma (NHL) revealed sufficient RNA-amounts and corresponding lymphocyte and neutrophile counts for running qRT-PCR, while acute-myeloid-leukemia (AML) and one myelofibrosis patient could not supply enough RNA. Generally, 1-2µg total RNA was isolated, whereas up to 10-fold differences in RNA-quantities (associated suppressed GE-changes) were identified among pre-exposure and exposure samples. From 31 genes, 23 were expressed in at least one of the pre-exposure samples. Relative to pre-exposure, the number of expressed genes could halve at 48 and 72h after irradiation. Using the LDA, 13 genes were validated in human samples. The four most promising genes (vid. sup.) were either undetermined or too close to pre-exposure. However, they were measured using the more sensitive 96-well format, except WNT3, which wasn´t detectable. As in previous studies, an opposite regulation in GE for FDXR in leukemia patients (up-regulated) relative to baboons (down-regulated) was reconfirmed. Radiation-induced GE-changes of DDB2 (up-regulated) and POU2AF1 (down-regulated) behaved similarly in both species. Hence, 16 out of 23 genes of two species showed GE-changes in the same direction, and up-regulated FDXR as in human studies were revalidated.

Conclusion: Identified genes for H-ARS severity prediction, previously detected in baboons, were validated in ALL but not in AML patients. Limitations related to leukemia type, associated reduced RNA amounts, suppressed GE changes, and methodological challenges must be considered as factors negatively affecting the total number of validated genes. Based on that, we propose additional controls including blood cell counts and preferably fluorescence-based RNA quantity measurements for selecting promising samples and using a more sensitive 96-well format for candidate genes with low baseline copy numbers.

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