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基因表达分类器ALLCatchR识别B细胞前体所有亚型和潜在的发展轨迹跨年龄

Thomas Beder¹, Björn-Thore Hansen¹, Alina M Hartmann^1,2

¹Medical Department II, Hematology and Oncology, University Hospital Schleswig-Holstein, Kiel, Germany.

|August 30, 2023

概括

机器学习工具ALLCatchR使用RNA-Seq基因表达数据准确分类B细胞前体急性淋巴细胞白血病 (BCP-ALL) 亚型. 这提高了BCP-ALL的诊断能力,为发展轨迹提供了洞察力.

科学领域:

计算生物学和生物信息学
血液学和瘤学血液学和瘤学
分子诊断学分子诊断学

背景情况:

目前的B细胞前体急性淋巴细胞白血病 (BCP-ALL) 分类依赖于基因组驱动因素,但系统的基因表达分析较不先进.
转录组测序 (RNA-Seq) 已建立用于识别驱动异常,但需要基于基因表达的亚型化强大的方法.

研究的目的:

开发ALLCatchR,一种机器学习分类器,用于使用RNA-Seq基因表达数据准确地分类BCP-ALL的分子亚型.
为了使RNA-Seq用于BCP-ALL诊断的常规应用,并为发育轨迹提供新的见解.

主要方法:

开发了一种机器学习分类器ALLCatchR,该分类器基于四个队伍的1869个BCP-ALL转录组配置文件进行训练.
在三个独立的保留队列 (n=1018) 上验证ALLCatchR,评估亚型分配的准确性,敏感性和特异性.
从FACS排序的健康骨髓前代阶段建立了人类B-淋巴体的新型RNA-Seq参考.

主要成果:

在独立队列中,ALLCatchR在亚型分配中实现了95.7%的整体准确性,在83.7%的样本中进行了高可信度预测.
该分类器的性能优于现有工具,并确定了新的驾驶员候选人,只有1.2%的样本未被分类.
将BCP-ALL样本投射到正常的淋巴发育轨迹上揭示了共享的近距离模式,为发育比较提供了一个新的框架.

结论:

ALLCatchR提供了基于RNA-Seq的系统和准确的BCP-ALL分子亚型的方法,增强了诊断能力.
该工具促进了在BCP-ALL诊断中的常规RNA-Seq应用,改善了亚型分配,并为疾病生物学提供了新的见解.
整合一个正常的淋巴发育参考轨迹为理解BCP-ALL发育起源和分类提供了一个框架.

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