Zhang Han, Cheng Hao, Wang Qingqing, Zeng Xianping, Chen Yanfen, Yan Jin, Sun Yanran, Zhao Xiaoxi, Li Weijing, Gao Chao, Gong Wenyu, Li Bei, Zhang Ruidong, Nan Li, Wu Yong, Bao Shilai, Han Jing-Dong J, Zheng Huyong
Beijing Key Laboratory of Pediatric Hematology Oncology, National Key Discipline of Pediatrics, Ministry of Education, Key Laboratory of Major Diseases in Children, Ministry of Education, Hematology Oncology Center, Beijing Children's Hospital, Capital Medical University. Beijing, China.
1] CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences. Shanghai, China [2] Graduate School of Chinese Academy of Sciences, Beijing, China.
Sci Rep. 2015 Jul 21;5:12435. doi: 10.1038/srep12435.
Pediatric acute lymphoblastic leukemia (ALL) is the most common neoplasm and one of the primary causes of death in children. Its treatment is highly dependent on the correct classification of subtype. Previously, we developed a microarray-based subtype classifier based on the relative expression levels of 62 marker genes, which can predict 7 different ALL subtypes with an accuracy as high as 97% in completely independent samples. Because the classifier is based on gene expression rank values rather than actual values, the classifier enables an individualized diagnosis, without the need to reference the background distribution of the marker genes in a large number of other samples, and also enables cross platform application. Here, we demonstrate that the classifier can be extended from a microarray-based technology to a multiplex qPCR-based technology using the same set of marker genes as the advanced fragment analysis (AFA). Compared to microarray assays, the new assay system makes the convenient, low cost and individualized subtype diagnosis of pediatric ALL a reality and is clinically applicable, particularly in developing countries.
小儿急性淋巴细胞白血病（ALL）是儿童最常见的肿瘤，也是儿童主要死因之一。其治疗高度依赖于亚型的正确分类。此前，我们基于62个标记基因的相对表达水平开发了一种基于微阵列的亚型分类器，该分类器在完全独立的样本中能够预测7种不同的ALL亚型，准确率高达97%。由于该分类器基于基因表达秩次值而非实际值，因此无需参考大量其他样本中标记基因的背景分布即可实现个体化诊断，还能实现跨平台应用。在此，我们证明该分类器可以从基于微阵列的技术扩展到基于多重定量PCR的技术，使用与高级片段分析（AFA）相同的一组标记基因。与微阵列检测相比，新的检测系统使小儿ALL便捷、低成本且个体化的亚型诊断成为现实，并且具有临床适用性，尤其在发展中国家。
