Li Lusheng, Xiao Hanyu, Wu Xinchao, Tang Zhenya, Khoury Joseph D, Wang Jieqiong, Wan Shibiao
Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, USA.
Department of Pathology, Microbiology and Immunology, University of Nebraska Medical Center, Omaha, NE, USA.
bioRxiv. 2025 Jan 2:2024.09.24.614777. doi: 10.1101/2024.09.24.614777.
As the most common pediatric malignancy, B-cell acute lymphoblastic leukemia (B-ALL) has multiple distinct subtypes characterized by recurrent and sporadic somatic and germline genetic alterations. Identifying B-ALL subtypes can facilitate risk stratification and enable tailored therapeutic design. Existing methods for B-ALL subtyping primarily depend on immunophenotyping, cytogenetic tests and genomic profiling, which would be costly, complicated, and laborious. To overcome these challenges, we present (an ensemble dom projection-based model for identifying subtypes), an accurate and cost-effective model for B-ALL subtype identification. By leveraging random projection (RP) and ensemble learning, RanBALL can preserve patient-to-patient distances after dimension reduction and yield robustly accurate classification performance for B-ALL subtyping. Benchmarking results based on > 1700 B-ALL patients demonstrated that RanBALL achieved remarkable performance (accuracy: 0.93, F1-score: 0.93, and Matthews correlation coefficient: 0.93), significantly outperforming state-of-the-art methods like ALLSorts in terms of all performance metrics. In addition, RanBALL performs better than tSNE in terms of visualizing B-ALL subtype information. We believe RanBALL will facilitate the discovery of B-ALL subtype-specific marker genes and therapeutic targets to have consequential positive impacts on downstream risk stratification and tailored treatment design. To extend its applicability and impacts, a Python-based RanBALL package is available at https://github.com/wan-mlab/RanBALL.
作为最常见的儿童恶性肿瘤，B细胞急性淋巴细胞白血病（B-ALL）有多种不同的亚型，其特征为反复出现和散发的体细胞及种系基因改变。识别B-ALL亚型有助于风险分层，并能实现量身定制的治疗方案设计。现有的B-ALL亚型分类方法主要依赖免疫表型分析、细胞遗传学检测和基因组分析，这些方法成本高、操作复杂且费力。为了克服这些挑战，我们提出了RanBALL（一种基于集成随机投影的B-ALL亚型识别模型），这是一种用于B-ALL亚型识别的准确且经济高效的模型。通过利用随机投影（RP）和集成学习，RanBALL在降维后能够保留患者之间的距离，并在B-ALL亚型分类中产生稳健准确的分类性能。基于1700多名B-ALL患者的基准测试结果表明，RanBALL取得了卓越的性能（准确率：0.93，F1分数：0.93，马修斯相关系数：0.93），在所有性能指标方面均显著优于ALLSorts等现有最佳方法。此外，在可视化B-ALL亚型信息方面，RanBALL比tSNE表现更好。我们相信RanBALL将有助于发现B-ALL亚型特异性标记基因和治疗靶点，对下游风险分层和量身定制的治疗设计产生积极影响。为了扩展其适用性和影响力，可在https://github.com/wan-mlab/RanBALL获取基于Python的RanBALL软件包。
