Background/Objectives:Massively parallel sequencing technologies have advanced chronic lymphocytic leukemia (CLL) diagnostics and precision oncology. Illumina platforms, while offering robust performance, require substantial infrastructure investment and a large number of samples for cost-efficiency. Conversely, third-generation long-read nanopore sequencing from Oxford Nanopore Technologies (ONT) can significantly reduce sequencing costs, making it a valuable tool in resource-limited settings. However, nanopore sequencing faces challenges with lower accuracy and throughput than Illumina platforms, necessitating additional computational strategies. In this paper, we demonstrate that integrating publicly available short-read data with in-house generated ONT data, along with the application of machine learning approaches, enables the characterization of the CLL transcriptome landscape, the identification of clinically relevant molecular subtypes, and the assignment of these subtypes to nanopore-sequenced samples.Methods:Public Illumina RNA sequencing data for 608 CLL samples were obtained from the CLL-Map Portal. CLL transcriptome analysis, gene module identification, and transcriptomic subtype classification were performed using the oposSOM R package for high-dimensional data visualization with self-organizing maps. Eight CLL patients were recruited from the Hematology Center After Prof. R. Yeolyan (Yerevan, Armenia). Sequencing libraries were prepared from blood total RNA using the PCR-cDNA sequencing-barcoding kit (SQK-PCB109) following the manufacturer’s protocol and sequenced on an R9.4.1 flow cell for 24–48 h. Raw reads were converted to TPM values. These data were projected into the SOMs space using the supervised SOMs portrayal (supSOM) approach to predict the SOMs portrait of new samples using support vector machine regression.Results:The CLL transcriptomic landscape reveals disruptions in gene modules (spots) associated with T cell cytotoxicity, B and T cell activation, inflammation, cell cycle, DNA repair, proliferation, and splicing. A specific gene module contained genes associated with poor prognosis in CLL. Accordingly, CLL samples were classified into T-cell cytotoxic, immune, proliferative, splicing, and three mixed types: proliferative–immune, proliferative–splicing, and proliferative–immune–splicing. These transcriptomic subtypes were associated with survival orthogonal to gender and mutation status. Using supervised machine learning approaches, transcriptomic subtypes were assigned to patient samples sequenced with nanopore sequencing.Conclusions:This study demonstrates that the CLL transcriptome landscape can be parsed into functional modules, revealing distinct molecular subtypes based on proliferative and immune activity, with important implications for prognosis and treatment that are orthogonal to other molecular classifications. Additionally, the integration of nanopore sequencing with public datasets and machine learning offers a cost-effective approach to molecular subtyping and prognostic prediction, facilitating more accessible and personalized CLL care.
背景/目的：大规模并行测序技术推动了慢性淋巴细胞白血病（CLL）诊断与精准肿瘤学的发展。Illumina平台虽性能稳健，但需大量基础设施投入及样本数量以实现成本效益。相比之下，牛津纳米孔技术公司（ONT）的第三代长读长纳米孔测序可显著降低测序成本，使其成为资源有限环境中的重要工具。然而，纳米孔测序在准确性和通量方面较Illumina平台存在不足，需借助额外计算策略。本研究证明，通过整合公开可用的短读长数据与内部生成的ONT数据，并应用机器学习方法，可实现CLL转录组图谱表征、临床相关分子亚型鉴定，并将这些亚型分配至纳米孔测序样本。 方法：从CLL-Map门户获取608例CLL样本的公共Illumina RNA测序数据。使用oposSOM R软件包通过自组织映射进行高维数据可视化，完成CLL转录组分析、基因模块识别及转录组亚型分类。从亚美尼亚埃里温R. Yeolyan教授血液学中心招募8例CLL患者。按照制造商协议，使用PCR-cDNA测序条形码试剂盒（SQK-PCB109）从血液总RNA制备测序文库，在R9.4.1流通池上测序24-48小时。原始读数转换为TPM值，通过监督式SOM表征方法，利用支持向量机回归将数据投影至SOM空间，以预测新样本的SOM图谱。 结果：CLL转录组图谱揭示了与T细胞毒性、B/T细胞活化、炎症、细胞周期、DNA修复、增殖及剪接相关基因模块（热点）的紊乱。特定基因模块包含与CLL不良预后相关的基因。据此，CLL样本被分为T细胞毒性型、免疫型、增殖型、剪接型及三种混合型（增殖-免疫型、增殖-剪接型、增殖-免疫-剪接型）。这些转录组亚型与患者生存期相关，且独立于性别和突变状态。通过监督机器学习方法，将转录组亚型分配至纳米孔测序的患者样本。 结论：本研究证实CLL转录组图谱可解析为功能模块，基于增殖和免疫活性揭示 distinct 分子亚型，对预后和治疗具有重要启示，且独立于其他分子分类。此外，纳米孔测序与公共数据集及机器学习的整合，为分子分型和预后预测提供了经济高效的方法，有助于实现更可及、个性化的CLL诊疗。
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