肿瘤教育血小板（TEP）是用于诊断和监测癌症的液体活检的潜在方法。然而，血小板肿瘤教育的机制尚不清楚，与 TEP 相关的转录本通常不是肿瘤相关转录本。我们证明，肿瘤转录物直接转移到循环血小板不太可能是 TEP 信号的来源。我们使用 CDSeq（一种潜在狄利克雷分配算法）对胶质母细胞瘤患者血液样本中的 TEP 信号进行解卷积。我们证明，血小板转录组中很大一部分转录物源自非血小板细胞，并且使用该算法可以去除污染转录物。此外，我们使用该算法的结果来证明 TEP 代表更活化的血小板的子集，其中还包含通常与非血小板炎症细胞相关的转录本，表明这些炎症细胞可能在肿瘤微环境中将转录本转移到血小板然后在流通中发现。我们的分析提出了一种处理 TEP 转录组数据的有用且有效的方法，以能够分离与特定肿瘤相关的独特 TEP 信号。

Tumor-educated platelets (TEPs) are a potential method of liquid biopsy for the diagnosis and monitoring of cancer. However, the mechanism underlying tumor education of platelets is not known, and transcripts associated with TEPs are often not tumor-associated transcripts. We demonstrated that direct tumor transfer of transcripts to circulating platelets is an unlikely source of the TEP signal. We used CDSeq, a latent Dirichlet allocation algorithm, to deconvolute the TEP signal in blood samples from patients with glioblastoma. We demonstrated that a substantial proportion of transcripts in the platelet transcriptome are derived from non-platelet cells, and the use of this algorithm allows the removal of contaminant transcripts. Furthermore, we used the results of this algorithm to demonstrate that TEPs represent a subset of more activated platelets, which also contain transcripts normally associated with non-platelet inflammatory cells, suggesting that these inflammatory cells, possibly in the tumor microenvironment, transfer transcripts to platelets that are then found in circulation. Our analysis suggests a useful and efficient method of processing TEP transcriptomic data to enable the isolation of a unique TEP signal associated with specific tumors.