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 nonplatelet 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 nonplatelet 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.
Jerome M. Karp, Aram S. Modrek, Ravesanker Ezhilarasan, Ze-Yan Zhang, Yingwen Ding, Melanie Graciani, Ali Sahimi, Michele Silvestro, Ting Chen, Shuai Li, Kwok-Kin Wong, Bhama Ramkhelawon, Krishna P.L. Bhat, Erik P. Sulman
Usage data is cumulative from February 2025 through February 2026.
| Usage | JCI | PMC |
|---|---|---|
| Text version | 1,645 | 289 |
| 162 | 50 | |
| Figure | 741 | 0 |
| Table | 32 | 0 |
| Supplemental data | 140 | 17 |
| Citation downloads | 88 | 0 |
| Totals | 2,808 | 356 |
| Total Views | 3,164 | |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.