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Deconvolution of the tumor-educated platelet transcriptome reveals activated platelet and inflammatory cell transcript signatures
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
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
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Research Article Oncology

Deconvolution of the tumor-educated platelet transcriptome reveals activated platelet and inflammatory cell transcript signatures

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Abstract

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.

Authors

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

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