Cancer Cell
Volume 40, Issue 9, 12 September 2022, Pages 999-1009.e6
Journal home page for Cancer Cell

Article
Detection and localization of early- and late-stage cancers using platelet RNA

https://doi.org/10.1016/j.ccell.2022.08.006Get rights and content
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open access

Highlights

  • Eighteen tumor types are identified by blood platelet RNA analysis with high specificity

  • Tumor-type-associated platelet RNA profiles allow for tumor-site-of-origin analysis

  • Platelets may be educated by multiple locations of tumor activity

  • Platelet RNAs may complement the field of liquid biopsies

Summary

Cancer patients benefit from early tumor detection since treatment outcomes are more favorable for less advanced cancers. Platelets are involved in cancer progression and are considered a promising biosource for cancer detection, as they alter their RNA content upon local and systemic cues. We show that tumor-educated platelet (TEP) RNA-based blood tests enable the detection of 18 cancer types. With 99% specificity in asymptomatic controls, thromboSeq correctly detected the presence of cancer in two-thirds of 1,096 blood samples from stage I–IV cancer patients and in half of 352 stage I–III tumors. Symptomatic controls, including inflammatory and cardiovascular diseases, and benign tumors had increased false-positive test results with an average specificity of 78%. Moreover, thromboSeq determined the tumor site of origin in five different tumor types correctly in over 80% of the cancer patients. These results highlight the potential properties of TEP-derived RNA panels to supplement current approaches for blood-based cancer screening.

Keywords

blood platelets
TEP
cancer
early detection
liquid biopsy
RNA
blood

Data and code availability

  • The raw sequencing data FASTQ-files are deposited in the NCBI GEO database under accession number GEO: GSE183635 and is publicly available as of the date of publication. Within this repository, a count table that served as input for the analyses is available as ‘TEP_Count_Matrix.RData’.

  • The code used to generate the thromboSeq algorithms including the thromboSeq dry-lab pipeline and a code reproducing the main manuscripts’ figures is available via GitHub (https://github.com/MyronBest/thromboSeq_source_code_v1.5 and https://github.com/MyronBest/InTVeld_Pancancer_TSOO), is available as of the date of publication, and is for research purposes only.

  • Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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Senior authors

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These authors contributed equally

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Lead contact