Tumor-Educated Platelet RNA for the Detection and (Pseudo)progression Monitoring of Glioblastoma

Cell Reports Medicine

卷:1

Adrienne Vancura¹  Sjors G.J.G. in ‘t Veld²  Joep Killestein³  Cyra Leurs³  Pepijn Schellen³  Sander Idema³  Jihane Tannous³  R. Jonas A. Nilsson³  David P. Noske³  Edward Post³  Ann Hoeben³  Anna C. Navis³  Jip Ramaker⁴  Myron G. Best⁴  Heleen Verschueren⁵  François Rustenburg⁵  Nik Sol⁵  Farrah Mateen⁵  William P.J. Leenders⁵  W. Peter Vandertop⁵  Maud Tjerkstra⁵  Laurine E. Wedekind⁵  Philip C. de Witt Hamer⁵  Bastiaan Moraal⁵  Bauke Ylstra⁵  Jaap C. Reijneveld⁶  Kenn Zwaan⁶  Bakhos A. Tannous⁷  Thomas Wurdinger⁷  Pieter Wesseling⁸ 

[1] Corresponding author;

[2] Department of Neurology, Cancer Center Amsterdam, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands;

[3] Department of Neurosurgery, Cancer Center Amsterdam, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands;

[4] MS Center Amsterdam, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands;

[5] Brain Tumor Center Amsterdam, Cancer Center Amsterdam, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands;

[6] Department of Neurology, Massachusetts General Hospital and Neuroscience Program, Harvard Medical School, Boston, MA, USA;

[7] Department of Pathology, Cancer Center Amsterdam, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands;

关键词: tumor-educated platelets;    blood platelets;    liquid biopsies;    glioblastoma;    machine learning;    swarm intelligence;   

DOI  :  

来源: DOAJ

【 摘 要 】

Summary: Tumor-educated platelets (TEPs) are potential biomarkers for cancer diagnostics. We employ TEP-derived RNA panels, determined by swarm intelligence, to detect and monitor glioblastoma. We assessed specificity by comparing the spliced RNA profile of TEPs from glioblastoma patients with multiple sclerosis and brain metastasis patients (validation series, n = 157; accuracy, 80%; AUC, 0.81 [95% CI, 0.74–0.89; p < 0.001]). Second, analysis of patients with glioblastoma versus asymptomatic healthy controls in an independent validation series (n = 347) provided a detection accuracy of 95% and AUC of 0.97 (95% CI, 0.95–0.99; p < 0.001). Finally, we developed the digitalSWARM algorithm to improve monitoring of glioblastoma progression and demonstrate that the TEP tumor scores of individual glioblastoma patients represent tumor behavior and could be used to distinguish false positive progression from true progression (validation series, n = 20; accuracy, 85%; AUC, 0.86 [95% CI, 0.70–1.00; p < 0.012]). In conclusion, TEPs have potential as a minimally invasive biosource for blood-based diagnostics and monitoring of glioblastoma patients.

【 授权许可】

Unknown