TY - JOUR
T1 - Tumor-Educated Platelet RNA for the Detection and (Pseudo)progression Monitoring of Glioblastoma
AU - Sol, Nik
AU - in ‘t Veld, Sjors G. J. G.
AU - Vancura, Adrienne
AU - Tjerkstra, Maud
AU - Leurs, Cyra
AU - Rustenburg, François
AU - Schellen, Pepijn
AU - Verschueren, Heleen
AU - Post, Edward
AU - Zwaan, Kenn
AU - Ramaker, Jip
AU - Wedekind, Laurine E.
AU - Tannous, Jihane
AU - Ylstra, Bauke
AU - Killestein, Joep
AU - Mateen, Farrah
AU - Idema, Sander
AU - de Witt Hamer, Philip C.
AU - Navis, Anna C.
AU - Leenders, William P. J.
AU - Hoeben, Ann
AU - Moraal, Bastiaan
AU - Noske, David P.
AU - Vandertop, W. Peter
AU - Nilsson, R. Jonas A.
AU - Tannous, Bakhos A.
AU - Wesseling, Pieter
AU - Reijneveld, Jaap C.
AU - Best, Myron G.
AU - Wurdinger, Thomas
N1 - © 2020 The Authors.
PY - 2020/10/20
Y1 - 2020/10/20
N2 - 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.
AB - 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.
KW - blood platelets
KW - glioblastoma
KW - liquid biopsies
KW - machine learning
KW - swarm intelligence
KW - tumor-educated platelets
UR - http://www.scopus.com/inward/record.url?scp=85096622064&partnerID=8YFLogxK
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85096622064&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/33103128
U2 - https://doi.org/10.1016/j.xcrm.2020.100101
DO - https://doi.org/10.1016/j.xcrm.2020.100101
M3 - Article
C2 - 33103128
SN - 2666-3791
VL - 1
SP - 100101
JO - Cell Reports Medicine
JF - Cell Reports Medicine
IS - 7
M1 - 100101
ER -