Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets
Best, Myron G.; Sol, Nik; In ‘t Veld, Sjors G.J.G.; Vancura, Adrienne; Muller, Mirte; Niemeijer, Anna Larissa N.; Fejes, Aniko V.; Tjon Kon Fat, Lee Ann; Huis in 't Veld, Anna E; Leurs, Cyra; Le Large, Tessa Y.; Meijer, Laura L.; Kooi, Irsan E.; Rustenburg, François; Schellen, Pepijn; Verschueren, Heleen; Post, Edward; Wedekind, Laurine E.; Bracht, Jillian; Esenkbrink, Michelle; Wils, Leon; Favaro, Francesca; Schoonhoven, Jilian D.; Tannous, Jihane; Meijers-Heijboer, Hanne; Kazemier, Geert; Giovannetti, Elisa; Reijneveld, Jaap C.; Idema, Sander; Killestein, Joep; Heger, Michal; de Jager, Saskia C.; Urbanus, Rolf T.; Hoefer, Imo E.; Pasterkamp, Gerard; Mannhalter, Christine; Gomez-Arroyo, Jose; Bogaard, Harm-Jan; Noske, David P.; Vandertop, W. Peter; van den Broek, Daan; Ylstra, Bauke; Nilsson, R. Jonas A; Wesseling, Pieter; Karachaliou, Niki; Rosell, Rafael; Lee-Lewandrowski, Elizabeth; Lewandrowski, Kent B.; Tannous, Bakhos A.; de Langen, Adrianus J.; Smit, Egbert F.; van den Heuvel, Michel M; Wurdinger, Thomas
(2017) Cancer Cell, volume 32, issue 2, pp. 238 - 252.e9
(Article)
Abstract
Blood-based liquid biopsies, including tumor-educated blood platelets (TEPs), have emerged as promising biomarker sources for non-invasive detection of cancer. Here we demonstrate that particle-swarm optimization (PSO)-enhanced algorithms enable efficient selection of RNA biomarker panels from platelet RNA-sequencing libraries (n = 779). This resulted in accurate TEP-based detection of early- and
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late-stage non-small-cell lung cancer (n = 518 late-stage validation cohort, accuracy, 88%; AUC, 0.94; 95% CI, 0.92–0.96; p < 0.001; n = 106 early-stage validation cohort, accuracy, 81%; AUC, 0.89; 95% CI, 0.83–0.95; p < 0.001), independent of age of the individuals, smoking habits, whole-blood storage time, and various inflammatory conditions. PSO enabled selection of gene panels to diagnose cancer from TEPs, suggesting that swarm intelligence may also benefit the optimization of diagnostics readout of other liquid biopsy biosources.
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Keywords: blood platelets, cancer diagnostics, liquid biopsies, NSCLC, particle-swarm optimization, RNA, self-learning algorithms, splicing, swarm intelligence, tumor-educated platelets, Oncology, Cell Biology, Cancer Research
ISSN: 1535-6108
Publisher: Cell Press
Note: Funding Information: Financial support was provided by European Research Council E8626 (R.J.A.N., E.F.S., and T.W.) and 336540 (T.W.), the Dutch Organisation of Scientific Research 93612003 and 91711366 (T.W.), the Dutch Cancer Society?(T.W. and H.M.H.), BMS IION (M.M.v.d.H. and T.W.), Stichting STOPhersentumoren.nl (M.G.B., P.W., and T.W.), the KNAW Van Walree stichting (M.G.B.), the NIH/NCI CA176359 and CA069246 (B.A.T.), CFF Norrland (R.J.A.N.), and Swedish Research Council (R.J.A.N.). We are thankful to Esther Drees, Thomas Kuilman, Oscar Krijgsman, Daniel S. Peeper, Dirk van Essen, Paul Eijk, Reno Bladergroen, Jan P.C. Lutgerink, the collaborators and team of the Cancer Pharmacology Lab, AIRC Start-Up Unit, Pisa, the NKI-AVL Core Facility Molecular Pathology and Biobanking (CFMPB) for supplying NKI-AVL Biobank material and lab support, Sebastiaan van de Sand (SIT B.V.) for computational resources, and Henk M. Verheul for continuous support. T.W. and R.J.A.N. received funding from Illumina and are shareholders of GRAIL, Inc. Publisher Copyright: © 2017 The Authors
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