Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets

Myron G Best; Nik Sol; Sjors G J G In 't Veld; Adrienne Vancura; Mirte Muller; Anna-Larissa N Niemeijer; Aniko V Fejes; Lee-Ann Tjon Kon Fat; Anna E Huis In 't Veld; Cyra Leurs; Tessa Y Le Large; Laura L Meijer; Irsan E Kooi; François Rustenburg; Pepijn Schellen; Heleen Verschueren; Edward Post; Laurine E Wedekind; Jillian Bracht; Michelle Esenkbrink; Leon Wils; Francesca Favaro; Jilian D Schoonhoven; Jihane Tannous; Hanne Meijers-Heijboer; Geert Kazemier; Elisa Giovannetti; Jaap C Reijneveld; Sander Idema; Joep Killestein; Michal Heger; Saskia C de Jager; Rolf T Urbanus; Imo E Hoefer; Gerard Pasterkamp; Christine Mannhalter; Jose Gomez-Arroyo; Harm-Jan Bogaard; David P Noske; W Peter Vandertop; Daan van den Broek; Bauke Ylstra; R Jonas A Nilsson; Pieter Wesseling; Niki Karachaliou; Rafael Rosell; Elizabeth Lee-Lewandrowski; Kent B Lewandrowski; Bakhos A Tannous; Adrianus J de Langen; Egbert F Smit; Michel M van den Heuvel; Thomas Wurdinger

doi:10.1016/j.ccell.2017.07.004

Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets

Myron G Best
, Nik Sol
, Sjors G J G In 't Veld
, Adrienne Vancura
, Mirte Muller
, Anna-Larissa N Niemeijer
, Aniko V Fejes
, Lee-Ann Tjon Kon Fat
, Anna E Huis In 't Veld
, Cyra Leurs
, Tessa Y Le Large
, Laura L Meijer
, Irsan E Kooi
, François Rustenburg
, Pepijn Schellen
, Heleen Verschueren
, Edward Post
, Laurine E Wedekind
, Jillian Bracht
, Michelle Esenkbrink

Leon Wils, Francesca Favaro, Jilian D Schoonhoven, Jihane Tannous, Hanne Meijers-Heijboer, Geert Kazemier, Elisa Giovannetti, Jaap C Reijneveld, Sander Idema, Joep Killestein, Michal Heger, Saskia C de Jager, Rolf T Urbanus, Imo E Hoefer, Gerard Pasterkamp, Christine Mannhalter, Jose Gomez-Arroyo, Harm-Jan Bogaard, David P Noske, W Peter Vandertop, Daan van den Broek, Bauke Ylstra, R Jonas A Nilsson, Pieter Wesseling, Niki Karachaliou, Rafael Rosell, Elizabeth Lee-Lewandrowski, Kent B Lewandrowski, Bakhos A Tannous, Adrianus J de Langen, Egbert F Smit, Michel M van den Heuvel, Thomas Wurdinger

Amsterdam UMC - Vrije Universiteit Amsterdam
VU University Medical Center
The Netherlands Cancer Institute–Antoni van Leeuwenhoek Hospital
Umeå University
Harvard Medical School Boston: Massachusetts General Hospital
Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, the Netherlands; Department of Medical Microbiology, University Medical Center Utrecht, Utrecht University, the Netherlands.
Medical University of Vienna
Quirón Dexeus University Hospital
Radboud University Medical Center, Radboud Institute for Health Sciences
Vrije Universiteit Amsterdam
Antoni van Leeuwenhoek Hospital
thromboDx B.V.
Harvard University
University of Amsterdam
Utrecht University
Pangaea Biotech
Autonomous University of Barcelona
Molecular Oncology Research (MORe) Foundation
Radboud University Nijmegen

Research output: Contribution to journal › Article › Academic › peer-review

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 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.

Original language	English
Pages (from-to)	238-252.e9
Journal	Cancer cell
Volume	32
Issue number	2
DOIs	https://doi.org/10.1016/j.ccell.2017.07.004
Publication status	Published - 14 Aug 2017

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Adult
Aged
Aged, 80 and over
Algorithms
Artificial Intelligence
Biomarkers, Tumor
Blood Platelets/physiology
Carcinoma, Non-Small-Cell Lung/blood
Cohort Studies
Diagnosis, Computer-Assisted/methods
Female
Gene Expression Profiling
High-Throughput Nucleotide Sequencing
Humans
Inflammation/blood
Lung Neoplasms/blood
Male
Middle Aged
Support Vector Machine
blood platelets
cancer diagnostics
liquid biopsies
NSCLC
particle-swarm optimization
RNA
self-learning algorithms
splicing
swarm intelligence
tumor-educated platelets

Access to Document

10.1016/j.ccell.2017.07.004

Cite this

Best, M. G., Sol, N., In 't Veld, S. G. J. G., Vancura, A., Muller, M., Niemeijer, A.-L. N., Fejes, A. V., Tjon Kon Fat, L.-A., Huis In 't Veld, A. E., Leurs, C., Le Large, T. Y., Meijer, L. L., Kooi, I. E., Rustenburg, F., Schellen, P., Verschueren, H., Post, E., Wedekind, L. E., Bracht, J., ... Wurdinger, T. (2017). Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets. Cancer cell, 32(2), 238-252.e9. https://doi.org/10.1016/j.ccell.2017.07.004

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title = "Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets",

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 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.",

keywords = "Adult, Aged, Aged, 80 and over, Algorithms, Artificial Intelligence, Biomarkers, Tumor, Blood Platelets/physiology, Carcinoma, Non-Small-Cell Lung/blood, Cohort Studies, Diagnosis, Computer-Assisted/methods, Female, Gene Expression Profiling, High-Throughput Nucleotide Sequencing, Humans, Inflammation/blood, Lung Neoplasms/blood, Male, Middle Aged, Support Vector Machine, blood platelets, cancer diagnostics, liquid biopsies, NSCLC, particle-swarm optimization, RNA, self-learning algorithms, splicing, swarm intelligence, tumor-educated platelets",

author = "Best, \{Myron G\} and Nik Sol and \{In 't Veld\}, \{Sjors G J G\} and Adrienne Vancura and Mirte Muller and Niemeijer, \{Anna-Larissa N\} and Fejes, \{Aniko V\} and \{Tjon Kon Fat\}, Lee-Ann and \{Huis In 't Veld\}, \{Anna E\} and Cyra Leurs and \{Le Large\}, \{Tessa Y\} and Meijer, \{Laura L\} and Kooi, \{Irsan E\} and Fran{\c c}ois Rustenburg and Pepijn Schellen and Heleen Verschueren and Edward Post and Wedekind, \{Laurine E\} and Jillian Bracht and Michelle Esenkbrink and Leon Wils and Francesca Favaro and Schoonhoven, \{Jilian D\} and Jihane Tannous and Hanne Meijers-Heijboer and Geert Kazemier and Elisa Giovannetti and Reijneveld, \{Jaap C\} and Sander Idema and Joep Killestein and Michal Heger and \{de Jager\}, \{Saskia C\} and Urbanus, \{Rolf T\} and Hoefer, \{Imo E\} and Gerard Pasterkamp and Christine Mannhalter and Jose Gomez-Arroyo and Harm-Jan Bogaard and Noske, \{David P\} and Vandertop, \{W Peter\} and \{van den Broek\}, Daan and Bauke Ylstra and Nilsson, \{R Jonas A\} and Pieter Wesseling and Niki Karachaliou and Rafael Rosell and Elizabeth Lee-Lewandrowski and Lewandrowski, \{Kent B\} and Tannous, \{Bakhos A\} and \{de Langen\}, \{Adrianus J\} and Smit, \{Egbert F\} and \{van den Heuvel\}, \{Michel M\} and Thomas Wurdinger",

year = "2017",

month = aug,

day = "14",

doi = "10.1016/j.ccell.2017.07.004",

language = "English",

volume = "32",

pages = "238--252.e9",

journal = "Cancer cell",

issn = "1535-6108",

publisher = "Cell Press",

number = "2",

}

Best, MG , Sol, N, In 't Veld, SGJG, Vancura, A, Muller, M, Niemeijer, A-LN, Fejes, AV, Tjon Kon Fat, L-A, Huis In 't Veld, AE , Leurs, C , Le Large, TY, Meijer, LL, Kooi, IE, Rustenburg, F, Schellen, P, Verschueren, H, Post, E, Wedekind, LE, Bracht, J, Esenkbrink, M, Wils, L, Favaro, F, Schoonhoven, JD, Tannous, J, Meijers-Heijboer, H , Kazemier, G , Giovannetti, E , Reijneveld, JC , Idema, S , Killestein, J, Heger, M, de Jager, SC, Urbanus, RT, Hoefer, IE, Pasterkamp, G, Mannhalter, C, Gomez-Arroyo, J, Bogaard, H-J , Noske, DP , Vandertop, WP, van den Broek, D, Ylstra, B, Nilsson, RJA, Wesseling, P, Karachaliou, N, Rosell, R, Lee-Lewandrowski, E, Lewandrowski, KB, Tannous, BA, de Langen, AJ, Smit, EF, van den Heuvel, MM & Wurdinger, T 2017, 'Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets', Cancer cell, vol. 32, no. 2, pp. 238-252.e9. https://doi.org/10.1016/j.ccell.2017.07.004

TY - JOUR

T1 - Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets

AU - Best, Myron G

AU - Sol, Nik

AU - In 't Veld, Sjors G J G

AU - Vancura, Adrienne

AU - Muller, Mirte

AU - Niemeijer, Anna-Larissa N

AU - Fejes, Aniko V

AU - Tjon Kon Fat, Lee-Ann

AU - Huis In 't Veld, Anna E

AU - Leurs, Cyra

AU - Le Large, Tessa Y

AU - Meijer, Laura L

AU - Kooi, Irsan E

AU - Rustenburg, François

AU - Schellen, Pepijn

AU - Verschueren, Heleen

AU - Post, Edward

AU - Wedekind, Laurine E

AU - Bracht, Jillian

AU - Esenkbrink, Michelle

AU - Wils, Leon

AU - Favaro, Francesca

AU - Schoonhoven, Jilian D

AU - Tannous, Jihane

AU - Meijers-Heijboer, Hanne

AU - Kazemier, Geert

AU - Giovannetti, Elisa

AU - Reijneveld, Jaap C

AU - Idema, Sander

AU - Killestein, Joep

AU - Heger, Michal

AU - de Jager, Saskia C

AU - Urbanus, Rolf T

AU - Hoefer, Imo E

AU - Pasterkamp, Gerard

AU - Mannhalter, Christine

AU - Gomez-Arroyo, Jose

AU - Bogaard, Harm-Jan

AU - Noske, David P

AU - Vandertop, W Peter

AU - van den Broek, Daan

AU - Ylstra, Bauke

AU - Nilsson, R Jonas A

AU - Wesseling, Pieter

AU - Karachaliou, Niki

AU - Rosell, Rafael

AU - Lee-Lewandrowski, Elizabeth

AU - Lewandrowski, Kent B

AU - Tannous, Bakhos A

AU - de Langen, Adrianus J

AU - Smit, Egbert F

AU - van den Heuvel, Michel M

AU - Wurdinger, Thomas

PY - 2017/8/14

Y1 - 2017/8/14

N2 - 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 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.

AB - 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 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.

KW - Adult

KW - Aged

KW - Aged, 80 and over

KW - Algorithms

KW - Artificial Intelligence

KW - Biomarkers, Tumor

KW - Blood Platelets/physiology

KW - Carcinoma, Non-Small-Cell Lung/blood

KW - Cohort Studies

KW - Diagnosis, Computer-Assisted/methods

KW - Female

KW - Gene Expression Profiling

KW - High-Throughput Nucleotide Sequencing

KW - Humans

KW - Inflammation/blood

KW - Lung Neoplasms/blood

KW - Male

KW - Middle Aged

KW - Support Vector Machine

KW - blood platelets

KW - cancer diagnostics

KW - liquid biopsies

KW - NSCLC

KW - particle-swarm optimization

KW - RNA

KW - self-learning algorithms

KW - splicing

KW - swarm intelligence

KW - tumor-educated platelets

UR - https://www.scopus.com/pages/publications/85027221443

U2 - 10.1016/j.ccell.2017.07.004

DO - 10.1016/j.ccell.2017.07.004

M3 - Article

C2 - 28810146

SN - 1535-6108

VL - 32

SP - 238-252.e9

JO - Cancer cell

JF - Cancer cell

IS - 2

ER -

Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets

Abstract

UN SDGs

Keywords

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