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Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets.
Cancer Cell ( IF 50.3 ) Pub Date : 2017-08-14 , DOI: 10.1016/j.ccell.2017.07.004 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
Cancer Cell ( IF 50.3 ) Pub Date : 2017-08-14 , DOI: 10.1016/j.ccell.2017.07.004 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
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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.
中文翻译：
使用肿瘤培养的血小板对非小细胞肺癌的群体智能增强检测。
基于血液的液体活检，包括肿瘤培养的血小板（TEP），已经成为无创检测癌症的有前途的生物标志物来源。在这里，我们证明了粒子群优化（PSO）增强算法能够从血小板RNA测序文库（n = 779）中有效选择RNA生物标志物面板。这样就可以对早期和晚期非小细胞肺癌进行准确的基于TEP的检测（n = 518个晚期验证队列，准确度为88％; AUC为0.94; 95％CI为0.92-0.96; p <0.001； n = 106个早期验证队列，准确性，81％； AUC，0.89； 95％CI，0.83-0.95； p <0.001），与个体年龄，吸烟习惯，全血储存时间无关，和各种炎性疾病。通过PSO，可以选择基因面板来诊断TEP所致的癌症，
更新日期：2017-08-14
中文翻译：
使用肿瘤培养的血小板对非小细胞肺癌的群体智能增强检测。
基于血液的液体活检，包括肿瘤培养的血小板（TEP），已经成为无创检测癌症的有前途的生物标志物来源。在这里，我们证明了粒子群优化（PSO）增强算法能够从血小板RNA测序文库（n = 779）中有效选择RNA生物标志物面板。这样就可以对早期和晚期非小细胞肺癌进行准确的基于TEP的检测（n = 518个晚期验证队列，准确度为88％; AUC为0.94; 95％CI为0.92-0.96; p <0.001； n = 106个早期验证队列，准确性，81％； AUC，0.89； 95％CI，0.83-0.95； p <0.001），与个体年龄，吸烟习惯，全血储存时间无关，和各种炎性疾病。通过PSO，可以选择基因面板来诊断TEP所致的癌症，
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