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

      research-article
      1 , 2 , 3 , , 3 , 4 , 26 , 1 , 3 , 26 , 1 , 3 , 26 , 5 , 6 , 1 , 3 , 7 , 8 , 6 , 4 , 9 , 10 , 10 , 11 , 1 , 2 , 3 , 1 , 3 , 1 , 3 , 12 , 1 , 3 , 12 , 1 , 3 , 1 , 1 , 1 , 1 , 1 , 13 , 11 , 10 , 14 , 3 , 4 , 1 , 4 , 9 , 15 , 16 , 17 , 17 , 16 , 7 , 6 , 6 , 1 , 3 , 1 , 3 , 18 , 2 , 1 , 8 , 2 , 3 , 19 , 20 , 20 , 21 , 22 , 23 , 24 , 24 , 13 , 6 , 5 , 6 , 5 , 25 , 1 , 3 , 13 , 27 , ∗∗
      Cancer Cell
      Cell Press
      tumor-educated platelets, blood platelets, RNA, cancer diagnostics, particle-swarm optimization, splicing, swarm intelligence, self-learning algorithms, liquid biopsies, NSCLC

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          Summary

          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.

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          Highlights

          • Tumor-educated platelet (TEP) RNA profiles allow for blood-based cancer diagnostics

          • Inflammatory conditions only minimally confound TEP-based cancer detection

          • Swarm intelligence algorithms enable efficient selection of biomarker gene panels

          • TEP gene panels enable support vector machine-based classification of lung cancer

          Abstract

          Best et al. use particle-swarm optimization algorithms and RNA-seq of tumor-educated platelets from patients to generate RNA sets capable of identifying patients with non-small-cell lung cancer, including those having early stage, from individuals without cancer, including those having inflammatory conditions.

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          Most cited references36

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          The tumour-induced systemic environment as a critical regulator of cancer progression and metastasis.

          Recent pre-clinical and clinical research has provided evidence that cancer progression is driven not only by a tumour's underlying genetic alterations and paracrine interactions within the tumour microenvironment, but also by complex systemic processes. We review these emerging paradigms of cancer pathophysiology and discuss how a clearer understanding of systemic regulation of cancer progression could guide development of new therapeutic modalities and efforts to prevent disease relapse following initial diagnosis and treatment.
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            The lung is a site of platelet biogenesis and a reservoir for hematopoietic progenitors

            Platelets are critical for hemostasis, thrombosis, and inflammatory responses 1,2 , yet the events leading to mature platelet production remain incompletely understood 3 . The bone marrow (BM) is proposed to be a major site of platelet production although indirect evidence points towards a potential pulmonary contribution to platelet biogenesis 4-7 . By directly imaging the lung microcirculation in mice 8 , we discovered that a large number of megakaryocytes (MKs) circulate through the lungs where they dynamically release platelets. MKs releasing platelets in the lung are of extrapulmonary origin, such as the BM, where we observed large MKs migrating out of the BM space. The lung contribution to platelet biogenesis is substantial with approximately 50% of total platelet production or 10 million platelets per hour. Furthermore, we identified populations of mature and immature MKs along with hematopoietic progenitors that reside in the extravascular spaces of the lung. Under conditions of thrombocytopenia and relative stem cell deficiency in the BM 9 , these progenitors can migrate out of the lung, repopulate the BM, completely reconstitute blood platelet counts, and contribute to multiple hematopoietic lineages. These results position the lung as a primary site of terminal platelet production and an organ with considerable hematopoietic potential.
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              RNA-Seq of Tumor-Educated Platelets Enables Blood-Based Pan-Cancer, Multiclass, and Molecular Pathway Cancer Diagnostics

              Summary Tumor-educated blood platelets (TEPs) are implicated as central players in the systemic and local responses to tumor growth, thereby altering their RNA profile. We determined the diagnostic potential of TEPs by mRNA sequencing of 283 platelet samples. We distinguished 228 patients with localized and metastasized tumors from 55 healthy individuals with 96% accuracy. Across six different tumor types, the location of the primary tumor was correctly identified with 71% accuracy. Also, MET or HER2-positive, and mutant KRAS, EGFR, or PIK3CA tumors were accurately distinguished using surrogate TEP mRNA profiles. Our results indicate that blood platelets provide a valuable platform for pan-cancer, multiclass cancer, and companion diagnostics, possibly enabling clinical advances in blood-based “liquid biopsies”.
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                Author and article information

                Contributors
                Journal
                Cancer Cell
                Cancer Cell
                Cancer Cell
                Cell Press
                1535-6108
                1878-3686
                14 August 2017
                14 August 2017
                : 32
                : 2
                : 238-252.e9
                Affiliations
                [1 ]Department of Neurosurgery, VU University Medical Center, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, the Netherlands
                [2 ]Department of Pathology, VU University Medical Center, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, the Netherlands
                [3 ]Brain Tumor Center Amsterdam, VU University Medical Center, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, the Netherlands
                [4 ]Department of Neurology, VU University Medical Center, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, the Netherlands
                [5 ]Department of Thoracic Oncology, The Netherlands Cancer Institute – Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
                [6 ]Department of Pulmonary Diseases, VU University Medical Center, De Boelelaan 1117, 1081 HV Amsterdam, the Netherlands
                [7 ]Clinical Institute of Laboratory Medicine, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
                [8 ]Department of Radiation Sciences, Oncology, Umeå University, 90185 Umeå, Sweden
                [9 ]MS Center Amsterdam, VU University Medical Center, De Boelelaan 1117, 1081 HV Amsterdam, the Netherlands
                [10 ]Department of Surgery, VU University Medical Center, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, the Netherlands
                [11 ]Department of Clinical Genetics, VU University Medical Center, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, the Netherlands
                [12 ]thromboDx B.V., 1098 EA Amsterdam, the Netherlands
                [13 ]Department of Neurology, Massachusetts General Hospital and Neuroscience Program, Harvard Medical School, 149 13 th Street, Charlestown, MA 02129, USA
                [14 ]Department of Medical Oncology, VU University Medical Center, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, the Netherlands
                [15 ]Department of Surgery, Amsterdam Medical Center, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands
                [16 ]Department of Experimental Cardiology, Utrecht University Medical Center, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
                [17 ]Laboratory of Clinical Chemistry and Hematology, Utrecht University Medical Center, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
                [18 ]Department of Clinical Chemistry, The Netherlands Cancer Institute – Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
                [19 ]Department of Pathology, Princess Máxima Center for Pediatric Oncology and University Medical Center Utrecht, Lundlaan 6, 3584 EA Utrecht, the Netherlands
                [20 ]Translational Research Unit, Dr. Rosell Oncology Institute, Quirón Dexeus University Hospital, Calle Sabine Arana 5-19, 08028 Barcelona, Spain
                [21 ]Pangaea Biotech SL, Calle Sabine Arana 5-19, 08028 Barcelona, Spain
                [22 ]Catalan Institute of Oncology, Hospital Germans Trias i Pujol, Carretera de Canyet, 08916 Barcelona, Spain
                [23 ]Molecular Oncology Research (MORe) Foundation, Calle Sabine Arana 5-19, 08028 Barcelona, Spain
                [24 ]Department of Pathology, Massachusetts General Hospital, Harvard Medical School, 149 13 th Street, Charlestown, MA 02129, USA
                [25 ]Department of Respiratory Diseases, Radboud University Medical Center, 6500 HB Nijmegen, the Netherlands
                Author notes
                []Corresponding author m.best@ 123456vumc.nl
                [∗∗ ]Corresponding author t.wurdinger@ 123456vumc.nl
                [26]

                These authors contributed equally

                [27]

                Lead Contact

                Article
                S1535-6108(17)30296-9
                10.1016/j.ccell.2017.07.004
                6381325
                28810146
                14f6f186-92fc-465c-84cc-092cb57f51e1
                © 2017 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 3 February 2017
                : 17 May 2017
                : 13 July 2017
                Categories
                Article

                Oncology & Radiotherapy
                tumor-educated platelets,blood platelets,rna,cancer diagnostics,particle-swarm optimization,splicing,swarm intelligence,self-learning algorithms,liquid biopsies,nsclc

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