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Classification of lung cancer histology by gold nanoparticle sensors

  • Orna Barash, MSc

      Affiliations

    • The Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion–Israel Institute of Technology, Haifa, Israel
    • These two authors contributed equally to this work.
    • ,
  • Nir Peled, MD, PhD

      Affiliations

    • University of Colorado Cancer Center, Division of Medical Oncology, University of Colorado–Denver, Aurora, Colorado, USA
    • The Lung Cancer Unit, Sheba Medical Center, Tel Aviv University, Tel Aviv, Israel
    • These two authors contributed equally to this work.
    • ,
  • Ulrike Tisch, PhD

      Affiliations

    • The Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion–Israel Institute of Technology, Haifa, Israel
    • ,
  • Paul A. Bunn Jr., MD

      Affiliations

    • University of Colorado Cancer Center, Division of Medical Oncology, University of Colorado–Denver, Aurora, Colorado, USA
    • ,
  • Fred R. Hirsch, MD, PhD

      Affiliations

    • University of Colorado Cancer Center, Division of Medical Oncology, University of Colorado–Denver, Aurora, Colorado, USA
    • ,
  • Hossam Haick, PhD

      Affiliations

    • The Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion–Israel Institute of Technology, Haifa, Israel
    • Corresponding Author InformationCorresponding author: Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion–Israel Institute of Technology, Haifa 32000, Israel.

Received 10 April 2011; accepted 3 October 2011. published online 26 October 2011.
Corrected Proof

Abstract 

We propose a nanomedical device for the classification of lung cancer (LC) histology. The device profiles volatile organic compounds (VOCs) in the headspace of (subtypes of) LC cells, using gold nanoparticle (GNP) sensors that are suitable for detecting LC-specific patterns of VOC profiles, as determined by gas chromatography–mass spectrometry analysis. Analyzing the GNP sensing signals by support vector machine allowed significant discrimination between (i) LC and healthy cells; (ii) small cell LC and non–small cell LC; and between (iii) two subtypes of non–small cell LC: adenocarcinoma and squamous cell carcinoma. The discriminative power of the GNP sensors was then linked with the chemical nature and composition of the headspace VOCs of each LC state. These proof-of-concept findings could totally revolutionize LC screening and diagnosis, and might eventually allow early and differential diagnosis of LC subtypes with detectable or unreachable lung nodules.

Graphical Abstract 

Experimental procedure for in vitro studies of lung cell line head space. The samples were analyzed by both the gold nanoparticle sensors (B, C, F) and gas chromatography–mass spectrometry (D, E, G).

Key words: Sensor, Gold nanoparticle, Lung cancer, Histology, Volatile organic compound

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 O.B., N.P., U.T., P.B., and H.H. have no conflict to declare related to the study. F.H is a member of the consultant/advisory boards of AstraZeneca, Roche, Lilly, Pfizer, Boehringer-Ingelheim, Merck Serono, Ventana-Roche, Glaxo Smith Kline, BMS/Imclone, and Syndax.

 The research leading to these results has received funding from the FP7-Health Program under the LCAOS (grant 258868; H.H. and N.P.) and FP7′s ERC grant under DIAG-CANCER (grant 256639; H.H.), the NIH/Lung SPORE (F.H., P.B.), and the International Association for the Study of Lung Cancer (N.P.).

 All authors designed the research; O.B. and N.P. performed the research; U.T., O.B., N.P., and H.H. analyzed data, and U.T. and H.H. wrote the article.

PII: S1549-9634(11)00374-1

doi:10.1016/j.nano.2011.10.001

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