Classification of lung cancer histology by gold nanoparticle sensors

doi:10.1016/j.nano.2011.10.001

Nanomedicine: Nanotechnology, Biology and Medicine

Volume 8, Issue 5, July 2012, Pages 580-589

https://doi.org/10.1016/j.nano.2011.10.001 Get rights and content

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.

From the Clinical Editor

In this study, a nanomedical device that profiles volatile organic compounds (VOCs) in lung cancer cells is investigated, using a matrix of gold nanoparticle (GNP) sensors that are suitable for detecting lung cancer (LC) specific patterns of VOC profiles. This device might eventually allow early differential diagnosis of LC subtypes including 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).

Section snippets

Collection of the headspace samples

The headspace samples were collected from commercially available cell lines. Fourteen NSCLC cell lines, subcategorized into 10 adenocarcinoma cell lines and 4 squamous cell carcinoma cell lines, as well as four SCLC cell lines (Table 1), were obtained from the Colorado cell bank registry. The cell lines were grown in 100-mm cell culture dishes from seeding (∼2 × 10⁶ cells) up to 95% confluency (7 × 10⁶ cells), using a two-dimensional medium (medium 1) under standard conditions (RPMI 1640 medium

Chemical analysis of the headspace LC cell lines

Our GC-MS/SPME analysis identified over 700 different VOCs in each headspace sample. Nonparametric Wilcoxon/Kruskal-Wallis tests could identify several VOCs from the families of aldehydes, alkanes, ketones, alcohols, and benzene derivatives that were on average significantly elevated or reduced in the LC subtypes studied, as compared to the empty medium (Table 3).

Marked differences were observed between the average headspace composition of all LC cell lines and of their simulated healthy

Discussion

The design of this study followed the hierarchical order of a possible future test for the screening and subsequent differential diagnosis of LC. In the first phase of such a test, a wide population would be screened for LC, using a test that can distinguish between LC and healthy states. In the second phase, the histological LC type would be determined in the LC-positive subjects, using a test that can distinguish between the two most prevalent histological types: NSCLC (80.4%) and SCLC

Acknowledgments

O.B. and U.T. acknowledge helpful discussions with Dr. Maya Ilouze.

References (43)

E. Vallieres et al.
The IASLC Lung Cancer Staging Project: proposals regarding the relevance of TNM in the pathologic staging of small cell lung cancer in the forthcoming (seventh) edition of the TNM classification for lung cancer
J Thorac Oncol
(2009)
A. Wehinger et al.
Lung cancer detection by proton transfer reaction mass-spectrometric analysis of human breath gas
Int J Mass Spectrom
(2007)
M. Phillips et al.
Volatile organic compounds in breath as markers of lung cancer: a cross-sectional study
Lancet
(1999)
G. Preti et al.
Analysis of lung air from patients with bronchogenic carcinoma and controls using gas chromatography–mass spectrometry
J Chromatogr
(1988)
A. Bryant et al.
Differences in epidemiology, histology, and survival between cigarette smokers and never-smokers who develop non-small cell lung cancer
Chest
(2007)
M. Patel et al.
ALDH1A1 and ALDH3A1 expression in lung cancers: correlation with histologic type and potential precursors
Lung Cancer
(2008)
P.P. Hsu et al.
Cancer cell metabolism: Warburg and beyond
Cell
(2008)
F. Barbone et al.
Cigarette smoking and histologic type of lung cancer in men
Chest
(1997)
Y. Joseph et al.
Chemiresistor coatings from Pt- and Au-nanoparticle/nonanedithiol films: sensitivity to gases and solvent vapors
Sens Actuat B
(2004)
G. Kroemer et al.
Tumor cell metabolism: cancer's Achilles' heel
Cancer Cell
(2008)

A. Jemal et al.

Cancer statistics, 2009

CA Cancer J Clin

(2009)

N. Peled et al.

Predictive and prognostic markers for epidermal growth factor receptor inhibitor therapy in non-small cell lung cancer

Ther Adv Med Oncol

(2009)

J.E. Holty et al.

Accuracy of transbronchial needle aspiration for mediastinal staging of non-small cell lung cancer: a meta-analysis

Thorax

(2005)

G. Peng et al.

Detection of lung, breast, colorectal, and prostate cancers from exhaled breath using a single array of nanosensors

Br J Cancer

(2010)

G. Peng et al.

Diagnosing lung cancer in exhaled breath using gold nanoparticles

Nat Nanotechnol

(2009)

X. Chen et al.

A study of the volatile organic compounds exhaled by lung cancer cells in vitro for breath diagnosis

Cancer

(2007)

O. Barash et al.

Sniffing the unique "odor print" of non-small-cell lung cancer with gold nanoparticles

Small

(2009)

S.M. Gordon et al.

Volatile organic-compounds in exhaled air from patients with lung-cancer

Clin Chem

(1985)

H.J. O'Neill et al.

A computerized classification technique for screening for the presence of breath biomarkers in lung cancer

Clin Chem

(1988)

U. Tisch et al.

Arrays of chemisensitive monolayer-capped metallic nanoparticles for diagnostic breath testing

Rev Chem Eng

(2011)

G. Shuster et al.

Classification of breast cancer precursors through exhaled breath

Breast Cancer Res Treat

(2011)

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: The topic of this invited contribution has been presented at the 4th IEEE International Conference on Nano/Molecular Medicine and Engineering (IEEE-NANOMED 2010), December 5-9, 2010, Hong Kong SAR, China.

: 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.

¹: These two authors contributed equally to this work.

View full text

Research ArticleClassification of lung cancer histology by gold nanoparticle sensors

Abstract

From the Clinical Editor

Graphical Abstract

Section snippets

Collection of the headspace samples

Chemical analysis of the headspace LC cell lines

Discussion

Acknowledgments

J Thorac Oncol

Int J Mass Spectrom

Lancet

J Chromatogr

Chest

Lung Cancer

Cell

Chest

Sens Actuat B

Cancer Cell

Cancer statistics, 2009

CA Cancer J Clin

Predictive and prognostic markers for epidermal growth factor receptor inhibitor therapy in non-small cell lung cancer

Ther Adv Med Oncol

Accuracy of transbronchial needle aspiration for mediastinal staging of non-small cell lung cancer: a meta-analysis

Thorax

Detection of lung, breast, colorectal, and prostate cancers from exhaled breath using a single array of nanosensors

Br J Cancer

Diagnosing lung cancer in exhaled breath using gold nanoparticles

Nat Nanotechnol

A study of the volatile organic compounds exhaled by lung cancer cells in vitro for breath diagnosis

Cancer

Sniffing the unique "odor print" of non-small-cell lung cancer with gold nanoparticles

Small

Volatile organic-compounds in exhaled air from patients with lung-cancer

Clin Chem

A computerized classification technique for screening for the presence of breath biomarkers in lung cancer

Clin Chem

Arrays of chemisensitive monolayer-capped metallic nanoparticles for diagnostic breath testing

Rev Chem Eng

Classification of breast cancer precursors through exhaled breath

Breast Cancer Res Treat

Research Article
Classification of lung cancer histology by gold nanoparticle sensors