• lung cancer;
  • sputum;
  • respiratory epithelial cells;
  • flow sorting;
  • early detection


  1. Top of page
  2. Abstract
  6. Acknowledgements


Induced sputum, in contrast to bronchoscopic biopsies and lavages, is an easily obtained source of biological specimens. However, obtaining abnormal exfoliated cells for detailed molecular studies is limited because respiratory epithelial cells comprise only about 1% of sputum cell populations.


We developed a multiparameter flow sorting strategy to purify epithelial cells from nonepithelial sputum cells, using anti-cytokeratin antibody AE1/AE3 to recognize human epithelial cells and DAPI to stain DNA. We excluded cells with a high degree of side-scatter, which were composed predominantly of squamous cells and contaminating macrophages. The remaining cytokeratin-positive respiratory epithelial cells were then sorted based on anti-cytokeratin (PE) vs DNA (DAPI) parameters.


In this proof of principle study, the AE1AE3 cytokeratin/DNA flow sorting strategy enriched rare diploid respiratory epithelial cells from an average of 1.1% of cells in unsorted induced sputum samples to average purities of 42%. Thus, AE1AE3 flow-sorting results in a 38-fold enrichment of these cells.


We report a multiparameter flow cytometric assay to detect and enrich rare respiratory epithelial cells from induced sputum samples to average purities of 42%. With further development, this methodology may be useful as part of a molecular screening approach of populations at high risk for lung cancer. © 2004 Wiley-Liss, Inc.

Carcinoma of the lung is the most common cause of cancer death worldwide and in the United States (1–3). In 2002, it was estimated that 169,400 Americans would be diagnosed with lung cancer and 154,900 will die (4). Using current standards of diagnosis and treatment, fewer than 15% of patients with lung cancer will survive five years after diagnosis (5). The prognosis for patients with lung cancer is strongly correlated with the stage of disease at the time of diagnosis. Over two-thirds of the patients have regional lymph node involvement or distant disease at the time of presentation, and the prognosis for these patients is poor, with five-year survival rates ranging from 16% (regional stage) to 2% (distant disease) (5). However, survival is markedly improved for patients with early stage disease, with five-year survival rates of 49% for localized disease (5) (staging according to the Surveillance, Epidemiology, and End Results (SEER) Program staging scheme (6, 7)). Thus, development of effective, noninvasive, diagnostic methods for early detection of lung cancer is clearly important to reduce lung cancer mortality.

Historically, chest radiography has been the principal diagnostic test for early detection of lung cancer. A recent review by Etzioni et al. (8) shows that relative survival (five- and 10-year) for lung cancer is highest among patients diagnosed with localized disease compared to regional or distant disease. In the 1970s, controlled trials sponsored by the U.S. National Cancer Institute (NCI) were undertaken to determine whether screening with sputum cytology and annual chest radiography would reduce lung cancer mortality. Although these trials showed that lung cancer was detected at an earlier stage and resectability and survival rates were higher in the study groups than in the controls, overall lung cancer mortality was not significantly improved (9–13).

These findings have stimulated a search for improved diagnostics for early detection of lung cancer. Interest in sputum as an inexpensive, noninvasive source of respiratory epithelial cells has been revived by several studies reporting detection of molecular abnormalities in sputum samples of patients with, or at risk for, lung cancer (14–27). However, a major limitation to the use of sputum samples for detailed molecular studies is that only a small proportion of sputum cell populations consist of respiratory epithelial cells. The contaminating inflammatory cell populations can obscure detection of important molecular abnormalities in the neoplastic respiratory epithelial cells.

Detailed analysis of induced sputum has been adopted as a relatively noninvasive method for the evaluation of airway inflammation in diseases such as asthma and chronic obstructive pulmonary disease (COPD). Two basic techniques for processing sputum have been described. The first consists of selecting all viscid portions from the expectorated sample to reduce squamous cell contamination, whereas the second processes the whole expectorate, containing sputum plus saliva (28). Studies comparing both techniques directly on samples from the same patient with either asthma or COPD found no significant differences in total or differential cell counts in the nonsquamous cell population (29, 30).

The cellular content of induced sputum samples have recently been established in healthy adults. Spanevello et al. (31) published reference values based on 96 healthy volunteers and reported that epithelial cells constituted only 1.5 ± 1.8% of cells in induced sputum samples. Belda et al. (32) also evaluated induced sputum of nonsmoking healthy adults and reported a mean of 1.6% respiratory epithelial cells. Differential cell counts in induced sputum have shown that the proportion of inflammatory cells in induced sputums is comparable between smokers and nonsmokers. For example, Lensmar et al. (33) found no significant differences in total cell counts and the percentages of macrophages, lymphocytes, neutrophils, and eosinophils in induced sputums of smokers and nonsmokers. Similar results were reported by Domagala-Kulawik et al. (34). These combined results indicate that respiratory epithelial cells, the targets of field carcinogenesis in the lung, constitute a very small percentage of cells in induced sputum samples.

We, and others, have previously shown that flow cytometric cell sorting can be used to identify and enrich epithelial cells from heterogeneous cell populations in fresh/frozen and formalin-fixed, paraffin-embedded human tissues, including breast cancer and Barrett's esophagus (35–43). Our goal was to develop a flow cytometric technique to enrich sputum respiratory epithelial cells from the heterogeneous mixture of sputum cellular elements. In the present study, we examined induced sputum specimens from current and former smokers who are at high risk for lung cancer and are enrolled in a low-dose spiral CT scan screening trial for lung cancer.


  1. Top of page
  2. Abstract
  6. Acknowledgements

Sample Collection and Preparation

The Mayo Clinic has participated in a study evaluating spiral CT scans in screening for early lung cancer. As part of this trial, induced sputum samples from current and ex-smokers were collected and fixed immediately in Saccomano's preservative and stored at 25°C. This study was approved by the Institutional Review Board of the Mayo Clinic on May 18, 2001. A total of 25 induced sputum samples were shipped to the Human Biology Division, Fred Hutchinson Cancer Research Center, and stored at 4°C.

The induced sputum samples were classified according to macroscopic appearance or visible clarity of the specimen in the fixative as “clear,” “intermediate,” or “cloudy,” corresponding to increasing cellularity. The specimens, typically 25–30 ml including preservative solution, were treated with a modification of a previously published protocol (44). Since the sputum processing method does not appear to impact the percentage of respiratory epithelial cells available for downstream analyses, the entire sample was processed. In brief, each sputum was homogenized by gentle vortexing and pipetting and 3–5-ml aliquots of sputum were mixed with an equal volume of 5.0 mM dithiothreitol (DTT). After vortexing, the aliquots were incubated for 15 min at 37°C. Next, the suspension was passed through a 70-μm nylon mesh filter, centrifuged for 5 min at 1,500 rpm, washed with 3 ml 1% albumin in phosphate buffered saline (PBS) and resuspended in 1 ml 10% normal goat serum in PBS.

The cell suspensions were evaluated microscopically and quantitated using a hemacytometer. A sample was considered unsatisfactory if the sample had less than 3× 106 cells, the percentage of squamous cells was greater than 80%, or alveolar macrophages were absent. Eight samples representative of the macroscopic appearance (clarity) of induced sputums, including three clear, three intermediate, and two cloudy samples, were assessed adequate and used in this study.

Staining and Flow Cytometry

The AE1AE3/DNA content flow-sorting strategy using an antibody that recognizes a cytoplasmic cytokeratin (AE1AE3) present in epithelial cells was adapted from a previously published protocol developed to discriminate epithelial cells from nonepithelial cells in breast tissue (39). AE1AE3 recognizes normal epithelial cells as well as those at all stages of neoplastic progression, and can be used to isolate epithelial cells from nonepithelial cells in sputum.

Approximately 1 × 106 cells were used for each flow experiment. A total of 2 × 105 cells were used for the isotype control, and 8 × 105 cells were used for cytokeratin staining. PBS + 10% normal goat serum was added to bring the final volume of each microcentrifuge tube to 200 μl. The DTT-treated sputum suspension was incubated with 6 μl of PE-conjugated anticytokeratin monoclonal antibody AE1AE3 (Roche Diagnostics, Indianapolis, IN; custom conjugate Intergen Co., Purchase, NY) and a purified mouse monoclonal IgG1 antibody conjugated with PE (DAKO, Carpenteria, CA) as isotypic control. After incubation, the IgG1 and AE1AE3 tubes were centrifuged at 2,000 rpm for 10 min at 4°C and decanted. DAPI (Boehringer, Mannheim, Germany) was then added, to a final concentration of 10 μg/ml. Flow cytometric discrimination of AE1AE3-positive cells was obtained by comparing the AE1AE3-stained sputum sample to the negative isotype control. The gating region for each sputum was set so that a maximum of 1% of the control sample stained with the isotype control fell into this window. Flow cytometry was performed on a dual laser Becton Dickinson (San Jose, CA) Vantage flow cytometer with 15-mW 488-nm excitation (PE) and 50-mW 354–361-nm (UV) excitation (DAPI), and the data was displayed in a bivariate histogram. Listmode data were analyzed by using Multicycle AV and MultiPlus AV software (Phoenix Flow Systems, San Diego, CA).


Smears were prepared from unsorted sputum samples. Cells were flow sorted onto slides from side-scatter and AE1AE3-positive gates. An average of 1,867 ± 420 (mean ± SD; range 364–2,000) cells were sorted onto slides. Slides were stained with hematoxylin and eosin (H&E) and counted by two independent readers. An average of 560 cells was counted on the smears. However, an average of 293 and 218 cells from the high side-scatter and AE1AE3-positive gates were counted, respectively, because cellular density and distribution could not be completely controlled while sorting onto slides.


  1. Top of page
  2. Abstract
  6. Acknowledgements

Sample Selection

Eight sputum representing different macroscopic appearances (clarity) in induced samples including three clear, three intermediate, and two cloudy samples that met microscopic criteria of cellularity were adequate for multiparameter flow cytometric analysis and cell sorting. Smears were prepared from each of the sputum samples, stained with H&E, and counted. On average, the eight sputum samples were comprised of 62% inflammatory, 37% squamous, and 1% respiratory epithelial cells.

Flow Cytometric Exclusion of Contaminating Cells

The first step for respiratory epithelial cell enrichment utilizes exclusion of cells with high side-scatter that would otherwise contaminate the AE1AE3-positive gate (Fig. 1). Of the cells with high side-scatter gate, 99% are macrophages and squamous cells, as they have large amounts of highly reflective cytoplasm. Furthermore, the squamous cells have dense and complex intermediate filaments in their cytoplasm, which stain nonspecifically with DAPI. We therefore selectively removed contaminating macrophages and squamous cells from other cells in the sample by displaying the cells in a bivariate histogram with the integral DAPI signal on the x-axis and the side scatter DAPI signal on the y-axis. An example of a sample with 18% contaminating cells excluded by side-scatter is shown in Figure 1. By using side-scatter gating, we excluded an average of 13.4% ± 8% (mean ± SD) of sputum cells (range: 4% to 28%). Cells in the side-scatter gate (Fig. 1) were sorted onto slides, stained with H&E, and counted. These cells consisted of 40% squamous cells, 59% macrophages, and less than 1% respiratory epithelial cells (Table 1).

thumbnail image

Figure 1. Side-scatter and DAPI fluorescence multiparameter flow cytometry. Side-scatter vs. DAPI fluorescence multiparameter flow cytometric gating to exclude contaminating squamous cells and macrophages of sputum sample no. 1279. Bivariate histogram displaying DAPI fluorescence (linear scale) on the x-axis and side-scatter (logarithmic scale) on the y-axis. In this example, 18% of cells were excluded, including squamous cells (48%), macrophages (51%), and respiratory epithelial cells (1%).

Download figure to PowerPoint

Table 1. Differential Sputum Sample Cell Counts From Sorted Cells in the Side Scatter Gate Region (n = 8)
Cell typeMean ± SDMedianRange
% respiratory epithelial cells0.9 ± 0.310.4–1.4
% squamous cells40.1 ± 26.942.16.9–86.3
% macrophages59.0 ± 27.156.912.3–92.7
No. total cells counted293 ± 40.3287.5227–362

Multiparameter AE1AE3 Cytokeratin/DNA Flow Sorting

After side-scatter exclusion, we displayed the remaining cells in a bivariate histogram with DNA content on the x-axis and cytokeratin (AE1AE3) on the y-axis. After the sorting gate was set as shown in Figure 2A, AE1AE3 detected an average of 1 ± 0.6% (mean ± SD) cytokeratin-positive epithelial cells (range: 0.2% to 2%) above the negative control threshold (Fig. 2B). The cytokeratin-positive cells were flow-sorted onto slides, stained with H&E, and counted (Fig. 3). The average percentage of respiratory epithelial cells from a mean of 218 cells per sample counted (N = 8) was 42% ± 19% (mean ± SD). The mean percentages of squamous cells and macrophages were 19% and 39%, respectively (Table 2).

thumbnail image

Figure 2. AE1AE3 cytokeratin and DNA content flow cytometry. Bivariate histogram displaying DAPI fluorescence on the x-axis (linear) and AE1AE3 cytokeratin fluorescence on the y-axis (logarithmic; 4 decades). Flow cytometric AE1AE3 cytokeratin gating to sort and enrich for respiratory epithelial cells from sputum sample no. 1567. A: AE1AE3 stained 1.9% of the cells as cytokeratin-positive epithelial cells. Cells in the sorted gate are shown in Figure 3. B: The gating region was set so that less than 1% of the control sample stained with the isotype control only fell into this window. In this example, 0.36% of the cells fell into the gating region, reflecting nonspecific staining.

Download figure to PowerPoint

thumbnail image

Figure 3. Cytology of flow cytometric AE1AE3 cytokeratin sorting to enrich for respiratory epithelial cells. The 1.9% gated cells shown in Figure 2B were sorted onto a slide and stained with H&E; shown at 20× (A) and 40× (B). The differential cell count showed 75% respiratory epithelial cells, 2% squamous cells, and 23% macrophages. [Color figure can be viewed in the online issue, which is available at].

Download figure to PowerPoint

Table 2. Differential Cell Counts From AE1AE3 Cytokeratin Flow-Sorted Sputum Samples (n = 8)
Cell typeMean ± SDMedianRange
% respiratory epithelial cells42.3 ± 18.646.814.9–74.7
% squamous cells18.6 ± 19.2122.3–60.8
% macrophages39.1 ± 10.842.822.9–50.6
No. total cells counted218 ± 74.924490–298

Enrichment of Respiratory Epithelial Cells

For each sample, we determined the enrichment of respiratory epithelial cells from the unsorted cells to the AE1AE3 sorted cells. In unsorted sputum samples, respiratory epithelial cells represent 1.1% of the cells, as assessed by differential counts of smears. After flow cytometric exclusion of contaminating cells the AE1AE3-positive respiratory epithelial cells represent 42.3% of the cells. Therefore, the average enrichment for the eight evaluated samples was 39-fold. In addition, we determined the enrichment for each individual sample, and the mean of all samples was 38-fold enrichment; however one patient whose smear contained no detectable respiratory epithelial cells could not be evaluated by this method because the calculation would have required division by zero.

Yield of Respiratory Epithelial Cells

We determined the total number of cells in each of the eight sputum samples after DTT treatment by hemacytometer, calculated the expected yield of respiratory epithelial cells by multiplying the total cell count by the percent cells in the cytokeratin-positive sorting gate, and multiplied this by the proportion of true respiratory cells in the sorted population (Fig. 2; Table 2). Based on these data, we estimated the average potential yield of respiratory epithelial cells in an induced sputum sample to be 32,285 ± 41,838 (mean ± SD), and the median was 12,806. Only one out of eight samples had a predicted yield of less than 6,500 cells.


  1. Top of page
  2. Abstract
  6. Acknowledgements

Cellular heterogeneity in sputum samples, which typically include only 1% respiratory epithelial cells, impairs biomarker analysis for early detection of lung cancer because the large excess of inflammatory cells limits detection and quantitation of neoplastic changes occurring in the epithelial cell populations. We describe a multiparameter flow cytometric approach to enrich the minority population of 1% respiratory epithelial cells in sputum samples to 42%, on average, using an AE1AE3 anti-cytokeratin/DNA content staining protocol, which is a 38-fold enrichment of respiratory epithelial cells relative to unsorted sputum samples. Although the absolute number of respiratory epithelial cells varies widely in different sputum samples, the mean predicted yield in the samples evaluated in this study was more than 32,000, sufficient for many types of quantitative biomarker analyses.

Previous studies have used flow cytometric cell sorting to enrich for defined cellular elements in sputum. For example, Alexis et al. (45) used flow cytometry to investigate whether sputum phagocytes have phenotypes indicative of increased functional activation and inflammation compared to phagocytes from the alveolar airways and peripheral blood in healthy subjects. In their study, gating of the different cell populations (macrophages, monocytes, polymorphonuclear neutrophils, eosinophils, and lymphocytes) in sputum was based on light-scatter properties, including forward-scatter for cell size, side-scatter for cell density/granularity, and antibody detection of positive/negative expression of relevant antigens such as CD45, a pan-leukocyte marker. This report is another example of this approach, in which respiratory epithelial cells can be selected based on similar criteria.

Accurate molecular analysis of human cancers and their precursor lesions requires identification and enrichment of subpopulations of cells from a composite background of cellular heterogeneity (35–43, 46–61). Multiparameter flow cytometry is a useful tool to purify cells of interest from human tissue and other human samples that are typically heterogenous. For our purposes, we adapted a multiparameter AE1AE3/DNA-content flow cytometric cell-sorting protocol that had been previously published for breast cancer (39), to enrich respiratory epithelial cells from sputum samples. Our results support the use of induced sputum specimens as an easily obtained and noninvasive source of biological material for flow cytometric detection and enrichment of rare respiratory epithelial subpopulations from a heterogeneous cellular background.

Recent biomarker studies of unprocessed sputum samples have detected abnormalities associated with lung cancer, thus demonstrating the feasibility of detecting molecular lesions in sputum (14–27). However, the ability to extend these results to early detection programs has not been established and may be limited in many cases. For example, some results required prior knowledge of specific p53 mutations in the tumor, a condition that cannot be readily adapted to mass screening (21). In other cases, positive signals have been detected in sputum, but it is not clear whether the abnormalities are in the epithelial cell targets of tobacco-induced field carcinogenesis or in other cells in the heterogeneous sputum mixtures (19, 20, 62, 63). Many biomarker tests, such as determination of loss of heterozygosity (LOH) or DNA sequencing, are inaccurate if the positive cells are a small minority of the total cells in a sample.

Although much work remains to be done to extend our results and those of others to early detection of lung cancer, our study shows that highly enriched populations of respiratory epithelial cells can be obtained from induced sputum samples by flow cytometric cell sorting. This helps overcome a major limitation to the study of neoplasia in sputum samples. Given the yield of enriched respiratory epithelial cells in these sputum samples, it should be possible to quantify biomarker abnormalities by fluorescence in situ hybridization (FISH), monoclonal antibodies, and other techniques. Purity may be further enhanced by adding a pan-leukocyte antibody (CD45) conjugated to FITC to further eliminate contaminating macrophages, or by depleting the samples with immunomagnetic beads to CD45 before flow sorting, which might make possible additional molecular testing, including DNA sequencing (for mutation analysis) and genotyping (for loss of heterozygosity analysis). For example, we have routinely used DNA content and multiparameter flow cytometric cell sorting to purify proliferating epithelial cells from Barrett's esophagus for molecular assays, including mutation detection, LOH analysis, and methylation detection (35–38, 40–43, 46, 47, 53, 64).

In summary, we report a technique to enrich sputum respiratory epithelial cells to 42% purity, with predicted average yields of greater than 32,000 cells. These proof-of-principle results overcome a major limitation to quantitative molecular analysis of induced sputum specimens as an easily obtained and noninvasive source of biological material in persons at increased risk for developing lung cancer. Development of quantitative biomarker panels that can be validated as predictors of progression to lung cancer in prospective studies may improve early detection as well as offering the possibility of validated surrogate endpoints in prevention trials (8, 65).


  1. Top of page
  2. Abstract
  6. Acknowledgements

This study was supported by SWOG 5 U10 CA 37 429 (to G.E.G.), Ryan Hill Research Foundation Seattle (to P.S.K.), NIH R01 CA 61202 (to B.J.R.), and NIH P01 CA 91955 (to B.J.R.).


  1. Top of page
  2. Abstract
  6. Acknowledgements
  • 1
    Greenlee RT, Hill-Harmon MB, Murray T, Thun M. Cancer statistics, 2001. CA Cancer J Clin 2001; 51: 1536.
  • 2
    Parkin DM, Bray F, Ferlay J, Pisani P. Estimating the world cancer burden: Globocan 2000. Int J Cancer 2001; 94: 153156.
  • 3
    Pisani P, Parkin DM, Bray F, Ferlay J. Estimates of the worldwide mortality from 25 cancers in 1990. Int J Cancer 1999; 83: 1829.
  • 4
    Jemal A, Thomas A, Murray T, Thun M. Cancer statistics. CA Cancer J Clin 2002; 52: 2347.
  • 5
    Ries LAG, Eisner MP, Kosary CL, Hankey BF, Miller BA, Clegg L, Mariott A, Fay MP, Feuer EJ, Edwards BK. SEER cancer statistics review, 1975–2000. Bethesda, MD: National Cancer Institute; 2003.
  • 6
    Clegg LX, Li FP, Hankey BF, Chu K, Edwards BK. Cancer survival among US whites and minorities: a SEER (surveillance, epidemiology, and end results) program population-based study. Arch Intern Med 2002; 162: 19851993.
  • 7
    Cancer Statistics Branch NCI. SEER extent of disease. Bethesda, MD: National Cancer Institute; 1988. p 1222.
  • 8
    Etzioni R, Urban N, Ramsey S, McIntosh M, Schwartz S, Reid B, Radich J, Anderson G, Hartwell L. The case for early detection. Nat Rev Cancer 2003; 3: 243252.
  • 9
    Melamed MR, Flehinger BJ, Zaman MB, Heelan RT, Perchick WA, Martini N. Screening for early lung cancer. Results of the Memorial Sloan-Kettering study in New York. Chest 1984; 86: 4453.
  • 10
    Fontana RS, Sanderson DR, Woolner LB, Taylor WF, Miller WE, Muhm JR, Bernatz PE, Payne WS, Pairolero PC, Bergstralh EJ. Screening for lung cancer. A critique of the Mayo Lung Project. Cancer 1991; 67: 11551164.
  • 11
    Fontana RS, Sanderson DR, Woolner LB, Taylor WF, Miller WE, Muhm JR. Lung cancer screening: the Mayo program. J Occup Med 1986; 28: 746750.
  • 12
    Marcus PM, Bergstralh EJ, Fagerstrom RM, Williams DE, Fontana R, Taylor WF, Prorok PC. Lung cancer mortality in the Mayo Lung Project: impact of extended follow-up. J Natl Cancer Inst 2000; 92: 13081316.
  • 13
    Melamed MR. Lung cancer screening results in the National Cancer Institute New York study. Cancer 2000; 89: 23562362.
  • 14
    Tockman MS. Clinical detection of lung cancer progression markers. J Cell Biochem Suppl 1996; 25: 177184.
  • 15
    Somers VA, Pietersen AM, Theunissen PH, Thunnissen FB. Detection of K-ras point mutations in sputum from patients with adenocarcinoma of the lung by point-EXACCT. J Clin Oncol 1998; 16: 30613068.
  • 16
    Yakubovskaya MS, Spiegelman V, Luo FC, Malaev S, Salnev A, Zborovskaya I, Gasparyan A, Polotsky B, Machaladze Z, Trachtenberg AC, Belitsky GA, Ronai Z. High frequency of K-ras mutations in normal appearing lung tissues and sputum of patients with lung cancer. Int J Cancer 1995; 63: 810814.
  • 17
    Miozzo M, Sozzi G, Musso K, Pilotti S, Incarbone M, Pastorino U, Pierotti MA. Microsatellite alterations in bronchial and sputum specimens of lung cancer patients. Cancer Res 1996; 56: 22852288.
  • 18
    Kersting M, Friedl C, Kraus A, Behn M, Pankow W, Schuermann M. Differential frequencies of p16(INK4a) promoter hypermethylation, p53 mutation, and K-ras mutation in exfoliative material mark the development of lung cancer in symptomatic chronic smokers. J Clin Oncol 2000; 18: 32213229.
  • 19
    Belinsky SA, Nikula KJ, Palmisano WA, Michels R, Saccomanno G, Gabrielson E, Baylin SB, Herman JG. Aberrant methylation of p16(INK4a) is an early event in lung cancer and a potential biomarker for early diagnosis. Proc Natl Acad Sci USA 1998; 95: 1189111896.
  • 20
    Palmisano WA, Divine KK, Saccomanno G, Gilliland FD, Baylin SB, Herman JG, Belinsky SA. Predicting lung cancer by detecting aberrant promoter methylation in sputum. Cancer Res 2000; 60: 59545958.
  • 21
    Mao L, Hruban RH, Boyle JO, Tockman M, Sidransky D. Detection of oncogene mutations in sputum precedes diagnosis of lung cancer. Cancer Res 1994; 54: 16341637.
  • 22
    Tockman MS, Gupta PK, Myers JD, Frost JK, Baylin SB, Gold EB, Chase AM, Wilkinson PH, Mulshine JL. Sensitive and specific monoclonal antibody recognition of human lung cancer antigen on preserved sputum cells: a new approach to early lung cancer detection. J Clin Oncol 1988; 6: 16851693.
  • 23
    Qiao YL, Tockman MS, Li L, Erozan YS, Yao SX, Barrett MJ, Zhou WH, Giffen CA, Luo XC, Taylor PR. A case-cohort study of an early biomarker of lung cancer in a screening cohort of Yunnan tin miners in China. Cancer Epidemiol Biomarkers Prev 1997; 6: 893900.
  • 24
    Gilliland FD, Harms HJ, Crowell RE, Li YF, Willink R, Belinsky SA. Glutathione S-transferase P1 and NADPH quinone oxidoreductase polymorphisms are associated with aberrant promoter methylation of P16(INK4a) and O(6)-methylguanine-DNA methyltransferase in sputum. Cancer Res 2002; 62: 22482252.
  • 25
    Belinsky SA, Palmisano WA, Gilliland FD, Crooks LA, Divine KK, Winters SA, Grimes MJ, Harms HJ, Tellez CS, Smith TM, Moots PP, Lechner JF, Stidley CA, Crowell RE. Aberrant promoter methylation in bronchial epithelium and sputum from current and former smokers. Cancer Res 2002; 62: 23702377.
  • 26
    Mao L, Lee DJ, Tockman MS, Erozan YS, Askin F, Sidransky D. Microsatellite alterations as clonal markers for the detection of human cancer. Proc Natl Acad Sci USA 1994; 91: 98719875.
  • 27
    Tockman MS, Mulshine JL, Piantadosi S, Erozan YS, Gupta PK, Ruckdeschel JC, Taylor PR, Zhukov T, Zhou WH, Qiao YL, Yao SX. Prospective detection of preclinical lung cancer: results from two studies of heterogeneous nuclear ribonucleoprotein A2/B1 overexpression. Clin Cancer Res 1997; 3: 22372246.
  • 28
    Kips JC, Peleman RA, Pauwels RA. Methods of examining induced sputum: do differences matter? Eur Respir J 1998; 11: 529533.
  • 29
    Gershman NH, Wong HH, Liu JT, Mahlmeister MJ, Fahy JV. Comparison of two methods of collecting induced sputum in asthmatic subjects. Eur Respir J 1996; 9: 24482453.
  • 30
    Peleman RA, Rytilae PH, Kips JC, Joos GF, Pauwels RA. Sputum analysis in chronic obstructive pulmonary disease (COPD): comparison of two methods. Am J Respir Crit Care Med 1995; 151: A486.
  • 31
    Spanevello A, Confalonieri M, Sulotto F, Romano F, Balzano G, Migliori GB, Bianchi A, Michetti G. Induced sputum cellularity. Reference values and distribution in normal volunteers. Am J Respir Crit Care Med 2000; 162: 11721174.
  • 32
    Belda J, Leigh R, Parameswaran K, O'Byrne PM, Sears MR, Hargreave FE. Induced sputum cell counts in healthy adults. Am J Respir Crit Care Med 2000; 161: 475478.
  • 33
    Lensmar C, Elmberger G, Sandgren P, Skold CM, Eklund A. Leukocyte counts and macrophage phenotypes in induced sputum and bronchoalveolar lavage fluid from normal subjects. Eur Respir J 1998; 12: 595600.
  • 34
    Domagala-Kulawik J, Maskey-Warzechowska M, Kraszewska I, Chazan R. The cellular composition and macrophage phenotype in induced sputum in smokers and ex-smokers with COPD. Chest 2003; 123: 10541059.
  • 35
    Galipeau PC, Cowan DS, Sanchez CA, Barrett MT, Emond MJ, Levine DS, Rabinovitch PS, Reid BJ. 17p (p53) allelic losses, 4N (G2/tetraploid) populations, and progression to aneuploidy in Barrett's esophagus. Proc Natl Acad Sci USA 1996; 93: 70817084.
  • 36
    Barrett MT, Sanchez CA, Prevo LJ, Wong DJ, Galipeau PC, Paulson TG, Rabinovitch PS, Reid BJ. Evolution of neoplastic cell lineages in Barrett oesophagus. Nat Genet 1999; 22: 106109.
  • 37
    Blount PL, Galipeau PC, Sanchez CA, Neshat K, Levine DS, Yin J, Suzuki H, Abraham JM, Meltzer SJ, Reid BJ. 17p allelic losses in diploid cells of patients with Barrett's esophagus who develop aneuploidy. Cancer Res 1994; 54: 22922295.
  • 38
    Barrett MT, Sanchez CA, Galipeau PC, Neshat K, Emond M, Reid BJ. Allelic loss of 9p21 and mutation of the CDKN2/p16 gene develop as early lesions during neoplastic progression in Barrett's esophagus. Oncogene 1996; 13: 18671873.
  • 39
    Glogovac JK, Porter PL, Banker DE, Rabinovitch PS. Cytokeratin labeling of breast cancer cells extracted from paraffin-embedded tissue for bivariate flow cytometric analysis. Cytometry 1996; 24: 260267.
  • 40
    Wong DJ, Paulson TG, Prevo LJ, Galipeau PC, Longton G, Blount PL, Reid BJ. p16 INK4a lesions are common, early abnormalities that undergo clonal expansion in Barrett's metaplastic epithelium. Cancer Res 2001; 61: 82848289.
  • 41
    Reid BJ, Prevo LJ, Galipeau PC, Sanchez CA, Longton G, Levine DS, Blount PL, Rabinovitch PS. Predictors of progression in Barrett's esophagus II: baseline 17p (p53) loss of heterozygosity identifies a patient subset at increased risk for neoplastic progression. Am J Gastroenterol 2001; 96: 28392848.
    Direct Link:
  • 42
    Galipeau PC, Prevo LJ, Sanchez CA, Longton GM, Reid BJ. Clonal expansion and loss of heterozygosity at chromosomes 9p and 17p in premalignant esophageal (Barrett's) tissue. J Natl Cancer Inst 1999; 91: 20872095.
  • 43
    Reid BJ, Sanchez CA, Blount PL, Levine DS. Barrett's esophagus: cell cycle abnormalities in advancing stages of neoplastic progression. Gastroenterology 1993; 105: 119129.
  • 44
    Tockman MS, Qiao Y, Li L, Zhao GZ, Sharma R, Cavenaugh LL, Erozan YS. Safe separation of sputum cells from mucoid glycoprotein. Acta Cytol 1995; 39: 11281136.
  • 45
    Alexis N, Soukup J, Ghio A, Becker S. Sputum phagocytes from healthy individuals are functional and activated: a flow cytometric comparison with cells in bronchoalveolar lavage and peripheral blood. Clin Immunol 2000; 97: 2132.
  • 46
    Blount PL, Ramel S, Raskind WH, Haggitt RC, Sanchez CA, Dean PJ, Rabinovitch PS, Reid BJ. 17p allelic deletions and p53 protein overexpression in Barrett's adenocarcinoma. Cancer Res 1991; 51: 54825486.
  • 47
    Ramel S, Reid BJ, Sanchez CA, Blount PL, Levine DS, Neshat K, Haggitt RC, Dean PJ, Thor K, Rabinovitch PS. Evaluation of p53 protein expression in Barrett's esophagus by two-parameter flow cytometry. Gastroenterology 1992; 102: 12201228.
  • 48
    Blount PL, Meltzer SJ, Yin J, Huang Y, Krasna MJ, Reid BJ. Clonal ordering of 17p and 5q allelic losses in Barrett dysplasia and adenocarcinoma. Proc Natl Acad Sci USA 1993; 90: 32213225.
  • 49
    Burmer GC, Rabinovitch PS, Loeb LA. Analysis of c-Ki-ras mutations in human colon carcinoma by cell sorting, polymerase chain reaction, and DNA sequencing. Cancer Res 1989; 49: 21412146.
  • 50
    Burmer GC, Levine DS, Kulander BG, Haggitt RC, Rubin CE, Rabinovitch PS. c-Ki-ras mutations in chronic ulcerative colitis and sporadic colon carcinoma. Gastroenterology 1990; 99: 416420.
  • 51
    Burmer GC, Crispin DA, Kolli VR, Haggitt RC, Kulander BG, Rubin CE, Rabinovitch PS. Frequent loss of a p53 allele in carcinomas and their precursors in ulcerative colitis. Cancer Commun 1991; 3: 167172.
  • 52
    Burmer GC, Rabinovitch PS, Haggitt RC, Crispin DA, Brentnall TA, Kolli VR, Stevens AC, Rubin CE. Neoplastic progression in ulcerative colitis: histology, DNA content, and loss of a p53 allele. Gastroenterology 1992; 103: 16021610.
  • 53
    Prevo LJ, Sanchez CA, Galipeau PC, Reid BJ. p53-mutant clones and field effects in Barrett's esophagus. Cancer Res 1999; 59: 47844787.
  • 54
    Barrett MT, Sanchez CA, Prevo LJ, Galipeau PC, Wong DJ, Reid BJ. Early events during neoplastic progression in Barrett's esophagus. In: SrivastavaS, HensonDE, GasdarA, editors. Molecular pathology of early cancer. IOS Press; Amsterdam, The Netherlands 1999. p 179188.
  • 55
    Reid BJ, Haggitt RC, Rubin CE, Rabinovitch PS. Barrett's esophagus. Correlation between flow cytometry and histology in detection of patients at risk for adenocarcinoma. Gastroenterology 1987; 93: 111.
  • 56
    Reid BJ, Blount PL, Rubin CE, Levine DS, Haggitt RC, Rabinovitch PS. Flow-cytometric and histological progression to malignancy in Barrett's esophagus: prospective endoscopic surveillance of a cohort. Gastroenterology 1992; 102: 12121219.
  • 57
    Reid BJ, Levine DS, Longton G, Blount PL, Rabinovitch PS. Predictors of progression to cancer in Barrett's esophagus: baseline histology and flow cytometry identify low- and high-risk patient subsets. Am J Gastroenterol 2000; 95: 16691676.
  • 58
    Reid BJ. p53 and neoplastic progression in Barrett's esophagus. Am J Gastroenterol 2001; 96: 13211323.
    Direct Link:
  • 59
    Rabinovitch PS, Longton G, Blount PL, Levine DS, Reid BJ. Predictors of progression in Barrett's esophagus III: baseline flow cytometric variables. Am J Gastroenterol 2001; 96: 30713083.
    Direct Link:
  • 60
    Schubert EL, Hsu L, Cousens LA, Glogovac J, Self S, Reid BJ, Rabinovitch PS, Porter PL. Single nucleotide polymorphism array analysis of flow-sorted epithelial cells from frozen versus fixed tissues for whole genome analysis of allelic loss in breast cancer. Am J Pathol 2002; 160: 7379.
  • 61
    Mei R, Galipeau PC, Prass C, Berno A, Ghandour G, Patil N, Wolff RK, Chee MS, Reid BJ, Lockhart DJ. Genome-wide detection of loss of heterozygosity with single nucleotide polymorphisms and high density DNA arrays. Genome Res 2000; 10: 11261137.
  • 62
    Issa JP. Epigenetic variation and human disease. J Nutr 2002; 132: 2388S2392S.
  • 63
    Toyota M, Itoh F, Kikuchi T, Satoh A, Obata T, Suzuki H, Ishii S, Endo T, Tokino T, Imai K. DNA methylation changes in gastrointestinal disease. J Gastroenterol 2002; 37 (Suppl 14): 97101.
  • 64
    Wong DJ, Barrett MT, Stoger R, Emond MJ, Reid BJ. p16INK4a promoter is hypermethylated at a high frequency in esophageal adenocarcinomas. Cancer Res 1997; 57: 26192622.
  • 65
    Sullivan Pepe M, Etzioni R, Feng Z, Potter JD, Thompson ML, Thornquist M, Winget M, Yasui Y. Phases of biomarker development for early detection of cancer. J Natl Cancer Inst 2001; 93: 10541061.