help button home button
AJRCMB
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Cave-Riant, F.
Right arrow Articles by Gaub, M. P.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Cave-Riant, F.
Right arrow Articles by Gaub, M. P.
American Journal of Respiratory Cell and Molecular Biology. Vol. 27, pp. 495-502, 2002
© 2002 American Thoracic Society
DOI: 10.1165/rcmb.4800

Association of Genetic Defects in Primary Resected Lung Adenocarcinoma Revealed by Targeted Allelic Imbalance Analysis

Florence Cave-Riant, Benoit Cuillerier, Michèle Beau-Faller, Nadine Martinet, François Alla, Christian Bronner, Anne Schneider, Pierre Oudet and Marie Pierre Gaub

Laboratoire de Biochimie-biologie Moléculaire and Laboratoire d'Immunologie, Hôpitaux Universitaires de Strasbourg, Strasbourg; Centre Lorrain d'Etude et de Recherche contre les Canceres Pulmonaires, Faculté de Médecine de Nancy, Vandoeuvre-Lès-Nancy; Centre d'informatique médicale, Centre Hospitalier Régional, Nancy; INSERM U 425, Faculté de Pharmacie, Illkirch; and Service de Pneumologie, HUS de Strasbourg, Strasbourg, France

Address correspondence to: Marie-Pierre Gaub, Ph.D., Laboratoire de Biochimie-biologie Moléculaire, Hôpital de Hautepierre, avenue Molière, 67098 Strasbourg cedex, France. E-Mail: gaub{at}dicdoc.inserm.fr


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Genetic mechanisms underlying origin and progression of lung cancer are still poorly understood, despite the numerous studies which identified many genomic alterations. Using polymorphic microsatellites, allelic imbalances have been frequently found at loci such as 3p, 5q, 8p, 9p and 9q, 11p and 11q, and 17q without either histologic specificity or prognosis value. We report allelotyping results in 54 cases (50 smokers) of primary lung adenocarcinoma (50 men/4 women) resected at one institution. To perform this study, a panel of seven microsatellites were chosen upon their likely involvement in lung cancer or in the cell cycle. A highly sensitive method was designed using fluorescent PCR coupled with quantification on an automated DNA sequencer. We report that at least one allelic imbalance was observed in 87% of adenocarcinoma. Alterations at 17q23 tended to be associated with early stage tumors (I and II) and longer survivals (P = 0.05 and P = 0.06, respectively). Furthermore, concomitant alterations were found at 9p21 and at either 9q34 or 3p24 loci (P = 0.003 and P = 0.004, respectively). The presence of genes coding for TGF-ß receptors I and II at these loci suggests that the TGF-ß/CDK inhibitor P16/P15 signaling pathway might be involved in lung adenocarcinoma development.

Abbreviations: allelic imbalance, AI • adenocarcinoma, ADC • cyclin-dependent kinase, CDK • microsatellite, MS • non–small cell lung carcinoma, NSCLC • polymerase chain reaction, PCR • small cell lung cancer, SCLC • transforming growth factor-ß, TGF-ß • TGF-ß receptor, TGF-ßR


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Lung cancer is one of the leading causes of cancer death in the industrialized world, with a rapidly increasing incidence in females (1–3). It is classified as non–small-cell and small-cell lung cancer (NSCLC and SCLC, respectively). About 80% of primary lung cancers are NSCLCs, including three major histologic subtypes: squamous-cell lung carcinoma (SQCLC), adenocarcinoma (ADC), and large cell carcinoma (4). Although lung cancer affects mainly men and smokers, frequency of ADC is increasing in women and nonsmokers by comparison to the other NSCLC subtypes, suggesting a different etiology (5–8).

The currently accepted model of carcinogenesis is the so-called stepwise accumulation/mutation model, in which the progressive accumulation of genomic defects confers growth and/or clonal advantages to the affected cells (9–11). The characterization of these defects, and more specifically the preferential association of defects involved in lung cancer, would help in the development of molecular markers for early detection and in the identification of poor prognosis tumors requiring postsurgical adjuvant therapy.

Extensive NSCLC cytogenetic analysis and comparative genomic hybridization allowed the characterization of several alterations in oncogenes such as Cyclin D1 and c-Myc amplifications and K-Ras mutations (12, 13). Likewise, inactivation of tumor suppressor genes were shown to be critical in lung cancer development. Losses of function for RB, P53, retinoid receptors, and cyclin-dependent kinase inhibitors like P16 have already been reported (14–18). Furthermore, frequent microsatellites (MS) allelic losses or gains were observed on chromosomes 3p, 5q, 8p, 9p, 9q, 11p, 11q, and 17q. Previous reported results were obtained mainly in either NSCLC or SCLC (19–20). Because NSCLCs include heterogeneous subgroups of lung tumors, determination of specific prognostic values of such alterations was impaired. In addition, the random choice of tested markers, and the low sensitivity of currently used assays, may explain discrepancies in reported results (17, 21–24).

To find frequently associated genetic defects involved in ADC, our study was focused on specific genes or loci either involved in the cell cycle or known to be highly rearranged in lung cancer. A consecutive population of 63 ADC was screened. Using fluorescent-labeled primers allowed a more sensitive quantification of PCR products compared with more classical radioactive assays (25). Prognosis value of allelotyping results was statistically for 54 fully resected lung ADCs. The selected MS were mapped at 17p13 in the first intron of P53 (TP53), at 9p21 close to the CDK inhibitors P15/P16 (D9S171), at 9q 34 (D9S179), at 17q23 (D17S794), and at 20q11 (D20S107). In these regions, several genes have been previously mapped, such as transforming growth factor-ß (TGF-ß) receptor I (TGFßRI) and RXR{alpha} (9q34), BRCA1 (17q23), or Znf217 (20q11). Finally, two microsatellites were studied, one (D3S1283) at 3p24-p22 in the vicinity of the TGFßRII) and another one which contains a mononucleotide repeat and was localized into the TGFßRII coding sequence (named TGFßRII). Our targeted allelotyping allowed us to detect at least one allelic imbalance (AI) in most of the ADCs. In addition, statistical analysis revealed that AI at 17q23 locus tended to be correlated with both a longer survival and an early stage tumor. Furthermore, AI associations mainly between loci localized at 9p21 and at either 3p24-p22 or 9q34 were observed. This suggests that co-operative functions mapping at these loci could be involved in tumor development.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Patients
Sixty-three consecutive patients referred for surgery to the Thoracic Surgery Department (Centre Hospitalier Universitaire, Nancy, France) were enrolled in this study. All were diagnosed with primitive resectable lung cancer by standard procedures. At the time of tumor resection, relevant nodes and tumor samples were taken for pathologic diagnosis and tumor staging according to the international tumor histotyping and staging nomenclature of lung cancer (4). Pathologic examinations of tumors were performed to confirm the presence of tumor cells in the resected sample. Only primitive lung ADCs were included in the study. In addition, non-tumor tissue was obtained at distance of the tumor specimen and used as paired control. This control DNA was prepared from a large cell population of healthy tissue. Because most of the microalterations in normal tissue have only been observed in few hundred microdissected cells (26–28), the presence of such microtumor clones would not significantly affect the sensitivity of our test. Tissue specimens were snap-frozen in liquid nitrogen and stored at -80°C before use.

Tissue DNA Extraction
Four to five slices (50 µm) of normal and tumor lung frozen paired tissues were incubated at 37°C in lysis buffer (500 µl) (urea 8 M, SDS 2%, EDTA 10 mM, NaCl 0.3 M, Tris 10 mM, pH 8) followed by an overnight digestion at 37°C with proteinase K (200 µg/ml). After two successive phenol-chloroform extractions, DNA was precipitated in ethanol, then dissolved in 200 µl of 20 mM Tris-HCL, 1 mM EDTA, pH 7.5.

MS, Polymerase Chain Reaction Amplifications, and Analysis
Extracted DNA from each paired non-tumor/tumor specimens were amplified by polymerase chain reaction (PCR) using previously described primers for seven polymorphic MS localized at 17p13 into P53 (TP53), at 9p21 near P16 (D9S171), at 9q34 (D9S179), at 3p24-p22 (D3S1283), into TGFßRII (3p22), at 17q23 (D17S794), and at 20q11 (D20S107) (29–31). Primer sequences were obtained from the Genome database (http://www.ncbi.nlm.nih.gov/genemap99). One primer for each MS was labeled at 5' end with the fluorescent dye Cy5. PCR amplifications were performed by combining 200 ng of genomic DNA in 25 µl of PCR buffer (1.5 mM MgCl2, 50 mM KCl, 20 mM Tris-HCl, pH 8.4), with 0.6 unit of Taq polymerase (GIBCO-BRL Life Technology, Cergy Pontaise Cedex, France), 80 µM of each deoxynucleotide phosphate, and 0.16 µM of each primer. After denaturation at 94°C for 7 min, amplifications were performed for 30 cycles as follows: 95°C for 1 min, 55°C for 1 min, 72°C for 1 min, and by a single final 5-min extension step at 72°C (Omnigen Hybaid Thermocycler; Thermolab System, Cergy Pontaise Cedex, France). The amplified fragments were fractionated on denaturing 6% urea gels on an ALF Sequencer (Amersham-Pharmacia Biotech Saclay, Orsay Cedex, France). Labeled PCR products were detected and quantified with the Allelelinks software package (Amersham-Pharmacia Biotech). Analysis were performed blindly without taking into account the clinico-pathologic features. AI was defined by an alteration of the allele ratio in tumor DNA compared with the allele ratio obtained from the paired normal DNA. The presence of an AI was confirmed by at least two independent PCRs of both paired DNAs.

Statistical Analysis
Statistical analysis was performed to identify any significant relationship between ADCs overall outcomes and AI. Quantities were expressed as means ± SD. Survival Kaplan-Meier curves were drawn together with log-rank tests. Association between two markers were tested by using the Fisher's exact two-tailed test and Bonferroni's correction when required. Probability values were calculated with the SPSS software.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Cutoff Values and Frequency of MS Alterations in the ADC Population
Only 54 of 63 patients (50 men and 4 women) with primary ADC were included in the final statistical analysis. Indeed, nine patients were excluded from the statistical analysis because they had malignant cells in the resection margins (three cases), DNA amplifications were unsuccessful (five cases), and one tumor demonstrated only MS instabilities at all loci, indicating a repair error phenotype (RER). The average age of patients was 59 ± 10 yr, with a mean of 30 ± 23 mo of survival. Follow-up clinical data and standard imaging methods were both used to determine the origin of patients' death due to cancer recurrence (relapsing patients) or other causes (non–cancer-related deaths). Survivors were patients free from recurrent local or distant ADC. One patient (number 36) presented a bronchioalveolar carcinoma. Other characteristics of the population are given in Table 1. At the end of the study, 22% patients were still alive, 67% had relapsed and died, and 11% died of non–cancer-related causes. Significant correlation was observed between the early stages (I and II) of the disease and the overall survival (P = 0.004).


View this table:
[in this window]
[in a new window]
  		TABLE 1 Population characteristics

 
Because AI value varied with the amount of tumor cells present in the sample, we aimed to increase the allelotyping sensitivity to detect the smallest possible fraction of tumor cells and thereby the smallest significant MS allele ratio alteration. (Figure 1). We first evaluated the method in testing the reproducibility of the PCR assays using a semiautomated fluorescent PCR sequencing analyzer (32). Peak height of the signal was measured for each allele of the considered MS, because the reproducibility obtained by measuring the peak height was found more accurate than when measuring the area under the curve. AI value was calculated as the difference between the height ratio of the two alleles for the tumor and for the paired normal control, divided by the alleles ratio for the normal paired control and expressed as a percentage (33). The feasibility of such analysis was determined in a previous study (32), and MS cutoffs were verified by amplifying twice each normal leukocyte and paired normal tissue DNA from 30 different patients (34). From these data, the AI cutoff values with a standard deviation of 3 were 14, 9, 10, 7, 9, and 14% for TP53, D9S171, D9S179, D3S1283, D17S794 and D20S107, respectively. However, no intensity of AI below 20% was found in our study because tumor specimens were always roughly macrodissected. To demonstrate the linearity of detection of tumor cells for each heterozygous MS, amplifications of serial increasing dilutions of tumor DNA in paired normal tissues DNA were performed. A significant AI value was observed with a 1/4 dilution for all tested samples, but not with a 1/10 dilution (Figure 2 and data not shown). Assuming that the AI intensities were dependent on the amount of tumor cells, our experiments suggested that an AI could be detected even when only 20% of rearranged clonal cells were present in the tested sample. As expected from the genome database (GDB), five markers were highly heterozygous (from 67–87%), but D17S794 was found less informative (52% instead of the expected 75%). The TGFßRII MS was chosen because it contains a mononucleotide repeat and allows microsatellite instability (MSI) assessment, but no MSI was observed at this locus. Only one case showed an RER phenotype. This case was excluded for further analysis because the RER phenotype corresponds to a quite different genetic lesion (mutations of DNA repair genes) by comparison to the classical loss of heterozygosity (LOH)/AI phenotype.



View larger version (34K):
[in this window]
[in a new window]
  		Figure 1. Allelic imbalance (AI) profiles. (A and B) Genomic DNA was extracted from normal lung tissue (B) and from tumors (L). A and B show an example of the reproducibility of PCR. D3S1283 amplified fragments were analyzed as described in MATERIALS AND METHODS. Alleles in tumor (L) presented an allele ratio identical to the one obtained in the normal tissue (B). (C and D). PCR fragments of D17S794 were analyzed as described in MATERIALS AND METHODS. C and D show an example of consecutive PCRs. In this case, the locus was informative and showed a loss of heterozygosity in the tumor (L) when compared with the normal tissue (B). Such a result is representative of an allelic imbalance (AI).

 


View larger version (39K):
[in this window]
[in a new window]
  		Figure 2. Example of serial dilution of tumor DNA with normal-paired DNA. PCR and electrophoresis were performed as described previously in MATERIALS AND METHODS. When an AI of 80% was observed in tumor DNA (lane 3) as compared with normal-paired DNA (lane 1), the 1/4 dilution (25/75, lane 13) still presented a significant AI (17%), in contrast to the 1/10 dilution (lane 15), which lead to no significant alteration of the allele ratio (6%).

 
Overall, with this panel of MS, 47/54 (87%) tumor specimens showed AI at least at one locus (Table 2). Most of the tumors appeared to be highly altered, because 33/54 (61%) showed AI at more than 50% of their informative loci (Table 2), but 26% (14/54) of the tumors presented only one AI. However, 7/54 showed no AI at all, even though the pathologist confirmed the presence of more than 50% of tumor cells in these samples.


View this table:
[in this window]
[in a new window]
  		TABLE 2 Clinicopathologic characteristics and allelotyping results of the 54 lung adenocarcinomas

 
Frequency of Alterations per MS: Evidence of Associations of Alteration
Individually, each MS appeared to be frequently altered in ADC (Table 2). Among the six MS, five showed AI frequencies ranging from 52–65%, TP53 and D9S171 demonstrating the highest AI levels. In contrast, MS localized at 20q11 was rearranged in only 31% of the tumors. Interestingly, loci at both arms of the chromosome 9 (D9S171 [9p21] and D9S179 [9q34]) were frequently lost (65 and 58% of the informative tumors, respectively).

Because the multistep origin of the cancer is now widely accepted, we were interested in searching for possible preferential associations of genetic defects in ADC. To determine preferential associations, a statistical analysis was performed using Fisher's exact test in multiple comparisons (Table 3). Interestingly, AI associations were found between D3S1283 and D9S171 (P = 0.004) and between D9S179 and D9S171 (P = 0.003).


View this table:
[in this window]
[in a new window]
  		TABLE 3 Search for significant allelic imbalance associations for the six informative microsatellites

 
Alterations of MS and Correlation to Survival and/or Clinical Data
Our allelotyping was compared with patients' survival and clinical data. No significant correlation was found between the frequency of AI (expressed as a percentage of the number of informative loci) and the smoking status of the patients, or the early I and II tumor staging versus III and IV (Table 2). Tumors were next separated into two groups depending on the number of AIs (group I, one AI; group II, more than one AI). Kaplan-Meier analyses did not show any significant correlation between the number of AI per patient and a longer survival (data not shown). Because TP53, D9S171 (9p21), or D9S179 (9q34) were frequently the only AI observed in tumors with a single MS alteration, we looked for a relation between the presence of a unique AI per tumor affecting one of these three loci and a longer survival, but we did not find any significant correlation. In addition, Kaplan-Meier analyses were performed to study the relationship between the presence of AI at a specific locus and a patient's survival. No correlation was observed between AI at TP53 and survival (P = 0.8) (Figure 3A), nor for the other markers (data not shown), except for AI at D17S794 (Figure 3B). Indeed, we observed that patients with 17q23 AI tended toward a longer survival (log-rank test P = 0.06). The presence of AI at 17q23 was also associated to early stage tumors (stages I and II) (P = 0.05).



View larger version (13K):
[in this window]
[in a new window]
  		Figure 3. Survival correlation to AI. (A) Kaplan–Meier survival curves depending on the presence of TP53-specific AI. No significant correlation (P = 0.8) was found between tumors with (n = 18) or without AI (n = 29) at TP53 locus and survival. Mean of survival with AI= 48.5 mo and without AI = 27 mo. (B) Kaplan–Meier survival curves depending on the presence of D17S794-specific AI. Only 28 patients were informative at this locus, 12 of 28 without and 16 of 28 with AI. The presence of AI at D17S794 (n = 16) was associated with a trend to a survival advantage (P = 0.06).

 

   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
Up to now, very few genetic markers have been shown to be of prognostic significance in ADC only, because most of the previous similar studies included all subtypes of NSCLC. In this study, a well-defined population (mainly men) of 54 fully resected ADCs, with complete clinical and pathologic follow-up, is presented. Allelotyping studies, requiring very small amounts of DNA, are powerful tools to reveal simultaneously AIs at several genes or loci in the same tumor. We identified only one case of RER phenotype in the analyzed cohort. This is in accordance with previous results demonstrating few mutator phenotypes in frozen samples of lung cancer (18, 35, 36). After exclusion of the RER case, AI were observed in 47/54 (87%) tumors at one or more of the loci examined, according to previous allelotyping studies (37–39). Furthermore, the overall high frequency of AI in our study is in agreement with those of Virmani and coworkers (23) and Zhou and colleagues (40) performed on cell lines or microdissected tumors, thus confirming the high sensitivity and reproducibility of fluorescent technology, which is reached without requirement of sample microdissection (Figure 1). Thus, the use of a fluorescent PCR technology in combination with the selection of a defined MS panel appear to be highly sensitive for the detection of lung clonal tumor cells (41).

Interestingly, we report a high level of alterations at the 9q34 locus (58%) in ADCs. Alteration at this locus was also described by Neville and coworkers (22) but in only 1/9 cases of ADC by using more telomeric markers than our own. Because their study included only a very small number of ADCs, it could not be stated whether several regions of rearrangement lay at 9q34. Our results suggest that the marker D9S179 could be informative for genes involved in ADCs. Genes such as ABL, TGFBRI, or TRAF1 have been mapped at this locus (42). Further studies are required to determine the smallest altered chromosomal region (deleted or amplified) to identify the cellular impaired function.

According to Virmani and associates (23), AI at the 9p21 locus was frequently observed in ADC (65%). LOH or AI at the 9p21 locus has been very frequently reported as associated with the loss of function of CDK inhibitor p15/p16, in a wide range of tumors including lung cancer (43–45). In our study, 39/47 (83%) tumors had an AI either at p53 or 9p21 (p15/p16INK4) loci, and both markers were most frequently the only altered MS in our ADC specimens, suggesting, once more, their involvement in the development of NSCLC as reported previously (14–17). Among the seven tumors without AI, two samples showed additional AI at the RB locus (data not shown), pointing out the impairment of the G1 to S cell cycle transition in ADC. Remarkably, unique AI were rarely observed at either the 17q23, 20q11, or the 3p24-p22 locus, suggesting that these alterations appear late in the tumor progression in comparison to the three others.

Taking into account the clinical data, we did not observe any correlation between AI and the smoking status of the patients but, possibly, our nonsmoker population is too small to conclude. No significant correlation was observed either between AI number and the tumor staging, or between a low number of AI per patient and a longer survival. Such latter results do not agree with the canonical multistep accumulation of genomic alterations associated with tumor progression as described for colorectal cancer and already observed in lung cancer (18). These discrepancies could probably be due to the choice of the panel of MS analyzed.

Altogether, our MS allelotyping is rather in agreement with the idea that association of some very specific chromosome regions are essential for the initial development of ADCs (46). Nevertheless, further studies are required to define the real implication of such genes in the ADC development.

Two preferential AI associations were observed in ADC at D9S179/D9S171 and at D3S1283/D9S171 loci. They involve a common locus at 9p21, suggesting that genes located at 9q34 and 3p24 could act independently, but co-operatively with 9p21 marker in the same oncogenesis pathway. From the genome database, very few genes close to these markers could be potentially involved in such cooperation, but, as mentioned above, ABL and TGFßRI genes lay at 9q34 and P16/P15 CDK inhibitors near D9S171. In addition, among other genes in the vicinity of D3S1283, it is interesting to notice the presence of the TGFßRII gene. Thus, in both types of significant associations of alteration, we observed that the p15/p16 locus could be involved together with TGFßR either through an alteration at the 9q34 locus containing TGFßRI or at the 3p24-p22 locus containing TGFßRII, both forming the heteromeric functional receptor. Involvement of TGFßRII but rarely TGFßRI has been already described in NSCLCs and in SCLCs (47–49). Until now, very few associations of genetic defects have been described in lung tumors. In NSCLC, an inverse correlation was described between RB and p16 (15) and between p19ARF and P53 alteration (50). Further precise mapping of the genetic defects at these loci would help to understand the molecular mechanisms underlying such associations.

Although accumulation of chromosome rearrangements is often observed during progression of most cancers, some rearrangements could result in neutral mutations or could function as regulators of tumor cell proliferation. According to this, patients with AI at the 17q23 locus showed a trend toward a longer survival (P = 0.06). Furthermore, we not only found a significant correlation between AI at 17q23 and early tumor stages (I and II) (P = 0.05), but also between patients with early stage tumors and significant longer survival (P = 0.004); these results are in agreement with the wildspread knowledge that longer survival are associated with lower tumor stages I and II in all lung cancer (51). Few genes, mapping at this locus, appear to be good candidates for a better prognosis when altered, because neither oncogenes nor tumor suppressor genes could be logically involved in such a positive effect. Because controversial results concerning the 17q arm have been reported (19–23), further analysis on an increasing number of patients would allow a more significant correlation and quantitative PCR will determine the type of alteration (amplification or deletion).

In conclusion, our present study allowed us to define a panel of very informative MS able to detect tumor cells in 87% of ADCs. It could be used as a new sensitive tool for diagnosis and follow-up. Finally, our results suggest that the TGFß/P16 signaling pathway would be mainly involved in ADCs. Furthermore, as previously described in SCLCs (52), our panel of MS could be used to detect MS alteration in plasma DNA of patients with lung cancer at diagnosis and during progression.


   Acknowledgments
 
The authors acknowledge Dr. G. Hamel and Dr. E. Guérin for helpful discussions during the redaction of this work, Michael Kelly and Marie Hélène Bohler for their careful advice, and Odile Régine and the technical staff for their expertise and their help in the training of the students. This work was supported by the Ligue contre le cancer de Lorraine, du Haut Rhin and du Bas Rhin, and the HUS of Strasbourg.

Received in original form December 14, 2001

Received in final form May 30, 2002


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

  1. Ernster, V. L. 1996. Female lung cancer. Annu. Rev. Public Health 17:97–114.[Medline]
  2. Levi, F., L. A. Vecchia, C. Lucchini, and F. E. Negri. 1996. World wide trends in cancer mortality in the elderly, 1955–1992. Eur. J. Cancer 32:652–672.
  3. Frieberg, J. S., and L. R. Kelser. 1997. Epidemiology of lung cancer. Semin. Thorac. Cardiovasc. Surgery 9:56–59.
  4. Mountain, C. F. 1997. Revisions in the international system for staging lung cancer. Chest 111:1710–1717.[Abstract/Free Full Text]
  5. Valaitis, J., S. Warren, and D. Gamble. 1981. Increasing incidence of adenocarcinoma of the lung. Cancer 47:1042–1046.[Medline]
  6. Andrews, J. L., S. Bloom, K. Balogh, and J. F. Beamis. 1985. Lung cancer in women: Lahey clinic experience, 1957–1980. Cancer 55:2894–2898.[Medline]
  7. Thun, M. J., C. A. Lally, J. T. Flannery, E. E. Calle, W. D. Flanders and C. W. Heath, Jr. 1997. Cigarette smoking and changes in the histopathology of lung cancer. J. Nat. Cancer Inst. 89:1580–1586.[Abstract/Free Full Text]
  8. Charloux, A., E. Quoix, N. Wolkowe, D. Small, G. Pauli, and H. Kreisman. 1997. The increasing incidence of lung adenocarcinoma: reality or artefact? A review of the epidemiology of lung adenocarcinoma. Int. J. Epidemiol 26:14–23.[Abstract/Free Full Text]
  9. Ilyas, M., J. Straub, I. P. M. Tomlinson, and W. F. Bodmer. 1999. Genetic pathways in colorectal and other cancers. Eur. J. Cancer 35:335–351.
  10. Lengauer, C., K. W. Kintzler, and B. Vogelstein. 1998. Genetic instabilities in human cancers. Nature 396:643–649.[Medline]
  11. Chung, D. C. 2000. The genetic basis of colorectal cancer: insights into critical pathways of tumorogenesis. Gastroenterology 119:854–865.[Medline]
  12. Marchetti, A., C. Doglioni, M. Barbareschi, F. Buttitta, S. Pellegrini, P. Gaeta, R. La Rocca, G. Merlo, A. Chella, C.A. Angeletti, P. Dalla Palma. and G. Bevilacqua. 1998. Cyclin D1 and retinoblastoma susceptibility gene alterations in non-small cell lung cancer. Int. J. Cancer 75: 187–192.[Medline]
  13. Hunchareck, M., J. Muscat, and J. F. Geschwind. 1999. K-ras oncogene mutation as a prognostic marker in non-small cell lung cancer: a combined analysis of 881 cases. Carcinogenesis 20:1507–1510.[Abstract/Free Full Text]
  14. Cairns, P., T. J. Polascik, Y. Eby, K. Tokino, J. Califano, A. Merlo, L. Mao, J. Herath, R. Jenkins, W. Westra, J. L. Rutter, A. Buckler, E. Gabrielson, M. Tockman, K. R. Cho, L. Hedrick, G. S. Bova, W. Isaacs, W. Koch, D. Schwab, and D. Sidransky. 1995. Frequency of homozygous deletion at p16/CDKN2 in primary human tumors. Nature Genet. 11:210–212.[Medline]
  15. Kratze, R. A., T. M. Greatens, J. B. Rubins, M. A. Maddaus, D. E. Niewoehner, G. A. Niehans, and J. Geradts. 1996. Rb and p16INK4a expression in resected non-small cell lung tumors. Cancer Res. 56:3415–3420.[Abstract/Free Full Text]
  16. Kinoshita, I., H. Dosaka-Akita, T. Mishina, K. Akie, M. Nishi, H. Hiroumi, F. Hommura, and Y. Kawakami. 1996. Altered p16INK4 and retinoblastoma protein status in non-small cell lung cancer: potential synergistic effect with altered p53 protein on proliferative activity. Cancer Res. 56:5557–5562.[Abstract/Free Full Text]
  17. Gazzeri, S., V. Gouyer, C. Vour'ch, C. Brambilla, and E. Brambilla. 1998. Mechanisms of p16INK4A inactivation in non small-cell lung cancers. Oncogene 16:497–504.[Medline]
  18. Martinet, N., F. Alla, G. Farré, T. Labib, H. Drouot, R. Vidili, E. Picard, M. P. Gaub, D. Le Faou, J. Siat, J. Borelly, P. Vermylen, T. Bazarbachi, J. M. Vignaud, and Y. Martinet. 2000. Retinoic acid receptor and retinoid X receptor alterations in lung cancer precursor lesions. Cancer Res. 60: 2869–2875.[Abstract/Free Full Text]
  19. Campedes-Cespedes, M., S. A. Ahrendt, S. Piantadosi, R. Rosell, M. Monzo, L. Wu, W. H. Westra, S. C. Yang, J. Jen, and D. Sidransky. 2001. Chromosomal alterations in lung adenocarcinoma from smokers and non-smokers. Cancer Res. 61:1309–1313.[Abstract/Free Full Text]
  20. Balsara, B. R., G. Sonoda, S. Du Manoir, J. M. Siegfried, E. Gabrielson, and J. R. Testa. 1997. Comparative genomic hybridization analysis detects frequent, often high-level, overrepresentation of DNA sequences at 3q, 5p, 7p, and 8q in human non-small cell lung carcinomas. Cancer Res. 57:2116–2120.[Abstract/Free Full Text]
  21. Fong, K. M., Y. Kida, P. V. Zimmerman, M. Ikenaga, and P. J. Smith. 1995. Loss of heterozygosity frequently affects chromosome 17q in non-small cell lung cancer. Cancer Res. 55:4268–4272.[Abstract/Free Full Text]
  22. Neville, E. M., M. P. Stewart, A. Swift, T. Liloglou, H. Ross, J. R. Gosney, R. J. Donnelly, and J. K. Field. 1996. Allelotype of non-small cell lung cancer. Int. J. Oncol. 9:533–539.
  23. Virmani, A. K., K. M. Fong, D. Kodagoda, D. McIntire, J. Hung, V. Tonk, J. D. Minna, and A. F. Gazdar. 1998. Allelotyping demonstrates common and distinct patterns of chromosomal loss in human lung cancer types. Genes Chromosomes Cancer 21:308–319.[Medline]
  24. Huncharek, M., B. Kupelnick, J. F. Geschwind, and J. F. Caubet. 2000. Prognostic significance of p53 mutations in non-small cell lung cancer: a meta-analysis of 829 cases from eight published studies. Cancer Lett. 153:219–226.[Medline]
  25. Niederacher, D., F. Picard, C. Van Roeyen, H. X. An, H. G. Benderand, and M. W. Beckmann. 1997. Patterns of allelic loss on chromosome 17 in sporadic breast carcinomas detected by fluorescent-labeled microsatellite analysis. Genes Chromosomes Cancer 18:181–192.[Medline]
  26. Mao, L., J. S. Lee, J. M. Kurie, Y. H. Fan, S. M. Lippman, J. J. Lee, J. Y. Ro, A. Broxson, R. Yu, R. C. Morice, B. L. Kemp, F. R. Khuri, G. L. Walsh, W. N. Hittelman, and W. H. Hong. 1997. Clonal genetic alterations in the lungs of current and former smokers.
  27. Park, I.-W., I. I. Wistuba, A. Maitra, S. Milchgrub, A. K. Virmani, J. D. Minna, and A. F. Gazdar. 1999. Multiple clonal abnormalities in the bronchial epithelium patients with lung cancer. J. Natl. Cancer Inst. 91:1863–1868.[Abstract/Free Full Text]
  28. Wistuba, I. I., S. Lam, C. Behrens, A. K. Virmani, K. M. Fong, J. Leriche, J. M. Samet, S. Srivastava, J. D. Minna, and A. F. Gazdar. 1997. Molecular damage in the bronchial epithelium of current and former smokers. J. Natl. Cancer Inst. 89:1366–1373.[Abstract/Free Full Text]
  29. Jones, M. H., and Y. Nakamura. 1992. Detection of loss of heterozygosity at the human TP53 locus using a dinucleotide repeat polymorphism. Genes Chromosomes Cancer 5:89–90.[Medline]
  30. Cannon Albright, L. A., D. E. Goldgar, S. Neuhausen, N. A. Gruis, D. E. Anderson, C. M. Lewis, M. Jost, T. D. Tran, K. Nyguen, A. Kamb, J. Weaver-Feldhaus, L. J. Meyer, J. J. Zone, and M. H. Skolnick. 1994. Localization of the 9p melanoma susceptibility locus (MLM) to a 2-cM region between D9S736 and D9S171. Genomics 23:265–268.[Medline]
  31. Parsons, R., L. L. Myeroff, B. Liu, J. K. V. Willson, S. D. Markowitz, K. W. Kinzler, and B. Vogelstein. 1995. Microsatellite instability and mutations of the transforming growth factor ß type II receptor gene in colorectal cancer. Cancer Res. 55:5548–5550.[Abstract/Free Full Text]
  32. Schneider, A., S. Borgnat, H. Lang, O. Regine, V. Lindner, M. Kassem, C. Saussine, P. Oudet, D. Jacqmin, and M.-P. Gaub. 2000. Evaluation of microsatellite analysis in urine sediment for diagnosis of bladder cancer. Cancer Res. 60:4617–4622.[Abstract/Free Full Text]
  33. Sugano, K., Y. Nakashima, K. Yamaguchi, N. Fukayama, M. Maekawa, H. Ohkura, T. Kakizoe, and T. Sekiya. 1996. Sensitive detection of loss of heterozygosity in the Tp53 gene in pancreatic adenocarcinoma by fluorescence-based single-strand conformation polymorphism analysis using blunt-end DNA fragments. Genes Chromosomes Cancer 15:157–164.[Medline]
  34. Weber, J. C., A. Schneider, S. Rohr, H. Nakano, P. Bachellier, A. Méchine, G. Hamel, M. Kanor, M. P. Chenard, M. P. Gaub, P. Oudet, C. Meyer, and D. Jaeck. 2001. Analysis of allelic imbalance in patients with colorectal cancer according to stage and presence of synchronous liver metastases. Ann. Surg. 6:795–803.
  35. Pylkkanen, L., A. Karjalainen, S. Anttila, H. Vainio, and K. Husgafvel-Pursiainen. 1997. No evidence of microsatellite instability but frequent loss of heterozygosity in primary resected lung cancer. Environ. Mol. Mutagen. 30:217–223.[Medline]
  36. Benachenhou, N., S. Guiral, I. Gorska-Flipot, D. Labuda, and D. Sinnett. 1998. High resolution deletion mapping reveals frequent allelic losses at the DNA mismatch repair loci hMLH1 and hMSH3 in non-small cell lung cancer. Int. J. Cancer 77:173–180.[Medline]
  37. Tsuchiya, E., Y. Nakamura, S. Y. Weng, K. Nakagawa, S. Tsuchiya, H. Sugand, and T. Kitagawa. 1992. Allelotype of non-small cell carcinoma: comparison between loss of heterozygosity in squamous cell carcinoma and adenocarcinoma. Cancer Res. 52:2478–2481.[Abstract/Free Full Text]
  38. Sato, S., Y. Nakamura, and E. Tsuchiya. 1994. Difference of allelotype between squamous-cell carcinoma and adenocarcinoma of the lung. Cancer Res. 54:5652–5655.[Abstract/Free Full Text]
  39. Thiberville, L., P. Payne, J. Vielkinds, J. Leriche, D. Horsman, G. Nouvet, B. Palcic, and S. Lam. 1995. Evidence of cumulative gene losses with progression of premalignant epithelial lesions to carcinoma of the bronchus. Cancer Res. 55:5133–5139.[Abstract/Free Full Text]
  40. Zhou, X., B. L. Kemp, F. R. Khuri, D. Liu, J. J. Lee, W. Wu, W. K. Hong, and L. Mao. 2000. Prognostic implication of microsatellite alteration profiles in early-stage non-small cell lung cancer. Clin. Cancer Res. 6: 559–565.[Abstract/Free Full Text]
  41. Liloglou, T., P. Maloney, G. Xinarianos, S. Fear, and J. K. Field. 2000. Sensitivity and limitations of high throughput fluorescent microsatellite analysis for the detection of allelic imbalance: application in lung tumors. Int. J. Oncol. 16:5–14.[Medline]
  42. Shtivelman, E., B. Lifshitz, R. P. Gale, and E. Canaani. 1985. Fused transcript of abl and bcr genes in chronic myelogenous leukaemia. Nature 315:550–554.[Medline]
  43. Nobori, T., K. Miura, D. J. Wu, A. Lois, K. Takabayashi, and D. A. Carson. 1994. Deletions of the cyclin-dependent kinase-4 inhibitor gene in multiple human cancers. Nature 368:753–756.[Medline]
  44. Kamb, A., N. A. Gruis, J. Weaver-Feldhaus, Q. Liu, K. Harshmam, S. V. Tavtigian, E. Stockert, R. S. Day III, B. E. Johnson, and M. H. Skolnick. 1994. A cell cycle regulator potentially involved in genesis of many tumor types. Science 264:436–440.[Abstract/Free Full Text]
  45. Cayuela, J. M., A. Madani, L. Sahnes, M. H. Stern, and F. Signaux. 1996. Multiple tumor-suppressor gene 1 inactivation is the most frequent genetic alteration in T-cell acute lymphoblastic leukemia. Blood 87:2180–2186.[Abstract/Free Full Text]
  46. Betticher, D. C., G. R. M. White, S. Vonlanthen, X. Liu, A. Kappeler, H. J. Altermatt, N. Thatcher, and J. Heighway. 1997. G1 control gene status is frequently altered in resectable non-small cell lung cancer. Int. J. Cancer (Pred. Oncol.) 74:556–562.[Medline]
  47. Takanami, I., T. Imamuma, T. Hashizume, K. Kikuchi, Y. Yamamoto, T. Yamamoto, and S. Kodair. 1996. Expression of PDGF, IGF-II, bFGF and TGF-beta 1 in pulmonary adenocarcinoma. Pathol. Res. Pract. 192:1113–1120.[Medline]
  48. de Jonge, R. R., L. Garrigue-Antar, V. F. Vellucci, and M. Reiss. 1997. Frequent inactivation of the transforming growth factor beta type II receptor in small-cell lung carcinoma cells. Oncol. Res. 9:89–98.[Medline]
  49. Kim, T. K., E. K. Mo, C. G. Yoo, C. T. Lee, S. K. Han, Y. S. Shim, and Y. W. Kim. 2001. Alteration of cell growth and morphology by overexpression of tranforming growth factor beta type II receptor in human lung adenocarcinoma cells. Lung Cancer 31:181–191.[Medline]
  50. Vonlanthen, S., J. Heigway, M. P. Tschan, M. M. Borner, H. J. Altermatt, A. Kappeler, A. Tobler, M. F. Fey, N. Thatcher, W. G. Yarbrough, and D. C. Betticher. 1998. Expression of p16INK4a/p16{alpha} and p19ARF/p16ß is frequently altered in non-small cell lung cancer and correlates with p53 overexpression. Oncogene 17:2779–2785.[Medline]
  51. Naruke, T., R. Tsuchiva, H. Kondo, H. Asamura, and H. Nakayama. 1997. Implications of staging in lung cancer. Chest 112:242S–248S.[Medline]
  52. Chen, X. Q., M. Stroun, J.-L. Magnenat, L. P. Nicod, A.-M. Kurt, J. Lyautey, C. Lederrey, and P. Anker. 1996. Microsatellite alterations in plasma DNA of small cell lung cancer patients. Nat. Med. 2:1033–1034.[Medline]



This article has been cited by other articles:


Home page
 J. Clin. Pathol.Home page
W W J de Leng, J J Keller, S Luiten, A R Musler, M Jansen, A F Baas, F W M de Rooij, J J P Gille, F H Menko, G J A Offerhaus, et al.
STRAD in Peutz-Jeghers syndrome and sporadic cancers
J. Clin. Pathol., October 1, 2005; 58(10): 1091 - 1095.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Cave-Riant, F.
Right arrow Articles by Gaub, M. P.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Cave-Riant, F.
Right arrow Articles by Gaub, M. P.

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Proc. Am. Thorac. Soc. Am. J. Respir. Crit. Care Med.
Copyright © 2002 American Thoracic Society.