Molecular classification and molecular genetics of human lung cancers

Molecular Classification and Molecular Genetics of Human Lung Cancers Matthew Meyerson, Wilbur A. Franklin, and Michael J. Kelley Recent advances in the molecular classification of lung carcinomas and the identification of causative genetic alterations will likely lead to improvements in the diagnosis and treatment of patients with lung cancer. It is now possible to identify gene expression profiles that associate with patient outcome in lung carcinomas, in particular adenocarcinoma. Furthermore, patient survival has been shown to correlate with lung cancer oligonucleotide microarray expression profiles. Largescale microarray technology may allow for the identification of useful biomarkers for early cancer detection. Oligonucleotide microarray data can be optimized by relating them to protein expression levels in tissue microarrays, by annotation with mutational data, and with results of testing for post-translational modification of cellular proteins. These data may be useful in tailoring chemotherapeutic protocols to individual tumors and identifying new targets for therapeutic intervention. Semin Oncol 31 (suppl 1):4-19. © 2004 Elsevier Inc. All rights reserved.

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ECENT ADVANCES in molecular classification and molecular genetics offer the hope of improving the diagnosis, treatment, and prognosis of patients with lung cancer. Carcinomas of the lung and bronchus continue to be the leading cause of cancer death in human populations in the United States and throughout the world.1,2 Despite significant efforts to develop effective clinical strategies for the treatment of lung cancer, the prognosis for patients remains poor. This review will focus on three issues: the molecular classification of human lung carcinomas by gene expression array analysis; the validation of

From the Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA; the Department of Pathology, Harvard Medical School, Boston, MA; the University of Colorado Health Sciences Center, Denver, CO; the Department of Medicine, Thoracic Oncology Program, Duke University Medical Center, Durham, NC; and the Department of Hematology/Oncology, Durham Veterans Affairs Hospital, Durham, NC. Dr Meyerson has received research grant support from and serves as a consultant to Novartis Pharmaceuticals. Address reprint requests to Matthew Meyerson, MD, PhD, Harvard Medical School, Department of Medical Oncology, DanaFarber Cancer Institute, Department of Pathology, 44 Binney St, Room M430, Boston, MA 02115. © 2004 Elsevier Inc. All rights reserved. 0093-7754/04/3101-0102$30.00/0 doi:10.1053/j.seminoncol.2003.12.009 4

such classifications by independent methods; and the identification of molecular genetic alterations that cause lung carcinoma in each of these categories, including inactivating mutations of tumor suppressor genes and activating mutations of oncogenes. MOLECULAR CLASSIFICATION OF LUNG CARCINOMAS BY GENE EXPRESSION ANALYSIS

The subclassification of human lung carcinomas presents an important diagnostic dilemma. While the distinction between small cell lung carcinoma (SCLC) and non–small cell lung carcinoma (NSCLC) shows distinct clinical courses and indicates different treatments,3,4 the role of the NSCLC subclassification is less critical at present. Treatment decisions are made on the basis of clinical-pathologic staging5 rather than histology. Furthermore, the morphologic subclassification of NSCLC remains somewhat imprecise. For example, while four morphologic subtypes of lung adenocarcinoma exist, one study showed that three independent pulmonary pathologists could agree on the subtype classification of only 40% of samples.6 Thus, a more precise and reproducible basis for lung cancer classification remains essential. In recent years, the development of gene expression profiling techniques has led to new approaches to cancer classification. cDNA and oligonucleotide microarrays can contain probes representing over 10,000 genes each. Supervised learning methods, in which an attempt is made to define genes that will predict a class of unknown cancer samples based on a training set of cancers of known class, can successfully identify genes that distinguish between existing classes of cancer.7 Unsupervised learning methods, where no assumption is made about cancer classes but rather the data are allowed to sort themselves statistically, have been applied successfully to discover new classes of cancer.8,9 Gene Expression Profiles of Small Cell Lung Cancer In recent years, a variety of studies have sought to identify diagnostic markers for SCLC, which has been defined histologically and immunohistoSeminars in Oncology, Vol 31, No 1, Suppl 1 (February), 2004: pp 4-19

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Table 1. Genes Associated With Small Cell Lung Carcinoma in Expression Profiling Studies Garber et al16

Bhattacharjee et al15

Sugita et al17

7b2 protein hASH1 Amyloid ␤ A4 precursor-like 1 Forkhead box G1B Glutaminyl cyclase Hypothalamus protein HBEX2 Imprinted in Prader-Willi syndrome 1 IA-1 (INSM1) Internexin neuronal filament Islet-1 transcription factor KIAA 0282 protein KIAA 0805 gene product KIAA 1051 protein Lin homeobox protein 2 Neuronal cell adhesion molecule Neuronal protein Thymosin ␤ Tyrosine phosphatase, receptor n2 v-myb viral oncogene homolog 1

ASH1 CDKN2C (p18) Core-binding protein (runt), ␣ subunit 2 Distal-less homeobox 6 Extra spindle poles homolog Forkhead box G1B Guanine nucleotide-binding protein 4 IA-1 Islet-1 transcription factor PSIP2 Thymosin ␤ Transcription factor 12 (HTF4) Tubulin, ␤ polypeptide

AADC (DOPA decarboxylase) ASH1 Chromogranin B (secretogranin I) Chromogranin C (secretogranin II) IA-1 MAGE-A2 (family A, 2) MAGE-A3 (family A, 3) MAGE-A6 (family A, 6) MAGE-A10 (family A, 10) MAGE-A12 (family A, 12) Na⫹,K⫹-ATPase (Na⫹,K⫹-ATPase, subunit ␣-III) NEFL NY-ESO-1 (cancer/testis antigen, CTAG1, LAGE2) TRIM9 (KIAA 0282 protein)

NOTE. Genes highlighted in multiple studies are shown in bold type. Abbreviations: AADC, aromatic amino acid decarboxylase; ASH1, achaete-scute homolog 1; HTF, helix-loop-helix transcription factor; IA-1, insulinoma associated 1; MAGE, melanoma antigen; NEFL, neurofilament light polypeptide; TRIM9, tripartite motif-containing 9.

chemically by features of neuroendocrine differentiation. Known markers include gastrin-releasing peptide, insulinoma-associated gene 1, chromogranin A, DOPA decarboxylase, and neuron-specific enolase.10-14 Small cell lung cancer expression profiles have been analyzed in two broad studies of human lung carcinomas15,16 and in one study of cell lines.17 Even though one of these studies used cDNA arrays and two used oligonucleotide arrays, the results of the three studies overlapped significantly (Table 1). These studies have identified a number of genes as defining characteristics of SCLC, including several known genes such as insulinoma-associated-1 (IA-1) and human achaetescute homologue-1 (hASH1). In addition, several novel possible markers for SCLC have been identified. Among the most interesting are two transcription factors that may underlie the neuroendocrine differentiation program, the islet (ISL)1 transcription factor and the forkhead box protein G1B. Both of these genes are clearly associated with SCLC, as they were found in both studies. ISL1 is required for formation of both motor neurons and pancreatic islet cells during embryonic development.18,19 Forkhead box

G1B, also known as Foxg1 or c-qin, is even more intriguing. Its expression is restricted to the developing telencephalon,20 and it was first identified as the transforming oncogene of avian sarcoma virus 31.21 It will be interesting to determine whether this gene, localized to chromosome 14q, plays any role in SCLC oncogenesis. Finally, it is striking that SCLC-correlated genes may be identified between any pair of the three studies. For example, the Garber et al16 and Sugita et al17 studies both show high-level expression of KIAA0282, while Fox G1 is found in the Garber and Bhattacharjee et al15 studies (Table 1). This also is consistent with a high correlation between the independent studies. Squamous Cell Lung Carcinoma Both microarray analysis15,16 and serial analysis of gene expression22,23 have been used to identify markers for squamous cell lung carcinoma. Again, comparison of the results across experimental platforms shows significant similarities. Major genes identified as squamous cell carcinoma markers include a variety of keratin genes (Table 2). This result is not surprising because the diagnostic cri-

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Table 2. Genes Associated With Squamous Cell Lung Carcinoma in Expression Profiling Studies Garber et al16

Bhattacharjee et al15

Nacht et al23

ADAM 23 Annexin A8 Ataxia-telangiectasia group D-associated protein Bullous pemphigoid antigen 1 Collagen VII ␣ 1 Cystatin A Galectin 7 Heparin-binding growth factor Keratin 5 Keratin 13 Keratin 17 Odd oziten-m homolog 1 Poliovirus receptor-related 1 S100 calcium-binding protein A2 Sialyltransferase Slug Transferrin receptor Tumor protein 63 Uronyl 2-sulfotransferase Visinin-like 1

Ataxia-telangiectasia group D-associated protein Bullous pemphigoid antigen 1 Collagen VII ␣ I Desmoglein 3 Galectin 7 Glypican 1 Keratin 5 Keratin 6 Keratin 17 s100 calcium-binding protein A2 Serine proteinase inhibitor, clade B Tumor protein 63

Cornifin Keratin 5 Keratin 6 Keratin 14 Keratin 16 Keratin 17 Keratin 19 s100 calcium-binding protein A2 Stratifin

NOTE. Genes highlighted in multiple studies are shown in bold type.

teria for squamous cell lung carcinoma include evidence of squamous differentiation such as keratin formation.4 Overlaps between the data sets also identify several other squamous cell lung carcinoma–associated markers, including the ataxiatelangiectasia group D–associated protein, bullous pemphigoid antigen 1, collagen VII ␣ 1, galectin 7, and the s100 calcium-binding protein A2 (Table 2). Finally, the squamous tumors also show overexpression of p63, a p53-related gene essential for the formation of squamous epithelia,24 in the two array studies. This overexpression has also been previously described.25 In summary, the gene expression studies of squamous cell lung carcinoma to date have confirmed the identity of a number of known markers and have shown several new candidate markers. Adenocarcinoma of the Lung: Subclassification Studies Adenocarcinoma is the most common form of lung carcinoma, responsible for nearly half of NSCLC cases, and this proportion is increasing.3 Furthermore, as described above, the subclassification of lung adenocarcinoma has been challenging.

Two recent studies have applied an unsupervised class discovery approach, hierarchical clustering,26 to the classification of human lung adenocarcinomas.15,16 Although these studies used different experimental platforms, oligonucleotide microarrays in one set of experiments15 and cDNA microarrays in the other,16 many of the results were highly congruent. In one study, oligonucleotide array analysis of 12,600 probe sets was performed on adenocarcinomas resected from the lungs of 139 patients.15 Using a combination of probabilistic clustering and hierarchical clustering, a distinct group of metastatic colon adenocarcinomas was identified (see below). Furthermore, approximately half of the lung adenocarcinoma specimens were divided into four major groups. The remaining samples were not assigned to any groups. The most prominent subgroup in this study was a set of tumors expressing neuroendocrine markers, such as hASH1, DOPA decarboxylase, and IA-1. This expression pattern was associated with a significantly inferior patient outcome.15 The samples that did not belong to any of the sharply defined subgroups are likely to be members of smaller

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Table 3. Expression Profiling Studies Revealing Similar Adenocarcinoma Subclasses Garber et al16

Bhattacharjee et al15

Adenocarcinoma group 1: ATPase, class 6, type IIA BENE Carcinoembryonic antigen-related 1 CD54 Citron Complement component 4-binding protein Cytochrome b5 Dipeptidylpeptidase IV Flavin-containing monooxygenase Folate receptor 1 GM3 synthase Hepsin KIAA0758 Ribonuclease, Rnase A family RNA-binding protein gene Selenium-binding protein 1 Solute carrier family 34, member 2 Surfactant protein A1 Thyroid transcription factor 1 Transcription factor 2, hepatic Transmembrane protease, serine 2

Adenocarcinoma group C4: ATP-binding cassette (ABC1) BENE Calcium channel, voltage-dependent Cytochrome b5 Deleted in liver cancer 1 N-acylsphingosine amidohydrolase Selenium-binding protein 1 Surfactant protein B Surfactant protein C Surfactant protein D

NOTE. Genes highlighted in multiple studies are shown in bold type.

groups that were not clearly defined in our sample size. It is likely that a larger sample size would allow more precise delineation of these groups. cDNA microarray analysis of 41 lung adenocarcinoma specimens, followed by hierarchical clustering, identified three major subgroups.16 Strikingly, adenocarcinoma group 1 of the cDNA array study showed dramatic similarities with adenocarcinoma group C4 of the oligonucleotide array study. Each of these tumor subtypes was characterized by high-level expression of surfactant protein genes and several other common genes including BENE, cytochrome b5, and seleniumbinding protein 1 (Table 3). These samples were often diagnosed as bronchioloalveolar carcinomas15 and appear to form a clear and distinct branch within the adenocarcinomas. As discussed above, the relatively small sample sizes may have discouraged the sharp identification of the other common groups between the two studies. Expression-based studies of several hundred lung adenocarcinoma specimens should provide sufficient power to identify the reproducible groups.

Finally, these studies showed that it is possible to use expression analysis to discriminate primary lung adenocarcinomas from metastatic adenocarcinomas that originated outside the lung. In one study, 12 tumors showed a signature of overexpression of genes such as liver-intestine cadherin 17 and galectin 4,15 which have been described as markers for adenocarcinoma of the colon.27,28 Nine of the 12 tumors expressing colonic markers were from patients known to have a previous history of colon carcinoma; the other three are likely to have represented metastatic colon adenocarcinoma from an unknown primary. Thus, expression profiling appears to be an effective approach to identify potentially unrecognized tumor metastases and might affect the treatment of a significant fraction of solitary lung adenocarcinomas. Outcome Prediction by Expression Analysis of Lung Carcinoma At this date, four expression profiling studies of lung cancer specimens have sought genes that distinguish between patients with longer and shorter survival times. Three of these studies used

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supervised approaches to identify survival-associated genes in lung adenocarcinomas29-31 while the fourth study used a hierarchical clustering approach to NSCLC.32 In one study, the authors used cDNA array analysis of over 18,000 genes to analyze lung adenocarcinoma cells purified by laser-capture microdissection.30 They identified 27 genes that correlate negatively or positively with patient survival. In an independent supervised analysis, oligonucleotide arrays containing 6,800 probe sets were used to survey gene expression in 86 lung adenocarcinoma specimens.29 The authors then performed a statistical analysis to identify 100 genes with the most significant association with patient survival. Using these 100 genes, the authors constructed a model to predict patient survival. This model clearly distinguished high-risk and low-risk groups in both a training set and an independent test set. In an alternative approach, a comparison of genes that are expressed in metastases of various cancers compared with the primary tumors (both lung and non-lung cancers were used in the training set) led to the identification of metastasisassociated genes. When these genes were used to separate lung adenocarcinomas into two groups, the group with higher expression of metastasisassociated genes was found to exhibit a significantly worse survival outcome.31 Finally, hierarchical clustering studies also give rise to distinctions between lung carcinoma subgroups according to patient outcome. In one lung adenocarcinoma classification study by hierarchical clustering, a group of tumors with neuroendocrine markers was associated with poor outcome.15 A cDNA array study found a distinct group associated with poor outcome, highlighted by high expression of ornithine decarboxylase and other genes.16 Of note, ornithine decarboxylase was one of the highly expressed genes in the neuroendocrine, poor-prognosis tumors as well.15 In addition, a hierarchical study of a mix of adenocarcinomas, squamous cell lung carcinomas, and large cell lung carcinomas identified a clustering pattern that did not correlate with tumor histology but instead correlated with patient outcome.32 While each of these studies show that it is possible to identify gene expression profiles that associate with patient outcome in lung carcinomas, in particular lung adenocarcinoma, the genes highlighted by each of the studies15,16,29-32 are not

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highly overlapping. One limitation is that the numbers of samples analyzed are small and are vastly exceeded by the numbers of genes that were assayed. In contrast, as described above, studies that attempt to define classifications by gene expression profiles readily yield overlapping gene sets. Thus, it is now essential to perform studies that reproduce, validate, and extend these first efforts at lung cancer classification using expression profiling. These integrative studies will allow us to generate a unified and consistent genomic model for lung cancer, which can be further integrated with clinical and pathologic observations. One approach to pathologic validation, by the use of tissue microarrays, is discussed in the next section. Finally, the ultimate goal of any classification system is to aid in identifying the genes whose mutations contribute to cancer pathogenesis; oncogene and tumor suppressor gene mutations in lung carcinoma are described in the third part of this review. ASSESSING THE BIOLOGICAL SIGNIFICANCE OF cDNA AND OLIGONUCLEOTIDE MICROARRAY DATA

Determining the biological significance of the vast data sets that are the output of microarray testing is a major challenge for this new technology. Much of the work that has so far been reported has attempted to relate microarray data to some external parameter such as tumor histology or patient survival. As described above, it has been shown by several groups that microarray data can be used to recapitulate the microanatomic classification of lung carcinoma and to subclassify the diagnostic category of adenocarcinoma.15,16 In addition, patient survival has been shown to correlate with lung cancer microarray expression profiles.15,16,29-32 However, this is not the only productive means to analyze the biological significance of microarray data. One alternative approach has been to use the power of global expression profiles to identify biomarkers that may be used for early detection of lung carcinoma. The prognosis of lung cancer has remained uniformly poor for decades despite improved survival for other types of malignancies, and lung cancer continues to be the greatest cause of cancer mortality in the United States.1 This has been attributed in part to late stage at diagnosis,

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and has prompted a vigorous search for biomarkers that might indicate the occurrence of lung cancer in high-risk populations.33 Large-scale microarray technology permits identification of overexpressed and underexpressed genes from among thousands, some of which might unexpectedly be useful biomarkers for early cancer detection. Application of relatively simple algorithms readily available using commercially available software can generate short lists of genes that are maximally imbalanced in relation to matched normal control lung tissue. The problem then becomes one of validating microarray data on large numbers of clinical samples. Tissue Microarrays One new avenue for rapidly validating oligonucleotide microarray data is the tissue microarray. Other approaches, such as quantitative reverse transcription-polymerase chain reaction (RT-PCR) amplification, are also important but will not be discussed in this review. The tissue microarray methodology permits the retrospective analysis of large clinical cohorts. Ordered tissue arrays can be constructed by sampling donor paraffin blocks using a biopsy needle and injecting the resulting tissue sample into a predetermined position in a recipient paraffin block.34-36 The resulting array may contain as many as 1,000 tissue samples. Heterogeneity with a tumor sample can be compensated for by multiply sampling the donor block. Numerous recent studies have shown the utility of using tissue microarrays for correlation of immunohistochemical patterns of protein expression to prognosis.37-43 Tissue microarray data are particularly helpful when linked to reliable outcomes databases. An example of the utility of this methodology is the demonstration that E-cadherin is an independent prognostic variable in NSCLC.38 E-cadherin is a cell surface receptor that is important in the regulation of cell adhesion44 and is a crucial part of a membrane complex including ␤-catenin, which regulates cell activation and proliferation. Loss of ␤-catenin might be expected to impair cell adhesion and affect downstream signal transduction so that tumor proliferative rates are enhanced. Using a tissue microarray, Bremnes et al38 were able to evaluate a large proportion of the proteins in the E-cadherin pathway and showed that only E-cadherin is an independent prognostic variable in NSCLC. Tissue microarray data also

Fig 1. Biomarker discovery using high-throughput technologies. Results are confirmed in a larger number of cell lines and tumors by RT-PCR. Potential biomarkers against which specific antibodies have been raised can be validated in TMAs linked to clinical and outcome data. Results may generate new hypotheses for further testing and validation. IHC, immunohistochemistry; FISH, fluorescence in situ hybridization; RT-PCR, reverse-transcriptase polymerase chain reaction; TMA, tissue microarray.

indicate that loss of E-cadherin may not be a prognostic variable in urothelial carcinoma.42 Integration of High Throughput Technologies to Analyze Gene Expression in Lung Cancer These experiments show that it is possible to rapidly accrue data regarding specific biomarkers and biomarker pathways in large horizontal cohorts for which follow-up data are available. How such data can be linked to cDNA and oligonucleotide microarray results has been shown in a study in which biomarkers discovered in a narrow subset of specimens through gene expression microarray analysis are validated in broad clinical cohorts using tissue microarrays.17 A typical testing schema for this approach is shown in Fig 1. In this example, a small number of cell lines are tested with a large oligonucleotide expression microarray. Cell lines used in this step are pure tumor cell populations and do not require microdissection or other accommodation to contaminating stromal cells. In the second step, candidate biomarkers are tested by RT-PCR against an expanded set of cell lines. This is an easy and economical way to confirm initial microarray data in a broad cross-section of cell lines with specific histologies. A third step

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consists of examination of protein expression profiles by immunohistochemistry (IHC) in a large tissue microarray containing clinical samples linked to clinical and follow-up data. This step is a rapid way to assess the prognostic importance of candidate biomarkers and their cellular distribution. Finally, the results of this multistep testing exercise can be used to generate new clinical and biological hypotheses that could be tested with either existing clinical resources or in new clinical trials. The stringent filtering algorithm, in which results for cell lines were normalized using both cultured epithelial cell and tumor homogenate controls, produced a list of 20 highly overexpressed genes, including 14 expressed only in SCLC, four only in NSCLC, and two in both SCLC and NSCLC. Many of the 14 genes found in SCLC in this analysis were also identified in other array studies15,16 as summarized in Table 1. Six of the 20 selected genes were CTAG genes, including five MAGE-A genes and NY-ESO-1. The remaining genes on the list encode proteins with a diversity of functions. Four of the genes, including ASH1, claudin 10, and the secretogranins I and II, contain signal peptide sequences, indicating that they may be secreted and may thus be useful serum biomarkers. To confirm the gene expression profiles in a larger cross-section of lung tumor cell lines, several of the most highly expressed genes were tested by RT-PCR in 25 cell lines with SCLC, NSCLC, and mesothelial histologies. It was found that CTAG gene expression profiles are cell-type specific. Most MAGE genes are expressed by both SCLC and NSCLC, but NY-ESO-1 and MAGE-10A are expressed almost exclusively by SCLC. MAGE-A expression could also be confirmed at the protein level in cell lines by IHC because an excellent anti-MAGE monoclonal antibody is commercially available (6C1; Novacastra, Newcastle, UK). Because MAGE-A proteins are highly homologous, individual anti-MAGE-A antibodies typically cross-react so that individual MAGE-A gene expression profiles cannot be distinguished by IHC. However, it was possible to confirm nearly complete concordance between RT-PCR and IHC for overall MAGE-A expression, suggesting that MAGE-A might be successfully detected in clinical samples by IHC. The availability of an excellent antibody that

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could be used on paraffin sections therefore made it possible to rapidly test a clinical cohort represented in a tissue microarray. A tissue microarray containing 187 early stage NSCLC samples was stained and scored. MAGE-A proteins may be expressed in either the nucleus or cytoplasm and are usually expressed in both. Forty-four percent of the arrayed tumors were stained at some level of intensity in the nucleus or cytoplasm by the antiMAGE-A antibody method. Staining was more frequent in squamous carcinomas than in nonsquamous carcinomas (␹2 P ⬍.000001), suggesting that CTAG proteins will be most useful for the detection and monitoring of central rather than peripheral airway tumors. No relationship was detected between MAGE-A expression status and survival. This study documented the utility of multimodal testing to confirm results of the new oligonucleotide microarray technologies. New biomarkers and therapeutic targets are likely to emerge from such an approach, but there are several limitations that still must be taken into account, which are discussed below. Annotation of Expression Profiles Several confounding factors could affect gene expression profiles, including genetic alterations and post-translational modifications of protein such as phosphorylation. The example of ErbB pathway activation is particularly relevant here. Although epidermal growth factor receptor is present at high levels in NSCLC, oligonucleotide microarray analysis has not indicated high overexpression of this gene at the RNA level in comparison with normal lung tissue. With the availability of antibodies to phosphorylated peptides, it has become possible to assess not only surface receptors of the ErbB signaling axis but activation of downstream signal transducers as well. Immunohistochemical analysis of phosphorylated Erk1 and Erk2 in tissue microarrays indicates that heterogeneity of downstream signal transduction is greater than expression patterns of epidermal growth factor receptor. It is not yet clear whether levels of phosphorylated protein will be of predictive value in the targeted therapy of NSCLC. Activation through phosphorylation of signal transduction pathways, such as the ErbB signaling axis, may induce marked changes in gene expression profiles as well as functional alternations and yet not be

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reflected in mRNA levels of the phosphorylated proteins themselves. Finally, genetic changes, such as mutations of p53 or K-ras, may affect gene expression profiles in specific ways. Little information is currently available in lung cancer relating genetic changes to oligonucleotide or cDNA expression profiles. However, it is to be expected that, within a short time, expression profiles will be linked to specific genetic alterations so that predictable molecular consequences can be followed from genetic alteration to RNA expression profile to protein abnormality. To achieve the greatest understanding of the processes driving gene expression, it will be of considerable importance to annotate gene expression data with mutational and post-translational information that cannot be inferred from the current understanding of expression profiles alone. Advances in microarray technology are rapidly revising the approach to the classification of lung cancer and the identification of potential lung cancer biomarkers. Oligonucleotide microarray data can be optimized by relating them to protein expression level in tissue microarrays, by annotation with mutational data, and with results of testing for post-translational modification of cellular proteins. Ultimately, these data may be useful in tailoring chemotherapeutic protocols to individual tumors and identifying new targets for therapeutic intervention. MOLECULAR GENETICS OF HUMAN LUNG CANCER

The accurate classification of lung carcinoma is a critical step in defining lung cancer categories for treatment protocols. However, developing new treatments requires the identification of the disrupted pathways and the particular genes mutated in lung cancer. The experimental transformation of a normal human cell to one capable of tumor formation requires at least four alterations: (1) inactivation of the p53 pathway; (2) inactivation of the p16/cdk4 or cdk6:cyclin D/Rb pathway; (3) constitutive growth signaling, such as occurs with a mutant ras gene; and (4) activation of telomerase activity.45,46 Molecular genetic analysis of lung cancer cell lines and tumor samples has identified a spectrum of genetic alterations, including chromosomal alterations (rearrangements, duplications, aneuploidy) and small mutations (missense mutations, non-sense mutations, splice-site

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mutations, and small insertions and deletions). Epigenetic alteration of gene expression associated with changes in the methylation of DNA in or near the promoter region has also been described. This review focuses on molecular genetic alterations of the four critical pathways involved in lung carcinogenesis, with emphasis on potential clinical implications of these alterations. p53 Mutations The TP53 gene on the short arm of chromosome 17 encodes a 53-kd phosphoprotein, p53 that, in response to genomic damage, limits cell cycle progression to allow genomic repair or, if repair does not occur, induces apoptosis. Mutations of p53 are currently the most common genetic alteration identified in human cancers.47-49 Mutations of p53 are found in approximately 75% of SCLC50-57 and in 50% of NSCLC52,54,56-62 tumor samples. The most common mutation type is a missense mutation, accounting for over 80% of more than 1,700 p53 mutations described in lung cancer tumors and tumor cell lines.47 The mutations of p53 in lung cancer are very diverse with over 500 unique mutations described, which are more than that found in most other common human malignancies. Over 80% of p53 mutations in lung cancer occur in the central, evolutionarily conserved region of exons 5-8. Approximately one third of point mutations of p53 in lung cancer are G to T transversions, whose frequency is proportional to the strength of epidemiological association of the subtype with tobacco smoking. G to T transversions are highest in SCLC, intermediate in squamous cell carcinoma, and lowest in adenocarcinoma. Some of the more commonly occurring G to T transversions occur at guanines that are preferentially adducted by activated metabolites of tobacco smoke carcinogens, particularly benzo(a)pyrene.63 Lung cancers from patients who have never smoked have a lower frequency of p53 mutations (about 25%). These findings are further evidence among the plethora of data linking tobacco smoking with lung cancer causation. There is evidence of genetic alteration at the p53 locus in preinvasive lesions of the bronchial epithelium. Loss of heterozygosity near p53,64 cytogenetic alterations of 17p,65,66 increased p53 protein expression,67-69 and p53 mutations65,70 have all been reported in squamous bronchial epithelium of smokers and/or adjacent to lung can-

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cers. Three meta-analyses have analyzed the numerous studies of p53 on prognosis in NSCLC.71-73 In each of these three meta-analyses, p53 mutation or overexpression, which is imperfectly associated with mutation, was associated with a poorer outcome. In the largest study, the relative risk of death varied between 1.37 and 2.24, depending on stage and histology, with the most significant effect in adenocarcinomas.73 A minority of patients with missense p53 mutations in their lung cancer has detectable serum antibodies directed against p53.74-77 The presence of these antibodies does not appear to confer a survival advantage but may provide an adjunct in diagnosis.78 Replacement of the p53 gene by direct injection into tumors or bronchoalveolar lavage has induced locoregional tumor regression in patients with advanced lung cancer.79 Rb/cdk4-cyclin D/p16 Pathway Alterations The retinoblastoma gene product, Rb, is an important regulator of the G1-S cell cycle checkpoint.80 Phosphorylation of the Rb protein from a hypophosphorylated form to a hyperphosphorylated form results in release of the transcription factor E2F1 and other Rb-binding proteins from Rb, thus allowing cell cycle progression. Retinoblastoma is regulated by the cyclin-dependent protein kinase 4 (CDK4):cyclin D complex and the CDK6:cyclin D complex, which can phosphorylate the Rb protein.81,82 The p16 cyclin-dependent kinase inhibitor blocks the ability of CDK4 and CDK6 to phosphorylate the Rb protein by a reversible, noncovalent binding interaction.83 Absence or mutation of the Rb protein or absence of p16 inhibition of CDK4:cyclin D kinase activity contributes to the uncontrolled growth of cancer cells. In SCLC, Rb alterations are common with deletion or rearrangement in 21%, absent or barely detectable mRNA expression in 55%, and undetectable protein in 70%.84 Furthermore, some SCLC tumor cells contain mutant protein that is unable to undergo phosphorylation changes necessary for cell cycle regulation.85 In contrast, p16 alterations are common in NSCLC. The mechanisms of inactivation of p16 in NSCLC include biallelic deletions (loss of both copies of the CDKN2 gene on chromosome 9p21) in approximately 17% of samples, intragenic mutations (mutations limited to the gene, such as missense and

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non-sense mutations, small insertions, and small deletions) in approximately 8%, and absent mRNA expression without genetic alteration in approximately 30%. Absent mRNA expression is associated with methylation of the promoter region of p16.86 Loss of Rb or p16 does not appear to have prognostic significance but rather is a requirement of malignant transformation. In NSCLC, biallelic deletions of p16 and some intragenic mutations and promoter methylation changes also disrupt the co-localized p19ARF protein, which interacts with the p53 pathway in regulating response to genomic damage.87,88 Genetic alteration (deletion and point mutation) of p16 appears to occur only in lung cancer of smokers.89 Cyclin D1, encoded by CCND1 located on chromosome 11q13, is a component of the CDK4: cyclin D and CDK6:cyclin D kinase complexes involved in regulation of the G1-S checkpoint of the cell cycle. Gene amplification and overexpression of CCND1 have been reported in 24% and 47% of NSCLC tumors, respectively.84 Methylation Changes in Lung Cancer In mammalian DNA, the sole covalently modified base is 5-methylcytosine. This modification is present in approximately 0.75% to 1.0% of all cytosine-guanine (CG) dinucleotides in somatic cells, while methylation of cytosine at locations other than CG dinucleotides does not occur. Clusters of CG dinucleotides, known as CG islands, are located near the promoter region of genes. The presence of methylcytosine in CG islands is associated with transcriptional inactivity. Promoter methylation is implicated in X-chromosome inactivation in females, genetic imprinting, silencing of parasitic sequences, and gene regulation. In cancer cells, global methylcytosine content is reduced by 20% to 60%. This reduced DNA methylation is implicated in chromosomal instability, reactivation of transposable elements, and loss of imprinting. However, there is also focal hypermethylation associated with transcriptional silencing. In lung cancer, the first gene found to have methylation of the promoter was p16.90 Other genes methylated in lung cancer include E-cadherin, adenomatous polyposis coli,91-94 O6-methylguanine-DNA methyltransferase, death-associated protein kinase, retinoic acid receptor ␤, tissue inhibitor of metalloprotease-3, and glutathione

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S-transferase pi 1. Methylation of promoter regions is detectable before invasion,95 suggesting a method for risk assessment. Methylation of DNA is heritable during cell proliferation. In conjunction with DNA replication, DNA methyltransferase 1 methylates the newly incorporated cytosine on the opposite strand of a methylated cytosine of a CG dinucleotide. The pyrimidine analog 5-aza-2⬘-deoxycytidine (decitabine) is a competitive inhibitor of DNA methyltransferase 1. Incorporation of decitabine into DNA prevents methylation of the newly synthesized strand. Two rounds of DNA synthesis are required for removal of methyl groups from both DNA strands, which is required for gene expression. Re-expression of methylation-silenced genes in vitro by decitabine has been shown in lung cancer cell lines86 and is being investigated in clinical trials of patients with NSCLC. ALTERATIONS OF GROWTH-SIGNALING PATHWAYS

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Initial studies suggested that the presence of a ras gene mutation is associated with a poor prognosis106; further studies are required to assess its impact independent of known prognostic factors. BRAF The BRAF protein serine/threonine kinase is a downstream effector of the Ras pathway, which transduces signals from Ras to the mitogen-activated protein kinase cascade. During the past year, mutations in BRAF have been identified in a variety of human cancers including melanoma107 and colon carcinoma.108 Point mutations in BRAF are found in approximately 2% of NSCLC specimens: two of 127 adenocarcinomas in one study,109 and two of 104 adenocarcinomas and 3 of 117 squamous cell carcinomas in another study.110 Although BRAF mutations are rare, the availability of BRAF inhibitors for lung cancer would permit the treatment of roughly 3,000 patients per year in the United States. PI3K/AKT/PTEN PATHWAY

The ras Gene Family The ras family of genes is composed of H-ras, K-ras, and N-ras, which code for membrane-associated proteins with 189 amino acids.96,97 These genes bind guanine nucleotides (GTP and GDP), have GTPase activity, and regulate cellular proliferation through transduction of signals across cellular membranes. Constitutive activation of a ras gene occurs by point mutation at the 12th, 13th, or 61st amino acid. The ras family of genes are mutated in approximately 20% of NSCLC but rarely, if ever, in SCLC.84 The mutations are most commonly found in adenocarcinomas, where 90% of the mutations are in the K-ras gene and 85% of these are at the 12th codon. The predominant mutation type in K-ras codon 12 is a G to T transversion, and these mutations are approximately equally distributed between the first and second position of codon 12.98 This mutation pattern is partially because of susceptibility of codon 12 to adduction with tobacco smoke carcinogen metabolites.99 K-ras mutations have been detected in atypical adenomatous hyperplasia and type II pneumocyte hyperplasia,100-104 potential precursor lesions of adenocarcinoma of the lung. In contrast, K-ras mutations are uncommonly found in bronchial epithelial metaplasia and dysplasia, the putative precursors of squamous cell carcinoma.105

PTEN The PTEN (phosphatase and tensin homolog deleted on chromosome 10) gene, located on chromosome 10q23, encodes a 47-kd protein with protein tyrosine and lipid phosphatase activity as well as a tensin domain. Growth-promoting cytokines and growth-factor stimulated signaling, such as through transmembrane growth factor receptors, activate the phosphatidylinositol-3 kinase (PI3K)/ Akt pathway. The lipid phosphatase activity of PTEN negatively regulates this signaling pathway by cleaving the D3 phosphate from phosphatidylinositol triphosphate, an important activator of Akt. The protein tyrosine phosphatase activity of PTEN may also have growth stimulatory effects. Germline mutations of PTEN are responsible for Cowden disease, a hamartomatous familial cancer syndrome with a predisposition to thyroid, breast, and skin cancers.111,112 Chromosome 10q23 is a frequent site of genetic loss by comparative genomic hybridization in lung cancer.113,114 Allelotype analysis in the region near the PTEN gene shows loss of heterozygosity in 25% to 41% of NSCLC and 75% to 91% of SCLC tumors.115,116 Genetic alteration of PTEN has been reported in about 17% of SCLC tumor cell lines and 12% of NSCLC cell lines (Table 4). In

14

MEYERSON, FRANKLIN, AND KELLEY

Table 4. Summary of Alterations of PTEN in Lung Cancer115,117,135-140 Cell Lines* Study Non–small cell lung cancer Hosoya et al115 Forgacs et al117 Petersen et al135 Kim et al136 Kohno et al137 Okami et al138 Sakurada et al139 Yokomizo et al140 Total Small cell lung cancer Hosoya et al115 Forgacs et al117 Petersen et al135 Kim et al136 Kohno et al137 Sakurada et al139 Yokomizo et al140 Total

Tumors

Deletion

Mutation

Absent mRNA

0/61 0/15

3/18

2/3 0/5

2/25

0/1 2/25 1/8

0/11 2/112 (2%)

0/11 6/63 (10%)

5/66 0/7

3/35

6/15

0/13 0/15

5/32 16/120 (13%)

1/32 4/97 (4%)

Deletion

Mutation

0/24

0/24

0/13

0/12

0/33

1/33 0/42 0/25 0/9 1/145 (1%)

0/25 0/9 0/104 (0%)

4/7 0/7

0/6 2/22 1/13 1/13 0/9 1/10 4/40 (10%)

Absent mRNA

0/20

0/6 0/22 0/12 1/13 0/9 0/10 1/52 (2%)

*Some cell lines were included in more than one study.

tumor samples, the frequency of PTEN genetic alterations is about 12% and 1%, in SCLC and NSCLC, respectively (Table 4). The study of NSCLC tumor samples is more likely to be representative of the occurrence of mutations, despite additional technical challenges in studying tumor samples because only about 10% of NSCLC tumor samples can be successfully established as a tumor cell line. In SCLC, the success rate of establishing a cell line is much higher and is consistent with the similar rate of PTEN genetic alterations in SCLC cell lines and tumors. In SCLC, the genetic alterations are more commonly biallelic deletion (10% to 13% of tumors) and less commonly small mutations (2% to 4% of tumors). Thus, PTEN is mutant in a minority of SCLC tumors and rarely in NSCLC. In addition to genetic alterations, some lung tumor cell lines without genetic alterations have absent PTEN mRNA.117 About 25% of resected NSCLC specimens lack PTEN staining by IHC.118 In one study of 125 patients with NSCLC, there was no prognostic significance of loss of PTEN protein.118 Methylation of the promoter region of PTEN may be responsible for loss of PTEN expres-

sion in a minority of NSCLC tumors. Identification of the subset of lung cancers with PTEN alterations may have therapeutic implications because these tumors may be sensitive to inhibitors of mTOR,119 the mammalian target of rapamycin serine/threonine kinase that is regulated by Akt. PIK3 Catalytic Alpha. The 100-kd catalytic subunit of PI3K is encoded by the PIK3CA gene on chromosome 3q26. This region is amplified in about 40% of squamous cell subtype of NSCLC120 and occurs almost exclusively in the squamous subtype.121 Increased activity of PI3K as a result of PIK3CA gene amplification results in increased growth stimulatory and cell survival (antiapoptosis) signals. Thus, PIK3CA may be viewed as a candidate oncogene. The restriction of PIK3CA gene amplification to the squamous cell subtype of NSCLC may have implications for therapeutic strategies that target growth stimulatory signals upstream of PI3K, such as epidermal growth factor receptor inhibitors. The recently observed relative activity of the epidermal growth factor receptor tyrosine kinase inhibitor, gefitinib, in nonsquamous

MOLECULAR CLASSIFICATION & MOLECULAR GENETICS

NSCLC compared with squamous NSCLC, could be hypothesized to arise from epidermal growth factor independence resulting from PIK3CA gene amplification in the squamous subtype. Other Phosphatases Mutations of the PPP1R3 gene, which encodes a regulatory subunit of protein phosphatase, were found in five of 33 (15%) NSCLC cell lines, 2 of 38 (5%) NSCLC tumors, and a single SCLC cell line.122 PPP2R1A and PPP2R1B, regulatory subunits of another protein phosphatase, were mutated in one and zero of 23 lung cancer samples, respectively.123 Genetic alteration of other catalytic and regulatory subunits of protein phosphatase have not been reported. Activation of Telomerase The ends of mammalian chromosomes contain telomeres, which are specialized structures necessary for maintenance of chromosome stability.124 Human telomeres may extend for up to 15 kb and are composed solely of the repeat TTAGGG. Shortening of the telomere occurs after each round of DNA replication. Telomerase, the enzyme that elongates telomeric DNA, maintains telomere integrity in immortalized cells but is not expressed in most normal human somatic cells.125 Telomerase is composed of both an RNA subunit and a protein catalytic subunit; the human catalytic subunit, hTERT, is believed to be limiting for telomerase activity and cellular immortality.126-128 Telomerase activity is detectable in most lung cancers, occurring in about 80% to 84% of resected NSCLC tumors129,130 and 98% of SCLC samples.131 Enzyme activity is detectable in histologic precursor lesions of squamous cell carcinoma,132,133 suggesting it is an early event in lung carcinogenesis. In lung cancers as in other cells, telomerase activity is associated with expression of the hTERT protein catalytic subunit,131 though the mechanism of re-expression of hTERT in cancer cells is poorly understood. Detection of hTERT mRNA from tumor is possible in biological samples containing a large excess of normal cells, suggesting a role in detection of subclinical disease.134 As for other cancers, the inhibition of telomerase activity may be a useful therapeutic strategy but has yet to be validated experimentally.

15

SUMMARY

Molecular biology approaches have begun to yield insights into the classification and pathogenesis of human lung carcinoma, although there remain many unanswered questions. Systematic classification efforts using microarray-based gene expression profiling have led to the development of provisional classification schemes for lung carcinoma. The distinction among SCLC, squamous cell lung carcinoma, and adenocarcinoma of the lung can be made quite readily by expression profiling. Furthermore, the first sets of comprehensive expression-based classifications for lung adenocarcinoma raise the promise of novel gene-based subsets, which appear to be highly correlated with patient outcome. Tissue microarray methods followed by immunohistochemical analysis provide a promising follow-up approach to extend and validate classification hypotheses developed by expression profiling studies of lung cancer. Finally, some progress has been made in the identification of the genetic alterations in oncogenes and tumor suppressor genes that lead to the development of lung carcinoma. The most common known alterations in SCLC include inactivation of the Rb and p53 tumor suppressors; PTEN inactivation also occurs in some SCLC tumors. In NSCLC, the major known gene alterations are found in p53, the Rb pathway (most notably p16), and the Ras signal transduction pathway (KRAS and BRAF). Telomerase activation is also a common event in lung carcinoma. REFERENCES 1. Jemal A, Thomas A, Murray T, et al: Cancer statistics, 2002. CA Cancer J Clin 52:23-47, 2002 2. Parkin DM, Pisani P, Ferlay J: Global cancer statistics. CA Cancer J Clin 49:33-64, 1999 3. Travis WD, Travis LB, Devesa SS: Lung cancer. Cancer 75:191-202, 1995 (suppl) 4. Travis WD, Colby TV, Corrin B, et al: Histological typing of lung and pleural tumors XIII. World Health Organization International Histological Classification of Tumors. (ed 3). Berlin/Heidelberg, Springer Verlag, 1999 5. Mountain CF: The international system for staging lung cancer. Semin Surg Oncol 18:106-115, 2000 6. Sorensen JB, Hirsch FR, Gazdar A, et al: Interobserver variability in histopathologic subtyping and grading of pulmonary adenocarcinoma. Cancer 71:2971-2976, 1993 7. Golub TR, Slonim DK, Tamayo P, et al: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286:531-537, 1999 8. Golub TR: Genome-wide views of cancer. N Engl J Med 344:601-602, 2001

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