MULTI-CENTER VALIDATION OF A LYMPH NODE METASTASIS GENE-EXPRESSION SIGNATURE FOR HEAD AND NECK SQUAMOUS CELL CARCINOMAS (original) (raw)
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Journal of Clinical Oncology, 2012
Current assessment of lymph node metastasis in patients with head and neck squamous cell carcinoma is not accurate enough to prevent overtreatment. The aim of this study was validation of a gene expression signature for distinguishing metastasizing (Nϩ) from nonmetastasizing (N0) squamous cell carcinoma of the oral cavity (OSCC) and oropharynx (OPSCC) in a large multicenter cohort, using a diagnostic DNA microarray in a Clinical Laboratory Improvement Amendments/ International Organization for Standardization-approved laboratory.
Head & Neck, 2008
For most patients with head and neck squamous cell cancer, a treatment decision concerning the neck is required. Since detection of small metastases in lymph nodes is difficult, many of the patients with no detectable metastases receive elective treatment of the neck. Additional information on the metastatic potential of the primary tumor before treatment may be useful to reduce the number of these elective procedures. Biomarkers may supply such information. Molecules involved in several pathways have been studied, but the complexity of the metastatic process makes it unlikely that a single marker for metastasis can de identified. Techniques allowing the study of many factors simultaneously seem to be the most promising ones. In recent years, microarray expression profiling and comparative genomic hybridization studies have yielded interesting results. If these results can be confirmed in larger studies, they may play a role in future clinical decision making on treatment of the clinically N0 neck.
Cancer Science, 2007
An accurate assessment of the cervical lymph node metastasis status in oral cavity cancer not only helps predict the prognosis of patients, but also helps surgeons to perform the appropriate treatment. We investigated the utilization of microarray technology focusing on the differences in gene expression profiles between primary tumors of oral squamous cell carcinoma that had metastasized to cervical lymph nodes and those that had not metastasized in the hope of finding new biomarkers to serve for diagnosis and treatment of oral cavity cancer. To design this experiment, we prepared two groups: the learning case group with 30 patients and the test case group with 13 patients. All tissue samples were performed using laser captured microdissection to yield cancer cells, and RNA was isolated from purified cancer cells. To identify a predictive gene expression signature, the different gene expressions between the two groups with and without metastasis in the learning case (n = 30) were analyzed, and the 85 genes expressed differentially were selected. Subsequently, to construct a more accurate prediction model, we further selected the genes with a high power for prediction from the 85 genes using the AdaBoost algorithm. The eight candidate genes, DCTD, IL-15, THBD, GSDML, SH3GL3, PTHLH, RP5-1022P6 and C9orf46, were selected to achieve the minimum error rate. Quantitative reverse transcription-polymerase chain reaction was carried out to validate the selected genes. From these statistical methods, the prediction model was constructed including the eight genes and this model was evaluated by using the test case group. The results in 12 of 13 cases (∼ ∼ ∼ ∼92.3%) were predicted correctly. (Cancer Sci 2007; 98: 740-746)
Multiple Robust Signatures for Detecting Lymph Node Metastasis in Head and Neck Cancer
Cancer Research, 2006
Genome-wide mRNA expression measurements can identify molecular signatures of cancer and are anticipated to improve patient management. Such expression profiles are currently being critically evaluated based on an apparent instability in gene composition and the limited overlap between signatures from different studies. We have recently identified a primary tumor signature for detection of lymph node metastasis in head and neck squamous cell carcinomas. Before starting a large multicenter prospective validation, we have thoroughly evaluated the composition of this signature. A multiple training approach was used for validating the original set of predictive genes. Based on different combinations of training samples, multiple signatures were assessed for predictive accuracy and gene composition. The initial set of predictive genes is a subset of a larger group of 825 genes with predictive power. Many of the predictive genes are interchangeable because of a similar expression pattern across the tumor samples. The head and neck metastasis signature has a more stable gene composition than previous predictors. Exclusion of the strongest predictive genes could be compensated by raising the number of genes included in the signature. Multiple accurate predictive signatures can be designed using various subsets of predictive genes. The absence of genes with strong predictive power can be compensated by including more genes with lower predictive power. Lack of overlap between predictive signatures from different studies with the same goal may be explained by the fact that there are more predictive genes than required to design an accurate predictor. (Cancer Res 2006; 66(4): 2361-6)
Carcinogenesis, 2012
The purpose of this study was to identify molecular markers associated with tumor recurrence and survival in patients with locally advanced head and neck squamous cell carcinoma (HNSCC). We studied the expression profile of 63 pre-treatment tumor biopsies obtained from locally advanced HNSCCs treated with standard treatments. Cluster analysis identified three tumor subtypes associated with significant differences in local recurrence-free survival (LRFS) (P<0.001), progression free-survival (PFS) (P<0.009) and overall survival (OS) (P<0.004). Tumor subtype 1, associated with short LRFS, PFS and OS, showed features of epithelial-mesenchymal transition and undifferentiation. It also overexpressed genes involved in cell adhesion, NF-κB and integrin signalling. Tumor subtype 3, associated with longer LRFS, PFS and OS, showed a high degree of differentiation and overexpressed genes located in chromosomal regions 19q13 and 1q21. Tumor subtype 2, which had an intermediate clinical outcome between subtype 1 and subtype 3, overexpressed genes involved in branching morphogenesis. Finally, we validated the association between gene cluster classification and patient survival using Gene Set Enrichment Analysis and two HNSCC data sets obtained from two independent patient cohorts. In conclusion, we generated a gene prognostic signature associated with survival in locally advanced patients using the expression profile of the pre-treatment tumor biopsy. Independent prospective studies would be necessary to assess if the proposed survival signature could help to guide clinical management of HNSCC.
Cancer Genomics - Proteomics, 2019
Background/Aim: In metastatic head and neck squamous cell carcinoma (HNSCC) the metastatic tumor does not always keep the same gene expression profile as the parental tumor, which may influence the course of the disease. The aim of this study was to compare the expression of genes implicated in HNSCC carcinogenesis between the primary tumor and the corresponding lymph node metastasis. Materials and Methods: Eighteen HNSCC, their corresponding node metastases and non-neoplastic tissues were studied by RT-qPCR for the expression of EGFR, VEGF, claudin7, maspin, survivin and SCCA. The levels of expression were correlated with histological characteristics and patients' prognosis. Results: All genes except for survivin displayed different expression in node metastasis compared to the primary tumor. The expression of EGFR, survivin, maspin, and claudin7 in node metastasis and SSCA in the primary tumor affected the prognosis. SCCA expression is associated with the expression of claudin7 and maspin. P16-positive tumors expressed low levels of VEGF and SCCA, while keratinizing tumors over-expressed VEGF. Conclusion: Differential gene expression levels in node metastases compared to the primary tumor is linked to the prognosis of HNSCC patients. The histological/immunohisto-chemical characteristics of the tumor are associated with these genes expression changes.
Four-gene expression model predictive of lymph node metastases in oral squamous cell carcinoma
Acta Oncologica, 2012
Background. Previous knowledge of cervical lymph node compromise may be crucial to choose the best treatment strategy in oral squamous cell carcinoma (OSCC). Here we propose a set four genes, whose mRNA expression in the primary tumor predicts nodal status in OSCC, excluding tongue. Material and methods. We identifi ed differentially expressed genes in OSCC with and without compromised lymph nodes using Differential Display RT-PCR. Known genes were chosen to be validated by means of Northern blotting or real time RT-PCR (qRT-PCR). Thereafter we constructed a Nodal Index (NI) using discriminant analysis in a learning set of 35 patients, which was further validated in a second independent group of 20 patients. Results. Of the 63 differentially expressed known genes identifi ed comparing three lymph node positive (pN ϩ) and three negative (pN0) primary tumors, 23 were analyzed by Northern analysis or RT-PCR in 49 primary tumors. Six genes confi rmed as differentially expressed were used to construct a NI, as the best set predictive of lymph nodal status, with the fi nal result including four genes. The NI was able to correctly classify 32 of 35 patients comprising the learning group (88.6%; p ϭ 0.009). Casein kinase 1alpha1 and scavenger receptor class B, member 2 were found to be up regulated in pN ϩ group in contrast to small proline-rich protein 2B and Ras-GTPase activating protein SH3 domainbinding protein 2 which were upregulated in the pN0 group. We validated further our NI in an independent set of 20 primary tumors, 11 of them pN0 and nine pN ϩ with an accuracy of 80.0% (p ϭ 0.012). Conclusions. The NI was an independent predictor of compromised lymph nodes, taking into the consideration tumor size and histological grade. The genes identifi ed here that integrate our " Nodal Index " model are predictive of lymph node metastasis in OSCC.