A transcriptional progression model for head and neck cancer

Patrick K. Ha, Nicole E. Benoit, Robert Yochem, James Sciubba, Marianna Zahurak, David Sidransky, Jonathan Pevsner, William H. Westra, Joseph Califano

Research output: Contribution to journalArticlepeer-review

107 Scopus citations


Purpose: A genetic progression model for head and neck squamous cell carcinoma (HNSC) has been established and implies the presence of transcriptional dysregulation as a consequence of accumulation of genetic alterations. Although expression array data have been provided for HNSC, the timing of transcriptional dysregulation in the progression from normal mucosa to dyplastic epithelium to invasive HNSC has not been described. Here, we describe a transcriptional progression model of HNSC. Experimental Design: Expression arrays representing >12,000 genes and expressed sequence tags were used to examine malignant lesions (M), premalignant lesions (PM), distant, histopathologically normal mucosa from patients with premalignant or malignant lesions (MN), and normal mucosa from the upper aerodigestive tract of patients with noncancer diagnoses (N). Significance analysis of microarrays, hierarchical clustering, and principal components analysis was used to identify genes with differential expression patterns. Results: Using a false discovery rate of <5% for significance analysis of microarray, the M group revealed 965 up-regulated and 1106 down-regulated genes relative to the N group. The PM group demonstrated 108 up-regulated and 226 down-regulated genes relative to the N group, whereas the M group demonstrated only 5 up-regulated and 13 down-regulated genes relative to the PM group. Both hierarchical cluster analysis and principal components analysis revealed a consistent separation between the N, PM, and M groups, with a closer association between the PM and M groups. To provide independent validation of the microarray data, quantitative reverse transcription-PCR was performed for a significantly up-regulated gene, integrin α 6, correlating well with microarray data (linear regression analysis, P < 0.0001). Conclusions: Similarly to the genetic progression model of HNSC, this transcriptional model shows that the majority of alterations occurs before the development of malignancy and identifies key targets of transcriptional dysregulation during progression from a normal to a premalignant state and from a premalignant to a malignant state.

Original languageEnglish (US)
Pages (from-to)3058-3064
Number of pages7
JournalClinical Cancer Research
Issue number8
StatePublished - Aug 1 2003

ASJC Scopus subject areas

  • Oncology
  • Cancer Research


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