Abstract LB-178: Genomic analysis identifies a subset of laryngeal tumors with poor prognosis
Authors: Suraj Peri, Michael J. Slifker, Ranee Mehra, Barbara Burtness, Eric Ross, Erica Golemis
Journal: Cancer Research (2015)

Abstract Squamous cell carcinoma of head and neck (HNSCC) is a highly heterogeneous tumor type with aggressive behavior. Clinical outcomes depend on tumor site, exposure to carcinogens or Human Papillomavirus (HPV) and other prognostic factors. Patients with HPV+ tumors, primarily arising in the oropharynx, have better survival outcomes than those with HPV- tumors. Using comprehensive genomic data resources, an integrative analysis to characterize these tumors by defining molecular classes and estimating their prognostic relevance has not been done. HNSCC data, from the TCGA repository representing non-silent mutations, copy number variations, gene-level read counts, and methylation were obtained. Data from 256 patients was used for which all four types of data were available. The level 3 copy number segmentation values were deconvoluted to obtain unique set of chromosomal regions. For gene-level read count matrices, the top 2000 genes were selected based on Median Absolute Deviation on RPKM values. In the case of methylation, top 2000 logit transformed ß-values were used. The processed data was then clustered using an integrative clustering methodology. Kaplan-Meier plots and log-rank tests were computed to compare survival distributions among clusters. Fisher's exact tests were used to test association of clinical factors with cluster membership. We performed clustering independently on all HNSCC samples and on laryngeal tumor samples alone. Integrative clustering identified 5 clusters and they can be defined based on tumor site and HPV status. While 74% of HPV+ cases grouped into one cluster; over 80% of oral tumors were distributed into two other clusters; and 64% of the laryngeal tumors cases were segregated into two clusters. We found significant differences in overall survival among these clusters (p = 0.00007). Based on the observation that laryngeal tumors showed different patterns of survival in distinct clusters, we repeated clustering on laryngeal samples alone. This analysis resulted in 2 clusters that recapitulated the survival differences shown in the first analysis (p = 0.0086). We observed distinguishing differences in methylation and expression among the two larynx tumor classes. The 2-year survival estimates for the two clusters were 45% and 75%, and no significant differences in age, gender, tumor stage, smoking status, or alcohol status were observed. Survival differences between these clusters (based on genomic and epigenomic measurements) suggest the possibility that this clustering model can predict survival in larynx cancer, independent of TNM stage. We have identified two classes of laryngeal tumors based on molecular profiles with substantially different clinical outcomes. These results provide a genomic signature that could lead to prognostic and therapeutic intervention for a debilitating malignancy. Note: This abstract was not presented at the meeting. Citation Format: Suraj Peri, Michael J. Slifker, Ranee Mehra, Barbara Burtness, Eric Ross, Erica Golemis. Genomic analysis identifies a subset of laryngeal tumors with poor prognosis. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-178. doi:10.1158/1538-7445.AM2015-LB-178