Variations in HPV function are associated with survival in squamous cell carcinoma
Frederico O. Gleber-Netto, Xiayu Rao, Theresa Guo, Yuanxin Xi, Meng Gao, Li Shen, Kelly Erikson, Nene N. Kalu, Shuling Ren, Guorong Xu, Kathleen M. Fisch, Keiko Akagi, Tanguy Seiwert, Maura Gillison, Mitchell J. Frederick, Faye M. Johnson, Jing Wang, Jeffrey N. Myers, Joseph Califano, Heath D. Skinner, Curtis R. Pickering
Frederico O. Gleber-Netto, Xiayu Rao, Theresa Guo, Yuanxin Xi, Meng Gao, Li Shen, Kelly Erikson, Nene N. Kalu, Shuling Ren, Guorong Xu, Kathleen M. Fisch, Keiko Akagi, Tanguy Seiwert, Maura Gillison, Mitchell J. Frederick, Faye M. Johnson, Jing Wang, Jeffrey N. Myers, Joseph Califano, Heath D. Skinner, Curtis R. Pickering
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Research Article Oncology Virology

Variations in HPV function are associated with survival in squamous cell carcinoma

Abstract

Incidence of HPV+ oropharyngeal squamous cell carcinoma (OPSCC) has been increasing dramatically. Although long-term survival rates for these patients are high, they often suffer from permanent radiotherapy-related morbidity. This has prompted the development of de-escalation clinical protocols to reduce morbidity. However, a subset of patients do not respond even to standard therapy and have poor outcomes. It is unclear how to properly identify and treat the high- and low-risk HPV+ OPSCC patients. Since HPV positivity drives radiotherapy sensitivity, we hypothesized that variations in HPV biology may cause differences in treatment response and outcome. By analyzing gene expression data, we identified variations in HPV-related molecules among HPV+ OPSCC. A subset of tumors presented a molecular profile distinct from that of typical HPV+ tumors and exhibited poor treatment response, indicating molecular and clinical similarities with HPV– tumors. These molecular changes were also observed in vitro and correlated with radiation sensitivity. Finally, we developed a prognostic biomarker signature for identification of this subgroup of HPV+ OPSCC and validated it in independent cohorts of oropharyngeal and cervical carcinomas. These findings could translate to improved patient stratification for treatment deintensification and new therapeutic approaches for treatment-resistant HPV-related cancer.

Authors

Frederico O. Gleber-Netto, Xiayu Rao, Theresa Guo, Yuanxin Xi, Meng Gao, Li Shen, Kelly Erikson, Nene N. Kalu, Shuling Ren, Guorong Xu, Kathleen M. Fisch, Keiko Akagi, Tanguy Seiwert, Maura Gillison, Mitchell J. Frederick, Faye M. Johnson, Jing Wang, Jeffrey N. Myers, Joseph Califano, Heath D. Skinner, Curtis R. Pickering

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Figure 2

Human transcriptome differences between HPV+ C1 and HPV+ C2 groups.

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Human transcriptome differences between HPV+ C1 and HPV+ C2 groups. Volc...
Volcano plot showing log2 fold-change (horizontal axis) and –log10FDR (vertical axis) values from the whole transcriptome analysis between HPV– (n = 28) and HPV+ C1 (n = 19) OPSCC (A) and between HPV– (n = 28) and HPV+ C2 (n = 33) OPSCC groups (B). Genes were considered differentially expressed at a significance level of 0.05 (n = 4366 in A and n = 8724 in B). Green dots represent differentially expressed genes with lower expression in HPV– cases than in HPV+ C1 cases (n = 2537 in A and n = 5268 in B). Purple dots represent differentially expressed genes with higher expression in HPV– cases than in HPV+ C1 cases (n = 1829 in A and n = 3456 in B). Black dots represent genes whose expression was not significantly different between groups. (C) 7760 differentially expressed genes between HPV+ (n = 52) and HPV– (n = 28) OPSCC cases and their corresponding log2 fold-change values when their expression was compared between HPV– (n = 28) and HPV+ C1 cases (n = 19) (vertical axis) and between HPV– (n = 28) and HPV+ C2 cases (n = 33) (horizontal axis). The red line represents the best-fit calculated by linear regression. The black line represents the 45° line. The line of best-fit slope was lower than 1 (0.72 ± 0.004), indicating greater log2 fold-change values in the horizontal axis (HPV– vs. HPV+ C2) than in the vertical axis (HPV– vs. HPV+ C1). This suggests that differences in expression levels of HPV-related genes were smaller between HPV+ C1 and HPV– cases than between HPV+ C2 and HPV– cases. (D) PCA using expression of 319 genes from the HPV-KEGG pathway database among all 80 TCGA OPSCC cases. All 3 groups (HPV+C1, n = 19; HPV+C2, n = 33; HPV–, n = 28) showed distinct expression profiles of HPV-related genes. (E) Venn diagram representing HPV-KEGG pathway genes differentially expressed between HPV+ C1 (n = 19) and HPV– cases (n = 28) and between HPV+ C2 (n = 33) and HPV– (n = 28) cases. A greater number of HPV-related genes was differentially expressed between HPV+ C2 and HPV– cases. This suggests that the HPV+ C1 group exhibited expression of some HPV-related genes similar to that in HPV– cases. (F) Mean expression of 61 HPV-KEGG pathway genes among HPV+ C1 (n = 19), HPV+ C2 (n = 33), and HPV– (n = 28) cases. These 61 genes were differentially expressed between HPV+ C1 and HPV+ C2 but not between HPV+ C1 and HPV– cases, suggesting that HPV+C1 cases lost part of their HPV characteristics.