Skip to content

Advertisement

You're viewing the new version of our site. Please leave us feedback.

Learn more

BMC Cancer

Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

NDN and CD1A are novel prognostic methylation markers in patients with head and neck squamous carcinomas

  • Shama Virani1,
  • Emily Bellile5,
  • Carol R. Bradford2,
  • Thomas E. Carey2,
  • Douglas B. Chepeha2,
  • Justin A. Colacino1,
  • Joseph I. Helman6, 7,
  • Jonathan B. McHugh4,
  • Lisa A. Peterson2,
  • Maureen A. Sartor3,
  • Jeremy MG Taylor5,
  • Heather M. Walline2,
  • Greg T. Wolf2 and
  • Laura S. Rozek1, 2, 8Email author
BMC Cancer201515:825

https://doi.org/10.1186/s12885-015-1806-8

Received: 27 April 2015

Accepted: 16 October 2015

Published: 30 October 2015

Abstract

Background

HPV-associated HNSCCs have a distinct etiologic mechanism and better prognosis than those with non-HPV associated HNSCCs. However, even within the each group, there is heterogeneity in survival time. Here, we test the hypothesis that specific candidate gene methylation markers (CCNA1, NDN, CD1A, DCC, p16, GADD45A) are associated with tumor recurrence and survival, in a well-characterized, prospective, cohort of 346 HNSCC patients.

Methods

Kaplan-Meier curves were used to estimate survival time distributions. Multivariable Cox Proportional Hazards models were used to test associations between each methylation marker and OST/RPFT after adjusting for known or identified prognostic factors. Stratified Cox models included an interaction term between HPV and methylation marker to test for differences in the associations of the biomarker with OST or RPFT across HPV status.

Results

Methylation markers were differentially associated with patient characteristics. DNA hypermethylation of NDN and CD1A was found to be significantly associated with overall survival time (OST) in all HNSCC patients (NDN hazard ratio (HR): 2.35, 95 % CI: 1.40-3.94; CD1A HR: 1.31, 95 % CI: 1.01-1.71). Stratification by HPV status revealed hypermethylation of CD1A was associated with better OST and recurrence/persistence-free time (RPFT) (OST HR: 3.34, 95 % CI: 1.88-5.93; RPFT HR: 2.06, 95 % CI: 1.21-3.49), while hypomethylation of CCNA1 was associated with increased RPFT in HPV (+) patients only (HR: 0.31, 95 % CI: 0.13-0.74).

Conclusions

This study is the first to describe novel epigenetic alterations associated with survival in an unselected, prospectively collected, consecutive cohort of patients with HNSCC. DNA hypermethylation of NDN and CD1A was found to be significantly associated with increased overall survival time in all HNSCC patients. However, stratification by the important prognostic factor of HPV status revealed the immune marker, CD1A, and the cell cycle regulator, CCNA1 to be associated with prognosis in HPV (+) patients, specifically. Here, we identified novel methylation markers and specific, epigenetic molecular differences associated with HPV status, which warrant further investigation.

Keywords

Head and neck cancerEpigeneticsSurvivalRecurrenceDNA methylation

Background

Head and neck cancer is the 6th most common cancer in the world with approximately 600,000 new cases each year and at least 90 % being squamous cell carcinomas [1, 2]. Heavy tobacco and alcohol use are well established risk factors, but high-risk human papillomavirus (HPV) infection has recently been identified as an independent etiologic factor for a subset of head and neck squamous cell carcinomas (HNSCCs) [3]. The overall 5-year survival rate for HNSCC has remained at 50-60 % for the past several decades, primarily due to locoregional or distant metastatic recurrence, which develop in 35- 55 % of patients within two years [4, 5]. Low survival rates are partially due to the fact that almost 60 % of patients are diagnosed after the disease has advanced locally, but also due to pathological, clinical and epidemiological heterogeneity [6, 7] and frequent association of significant co-morbidities.

The incidence of HPV-associated HNSCC has steadily increased, especially in younger patients, while incidence of non-HPV associated HNSCC has declined in recent years [810]. HPV-associated HNSCCs have a unique risk profile, a distinct etiologic mechanism, and better prognosis than non-HPV associated HNSCCs [1114]. HPV (+) patients tend to have cancers almost exclusively located in the oropharynx, be younger with a higher socioeconomic status, and have a less profound use of alcohol and tobacco [13, 15, 16]. Studies show a 60-80 % reduction in mortality in HPV (+) patients compared to patients with non-HPV associated HNSCC [3, 17, 18] regardless of treatment modality or tumor stage. Within each group, however, there is heterogeneity in survival time, with up to 20 % of HNSCCs progressing with distant metastases. Thus, there is strong interest in identifying prognostic markers for both HPV (+) and HPV (-) patients with HNSCC.

Gene-specific DNA methylation has been increasingly recognized as a contributor to the molecular heterogeneity of HNSCC [19, 20]. Several markers have been proposed as biomarkers of prognosis and/or diagnosis [19, 21]. However, there is a need to determine the validity of epigenetic markers considering the divergent etiologic mechanisms. In addition, the extent to which epidemiologic characteristics contribute to the prognostic advantage of HPV (+) tumors is unclear. Combining methylation information with clinical characteristics known to affect survival is crucial to understanding the differences in survival rates by these characteristics and how they may be targeted for intervention.

Here we test the hypothesis that specific candidate gene methylation markers (CCNA1, NDN, CD1A, DCC, GADD45a, and p16) are associated with tumor recurrence and survival, in a well-characterized, prospective cohort of HNSCC patients with extensive epidemiologic, clinical and outcome information, who were treated by a single group of clinicians with a homogenous treatment approach. This approach allows careful consideration of the epigenetic biomarkers in the context of epidemiologic and clinicopathologic characteristics that influence overall and recurrence-free survival.

Methods

Recruitment

The University of Michigan’s Head and Neck Specialized Program of Research Excellence (SPORE) approaches every incident, previously untreated HNSCC patient to participate in longitudinal epidemiology studies. This unselected study population represents 28 % of incident HNSCC cases in the state of Michigan. From November 2008 through June 2012, subjects were screened for eligibility and 92 % (n = 513) of subjects approached signed a written, informed consent. Consented subjects completed a baseline questionnaire of demographics, epidemiologic characteristics, and behavior modules. Comorbidity data were abstracted from the medical record and graded by severity (none, mild, moderate, severe) using the Adult Comorbidity Evaluation of 27 conditions organized by 12 systems (ACE-27). Research assistants collected formalin-fixed, paraffin-embedded (FFPE) HNSCC tissue blocks and detailed pathophysiological and clinical data annually until death or the patient was lost to follow-up. This study was approved by the Institutional Review Board of the University of Michigan Medical School.

Tissue acquisition

The FFPE tissue blocks were collected pretreatment from three possible sources: (1) a biopsy obtained from an outside hospital, (2) a biopsy performed at the University of Michigan hospital, and/or (3) from surgery performed at the University of Michigan. Tissue acquired from the three sources yielded at least one sample for 88 % (n = 450) of the subjects.

Study population

An expert head and neck pathologist (JM) confirmed tumor histology and screened representative blocks for areas of >70 % cellularity and minimal necrosis. Seventy-two percent (n = 369) of all subjects had sufficient tissue and DNA to yield methylation results. Of these, 15 subjects were excluded for tumors arising from rare sites or non-squamous histology (e.g., unknown primary, nasopharynx, salivary gland, sinus) and 7 subjects were excluded for indeterminate HPV status. One subject was lost to follow up. This resulted in a total of 346 subjects used in the methylation analyses, representing 67 % of the 513 eligible participants screened.

Follow-up

All patients were followed prospectively at designated intervals by clinicians at the University of Michigan or through contact with referring physicians. The median follow-up period was 27 months for survival and 24 months for recurrence (range: 1-54 months). Number of patients alive and followed for OS at 1, 2 and 3 years were 307, 90 and 85 patients, respectively. Number of patients alive and followed for RPFT at 1, 2 and 3 years were 242, 129 and 53 patients, respectively. Deaths were captured through the Social Security Death Index, yearly survey updates, notification from family, and medical record reviews. Survival time and events were censored as of 4/30/13. Recurrence and persistent disease events were confirmed updated annually during a chart review at every subject’s yearly anniversary of their date of initial diagnosis.

Target gene selection

Our group recently completed a discovery-based study designed to identify novel prognostic epigenetic biomarkers for patients with HNSCC [22, 23]. CCNA1 (cyclin A1) was chosen for further testing based on its potential for clinical relevance and the discovery analysis that identified regions of their promoters to be significantly differentially methylated in head and neck cancer patients by HPV status [22]. NDN (necdin) and CD1a (cluster of differentiation 1a) were also differentially methylated in this discovery analysis, however they were not significant, potentially due to small sample size. NDN is an imprinted gene previously implicated in epithelial ovarian, bladder, breast, colorectal, and urothelial cancers, as well as premalignant lesions such as vulval intraepithelial neoplasia and Barrett’s oesophagus, although has not been studied in the context of HNSCC [2229]. CD1A was the first immune gene found to be differentially methylated in the discovery analysis. CD1A methylation has not been previously studied in HNSCC, however significant hypermethylation of CD1B, CD1C, CD1D and CD1E has been found in HPV (+) HNSCC tumors compared to HPV(-) tumors [30]. DCC (deleted in colorectal carcinoma), GADD45 (growth arrest and DNA damage 45) and p16 (cyclin-dependent kinase inhibitor) were all previously found to be hypermethylated in HNSCC and were chosen for their role as tumor suppressors and potential involvement with HPV [9, 3134].

Microdissection/DNA extraction/bisulfite conversion

Designated areas of FFPE tissue were microdissected from unstained slides and DNA was extracted using the QIAamp DNA FFPE Tissue Kit (Qiagen, Valencia, CA) according to the manufacturer’s protocol. DNA concentration and purity was measured with a NanoDrop spectrophotometer (Thermo Scientific, Waltham, MA). Sodium bisulfite treatment was performed on 250 ng of DNA using the Epitect Bisulfite Kit (Qiagen, Valencia, CA) according to the manufacturer’s recommended protocol.

HPV testing

HPV status was determined by an ultrasensitive method using real-time competitive polymerase chain reaction (PCR) and matrix-assisted laser desorption/ionization mass spectroscopy, as described in Tang et al. [35]. Multiplex PCR amplification of the E6 region of 15 discrete high risk HPV types (HPV 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, 68 and 73), and human GAPDH control included a competitor oligo identical to each natural amplicon except for a single nucleotide difference. Probes that identify unique sequences in the oncogenic E6 region of each type were used in multiplex single base extension reactions extending at the single base difference between wild-type and competitor HPV so that each HPV type and its competitor were distinguished by mass when analyzed on the MALDI-TOF mass spectrometer as described previously [32, 3639].

Methylation analysis

Methylation assays for promoter regions of DCC, CD1A, GADD45 and NDN, were designed using PyroMark Assay Design 2.0 software across 5, 2, 5 and 3 CpG sites, respectively (Qiagen, Valencia, CA). The methylation assay for p16 was adapted from Shaw, et al. and covered 4 CpG sites [40]. The promoter region of CCNA1 was sequenced using the Sequenom EpiTyper, a MALDI-TOF mass spectrometry based platform across 4 CpG sites. Primer sets are shown in Additional file 1: Table S1. Location of each CpG site and distance to transcription start site are denoted in Additional file 1: Table S2. Bisulfite singleplex PCR amplification was performed using FastStart Taq Polymerase (Roche Diagnostics, Indiana, US) for CCNA1, and HotStar Taq® Master Mix Kit (Qiagen Valencia, CA) for all other genes, with a forward and reverse primer concentration of 0.2 mM and 30 ng of bisulfite-converted DNA. Confirmation of PCR product quality and absence of contamination was established from 2 % agarose gels with ethidium bromide staining. Fifteen microliters of each PCR product was combined with the respective sequencing primer and methylation analysis by pyrosequencing was conducted using the Pyromark™ MD System (Biotage) according to manufacturer’s protocol, including single strand binding protein (PyroGold reagents). Four bisulfite and four pyrosequencing controls were generated by mixing unmethylated and methylated control DNA (genomic: EpigenDX; bisulfite-converted: Epitect) to obtain controls with 0 %, 30 %, 60 % and 100 % methylation. Each sample plate was run with all controls. If methylation values of controls were incorrect, all samples on plate were re-run. Measurement of all samples for every methylation marker selected was not possible if there was insufficient quantity of total extracted DNA. Average methylation across all CpG sites measured for each gene were used in all statistical analyses as strong associations between CpG sites of each gene has been previously shown [41].

Statistical analysis

Overall survival time (OST) and recurrence/persistence-free time (RPFT) were calculated beginning at date of diagnosis. An OST event was defined as death from any cause. For RPFT, an event was defined as any recurrence (local, regional or distant) of the tumor or persistence of the tumor after definitive treatment. In the case of persistence, an RPFT of 1 day after diagnosis was assigned. In the case of death prior to recurrence, a subject was censored for RPFT at the last known date recurrence-free.

Univariate analyses, including Kruskal-Wallis and Wilcoxon-rank tests, were conducted to test for differences in methylation of each gene by clinico-pathological and epidemiological characteristics. The Kaplan-Meier method was employed to estimate survival time distributions and graphically visualize time-to-event outcomes for overall survival time (OST) and recurrence/persistence-free time (RPFT) by methylation. Methylation of each marker was categorized into quartiles for the Kaplan-Meier curves, with quartile 1 containing the lowest values and quartile 4 containing the highest (Additional file 1: Table S3). Statistical differences in curves were tested using the log-rank test.

Multivariable Cox proportional hazard models were used to test associations between each methylation marker and OST/RPFT after adjusting for known or identified prognostic factors. All mean methylation values were log-transformed after adding an offset value of 1. For each outcome, a model with only clinical predictors was developed using a backward selection algorithm (alpha criteria = 0.05) to arrive at a parsimonious model, with the stipulation that stage and disease site would remain in the model regardless of their significance. Variables introduced for potential inclusion were: age, HPV status, ACE comorbidity score (none, mild, moderate, severe), tobacco use (never, former, current within 12 months), and alcohol use (never, former, current within 12 months). The final clinical model for both OST and RPFT included age, tumor stage, disease site, and HPV; the final clinical model for OST included comorbidity score in addition. After data exploration, significant violation to the proportional hazards assumption was observed for HPV. Stratified Cox proportional hazard models were performed that allowed differing baseline hazard functions for HPV+ and HPV- groups accounting for the non-proportional hazards observed in our data. These stratified Cox proportional hazards models included the same adjustment covariates as the unstratified version, and included an additional interaction term between HPV and methylation marker to test for differences in the associations of the biomarker with OST or RPFT across HPV status. Finally, each methylation marker was added to both the stratified and un-stratified version of the clinical Cox models to assess associations between the marker and outcome after covariate adjustment. All methylation measurements were standardized to interquartile ranges (IQR) of each respective marker (Additional file 1: Table S3). Therefore, hazard ratios (HRs) are interpreted as a comparison between those with methylation in the 25th percentile compared to those with methylation in the 75th percentile.

Unadjusted p-values are presented, however authors advise that a significance finding near the threshold of p < 0.05 should be interpreted with caution. Due to multiple tests being performed (each outcome was modeled for 6 genes), a more conservative Bonferroni threshold for significance was calculated as p < 0.004 (0.05/12) and reflected in superscripts in the Cox model results (Table 4).

Statistical analyses were conducted in R 3.1.1 and SAS 9.3.

Results

HNSCC patient characteristics

The mean age of the HNSCC patients was 59.7 years and consisted of 75 % males (Table 1). Cancer sites were mostly oropharyngeal and oral cavity (36 % each) while laryngeal cancers made up about 24 % of cases and only 3 % of cases were hypopharyngeal. Sixty-one percent of cases were stage IV. Forty-six percent of patients had mild comorbidity status, while 26 % had moderate and 8 % had severe comorbidity. Forty-two percent of patients were classified as current smokers, or having quit within the past 12 months, while 36 % were former smokers (quit more than one year ago) and 22 % were never smokers. Distributions of all patient characteristics are listed in Table 1.
Table 1

Patient clinical and epidemiological characteristics, N = 346

Characteristic

 

N

(%)

Mean (std), range

Age at Dx (years)

   

59.7 (11.5), 25-93

Gender

Male

259

75 %

 
 

Female

87

25 %

 

Cancer Site

Larynx/Glottic

83

24 %

 
 

Oral Cavity

126

36 %

 
 

Oropharynx

125

36 %

 
 

Hypopharynx

12

3 %

 

Cancer Stage

I/Cis

47

14 %

 
 

II

35

10 %

 
 

III

52

15 %

 
 

IV

212

61 %

 

Comorbidities (ACE)

None

91

26 %

 
 

Mild

158

46 %

 
 

Moderate

70

20 %

 
 

Severe

27

8 %

 

HPV status

Positive

135

39 %

 
 

Negative

211

61 %

 

Tobacco Use

Current (within past 12 months)

145

42 %

 
 

Former (quit > 12 months)

125

36 %

 
 

Never

76

22 %

 

Pack Years, n = 257

(cigs only)

  

35.6 (30.0), 0.1-171.0

Pack Years, n = 264

(sum of cigs, cigars, pipe)

  

37.7 (33.3), 0.07-242.9

Alcohol Use, n = 244

Never

29

8 %

 
 

Former (quit >12 months)

227

66 %

 
 

Current (within past 12 months)

90

26 %

 

Treatment, n = 335

Surgery alone

67

20 %

 
 

Radiation alone

34

10 %

 
 

Surgery + Radiation

34

10 %

 
 

Radiation + Chemotherapy

138

41 %

 
 

Surgery + Radiation + Chemotherapy

39

12 %

 
 

No Treatment prior to death

23

7 %

 

Persistent Diseasea

 

29

8 %

 

Median Follow-up for Survival

 

27 months

  

Median Follow-up for Recurrence/Persistence

 

24 months

  

Recurrence/Persistenceb

 

81

  

Death

 

78

  

KM estimate 2 year RPFTc

  

75 %

 

KM estimate 2 year OSTd

  

79 %

 

aDisease considered persistent if patient never became disease free. bRecurrence of the HNSCC in the primary, regional and/or a distant location. Persistent patients whose disease never cleared after treatment are included and considered recurrent with a recurrence time = 1 day. cRPFT Recurrence/Persistent Free Time defined as time from diagnosis to recurrence event or end of follow-up. End of follow-up is the last date where patient was reviewed for recurrence. Patients whose disease never cleared after treatment are considered recurrent with a recurrence time = 1 day. dOST Overall Survival Time. Death from any cause considered an event. Overall survival time defined from date of diagnosis by UM physician

Patients with HPV (+) tumors (n = 135) were on average younger than patients with HPV (-) tumors (n = 211) (mean age = 57 years, SD: 9.6 years for HPV (+) patients and mean age = 61.4 years, SD: 12.3 years for HPV (-) patients, p-value = 0.0002) (Table 2). The majority of HPV (-) patients had cancers of the oral cavity (52 %) whereas the majority of HPV (+) patients had cancer of the oropharynx (OP) (78 %). HPV was detected in 22 % of non-oropharyngeal (non OP) sites. Comparisons between HPV (+) patients presenting in OP and non-OP sites revealed similar 2-year overall survival times (Kaplan Meier Estimate (KM) (95 % CI): 92 % (87 %, 98 %); 89 % (77 %, 100 %); respectively, p-value = 0.71) as well as similar recurrence-free survival times (KM (95 % CI): 85 % (78 %, 93 %); 80 % (66 %, 98 %), respectively, p-value = 0.59; Additional file 1: Figure S1) More patients with HPV (+) tumors were diagnosed with stage IV tumors than those patients with HPV (-) tumors (77 % vs 51 %) primarily due to a higher frequency of patients with N2 neck disease that is common in HPV related cancers [42, 43]. Most HPV (-) patients were current smokers (48 %) while HPV (+) patients had lower proportions of current (32 %) smokers and similar frequencies of former (37 %) and never (31 %) smokers. Pack-years of cigarettes only use was higher in HPV (-) patients (mean: 38.4 pack-years) compared to HPV (+) patients (29.9 pack-years, p-value = 0.02). Distributions of patient characteristics by HPV status are listed in Table 2.
Table 2

Patient clinical and epidemiological characteristics by HPV status, N = 346

  

HPV (–)

HPV (+)

HPV (-)

HPV (+)

 

Characteristic

 

N

(%) among HPV(-)

N

(%) among HPV(+)

Mean (SD)

Mean (SD)

p-value

Age at Dx (years)

 

211

 

135

 

61.4 (12.3)

57.0 (9.6)

0.0002

Gender

Male

139

66 %

120

89 %

  

<0.0001

 

Female

72

34 %

15

11 %

   

Cancer Site

Larynx/Glottic

72

34 %

11

8 %

  

<0.0001

 

Oral Cavity

110

52 %

16

12 %

   
 

Oropharynx

20

9 %

105

78 %

   
 

Hypopharynx

9

4 %

3

2 %

   

Cancer Stage

I/Cis

36

17 %

11

8 %

  

<0.0001

 

II

28

13 %

7

5 %

   
 

III

39

18 %

13

10 %

   
 

IV

108

51 %

104

77 %

   

Comorbidities (ACE)

None

41

19 %

50

37 %

  

0.001

 

Mild

99

47 %

59

44 %

   
 

Moderate

50

24 %

20

15 %

   
 

Severe

21

10 %

6

4 %

   

Tobacco Use

Current (within past 12 months)

102

48 %

43

32 %

  

0.001

 

Former (quit > 12 months)

75

36 %

50

37 %

   
 

Never

34

16 %

42

31 %

   

Pack Years, n = 257

(cigs only)

    

38.4 (25.6)

29.9 (28.8)

0.02

Pack Years, n = 264

(cigs, cigars, pipe)

    

41.8 (34.5)

29.6 (29.4)

0.005

Alcohol Use, n = 244

Current (within past 12 months)

63

30 %

27

20 %

  

0.01

 

Former (quit > 12 months)

126

60 %

101

75 %

   
 

Never

22

10 %

7

5 %

   

Treatment, n = 335

Surg alone

53

26 %

14

11 %

  

<0.0001

 

Rad alone

23

11 %

11

8 %

   
 

Surg + Rad

30

15 %

4

3 %

   
 

Rad + Chemo

45

22 %

93

70 %

   
 

Surg + Rad + Chemo

29

14 %

10

8 %

   
 

No Treatment prior to death

22

11 %

1

1 %

   

Post Treatment Status

Free of Disease

186

88 %

131

97 %

  

0.004

 

Persistent Disease

25

12 %

4

3 %

   

KM estimate 2 year RPFT

  

69 %

 

84 %

  

0.001

KM estimate 2 year OST

  

71 %

 

91 %

  

<0.0001

-Percentages will not add to 100 % horizontally. % presented is proportion of clinical or epidemiological subgroup among HPV status category. -p-values calculated by Wilcoxon-rank tests for continuous variables and Kruskal-Wallis tests for categorical variables. Log-rank test used for KM analysis

Tumor methylation differs by epidemiologic characteristics

Methylation of CD1A differed across clinically relevant age groups, decreasing with increasing age (Table 3). HPV status was significantly associated with several markers, as expected. Methylation of CCNA1, NDN, CD1A, and DCC was higher, while methylation of p16was lower, in HPV (+) tumors compared to HPV (-) tumors. Increasing number of total pack-years across all tobacco types was significantly associated with decreased methylation of NDN and CD1A. Tobacco use (current, former, never user) was also considered separately as these data were complete and more reliable than pack-years for most patients (Table 3). Tobacco use was significantly associated with methylation of NDN, CD1A, and DCC (p-value  = 0.02; p-value  < 0.001; p-value  = 0.03, respectively). Interestingly, former smokers had lower methylation of these genes compared to never or current smokers. Trends of each methylation marker differed significantly across disease site. Methylation of NDN was higher in patients with higher stage tumors (p -value < 0.001), while a trend for CCNA1 methylation with stage was U-shaped, with higher methylation at stages I and IV (p -value = 0.01). As comorbidity status increased, methylation of CD1A increased (p-value = 0.006). Finally, methylation of NDN and DCC was significantly higher in males compared to females (p-value =0.003; p-value =0.002, respectively).
Table 3

Methylation markers by patient characteristics

 

CCNA1

NDN

CD1A

DCC

p16

GADD45

 

median (range)

p

median (range)

p

median (range)

p

median (range)

p

median (range)

p

median (range)

p

Age

            

 <40 years

19.5 (14.5, 64.7)

0.4

40.8 (34.5, 72.8)

0.4

78.6 (56.8, 91.2))

0.02

21.3 (9.9, 62.3)

0.4

3.1 (0.6, 25.0)

0.5

1.5 (1.0, 2.8)

0.5

 40-60

25.8 (3.0, 90.5)

 

44.6 (10.8, 84.2)

 

71.0 (22.2, 93.9)

 

33.2 (3.1, 85.8)

 

2.3 (0, 70.1)

 

1.4 (0, 3.9)

 

 ≥60

27.3 (5.5, 77.0)

 

43.9 (11.2, 84.9)

 

68.6 (21.4, 90.9)

 

33.7 (3.0, 79.8)

 

2.2 (0, 59.8)

 

1.4 (0, 16.0)

 

HPV

            

 Positive

33.3 (3.0, 76.0)

<0.001

52.7 (14.0, 84.9)

<0.001

77.6 (25.1, 93.9)

<0.001

44.7 (3.1, 85.8)

<0.001

1.9 (0, 59.8)

<0.001

1.4 (0.4, 3.9)

0.8

 Negative

20.9 (5.5, 90.5)

 

40.0 (10.8, 80.3)

 

65.4 (21.4 (92.2)

 

26.0 (3.0, 83.3)

 

2.5 (0, 70.1)

 

1.4 (0, 16)

 

Pack Years

            

 0

26.6 (3.0, 90.5)

0.2

46.4 (14.0, 84.2)

0.01

79.7 (47.2, 93.9)

<0.001

36.7 (3.1, 79.8)

0.1

2.2 (0, 26.9)

0.7

1.5 (0.3, 3.7)

0.2

 0-10

31.5 (13.3, 63.3)

 

45.9 (24.6, 75.7)

 

70.5 (32.4, 91.1)

 

40.5 (10.8, 71.7)

 

2.1 (0, 46.6)

 

1.4 (0.4, 16.0)

 

 10-20

23.6 (6.8, 64.8)

 

46.4 (10.8, 76.8)

 

73.0 (26.7, 91.7)

 

32.7 (6.1, 75.1)

 

2.2 (0, 70.1)

 

1.6 (0.7, 3.9)

 

 20+

25.5 (6.3, 77.0)

 

41.3 (11.2, 84.9)

 

64.2 (21.4, 92.0)

 

31.4 (3.0, 85.8)

 

2.4 (0, 59.8)

 

1.4 (0, 3.9)

 

Tobacco

            

 Current

25.5 (6.3, 76.0)

0.8

45.0 (10.8, 84.9)

0.02

69.5 (25.1, 91.7)

<0.001

39.5 (4.2, 83.3)

0.03

2.2 (0, 70.1)

0.7

1.4 (0, 16)

0.5

 Former

25.6 (6.8, 77.0)

 

42.2 (11.2, 80.3)

 

64.4 (21.4, 90.0)

 

29.3 (3.0, 85.8)

 

2.4 (0, 46.6)

 

1.4 (0, 3.9)

 

 Never

26.6 (3, 90.5)

 

46.4 (14.0, 84.2)

 

79.7 (47.2, 93.9)

 

35.7 (3.1, 79.8)

 

2.2 (0, 26.9)

 

1.5 (0.3, 3.7)

 

Site

            

 Larynx/Glottic

24.5 (8.0, 90.5)

<0.001

41.2 (11.2, 84.9)

<0.001

56.2 (21.6, 92.2)

<0.001

26.3 (3.1, 83.3)

<0.001

2.4 (0, 51.8)

<0.001

1.4 (0, 3.9)

0.02

 Oral Cavity

22.0 (5.5, 77.0)

 

40.0 (10.8, 62.8)

 

70.6 (21.4, 90.2)

 

28.4 (3.0, 67.2)

 

2.6 (0, 70.1)

 

1.4 (0, 16.0)

 

 Oropharynx

32.3 (3.0, 76.0)

 

52.7 (21.8, 84.2)

 

77.5 (21.6, 93.9)

 

42.5 (6.1, 81.6)

 

2.0 (0, 39.9)

 

1.4 (0.4, 3.9)

 

 Hypopharynx

15.6 (9.0, 64.0)

 

44.2 (17.2, 91.0)

 

60.5 (25.1, 90.6)

 

54.5 (12.3, 85.8)

 

2.6 90, 59.8)

 

1.1 (0, 1.6)

 

Stage

            

 I/Cis

27.5 (11.8, 77.0)

0.01

38.6 (11.2, 80.3)

<0.001

68.1 (30.3, 89.4)

0.8

26.7 (3.0, 67.2)

0.1

2.5 (0, 39.0)

0.1

1.3 (0, 3.5)

0.3

 II

22.4 (12, 74.3)

 

41.2 (17.2, 84.2)

 

68.8 (21.6, 88.3)

 

40.8 (7.9, 83.3)

 

2.3 (0, 59.8)

 

1.4 (0.6, 4.6)

 

 III

21.3 (6.8, 61.3)

 

40.4 (15.3, 69.7)

 

71.2 (21.4, 90.2)

 

28.8 (3.9, 75.1)

 

2.6 (0, 70.1)

 

1.5 (0, 16.0)

 

 IV

27.6 (3.0, 90.5)

 

46.8 (10.8, 84.9)

 

70.5 (21.6, 93.9)

 

35.9 (3.1, 85.8)

 

2.2 (0, 51.8)

 

1.4 (0, 3.9)

 

Comorbidities

            

 None

29.5 (6.3, 74.3)

0.4

46.0 (20.2, 84.2)

0.3

74.8 (32.4, 93.9)

0.006

33.1 (5.6, 75.2)

0.6

2.5 (0, 39.9)

0.4

1.5 (0.4, 4.6)

0.1

 Mild

25.5 (3.0, 77.0)

 

43.3 (10.8, 84.9)

 

69.5 (21.6, 92.0)

 

35.4 (3.0, 85.8)

 

2.2 (0, 70.1)

 

1.4 (0, 16.0)

 

 Moderate

23.0 (6.8, 90.5)

 

44.0 (15.0, 79.3)

 

69.2 (21.4, 92.2)

 

29.8 (3.5, 79.8)

 

2.2 (0, 59.8)

 

1.3 (0, 3.9)

 

 Severe

30.5 (13.5,63.3)

 

44.6 (11.2, 80.3)

 

65.4 (21.6, 85.1)

 

43.0 (6.2, 83.3)

 

1.8 (0, 51.8)

 

1.2 (0, 3.9)

 

Gender

            

 Male

26.5 (3.0, 90.5)

0.2

45.6 (13.0, 84.9)

0.003

69.9 (21.4, 93.9)

0.4

36.1 (3.1, 85.8)

0.002

2.3 (0, 70.1)

0.9

1.4 (0, 3.9)

0.5

 Female

23.8 (6.3, 77.0)

 

40.5 (10.8, 68.9)

 

69.5 (21.6, 91.7)

 

25.4 (3.0, 75.1)

 

2.1 (0, 46.6)

 

1.4 (0, 16.0)

 

Distribution of methylation markers across patient characteristics. p-values calculated by Kruskal-Wallis tests. Methylation values are expressed as percentages

Stratification by HPV status across these epidemiologic and epigenetic characteristics revealed HPV (+) tumors were hypermethylated across CCNA1, NDN, CD1A, and DCC, but hypomethylated in p16 as compared to HPV(-) tumors, regardless of clinical epidemiological traits (data not shown).

Tumor methylation and survival/recurrence

Kaplan-Meier curves for the association between methylation, categorized into quartiles, and OST and RPFT across all HNSCC patients, are shown in Additional file 1: Figure S2. Differences in methylation of both CD1A and NDN were significantly associated with OST in all HNSCC patients (p-value = 0.005; p-value = 0.001, respectively). The lowest quartile of methylation of CD1A (Q1) was associated with the lowest probability of OST (0.68 at 24 months) whereas higher methylation in Q2, Q3 and Q4 clustered above a probability of OST of 0.80 at 24 months. Methylation of the lowest quartile of NDN was associated with the lowest probability of OST. In terms of RPFT, there were significant associations with CCNA1, DCC and NDN methylation in all HNSCC patients. NDN revealed a trend of increasing probability of RPFT with increasing quartile of methylation. The lowest and highest quartiles of CCNA1 and DCC had the lowest probabilities of RPFT.

When patients were separated and analyzed by HPV status, distinct associations between methylation markers and OST/RPFT were identified (Additional file 1: Figures S3 and S4). In HPV (+) patients, higher quartiles of methylation of CD1A in Q3 and Q4 were associated with better OST compared to the lower quartiles (p-value = 0.03). Within HPV (-) patients, NDN and DCC were significantly associated with probability of OST. Low methylation of NDN in Q1 had the lowest probability of OST compared to methylation in all other quartiles (p -value = 0.04). DCC methylation in Q2 was associated with the highest probability of OST (p -value = 0.005). A similar pattern was observed for DCC methylation and RPFT, although with DCC, it was Q1 had much poorer probability of recurrence/persistence compared to other quartiles (p-value = 0.002). (Additional file 1: Figure S4)

NDN and CD1A methylation are novel markers of survival in HNSCC patients

Multivariable Cox proportional hazards models adjusting for site, stage, HPV status, age and comorbidity score showed significant associations of NDN and CD1A gene promoter methylation with survival in all HNSCC patients (Table 4). Treatment was not included in any models as it was confounded by site. Those with NDN methylation in the 25th percentile had a 2.4 times higher hazard of a death event compared to those with methylation in the 75th percentile (95 % CI: 1.40-3.94). Patients with CD1A methylation in the 25th percentile had a 1.3 times higher hazard of a death event compared to those with methylation in the 75th percentile upon adjustment (95 % CI: 1.01-1.71). These results indicate that for all HNSCC patients, hypermethylation of NDN and CD1A are associated with better patient survival.
Table 4

Stratified Cox Proportional Hazards Analysis comparing overall survival time (OST) and recurrence/persistence free time (RPFT) by gene methylation

   

Multivariable cox model

Stratified cox proportional hazards model

Gene methylationa

Nb

Multivariable HR (95 % CI)c,d

Stratified model among HPV + c,e

Stratified model among HPV-c,e

p-valuef

OST

CCNA1

341

1.13 (0.81, 1.59)

0.62 (0.25, 1.52)

1.25 (0.86, 1.81)

0.16

NDN

346

2.35 (1.40, 3.94) **

3.12 (0.95, 10.21)

2.22 (1.25, 3.92)*

0.61

CD1A

342

1.31 (1.01, 1.71)*

3.34 (1.88, 5.93)**

1.13 (0.84, 1.51)

0.001**

DCC

344

1.30 (0.95, 1.78)

1.45 (0.59, 3.60)

1.28 (0.91, 1.79)

0.79

p16

343

1.11 (0.93, 1.32)

0.76 (0.48, 1.19)

1.17 (0.97, 1.42)

0.08

GADD45

340

0.88 (0.66, 1.17)

0.80 (0.43, 1.50)

0.91 (0.66, 1.25)

0.73

RPFT

CCNA1

334

0.81 (0.57, 1.14)

0.31 (0.13, 0.74)*

0.96 (0.66, 1.42)

0.02*

NDN

339

1.86 (1.12, 3.07)*

2.08 (0.70, 6.19)

1.77 (1.00, 3.13)

0.79

CD1A

335

1.24 (0.95, 1.62)

2.06 (1.21, 3.49)*

1.07 (0.79, 1.46)

0.04*

DCC

337

1.32 (0.96, 1.83)

0.97 (0.40, 2.34)

1.37 (0.97, 1.94)

0.47

p16

336

1.00 (0.84, 1.19)

0.91 (0.57, 1.46)

1.00 (0.84, 1.20)

0.72

GADD45

333

1.12 (0.87, 1.46)

0.84 (0.49, 1.42)

1.23 (0.91, 1.66)

0.22

Each row represents the hazard ratios in the multivariable or stratified Cox Proportional Hazards model for each gene. The top panel lists all overall survival time models and the bottom panel lists all recurrence/persistence-free survival time models. aAll genes log-transformed to better fit the linear functional relationship between predictor and outcome bn number of subjects in models cAll methylation measurements standardized to interquartile ranges. HRs are interpreted as comparison between those with methylation in the 25th percentile compared to those with methylation in the 75th percentile dMultivariable HR calculated from a multivariable Cox Proportional Hazards model after controlling for: age, HPV status, disease site, stage, and comorbidity score in the OST model and age, HPV status, disease site and stage in the RPFT model. eMultivariable HR (95 % CI) calculated from a stratified multivariable Cox Proportional Hazards model to allow for differing baseline hazard functions for HPV+ and HPV- groups. These models include an interaction of HPV status and gene methylation. Hazards listed for each HPV group in the stratified models calculated from this interaction term. Stratified OST models control for: age, disease site, stage, and comorbidity score and stratified RPFT models control for: age, disease site and stage. fp-value for interaction term. *p-value < 0.05; **significance at the Bonferroni-adjusted p-value < 0.004

To determine the extent to which HPV status may have played a role in these findings, stratified Cox models with interaction between methylation level and HPV status were used to measure associations separately within each group (Table 4). Divergent associations were found in patients based on their HPV status. The test for interaction indicated differences in OST for CD1A (p-value = 0.001) and in RPFT for CD1A (p-value = 0.04) and CCNA1 (p-value = 0.02). In HPV (+) patients, CD1A methylation in the 25th percentile revealed a 3.34 times higher hazard of a death event than those in the 75th percentile (95 % CI: 1.88-5.93). In addition, patients had twice the hazard of a tumor recurrence or persistence if they were at the 25th percentile of CD1A methylation compared to the 75th percentile. These results suggest that hypermethylation of CD1A is associated with better survival and lower recurrence/persistence in HPV (+) patients specifically. Conversely, for CCNA1, HPV (+) patients had a 0.31 times lower hazard of a recurrent event or persistence comparing those in the 25th percentile to those in the 75th percentile of methylation, indicating that hypomethylation of this gene is associated with lower risk of recurrence/persistence in HPV(+) patients.

Discussion

Changes in methylation patterns are one of the most frequent events in human neoplasms. Epigenetic alterations have been increasingly recognized to play a role in the complex mechanisms of head and neck carcinogenesis [19, 21]. This study is the first to describe novel epigenetic alterations associated with survival in an unselected, prospectively collected, consecutive cohort of patients with HNSCC. Our strongest findings support overall survival from HNSCC to be associated with NDN methylation in all patients and CD1A methylation in HPV (+) patients. These associations are significant at p-value < 0.004, the Bonferroni-adjusted p-value for multiple testing. Despite weaker associations, likely due to lower number of patients and events, there are indications that gene methylation is important in recurrence/persistence of HNSCC. Hypermethylation of NDN is associated with lower recurrence/persistence overall whereas hypermethylation of CD1A and hypomethylation of CCNA1 is associated with lower recurrence/persistence in HPV (+) patients only. These unique discoveries raise new questions about why these specific epigenetic changes differ among biologically distinct subsets of HNSCC patients (HPV + versus HPV -) and if these differences are linked to HPV status or other factors (e.g. tumor-host immunity, oncogene mutations).

NDN is a maternally imprinted gene that has monoallelic expression. It encodes necdin, a protein that interacts with p53 to suppress growth and induce cell cycle arrest [26]. Although necdin is a p53 target gene involved in cell growth arrest and found to be dysregulated in urothelial, prostate, breast, bladder, lung and ovarian cancers, it has only recently been implicated in HNSCC [22, 23, 2630]. Our adjusted Cox model analyses revealed hypermethylation of NDN was associated with better survival in all HNSCC patients. Although the function of NDN is unknown, previous literature has implicated necdin to act as a “switch”, promoting quiescence under steady state conditions but bypassing p53 responses to promote proliferation or suppressing p53-induced apoptosis in a stressful state [25, 26, 44]. Our findings support the oncogenic function of this gene within tumor cells in that hypermethylation of this gene is beneficial for patient survival.

CD1A encodes an immune protein responsible for presenting antigens by dendritic Langerhans cells to T lymphocytes, such as natural killer cells. Findings from our adjusted Cox model validated the difference seen in Kaplan-Meier survival curves across quartiles of CD1A methylation. Our analyses revealed hypermethylation was associated with better overall survival for all HNSCC patients and, for better survival and lower recurrence in HPV (+) patients specifically, even after adjusting for important clinical variables. Recent studies have highlighted important differences in immunologic status in patients’ peripheral blood and tumor microenvironment according to HPV status [45, 46]. Patients with non-HPV related HNSCC tend to be significantly immunosuppressed and this immune suppression is associated with worse outcomes. CD1A dysregulation could negatively impact activation of suppressor and regulatory T cells systemically as well as tumor associated macrophages in the microenvironment, benefiting patients with HPV+ cancers. Further studies of CD1A expression and the specific immune abnormalities associated with HPV status are underway.

Hypomethylation of CCNA1 was associated with lower hazard of recurrence/persistence in HPV (+) patients only. CCNA1 is a cell cycle regulator that binds to retinoblastoma, E2F transcription factor and p21 family proteins to promote cell cycle progression. This pathway plays a particularly important regulatory role in cell proliferation with HPV infection. Our results suggest that decreased methylation of this cell cycle regulator is protective against recurrence events. Our findings are consistent with previous findings in HPV (+) HNSCC patients [22, 30, 47, 48]. However, several studies have shown that promoter methylation of CCNA1 had no effect on protein or gene expression in HPV-positive HNSCC in spite of a strong correlation between CCNA1 overexpression and HPV positivity, suggesting that HPV may induce both promoter hypermethylation and overexpression [47, 49, 50]. Prognostically, HPV induced cyclin A1 overexpression has been associated with a lower recurrence rate in HNSCC [47].

Considering CD1A is an immune regulatory gene and CCNA1 is involved in response pathways to HPV infection, it is plausible that these genes would play a larger role in more antigenic cancers with a viral etiology. NDN, which is involved in cell cycle regulation via p53 pathways, is likely to be important in regulation of altered cell cycle pathways contributing to the process of tumorigenesis [26]. However, it remains unclear to what extent these significant associations with survival are due to treatment efficacy or reflect epigenetic variations due to co-morbidities or health behaviors. In contrast to other reports from large outcome studies, there were no significant differences in survival outcomes with respect to co-morbidities, suggesting that the epigenetic changes more likely were reflective of differences in tumor biology rather than co-morbidity. Larger epigenetic studies of smoking, diet and co-morbidities and associations with known genetic mutations are currently underway.

Our sensitive method of HPV detection has been previously validated with other commonly used assays to detect HPV [39]. As expected, a majority of HPV (+) patients presented in the oropharynx, although our use of this assay allowed us to detect HPV in 22 % of HPV (+) patients with non-oropharynx sites, 16 % of which were in the oral cavity. In the last decade, oral HPV prevalence has increased in HNSCC. Previous studies have reported prevalence of HPV in oral cavity tumors to be between 4-20 % [3, 5153] . This is likely explained by the significant increase in incidence of HPV-associated oral squamous cell carcinomas (OSCCs) since 2000, compared to the relatively stable incidence trends of non-HPV associated OSCCs [11]. Our cohort is a prospective, unselected patient population that is likely representing the changing epidemiology affecting HPV-associated OSCC incidence. This is further supported by our finding of similar 2-year survival times of HPV (+) patients with OP and non-OP tumors. The low proportion of HPV (+) patients with tumors in the larynx and hypopharynx in our study fell within previously reported figures [54, 55].

Epidemiologic and clinical characteristics have generally been used to understand cancer phenotype, determine prognosis and inform treatment plans for patients. Epidemiologic factors such as smoking history, nutrition and comorbidity are well known significant prognostic factors for overall survival and indicate the importance of including such factors in studies of new molecular markers [8, 56, 57]. In the last decade, the clinical importance of better understanding tumor biology in HNSCC has emerged through validation of HPV status as a significant molecular predictor of patient survival and recurrence. Differences in patient outcomes according to HPV status are so dramatic that many investigators believe they reflect a new and unique phenotype that could justify significant de-intensification of therapy [8, 58]. In this study we have identified novel methylation markers and specific, epigenetic molecular differences within the setting of the generalized hypermethylation phenotype associated with HPV status, which warrant further investigation. The findings support biological implications of epigenetic markers on patient survival and their potential usefulness in identifying unique subsets of patients with varied outcomes. Several markers show expected associations with patient characteristics. Methylation of the immune marker, CD1A, is indicative of comorbidity status, while several markers are differentially methylated according to HPV status, validating our previous study [22]. However, the finding of associations of methylation markers with characteristics such as stage, gender and tobacco status is new. Former smokers had lower methylation of NDN and CD1A compared to never and current smokers. Because total pack years is inversely associated with these genes, perhaps former smokers differ because duration of exposure and/or exposure at an early age is integral in the initiation of processes that permits carcinogenesis.

Conclusions

Our cohort shows the expected associations established in previous literature, such as the relationships between stage, site and HPV status with overall survival time, and the expected population characteristics of a HNSCC cohort established by previous studies, providing assurance that the new associations with survival demonstrated with this cohort are meaningful and can be extrapolated to the general HNSCC patient population. Identification of significant epigenetic markers of biologic tumor behavior and outcome should open new horizons for investigations and interventions directed at reversible gene alterations and potentially identify novel therapeutic targets.

Abbreviations

HPV: 

Human papilloma virus

HNSCC: 

Head and neck squamous cell carcinoma

OST: 

Overall survival time

RPFT: 

Recurrence/persistence-free time

IQR: 

Interquartile range

CCNA1

Cyclin A1

NDN

Necdin

CD1a

Cluster of differentiation 1a

DCC

Deleted in colorectal carcinoma

GADD45

Growth arrest and DNA damage 45

p16

Cyclin-dependent kinase inhibitor

Declarations

Funding sources

This project was supported by Federal Funds from the National Institutes of Health/National Cancer Institute (NIH/NCI) under the University of Michigan Specialized Programs of Research Excellence (SPORE) Grant P50CA097248, as well as through NIH/NCI R01CA158286. Support for SV was also provided by the National Institute on Aging Institutional Training Grant, (T32 AG027708) and the Rackham Predoctoral Fellowship from the University of Michigan. Support for JAC was provided by institutional training grants from the National Institute of Environmental Health Sciences (NIEHS) (T32 ES007062) and the National Human Genome Research Institute (NHGRI) (T32 HG00040) as well as a Rackham Predoctoral Fellowship from the University of Michigan.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Environmental Health Sciences, University of Michigan School of Public Health
(2)
Department of Otolaryngology, University of Michigan Medical School
(3)
Department of Computational Medicine and Bioinformatics, University of Michigan
(4)
Department of Pathology, University of Michigan Medical School
(5)
Department of Biostatistics, University of Michigan, School of Public Health
(6)
Department of Oral-Maxillofacial Surgery, University of Michigan Dental School
(7)
Department of Surgery, University of Michigan Medical School
(8)
1415 Washington Heights, Environmental Health Sciences 6630 SPH

References

  1. Hoffman HT, Karnell LH, Funk GF, Robinson RA, Menck HR. The National Cancer Data Base report on cancer of the head and neck. Arch Otolaryngol Head Neck Surg. 1998;124(9):951–62.View ArticlePubMedGoogle Scholar
  2. Parkin DM, Bray F, Ferlay J, Pisani P. Global cancer statistics, 2002. CA Cancer J Clin. 2005;55(2):74–108.View ArticlePubMedGoogle Scholar
  3. Gillison ML, Koch WM, Capone RB, Spafford M, Westra WH, Wu L, et al. Evidence for a causal association between human papillomavirus and a subset of head and neck cancers. J Natl Cancer Inst. 2000;92(9):709–20.View ArticlePubMedGoogle Scholar
  4. Leemans CR, Braakhuis BJ, Brakenhoff RH. The molecular biology of head and neck cancer. Nat Rev Cancer. 2011;11(1):9–22.View ArticlePubMedGoogle Scholar
  5. Wise-Draper TM, Draper DJ, Gutkind JS, Molinolo AA, Wikenheiser-Brokamp KA, Wells SI. Future directions and treatment strategies for head and neck squamous cell carcinomas. Transl Res. 2012;160(3):167–77.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Forastiere AA, Ang KK, Brizel D, Brockstein BE, Burtness BA, Cmelak AJ, et al. Head and neck cancers. J Natl Compr Canc Netw. 2008;6(7):646–95.PubMedGoogle Scholar
  7. Jung AC, Job S, Ledrappier S, Macabre C, Abecassis J, de Reynies A, et al. A poor prognosis subtype of HNSCC is consistently observed across methylome, transcriptome, and miRNome analysis. Clin Cancer Res. 2013;19(15):4174–84.View ArticlePubMedGoogle Scholar
  8. Marur S, D'Souza G, Westra WH, Forastiere AA. HPV-associated head and neck cancer: a virus-related cancer epidemic. Lancet Oncol. 2010;11(8):781–9.View ArticlePubMedGoogle Scholar
  9. Gillison ML. Human papillomavirus and prognosis of oropharyngeal squamous cell carcinoma: implications for clinical research in head and neck cancers. J Clin Oncol. 2006;24(36):5623–5.View ArticlePubMedGoogle Scholar
  10. Sturgis EM, Cinciripini PM. Trends in head and neck cancer incidence in relation to smoking prevalence: an emerging epidemic of human papillomavirus-associated cancers? Cancer. 2007;110(7):1429–35.View ArticlePubMedGoogle Scholar
  11. Chaturvedi AK, Engels EA, Anderson WF, Gillison ML. Incidence trends for human papillomavirus-related and -unrelated oral squamous cell carcinomas in the United States. J Clin Oncol. 2008;26(4):612–9.View ArticlePubMedGoogle Scholar
  12. Fakhry C, Gillison ML. Clinical implications of human papillomavirus in head and neck cancers. J Clin Oncol. 2006;24(17):2606–11.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Gillison ML, D'Souza G, Westra W, Sugar E, Xiao W, Begum S, et al. Distinct risk factor profiles for human papillomavirus type 16-positive and human papillomavirus type 16-negative head and neck cancers. J Natl Cancer Inst. 2008;100(6):407–20.View ArticlePubMedGoogle Scholar
  14. Joseph AW, D'Souza G. Epidemiology of human papillomavirus-related head and neck cancer. Otolaryngol Clin North Am. 2012;45(4):739–64.View ArticlePubMedGoogle Scholar
  15. Chaturvedi AK, Engels EA, Pfeiffer RM, Hernandez BY, Xiao W, Kim E, et al. Human papillomavirus and rising oropharyngeal cancer incidence in the United States. J Clin Oncol. 2011;29(32):4294–301.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Ragin CC, Modugno F, Gollin SM. The epidemiology and risk factors of head and neck cancer: a focus on human papillomavirus. J Dent Res. 2007;86(2):104–14.View ArticlePubMedGoogle Scholar
  17. Schwartz SR, Yueh B, McDougall JK, Daling JR, Schwartz SM. Human papillomavirus infection and survival in oral squamous cell cancer: a population-based study. Otolaryngol Head Neck Surg. 2001;125(1):1–9.View ArticlePubMedGoogle Scholar
  18. Weinberger PM, Yu Z, Haffty BG, Kowalski D, Harigopal M, Brandsma J, et al. Molecular classification identifies a subset of human papillomavirus--associated oropharyngeal cancers with favorable prognosis. J Clin Oncol. 2006;24(5):736–47.View ArticlePubMedGoogle Scholar
  19. Demokan S, Dalay N. Role of DNA methylation in head and neck cancer. Clin Epigenetics. 2011;2(2):123–50.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Worsham MJ, Stephen JK, Chen KM, Havard S, Shah V, Gardner G, et al. Delineating an epigenetic continuum in head and neck cancer. Cancer Lett. 2014;342(2):178–84.View ArticlePubMedGoogle Scholar
  21. Arantes LM, de Carvalho AC, Melendez ME, Carvalho AL, Goloni-Bertollo EM. Methylation as a biomarker for head and neck cancer. Oral Oncol. 2014;50(6):587–92.View ArticlePubMedGoogle Scholar
  22. Colacino JA, Dolinoy DC, Duffy SA, Sartor MA, Chepeha DB, Bradford CR, et al. Comprehensive analysis of DNA methylation in head and neck squamous cell carcinoma indicates differences by survival and clinicopathologic characteristics. PLoS One. 2013;8(1):e54742.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Sartor MA, Dolinoy DC, Jones TR, Colacino JA, Prince ME, Carey TE, et al. Genome-wide methylation and expression differences in HPV(+) and HPV(-) squamous cell carcinoma cell lines are consistent with divergent mechanisms of carcinogenesis. Epigenetics. 2011;6(6):777–87.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Rhodes DR, Kalyana-Sundaram S, Mahavisno V, Varambally R, Yu J, Briggs BB, et al. Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. Neoplasia. 2007;9(2):166–80.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Asai T, Liu Y, Nimer SD. Necdin, a p53 target gene, in normal and cancer stem cells. Oncotarget. 2013;4(6):806–807.View ArticlePubMedPubMed CentralGoogle Scholar
  26. De Faveri LE, Hurst CD, Platt FM, Taylor CF, Roulson JA, Sanchez-Carbayo M, et al. Putative tumour suppressor gene necdin is hypermethylated and mutated in human cancer. Br J Cancer. 2013;108(6):1368–77.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Haviland R, Eschrich S, Bloom G, Ma Y, Minton S, Jove R, et al. Necdin, a negative growth regulator, is a novel STAT3 target gene down-regulated in human cancer. PLoS One. 2011;6(10):e24923.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Sanchez-Carbayo M, Socci ND, Lozano J, Saint F, Cordon-Cardo C. Defining molecular profiles of poor outcome in patients with invasive bladder cancer using oligonucleotide microarrays. J Clin Oncol. 2006;24(5):778–89.View ArticlePubMedGoogle Scholar
  29. Tan AC, Jimeno A, Lin SH, Wheelhouse J, Chan F, Solomon A, et al. Characterizing DNA methylation patterns in pancreatic cancer genome. Mol Oncol. 2009;3(5-6):425–38.View ArticlePubMedGoogle Scholar
  30. Cancer Genome Atlas Network. Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature. 2015;517(7536):576–582.View ArticleGoogle Scholar
  31. Butz K, Whitaker N, Denk C, Ullmann A, Geisen C, Hoppe-Seyler F. Induction of the p53-target gene GADD45 in HPV-positive cancer cells. Oncogene. 1999;18(14):2381–6.View ArticlePubMedGoogle Scholar
  32. Kumar B, Cordell KG, Lee JS, Worden FP, Prince ME, Tran HH, et al. EGFR, p16, HPV Titer, Bcl-xL and p53, sex, and smoking as indicators of response to therapy and survival in oropharyngeal cancer. J Clin Oncol. 2008;26(19):3128–37.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Ying J, Srivastava G, Hsieh WS, Gao Z, Murray P, Liao SK, et al. The stress-responsive gene GADD45G is a functional tumor suppressor, with its response to environmental stresses frequently disrupted epigenetically in multiple tumors. Clin Cancer Res. 2005;11(18):6442–9.View ArticlePubMedGoogle Scholar
  34. Langevin SM, Butler RA, Eliot M, Pawlita M, Maccani JZ, McClean MD, et al. Novel DNA methylation targets in oral rinse samples predict survival of patients with oral squamous cell carcinoma. Oral Oncol. 2014;50(11):1072–80.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Tang AL, Hauff SJ, Owen JH, Graham MP, Czerwinski MJ, Park JJ, et al. UM-SCC-104: a new human papillomavirus-16-positive cancer stem cell-containing head and neck squamous cell carcinoma cell line. Head Neck. 2012;34(10):1480–91.View ArticlePubMedGoogle Scholar
  36. Worden FP, Kumar B, Lee JS, Wolf GT, Cordell KG, Taylor JM, et al. Chemoselection as a strategy for organ preservation in advanced oropharynx cancer: response and survival positively associated with HPV16 copy number. J Clin Oncol. 2008;26(19):3138–46.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Maxwell JH, Kumar B, Feng FY, McHugh JB, Cordell KG, Eisbruch A, et al. HPV-positive/p16-positive/EBV-negative nasopharyngeal carcinoma in white North Americans. Head Neck. 2010;32(5):562–7.PubMedPubMed CentralGoogle Scholar
  38. Maxwell JH, Kumar B, Feng FY, Worden FP, Lee JS, Eisbruch A, et al. Tobacco use in human papillomavirus-positive advanced oropharynx cancer patients related to increased risk of distant metastases and tumor recurrence. Clin Cancer Res. 2010;16(4):1226–35.View ArticlePubMedPubMed CentralGoogle Scholar
  39. Walline HM, Komarck C, McHugh JB, Byrd SA, Spector ME, Hauff SJ, et al. High-risk human papillomavirus detection in oropharyngeal, nasopharyngeal, and oral cavity cancers: comparison of multiple methods. JAMA Otolaryngol Head Neck Surg. 2013;139(12):1320–7.View ArticlePubMedPubMed CentralGoogle Scholar
  40. Shaw RJ, Liloglou T, Rogers SN, Brown JS, Vaughan ED, Lowe D, et al. Promoter methylation of P16, RARbeta, E-cadherin, cyclin A1 and cytoglobin in oral cancer: quantitative evaluation using pyrosequencing. Br J Cancer. 2006;94(4):561–8.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Virani S, Bellile, E, Peterson, LA, Sartor, MA, Taylor, JMG, McHugh, JB, et al. Stability of methylation markers in head and neck squamous cell carcinomas. Head Neck: J Sci Spec Head Neck. 2015, (Accepted).Google Scholar
  42. Goldenberg D, Begum S, Westra WH, Khan Z, Sciubba J, Pai SI, et al. Cystic lymph node metastasis in patients with head and neck cancer: An HPV-associated phenomenon. Head Neck. 2008;30(7):898–903.View ArticlePubMedGoogle Scholar
  43. Lajer CB, von Buchwald C. The role of human papillomavirus in head and neck cancer. APMIS. 2010;118(6-7):510–9.View ArticlePubMedGoogle Scholar
  44. Lafontaine J, Rodier F, Ouellet V, Mes-Masson AM. Necdin, a p53-target gene, is an inhibitor of p53-mediated growth arrest. PLoS One. 2012;7(2):e31916.View ArticlePubMedPubMed CentralGoogle Scholar
  45. Wansom D, Light E, Worden F, Prince M, Urba S, Chepeha DB, et al. Correlation of cellular immunity with human papillomavirus 16 status and outcome in patients with advanced oropharyngeal cancer. Arch Otolaryngol Head Neck Surg. 2010;136(12):1267–73.View ArticlePubMedPubMed CentralGoogle Scholar
  46. Wansom D, Light E, Thomas D, Worden F, Prince M, Urba S, et al. Infiltrating lymphocytes and human papillomavirus-16--associated oropharyngeal cancer. Laryngoscope. 2012;122(1):121–7.View ArticlePubMedPubMed CentralGoogle Scholar
  47. Weiss D, Koopmann M, Basel T, Rudack C. Cyclin A1 shows age-related expression in benign tonsils, HPV16-dependent overexpression in HNSCC and predicts lower recurrence rate in HNSCC independently of HPV16. BMC Cancer. 2012;12:259.View ArticlePubMedPubMed CentralGoogle Scholar
  48. Weiss D, Basel T, Sachse F, Braeuninger A, Rudack C. Promoter methylation of cyclin A1 is associated with human papillomavirus 16 induced head and neck squamous cell carcinoma independently of p53 mutation. Mol Carcinog. 2011;50(9):680–8.View ArticlePubMedGoogle Scholar
  49. Martinez I, Wang J, Hobson KF, Ferris RL, Khan SA. Identification of differentially expressed genes in HPV-positive and HPV-negative oropharyngeal squamous cell carcinomas. Eur J Cancer. 2007;43(2):415–32.View ArticlePubMedGoogle Scholar
  50. van Kempen PM, Noorlag R, Braunius WW, Stegeman I, Willems SM, Grolman W. Differences in methylation profiles between HPV-positive and HPV-negative oropharynx squamous cell carcinoma: a systematic review. Epigenetics. 2014;9(2):194–203.View ArticlePubMedGoogle Scholar
  51. Isayeva T, Li Y, Maswahu D, Brandwein-Gensler M. Human papillomavirus in non-oropharyngeal head and neck cancers: a systematic literature review. Head Neck Pathol. 2012;6 Suppl 1:S104–120.View ArticlePubMedGoogle Scholar
  52. Sharma A, Mendez E, Yueh B, Lohavanichbutr P, Houck J, Doody DR, et al. Human papillomavirus-positive oral cavity and oropharyngeal cancer patients do not have better quality-of-life trajectories. Otolaryngol Head Neck Surg. 2012;146(5):739–45.View ArticlePubMedPubMed CentralGoogle Scholar
  53. Smith EM, Rubenstein LM, Haugen TH, Pawlita M, Turek LP. Complex etiology underlies risk and survival in head and neck cancer human papillomavirus, tobacco, and alcohol: a case for multifactor disease. J Oncol. 2012;2012:571862.View ArticlePubMedPubMed CentralGoogle Scholar
  54. Gheit T, Abedi-Ardekani B, Carreira C, Missad CG, Tommasino M, Torrente MC. Comprehensive analysis of HPV expression in laryngeal squamous cell carcinoma. J Med Virol. 2014;86(4):642–6.View ArticlePubMedGoogle Scholar
  55. Shaughnessy JN, Farghaly H, Wilson L, Redman R, Potts K, Bumpous J, et al. HPV: a factor in organ preservation for locally advanced larynx and hypopharynx cancer? Am J Otolaryngol. 2014;35(1):19–24.View ArticlePubMedGoogle Scholar
  56. Arthur AE, Duffy SA, Sanchez GI, Gruber SB, Terrell JE, Hebert JR, et al. Higher micronutrient intake is associated with human papillomavirus-positive head and neck cancer: a case-only analysis. Nutr Cancer. 2011;63(5):734–42.View ArticlePubMedPubMed CentralGoogle Scholar
  57. Piccirillo J, Costas I, Reichman ME. SEER Survival Monograph: Cancer Survival Among Adults: U.S. SEER Program, 1988-2001, Patient and Tumor Characteristics. Bethesda, MD: National Cancer Institute, SEER Program; 2007.Google Scholar
  58. Deschler DG, Richmon JD, Khariwala SS, Ferris RL, Wang MB. The “New” Head and neck cancer patient-young, nonsmoker, nondrinker, and HPV positive: evaluation. Otolaryngol Head Neck Surg. 2014;151(3):375–80.View ArticlePubMedPubMed CentralGoogle Scholar

Copyright

© Virani et al. 2015

Advertisement