Abstract
Objective
The causes of the differences in the higher incidence of and the mortality from head and neck squamous cell carcinoma (HNSCC) in African American (AA) versus Caucasian Americans (CA) lack a consensus. We examined a comprehensive array of risk factors influencing health and disease in an access to care, racially diverse, primary HNSCC cohort.
Study Design
Cross-sectional study.
Setting
Primary care academic health care system.
Subjects and Methods
The cohort of 673 comprised 391 CA and 282 AA (42%). Risk variables included demographic, histopathology, and clinical/epidemiologic factors. Tumor DNA was interrogated for loss and gain of 113 genes with known involvement in HNSCC/cancer. Logistic regression for univariate analysis was followed by multivariate modeling with determination of model predictability (c-index).
Results
Of the 39 univariate differences between AA and CA, multivariate modeling (c-index=0.81) retained seven (p<0.05). AA were less likely to be married, more likely to have tumor lymphocytic response, undergo radiation treatment, and smoke. Insurance type was a significant predictor of race. AA were more likely to have Medicaid, Medicare, and other HMO types. AA tumors were more likely to have loss of CDKN2A and gain of SCYA3 versus CA.
Conclusions
Multivariate modeling indicated significant differences between AA and CA HNSCC for histopathology, treatment, smoking, marital status, type of insurance, as well as tumor gene copy number alterations. Our data reiterate that for HNSCC as in the case of other complex diseases, tumor genetics or biology is only one of many potential contributors to differences among racial groups.
Keywords: African American, Caucasian American, risk factor variables, tumor biology
INTRODUCTION
There is abundant epidemiological evidence that self-identified race/ethnicity is associated with differences in cancer incidence and mortality. The high mortality rate for HNSCC continues to be driven by the disparate unfavorable diagnosis and prognosis outcomes for African Americans (AA)1–3. There is no consensus on the causes of the differences in the higher incidence of and the mortality from HNSCC for AA when compared to Caucasian Americans (CA), but they can include differences in access to care, stage at diagnosis, insurance, attitudes of health providers, as well as human papilloma virus (HPV) infection status3–8. HPV is now regarded, in addition to tobacco and alcohol9, as a causative agent for some HNSCC 10 and an independent risk factor for oropharyngeal cancer (OPSCC)11.
Racial/ethnic difference such as genetic features, in combination with environmental factors can also influence carcinogenic mechanisms and lead to biologically important differences in the molecular profile of a tumor12. Epidemiological and clinical studies using molecular markers indicate that racial differences in cancer types do exist12, supporting a systematic evaluation of these issues.
The objective of this study was to gain insights into differences in the higher incidence of and the mortality from HNSCC in AA versus CA through the examination of a comprehensive array of risk factors influencing health and disease in an access to care, racially diverse, primary HNSCC cohort.
MATERIALS AND METHODS
Study design
Study Cohort
The study cohort of 673 primary HNSCC was drawn from a large, clinically well characterized multi-ethnic (40% AA), primary care patient population in the Detroit area. Patients, 21 years or older with a primary HNSCC diagnostic biopsy in the Henry Ford Health System with available tumor tissue blocks were identified through tumor registry and ENT clinic records between 1986 and 2005. The Henry Ford Health System Tumor Registry (HFHS-TR) is certified by the Commission on Cancer (CoC) of the American College of Surgeons and has been a division of the Department of Medical Records since the mid-1960s. All HFHS Tumor Registry registrars are Registered Health Information Technicians (RHIT) and have passed the National Exam for RHIT”. Follow-up spanned 4–23 years (through 9/25/2009). This study included only self-reported AA and CA HNSCC. Other ethnicities were excluded.
Risk Factor Variables
Risk variables included demographic, histopathology, and clinical/epidemiologic risk factors for a total of 23 non-gene variables and are described in Tables 1 & 2. Tumor DNA was interrogated for loss and gain of 113 genes with known involvement in HNSCC/cancer (gene panels p005, p006, p007, www.mlpa.com).
Table 1.
Demographic and clinical variables by race
| Variable | Response | White (N= 391) | Black (N= 282) | p-value |
|---|---|---|---|---|
| Demographics | ||||
| Age category | <=50 | 49 (13%) | 44 (16%) | 0.516 |
| 51–65 | 150 (38%) | 106 (38%) | ||
| >65 | 192 (49%) | 132 (47%) | ||
| Gender | Male | 286 (73%) | 212 (75%) | 0.553 |
| Female | 105 (27%) | 70 (25%) | ||
| Clinical/Epidemiologic | ||||
| Marital Status | No | 116 (31%) | 140 (53%) | <.001 |
| Yes | 257 (69%) | 122 (47%) | ||
| Insurance Type | Hap | 127 (38%) | 38 (15%) | <.001 |
| Other HMO | 21 (6%) | 32 (13%) | ||
| Blue Cross | 44 (13%) | 15 (6%) | ||
| Medicare | 144 (42%) | 143 (58%) | ||
| Medicaid | 3 (1%) | 21 (8%) | ||
| Allergy | No | 341 (87%) | 247 (88%) | 0.885 |
| Yes | 50 (13%) | 35 (12%) | ||
| Overall Comorbidity | 0. None | 83 (21%) | 21 (7%) | <.001 |
| 1. Mild | 175 (45%) | 132 (47%) | ||
| 2. Moderate | 79 (20%) | 78 (28%) | ||
| 3. Severe | 54 (14%) | 51 (18%) | ||
| Pneumonia | No | 341 (87%) | 237 (84%) | 0.244 |
| Yes | 50 (13%) | 45 (16%) | ||
| Hyperthyroid Disease | No | 359 (92%) | 270 (96%) | 0.042 |
| Yes | 32 (8%) | 12 (4%) | ||
| Family History of Cancer | No | 68 (39%) | 58 (47%) | 0.160 |
| Yes | 108 (61%) | 66 (53%) | ||
| Cigarette Smoking Category | 0:Never Smk | 61 (16%) | 17 (7%) | <.001 |
| 1:Past Smk | 150 (40%) | 84 (32%) | ||
| 2:Current Smk 1–15 | 17 (5%) | 31 (12%) | ||
| 3:Current Smk 15–25 | 73 (20%) | 98 (38%) | ||
| 4:Current Smk 25–35 | 30 (8%) | 16 (6%) | ||
| 5:Current Smk >35 | 41 (11%) | 15 (6%) | ||
| Cigarette Smoking Category Collapsed | Never | 61 (17%) | 17 (7%) | <.001 |
| Current | 150 (40%) | 84 (32%) | ||
| Past | 161 (43%) | 160 (61%) | ||
| Alcohol | No | 27 (8%) | 8 (3%) | 0.007 |
| Yes | 294 (92%) | 252 (97%) | ||
| Radiation | No | 162 (41%) | 92 (33%) | 0.020 |
| Yes | 229 (59%) | 190 (67%) | ||
| Chemotherapy | No | 316 (81%) | 204 (72%) | 0.010 |
| Yes | 75 (19%) | 78 (28%) | ||
| Surgery | No | 198 (51%) | 160 (57%) | 0.118 |
| Yes | 193 (49%) | 122 (43%) |
Table 2.
Histopathology variables by race
| Variable | Response | White (N= 391) | Black (N= 282) | p-value |
|---|---|---|---|---|
| Histopathology | ||||
| Overall Grade | Well Differentiated | 117 (30%) | 65 (23%) | 0.097 |
| Moderately Differentiated | 177 (46%) | 135 (48%) | ||
| Poorly Differentiated | 94 (24%) | 82 (29%) | ||
| Tumor Type: Keratinizing | No | 139 (36%) | 98 (35%) | 0.793 |
| Yes | 250 (64%) | 184 (65%) | ||
| Tumor Type: Basaloid | No | 385 (99%) | 275 (98%) | 0.143 |
| Yes | 4 (1%) | 7 (2%) | ||
| Tumor Type: Adenocarcinoma | No | 388 (100%) | 282 (100%) | 0.394 |
| Yes | 1 (0%) | 0 (0%) | ||
| Lymphocytic Response | Continuous rim | 182 (47%) | 120 (43%) | 0.003 |
| Patchy Infiltrate | 177 (46%) | 154 (55%) | ||
| Absent | 25 (7%) | 5 (2%) | ||
| Desmoplastic Response | Prominent & Diffuse | 176 (46%) | 135 (48%) | 0.282 |
| Patchy & Irregular | 151 (39%) | 117 (42%) | ||
| Focal | 30 (8%) | 16 (6%) | ||
| Absent | 26 (7%) | 11 (4%) | ||
| Pattern Invasion | Pushing Cohesive Borders | 71 (19%) | 32 (11%) | 0.092 |
| Solid Cords | 192 (50%) | 153 (55%) | ||
| Thin Irregular Cords | 98 (26%) | 74 (27%) | ||
| Single Cell | 22 (6%) | 20 (7%) | ||
| Vascular Invasion | Identified | 30 (8%) | 21 (7%) | 0.886 |
| Not Identified | 356 (92%) | 260 (93%) | ||
| Perineural Invasion | Identified | 27 (7%) | 15 (5%) | 0.374 |
| Not Identified | 358 (93%) | 267 (95%) | ||
| Mitotic Index :Mitoses Per10 High Power Field (HPF) | Frequent<=5/10HPF | 5 (1%) | 4 (1%) | 0.885 |
| Frequent>5/10HPF | 383 (99%) | 278 (99%) | ||
| Tumor Necrosis. | Extensive | 49 (13%) | 32 (11%) | 0.003 |
| Minimal | 50 (13%) | 65 (23%) | ||
| None | 289 (74%) | 185 (66%) | ||
| Location of Primary Tumor | Oral cavity | 110 (28%) | 49 (17%) | 0.025 |
| Larynx | 112 (29%) | 97 (34%) | ||
| Oropharyngeal | 67 (17%) | 49 (17%) | ||
| Hypopharngeal | 30 (8%) | 26 (9%) | ||
| Other | 72 (18%) | 61 (22%) | ||
| Stage | Early (1 & 2) | 165 (46%) | 88 (34%) | 0.003 |
| Late (3 & 4) | 195 (54%) | 172 (66%) |
The Pathology Review Form recorded all squamous head and neck (SHN) diagnoses from a specific biopsy specimen (Table 2). The Squamous Head and Neck (SHN) Medical Record Abstraction Form captured a patient’s clinical, demographical, and epidemiological information, including smoking, alcohol, and comorbidity. Overall comorbidity was determined using the Adult Comorbidity Evaluation 27 (ACE-27) index for cancer patients13. The ACE-27 categorizes specific diseases into one of three grades: grade 1 (mild), grade 2 (moderate), and grade 3 (severe), based on organ decompensation and prognostic impact. Comorbidity scores include none - 0; mild - 1; moderate - 2; or severe - 3 and are assigned based on the highest ranked/graded single ailment14. Collection of clinical, epidemiological and demographical data was conducted by experienced Medical Record Abstractors trained in the use of study forms. Age cut-offs of < 50 yrs, 51–65 yrs and > 65 yrs were set to capture younger patients and those in the Medicare group.
Health insurance payors included Blue Cross, Medicare, Medicaid, Health Alliance Plan (HAP, a Health Maintenance Organization [HMO]), and other non-HAP HMO’s (other HMO). Patients were categorized into treatment groups of surgery, radiation, and chemotherapy. This study was approved by the Henry Ford Health System Institutional Review Board committee.
Molecular Methods
Whole 5 micron tissue sections or microdissected tumor lesions and adjacent normal when present were processed for DNA extraction15. DNA was interrogated for gene copy number alterations (losses and gains) using the multiplex ligation-dependent probe amplification assay (MLPA) as previously described16–19. MLPA is a high throughput assay allowing simultaneous interrogation of 41 genes using minute amounts (20 ng) of DNA. Validated using real-time PCR, it is ideally suited for DNA from formalin-fixed paraffin embedded tissues16–19. Gene gain and loss by MLPA concurred with chromosomal aberrations, and provide a novel index to estimate the extent of genomic abnormality with disease progression.16. Three gene probe panels, p005, p006, and p007 (www.mlpa.com), comprising 113 unique genes were examined. The panels detects primarily oncogenes and tumor suppressor genes that are located at chromosomal segments that have been implicated in cancer and distributed throughout the genome16–19.
Statistical Methods
All analyses were done using SAS 9.2. Logistic regression modeling was used to examine risk factor differences between AA and CA HNSCC. The analysis started with testing for individual variable effects followed by inclusion of variable(s) of p< 0.10 in the stepwise multivariable modeling process. Multivariable modeling is an analytical approach to increase model predictability (e.g., c-index) based on a set of risk factors rather than a single factor. The risk factors retained in the final multivariable model become independent predictors after adjusting for the other covariate factors. The final multivariate model included variable(s) of p<0.05 with determination of the c-index estimation for goodness-of-fit. The c-index ranges from 0 to 1, where 1 indicates perfect fit and 0 none. The usual interpretation of the c-index is in context of prediction, but here it reflects the general amount of separation between AA and CA for the variables in the model.
RESULTS
Of the 673 primary HNSCC cohort, 391 were CA and 282 AA (42%); the mean age was 64 years (SD=12) with 14% 50 years or younger, 38% 51–65 years, and 48% >65 years of age. Demographic, histopathology, and clinical/epidemiological variables examined in this study for AA and CA patients are presented in Tables 1 & 2.
Of the 136 total variables (2 demographic, 11 histopathology, 10 clinical/epidemiology, 113 MLPA), univariate differences in race as AA and CA, were noted for 39 risk factors (p<0.10). Of the 39 univariate factors, 25 were gene and 14 were non-gene variables.
Multivariate modeling retained seven variables (p<0.05) with a c-index of 0.81 (Table 3). Type of insurance such as HAP (Health Alliance Plan), Blue Cross, Medicaid, Medicare, and other HMO was a significant predictor of race. Insurance type (including Medicaid) was noted for 87% CA (339/391) and 88% AA ((249/282). A larger proportion of the CA group (87%, 336/391) had private insurance (HAP, Medicare, Blue Cross, other HMO) as compared to 81% (228/282) of AA patients. With HAP as reference, AA were more likely to have Medicaid (OR=24.5), Medicare (OR=3.69) and other HMO (OR=3.99). Insurance status by stage was not significant. AA were more likely to be unmarried (OR=2.44). AA were more likely to be current and past smokers (OR=2.83 and OR=3.89, respectively). AA were more likely to have a tumor lymphocytic response, whether continuous rim or patchy infiltrate (OR=6.12, OR=7.83, respectively). Of the treatment types (surgery, radiation, chemotherapy), radiation (p=0.020) and chemotherapy (p=0.010) were univariate predictors of race as AA. Radiation remained in the final model; AA were more likely to receive radiation treatment than CA (OR=2.23). Radiation by stage was not significant (p=0.09).
Table 3.
Multivariate Model: c-index=0.81
| Variable | Beta | Standard Error | Wald Chi-Square | P Value | Odds Ratio | 95% Confidence Limits | ||
|---|---|---|---|---|---|---|---|---|
| Insurance Type | Blue Cross vs Hap | 0.2286 | 0.4402 | 0.2697 | 0.6035 | 1.257 | 0.530 | 2.978 |
| Insurance Type | Medicaid vs Hap | 3.1995 | 0.7294 | 19.2411 | <.0001 | 24.521 | 5.870 | 102.427 |
| Insurance Type | Medicare vs Hap | 1.3063 | 0.2656 | 24.1945 | <.0001 | 3.693 | 2.194 | 6.214 |
| Insurance Type | Other HMO vs Hap | 1.3838 | 0.4044 | 11.7127 | 0.0006 | 3.990 | 1.806 | 8.814 |
| Lymphocyte Response: Continuous rim vs Absent | 1.8111 | 0.7159 | 6.4010 | 0.0114 | 6.117 | 1.504 | 24.883 | |
| Lymphocyte Response Patchy Infiltrate vs Absent | 2.0587 | 0.7129 | 8.3395 | 0.0039 | 7.835 | 1.938 | 31.686 | |
| Married | No vs Yes | 0.8932 | 0.2222 | 16.1582 | <.0001 | 2.443 | 1.580 | 3.776 |
| Smoking | Current vs Never | 1.0426 | 0.3898 | 7.1523 | 0.0075 | 2.837 | 1.321 | 6.090 |
| Smoking | Past vs Never | 1.3588 | 0.3807 | 12.7402 | 0.0004 | 3.892 | 1.845 | 8.207 |
| Radiation | Yes vs No | 0.8002 | 0.2336 | 11.7339 | 0.0006 | 2.226 | 1.408 | 3.519 |
| CDKN2A | Gain vs Normal | −1.0190 | 0.6783 | 2.2569 | 0.1330 | 0.361 | 0.096 | 1.364 |
| CDKN2A | Loss vs Normal | 0.3837 | 0.2210 | 3.0152 | 0.0825 | 1.468 | 0.952 | 2.263 |
| SCYA3 | Gain vs Normal | 1.0350 | 0.2856 | 13.1342 | 0.0003 | 2.815 | 1.608 | 4.927 |
| SCYA3 | Loss vs Normal | −0.5521 | 0.2504 | 4.8597 | 0.0275 | 0.576 | 0.352 | 0.941 |
With regard to tumor genetic alterations, AA tumors were more likely to have gain of SCYA3 (OR=2.81) and less likely to have loss of SCYA3 (OR=0.58) than CA tumors. For the CDKN2A gene, AA tumors were more likely to have loss of CDKN2A (OR=1.47) when compared to CA tumors.
DISCUSSION
There is considerable evidence to support the disproportionate increase in HNSCC incidence and mortality in AA as compared to CA1–3,20. The age-adjusted incidence of head and neck cancers of the larynx and oral cavity and pharynx in African Americans in the US between 2003–2007 was higher than whites21. For the period 1975–2007, the death rates for all cancers combined continued to be substantially higher among African Americans than whites with improving rates for women21
The ability to dissect out factors that contribute to racial disparities, while gaining ground continues to be challenging. Some reasons for this include a dearth of large multi-ethnic studies of primary HNSCC cohorts with sizable AA patients. Also, a majority of studies have examined HNSCC outcomes in narrower contexts of a limited number of pathologic and clinical risk factors. This large multi-ethnic Detroit HNSCC study with 42% AA took a broader more inclusive approach by examining many intertwined variables influencing health and disease.
Access to Care
Much of the disparities in HNSCC outcomes for AA, as in other cancers, are likely due to access to care barriers that prevent timely and high-quality medical care, resulting in later stage at diagnosis as well as inequalities in treatment3,22,23. In this study, though a majority of patients had some form of medical insurance, insurance type was a significant predictor of race. The primary health care environment of the Henry Ford Health System (HFHS) is dominated by Health Alliance Plan (HAP), a non-profit health maintenance organization (HMO) serving more than 2,800 Detroit area employers and more than 580,000 members. Nearly half of the cohort (287/588) had Medicare, reflecting the age distribution of the two groups. When compared to CA, AA were significantly more likely to have Medicaid (8% AA, 1% CA), Medicare (58% AA, 42% CA), and other HMO types (13%AA, 6% CA). Stage at diagnosis, a significant univariate predictor (p=0.003) for race (late stage: 66% AA, 54% CA) did not remain in the final multivariate model as an independent factor for race. The latter is likely due to the primary care environment of the Henry Ford Health system and access to some form of insurance for the majority of cohort subjects.
Marital Status
Quality of life and survival rates for head and neck cancer patients are better for married persons and those not living alone compared with unmarried persons and those living alone24. In our study, marital status was an independent predictor of race as AA versus CA (OR=2.44) and a likely underlying contributing social factor for the observed disparate outcomes for AA as compared to CA with HNSCC in reported studies.
Smoking
Smoking is responsible for about 30% of all cancer deaths and is associated with increased risk of at least 16 types of cancer including HNSCC25. Unlike past decades where the rate of adult smoking has been higher in AA than CA, in recent years these rates are more comparable21. Tobacco is an etiologic agent in some HNSCC and in this study, smoking status as past (OR=2.83) or current (OR=2.44) was significantly associated with race as AA.
Radiation treatment
In this cohort, radiation treatment was an independent predictor of race. AA were 2.2 times more likely to receive radiation than CA. Fewer AA (43%) received surgery than CA (49%) and chemotherapy was a univariate predictor of race (p=0.010: 28% AA received chemotherapy vs 19% of CA). This observation is interesting from two aspects. First, it suggests that treatment was likely not a barrier given that 87% of AA had some type of insurance, similar to that of the CA group (86%). Second, though radiation by stage was not significant (p=0.09), it remains suspect that late stage disease contributed to chemo-radiation and less surgery and requires validation in a larger 1:1 matched case (AA) and control (CA) study.
Tumor lymphocytic response
In HNSCC, studies of lymphocytic response, a histologic parameter has been based on the subjective observation of lymphocyte infiltration on H&E (hematoxylin and eosin) tissue sections. These earlier studies have indicated an inverse correlation between lymphocytic infiltrate, local recurrence26, potential for lymph node metastasis27, as well as survival28. In this study, presence of a lymphocytic response as continuous rim or patchy infiltrate was an independent predictor of race. AA tumors were 6–7 times more likely than CA tumors to present with a lymphocytic response. The latter suggests a growing support for the dual roles of immune system cells, on one hand being summoned as first responders in local host defenses against tumors29 and on the other hand being maneuvered into behaving as if they were healing a wound30, emitting growth factors that embolden the tumor into activities such as stimulating angiogenesis31. Molecular insights into these responses would greatly aid in our understanding of HNSCC pathogenesis and is further discussed in the section below.
Tumor molecular characteristics
Genetic alterations provide means of identifying tumor cells as well as defining changes that presumably determine biological differences from their normal counterparts. Molecular genetic prognosticators can influence prevention, diagnosis, appropriateness of adjuvant chemotherapy, and, possibly, the chemotherapeutic regimen of cancer patients. Dissecting out processes specific to the pathogenesis of malignancy can distill key genetic biomarkers of HNSCC etiology, transformation, and progression.
In this study, the AA tumors were 1.4 times more likely to have loss of the CDKN2A gene as compared to CA tumors. The CDKN2A p14ARF, p16INK4a locus at 9p21, which generates two gene products, p16 and p1432,33, has been linked to malignant progression in HNSCC34,35. Loss of p16 expression by deletion, mutation, or hypermethylation is common in HNSCC17,36 and is associated with worse prognosis37. Also, tobacco/alcohol-associated HNSCC appears to be associated with p16 downregulation (as an early event) and the TP53 gene mutation resulting in p53 overexpression38.
SCYA3, otherwise known as macrophage inflammatory protein-1α (MIP-1-alpha) or CCL3, belongs to the CC (beta) subfamily of chemokines that recruit a variety of cells to sites of inflammation39. In oral squamous cell carcinoma, CCL3/CCR1 appears to have dual roles of tumor lymph node metastasis and local host defense against tumor29. This was also demonstrated in hepatocelluar carcinoma progression40, and support multiple and opposite functions in tumorigenesis for the CCL3/CCR1 system.
In our study, the lack of fidelity of the SCYA3 gene with respect to loss and gain was an independent factor for race. AA tumors were almost three times more likely to have gain of SCYA3 and 57% less likely to have loss when compared to CA tumors. This observation suggests a tantalizing connection to the finding that AA tumors were almost 6–7 times more likely to have presence of lymphocytic response, when presenting as a patchy infiltrate or as a continuous rim, when compared to CA tumors.
Immunohistochemistry to correlate CCL3/CCR1 and SCYA3 copy number with presence or absence of lymphocytic response in AA and CA in this cohort is a next step.
Conclusion
Understanding and accounting for factors contributing to differences in HNSCC racial groups should provide much needed insights into disparities of incidence and mortality in AA. The contribution of HPV and p16 involvement was not evaluated, and this was a study limitation. Major strengths of this study include the large multi-ethnic primary HNSCC cohort with 42% AA and a comprehensive examination of patient and tumor variables influencing health and disease. We found significant differences between AA and CA HNSCC for histopathology, treatment, smoking, marital status, type of insurance, as well as tumor gene copy number alterations. Our data reiterate that for HNSCC as in the case of other complex diseases, tumor genetics or biology is only one of many potential contributors to differences among racial groups.
Acknowledgments
Supported by NIH R01 DE 15990
Dr. Worsham had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. This study was supported by R01 NIH DE 15990 (Dr. Worsham).
Footnotes
Podium presentation at the 2011 AAO-HNSF Annual Meeting & OTO EXPO, San Francisco, CA, September 11-14, 2011
References
- 1.Cancer Facts & Figures 2011. American Cancer Society; 2011. [Google Scholar]
- 2.Hoffman HT, Karnell LH, Funk GF, et al. The National Cancer Data Base report on cancer of the head and neck. Arch Otolaryngol Head Neck Surg. 1998;124:951–62. doi: 10.1001/archotol.124.9.951. [DOI] [PubMed] [Google Scholar]
- 3.Shavers VL, Harlan LC, Winn D, et al. Racial/ethnic patterns of care for cancers of the oral cavity, pharynx, larynx, sinuses, and salivary glands. Cancer Metastasis Rev. 2003;22:25–38. doi: 10.1023/a:1022255800411. [DOI] [PubMed] [Google Scholar]
- 4.Arbes SJ, Jr, Olshan AF, Caplan DJ, et al. Factors contributing to the poorer survival of black Americans diagnosed with oral cancer (United States) Cancer Causes Control. 1999;10:513–23. doi: 10.1023/a:1008911300100. [DOI] [PubMed] [Google Scholar]
- 5.Goodwin WJ, Thomas GR, Parker DF, et al. Unequal burden of head and neck cancer in the United States. Head Neck. 2008;30:358–71. doi: 10.1002/hed.20710. [DOI] [PubMed] [Google Scholar]
- 6.Gourin CG, Podolsky RH. Racial disparities in patients with head and neck squamous cell carcinoma. Laryngoscope. 2006;116:1093–106. doi: 10.1097/01.mlg.0000224939.61503.83. [DOI] [PubMed] [Google Scholar]
- 7.Murdock JM, Gluckman JL. African-American and white head and neck carcinoma patients in a university medical center setting. Are treatments provided and are outcomes similar or disparate? Cancer. 2001;91:279–83. doi: 10.1002/1097-0142(20010101)91:1+<279::aid-cncr19>3.0.co;2-x. [DOI] [PubMed] [Google Scholar]
- 8.Settle K, Posner MR, Schumaker LM, et al. Racial survival disparity in head and neck cancer results from low prevalence of human papillomavirus infection in black oropharyngeal cancer patients. Cancer Prev Res (Phila Pa) 2009;2:776–81. doi: 10.1158/1940-6207.CAPR-09-0149. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Hashibe M, Brennan P, Benhamou S, et al. Alcohol drinking in never users of tobacco, cigarette smoking in never drinkers, and the risk of head and neck cancer: pooled analysis in the International Head and Neck Cancer Epidemiology Consortium. J Natl Cancer Inst. 2007;99:777–89. doi: 10.1093/jnci/djk179. [DOI] [PubMed] [Google Scholar]
- 10.Gillison ML, Lowy DR. A causal role for human papillomavirus in head and neck cancer. Lancet. 2004;363:1488–9. doi: 10.1016/S0140-6736(04)16194-1. [DOI] [PubMed] [Google Scholar]
- 11.Ang KK, Harris J, Wheeler R, et al. Human papillomavirus and survival of patients with oropharyngeal cancer. N Engl J Med. 2010;363:24–35. doi: 10.1056/NEJMoa0912217. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Wiencke JK. Impact of race/ethnicity on molecular pathways in human cancer. Nat Rev Cancer. 2004;4:79–84. doi: 10.1038/nrc1257. [DOI] [PubMed] [Google Scholar]
- 13.Piccirillo JF, Costas I, Claybour P, et al. The Measurement of Comorbidity by Cancer Registries. Journal of Registry Management. 2003;30:8–14. [Google Scholar]
- 14.Naqvi K, Garcia-Manero G, Sardesai S, et al. Association of comorbidities with overall survival in myelodysplastic syndrome: development of a prognostic model. J Clin Oncol. 2011;29:2240–6. doi: 10.1200/JCO.2010.31.3353. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Chen K, Sawhney R, Khan M, et al. Methylation of multiple genes as diagnostic and therapeutic markers in primary head and neck squamous cell carcinoma. Arch Otolaryngol Head Neck Surg. 2007;133:1131–8. doi: 10.1001/archotol.133.11.1131. [DOI] [PubMed] [Google Scholar]
- 16.Worsham MJ, Pals G, Schouten JP, et al. Delineating genetic pathways of disease progression in head and neck squamous cell carcinoma. Arch Otolaryngol Head Neck Surg. 2003;129:702–8. doi: 10.1001/archotol.129.7.702. [DOI] [PubMed] [Google Scholar]
- 17.Worsham MJ, Chen KM, Tiwari N, et al. Fine-mapping loss of gene architecture at the CDKN2B (p15INK4b), CDKN2A (p14ARF, p16INK4a), and MTAP genes in head and neck squamous cell carcinoma. Arch Otolaryngol Head Neck Surg. 2006;132:409–15. doi: 10.1001/archotol.132.4.409. [DOI] [PubMed] [Google Scholar]
- 18.Worsham MJ, Pals G, Schouten JP, et al. High-resolution mapping of molecular events associated with immortalization, transformation, and progression to breast cancer in the MCF10 model. Breast Cancer Res Treat. 2006;96:177–86. doi: 10.1007/s10549-005-9077-8. [DOI] [PubMed] [Google Scholar]
- 19.Kunjoonju JP, Raitanen M, Grenman S, et al. Identification of individual genes altered in squamous cell carcinoma of the vulva. Genes Chromosomes Cancer. 2005;44:185–93. doi: 10.1002/gcc.20230. [DOI] [PubMed] [Google Scholar]
- 20.Siegel RWE, Brawley O, Jemal A. Cancer statistics, 2011:The impact of eliminating socioeconomic and racial disparities on premature cancer deaths. CA Cancer Journal Clinical. 2011 doi: 10.3322/caac.20121. [DOI] [PubMed] [Google Scholar]
- 21.American Cancer Society. Cancer Facts & Figures for African Americans 2011–2012. 2011. [Google Scholar]
- 22.Ward E, Halpern M, Schrag N, et al. Association of insurance with cancer care utilization and outcomes. CA Cancer J Clin. 2008;58:9–31. doi: 10.3322/CA.2007.0011. [DOI] [PubMed] [Google Scholar]
- 23.Bach PB, Schrag D, Brawley OW, et al. Survival of blacks and whites after a cancer diagnosis. Jama. 2002;287:2106–13. doi: 10.1001/jama.287.16.2106. [DOI] [PubMed] [Google Scholar]
- 24.Long SA, D’Antonio LL, Robinson EB, et al. Factors related to quality of life and functional status in 50 patients with head and neck cancer. Laryngoscope. 1996;106:1084–8. doi: 10.1097/00005537-199609000-00008. [DOI] [PubMed] [Google Scholar]
- 25.Annual smoking-attributable mortality, years of potential life lost, and economic costs--United States, 1995–1999. MMWR Morb Mortal Wkly Rep. 2002;51:300–3. [PubMed] [Google Scholar]
- 26.Brandwein-Gensler M, Teixeira MS, Lewis CM, et al. Oral squamous cell carcinoma: histologic risk assessment, but not margin status, is strongly predictive of local disease-free and overall survival. Am J Surg Pathol. 2005;29:167–78. doi: 10.1097/01.pas.0000149687.90710.21. [DOI] [PubMed] [Google Scholar]
- 27.Hiratsuka H, Miyakawa A, Nakamori K, et al. Multivariate analysis of occult lymph node metastasis as a prognostic indicator for patients with squamous cell carcinoma of the oral cavity. Cancer. 1997;80:351–6. [PubMed] [Google Scholar]
- 28.Kurokawa H, Yamashita Y, Murata T, et al. Histological grading of malignancy correlates with regional lymph node metastasis and survival of patients with oral squamous cell carcinoma. Fukuoka Igaku Zasshi. 1998;89:225–31. [PubMed] [Google Scholar]
- 29.Silva TA, Ribeiro FL, Oliveira-Neto HH, et al. Dual role of CCL3/CCR1 in oral squamous cell carcinoma: implications in tumor metastasis and local host defense. Oncol Rep. 2007;18:1107–13. [PubMed] [Google Scholar]
- 30.Johnson G. New York Times. 2011. Cancer’s Secrets Come Into Sharper Focus. [Google Scholar]
- 31.Folkman J. Angiogenesis. Annu Rev Med. 2006;57:1–18. doi: 10.1146/annurev.med.57.121304.131306. [DOI] [PubMed] [Google Scholar]
- 32.Haber DA. Splicing into senescence: the curious case of p16 and p19ARF. Cell. 1997;91:555–8. doi: 10.1016/s0092-8674(00)80441-9. [DOI] [PubMed] [Google Scholar]
- 33.Xing EP, Nie Y, Song Y, et al. Mechanisms of inactivation of p14ARF, p15INK4b, and p16INK4a genes in human esophageal squamous cell carcinoma. Clin Cancer Res. 1999;5:2704–13. [PubMed] [Google Scholar]
- 34.Lydiatt WM, Davidson BJ, Schantz SP, et al. 9p21 deletion correlates with recurrence in head and neck cancer. Head Neck. 1998;20:113–8. doi: 10.1002/(sici)1097-0347(199803)20:2<113::aid-hed3>3.0.co;2-5. [DOI] [PubMed] [Google Scholar]
- 35.Reed AL, Califano J, Cairns P, et al. High frequency of p16 (CDKN2/MTS-1/INK4A) inactivation in head and neck squamous cell carcinoma. Cancer Res. 1996;56:3630–3. [PubMed] [Google Scholar]
- 36.Yarbrough WG. The ARF-p16 gene locus in carcinogenesis and therapy of head and neck squamous cell carcinoma. Laryngoscope. 2002;112:2114–28. doi: 10.1097/00005537-200212000-00002. [DOI] [PubMed] [Google Scholar]
- 37.Namazie A, Alavi S, Olopade OI, et al. Cyclin D1 amplification and p16(MTS1/CDK4I) deletion correlate with poor prognosis in head and neck tumors. Laryngoscope. 2002;112:472–81. doi: 10.1097/00005537-200203000-00013. [DOI] [PubMed] [Google Scholar]
- 38.Psyrri A, DiMaio D. Human papillomavirus in cervical and head-and-neck cancer. Nat Clin Pract Oncol. 2008;5:24–31. doi: 10.1038/ncponc0984. [DOI] [PubMed] [Google Scholar]
- 39.Proost P, Wuyts A, van Damme J. The role of chemokines in inflammation. Int J Clin Lab Res. 1996;26:211–23. doi: 10.1007/BF02602952. [DOI] [PubMed] [Google Scholar]
- 40.Yang X, Lu P, Fujii C, et al. Essential contribution of a chemokine, CCL3, and its receptor, CCR1, to hepatocellular carcinoma progression. Int J Cancer. 2006;118:1869–76. doi: 10.1002/ijc.21596. [DOI] [PubMed] [Google Scholar]