Abstract
Introduction
Lung cancer is a leading cause of cancer-related death worldwide. Racial disparities in LC survival exist between blacks and whites, yet are limited by categorical definitions of race. We sought to examine the impact of African ancestry on overall survival among black and white non-small cell lung cancer (NSCLC) cases.
Methods
Black and white incident NSCLC cases from the prospective Southern Community Cohort Study (N=425) were identified via linkage with state cancer registries in 12 Southern states. Vital status was determined by linkage with the National Death Index and Social Security Administration. We evaluated the impact of African ancestry, estimated using genome-wide ancestry informative markers, on overall survival by calculating the time-dependent area under the curve (AUC) for Cox proportional hazards models, adjusting for relevant covariates such as stage and treatment. We replicated our findings in an independent population of black NSCLC cases.
Results
Global African ancestry was not significantly associated with overall survival among NSCLC cases. There was no change in model performance when comparing Cox proportional hazards models with and without African ancestry (AUC=0.79 for each model). Removal of stage and treatment reduced the average time-dependent AUC from 0.79 to 0.65. Similar findings were observed in our replication study.
Conclusions
Stage and treatment are more important predictors of survival than African ancestry. These findings suggest that racial disparities in lung cancer survival may disappear with similar early detection efforts for blacks and whites alike.
Keywords: Lung cancer, genetic ancestry, survival
Introduction
Lung cancer is the leading cause of cancer death among both men and women in the United States, with a 5-year relative survival of 18%.1, 2 While lung cancer mortality has decreased in recent years in large part due to greater smoking cessation efforts, a racial disparity exists such that blacks experience poorer survival compared to whites.1, 3–5 Specifically, the national five-year survival is 18% among white individuals and 15% among blacks.2 Blacks are more frequently diagnosed at late stage disease compared to whites and less likely to receive the recommended course of treatment based on disease stage.2, 6–8 Several recent studies have suggested that controlling for differential access to healthcare results in no difference in survival outcomes among blacks and whites.9–11 We and others have demonstrated blacks and whites experience no difference in lung cancer survival after controlling for stage and socioeconomic factors.12, 13 A recent analysis of Surveillance, Epidemiology, and End Results (SEER) Program data also suggests that blacks have similar lung cancer survival compared to whites.14 However, blacks are an admixed population with varying proportions of African ancestry15, 16 and self-identified whites can carry African ancestry.17 Identification of ancestry informative markers, genetic variants that differ in frequency between ancestral populations, allows us to distinguish individual-level ancestral origins at the genetic level, i.e. genetic ancestry. Prior studies have shown important associations between genetic ancestry and biomedical phenotypes18–20 such as lung function21–23 and breast cancer risk;24, 25 however, the association between genetic ancestry and survival after a diagnosis of lung cancer has yet to be examined. We examined the effect of African ancestry on lung cancer survival in blacks and whites with non-small cell lung cancer in the Southern Community Cohort Study (SCCS), a cohort with the largest representation of blacks in the United States. Black and white SCCS participants were primarily recruited from community health centers across the Southeast and thus have similar access to healthcare. Analyses were replicated in a population of black lung cancer cases ascertained from the population-based Metropolitan Detroit Cancer Surveillance System.
Methods
Study Population
Study participants were selected from the SCCS, a prospective cohort study of ~86,000 adults aged 40–79 years. Participants were enrolled between March 2002 and September 2009 and from a 12-state region across the Southeastern United States (Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia). Approximately 15% of participants were recruited through mail-in questionnaires, which were sent to a random subset of adults across the 12-state region. The remaining 85% of participants were enrolled at community health centers throughout the region. Individuals were eligible to participate if they were between the ages of 40–79 years. Demographic characteristics, family history of disease, insurance coverage, tobacco use and other information were collected via in-person interviews by a trained interviewer upon enrollment at the health centers and by completion of the same questionnaire for the general population recruits. Individuals self-reported race/ethnicity by selecting any of the following investigator-defined racial/ethnic groups: white, black/African American, Hispanic/Latino, Asian or Pacific Islander, American Indian or Alaska Native, or Other racial or ethnic group. Approximately two-thirds of participants self-identified as “Black/African American”. Upon enrollment, all individuals were asked to donate a biologic specimen (blood, urine, saliva, or buccal cell), of which ~90% of participants agreed. A detailed description of study design and recruitment has been previously published.26, 27 The SCCS was approved by institutional review boards at Vanderbilt University and Meharry Medical College. Written informed consent was obtained from all participants.
Case Identification and Mortality Assessment
All incident non-small cell lung cancer (NSCLC) cases occurring within the SCCS between 2002 and 2010 were identified through linkage with the 12 state cancer registries. Individuals with a lung cancer diagnosis prior to study enrollment were excluded. Histology, stage at diagnosis, and treatment information were obtained from individual state cancer registries. Stage was derived using the American Joint Committee on Cancers (AJCC) TNM System staging guidelines (6th and 7th Editions). Due to small sample size, we combined individuals with stage II and III disease. For individuals missing stage information, we used the Surveillance, Epidemiology, and End Results (SEER) Summary Stage guidelines, assuming local disease was equivalent to stage I, regional disease was equivalent to stage II/III, and distant disease was equivalent to stage IV. Treatment information describing the administration of chemotherapy, radiation therapy, hormone therapy, immunotherapy, surgery, or other cancer-directed treatment was summarized into a design variable with five levels: no treatment, chemotherapy only, radiation only, surgery only, and multi-modality (patients receiving any combination of the above treatment options). Participants were followed for all-cause mortality. Vital status was determined at end-of-follow-up (December 31, 2011) through linkage with the Social Security Administration or the National Death Index. Survival time was defined as the time from the date of diagnosis to the date of death, loss to followup, or censoring.
Genotyping and Quality Control
SCCS individuals were genotyped on the Illumina HumanExome BeadChip v1.1, which contains a panel of >3,000 ancestry informative markers for distinguishing between African and European ancestries. To remove technical artifacts and ensure high quality data for ancestry analysis, we performed standard quality control of all genotyping data. Briefly, quality control removed individuals with sex inconsistencies, <98% genotyping efficiency, or self-reported race other than “Black/African American” or “White”. Relatedness among individuals was also examined and the individual with the lowest call rate in each relationship pair was removed. Variants were removed during quality control if they were non-autosomal, had <98% genotyping efficiency, or <5% minor allele frequency. Mendelian errors were examined using HapMap trio controls. Variants were pruned based on linkage disequilibrium (window size = 50, step size = 10, r2 > 0.4) for ancestry estimation in blacks and whites separately. All quality control measures described were applied using PLINK28 (version 1.07).
Ancestry Estimation
Global ancestry estimates describe the proportion of an individual’s total genome inherited from each contributing ancestral population. To distinguish European and African ancestry, we used a panel of ancestry informative markers. The number of markers available for global ancestry estimation was N=553 for whites and N=1137 for blacks. Supervised admixture analysis was performed using the software program ADMIXTURE29 to estimate individual ancestry proportions assuming two ancestral populations with CEU (Utah residents with ancestry from northern and western Europe) and YRI (Yoruban in Ibadan, Nigeria) HapMap30 populations as representative ancestral populations. The resulting output contains the estimated proportions of African and European ancestry for each individual; proportion of African ancestry was then converted to a percent and used for analyses (hereafter referred to as “African ancestry”).
Statistical Analysis
Chi-square tests and t-tests were used to assess racial differences in categorical and continuous descriptive characteristics, respectively. The impact of African ancestry on overall mortality among self-reported black and white lung cancer cases was examined using a Cox proportional hazards model. Individuals with less than 30 days survival time were excluded from analysis to remove potential bias related to treatment effects. Model fit statistics for the Cox model are well established, so we used time-dependent AUC (area under the Receiver Operating Characteristic curve) for the purpose of describing the model’s discriminative ability over time. At every observable time, subjects were classified as alive or dead (censoring was handled as described by Heagerty et al.31) and an ROC curve was constructed. The AUC was then graphed as a function of time, so the ability of the model to correctly classify subjects was assessed as a function of time. The AUC for an unadjusted Cox proportional hazards model with African ancestry only was estimated first. Then covariates, selected based on a priori knowledge, were added to the model and the time-dependent predictive ability of the new model was estimated. The model examined overall mortality as a function of African ancestry, age at diagnosis, sex, BMI (kg/m2), number of cigarettes smoked per day, stage at diagnosis, treatment, highest education level achieved, and family history of lung cancer (hereafter called the “main effects model”). Using a flexible parametric additive model, it was determined that self-reported race could be predicted from African ancestry and the other model covariates, so self-reported race was removed from all Cox regression models. Continuous variables, including African ancestry, were modeled with restricted cubic splines using three knots, which allows for a non-linear relationship between a continuous variable and the outcome. Missing data was multiply imputed ten times using predictive mean matching among the eight potential confounding variables. The Cox proportional hazards models were fit with each of the 10 completed data sets, and results were pooled using Rubin’s rules.32 A time-dependent ROC curve31 and AUC was calculated for every time point between 30 days and 4 years to estimate how well the model predicts mortality. The time-dependent AUCs were then averaged over the time interval to obtain the average time-dependent AUC. We then compared the time-dependent AUC of our main effects model to an over-fit Cox proportional hazards model with two-way interaction terms (i.e. African ancestry x treatment, African ancestry x stage, African ancestry x education, education x treatment, education x stage, treatment x stage, sex x age, sex x BMI and sex x cigarettes per day) to assess how well our main effects model performed. We examined the impact of African ancestry, stage, and treatment by removing these variables from the interaction and main effects models and compared the time-dependent AUC with and without these variables. Since the model did not assume a linear effect of African ancestry (i.e. splines were used), there is not a single p-value or hazard ratio associated with African ancestry. The effect of African ancestry was assessed using a likelihood ratio test to estimate non-constant hazard ratios for mortality and corresponding 95% confidence intervals (CIs). Unadjusted survival models for race, stage, and race x stage and stratified survival curves were fit to evaluate the individual effects of these variables on survival. An analysis was performed among only self-reported black individuals to verify results. All statistical analyses were performed in R version 3.2.2 with packages Hmisc and survivalROC.
Replication
Incident black NSCLC cases were identified from three lung cancer case-control studies (Family Health Study III; Women’s Epidemiology of Lung Disease Study; and Exploring Health, Ancestry and Lung Epidemiology Study) conducted at the Barbara Ann Karmanos Cancer Institute affiliated with Wayne State University in Detroit, MI. These studies have been previously described.33 Rapid case ascertainment was used to identify cases in the population-based Metropolitan Detroit Cancer Surveillance System, an NCI-funded SEER registry. The institutional review board at Wayne State University approved this study and written informed consent was provided by all participants. Stage, treatment, and vital status were obtained through linkage with the Detroit SEER registry. Treatment and stage variables were summarized in the same manner as the SCCS using both AJCC and SEER staging information. Individuals were previously genotyped on the Illumina 1M-Duo BeadChip. Supervised analysis (K=2) was performed with the use of genome-wide single nucleotide polymorphisms, CEU and YRI reference populations, and the software program ADMIXTURE. Time-dependent AUCs were estimated using the same methods described above.
Results
Among the SCCS individuals, 450 incident NSCLC cases occurred with 425 (286 black and 139 white) remaining after quality control procedures. Individuals had a median survival time of 0.7 years (range: 0.003–8.6 years), during which 359 deaths occurred (248 blacks and 111 whites). Forty-two individuals (32 black and 10 white) were excluded for having survival times less than 30 days. Forty-seven percent of blacks with lung cancer had less than 12 years of education compared to 35% of whites (Table 1). The mean age at lung cancer diagnosis was 60 years for blacks and 63 years for whites. More males were diagnosed with lung cancer among blacks than among whites (60% vs. 42%). Smoking status (current/former/never) did not differ between blacks and whites, with 94% of blacks and 96% of whites having smoked cigarettes. Twenty-three percent of whites reported a positive family history of lung cancer compared to 9% of blacks. A greater percentage of blacks were diagnosed at stage IV disease compared to whites (52% vs. 43%). Although similar numbers of blacks and whites were diagnosed with stage I disease (16% vs. 21%), almost twice as many whites received a surgery only course of treatment compared to blacks (11% vs. 21%) (Supplemental Figure 1). Median African ancestry for self-reported blacks was 85.6%, and was 1.3% for self-reported whites (Table 1 and Figure 1).
Table 1.
Descriptive characteristics by race of incident non-small cell lung cancer (NSCLC) cases in the Southern Community Cohort Study.
| Blacks (N=286) | Whites (N=139) | Total (N=425) | ||
|---|---|---|---|---|
| N (%) | N (%) | N (%) | p-valuea | |
| Sex | 6.7x10−4 | |||
| Male | 171 (59.8) | 58 (41.7) | 229 (53.9) | |
| Female | 115 (40.2) | 81 (58.3) | 196 (46.1) | |
| Vital status | 0.09 | |||
| Alive | 38 (13.3) | 28 (20.1) | 66 (15.5) | |
| Dead | 248 (86.7) | 111 (79.9) | 359 (84.5) | |
| Median African ancestry, % (range) | 85.6 | 1.3 | 80.1 | 2.2x10−16 |
| (<0.01–98.7) | (<0.01–91.1) | (<0.01–98.7) | ||
| Lung cancer stage at diagnosis | 0.15 | |||
| I | 44 (15.7) | 29 (21.3) | 73 (17.5) | |
| II/III | 90 (32.0) | 49 (36.0) | 139 (33.3) | |
| IV | 147 (52.3) | 58 (42.6) | 205 (49.2) | |
| Unknown | 5 | 3 | 8 | |
| Treatment | 0.10 | |||
| No treatment | 75 (26.8) | 36 (27.3) | 111 (26.9) | |
| Surgery only | 31 (11.1) | 27 (20.5) | 58 (14.1) | |
| Chemotherapy only | 51 (18.2) | 18 (13.6) | 69 (16.7) | |
| Radiation only | 36 (12.9) | 12 (9.1) | 48 (11.7) | |
| Multi-modality | 87 (31.1) | 39 (29.5) | 126 (30.6) | |
| Unknown | 6 | 7 | 13 | |
| Histology | 0.74b | |||
| Adenocarcinoma | 113 (39.5) | 51 (36.7) | 164 (38.6) | |
| NSCLC-NOS | 78 (27.3) | 34 (24.5) | 112 (26.4) | |
| Squamous | 72 (25.2) | 41 (29.5) | 113 (26.6) | |
| Other NSCLC | 22 (7.7) | 12 (8.6) | 34 (8.0) | |
| Multiple histologies | 1 (0.3) | 1 (0.7) | 2 (0.5) | |
| Mean age at enrollment, yr (SD) | 56.5 (9.0) | 60.4 (8.6) | 57.8 (9.1) | 2.7x10−5 |
| Mean age at diagnosis, yr (SD) | 59.6 (9.1) | 62.8 (8.7) | 60.6 (9.1) | 4.5x10−4 |
| Median observed duration of disease among those who died, yr (range) | 0.50 (0.003–8.61) | 0.54 (0.01–5.4) | 0.52 (0.003–8.61) | 0.43 |
| Median observed duration of disease among those alive at last follow-up, yr (range) | 3.8 (1.5–8.2) | 4.0 (1.5–7.7) | 3.5 (1.5–8.2) | 0.54 |
| Highest education level, yr | 0.02 | |||
| <12 | 134 (47.2) | 48 (34.8) | 182 (43.1) | |
| ≥12 | 150 (52.8) | 90 (65.2) | 240 (56.9) | |
| Unknown | 2 | 1 | 3 | |
| Household income in last year | 0.53 | |||
| <$15,000 | 190 (67.6) | 87 (64.0) | 277 (66.4) | |
| ≥$15,000 | 91 (32.4) | 49 (36.0) | 140 (33.6) | |
| Unknown | 5 | 3 | 8 | |
| Smoking status at cohort entry | 0.29 | |||
| Current | 206 (72.8) | 93 (68.4) | 299 (71.4) | |
| Former | 59 (20.8) | 37 (27.2) | 96 (22.9) | |
| Never | 18 (6.4) | 6 (4.4) | 24 (5.7) | |
| Unknown | 3 | 3 | 6 | |
| Mean cigarettes per day (SD) | 15.7 (13.1) | 23.2 (14.7) | 18.1 (14.1) | 8.7x10−7 |
| Smokes menthol cigarettes | 2.2x10−16 | |||
| Yes | 182 (69.2) | 23 (17.8) | 205 (52.3) | |
| No | 81 (30.8) | 106 (82.2) | 187 (47.7) | |
| Unknown | 23 | 10 | 33 | |
| Self-reported doctor diagnosis of emphysema or chronic bronchitis | 5.3x10−7 | |||
| Yes | 28 (9.9) | 41 (29.7) | 69 (16.4) | |
| No | 255 (90.1) | 97 (70.3) | 352 (83.6) | |
| Unknown | 3 | 1 | 4 | |
| First-degree relative with lung cancer | 0.001 | |||
| Yes | 21 (9.3) | 26 (22.6) | 47 (13.8) | |
| No | 205 (90.7) | 89 (77.4) | 294 (86.2) | |
| Unknown | 60 | 24 | 84 | |
| Mean BMI, kg/m2 (SD) | 26.8 (6.1) | 27.1 (6.0) | 26.9 (6.1) | 0.59 |
| Health insurance status | 0.06 | |||
| Yes | 174 (61.7) | 98 (71.5) | 272 (64.9) | |
| No | 108 (38.3) | 39 (28.5) | 147 (35.1) | |
| Unknown | 4 | 2 | 6 | |
| Enrollment source | 1.6x10−4 | |||
| Community Health Center | 268 (93.7) | 113 (81.3) | 381 (89.6) | |
| General Population | 18 (6.3) | 26 (18.7) | 44 (10.4) |
NOS=Not otherwise specified
Yr=Year
SD=Standard deviation
BMI=Body mass index
Chi-square and t-test p-values reported for categorical and continuous variables, respectively
Chi-square test conducted with ‘Multiple histologies’ category excluded due to small cell count.
Figure 1. Genetic ancestry estimates for blacks (N=286) and whites (N=139) with non-small cell lung cancer in the Southern Community Cohort Study.
Global ancestry was estimated using ADMIXTURE software and ancestry informative markers (N=1137 in blacks and N=553 in whites) using a supervised method including CEU (European) and YRI (African) reference populations from the International HapMap Project. Individuals are plotted along the x-axis and along the y-axis is the percent African ancestry (dark blue) and percent European ancestry (light blue) for each individual.
Cox proportional hazards models were implemented to determine the impact of African ancestry on overall mortality. The unadjusted Cox model for percent African ancestry had an average time-dependent AUC of 0.54 (Table 2). In the main effects model, African ancestry was not associated with overall mortality, with or without stage and treatment (Figure 2A and 2B), although at smaller values of African ancestry a reduction in mortality was observed. We then estimated the area under the curve to assess the predictive ability of each model. With an average time-dependent AUC of 0.79, the main effects multivariable model examining the association between African ancestry and overall mortality, performed dramatically better than the univariate model with African ancestry alone (Figure 3). The inclusion of interaction terms to the main effects model to create an “over fit” model increased the average time-dependent AUC slightly to 0.83, indicating that the main effects model had high predictive ability. Removing African ancestry had no impact on the average time-dependent AUC for either the main effects model (Table 2 and Figure 3) or the interactions model (Table 2). Removing stage and treatment from the main effects and interaction models substantially decreased the average time-dependent AUC to 0.65 and 0.67, respectively (Table 2). Further removal of African ancestry from the main effects and interaction models without stage and treatment resulted in little change in the average time-dependent AUC (Figure 3 and Table 2). We then removed whites from the SCCS and examined each of the Cox proportional hazards models among blacks only. Observations were similar to those of the overall NSCLC population and are presented in Supplemental Figure 2 and Supplemental Table 1. The unadjusted survival curves stratified by race, stage, and race x stage (Figure 4) show that a clinically relevant difference in lung cancer survival between black and white individuals is not supported by the data. Instead, stage and treatment appear to play a more significant role in survival.
Table 2.
Average time-dependent AUCs for Cox proportional hazards models in the Southern Community Cohort Study
| Model | Average time- dependent AUC |
|---|---|
| African ancestry only (unadjusted) | 0.54 |
| Main effectsa | 0.79 |
| Interactionsb | 0.83 |
| Main effects without African ancestry | 0.79 |
| Interactions without African ancestry | 0.82 |
| Main effects without stage and treatment | 0.65 |
| Interactions without stage and treatment | 0.67 |
| Main effects without stage, treatment, and African ancestry | 0.63 |
| Interactions without stage, treatment, and African ancestry | 0.66 |
Main effects model is a Cox proportional hazards model examining association between African ancestry and lung cancer mortality, adjusting for age at diagnosis, sex, BMI (kg/m2), cigarettes per day, stage at diagnosis, treatment, highest education level, and family history of lung cancer.
Interaction model is a Cox proportional hazards model examining association between African ancestry and lung cancer mortality, adjusting for the same variables in the main effects model but also including the following two-way interactions (percent African ancestry x treatment, African ancestry x stage, African ancestry x education, education x treatment, education x stage, treatment x stage, sex x age, sex x BMI, sex x cigarettes per day).
Figure 2. African ancestry is not associated with lung cancer mortality, with or without stage and treatment included in the model.
Hazard ratios and 95% confidence intervals are plotted on the y-axis for the association between splined percent African ancestry (x-axis) and lung cancer mortality in the Southern Community Cohort Study for the (A) main effects model and the (B) main effects model without stage and treatment (see Methods). Median African ancestry (80%) is the referent (vertical grey line). Main effects model is a Cox proportional hazards model examining the association between African ancestry and lung cancer mortality, adjusting for age at diagnosis, sex, BMI (kg/m2), cigarettes per day, stage at diagnosis, treatment, highest education level, and family history of lung cancer.
Figure 3. Time-dependent AUCs for each Cox proportional hazards model in the Southern Community Cohort Study.
Removal of stage and treatment, not African ancestry, resulted in a reduction in the predictive ability of the main effects model. African ancestry had no effect on the time-dependent AUC of the main effects model, with or without stage and treatment.
The Kaplan-Meier survival probability estimates below the x-axis represent the probability of surviving to the indicated 6 month intervals as estimated from the main effects model (black text) and from the main effects model without stage or treatment (blue text).
Figure 4.
Probability of survival stratified by (A) race, (B) stage, and (C) race and stage.
We evaluated the impact of African ancestry on overall mortality in a population of 316 black NSCLC cases ascertained from the Karmanos Cancer Institute at Wayne State University. Briefly, the mean age of diagnosis was 60 years and 41% of lung cancer cases were male. Thirty-four percent of cases were diagnosed at stage I disease, 39% were diagnosed at stage II/III disease, and 26% were diagnosed at stage IV disease (Supplemental Table 2 and Supplemental Figure 1). The median African ancestry was 83.3% (Supplemental Figure 3). Additional descriptive characteristics of this population are provided in Supplemental Table 2. Similar findings for the impact of African ancestry on overall mortality were observed in the blacks in the SCCS (Supplemental Table 1 and Supplemental Figure 4). Removal of African ancestry from both the main effects model and the interaction model had a negligible effect on the average time-dependent AUC (0.74 and 0.76, respectively, Supplemental Table 1). Like the SCCS, the removal of stage and treatment from the main effects model resulted in a dramatic decrease in the average time-dependent AUC (0.63) and removal of African ancestry in addition to stage and treatment had little impact (average time-dependent AUC = 0.61).
Discussion
We found that African ancestry was not associated with NSCLC mortality in the SCCS, using a Cox proportional hazard model adjusted for stage and treatment. Since African ancestry did not have a linear relationship with mortality, we chose to model the predictor as a restricted cubic spline. As such, there is no single hazard ratio and p-value to describe the magnitude and significance of the association. Instead, we present the hazard ratio and confidence interval as a function of African ancestry in Figure 2, using the median African ancestry value as the referent. It is worth noting that it is impossible to measure evidence of no effect using a p-value.34 Hence the display of the 95% CI for hazard ratios over African ancestry is critically important. We find the 95% CI is tight around the estimated hazard ratio of approximately 1.0 for African ancestry, providing evidence to support the null hypothesis of no association. Furthermore, we examined the impact of African ancestry on mortality by comparing the average time-dependent area under the curve for Cox proportional hazards models with and without African ancestry or stage and treatment. This allows us to examine the clinical impact of such predictors, rather than simply the strength of the association. Using these methods, we find that stage and treatment were strongly predictive of overall mortality and are more important predictors of mortality than African ancestry. This result was recapitulated in an independent population of blacks with NSCLC from Karmanos Cancer Institute at Wayne State University, providing further evidence of the null association between African ancestry and overall mortality.
In this study we used genetic ancestry as a continuous proxy for race. While genetic ancestry and race are highly correlated, race can be viewed as a social construct that captures both genetic and non-genetic factors, such as culture and social perception. Given the high variability of genetic ancestry that is not captured by categorically-defined race, adjusting for race alone while examining diseases with established racial disparities may not entirely account for underlying population substructure.15, 35 Here, the utilization of African ancestry instead of race allows us to attempt to disentangle the genetic and social disparities associated with lung cancer survival.36–38 We find that African ancestry is not associated with overall mortality when adjusting for stage and treatment (Figure 2A), with hazard ratios and confidence intervals encompassing 1.0 for all values of African ancestry. When stage and treatment are removed from the model (Figure 2B), we observed a slightly, although not statistically significant, decreased mortality for individuals with smaller proportions of African ancestry in the SCCS. We hypothesize that this observed reduction in mortality is due to the influence of t treatment, social, environmental, or other factors not captured by genetic ancestry alone. It is possible that these observed differences are the result of cultural differences in the perception of disease and the willingness to seek treatment.39, 40
Herein we observed a striking decrease in the predictive ability of the model when stage and treatment are excluded from Cox models for lung cancer. Prior work has shown that racial disparities in lung cancer survival disappear when controlling for stage at diagnosis or treatment.10, 11, 13 Together these studies along with the present analysis suggest that the observed disparity in survival between blacks and whites can be attributed to differences in stage at diagnosis or receipt of treatment rather than race. It is important to note that while this study shows no association between genetic ancestry and overall mortality, it does not eliminate the potential for a genetic contribution to lung cancer survival or the possibility of race-specific genetic risk factors.
To our knowledge, this is the first study to examine the relationship between genetic ancestry and lung cancer mortality in blacks and whites with NSCLC. By utilizing the unique SCCS resource, we were able to control for multiple factors potentially influencing lung cancer survival, including socioeconomic status, family history of lung cancer, cigarette smoking, disease stage, and treatment received. Despite our limited sample size and the different characteristics between our discovery and replication study populations, including stage at diagnosis, our confidence intervals are narrow and the consistency of our findings between two independent study populations emphasizes the robustness of our findings. Furthermore, we acknowledge that the staging and treatment information obtained through linkage with cancer registries may not depict the most accurate and up to date clinical and staging information. Additionally, these data do not capture recent advances in lung cancer treatment, which include targeted therapeutics and immunotherapies. Future studies should be conducted in a clinical population with carefully annotated clinical information for examining racial differences in lung cancer survival.
In summary, we find that African ancestry is not associated with NSCLC mortality and that stage and treatment are robust predictors of lung cancer mortality. These findings suggest that efforts to increase the early detection of lung cancer will improve lung cancer outcomes for both blacks and whites.
Supplementary Material
Acknowledgments
Funding/Support
This work was supported by a Department of Defense Early Investigator Synergistic Idea Award granted to M.C.A. (W81XWH-12-1-0547) and E.L.G. (W81XWH-12-1-0544); the National Cancer Institute at the National Institutes of Health (K07 CA172294) to M.C.A.; a Department of Veterans Affairs Career Development Award (10-024) to E.L.G.; the National Institutes of Health to A.G.S (R01CA60691, R01CA87895 and HHSN261201300011I) and to W.J.B. (R01CA092447 and U01202979); and a National Institute of General Medical Sciences at the National Institutes of Health training grant (5T32GM080178, PI: Nancy Cox) to C.C.J. The funding bodies had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Data on SCCS cancer cases used in this publication were provided by the Alabama Statewide Cancer Registry; Kentucky Cancer Registry, Lexington, KY; Tennessee Department of Health, Office of Cancer Surveillance; Florida Cancer Data System; North Carolina Central Cancer Registry, North Carolina Division of Public Health; Georgia Comprehensive Cancer Registry; Louisiana Tumor Registry; Mississippi Cancer Registry; South Carolina Central Cancer Registry; Virginia Department of Health, Virginia Cancer Registry; Arkansas Department of Health, Cancer Registry, 4815 W. Markham, Little Rock, AR 72205. The Arkansas Central Cancer Registry is fully funded by a grant from National Program of Cancer Registries, Centers for Disease Control and Prevention (CDC). Data on SCCS cancer cases from Mississippi were collected by the Mississippi Cancer Registry, which participates in the National Program of Cancer Registries (NPCR) of the Centers for Disease Control and Prevention (CDC). The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the CDC or the Mississippi Cancer Registry.
Footnotes
Conflict of Interest
The authors have no conflicts of interest to disclose.
List of Supplementary Material:
Jones_Aldrich_Supplement_JTO.docx
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