Genetic factors associated with cancer racial disparity - an integrative study across twenty-one cancer types

Racial and Ethnic Disparity for Cancer Mortality in General and Single-Payer Healthcare Systems in the United States

BACKGROUND

It remains unclear what factors significantly drive racial disparity in cancer survival in the United States (US). We compared adjusted mortality outcomes in cancer patients from different racial and ethnic groups on a population level in the US and a single-payer healthcare system.

PATIENTS AND METHODS

We selected adult patients with incident solid and hematologic malignancies from the Surveillance, Epidemiology, and End Results (SEER) 2011-2020 and Veteran Affairs national healthcare system (VA) 2011-2021. We classified the self-reported NIH race and ethnicity into non-Hispanic White (NHW), non-Hispanic Black (NHB), non-Hispanic Asian Pacific Islander (API), and Hispanic. Cox regression models for hazard ratio of racial and ethnic groups were built after adjusting confounders in each cohort.

RESULTS

The study included 3,104,657 patients from SEER and 287,619 patients from VA. There were notable differences in baseline characteristics in the two cohorts. In SEER, adjusted HR for mortality was 1.12 (95% CI, 1.12-1.13), 1.03 (95% CI, 1.03-1.04), and 0.91 (95% CI, 0.90-0.92), for NHB, Hispanic, and API patients, respectively, vs. NHW. In VA, adjusted HR was 0.94 (95% CI, 0.92-0.95), 0.84 (95% CI, 0.82-0.87), and 0.96 (95% CI, 0.93-1.00) for NHB, Hispanic, and API, respectively, vs. NHW. Additional subgroup analyses by cancer types, age, and sex did not significantly change these associations.

CONCLUSIONS

Racial disparity continues to persist on a population level in the US especially for NHB vs. NHW patients, where the adjusted mortality was 12% higher in the general population but 6% lower in the single-payer VA system.

Cancer mortality by country of birth and cancer type in Sweden: A 25-year registry-based cohort study

Numerous studies have reported lower overall cancer mortality rates among immigrants compared to native populations. However, limited information exists regarding cancer mortality among immigrants based on specific birth countries and cancer types. We used population-based registries and followed 10 million individuals aged 20 years or older in Sweden between 1992 and 2016. The Cox proportional hazard model was used to explore the disparities in cancer mortality by country of birth and cancer type, stratified by gender. Age-standardized mortality rates were also computed using the world standard population. Hazard ratio (HR) of all-site cancer was slightly lower among immigrants (males: HRm = 0.97: 95% confidence interval: 0.95, 0.98; females: HRf = 0.93: 0.91, 0.94) than Swedish-born population. However, the immigrants showed higher mortality for infection-related cancers, including liver (HRf = 1.10: 1.01, 1.19; HRm = 1.10: 1.02, 1.17), stomach (HRf = 1.39: 1.31, 1.49; HRm = 1.33: 1.26, 1.41) cancers, and tobacco-related cancers, including lung (HRm = 1.44: 1.40, 1.49), and laryngeal cancers (HRm = 1.47: 1.24, 1.75). The HR of mesothelioma was also significantly higher in immigrants (HRf = 1.44: 1.10, 1.90). Mortality from lung cancer was specifically higher in men from Nordic (HRm = 1.41: 1.27, 1.55) and non-Nordic Europe (HRm = 1.49: 1.43, 1.55) countries and lower in Asian (HRm = 0.78: 0.66, 0.93) and South American men (HRm = 0.70: 0.57, 0.87). In conclusion, there are large variations in cancer mortality by country of birth, and cancer type and require regular surveillance. Our detailed analyses lead to some novel findings such as excess mortality rate of mesothelioma and laryngeal cancers in Immigrants in Sweden. A targeted cancer prevention program among immigrants in Sweden is needed.

Persistence of Racial and Ethnic Disparities in Risk and Survival for Patients with Neuroblastoma over Two Decades

BACKGROUND

Racial/ethnic survival disparities in neuroblastoma were first reported more than a decade ago. We sought to investigate if these disparities have persisted with current era therapy.

METHODS

Two patient cohorts were identified in the International Neuroblastoma Risk Group Data Commons (INRGdc) (Cohort 1: diagnosed 2001-2009, n=4359; Cohort 2: diagnosed 2010-2019, n=4891). Chi-squared tests were used to assess the relationship between race/ethnicity and clinical and biologic features. Survival was estimated by the Kaplan-Meier method. Cox proportional hazards regression analyses were performed to investigate the association between racial/ethnic groups and prognostic markers.

RESULTS

Significantly higher 5-year event-free survival (EFS) and overall survival (OS) were observed for Cohort 2 compared to Cohort 1 (P<0.001 and P<0.001, respectively). Compared to White patients, Black patients in both cohorts had a higher proportion of high-risk disease (Cohort 1: P<0.001; Cohort 2: P<0.001) and worse EFS (Cohort 1: P<0.001; Cohort 2 P<0.001) and OS (Cohort 1: P<0.001; Cohort 2: P<0.001). In Cohort 1, Native Americans also had a higher proportion of high-risk disease (P=0.03) and inferior EFS/OS. No significant survival disparities were observed for low- or intermediate-risk patients in either cohort or high-risk patients in Cohort 1. Hispanic patients with high-risk disease in Cohort 2 had significantly inferior OS (P=0.047). Significantly worse OS, but not EFS, (P=0.006 and P=0.02, respectively) was also observed among Black and Hispanic patients assigned to receive post-Consolidation dinutuximab on clinical trials (n=885).

CONCLUSION

Racial/ethnic survival disparities have persisted over time and were observed among high-risk patients assigned to receive post-Consolidation dinutuximab.

TCGA Expression Analyses of 10 Carcinoma Types Reveal Clinically Significant Racial Differences

Epidemiological studies reveal disparities in cancer incidence and outcome rates between racial groups in the United States. In our study, we investigated molecular differences between racial groups in 10 carcinoma types. We used publicly available data from The Cancer Genome Atlas to identify patterns of differential gene expression in tumor samples obtained from 4112 White, Black/African American, and Asian patients. We identified race-dependent expression of numerous genes whose mRNA transcript levels were significantly correlated with patients' survival. Only a small subset of these genes was differentially expressed in multiple carcinomas, including genes involved in cell cycle progression such as CCNB1, CCNE1, CCNE2, and FOXM1. In contrast, most other genes, such as transcriptional factor ETS1 and apoptotic gene BAK1, were differentially expressed and clinically significant only in specific cancer types. Our analyses also revealed race-dependent, cancer-specific regulation of biological pathways. Importantly, homology-directed repair and ERBB4-mediated nuclear signaling were both upregulated in Black samples compared to White samples in four carcinoma types. This large-scale pan-cancer study refines our understanding of the cancer health disparity and can help inform the use of novel biomarkers in clinical settings and the future development of precision therapies.

The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review

BACKGROUND

The field of oncology is at the forefront of advances in artificial intelligence (AI) in health care, providing an opportunity to examine the early integration of these technologies in clinical research and patient care. Hope that AI will revolutionize health care delivery and improve clinical outcomes has been accompanied by concerns about the impact of these technologies on health equity.

OBJECTIVE

We aimed to conduct a scoping review of the literature to address the question, "What are the current and potential impacts of AI technologies on health equity in oncology?"

METHODS

Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines for scoping reviews, we systematically searched MEDLINE and Embase electronic databases from January 2000 to August 2021 for records engaging with key concepts of AI, health equity, and oncology. We included all English-language articles that engaged with the 3 key concepts. Articles were analyzed qualitatively for themes pertaining to the influence of AI on health equity in oncology.

RESULTS

Of the 14,011 records, 133 (0.95%) identified from our review were included. We identified 3 general themes in the literature: the use of AI to reduce health care disparities (58/133, 43.6%), concerns surrounding AI technologies and bias (16/133, 12.1%), and the use of AI to examine biological and social determinants of health (55/133, 41.4%). A total of 3% (4/133) of articles focused on many of these themes.

CONCLUSIONS

Our scoping review revealed 3 main themes on the impact of AI on health equity in oncology, which relate to AI's ability to help address health disparities, its potential to mitigate or exacerbate bias, and its capability to help elucidate determinants of health. Gaps in the literature included a lack of discussion of ethical challenges with the application of AI technologies in low- and middle-income countries, lack of discussion of problems of bias in AI algorithms, and a lack of justification for the use of AI technologies over traditional statistical methods to address specific research questions in oncology. Our review highlights a need to address these gaps to ensure a more equitable integration of AI in cancer research and clinical practice. The limitations of our study include its exploratory nature, its focus on oncology as opposed to all health care sectors, and its analysis of solely English-language articles.

Biological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo

Bayesian networks (BNs) provide a probabilistic, graphical framework for modeling high-dimensional joint distributions with complex correlation structures. BNs have wide applications in many disciplines, including biology, social science, finance and biomedical science. Despite extensive studies in the past, network structure learning from data is still a challenging open question in BN research. In this study, we present a sequential Monte Carlo (SMC)-based three-stage approach, GRowth-based Approach with Staged Pruning (GRASP). A double filtering strategy was first used for discovering the overall skeleton of the target BN. To search for the optimal network structures we designed an adaptive SMC (adSMC) algorithm to increase the quality and diversity of sampled networks which were further improved by a third stage to reclaim edges missed in the skeleton discovery step. GRASP gave very satisfactory results when tested on benchmark networks. Finally, BN structure learning using multiple types of genomics data illustrates GRASP’s potential in discovering novel biological relationships in integrative genomic studies.

HSD17B7 gene in self-renewal and oncogenicity of keratinocytes from Black versus White populations

Human populations of Black African ancestry have a relatively high risk of aggressive cancer types, including keratinocyte-derived squamous cell carcinomas (SCCs). We show that primary keratinocytes (HKCs) from Black African (Black) versus White Caucasian (White) individuals have on average higher oncogenic and self-renewal potential, which are inversely related to mitochondrial electron transfer chain activity and ATP and ROS production. HSD17B7 is the top-ranked differentially expressed gene in HKCs and Head/Neck SCCs from individuals of Black African versus Caucasian ancestries, with several ancestry-specific eQTLs linked to its expression. Mirroring the differences between Black and White HKCs, modulation of the gene, coding for an enzyme involved in sex steroid and cholesterol biosynthesis, determines HKC and SCC cell proliferation and oncogenicity as well as mitochondrial OXPHOS activity. Overall, the findings point to a targetable determinant of cancer susceptibility among different human populations, amenable to prevention and management of the disease.