Looking at cancer health disparities without the colored lenses

Extensive set of African ancestry-informative markers (AIMs) to study ancestry and population health

Introduction: Human populations are often highly structured due to differences in genetic ancestry among groups, posing difficulties in associating genes with diseases. Ancestry-informative markers (AIMs) aid in the detection of population stratification and provide an alternative approach to map population-specific alleles to disease. Here, we identify and characterize a novel set of African AIMs that separate populations of African ancestry from other global populations including those of European ancestry. Methods: Using data from the 1000 Genomes Project, highly informative SNP markers from five African subpopulations were selected based on estimates of informativeness (In) and compared against the European population to generate a final set of 46,737 African ancestry-informative markers (AIMs). The AIMs identified were validated using an independent set and functionally annotated using tools like SIFT, PolyPhen. They were also investigated for representation of commonly used SNP arrays. Results: This set of African AIMs effectively separates populations of African ancestry from other global populations and further identifies substructure between populations of African ancestry. When a subset of these AIMs was studied in an independent dataset, they differentiated people who self-identify as African American or Black from those who identify their ancestry as primarily European. Most of the AIMs were found to be in their intergenic and intronic regions with only 0.6% in the coding regions of the genome. Most of the commonly used SNP array investigated contained less than 10% of the AIMs. Discussion: While several functional annotations of both coding and non-coding African AIMs are supported by the literature and linked these high-frequency African alleles to diseases in African populations, more effort is needed to map genes to diseases in these genetically diverse subpopulations. The relative dearth of these African AIMs on current genotyping platforms (the array with the highest fraction, llumina's Omni 5, harbors less than a quarter of AIMs), further demonstrates a greater need to better represent historically understudied populations.

Implementing patient navigation programs: Considerations and lessons learned from the Alliance to Advance Patient-Centered Cancer Care

BACKGROUND

Six multidisciplinary cancer centers were selected and funded by the Merck Foundation (2017-2021) to collaborate in the Alliance to Advance Patient-Centered Cancer Care ("Alliance"), an initiative to improve patient access, minimize health disparities, and enhance the quality of patient-centered cancer care. These sites share their insights on implementation and expansion of their patient navigation efforts.

METHODS

Patient navigation represents an evidence-based health care intervention designed to enhance patient-centered care and care coordination. Investigators at 6 National Cancer Institute-designated cancer centers outline their approaches to reducing health care disparities and synthesize their efforts to ensure sustainability and successful transferability in the management of patients with cancer and their families in real-world health care settings.

RESULTS

Insights are outlined within the context of patient navigation program effectiveness and supported by examples from Alliance cancer center sites: 1) understand the patient populations, particularly underserved and high-risk patients; 2) capitalize on the existing infrastructure and institutional commitment to support and sustain patient navigation; and 3) build capacity by mobilizing community support outside of the cancer center.

CONCLUSIONS

This process-level article reflects the importance of collaboration and the usefulness of partnering with other cancer centers to share interdisciplinary insights while undergoing intervention development, implementation, and expansion. These collective insights may be useful to staff at other cancer centers that look to implement, enhance, or evaluate the effectiveness of their patient navigation interventions.

A Signature for Smoking Status of Coronary Heart Disease Patients through Weighted Gene Coexpression Network Analysis

Background. Smoking is one of the risk factors of coronary heart disease (CHD), while its underlying mechanism is less well defined. Purpose. To identify and testify 6 key genes of CHD related to smoking through weighted gene coexpression network analysis (WGCNA), protein-protein interaction (PPI) network analysis, and pathway analysis. Methods. CHD patients’ samples were first downloaded from Gene Expression Omnibus (GEO). Then, genes of interest were obtained after analysis of variance (ANOVA). Thereafter, 23 coexpressed modules that were determined after genes with similar expression were incorporated via WGCNA. The biological functions of genes in the modules were researched by enrichment analysis. Pearson correlation analysis and PPI network analysis were used to screen core genes related to smoking in CHD. Results. The violet module was the most significantly associated with smoking ( $r=-0.28$ , $p=0.006$ ). Genes in this module mainly participated in biological functions related to the heart. Altogether, 6 smoking-related core genes were identified through bioinformatics analyses. Their expressions in animal models were detected through the animal experiment. Conclusion. This study identified 6 core genes to serve as underlying biomarkers for monitoring and predicting smoker’s CHD risk.