Illuminating cancer health disparities using ethnogenetic layering (EL) and phenotype segregation network analysis (PSNA)

Pancreatic Cancer Health Disparity: Pharmacologic Anthropology

Pancreatic cancer (PCa) remains a formidable global health challenge, with high mortality rates and limited treatment options. While advancements in pharmacology have led to improved outcomes for various cancers, PCa continues to exhibit significant health disparities, disproportionately affecting certain populations. This paper explores the intersection of pharmacology and anthropology in understanding the health disparities associated with PCa. By considering the socio-cultural, economic, and behavioral factors that influence the development, diagnosis, treatment, and outcomes of PCa, pharmacologic anthropology provides a comprehensive framework to address these disparities and improve patient care.

Protein interacting with C alpha kinase 1 (PICK1) is involved in promoting tumor growth and correlates with poor prognosis of human breast cancer

Protein interacting with C alpha kinase 1 (PICK1), which interacts with multiple different proteins in a variety of cellular contexts, is believed to play important roles in diverse pathological conditions including cancer. In this study, we attempted to investigate the correlation of PICK1 with clinicopathological features as well as prognosis of human breast cancer. In addition, we aimed at a better understanding of the biological function of PICK1 in breast cancer cell biology. As judged by semi- quantitative RT-PCR and western blotting, PICK1 was overexpressed in tumor cells as compared to adjacent normal epithelia in breast, lung, gastric, colorectal, and ovarian cancer. As judged by immunostaining breast cancer tissue microarrays, high levels of PICK1 expression correlated with shortened span of overall survival (OS). Protein interacting with C alpha kinase 1 (PICK1) expression seemed to be specifically associated with reduced OS in lymph node-positive, Her/neu-2 positive, and the basal-like type subgroups, respectively. Consistently, the expression of PICK1 correlated with histological grade, lymph node metastasis, Her-2/neu-positivity, and triple-negative basal-like breast cancer. Protein interacting with C alpha kinase 1 (PICK1) was not correlated with menopausal status, tumor size, or hormone receptor status. In a complementary study, transfection of MDA-MB-231 cells with PICK1 siRNA decreased cell proliferation and colony formation in vitro and inhibited tumorigenicity in nude mice. Our clinical and experimental evidence supports an oncogenic role of PICK1 in human breast cancer. In particular, our data suggest that PICK1 promotes tumor cell proliferation. Taken together, PICK1 may serve not only as a marker for poor prognosis, but also as a therapeutic target in breast cancer.

Ethnogenetic layering (EL): an alternative to the traditional race model in human variation and health disparity studies

BACKGROUND

Traditionally, studies in human biodiversity, disease risk, and health disparities have defined populations in the context of typological racial models. However, such racial models are often imprecise generalizations that fail to capture important local patterns of human biodiversity.

AIM

More explicit, detailed, and integrated information on relevant geographic, environmental, cultural, genetic, historical, and demographic variables are needed to understand local group expressions of disease inequities. This paper details the methods used in ethnogenetic layering (EL), a non-typological alternative to the current reliance of the biological racial paradigm in public health, epidemiology, and biomedicine.

SUBJECTS AND METHODS

EL is focused on geographically identified microethnic groups or MEGs, a more nuanced and sensitive level of analysis than race. Using the MEG level of analysis, EL reveals clinical variations, details the causes of health disparities, and provides a foundation for bioculturally effective intervention strategies. EL relies on computational approaches by using GIS-facilitated maps to produce horizontally stratified geographical regional profiles which are then stacked and evaluated vertically. Each horizontal digital map details local geographic variation in the attributes of a particular database; usually this includes data on local historical demography, genetic diversity, cultural patterns, and specific chronic disease risks (e.g. dietary and toxicological exposures). Horizontal visual display of these layered maps permits vertical analysis at various geographic hot spots.

RESULTS AND CONCLUSIONS

From these analyses, geographical areas and their associated MEGs with highly correlated chronic disease risk factors can be identified and targeted for further study.

Ancestral links of Chesapeake Bay region African Americans to specific Bight of Bonny (West Africa) microethnic groups and increased frequency of aggressive breast cancer in both regions

The high frequency of aggressive, early onset, and highly fatal breast cancer among Chesapeake Bay region African Americans suggests that there may be a contributing ancestral component. This study identifies the region's founding African, European, and Native American Indian populations using ethnogenetic layering and identifies the microethnic substructure of each founding continental aggregate. The largest component (38%) of the enslaved Africans brought to the Chesapeake Bay originally came from the coastal and hinterlands of the Bight of Bonny, a region with very high rates of aggressive, early onset breast cancer. Ethnogenetic layering is applied a second time to reveal the microethnic groups of the Bight of Bonny hinterlands with historical links to the Chesapeake Bay region. These analyses identify the specific microethnic groups within this region of Africa that may be the sources of relevant polymorphisms contributing to the etiology of aggressive breast cancer in the Chesapeake Bay. This report suggests a historical link between specific African microethnic groups and a US health disparity.