SAMBAI - Yemaachi Biotech - SAMBAI: Societal, Ancestry, Molecular and Biological Analyses of Inequalities
Research Abstract
Background
Prostate, breast, and pancreatic cancers all have a disproportionately higher rate of aggressive tumor
grade and early onset in Black patients, with recent spikes of high incidence in West African nations
compared to other African regions. The genetic background correlations implicate predispositions.
Members of our SAMBAI team of investigators have pioneered genomics in cancer disparities research,
and over the past two decades, we have uncovered compelling evidence of distinct immunological
mechanisms associated with genetic ancestry. Our SAMBAI team members have developed methods to
quantify environmental exposures and interrogate the lived experiences of marginalized populations,
including epigenetic responses to racism.
Aims
We will partner with scientists across the US, Africa, and the UK to build an unprecedented resource, the
SAMBAI Biobank and Data Repository for Cancer Equity Research. We will generate a comprehensive,
accurate, and relevant measurement of social, environmental, genetic, and immunological factors to
complete an integrated set of analyses to define the causal vs. modifier relationships of disparate
outcomes in diverse underserved populations. We will establish a sustainable framework for team science
approaches with under-represented partners and establish best practices for coordinating cancer equity
research on a global scale.
Methods
We propose to utilize multiple methods across our different work packages. Social Determinants include
self-reporting surveys and database abstractions. Exposomes utilize mass spectrometry of plasma.
Genomics will utilize three sequencing methods on germline and tumor tissue, including long read,
short/deep, and ultra-low pass whole genome sequencing. Lastly, immunological profiles will be
measured with spatial transcriptomics and circulating multiplex immunoassays. These data require novel
computational frameworks, including cloud-based virtualization and the use of machine learning
technologies to identify novel associations across the strata of social to spatial data elements and across
our diverse geographic and ancestral SAMBAI cohorts.
Utility and Impact
We will improve research capacity in under-resourced environments for large-scale cancer research and
equitable access to data with equitable feasibility to improve treatment and outcomes. We will define
interactions of environmental exposures, social determinants, and genetic ancestry that determine
immunological landscapes of primary tumors and/or circulating immunological profiles in patients of
African descent. Our project will contribute a data repository with 100K features/patient, for 40,000
patients. The impact on this population includes a novel trial design, in collaboration with our patient
advocacy partners, to ensure that the specific genomic and immunological features we uncover become
part of targeted precision oncology theragnostic options.
